From 04998b2c02a194a008c71c51ae229ae8032481e5 Mon Sep 17 00:00:00 2001
From: Miguel Alonso Jr <miguel.alonso@unity3d.com>
Date: Tue, 27 Jun 2023 10:16:13 -0400
Subject: [PATCH] Deployed 3fd3fdf15 with MkDocs version: 1.4.2

---
 404.html                                      |  64 +-
 API-Reference/index.html                      |  70 +-
 Background-Machine-Learning/index.html        | 112 ++-
 Background-PyTorch/index.html                 | 112 ++-
 Background-Unity/index.html                   | 112 ++-
 CODE_OF_CONDUCT/index.html                    |  70 +-
 CONTRIBUTING/index.html                       |  70 +-
 Custom-SideChannels/index.html                |  70 +-
 ELO-Rating-System/index.html                  | 809 ++++++++++++++++++
 FAQ/index.html                                | 112 ++-
 Getting-Started/index.html                    |  70 +-
 Glossary/index.html                           |  70 +-
 Hugging-Face-Integration/index.html           | 792 +++++++++++++++++
 Installation-Anaconda-Windows/index.html      |  70 +-
 Installation/index.html                       |  76 +-
 Integrations-Match3/index.html                |  70 +-
 Integrations/index.html                       |  70 +-
 LICENSE/index.html                            |  70 +-
 Learning-Environment-Create-New/index.html    |  70 +-
 Learning-Environment-Design-Agents/index.html |  79 +-
 Learning-Environment-Design/index.html        |  70 +-
 Learning-Environment-Examples/index.html      |  70 +-
 Learning-Environment-Executable/index.html    |  70 +-
 Limitations/index.html                        | 112 ++-
 ML-Agents-Envs-README/index.html              |  70 +-
 ML-Agents-Overview/index.html                 | 115 ++-
 ML-Agents-README/index.html                   |  70 +-
 ML-Agents-Toolkit-Documentation/index.html    | 130 ++-
 Migrating/index.html                          | 112 ++-
 Package-Settings/index.html                   |  70 +-
 Profiling-Python/index.html                   |  70 +-
 Python-Custom-Trainer-Plugin/index.html       |  70 +-
 Python-Gym-API-Documentation/index.html       | 112 ++-
 Python-Gym-API/index.html                     | 112 ++-
 Python-LLAPI-Documentation/index.html         | 112 ++-
 Python-LLAPI/index.html                       | 112 ++-
 .../index.html                                | 112 ++-
 Python-Optimizer-Documentation/index.html     |  70 +-
 .../index.html                                | 112 ++-
 Python-PettingZoo-API/index.html              | 112 ++-
 Readme/index.html                             |  70 +-
 Training-Configuration-File/index.html        |  70 +-
 Training-ML-Agents/index.html                 |  71 +-
 Training-Plugins/index.html                   |  70 +-
 Training-on-Amazon-Web-Service/index.html     |  70 +-
 Training-on-Microsoft-Azure/index.html        |  70 +-
 Tutorial-Custom-Trainer-Plugin/index.html     | 112 ++-
 Unity-Environment-Registry/index.html         |  70 +-
 Unity-Inference-Engine/index.html             |  70 +-
 Using-Docker/index.html                       |  70 +-
 Using-Tensorboard/index.html                  |  70 +-
 Using-Virtual-Environment/index.html          |  70 +-
 Versioning/index.html                         |  95 +-
 assets/javascripts/bundle.b4d07000.min.js     |  29 -
 assets/javascripts/bundle.b4d07000.min.js.map |   8 -
 assets/javascripts/bundle.d6c3db9e.min.js     |  29 +
 assets/javascripts/bundle.d6c3db9e.min.js.map |   8 +
 assets/javascripts/lunr/min/lunr.hy.min.js    |   1 -
 assets/javascripts/lunr/min/lunr.kn.min.js    |   1 -
 assets/javascripts/lunr/min/lunr.ko.min.js    |   2 +-
 assets/javascripts/lunr/min/lunr.sa.min.js    |   1 -
 assets/javascripts/lunr/min/lunr.te.min.js    |   1 -
 assets/javascripts/lunr/min/lunr.zh.min.js    |   2 +-
 assets/javascripts/lunr/wordcut.js            |   4 +-
 ...208ed371.min.js => search.16e2a7d4.min.js} |  24 +-
 ....min.js.map => search.16e2a7d4.min.js.map} |   8 +-
 assets/stylesheets/main.26e3688c.min.css      |   1 -
 assets/stylesheets/main.26e3688c.min.css.map  |   1 -
 assets/stylesheets/main.472b142f.min.css      |   1 +
 assets/stylesheets/main.472b142f.min.css.map  |   1 +
 assets/stylesheets/palette.08040f6c.min.css   |   1 +
 .../stylesheets/palette.08040f6c.min.css.map  |   1 +
 assets/stylesheets/palette.ecc896b0.min.css   |   1 -
 .../stylesheets/palette.ecc896b0.min.css.map  |   1 -
 com.unity.ml-agents.extensions/index.html     |  70 +-
 com.unity.ml-agents/index.html                |  70 +-
 doxygen/Readme/index.html                     |  70 +-
 images/elo_example.png                        | Bin 0 -> 78772 bytes
 images/elo_expected_score_formula.png         | Bin 0 -> 105136 bytes
 images/elo_score_update_formula.png           | Bin 0 -> 125155 bytes
 index.html                                    | 444 +++++++++-
 ml-agents-envs/index.html                     |  70 +-
 ml-agents/index.html                          |  70 +-
 search/search_index.json                      |   2 +-
 sitemap.xml                                   | 122 +--
 sitemap.xml.gz                                | Bin 778 -> 806 bytes
 86 files changed, 4621 insertions(+), 2322 deletions(-)
 create mode 100644 ELO-Rating-System/index.html
 create mode 100644 Hugging-Face-Integration/index.html
 delete mode 100644 assets/javascripts/bundle.b4d07000.min.js
 delete mode 100644 assets/javascripts/bundle.b4d07000.min.js.map
 create mode 100644 assets/javascripts/bundle.d6c3db9e.min.js
 create mode 100644 assets/javascripts/bundle.d6c3db9e.min.js.map
 delete mode 100644 assets/javascripts/lunr/min/lunr.hy.min.js
 delete mode 100644 assets/javascripts/lunr/min/lunr.kn.min.js
 delete mode 100644 assets/javascripts/lunr/min/lunr.sa.min.js
 delete mode 100644 assets/javascripts/lunr/min/lunr.te.min.js
 rename assets/javascripts/workers/{search.208ed371.min.js => search.16e2a7d4.min.js} (57%)
 rename assets/javascripts/workers/{search.208ed371.min.js.map => search.16e2a7d4.min.js.map} (53%)
 delete mode 100644 assets/stylesheets/main.26e3688c.min.css
 delete mode 100644 assets/stylesheets/main.26e3688c.min.css.map
 create mode 100644 assets/stylesheets/main.472b142f.min.css
 create mode 100644 assets/stylesheets/main.472b142f.min.css.map
 create mode 100644 assets/stylesheets/palette.08040f6c.min.css
 create mode 100644 assets/stylesheets/palette.08040f6c.min.css.map
 delete mode 100644 assets/stylesheets/palette.ecc896b0.min.css
 delete mode 100644 assets/stylesheets/palette.ecc896b0.min.css.map
 create mode 100644 images/elo_example.png
 create mode 100644 images/elo_expected_score_formula.png
 create mode 100644 images/elo_score_update_formula.png

diff --git a/404.html b/404.html
index 4146ac45d7..6f1fdaa7bf 100644
--- a/404.html
+++ b/404.html
@@ -12,10 +12,8 @@
         <meta name="author" content="Unity Technologies">
       
       
-      
-      
       <link rel="icon" href="/ml-agents/images/unity-logo-black.png">
-      <meta name="generator" content="mkdocs-1.4.3, mkdocs-material-9.1.15">
+      <meta name="generator" content="mkdocs-1.4.2, mkdocs-material-8.5.10">
     
     
       
@@ -23,10 +21,15 @@
       
     
     
-      <link rel="stylesheet" href="/ml-agents/assets/stylesheets/main.26e3688c.min.css">
+      <link rel="stylesheet" href="/ml-agents/assets/stylesheets/main.472b142f.min.css">
       
         
-        <link rel="stylesheet" href="/ml-agents/assets/stylesheets/palette.ecc896b0.min.css">
+        <link rel="stylesheet" href="/ml-agents/assets/stylesheets/palette.08040f6c.min.css">
+        
+          
+          
+          <meta name="theme-color" content="#2094f3">
+        
       
       
 
@@ -76,9 +79,7 @@
     
       
 
-  
-
-<header class="md-header md-header--shadow" data-md-component="header">
+<header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
     <a href="/ml-agents/." title="Unity ML-Agents Toolkit" class="md-header__button md-logo" aria-label="Unity ML-Agents Toolkit" data-md-component="logo">
       
@@ -105,8 +106,6 @@
       </div>
     </div>
     
-      
-    
     
     
       <label class="md-header__button md-icon" for="__search">
@@ -135,7 +134,7 @@
           <div class="md-search-result__meta">
             Initializing search
           </div>
-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
         </div>
       </div>
     </div>
@@ -147,7 +146,7 @@
         <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -191,7 +190,7 @@
       <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -257,18 +256,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_4" type="checkbox" id="__nav_4" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
       
       
-      
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_4">
           Background
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Background" data-md-level="1">
         <label class="md-nav__title" for="__nav_4">
           <span class="md-nav__icon md-icon"></span>
           Background
@@ -334,18 +332,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_5" type="checkbox" id="__nav_5" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
-      
       
       
-        <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_5">
           Interfacing with Unity Builds
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Interfacing with Unity Builds" data-md-level="1">
         <label class="md-nav__title" for="__nav_5">
           <span class="md-nav__icon md-icon"></span>
           Interfacing with Unity Builds
@@ -411,18 +408,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_6" type="checkbox" id="__nav_6" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" >
       
       
-      
-        <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_6">
           Python API Docs
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Python API Docs" data-md-level="1">
         <label class="md-nav__title" for="__nav_6">
           <span class="md-nav__icon md-icon"></span>
           Python API Docs
@@ -502,18 +498,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_7" type="checkbox" id="__nav_7" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_7" >
-      
       
       
-        <label class="md-nav__link" for="__nav_7" id="__nav_7_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_7">
           Tutorials
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_7_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Tutorials" data-md-level="1">
         <label class="md-nav__title" for="__nav_7">
           <span class="md-nav__icon md-icon"></span>
           Tutorials
@@ -551,18 +546,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_8" type="checkbox" id="__nav_8" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_8" >
-      
       
       
-        <label class="md-nav__link" for="__nav_8" id="__nav_8_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_8">
           About
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="About" data-md-level="1">
         <label class="md-nav__title" for="__nav_8">
           <span class="md-nav__icon md-icon"></span>
           About
@@ -696,10 +690,10 @@ <h1>404 - Not found</h1>
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
-    <script id="__config" type="application/json">{"base": "/ml-agents/", "features": [], "search": "/ml-agents/assets/javascripts/workers/search.208ed371.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": "/ml-agents/", "features": [], "search": "/ml-agents/assets/javascripts/workers/search.16e2a7d4.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-      <script src="/ml-agents/assets/javascripts/bundle.b4d07000.min.js"></script>
+      <script src="/ml-agents/assets/javascripts/bundle.d6c3db9e.min.js"></script>
       
     
   </body>
diff --git a/API-Reference/index.html b/API-Reference/index.html
index 9d6186e70b..631e770227 100644
--- a/API-Reference/index.html
+++ b/API-Reference/index.html
@@ -14,10 +14,8 @@
       
         <link rel="canonical" href="https://unity-technologies.github.io/ml-agents/API-Reference/">
       
-      
-      
       <link rel="icon" href="../images/unity-logo-black.png">
-      <meta name="generator" content="mkdocs-1.4.3, mkdocs-material-9.1.15">
+      <meta name="generator" content="mkdocs-1.4.2, mkdocs-material-8.5.10">
     
     
       
@@ -25,10 +23,15 @@
       
     
     
-      <link rel="stylesheet" href="../assets/stylesheets/main.26e3688c.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.472b142f.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.ecc896b0.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.08040f6c.min.css">
+        
+          
+          
+          <meta name="theme-color" content="#2094f3">
+        
       
       
 
@@ -83,9 +86,7 @@
     
       
 
-  
-
-<header class="md-header md-header--shadow" data-md-component="header">
+<header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
     <a href=".." title="Unity ML-Agents Toolkit" class="md-header__button md-logo" aria-label="Unity ML-Agents Toolkit" data-md-component="logo">
       
@@ -112,8 +113,6 @@
       </div>
     </div>
     
-      
-    
     
     
       <label class="md-header__button md-icon" for="__search">
@@ -142,7 +141,7 @@
           <div class="md-search-result__meta">
             Initializing search
           </div>
-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
         </div>
       </div>
     </div>
@@ -154,7 +153,7 @@
         <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
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@@ -418,18 +415,17 @@
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+      <nav class="md-nav" aria-label="Python API Docs" data-md-level="1">
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           Python API Docs
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+        <label class="md-nav__link" for="__nav_8">
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="false">
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           About
@@ -670,8 +664,10 @@
                 
                   
 
-  
-  
+  <a href="https://github.com/Unity-Technologies/ml-agents/edit/main/docs/API-Reference.md" title="Edit this page" class="md-content__button md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
 
 <h1 id="api-reference">API Reference</h1>
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+    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.16e2a7d4.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.b4d07000.min.js"></script>
+      <script src="../assets/javascripts/bundle.d6c3db9e.min.js"></script>
       
     
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diff --git a/Background-Machine-Learning/index.html b/Background-Machine-Learning/index.html
index 18f60128c5..e59e423bbe 100644
--- a/Background-Machine-Learning/index.html
+++ b/Background-Machine-Learning/index.html
@@ -14,14 +14,8 @@
       
         <link rel="canonical" href="https://unity-technologies.github.io/ml-agents/Background-Machine-Learning/">
       
-      
-        <link rel="prev" href="../ML-Agents-Toolkit-Documentation/">
-      
-      
-        <link rel="next" href="../Background-PyTorch/">
-      
       <link rel="icon" href="../images/unity-logo-black.png">
-      <meta name="generator" content="mkdocs-1.4.3, mkdocs-material-9.1.15">
+      <meta name="generator" content="mkdocs-1.4.2, mkdocs-material-8.5.10">
     
     
       
@@ -29,10 +23,15 @@
       
     
     
-      <link rel="stylesheet" href="../assets/stylesheets/main.26e3688c.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.472b142f.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.ecc896b0.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.08040f6c.min.css">
+        
+          
+          
+          <meta name="theme-color" content="#2094f3">
+        
       
       
 
@@ -87,9 +86,7 @@
     
       
 
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+<header class="md-header" data-md-component="header">
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     <a href=".." title="Unity ML-Agents Toolkit" class="md-header__button md-logo" aria-label="Unity ML-Agents Toolkit" data-md-component="logo">
       
@@ -116,8 +113,6 @@
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@@ -146,7 +141,7 @@
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             Initializing search
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+          <ol class="md-search-result__list"></ol>
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@@ -158,7 +153,7 @@
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@@ -202,7 +197,7 @@
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-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" checked>
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+        <label class="md-nav__link" for="__nav_4">
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="true">
+      <nav class="md-nav" aria-label="Background" data-md-level="1">
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+        <label class="md-nav__link" for="__nav_5">
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Interfacing with Unity Builds" data-md-level="1">
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-        <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_6">
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Python API Docs" data-md-level="1">
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_7_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Tutorials" data-md-level="1">
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           Tutorials
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-        <label class="md-nav__link" for="__nav_8" id="__nav_8_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_8">
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           <span class="md-nav__icon md-icon"></span>
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="About" data-md-level="1">
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           About
@@ -788,8 +778,10 @@
                 
                   
 
-  
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+  <a href="https://github.com/Unity-Technologies/ml-agents/edit/main/docs/Background-Machine-Learning.md" title="Edit this page" class="md-content__button md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
 
 <h1 id="background-machine-learning">Background: Machine Learning</h1>
@@ -983,6 +975,42 @@ <h2 id="deep-learning">Deep Learning</h2>
       
         <footer class="md-footer">
   
+    
+    <nav class="md-footer__inner md-grid" aria-label="Footer" >
+      
+        
+        <a href="../ML-Agents-Toolkit-Documentation/" class="md-footer__link md-footer__link--prev" aria-label="Previous: Toolkit Documentation" rel="prev">
+          <div class="md-footer__button md-icon">
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
+          </div>
+          <div class="md-footer__title">
+            <div class="md-ellipsis">
+              <span class="md-footer__direction">
+                Previous
+              </span>
+              Toolkit Documentation
+            </div>
+          </div>
+        </a>
+      
+      
+        
+        <a href="../Background-PyTorch/" class="md-footer__link md-footer__link--next" aria-label="Next: PyTorch" rel="next">
+          <div class="md-footer__title">
+            <div class="md-ellipsis">
+              <span class="md-footer__direction">
+                Next
+              </span>
+              PyTorch
+            </div>
+          </div>
+          <div class="md-footer__button md-icon">
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M4 11v2h12l-5.5 5.5 1.42 1.42L19.84 12l-7.92-7.92L10.5 5.5 16 11H4Z"/></svg>
+          </div>
+        </a>
+      
+    </nav>
+  
   <div class="md-footer-meta md-typeset">
     <div class="md-footer-meta__inner md-grid">
       <div class="md-copyright">
@@ -1008,10 +1036,10 @@ <h2 id="deep-learning">Deep Learning</h2>
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
-    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.208ed371.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.16e2a7d4.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.b4d07000.min.js"></script>
+      <script src="../assets/javascripts/bundle.d6c3db9e.min.js"></script>
       
     
   </body>
diff --git a/Background-PyTorch/index.html b/Background-PyTorch/index.html
index 084ac63549..e882991558 100644
--- a/Background-PyTorch/index.html
+++ b/Background-PyTorch/index.html
@@ -14,14 +14,8 @@
       
         <link rel="canonical" href="https://unity-technologies.github.io/ml-agents/Background-PyTorch/">
       
-      
-        <link rel="prev" href="../Background-Machine-Learning/">
-      
-      
-        <link rel="next" href="../Background-Unity/">
-      
       <link rel="icon" href="../images/unity-logo-black.png">
-      <meta name="generator" content="mkdocs-1.4.3, mkdocs-material-9.1.15">
+      <meta name="generator" content="mkdocs-1.4.2, mkdocs-material-8.5.10">
     
     
       
@@ -29,10 +23,15 @@
       
     
     
-      <link rel="stylesheet" href="../assets/stylesheets/main.26e3688c.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.472b142f.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.ecc896b0.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.08040f6c.min.css">
+        
+          
+          
+          <meta name="theme-color" content="#2094f3">
+        
       
       
 
@@ -87,9 +86,7 @@
     
       
 
-  
-
-<header class="md-header md-header--shadow" data-md-component="header">
+<header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
     <a href=".." title="Unity ML-Agents Toolkit" class="md-header__button md-logo" aria-label="Unity ML-Agents Toolkit" data-md-component="logo">
       
@@ -116,8 +113,6 @@
       </div>
     </div>
     
-      
-    
     
     
       <label class="md-header__button md-icon" for="__search">
@@ -146,7 +141,7 @@
           <div class="md-search-result__meta">
             Initializing search
           </div>
-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
         </div>
       </div>
     </div>
@@ -158,7 +153,7 @@
         <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -202,7 +197,7 @@
       <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -270,18 +265,17 @@
     <li class="md-nav__item md-nav__item--active md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_4" type="checkbox" id="__nav_4" checked>
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" checked>
-      
       
       
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_4">
           Background
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="true">
+      <nav class="md-nav" aria-label="Background" data-md-level="1">
         <label class="md-nav__title" for="__nav_4">
           <span class="md-nav__icon md-icon"></span>
           Background
@@ -311,7 +305,7 @@
   
     <li class="md-nav__item md-nav__item--active">
       
-      <input class="md-nav__toggle md-toggle" type="checkbox" id="__toc">
+      <input class="md-nav__toggle md-toggle" data-md-toggle="toc" type="checkbox" id="__toc">
       
       
         
@@ -395,18 +389,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_5" type="checkbox" id="__nav_5" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
       
       
-      
-        <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_5">
           Interfacing with Unity Builds
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Interfacing with Unity Builds" data-md-level="1">
         <label class="md-nav__title" for="__nav_5">
           <span class="md-nav__icon md-icon"></span>
           Interfacing with Unity Builds
@@ -472,18 +465,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_6" type="checkbox" id="__nav_6" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" >
-      
       
       
-        <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_6">
           Python API Docs
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Python API Docs" data-md-level="1">
         <label class="md-nav__title" for="__nav_6">
           <span class="md-nav__icon md-icon"></span>
           Python API Docs
@@ -563,18 +555,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_7" type="checkbox" id="__nav_7" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_7" >
       
       
-      
-        <label class="md-nav__link" for="__nav_7" id="__nav_7_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_7">
           Tutorials
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_7_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Tutorials" data-md-level="1">
         <label class="md-nav__title" for="__nav_7">
           <span class="md-nav__icon md-icon"></span>
           Tutorials
@@ -612,18 +603,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_8" type="checkbox" id="__nav_8" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_8" >
-      
       
       
-        <label class="md-nav__link" for="__nav_8" id="__nav_8_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_8">
           About
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="About" data-md-level="1">
         <label class="md-nav__title" for="__nav_8">
           <span class="md-nav__icon md-icon"></span>
           About
@@ -746,8 +736,10 @@
                 
                   
 
-  
-  
+  <a href="https://github.com/Unity-Technologies/ml-agents/edit/main/docs/Background-PyTorch.md" title="Edit this page" class="md-content__button md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
 
 <h1 id="background-pytorch">Background: PyTorch</h1>
@@ -796,6 +788,42 @@ <h2 id="tensorboard">TensorBoard</h2>
       
         <footer class="md-footer">
   
+    
+    <nav class="md-footer__inner md-grid" aria-label="Footer" >
+      
+        
+        <a href="../Background-Machine-Learning/" class="md-footer__link md-footer__link--prev" aria-label="Previous: Machine Learning" rel="prev">
+          <div class="md-footer__button md-icon">
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
+          </div>
+          <div class="md-footer__title">
+            <div class="md-ellipsis">
+              <span class="md-footer__direction">
+                Previous
+              </span>
+              Machine Learning
+            </div>
+          </div>
+        </a>
+      
+      
+        
+        <a href="../Background-Unity/" class="md-footer__link md-footer__link--next" aria-label="Next: Unity" rel="next">
+          <div class="md-footer__title">
+            <div class="md-ellipsis">
+              <span class="md-footer__direction">
+                Next
+              </span>
+              Unity
+            </div>
+          </div>
+          <div class="md-footer__button md-icon">
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M4 11v2h12l-5.5 5.5 1.42 1.42L19.84 12l-7.92-7.92L10.5 5.5 16 11H4Z"/></svg>
+          </div>
+        </a>
+      
+    </nav>
+  
   <div class="md-footer-meta md-typeset">
     <div class="md-footer-meta__inner md-grid">
       <div class="md-copyright">
@@ -821,10 +849,10 @@ <h2 id="tensorboard">TensorBoard</h2>
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
-    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.208ed371.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.16e2a7d4.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.b4d07000.min.js"></script>
+      <script src="../assets/javascripts/bundle.d6c3db9e.min.js"></script>
       
     
   </body>
diff --git a/Background-Unity/index.html b/Background-Unity/index.html
index ed04a1e2a1..b065bebd4c 100644
--- a/Background-Unity/index.html
+++ b/Background-Unity/index.html
@@ -14,14 +14,8 @@
       
         <link rel="canonical" href="https://unity-technologies.github.io/ml-agents/Background-Unity/">
       
-      
-        <link rel="prev" href="../Background-PyTorch/">
-      
-      
-        <link rel="next" href="../Python-Gym-API/">
-      
       <link rel="icon" href="../images/unity-logo-black.png">
-      <meta name="generator" content="mkdocs-1.4.3, mkdocs-material-9.1.15">
+      <meta name="generator" content="mkdocs-1.4.2, mkdocs-material-8.5.10">
     
     
       
@@ -29,10 +23,15 @@
       
     
     
-      <link rel="stylesheet" href="../assets/stylesheets/main.26e3688c.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.472b142f.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.ecc896b0.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.08040f6c.min.css">
+        
+          
+          
+          <meta name="theme-color" content="#2094f3">
+        
       
       
 
@@ -87,9 +86,7 @@
     
       
 
-  
-
-<header class="md-header md-header--shadow" data-md-component="header">
+<header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
     <a href=".." title="Unity ML-Agents Toolkit" class="md-header__button md-logo" aria-label="Unity ML-Agents Toolkit" data-md-component="logo">
       
@@ -116,8 +113,6 @@
       </div>
     </div>
     
-      
-    
     
     
       <label class="md-header__button md-icon" for="__search">
@@ -146,7 +141,7 @@
           <div class="md-search-result__meta">
             Initializing search
           </div>
-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
         </div>
       </div>
     </div>
@@ -158,7 +153,7 @@
         <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
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+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
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     GitHub
@@ -202,7 +197,7 @@
       <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -270,18 +265,17 @@
     <li class="md-nav__item md-nav__item--active md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_4" type="checkbox" id="__nav_4" checked>
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" checked>
       
       
-      
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_4">
           Background
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="true">
+      <nav class="md-nav" aria-label="Background" data-md-level="1">
         <label class="md-nav__title" for="__nav_4">
           <span class="md-nav__icon md-icon"></span>
           Background
@@ -325,7 +319,7 @@
   
     <li class="md-nav__item md-nav__item--active">
       
-      <input class="md-nav__toggle md-toggle" type="checkbox" id="__toc">
+      <input class="md-nav__toggle md-toggle" data-md-toggle="toc" type="checkbox" id="__toc">
       
       
         
@@ -357,18 +351,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_5" type="checkbox" id="__nav_5" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
-      
       
       
-        <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_5">
           Interfacing with Unity Builds
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Interfacing with Unity Builds" data-md-level="1">
         <label class="md-nav__title" for="__nav_5">
           <span class="md-nav__icon md-icon"></span>
           Interfacing with Unity Builds
@@ -434,18 +427,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_6" type="checkbox" id="__nav_6" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" >
       
       
-      
-        <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_6">
           Python API Docs
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Python API Docs" data-md-level="1">
         <label class="md-nav__title" for="__nav_6">
           <span class="md-nav__icon md-icon"></span>
           Python API Docs
@@ -525,18 +517,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_7" type="checkbox" id="__nav_7" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_7" >
-      
       
       
-        <label class="md-nav__link" for="__nav_7" id="__nav_7_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_7">
           Tutorials
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         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_7_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Tutorials" data-md-level="1">
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           <span class="md-nav__icon md-icon"></span>
           Tutorials
@@ -574,18 +565,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_8" type="checkbox" id="__nav_8" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_8" >
       
       
-      
-        <label class="md-nav__link" for="__nav_8" id="__nav_8_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_8">
           About
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="About" data-md-level="1">
         <label class="md-nav__title" for="__nav_8">
           <span class="md-nav__icon md-icon"></span>
           About
@@ -686,8 +676,10 @@
                 
                   
 
-  
-  
+  <a href="https://github.com/Unity-Technologies/ml-agents/edit/main/docs/Background-Unity.md" title="Edit this page" class="md-content__button md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
 
 <h1 id="background-unity">Background: Unity</h1>
@@ -725,6 +717,42 @@ <h1 id="background-unity">Background: Unity</h1>
       
         <footer class="md-footer">
   
+    
+    <nav class="md-footer__inner md-grid" aria-label="Footer" >
+      
+        
+        <a href="../Background-PyTorch/" class="md-footer__link md-footer__link--prev" aria-label="Previous: PyTorch" rel="prev">
+          <div class="md-footer__button md-icon">
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
+          </div>
+          <div class="md-footer__title">
+            <div class="md-ellipsis">
+              <span class="md-footer__direction">
+                Previous
+              </span>
+              PyTorch
+            </div>
+          </div>
+        </a>
+      
+      
+        
+        <a href="../Python-Gym-API/" class="md-footer__link md-footer__link--next" aria-label="Next: Getting started with the Gym API" rel="next">
+          <div class="md-footer__title">
+            <div class="md-ellipsis">
+              <span class="md-footer__direction">
+                Next
+              </span>
+              Getting started with the Gym API
+            </div>
+          </div>
+          <div class="md-footer__button md-icon">
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M4 11v2h12l-5.5 5.5 1.42 1.42L19.84 12l-7.92-7.92L10.5 5.5 16 11H4Z"/></svg>
+          </div>
+        </a>
+      
+    </nav>
+  
   <div class="md-footer-meta md-typeset">
     <div class="md-footer-meta__inner md-grid">
       <div class="md-copyright">
@@ -750,10 +778,10 @@ <h1 id="background-unity">Background: Unity</h1>
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
-    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.208ed371.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.16e2a7d4.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.b4d07000.min.js"></script>
+      <script src="../assets/javascripts/bundle.d6c3db9e.min.js"></script>
       
     
   </body>
diff --git a/CODE_OF_CONDUCT/index.html b/CODE_OF_CONDUCT/index.html
index 86cb0e5381..09c2cf15a9 100644
--- a/CODE_OF_CONDUCT/index.html
+++ b/CODE_OF_CONDUCT/index.html
@@ -14,10 +14,8 @@
       
         <link rel="canonical" href="https://unity-technologies.github.io/ml-agents/CODE_OF_CONDUCT/">
       
-      
-      
       <link rel="icon" href="../images/unity-logo-black.png">
-      <meta name="generator" content="mkdocs-1.4.3, mkdocs-material-9.1.15">
+      <meta name="generator" content="mkdocs-1.4.2, mkdocs-material-8.5.10">
     
     
       
@@ -25,10 +23,15 @@
       
     
     
-      <link rel="stylesheet" href="../assets/stylesheets/main.26e3688c.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.472b142f.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.ecc896b0.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.08040f6c.min.css">
+        
+          
+          
+          <meta name="theme-color" content="#2094f3">
+        
       
       
 
@@ -83,9 +86,7 @@
     
       
 
-  
-
-<header class="md-header md-header--shadow" data-md-component="header">
+<header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
     <a href=".." title="Unity ML-Agents Toolkit" class="md-header__button md-logo" aria-label="Unity ML-Agents Toolkit" data-md-component="logo">
       
@@ -112,8 +113,6 @@
       </div>
     </div>
     
-      
-    
     
     
       <label class="md-header__button md-icon" for="__search">
@@ -142,7 +141,7 @@
           <div class="md-search-result__meta">
             Initializing search
           </div>
-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
         </div>
       </div>
     </div>
@@ -154,7 +153,7 @@
         <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -198,7 +197,7 @@
       <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
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   <div class="md-source__repository">
     GitHub
@@ -264,18 +263,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_4" type="checkbox" id="__nav_4" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
       
       
-      
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_4">
           Background
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Background" data-md-level="1">
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           <span class="md-nav__icon md-icon"></span>
           Background
@@ -341,18 +339,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
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-      
       
       
-        <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_5">
           Interfacing with Unity Builds
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Interfacing with Unity Builds" data-md-level="1">
         <label class="md-nav__title" for="__nav_5">
           <span class="md-nav__icon md-icon"></span>
           Interfacing with Unity Builds
@@ -418,18 +415,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_6" type="checkbox" id="__nav_6" >
       
       
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-      
       
       
-        <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_6">
           Python API Docs
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Python API Docs" data-md-level="1">
         <label class="md-nav__title" for="__nav_6">
           <span class="md-nav__icon md-icon"></span>
           Python API Docs
@@ -509,18 +505,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_7" type="checkbox" id="__nav_7" >
       
       
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-      
-        <label class="md-nav__link" for="__nav_7" id="__nav_7_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_7">
           Tutorials
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_7_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Tutorials" data-md-level="1">
         <label class="md-nav__title" for="__nav_7">
           <span class="md-nav__icon md-icon"></span>
           Tutorials
@@ -558,18 +553,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_8" type="checkbox" id="__nav_8" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_8" >
-      
       
       
-        <label class="md-nav__link" for="__nav_8" id="__nav_8_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_8">
           About
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="About" data-md-level="1">
         <label class="md-nav__title" for="__nav_8">
           <span class="md-nav__icon md-icon"></span>
           About
@@ -720,8 +714,10 @@
                 
                   
 
-  
-  
+  <a href="https://github.com/Unity-Technologies/ml-agents/edit/main/docs/CODE_OF_CONDUCT.md" title="Edit this page" class="md-content__button md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
 
 <h1 id="contributor-covenant-code-of-conduct">Contributor Covenant Code of Conduct</h1>
@@ -824,10 +820,10 @@ <h2 id="attribution">Attribution</h2>
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
-    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.208ed371.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.16e2a7d4.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.b4d07000.min.js"></script>
+      <script src="../assets/javascripts/bundle.d6c3db9e.min.js"></script>
       
     
   </body>
diff --git a/CONTRIBUTING/index.html b/CONTRIBUTING/index.html
index e492ed99d4..45fbacde2a 100644
--- a/CONTRIBUTING/index.html
+++ b/CONTRIBUTING/index.html
@@ -14,10 +14,8 @@
       
         <link rel="canonical" href="https://unity-technologies.github.io/ml-agents/CONTRIBUTING/">
       
-      
-      
       <link rel="icon" href="../images/unity-logo-black.png">
-      <meta name="generator" content="mkdocs-1.4.3, mkdocs-material-9.1.15">
+      <meta name="generator" content="mkdocs-1.4.2, mkdocs-material-8.5.10">
     
     
       
@@ -25,10 +23,15 @@
       
     
     
-      <link rel="stylesheet" href="../assets/stylesheets/main.26e3688c.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.472b142f.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.ecc896b0.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.08040f6c.min.css">
+        
+          
+          
+          <meta name="theme-color" content="#2094f3">
+        
       
       
 
@@ -83,9 +86,7 @@
     
       
 
-  
-
-<header class="md-header md-header--shadow" data-md-component="header">
+<header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
     <a href=".." title="Unity ML-Agents Toolkit" class="md-header__button md-logo" aria-label="Unity ML-Agents Toolkit" data-md-component="logo">
       
@@ -112,8 +113,6 @@
       </div>
     </div>
     
-      
-    
     
     
       <label class="md-header__button md-icon" for="__search">
@@ -142,7 +141,7 @@
           <div class="md-search-result__meta">
             Initializing search
           </div>
-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
         </div>
       </div>
     </div>
@@ -154,7 +153,7 @@
         <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -198,7 +197,7 @@
       <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -264,18 +263,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_4" type="checkbox" id="__nav_4" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
       
       
-      
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_4">
           Background
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Background" data-md-level="1">
         <label class="md-nav__title" for="__nav_4">
           <span class="md-nav__icon md-icon"></span>
           Background
@@ -341,18 +339,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_5" type="checkbox" id="__nav_5" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
-      
       
       
-        <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_5">
           Interfacing with Unity Builds
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Interfacing with Unity Builds" data-md-level="1">
         <label class="md-nav__title" for="__nav_5">
           <span class="md-nav__icon md-icon"></span>
           Interfacing with Unity Builds
@@ -418,18 +415,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_6" type="checkbox" id="__nav_6" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" >
-      
       
       
-        <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_6">
           Python API Docs
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Python API Docs" data-md-level="1">
         <label class="md-nav__title" for="__nav_6">
           <span class="md-nav__icon md-icon"></span>
           Python API Docs
@@ -509,18 +505,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_7" type="checkbox" id="__nav_7" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_7" >
       
       
-      
-        <label class="md-nav__link" for="__nav_7" id="__nav_7_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_7">
           Tutorials
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_7_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Tutorials" data-md-level="1">
         <label class="md-nav__title" for="__nav_7">
           <span class="md-nav__icon md-icon"></span>
           Tutorials
@@ -558,18 +553,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_8" type="checkbox" id="__nav_8" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_8" >
-      
       
       
-        <label class="md-nav__link" for="__nav_8" id="__nav_8_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_8">
           About
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="About" data-md-level="1">
         <label class="md-nav__title" for="__nav_8">
           <span class="md-nav__icon md-icon"></span>
           About
@@ -733,8 +727,10 @@
                 
                   
 
-  
-  
+  <a href="https://github.com/Unity-Technologies/ml-agents/edit/main/docs/CONTRIBUTING.md" title="Edit this page" class="md-content__button md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
 
 <h1 id="contribution-guidelines">Contribution Guidelines</h1>
@@ -854,10 +850,10 @@ <h2 id="contributor-license-agreements">Contributor License Agreements</h2>
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
-    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.208ed371.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.16e2a7d4.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.b4d07000.min.js"></script>
+      <script src="../assets/javascripts/bundle.d6c3db9e.min.js"></script>
       
     
   </body>
diff --git a/Custom-SideChannels/index.html b/Custom-SideChannels/index.html
index 661c13b090..ff387a8360 100644
--- a/Custom-SideChannels/index.html
+++ b/Custom-SideChannels/index.html
@@ -14,10 +14,8 @@
       
         <link rel="canonical" href="https://unity-technologies.github.io/ml-agents/Custom-SideChannels/">
       
-      
-      
       <link rel="icon" href="../images/unity-logo-black.png">
-      <meta name="generator" content="mkdocs-1.4.3, mkdocs-material-9.1.15">
+      <meta name="generator" content="mkdocs-1.4.2, mkdocs-material-8.5.10">
     
     
       
@@ -25,10 +23,15 @@
       
     
     
-      <link rel="stylesheet" href="../assets/stylesheets/main.26e3688c.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.472b142f.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.ecc896b0.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.08040f6c.min.css">
+        
+          
+          
+          <meta name="theme-color" content="#2094f3">
+        
       
       
 
@@ -83,9 +86,7 @@
     
       
 
-  
-
-<header class="md-header md-header--shadow" data-md-component="header">
+<header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
     <a href=".." title="Unity ML-Agents Toolkit" class="md-header__button md-logo" aria-label="Unity ML-Agents Toolkit" data-md-component="logo">
       
@@ -112,8 +113,6 @@
       </div>
     </div>
     
-      
-    
     
     
       <label class="md-header__button md-icon" for="__search">
@@ -142,7 +141,7 @@
           <div class="md-search-result__meta">
             Initializing search
           </div>
-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
         </div>
       </div>
     </div>
@@ -154,7 +153,7 @@
         <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -198,7 +197,7 @@
       <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
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-      
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_4">
           Background
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         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Background" data-md-level="1">
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           Background
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+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_5" type="checkbox" id="__nav_5" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
-      
       
       
-        <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_5">
           Interfacing with Unity Builds
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         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Interfacing with Unity Builds" data-md-level="1">
         <label class="md-nav__title" for="__nav_5">
           <span class="md-nav__icon md-icon"></span>
           Interfacing with Unity Builds
@@ -418,18 +415,17 @@
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+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_6" type="checkbox" id="__nav_6" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" >
-      
       
       
-        <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_6">
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         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Python API Docs" data-md-level="1">
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           Python API Docs
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-      
-        <label class="md-nav__link" for="__nav_7" id="__nav_7_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_7">
           Tutorials
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_7_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Tutorials" data-md-level="1">
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           <span class="md-nav__icon md-icon"></span>
           Tutorials
@@ -558,18 +553,17 @@
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-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_8" >
-      
       
       
-        <label class="md-nav__link" for="__nav_8" id="__nav_8_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_8">
           About
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="About" data-md-level="1">
         <label class="md-nav__title" for="__nav_8">
           <span class="md-nav__icon md-icon"></span>
           About
@@ -732,8 +726,10 @@
                 
                   
 
-  
-  
+  <a href="https://github.com/Unity-Technologies/ml-agents/edit/main/docs/Custom-SideChannels.md" title="Edit this page" class="md-content__button md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
 
 <h1 id="custom-side-channels">Custom Side Channels</h1>
@@ -975,10 +971,10 @@ <h3 id="example-python-code">Example Python code</h3>
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+    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.16e2a7d4.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.b4d07000.min.js"></script>
+      <script src="../assets/javascripts/bundle.d6c3db9e.min.js"></script>
       
     
   </body>
diff --git a/ELO-Rating-System/index.html b/ELO-Rating-System/index.html
new file mode 100644
index 0000000000..1240606e04
--- /dev/null
+++ b/ELO-Rating-System/index.html
@@ -0,0 +1,809 @@
+
+<!doctype html>
+<html lang="en" class="no-js">
+  <head>
+    
+      <meta charset="utf-8">
+      <meta name="viewport" content="width=device-width,initial-scale=1">
+      
+        <meta name="description" content="The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents.">
+      
+      
+        <meta name="author" content="Unity Technologies">
+      
+      
+        <link rel="canonical" href="https://unity-technologies.github.io/ml-agents/ELO-Rating-System/">
+      
+      <link rel="icon" href="../images/unity-logo-black.png">
+      <meta name="generator" content="mkdocs-1.4.2, mkdocs-material-8.5.10">
+    
+    
+      
+        <title>ELO Rating System - Unity ML-Agents Toolkit</title>
+      
+    
+    
+      <link rel="stylesheet" href="../assets/stylesheets/main.472b142f.min.css">
+      
+        
+        <link rel="stylesheet" href="../assets/stylesheets/palette.08040f6c.min.css">
+        
+          
+          
+          <meta name="theme-color" content="#2094f3">
+        
+      
+      
+
+    
+    
+    
+      
+        
+        
+        <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
+        <link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Roboto:300,300i,400,400i,700,700i%7CRoboto+Mono:400,400i,700,700i&display=fallback">
+        <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
+      
+    
+    
+      <link rel="stylesheet" href="../extra.css">
+    
+    <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
+    
+      
+
+    
+    
+    
+  </head>
+  
+  
+    
+    
+    
+    
+    
+    <body dir="ltr" data-md-color-scheme="default" data-md-color-primary="blue" data-md-color-accent="light-blue">
+  
+    
+    
+    <input class="md-toggle" data-md-toggle="drawer" type="checkbox" id="__drawer" autocomplete="off">
+    <input class="md-toggle" data-md-toggle="search" type="checkbox" id="__search" autocomplete="off">
+    <label class="md-overlay" for="__drawer"></label>
+    <div data-md-component="skip">
+      
+        
+        <a href="#elo-rating-system" class="md-skip">
+          Skip to content
+        </a>
+      
+    </div>
+    <div data-md-component="announce">
+      
+    </div>
+    
+    
+      
+
+<header class="md-header" data-md-component="header">
+  <nav class="md-header__inner md-grid" aria-label="Header">
+    <a href=".." title="Unity ML-Agents Toolkit" class="md-header__button md-logo" aria-label="Unity ML-Agents Toolkit" data-md-component="logo">
+      
+  <img src="../images/unity-logo.png" alt="logo">
+
+    </a>
+    <label class="md-header__button md-icon" for="__drawer">
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
+    </label>
+    <div class="md-header__title" data-md-component="header-title">
+      <div class="md-header__ellipsis">
+        <div class="md-header__topic">
+          <span class="md-ellipsis">
+            Unity ML-Agents Toolkit
+          </span>
+        </div>
+        <div class="md-header__topic" data-md-component="header-topic">
+          <span class="md-ellipsis">
+            
+              ELO Rating System
+            
+          </span>
+        </div>
+      </div>
+    </div>
+    
+    
+    
+      <label class="md-header__button md-icon" for="__search">
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
+      </label>
+      <div class="md-search" data-md-component="search" role="dialog">
+  <label class="md-search__overlay" for="__search"></label>
+  <div class="md-search__inner" role="search">
+    <form class="md-search__form" name="search">
+      <input type="text" class="md-search__input" name="query" aria-label="Search" placeholder="Search" autocapitalize="off" autocorrect="off" autocomplete="off" spellcheck="false" data-md-component="search-query" required>
+      <label class="md-search__icon md-icon" for="__search">
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
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+      </label>
+      <nav class="md-search__options" aria-label="Search">
+        
+        <button type="reset" class="md-search__icon md-icon" title="Clear" aria-label="Clear" tabindex="-1">
+          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M19 6.41 17.59 5 12 10.59 6.41 5 5 6.41 10.59 12 5 17.59 6.41 19 12 13.41 17.59 19 19 17.59 13.41 12 19 6.41Z"/></svg>
+        </button>
+      </nav>
+      
+    </form>
+    <div class="md-search__output">
+      <div class="md-search__scrollwrap" data-md-scrollfix>
+        <div class="md-search-result" data-md-component="search-result">
+          <div class="md-search-result__meta">
+            Initializing search
+          </div>
+          <ol class="md-search-result__list"></ol>
+        </div>
+      </div>
+    </div>
+  </div>
+</div>
+    
+    
+      <div class="md-header__source">
+        <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
+  <div class="md-source__icon md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+  </div>
+  <div class="md-source__repository">
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+</a>
+      </div>
+    
+  </nav>
+  
+</header>
+    
+    <div class="md-container" data-md-component="container">
+      
+      
+        
+          
+        
+      
+      <main class="md-main" data-md-component="main">
+        <div class="md-main__inner md-grid">
+          
+            
+              
+              <div class="md-sidebar md-sidebar--primary" data-md-component="sidebar" data-md-type="navigation" >
+                <div class="md-sidebar__scrollwrap">
+                  <div class="md-sidebar__inner">
+                    
+
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+<nav class="md-nav md-nav--primary" aria-label="Navigation" data-md-level="0">
+  <label class="md-nav__title" for="__drawer">
+    <a href=".." title="Unity ML-Agents Toolkit" class="md-nav__button md-logo" aria-label="Unity ML-Agents Toolkit" data-md-component="logo">
+      
+  <img src="../images/unity-logo.png" alt="logo">
+
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+    Unity ML-Agents Toolkit
+  </label>
+  
+    <div class="md-nav__source">
+      <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
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+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+  </div>
+  <div class="md-source__repository">
+    GitHub
+  </div>
+</a>
+    </div>
+  
+  <ul class="md-nav__list" data-md-scrollfix>
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href=".." class="md-nav__link">
+        Home
+      </a>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../ML-Agents-Overview/" class="md-nav__link">
+        ML-Agents Overview
+      </a>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../ML-Agents-Toolkit-Documentation/" class="md-nav__link">
+        Toolkit Documentation
+      </a>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
+  
+    
+    <li class="md-nav__item md-nav__item--nested">
+      
+      
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_4" type="checkbox" id="__nav_4" >
+      
+      
+      
+      
+        <label class="md-nav__link" for="__nav_4">
+          Background
+          <span class="md-nav__icon md-icon"></span>
+        </label>
+      
+      <nav class="md-nav" aria-label="Background" data-md-level="1">
+        <label class="md-nav__title" for="__nav_4">
+          <span class="md-nav__icon md-icon"></span>
+          Background
+        </label>
+        <ul class="md-nav__list" data-md-scrollfix>
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../Background-Machine-Learning/" class="md-nav__link">
+        Machine Learning
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../Background-PyTorch/" class="md-nav__link">
+        PyTorch
+      </a>
+    </li>
+  
+
+            
+          
+            
+              
+  
+  
+  
+    <li class="md-nav__item">
+      <a href="../Background-Unity/" class="md-nav__link">
+        Unity
+      </a>
+    </li>
+  
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+          
+        </ul>
+      </nav>
+    </li>
+  
+
+    
+      
+      
+      
+
+  
+  
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+    <li class="md-nav__item md-nav__item--nested">
+      
+      
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_5" type="checkbox" id="__nav_5" >
+      
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+      
+      
+        <label class="md-nav__link" for="__nav_5">
+          Interfacing with Unity Builds
+          <span class="md-nav__icon md-icon"></span>
+        </label>
+      
+      <nav class="md-nav" aria-label="Interfacing with Unity Builds" data-md-level="1">
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+          Interfacing with Unity Builds
+        </label>
+        <ul class="md-nav__list" data-md-scrollfix>
+          
+            
+              
+  
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+      
+        <li class="md-nav__item">
+  <a href="#what-is-a-zero-sum-game" class="md-nav__link">
+    What is a zero-sum game?
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#how-works-the-elo-rating-system" class="md-nav__link">
+    How works the ELO Rating System
+  </a>
+  
+    <nav class="md-nav" aria-label="How works the ELO Rating System">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#the-tennis-example" class="md-nav__link">
+    The Tennis example
+  </a>
+  
+</li>
+        
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+    </nav>
+  
+</li>
+      
+    </ul>
+  
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+                  </div>
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+              </div>
+            
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+          
+            <div class="md-content" data-md-component="content">
+              <article class="md-content__inner md-typeset">
+                
+                  
+
+  <a href="https://github.com/Unity-Technologies/ml-agents/edit/main/docs/ELO-Rating-System.md" title="Edit this page" class="md-content__button md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
+
+
+<h1 id="elo-rating-system">ELO Rating System</h1>
+<p>In adversarial games, the cumulative environment reward may <strong>not be a meaningful metric</strong> by which to track
+learning progress.</p>
+<p>This is because the cumulative reward is <strong>entirely dependent on the skill of the opponent</strong>.</p>
+<p>An agent at a particular skill level will get more or less reward against a worse or better agent,
+respectively.</p>
+<p>Instead, it's better to use ELO rating system, a method to calculate <strong>the relative skill level between two players in a zero-sum game</strong>.</p>
+<p>If the training performs correctly, <strong>this value should steadily increase</strong>.</p>
+<h2 id="what-is-a-zero-sum-game">What is a zero-sum game?</h2>
+<p>A zero-sum game is a game where <strong>each player's gain or loss of utility is exactly balanced by the gain or loss of the utility of the opponent</strong>.</p>
+<p>Simply explained, we face a zero-sum game <strong>when one agent gets +1.0, its opponent gets -1.0 reward</strong>.</p>
+<p>For instance, Tennis is a zero-sum game: if you win the point you get +1.0 and your opponent gets -1.0 reward.</p>
+<h2 id="how-works-the-elo-rating-system">How works the ELO Rating System</h2>
+<ul>
+<li>
+<p>Each player <strong>has an initial ELO score</strong>. It's defined in the <code>initial_elo</code> trainer config hyperparameter.</p>
+</li>
+<li>
+<p>The <strong>difference in rating between the two players</strong> serves as the predictor of the outcomes of a match.</p>
+</li>
+</ul>
+<p><img alt="Example Elo" src="../images/elo_example.png" />
+<em>For instance, if player A has an Elo score of 2100 and player B has an ELO score of 1800 the chance that player A wins is 85% against 15% for player b.</em></p>
+<ul>
+<li>We calculate the <strong>expected score of each player</strong> using this formula:</li>
+</ul>
+<p><img alt="Elo Expected Score Formula" src="../images/elo_expected_score_formula.png" /></p>
+<ul>
+<li>At the end of the game, based on the outcome <strong>we update the player’s actual Elo score</strong>, we use a linear adjustment proportional to the amount by which the player over-performed or under-performed.
+The winning player takes points from the losing one:</li>
+<li>If the <em>higher-rated player wins</em> → <strong>a few points</strong> will be taken from the lower-rated player.</li>
+<li>If the <em>lower-rated player wins</em> → <strong>a lot of points</strong> will be taken from the high-rated player.</li>
+<li>
+<p>If it’s <em>a draw</em> → the lower-rated player gains <strong>a few points</strong> from higher.</p>
+</li>
+<li>
+<p>We update players rating using this formula:</p>
+</li>
+</ul>
+<p><img alt="Elo Score Update Formula" src="../images/elo_score_update_formula.png" /></p>
+<h3 id="the-tennis-example">The Tennis example</h3>
+<ul>
+<li>We start to train our agents.</li>
+<li>Both of them have the same skills. So ELO score for each of them that we defined using parameter <code>initial_elo = 1200.0</code>.</li>
+</ul>
+<p>We calculate the expected score E:
+Ea = 0.5
+Eb = 0.5</p>
+<p>So it means that each player has 50% chances of winning the point.</p>
+<p>If A wins, the new rating R would be:</p>
+<p>Ra = 1200 + 16 * (1 - 0.5) → 1208</p>
+<p>Rb = 1200 + 16 * (0 - 0.5) → 1192</p>
+<p>Player A has now an ELO score of 1208 and Player B an ELO score of 1192. Therefore, Player A is now a little bit <strong>better than Player B</strong>.</p>
+
+
+
+
+
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-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
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+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
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   <div class="md-source__repository">
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@@ -202,7 +197,7 @@
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+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
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   <div class="md-source__repository">
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@@ -268,18 +263,17 @@
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+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_7" type="checkbox" id="__nav_7" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_7" >
       
       
-      
-        <label class="md-nav__link" for="__nav_7" id="__nav_7_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_7">
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_7_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Tutorials" data-md-level="1">
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           Tutorials
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-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_8" checked>
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-        <label class="md-nav__link" for="__nav_8" id="__nav_8_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_8">
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="true">
+      <nav class="md-nav" aria-label="About" data-md-level="1">
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           About
@@ -591,7 +581,7 @@
   
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+      <input class="md-nav__toggle md-toggle" data-md-toggle="toc" type="checkbox" id="__toc">
       
       
         
@@ -802,8 +792,10 @@
                 
                   
 
-  
-  
+  <a href="https://github.com/Unity-Technologies/ml-agents/edit/main/docs/FAQ.md" title="Edit this page" class="md-content__button md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
 
 <h1 id="frequently-asked-questions">Frequently Asked Questions</h1>
@@ -879,6 +871,42 @@ <h2 id="file-name-cannot-be-opened-because-the-developer-cannot-be-verified">"Fi
       
         <footer class="md-footer">
   
+    
+    <nav class="md-footer__inner md-grid" aria-label="Footer" >
+      
+        
+        <a href="../Tutorial-Custom-Trainer-Plugin/" class="md-footer__link md-footer__link--prev" aria-label="Previous: Custom Trainer Plugin" rel="prev">
+          <div class="md-footer__button md-icon">
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
+          </div>
+          <div class="md-footer__title">
+            <div class="md-ellipsis">
+              <span class="md-footer__direction">
+                Previous
+              </span>
+              Custom Trainer Plugin
+            </div>
+          </div>
+        </a>
+      
+      
+        
+        <a href="../Limitations/" class="md-footer__link md-footer__link--next" aria-label="Next: Limitations" rel="next">
+          <div class="md-footer__title">
+            <div class="md-ellipsis">
+              <span class="md-footer__direction">
+                Next
+              </span>
+              Limitations
+            </div>
+          </div>
+          <div class="md-footer__button md-icon">
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M4 11v2h12l-5.5 5.5 1.42 1.42L19.84 12l-7.92-7.92L10.5 5.5 16 11H4Z"/></svg>
+          </div>
+        </a>
+      
+    </nav>
+  
   <div class="md-footer-meta md-typeset">
     <div class="md-footer-meta__inner md-grid">
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-    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.208ed371.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.16e2a7d4.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.b4d07000.min.js"></script>
+      <script src="../assets/javascripts/bundle.d6c3db9e.min.js"></script>
       
     
   </body>
diff --git a/Getting-Started/index.html b/Getting-Started/index.html
index 24de20c9ce..fcec16e4af 100644
--- a/Getting-Started/index.html
+++ b/Getting-Started/index.html
@@ -14,10 +14,8 @@
       
         <link rel="canonical" href="https://unity-technologies.github.io/ml-agents/Getting-Started/">
       
-      
-      
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-      <meta name="generator" content="mkdocs-1.4.3, mkdocs-material-9.1.15">
+      <meta name="generator" content="mkdocs-1.4.2, mkdocs-material-8.5.10">
     
     
       
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-      <link rel="stylesheet" href="../assets/stylesheets/main.26e3688c.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.472b142f.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.ecc896b0.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.08040f6c.min.css">
+        
+          
+          
+          <meta name="theme-color" content="#2094f3">
+        
       
       
 
@@ -83,9 +86,7 @@
     
       
 
-  
-
-<header class="md-header md-header--shadow" data-md-component="header">
+<header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
     <a href=".." title="Unity ML-Agents Toolkit" class="md-header__button md-logo" aria-label="Unity ML-Agents Toolkit" data-md-component="logo">
       
@@ -112,8 +113,6 @@
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     </div>
     
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-    
     
     
       <label class="md-header__button md-icon" for="__search">
@@ -142,7 +141,7 @@
           <div class="md-search-result__meta">
             Initializing search
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-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
         </div>
       </div>
     </div>
@@ -154,7 +153,7 @@
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+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
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@@ -198,7 +197,7 @@
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-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
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   </div>
   <div class="md-source__repository">
     GitHub
@@ -264,18 +263,17 @@
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-      
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+        <label class="md-nav__link" for="__nav_4">
           Background
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         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Background" data-md-level="1">
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           <span class="md-nav__icon md-icon"></span>
           Background
@@ -341,18 +339,17 @@
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+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_5" type="checkbox" id="__nav_5" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
-      
       
       
-        <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_5">
           Interfacing with Unity Builds
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Interfacing with Unity Builds" data-md-level="1">
         <label class="md-nav__title" for="__nav_5">
           <span class="md-nav__icon md-icon"></span>
           Interfacing with Unity Builds
@@ -418,18 +415,17 @@
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+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_6" type="checkbox" id="__nav_6" >
       
       
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-      
       
       
-        <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_6">
           Python API Docs
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         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Python API Docs" data-md-level="1">
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           <span class="md-nav__icon md-icon"></span>
           Python API Docs
@@ -509,18 +505,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
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-      
-        <label class="md-nav__link" for="__nav_7" id="__nav_7_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_7">
           Tutorials
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_7_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Tutorials" data-md-level="1">
         <label class="md-nav__title" for="__nav_7">
           <span class="md-nav__icon md-icon"></span>
           Tutorials
@@ -558,18 +553,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_8" type="checkbox" id="__nav_8" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_8" >
-      
       
       
-        <label class="md-nav__link" for="__nav_8" id="__nav_8_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_8">
           About
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="About" data-md-level="1">
         <label class="md-nav__title" for="__nav_8">
           <span class="md-nav__icon md-icon"></span>
           About
@@ -773,8 +767,10 @@
                 
                   
 
-  
-  
+  <a href="https://github.com/Unity-Technologies/ml-agents/edit/main/docs/Getting-Started.md" title="Edit this page" class="md-content__button md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
 
 <h1 id="getting-started-guide">Getting Started Guide</h1>
@@ -1047,10 +1043,10 @@ <h2 id="next-steps">Next Steps</h2>
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
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+    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.16e2a7d4.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.b4d07000.min.js"></script>
+      <script src="../assets/javascripts/bundle.d6c3db9e.min.js"></script>
       
     
   </body>
diff --git a/Glossary/index.html b/Glossary/index.html
index 0fdc8b53c6..e73cbe6614 100644
--- a/Glossary/index.html
+++ b/Glossary/index.html
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         <link rel="canonical" href="https://unity-technologies.github.io/ml-agents/Glossary/">
       
-      
-      
       <link rel="icon" href="../images/unity-logo-black.png">
-      <meta name="generator" content="mkdocs-1.4.3, mkdocs-material-9.1.15">
+      <meta name="generator" content="mkdocs-1.4.2, mkdocs-material-8.5.10">
     
     
       
@@ -25,10 +23,15 @@
       
     
     
-      <link rel="stylesheet" href="../assets/stylesheets/main.26e3688c.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.472b142f.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.ecc896b0.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.08040f6c.min.css">
+        
+          
+          
+          <meta name="theme-color" content="#2094f3">
+        
       
       
 
@@ -83,9 +86,7 @@
     
       
 
-  
-
-<header class="md-header md-header--shadow" data-md-component="header">
+<header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
     <a href=".." title="Unity ML-Agents Toolkit" class="md-header__button md-logo" aria-label="Unity ML-Agents Toolkit" data-md-component="logo">
       
@@ -112,8 +113,6 @@
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     </div>
     
-      
-    
     
     
       <label class="md-header__button md-icon" for="__search">
@@ -142,7 +141,7 @@
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             Initializing search
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-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
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@@ -154,7 +153,7 @@
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+        <label class="md-nav__link" for="__nav_4">
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Background" data-md-level="1">
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           Background
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+        <label class="md-nav__link" for="__nav_5">
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         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Interfacing with Unity Builds" data-md-level="1">
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           Interfacing with Unity Builds
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-      
-        <label class="md-nav__link" for="__nav_8" id="__nav_8_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_8">
           About
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="About" data-md-level="1">
         <label class="md-nav__title" for="__nav_8">
           <span class="md-nav__icon md-icon"></span>
           About
@@ -670,8 +664,10 @@
                 
                   
 
-  
-  
+  <a href="https://github.com/Unity-Technologies/ml-agents/edit/main/docs/Glossary.md" title="Edit this page" class="md-content__button md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
 
 <h1 id="ml-agents-toolkit-glossary">ML-Agents Toolkit Glossary</h1>
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-      <script src="../assets/javascripts/bundle.b4d07000.min.js"></script>
+      <script src="../assets/javascripts/bundle.d6c3db9e.min.js"></script>
       
     
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diff --git a/Hugging-Face-Integration/index.html b/Hugging-Face-Integration/index.html
new file mode 100644
index 0000000000..e39ebc0277
--- /dev/null
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@@ -0,0 +1,792 @@
+
+<!doctype html>
+<html lang="en" class="no-js">
+  <head>
+    
+      <meta charset="utf-8">
+      <meta name="viewport" content="width=device-width,initial-scale=1">
+      
+        <meta name="description" content="The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents.">
+      
+      
+        <meta name="author" content="Unity Technologies">
+      
+      
+        <link rel="canonical" href="https://unity-technologies.github.io/ml-agents/Hugging-Face-Integration/">
+      
+      <link rel="icon" href="../images/unity-logo-black.png">
+      <meta name="generator" content="mkdocs-1.4.2, mkdocs-material-8.5.10">
+    
+    
+      
+        <title>The Hugging Face Integration - Unity ML-Agents Toolkit</title>
+      
+    
+    
+      <link rel="stylesheet" href="../assets/stylesheets/main.472b142f.min.css">
+      
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+        <link rel="stylesheet" href="../assets/stylesheets/palette.08040f6c.min.css">
+        
+          
+          
+          <meta name="theme-color" content="#2094f3">
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+        <link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Roboto:300,300i,400,400i,700,700i%7CRoboto+Mono:400,400i,700,700i&display=fallback">
+        <style>:root{--md-text-font:"Roboto";--md-code-font:"Roboto Mono"}</style>
+      
+    
+    
+      <link rel="stylesheet" href="../extra.css">
+    
+    <script>__md_scope=new URL("..",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
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+    <input class="md-toggle" data-md-toggle="drawer" type="checkbox" id="__drawer" autocomplete="off">
+    <input class="md-toggle" data-md-toggle="search" type="checkbox" id="__search" autocomplete="off">
+    <label class="md-overlay" for="__drawer"></label>
+    <div data-md-component="skip">
+      
+        
+        <a href="#the-hugging-face-integration" class="md-skip">
+          Skip to content
+        </a>
+      
+    </div>
+    <div data-md-component="announce">
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+    </div>
+    
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+      
+
+<header class="md-header" data-md-component="header">
+  <nav class="md-header__inner md-grid" aria-label="Header">
+    <a href=".." title="Unity ML-Agents Toolkit" class="md-header__button md-logo" aria-label="Unity ML-Agents Toolkit" data-md-component="logo">
+      
+  <img src="../images/unity-logo.png" alt="logo">
+
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+    <label class="md-header__button md-icon" for="__drawer">
+      <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M3 6h18v2H3V6m0 5h18v2H3v-2m0 5h18v2H3v-2Z"/></svg>
+    </label>
+    <div class="md-header__title" data-md-component="header-title">
+      <div class="md-header__ellipsis">
+        <div class="md-header__topic">
+          <span class="md-ellipsis">
+            Unity ML-Agents Toolkit
+          </span>
+        </div>
+        <div class="md-header__topic" data-md-component="header-topic">
+          <span class="md-ellipsis">
+            
+              The Hugging Face Integration
+            
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+      </div>
+    </div>
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+      <label class="md-header__button md-icon" for="__search">
+        <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M9.5 3A6.5 6.5 0 0 1 16 9.5c0 1.61-.59 3.09-1.56 4.23l.27.27h.79l5 5-1.5 1.5-5-5v-.79l-.27-.27A6.516 6.516 0 0 1 9.5 16 6.5 6.5 0 0 1 3 9.5 6.5 6.5 0 0 1 9.5 3m0 2C7 5 5 7 5 9.5S7 14 9.5 14 14 12 14 9.5 12 5 9.5 5Z"/></svg>
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+  <label class="md-search__overlay" for="__search"></label>
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+        <div class="md-search-result" data-md-component="search-result">
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+</div>
+    
+    
+      <div class="md-header__source">
+        <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
+  <div class="md-source__icon md-icon">
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+    <div class="md-container" data-md-component="container">
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+    GitHub
+  </div>
+</a>
+    </div>
+  
+  <ul class="md-nav__list" data-md-scrollfix>
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+        Home
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+      <a href="../ML-Agents-Overview/" class="md-nav__link">
+        ML-Agents Overview
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+    <li class="md-nav__item">
+      <a href="../ML-Agents-Toolkit-Documentation/" class="md-nav__link">
+        Toolkit Documentation
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+<nav class="md-nav md-nav--secondary" aria-label="Table of contents">
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+    <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
+      
+        <li class="md-nav__item">
+  <a href="#download-a-model-from-the-hub" class="md-nav__link">
+    Download a model from the Hub
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#upload-a-model-to-the-hub" class="md-nav__link">
+    Upload a model to the Hub
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#visualize-an-agent-playing" class="md-nav__link">
+    Visualize an agent playing
+  </a>
+  
+</li>
+      
+    </ul>
+  
+</nav>
+                  </div>
+                </div>
+              </div>
+            
+          
+          
+            <div class="md-content" data-md-component="content">
+              <article class="md-content__inner md-typeset">
+                
+                  
+
+  <a href="https://github.com/Unity-Technologies/ml-agents/edit/main/docs/Hugging-Face-Integration.md" title="Edit this page" class="md-content__button md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
+
+
+<h1 id="the-hugging-face-integration">The Hugging Face Integration</h1>
+<p>The <a href="https://huggingface.co/models?pipeline_tag=reinforcement-learning">Hugging Face Hub 🤗</a> is a central place <strong>where anyone can share and download models</strong>.</p>
+<p>It allows you to:
+- <strong>Host</strong> your trained models.
+- <strong>Download</strong> trained models from the community.
+- Visualize your agents <strong>playing directly on your browser</strong>.</p>
+<p>You can see the list of ml-agents models <a href="https://huggingface.co/models?library=ml-agents">here</a>.</p>
+<p>We wrote a <strong>complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub</strong>:</p>
+<ul>
+<li>A short tutorial where you <a href="https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction">teach <strong>Huggy the Dog to fetch the stick</strong> and then play with him directly in your browser</a></li>
+<li>A <a href="https://huggingface.co/learn/deep-rl-course/unit5/introduction">more in-depth tutorial</a></li>
+</ul>
+<h2 id="download-a-model-from-the-hub">Download a model from the Hub</h2>
+<p>You can simply download a model from the Hub using <code>mlagents-load-from-hf</code>.</p>
+<p>You need to define two parameters:</p>
+<ul>
+<li><code>--repo-id</code>: the name of the Hugging Face repo you want to download.</li>
+<li><code>--local-dir</code>: the path to download the model.</li>
+</ul>
+<p>For instance, I want to load the model with model-id "ThomasSimonini/MLAgents-Pyramids" and put it in the downloads directory:</p>
+<pre><code class="language-sh">mlagents-load-from-hf --repo-id=&quot;ThomasSimonini/MLAgents-Pyramids&quot; --local-dir=&quot;./downloads&quot;
+</code></pre>
+<h2 id="upload-a-model-to-the-hub">Upload a model to the Hub</h2>
+<p>You can simply upload a model to the Hub using <code>mlagents-push-to-hf</code></p>
+<p>You need to define four parameters:</p>
+<ul>
+<li><code>--run-id</code>: the name of the training run id.</li>
+<li><code>--local-dir</code>: where the model was saved</li>
+<li><code>--repo-id</code>: the name of the Hugging Face repo you want to create or update. It’s always <your huggingface username>/<the repo name> If the repo does not exist it will be created automatically</li>
+<li><code>--commit-message</code>: since HF repos are git repositories you need to give a commit message.</li>
+</ul>
+<p>For instance, I want to upload my model trained with run-id "SnowballTarget1" to the repo-id: ThomasSimonini/ppo-SnowballTarget:</p>
+<pre><code class="language-sh">  mlagents-push-to-hf --run-id=&quot;SnowballTarget1&quot; --local-dir=&quot;./results/SnowballTarget1&quot; --repo-id=&quot;ThomasSimonini/ppo-SnowballTarget&quot; --commit-message=&quot;First Push&quot;
+</code></pre>
+<h2 id="visualize-an-agent-playing">Visualize an agent playing</h2>
+<p>You can watch your agent playing directly in your browser (if the environment is from the <a href="../Learning-Environment-Examples/">ML-Agents official environments</a>)</p>
+<ul>
+<li>Step 1: Go to https://huggingface.co/unity and select the environment demo.</li>
+<li>Step 2: Find your model_id in the list.</li>
+<li>Step 3: Select your .nn /.onnx file.</li>
+<li>Step 4: Click on Watch the agent play</li>
+</ul>
+
+
+
+
+
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+              </article>
+            </div>
+          
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diff --git a/Installation-Anaconda-Windows/index.html b/Installation-Anaconda-Windows/index.html
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-        <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_5">
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+      <nav class="md-nav" aria-label="Interfacing with Unity Builds" data-md-level="1">
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@@ -418,18 +415,17 @@
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-        <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_6">
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Python API Docs" data-md-level="1">
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           <span class="md-nav__icon md-icon"></span>
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@@ -509,18 +505,17 @@
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-        <label class="md-nav__link" for="__nav_7" id="__nav_7_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_7">
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_7_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Tutorials" data-md-level="1">
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           <span class="md-nav__icon md-icon"></span>
           Tutorials
@@ -558,18 +553,17 @@
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-      
       
       
-        <label class="md-nav__link" for="__nav_8" id="__nav_8_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_8">
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           <span class="md-nav__icon md-icon"></span>
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="About" data-md-level="1">
         <label class="md-nav__title" for="__nav_8">
           <span class="md-nav__icon md-icon"></span>
           About
@@ -760,8 +754,10 @@
                 
                   
 
-  
-  
+  <a href="https://github.com/Unity-Technologies/ml-agents/edit/main/docs/Installation-Anaconda-Windows.md" title="Edit this page" class="md-content__button md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
 
 <h1 id="installing-ml-agents-toolkit-for-windows-deprecated">Installing ML-Agents Toolkit for Windows (Deprecated)</h1>
@@ -1078,10 +1074,10 @@ <h2 id="acknowledgments">Acknowledgments</h2>
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+    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.16e2a7d4.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.b4d07000.min.js"></script>
+      <script src="../assets/javascripts/bundle.d6c3db9e.min.js"></script>
       
     
   </body>
diff --git a/Installation/index.html b/Installation/index.html
index 7f432eef2a..b065df149d 100644
--- a/Installation/index.html
+++ b/Installation/index.html
@@ -14,10 +14,8 @@
       
         <link rel="canonical" href="https://unity-technologies.github.io/ml-agents/Installation/">
       
-      
-      
       <link rel="icon" href="../images/unity-logo-black.png">
-      <meta name="generator" content="mkdocs-1.4.3, mkdocs-material-9.1.15">
+      <meta name="generator" content="mkdocs-1.4.2, mkdocs-material-8.5.10">
     
     
       
@@ -25,10 +23,15 @@
       
     
     
-      <link rel="stylesheet" href="../assets/stylesheets/main.26e3688c.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.472b142f.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.ecc896b0.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.08040f6c.min.css">
+        
+          
+          
+          <meta name="theme-color" content="#2094f3">
+        
       
       
 
@@ -83,9 +86,7 @@
     
       
 
-  
-
-<header class="md-header md-header--shadow" data-md-component="header">
+<header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
     <a href=".." title="Unity ML-Agents Toolkit" class="md-header__button md-logo" aria-label="Unity ML-Agents Toolkit" data-md-component="logo">
       
@@ -112,8 +113,6 @@
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-    
     
     
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@@ -142,7 +141,7 @@
           <div class="md-search-result__meta">
             Initializing search
           </div>
-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
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       </div>
     </div>
@@ -154,7 +153,7 @@
         <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
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   <div class="md-source__repository">
     GitHub
@@ -198,7 +197,7 @@
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   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -264,18 +263,17 @@
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+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_4" type="checkbox" id="__nav_4" >
       
       
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-      
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_4">
           Background
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         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Background" data-md-level="1">
         <label class="md-nav__title" for="__nav_4">
           <span class="md-nav__icon md-icon"></span>
           Background
@@ -341,18 +339,17 @@
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-      
       
       
-        <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_5">
           Interfacing with Unity Builds
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Interfacing with Unity Builds" data-md-level="1">
         <label class="md-nav__title" for="__nav_5">
           <span class="md-nav__icon md-icon"></span>
           Interfacing with Unity Builds
@@ -418,18 +415,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_6" type="checkbox" id="__nav_6" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" >
-      
       
       
-        <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_6">
           Python API Docs
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Python API Docs" data-md-level="1">
         <label class="md-nav__title" for="__nav_6">
           <span class="md-nav__icon md-icon"></span>
           Python API Docs
@@ -509,18 +505,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_7" type="checkbox" id="__nav_7" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_7" >
       
       
-      
-        <label class="md-nav__link" for="__nav_7" id="__nav_7_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_7">
           Tutorials
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_7_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Tutorials" data-md-level="1">
         <label class="md-nav__title" for="__nav_7">
           <span class="md-nav__icon md-icon"></span>
           Tutorials
@@ -558,18 +553,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_8" type="checkbox" id="__nav_8" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_8" >
-      
       
       
-        <label class="md-nav__link" for="__nav_8" id="__nav_8_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_8">
           About
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="About" data-md-level="1">
         <label class="md-nav__title" for="__nav_8">
           <span class="md-nav__icon md-icon"></span>
           About
@@ -787,8 +781,10 @@
                 
                   
 
-  
-  
+  <a href="https://github.com/Unity-Technologies/ml-agents/edit/main/docs/Installation.md" title="Edit this page" class="md-content__button md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
 
 <h1 id="installation">Installation</h1>
@@ -846,14 +842,14 @@ <h3 id="clone-the-ml-agents-toolkit-repository-optional">Clone the ML-Agents Too
 of our tutorials / guides assume you have access to our example environments).</p>
 <p><strong>NOTE:</strong> There are samples shipped with the Unity Package.  You only need to clone
 the repository if you would like to explore more examples.</p>
-<pre><code class="language-sh">git clone --branch release_19 https://github.com/Unity-Technologies/ml-agents.git
+<pre><code class="language-sh">git clone --branch release_20 https://github.com/Unity-Technologies/ml-agents.git
 </code></pre>
-<p>The <code>--branch release_19</code> option will switch to the tag of the latest stable
+<p>The <code>--branch release_20</code> option will switch to the tag of the latest stable
 release. Omitting that will get the <code>main</code> branch which is potentially unstable.</p>
 <h4 id="advanced-local-installation-for-development">Advanced: Local Installation for Development</h4>
 <p>You will need to clone the repository if you plan to modify or extend the
 ML-Agents Toolkit for your purposes. If you plan to contribute those changes
-back, make sure to clone the <code>main</code> branch (by omitting <code>--branch release_19</code>
+back, make sure to clone the <code>main</code> branch (by omitting <code>--branch release_20</code>
 from the command above). See our
 <a href="../com.unity.ml-agents/CONTRIBUTING.md">Contributions Guidelines</a> for more
 information on contributing to the ML-Agents Toolkit.</p>
@@ -990,10 +986,10 @@ <h2 id="help">Help</h2>
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
-    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.208ed371.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.16e2a7d4.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.b4d07000.min.js"></script>
+      <script src="../assets/javascripts/bundle.d6c3db9e.min.js"></script>
       
     
   </body>
diff --git a/Integrations-Match3/index.html b/Integrations-Match3/index.html
index 42403c448c..0d2c7125ae 100644
--- a/Integrations-Match3/index.html
+++ b/Integrations-Match3/index.html
@@ -14,10 +14,8 @@
       
         <link rel="canonical" href="https://unity-technologies.github.io/ml-agents/Integrations-Match3/">
       
-      
-      
       <link rel="icon" href="../images/unity-logo-black.png">
-      <meta name="generator" content="mkdocs-1.4.3, mkdocs-material-9.1.15">
+      <meta name="generator" content="mkdocs-1.4.2, mkdocs-material-8.5.10">
     
     
       
@@ -25,10 +23,15 @@
       
     
     
-      <link rel="stylesheet" href="../assets/stylesheets/main.26e3688c.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.472b142f.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.ecc896b0.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.08040f6c.min.css">
+        
+          
+          
+          <meta name="theme-color" content="#2094f3">
+        
       
       
 
@@ -83,9 +86,7 @@
     
       
 
-  
-
-<header class="md-header md-header--shadow" data-md-component="header">
+<header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
     <a href=".." title="Unity ML-Agents Toolkit" class="md-header__button md-logo" aria-label="Unity ML-Agents Toolkit" data-md-component="logo">
       
@@ -112,8 +113,6 @@
       </div>
     </div>
     
-      
-    
     
     
       <label class="md-header__button md-icon" for="__search">
@@ -142,7 +141,7 @@
           <div class="md-search-result__meta">
             Initializing search
           </div>
-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
         </div>
       </div>
     </div>
@@ -154,7 +153,7 @@
         <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -198,7 +197,7 @@
       <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -264,18 +263,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_4" type="checkbox" id="__nav_4" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
       
       
-      
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_4">
           Background
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Background" data-md-level="1">
         <label class="md-nav__title" for="__nav_4">
           <span class="md-nav__icon md-icon"></span>
           Background
@@ -341,18 +339,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_5" type="checkbox" id="__nav_5" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
-      
       
       
-        <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_5">
           Interfacing with Unity Builds
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Interfacing with Unity Builds" data-md-level="1">
         <label class="md-nav__title" for="__nav_5">
           <span class="md-nav__icon md-icon"></span>
           Interfacing with Unity Builds
@@ -418,18 +415,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_6" type="checkbox" id="__nav_6" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" >
-      
       
       
-        <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_6">
           Python API Docs
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Python API Docs" data-md-level="1">
         <label class="md-nav__title" for="__nav_6">
           <span class="md-nav__icon md-icon"></span>
           Python API Docs
@@ -509,18 +505,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_7" type="checkbox" id="__nav_7" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_7" >
       
       
-      
-        <label class="md-nav__link" for="__nav_7" id="__nav_7_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_7">
           Tutorials
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_7_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Tutorials" data-md-level="1">
         <label class="md-nav__title" for="__nav_7">
           <span class="md-nav__icon md-icon"></span>
           Tutorials
@@ -558,18 +553,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_8" type="checkbox" id="__nav_8" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_8" >
-      
       
       
-        <label class="md-nav__link" for="__nav_8" id="__nav_8_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_8">
           About
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="About" data-md-level="1">
         <label class="md-nav__title" for="__nav_8">
           <span class="md-nav__icon md-icon"></span>
           About
@@ -821,8 +815,10 @@
                 
                   
 
-  
-  
+  <a href="https://github.com/Unity-Technologies/ml-agents/edit/main/docs/Integrations-Match3.md" title="Edit this page" class="md-content__button md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
 
 <h1 id="match-3-with-ml-agents">Match-3 with ML-Agents</h1>
@@ -954,10 +950,10 @@ <h2 id="feedback">Feedback</h2>
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
-    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.208ed371.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.16e2a7d4.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.b4d07000.min.js"></script>
+      <script src="../assets/javascripts/bundle.d6c3db9e.min.js"></script>
       
     
   </body>
diff --git a/Integrations/index.html b/Integrations/index.html
index 9a0f59ffd9..17ba0b4ae5 100644
--- a/Integrations/index.html
+++ b/Integrations/index.html
@@ -14,10 +14,8 @@
       
         <link rel="canonical" href="https://unity-technologies.github.io/ml-agents/Integrations/">
       
-      
-      
       <link rel="icon" href="../images/unity-logo-black.png">
-      <meta name="generator" content="mkdocs-1.4.3, mkdocs-material-9.1.15">
+      <meta name="generator" content="mkdocs-1.4.2, mkdocs-material-8.5.10">
     
     
       
@@ -25,10 +23,15 @@
       
     
     
-      <link rel="stylesheet" href="../assets/stylesheets/main.26e3688c.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.472b142f.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.ecc896b0.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.08040f6c.min.css">
+        
+          
+          
+          <meta name="theme-color" content="#2094f3">
+        
       
       
 
@@ -83,9 +86,7 @@
     
       
 
-  
-
-<header class="md-header md-header--shadow" data-md-component="header">
+<header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
     <a href=".." title="Unity ML-Agents Toolkit" class="md-header__button md-logo" aria-label="Unity ML-Agents Toolkit" data-md-component="logo">
       
@@ -112,8 +113,6 @@
       </div>
     </div>
     
-      
-    
     
     
       <label class="md-header__button md-icon" for="__search">
@@ -142,7 +141,7 @@
           <div class="md-search-result__meta">
             Initializing search
           </div>
-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
         </div>
       </div>
     </div>
@@ -154,7 +153,7 @@
         <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -198,7 +197,7 @@
       <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -264,18 +263,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_4" type="checkbox" id="__nav_4" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
       
       
-      
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_4">
           Background
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Background" data-md-level="1">
         <label class="md-nav__title" for="__nav_4">
           <span class="md-nav__icon md-icon"></span>
           Background
@@ -341,18 +339,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_5" type="checkbox" id="__nav_5" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
-      
       
       
-        <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_5">
           Interfacing with Unity Builds
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Interfacing with Unity Builds" data-md-level="1">
         <label class="md-nav__title" for="__nav_5">
           <span class="md-nav__icon md-icon"></span>
           Interfacing with Unity Builds
@@ -418,18 +415,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_6" type="checkbox" id="__nav_6" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" >
-      
       
       
-        <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_6">
           Python API Docs
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Python API Docs" data-md-level="1">
         <label class="md-nav__title" for="__nav_6">
           <span class="md-nav__icon md-icon"></span>
           Python API Docs
@@ -509,18 +505,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_7" type="checkbox" id="__nav_7" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_7" >
       
       
-      
-        <label class="md-nav__link" for="__nav_7" id="__nav_7_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_7">
           Tutorials
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_7_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Tutorials" data-md-level="1">
         <label class="md-nav__title" for="__nav_7">
           <span class="md-nav__icon md-icon"></span>
           Tutorials
@@ -558,18 +553,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_8" type="checkbox" id="__nav_8" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_8" >
-      
       
       
-        <label class="md-nav__link" for="__nav_8" id="__nav_8_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_8">
           About
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="About" data-md-level="1">
         <label class="md-nav__title" for="__nav_8">
           <span class="md-nav__icon md-icon"></span>
           About
@@ -692,8 +686,10 @@
                 
                   
 
-  
-  
+  <a href="https://github.com/Unity-Technologies/ml-agents/edit/main/docs/Integrations.md" title="Edit this page" class="md-content__button md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
 
 <h1 id="game-integrations">Game Integrations</h1>
@@ -745,10 +741,10 @@ <h2 id="interested-in-more-game-templates">Interested in more game templates?</h
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
-    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.208ed371.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.16e2a7d4.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.b4d07000.min.js"></script>
+      <script src="../assets/javascripts/bundle.d6c3db9e.min.js"></script>
       
     
   </body>
diff --git a/LICENSE/index.html b/LICENSE/index.html
index 4ef045e8d5..f6057064e9 100644
--- a/LICENSE/index.html
+++ b/LICENSE/index.html
@@ -14,10 +14,8 @@
       
         <link rel="canonical" href="https://unity-technologies.github.io/ml-agents/LICENSE/">
       
-      
-      
       <link rel="icon" href="../images/unity-logo-black.png">
-      <meta name="generator" content="mkdocs-1.4.3, mkdocs-material-9.1.15">
+      <meta name="generator" content="mkdocs-1.4.2, mkdocs-material-8.5.10">
     
     
       
@@ -25,10 +23,15 @@
       
     
     
-      <link rel="stylesheet" href="../assets/stylesheets/main.26e3688c.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.472b142f.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.ecc896b0.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.08040f6c.min.css">
+        
+          
+          
+          <meta name="theme-color" content="#2094f3">
+        
       
       
 
@@ -78,9 +81,7 @@
     
       
 
-  
-
-<header class="md-header md-header--shadow" data-md-component="header">
+<header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
     <a href=".." title="Unity ML-Agents Toolkit" class="md-header__button md-logo" aria-label="Unity ML-Agents Toolkit" data-md-component="logo">
       
@@ -107,8 +108,6 @@
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     </div>
     
-      
-    
     
     
       <label class="md-header__button md-icon" for="__search">
@@ -137,7 +136,7 @@
           <div class="md-search-result__meta">
             Initializing search
           </div>
-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
         </div>
       </div>
     </div>
@@ -149,7 +148,7 @@
         <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -193,7 +192,7 @@
       <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -259,18 +258,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_4" type="checkbox" id="__nav_4" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
-      
       
       
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_4">
           Background
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Background" data-md-level="1">
         <label class="md-nav__title" for="__nav_4">
           <span class="md-nav__icon md-icon"></span>
           Background
@@ -336,18 +334,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_5" type="checkbox" id="__nav_5" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
-      
       
       
-        <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_5">
           Interfacing with Unity Builds
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Interfacing with Unity Builds" data-md-level="1">
         <label class="md-nav__title" for="__nav_5">
           <span class="md-nav__icon md-icon"></span>
           Interfacing with Unity Builds
@@ -413,18 +410,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_6" type="checkbox" id="__nav_6" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" >
       
       
-      
-        <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_6">
           Python API Docs
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Python API Docs" data-md-level="1">
         <label class="md-nav__title" for="__nav_6">
           <span class="md-nav__icon md-icon"></span>
           Python API Docs
@@ -504,18 +500,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_7" type="checkbox" id="__nav_7" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_7" >
-      
       
       
-        <label class="md-nav__link" for="__nav_7" id="__nav_7_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_7">
           Tutorials
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_7_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Tutorials" data-md-level="1">
         <label class="md-nav__title" for="__nav_7">
           <span class="md-nav__icon md-icon"></span>
           Tutorials
@@ -553,18 +548,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_8" type="checkbox" id="__nav_8" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_8" >
       
       
-      
-        <label class="md-nav__link" for="__nav_8" id="__nav_8_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_8">
           About
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="About" data-md-level="1">
         <label class="md-nav__title" for="__nav_8">
           <span class="md-nav__icon md-icon"></span>
           About
@@ -663,8 +657,10 @@
                 
                   
 
-  
-  
+  <a href="https://github.com/Unity-Technologies/ml-agents/edit/main/docs/LICENSE.md" title="Edit this page" class="md-content__button md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
 
   <h1>LICENSE</h1>
@@ -902,10 +898,10 @@ <h1>LICENSE</h1>
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
-    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.208ed371.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.16e2a7d4.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.b4d07000.min.js"></script>
+      <script src="../assets/javascripts/bundle.d6c3db9e.min.js"></script>
       
     
   </body>
diff --git a/Learning-Environment-Create-New/index.html b/Learning-Environment-Create-New/index.html
index c67f2cd68a..e6077fc5cd 100644
--- a/Learning-Environment-Create-New/index.html
+++ b/Learning-Environment-Create-New/index.html
@@ -14,10 +14,8 @@
       
         <link rel="canonical" href="https://unity-technologies.github.io/ml-agents/Learning-Environment-Create-New/">
       
-      
-      
       <link rel="icon" href="../images/unity-logo-black.png">
-      <meta name="generator" content="mkdocs-1.4.3, mkdocs-material-9.1.15">
+      <meta name="generator" content="mkdocs-1.4.2, mkdocs-material-8.5.10">
     
     
       
@@ -25,10 +23,15 @@
       
     
     
-      <link rel="stylesheet" href="../assets/stylesheets/main.26e3688c.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.472b142f.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.ecc896b0.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.08040f6c.min.css">
+        
+          
+          
+          <meta name="theme-color" content="#2094f3">
+        
       
       
 
@@ -83,9 +86,7 @@
     
       
 
-  
-
-<header class="md-header md-header--shadow" data-md-component="header">
+<header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
     <a href=".." title="Unity ML-Agents Toolkit" class="md-header__button md-logo" aria-label="Unity ML-Agents Toolkit" data-md-component="logo">
       
@@ -112,8 +113,6 @@
       </div>
     </div>
     
-      
-    
     
     
       <label class="md-header__button md-icon" for="__search">
@@ -142,7 +141,7 @@
           <div class="md-search-result__meta">
             Initializing search
           </div>
-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
         </div>
       </div>
     </div>
@@ -154,7 +153,7 @@
         <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -198,7 +197,7 @@
       <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -264,18 +263,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_4" type="checkbox" id="__nav_4" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
       
       
-      
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_4">
           Background
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Background" data-md-level="1">
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           <span class="md-nav__icon md-icon"></span>
           Background
@@ -341,18 +339,17 @@
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-        <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_5">
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+      <nav class="md-nav" aria-label="Interfacing with Unity Builds" data-md-level="1">
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@@ -418,18 +415,17 @@
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-        <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_6">
           Python API Docs
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         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Python API Docs" data-md-level="1">
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           <span class="md-nav__icon md-icon"></span>
           Python API Docs
@@ -509,18 +505,17 @@
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-      
-        <label class="md-nav__link" for="__nav_7" id="__nav_7_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_7">
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           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_7_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Tutorials" data-md-level="1">
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           <span class="md-nav__icon md-icon"></span>
           Tutorials
@@ -558,18 +553,17 @@
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-      
       
       
-        <label class="md-nav__link" for="__nav_8" id="__nav_8_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_8">
           About
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="About" data-md-level="1">
         <label class="md-nav__title" for="__nav_8">
           <span class="md-nav__icon md-icon"></span>
           About
@@ -829,8 +823,10 @@
                 
                   
 
-  
-  
+  <a href="https://github.com/Unity-Technologies/ml-agents/edit/main/docs/Learning-Environment-Create-New.md" title="Edit this page" class="md-content__button md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
 
 <h1 id="making-a-new-learning-environment">Making a New Learning Environment</h1>
@@ -1298,10 +1294,10 @@ <h2 id="optional-training-using-concurrent-unity-instances">Optional: Training U
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+    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.16e2a7d4.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.b4d07000.min.js"></script>
+      <script src="../assets/javascripts/bundle.d6c3db9e.min.js"></script>
       
     
   </body>
diff --git a/Learning-Environment-Design-Agents/index.html b/Learning-Environment-Design-Agents/index.html
index 35cea90fe7..a805f6ee4a 100644
--- a/Learning-Environment-Design-Agents/index.html
+++ b/Learning-Environment-Design-Agents/index.html
@@ -14,10 +14,8 @@
       
         <link rel="canonical" href="https://unity-technologies.github.io/ml-agents/Learning-Environment-Design-Agents/">
       
-      
-      
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-      <meta name="generator" content="mkdocs-1.4.3, mkdocs-material-9.1.15">
+      <meta name="generator" content="mkdocs-1.4.2, mkdocs-material-8.5.10">
     
     
       
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-      <link rel="stylesheet" href="../assets/stylesheets/main.26e3688c.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.472b142f.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.ecc896b0.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.08040f6c.min.css">
+        
+          
+          
+          <meta name="theme-color" content="#2094f3">
+        
       
       
 
@@ -83,9 +86,7 @@
     
       
 
-  
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+<header class="md-header" data-md-component="header">
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     <a href=".." title="Unity ML-Agents Toolkit" class="md-header__button md-logo" aria-label="Unity ML-Agents Toolkit" data-md-component="logo">
       
@@ -112,8 +113,6 @@
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       <label class="md-header__button md-icon" for="__search">
@@ -142,7 +141,7 @@
           <div class="md-search-result__meta">
             Initializing search
           </div>
-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
         </div>
       </div>
     </div>
@@ -154,7 +153,7 @@
         <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -198,7 +197,7 @@
       <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -264,18 +263,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_4" type="checkbox" id="__nav_4" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
       
       
-      
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_4">
           Background
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Background" data-md-level="1">
         <label class="md-nav__title" for="__nav_4">
           <span class="md-nav__icon md-icon"></span>
           Background
@@ -341,18 +339,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_5" type="checkbox" id="__nav_5" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
-      
       
       
-        <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_5">
           Interfacing with Unity Builds
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Interfacing with Unity Builds" data-md-level="1">
         <label class="md-nav__title" for="__nav_5">
           <span class="md-nav__icon md-icon"></span>
           Interfacing with Unity Builds
@@ -418,18 +415,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_6" type="checkbox" id="__nav_6" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" >
-      
       
       
-        <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_6">
           Python API Docs
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Python API Docs" data-md-level="1">
         <label class="md-nav__title" for="__nav_6">
           <span class="md-nav__icon md-icon"></span>
           Python API Docs
@@ -509,18 +505,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_7" type="checkbox" id="__nav_7" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_7" >
       
       
-      
-        <label class="md-nav__link" for="__nav_7" id="__nav_7_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_7">
           Tutorials
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_7_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Tutorials" data-md-level="1">
         <label class="md-nav__title" for="__nav_7">
           <span class="md-nav__icon md-icon"></span>
           Tutorials
@@ -558,18 +553,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_8" type="checkbox" id="__nav_8" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_8" >
-      
       
       
-        <label class="md-nav__link" for="__nav_8" id="__nav_8_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_8">
           About
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="About" data-md-level="1">
         <label class="md-nav__title" for="__nav_8">
           <span class="md-nav__icon md-icon"></span>
           About
@@ -1015,8 +1009,10 @@
                 
                   
 
-  
-  
+  <a href="https://github.com/Unity-Technologies/ml-agents/edit/main/docs/Learning-Environment-Design-Agents.md" title="Edit this page" class="md-content__button md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
 
 <h1 id="agents">Agents</h1>
@@ -1136,9 +1132,12 @@ <h2 id="decisions">Decisions</h2>
 Making decisions at regular step intervals is generally most appropriate for
 physics-based simulations. For example, an agent in a robotic simulator that
 must provide fine-control of joint torques should make its decisions every step
-of the simulation. On the other hand, an agent that only needs to make decisions
-when certain game or simulation events occur, such as in a turn-based game,
-should call <code>Agent.RequestDecision()</code> manually.</p>
+of the simulation. In games such as real-time strategy, where many agents make
+their decisions at regular intervals, the decision timing for each agent can be
+staggered by setting the <code>DecisionStep</code> parameter in the <code>Decision Requester</code>
+component for each agent. On the other hand, an agent that only needs to make
+decisions when certain game or simulation events occur, such as in a turn-based
+game, should call <code>Agent.RequestDecision()</code> manually.</p>
 <h2 id="observations-and-sensors">Observations and Sensors</h2>
 <p>In order for an agent to learn, the observations should include all the
 information an agent needs to accomplish its task. Without sufficient and
@@ -2104,10 +2103,10 @@ <h2 id="recording-demonstrations">Recording Demonstrations</h2>
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
-    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.208ed371.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.16e2a7d4.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.b4d07000.min.js"></script>
+      <script src="../assets/javascripts/bundle.d6c3db9e.min.js"></script>
       
     
   </body>
diff --git a/Learning-Environment-Design/index.html b/Learning-Environment-Design/index.html
index 604f8caf9c..1c649e2b6d 100644
--- a/Learning-Environment-Design/index.html
+++ b/Learning-Environment-Design/index.html
@@ -14,10 +14,8 @@
       
         <link rel="canonical" href="https://unity-technologies.github.io/ml-agents/Learning-Environment-Design/">
       
-      
-      
       <link rel="icon" href="../images/unity-logo-black.png">
-      <meta name="generator" content="mkdocs-1.4.3, mkdocs-material-9.1.15">
+      <meta name="generator" content="mkdocs-1.4.2, mkdocs-material-8.5.10">
     
     
       
@@ -25,10 +23,15 @@
       
     
     
-      <link rel="stylesheet" href="../assets/stylesheets/main.26e3688c.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.472b142f.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.ecc896b0.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.08040f6c.min.css">
+        
+          
+          
+          <meta name="theme-color" content="#2094f3">
+        
       
       
 
@@ -83,9 +86,7 @@
     
       
 
-  
-
-<header class="md-header md-header--shadow" data-md-component="header">
+<header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
     <a href=".." title="Unity ML-Agents Toolkit" class="md-header__button md-logo" aria-label="Unity ML-Agents Toolkit" data-md-component="logo">
       
@@ -112,8 +113,6 @@
       </div>
     </div>
     
-      
-    
     
     
       <label class="md-header__button md-icon" for="__search">
@@ -142,7 +141,7 @@
           <div class="md-search-result__meta">
             Initializing search
           </div>
-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
         </div>
       </div>
     </div>
@@ -154,7 +153,7 @@
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           About
@@ -753,8 +747,10 @@
                 
                   
 
-  
-  
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+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
 
 <h1 id="designing-a-learning-environment">Designing a Learning Environment</h1>
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+    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.16e2a7d4.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.b4d07000.min.js"></script>
+      <script src="../assets/javascripts/bundle.d6c3db9e.min.js"></script>
       
     
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diff --git a/Learning-Environment-Examples/index.html b/Learning-Environment-Examples/index.html
index 44bdfe3cdf..46054621d1 100644
--- a/Learning-Environment-Examples/index.html
+++ b/Learning-Environment-Examples/index.html
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         <link rel="canonical" href="https://unity-technologies.github.io/ml-agents/Learning-Environment-Examples/">
       
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-      
       <link rel="icon" href="../images/unity-logo-black.png">
-      <meta name="generator" content="mkdocs-1.4.3, mkdocs-material-9.1.15">
+      <meta name="generator" content="mkdocs-1.4.2, mkdocs-material-8.5.10">
     
     
       
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-      <link rel="stylesheet" href="../assets/stylesheets/main.26e3688c.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.472b142f.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.ecc896b0.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.08040f6c.min.css">
+        
+          
+          
+          <meta name="theme-color" content="#2094f3">
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+<header class="md-header" data-md-component="header">
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@@ -142,7 +141,7 @@
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+          <ol class="md-search-result__list"></ol>
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@@ -154,7 +153,7 @@
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@@ -198,7 +197,7 @@
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           Python API Docs
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_7_label" aria-expanded="false">
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-        <label class="md-nav__link" for="__nav_8" id="__nav_8_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_8">
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="About" data-md-level="1">
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           About
@@ -797,8 +791,10 @@
                 
                   
 
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+  <a href="https://github.com/Unity-Technologies/ml-agents/edit/main/docs/Learning-Environment-Examples.md" title="Edit this page" class="md-content__button md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
 
 <h1 id="example-learning-environments">Example Learning Environments</h1>
@@ -1364,10 +1360,10 @@ <h2 id="dungeon-escape">Dungeon Escape</h2>
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
-    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.208ed371.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.16e2a7d4.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.b4d07000.min.js"></script>
+      <script src="../assets/javascripts/bundle.d6c3db9e.min.js"></script>
       
     
   </body>
diff --git a/Learning-Environment-Executable/index.html b/Learning-Environment-Executable/index.html
index 6402315320..c6815bfd7f 100644
--- a/Learning-Environment-Executable/index.html
+++ b/Learning-Environment-Executable/index.html
@@ -14,10 +14,8 @@
       
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-      
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-      <meta name="generator" content="mkdocs-1.4.3, mkdocs-material-9.1.15">
+      <meta name="generator" content="mkdocs-1.4.2, mkdocs-material-8.5.10">
     
     
       
@@ -25,10 +23,15 @@
       
     
     
-      <link rel="stylesheet" href="../assets/stylesheets/main.26e3688c.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.472b142f.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.ecc896b0.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.08040f6c.min.css">
+        
+          
+          
+          <meta name="theme-color" content="#2094f3">
+        
       
       
 
@@ -83,9 +86,7 @@
     
       
 
-  
-
-<header class="md-header md-header--shadow" data-md-component="header">
+<header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
     <a href=".." title="Unity ML-Agents Toolkit" class="md-header__button md-logo" aria-label="Unity ML-Agents Toolkit" data-md-component="logo">
       
@@ -112,8 +113,6 @@
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-      
-    
     
     
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@@ -142,7 +141,7 @@
           <div class="md-search-result__meta">
             Initializing search
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-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
         </div>
       </div>
     </div>
@@ -154,7 +153,7 @@
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   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -198,7 +197,7 @@
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   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
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   </div>
   <div class="md-source__repository">
     GitHub
@@ -264,18 +263,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_4" type="checkbox" id="__nav_4" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
       
       
-      
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_4">
           Background
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Background" data-md-level="1">
         <label class="md-nav__title" for="__nav_4">
           <span class="md-nav__icon md-icon"></span>
           Background
@@ -341,18 +339,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_5" type="checkbox" id="__nav_5" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
-      
       
       
-        <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_5">
           Interfacing with Unity Builds
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Interfacing with Unity Builds" data-md-level="1">
         <label class="md-nav__title" for="__nav_5">
           <span class="md-nav__icon md-icon"></span>
           Interfacing with Unity Builds
@@ -418,18 +415,17 @@
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+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_6" type="checkbox" id="__nav_6" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" >
-      
       
       
-        <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_6">
           Python API Docs
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Python API Docs" data-md-level="1">
         <label class="md-nav__title" for="__nav_6">
           <span class="md-nav__icon md-icon"></span>
           Python API Docs
@@ -509,18 +505,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_7" type="checkbox" id="__nav_7" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_7" >
       
       
-      
-        <label class="md-nav__link" for="__nav_7" id="__nav_7_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_7">
           Tutorials
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_7_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Tutorials" data-md-level="1">
         <label class="md-nav__title" for="__nav_7">
           <span class="md-nav__icon md-icon"></span>
           Tutorials
@@ -558,18 +553,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_8" type="checkbox" id="__nav_8" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_8" >
-      
       
       
-        <label class="md-nav__link" for="__nav_8" id="__nav_8_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_8">
           About
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="About" data-md-level="1">
         <label class="md-nav__title" for="__nav_8">
           <span class="md-nav__icon md-icon"></span>
           About
@@ -706,8 +700,10 @@
                 
                   
 
-  
-  
+  <a href="https://github.com/Unity-Technologies/ml-agents/edit/main/docs/Learning-Environment-Executable.md" title="Edit this page" class="md-content__button md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
 
 <h1 id="using-an-environment-executable">Using an Environment Executable</h1>
@@ -931,10 +927,10 @@ <h2 id="training-on-headless-server">Training on Headless Server</h2>
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
-    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.208ed371.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.16e2a7d4.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.b4d07000.min.js"></script>
+      <script src="../assets/javascripts/bundle.d6c3db9e.min.js"></script>
       
     
   </body>
diff --git a/Limitations/index.html b/Limitations/index.html
index 75ac315620..e13784a9eb 100644
--- a/Limitations/index.html
+++ b/Limitations/index.html
@@ -14,14 +14,8 @@
       
         <link rel="canonical" href="https://unity-technologies.github.io/ml-agents/Limitations/">
       
-      
-        <link rel="prev" href="../FAQ/">
-      
-      
-        <link rel="next" href="../Migrating/">
-      
       <link rel="icon" href="../images/unity-logo-black.png">
-      <meta name="generator" content="mkdocs-1.4.3, mkdocs-material-9.1.15">
+      <meta name="generator" content="mkdocs-1.4.2, mkdocs-material-8.5.10">
     
     
       
@@ -29,10 +23,15 @@
       
     
     
-      <link rel="stylesheet" href="../assets/stylesheets/main.26e3688c.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.472b142f.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.ecc896b0.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.08040f6c.min.css">
+        
+          
+          
+          <meta name="theme-color" content="#2094f3">
+        
       
       
 
@@ -87,9 +86,7 @@
     
       
 
-  
-
-<header class="md-header md-header--shadow" data-md-component="header">
+<header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
     <a href=".." title="Unity ML-Agents Toolkit" class="md-header__button md-logo" aria-label="Unity ML-Agents Toolkit" data-md-component="logo">
       
@@ -116,8 +113,6 @@
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     </div>
     
-      
-    
     
     
       <label class="md-header__button md-icon" for="__search">
@@ -146,7 +141,7 @@
           <div class="md-search-result__meta">
             Initializing search
           </div>
-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
         </div>
       </div>
     </div>
@@ -158,7 +153,7 @@
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   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -202,7 +197,7 @@
       <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -268,18 +263,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_4" type="checkbox" id="__nav_4" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
       
       
-      
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_4">
           Background
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         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Background" data-md-level="1">
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+        <label class="md-nav__link" for="__nav_5">
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="true">
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@@ -686,8 +676,10 @@
                 
                   
 
-  
-  
+  <a href="https://github.com/Unity-Technologies/ml-agents/edit/main/docs/Limitations.md" title="Edit this page" class="md-content__button md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
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 <h1 id="limitations">Limitations</h1>
@@ -713,6 +705,42 @@ <h1 id="limitations">Limitations</h1>
       
         <footer class="md-footer">
   
+    
+    <nav class="md-footer__inner md-grid" aria-label="Footer" >
+      
+        
+        <a href="../FAQ/" class="md-footer__link md-footer__link--prev" aria-label="Previous: FAQs" rel="prev">
+          <div class="md-footer__button md-icon">
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
+          </div>
+          <div class="md-footer__title">
+            <div class="md-ellipsis">
+              <span class="md-footer__direction">
+                Previous
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+              FAQs
+            </div>
+          </div>
+        </a>
+      
+      
+        
+        <a href="../Migrating/" class="md-footer__link md-footer__link--next" aria-label="Next: Migrating" rel="next">
+          <div class="md-footer__title">
+            <div class="md-ellipsis">
+              <span class="md-footer__direction">
+                Next
+              </span>
+              Migrating
+            </div>
+          </div>
+          <div class="md-footer__button md-icon">
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M4 11v2h12l-5.5 5.5 1.42 1.42L19.84 12l-7.92-7.92L10.5 5.5 16 11H4Z"/></svg>
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+      
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+  
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+    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.16e2a7d4.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.b4d07000.min.js"></script>
+      <script src="../assets/javascripts/bundle.d6c3db9e.min.js"></script>
       
     
   </body>
diff --git a/ML-Agents-Envs-README/index.html b/ML-Agents-Envs-README/index.html
index 3d3099f297..d6c8c48751 100644
--- a/ML-Agents-Envs-README/index.html
+++ b/ML-Agents-Envs-README/index.html
@@ -14,10 +14,8 @@
       
         <link rel="canonical" href="https://unity-technologies.github.io/ml-agents/ML-Agents-Envs-README/">
       
-      
-      
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-      <meta name="generator" content="mkdocs-1.4.3, mkdocs-material-9.1.15">
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+      <link rel="stylesheet" href="../assets/stylesheets/main.472b142f.min.css">
       
         
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+        <link rel="stylesheet" href="../assets/stylesheets/palette.08040f6c.min.css">
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+          <meta name="theme-color" content="#2094f3">
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+<header class="md-header" data-md-component="header">
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     <a href=".." title="Unity ML-Agents Toolkit" class="md-header__button md-logo" aria-label="Unity ML-Agents Toolkit" data-md-component="logo">
       
@@ -112,8 +113,6 @@
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@@ -142,7 +141,7 @@
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+          <ol class="md-search-result__list"></ol>
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+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
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@@ -264,18 +263,17 @@
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+        <label class="md-nav__link" for="__nav_4">
           Background
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         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Background" data-md-level="1">
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           <span class="md-nav__icon md-icon"></span>
           Background
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+        <label class="md-nav__link" for="__nav_5">
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Interfacing with Unity Builds" data-md-level="1">
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           <span class="md-nav__icon md-icon"></span>
           Interfacing with Unity Builds
@@ -418,18 +415,17 @@
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+        <label class="md-nav__link" for="__nav_6">
           Python API Docs
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         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Python API Docs" data-md-level="1">
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           <span class="md-nav__icon md-icon"></span>
           Python API Docs
@@ -509,18 +505,17 @@
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-      
-        <label class="md-nav__link" for="__nav_7" id="__nav_7_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_7">
           Tutorials
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_7_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Tutorials" data-md-level="1">
         <label class="md-nav__title" for="__nav_7">
           <span class="md-nav__icon md-icon"></span>
           Tutorials
@@ -558,18 +553,17 @@
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-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_8" >
-      
       
       
-        <label class="md-nav__link" for="__nav_8" id="__nav_8_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_8">
           About
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="About" data-md-level="1">
         <label class="md-nav__title" for="__nav_8">
           <span class="md-nav__icon md-icon"></span>
           About
@@ -699,8 +693,10 @@
                 
                   
 
-  
-  
+  <a href="https://github.com/Unity-Technologies/ml-agents/edit/main/docs/ML-Agents-Envs-README.md" title="Edit this page" class="md-content__button md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
 
 <h1 id="unity-ml-agents-python-interface">Unity ML-Agents Python Interface</h1>
@@ -778,10 +774,10 @@ <h2 id="limitations">Limitations</h2>
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
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+    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.16e2a7d4.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.b4d07000.min.js"></script>
+      <script src="../assets/javascripts/bundle.d6c3db9e.min.js"></script>
       
     
   </body>
diff --git a/ML-Agents-Overview/index.html b/ML-Agents-Overview/index.html
index 63a8bbf29b..74140d8dbf 100644
--- a/ML-Agents-Overview/index.html
+++ b/ML-Agents-Overview/index.html
@@ -14,14 +14,8 @@
       
         <link rel="canonical" href="https://unity-technologies.github.io/ml-agents/ML-Agents-Overview/">
       
-      
-        <link rel="prev" href="..">
-      
-      
-        <link rel="next" href="../ML-Agents-Toolkit-Documentation/">
-      
       <link rel="icon" href="../images/unity-logo-black.png">
-      <meta name="generator" content="mkdocs-1.4.3, mkdocs-material-9.1.15">
+      <meta name="generator" content="mkdocs-1.4.2, mkdocs-material-8.5.10">
     
     
       
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+      <link rel="stylesheet" href="../assets/stylesheets/main.472b142f.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.ecc896b0.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.08040f6c.min.css">
+        
+          
+          
+          <meta name="theme-color" content="#2094f3">
+        
       
       
 
@@ -87,9 +86,7 @@
     
       
 
-  
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+<header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
     <a href=".." title="Unity ML-Agents Toolkit" class="md-header__button md-logo" aria-label="Unity ML-Agents Toolkit" data-md-component="logo">
       
@@ -116,8 +113,6 @@
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@@ -146,7 +141,7 @@
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             Initializing search
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+          <ol class="md-search-result__list"></ol>
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-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
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@@ -202,7 +197,7 @@
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-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
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     GitHub
@@ -238,7 +233,7 @@
   
     <li class="md-nav__item md-nav__item--active">
       
-      <input class="md-nav__toggle md-toggle" type="checkbox" id="__toc">
+      <input class="md-nav__toggle md-toggle" data-md-toggle="toc" type="checkbox" id="__toc">
       
       
         
@@ -547,18 +542,17 @@
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-      
       
       
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_4">
           Background
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         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Background" data-md-level="1">
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           <span class="md-nav__icon md-icon"></span>
           Background
@@ -624,18 +618,17 @@
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-      
-        <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_5">
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         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Interfacing with Unity Builds" data-md-level="1">
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           <span class="md-nav__icon md-icon"></span>
           Interfacing with Unity Builds
@@ -701,18 +694,17 @@
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+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_6" type="checkbox" id="__nav_6" >
       
       
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+        <label class="md-nav__link" for="__nav_6">
           Python API Docs
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         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Python API Docs" data-md-level="1">
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           <span class="md-nav__icon md-icon"></span>
           Python API Docs
@@ -792,18 +784,17 @@
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-      
-        <label class="md-nav__link" for="__nav_7" id="__nav_7_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_7">
           Tutorials
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         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_7_label" aria-expanded="false">
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           <span class="md-nav__icon md-icon"></span>
           Tutorials
@@ -841,18 +832,17 @@
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-      
       
       
-        <label class="md-nav__link" for="__nav_8" id="__nav_8_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_8">
           About
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="About" data-md-level="1">
         <label class="md-nav__title" for="__nav_8">
           <span class="md-nav__icon md-icon"></span>
           About
@@ -1206,8 +1196,10 @@
                 
                   
 
-  
-  
+  <a href="https://github.com/Unity-Technologies/ml-agents/edit/main/docs/ML-Agents-Overview.md" title="Edit this page" class="md-content__button md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
 
 <h1 id="ml-agents-toolkit-overview">ML-Agents Toolkit Overview</h1>
@@ -1747,7 +1739,8 @@ <h3 id="training-in-competitive-multi-agent-environments-with-self-play">Trainin
 page for more information on setting up teams in your Unity scene. Also, read
 our
 <a href="https://blogs.unity3d.com/2020/02/28/training-intelligent-adversaries-using-self-play-with-ml-agents/">blog post on self-play</a>
-for additional information.</p>
+for additional information. Additionally, check <a href="../ELO-Rating-System/">ELO Rating System</a> the method we use to calculate
+the relative skill level between two players.</p>
 <h3 id="training-in-cooperative-multi-agent-environments-with-ma-poca">Training In Cooperative Multi-Agent Environments with MA-POCA</h3>
 <p><img alt="PushBlock with Agents Working Together" src="../images/cooperative_pushblock.png" /></p>
 <p>ML-Agents provides the functionality for training cooperative behaviors - i.e.,
@@ -1963,6 +1956,42 @@ <h2 id="summary-and-next-steps">Summary and Next Steps</h2>
       
         <footer class="md-footer">
   
+    
+    <nav class="md-footer__inner md-grid" aria-label="Footer" >
+      
+        
+        <a href=".." class="md-footer__link md-footer__link--prev" aria-label="Previous: Home" rel="prev">
+          <div class="md-footer__button md-icon">
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
+          </div>
+          <div class="md-footer__title">
+            <div class="md-ellipsis">
+              <span class="md-footer__direction">
+                Previous
+              </span>
+              Home
+            </div>
+          </div>
+        </a>
+      
+      
+        
+        <a href="../ML-Agents-Toolkit-Documentation/" class="md-footer__link md-footer__link--next" aria-label="Next: Toolkit Documentation" rel="next">
+          <div class="md-footer__title">
+            <div class="md-ellipsis">
+              <span class="md-footer__direction">
+                Next
+              </span>
+              Toolkit Documentation
+            </div>
+          </div>
+          <div class="md-footer__button md-icon">
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M4 11v2h12l-5.5 5.5 1.42 1.42L19.84 12l-7.92-7.92L10.5 5.5 16 11H4Z"/></svg>
+          </div>
+        </a>
+      
+    </nav>
+  
   <div class="md-footer-meta md-typeset">
     <div class="md-footer-meta__inner md-grid">
       <div class="md-copyright">
@@ -1988,10 +2017,10 @@ <h2 id="summary-and-next-steps">Summary and Next Steps</h2>
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
-    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.208ed371.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.16e2a7d4.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.b4d07000.min.js"></script>
+      <script src="../assets/javascripts/bundle.d6c3db9e.min.js"></script>
       
     
   </body>
diff --git a/ML-Agents-README/index.html b/ML-Agents-README/index.html
index 87801baad9..84ec425589 100644
--- a/ML-Agents-README/index.html
+++ b/ML-Agents-README/index.html
@@ -14,10 +14,8 @@
       
         <link rel="canonical" href="https://unity-technologies.github.io/ml-agents/ML-Agents-README/">
       
-      
-      
       <link rel="icon" href="../images/unity-logo-black.png">
-      <meta name="generator" content="mkdocs-1.4.3, mkdocs-material-9.1.15">
+      <meta name="generator" content="mkdocs-1.4.2, mkdocs-material-8.5.10">
     
     
       
@@ -25,10 +23,15 @@
       
     
     
-      <link rel="stylesheet" href="../assets/stylesheets/main.26e3688c.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.472b142f.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.ecc896b0.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.08040f6c.min.css">
+        
+          
+          
+          <meta name="theme-color" content="#2094f3">
+        
       
       
 
@@ -83,9 +86,7 @@
     
       
 
-  
-
-<header class="md-header md-header--shadow" data-md-component="header">
+<header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
     <a href=".." title="Unity ML-Agents Toolkit" class="md-header__button md-logo" aria-label="Unity ML-Agents Toolkit" data-md-component="logo">
       
@@ -112,8 +113,6 @@
       </div>
     </div>
     
-      
-    
     
     
       <label class="md-header__button md-icon" for="__search">
@@ -142,7 +141,7 @@
           <div class="md-search-result__meta">
             Initializing search
           </div>
-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
         </div>
       </div>
     </div>
@@ -154,7 +153,7 @@
         <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -198,7 +197,7 @@
       <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
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@@ -264,18 +263,17 @@
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-      
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_4">
           Background
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         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Background" data-md-level="1">
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           <span class="md-nav__icon md-icon"></span>
           Background
@@ -341,18 +339,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
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-      
       
       
-        <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_5">
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+      <nav class="md-nav" aria-label="Python API Docs" data-md-level="1">
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+      <nav class="md-nav" aria-label="Tutorials" data-md-level="1">
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           Tutorials
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-        <label class="md-nav__link" for="__nav_8" id="__nav_8_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_8">
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="About" data-md-level="1">
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           About
@@ -699,8 +693,10 @@
                 
                   
 
-  
-  
+  <a href="https://github.com/Unity-Technologies/ml-agents/edit/main/docs/ML-Agents-README.md" title="Edit this page" class="md-content__button md-icon">
+    
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+  </a>
 
 
 <h1 id="unity-ml-agents-trainers">Unity ML-Agents Trainers</h1>
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+    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.16e2a7d4.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.b4d07000.min.js"></script>
+      <script src="../assets/javascripts/bundle.d6c3db9e.min.js"></script>
       
     
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diff --git a/ML-Agents-Toolkit-Documentation/index.html b/ML-Agents-Toolkit-Documentation/index.html
index 4d2169f28a..214f73e483 100644
--- a/ML-Agents-Toolkit-Documentation/index.html
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@@ -14,14 +14,8 @@
       
         <link rel="canonical" href="https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/">
       
-      
-        <link rel="prev" href="../ML-Agents-Overview/">
-      
-      
-        <link rel="next" href="../Background-Machine-Learning/">
-      
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-      <meta name="generator" content="mkdocs-1.4.3, mkdocs-material-9.1.15">
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-      <link rel="stylesheet" href="../assets/stylesheets/main.26e3688c.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.472b142f.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.ecc896b0.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.08040f6c.min.css">
+        
+          
+          
+          <meta name="theme-color" content="#2094f3">
+        
       
       
 
@@ -87,9 +86,7 @@
     
       
 
-  
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+<header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
     <a href=".." title="Unity ML-Agents Toolkit" class="md-header__button md-logo" aria-label="Unity ML-Agents Toolkit" data-md-component="logo">
       
@@ -116,8 +113,6 @@
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@@ -146,7 +141,7 @@
           <div class="md-search-result__meta">
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-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
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       </div>
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@@ -158,7 +153,7 @@
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   <div class="md-source__repository">
     GitHub
@@ -253,7 +248,7 @@
   
     <li class="md-nav__item md-nav__item--active">
       
-      <input class="md-nav__toggle md-toggle" type="checkbox" id="__toc">
+      <input class="md-nav__toggle md-toggle" data-md-toggle="toc" type="checkbox" id="__toc">
       
       
         
@@ -316,6 +311,13 @@
     Extending ML-Agents
   </a>
   
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#hugging-face-integration" class="md-nav__link">
+    Hugging Face Integration
+  </a>
+  
 </li>
       
         <li class="md-nav__item">
@@ -372,18 +374,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_4" type="checkbox" id="__nav_4" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
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-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_4">
           Background
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         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Background" data-md-level="1">
         <label class="md-nav__title" for="__nav_4">
           <span class="md-nav__icon md-icon"></span>
           Background
@@ -449,18 +450,17 @@
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+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_5" type="checkbox" id="__nav_5" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
-      
       
       
-        <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_5">
           Interfacing with Unity Builds
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Interfacing with Unity Builds" data-md-level="1">
         <label class="md-nav__title" for="__nav_5">
           <span class="md-nav__icon md-icon"></span>
           Interfacing with Unity Builds
@@ -526,18 +526,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_6" type="checkbox" id="__nav_6" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" >
       
       
-      
-        <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_6">
           Python API Docs
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Python API Docs" data-md-level="1">
         <label class="md-nav__title" for="__nav_6">
           <span class="md-nav__icon md-icon"></span>
           Python API Docs
@@ -617,18 +616,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
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-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_7" >
-      
       
       
-        <label class="md-nav__link" for="__nav_7" id="__nav_7_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_7">
           Tutorials
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_7_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Tutorials" data-md-level="1">
         <label class="md-nav__title" for="__nav_7">
           <span class="md-nav__icon md-icon"></span>
           Tutorials
@@ -666,18 +664,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_8" type="checkbox" id="__nav_8" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_8" >
-      
       
       
-        <label class="md-nav__link" for="__nav_8" id="__nav_8_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_8">
           About
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="About" data-md-level="1">
         <label class="md-nav__title" for="__nav_8">
           <span class="md-nav__icon md-icon"></span>
           About
@@ -805,6 +802,13 @@
     Extending ML-Agents
   </a>
   
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#hugging-face-integration" class="md-nav__link">
+    Hugging Face Integration
+  </a>
+  
 </li>
       
         <li class="md-nav__item">
@@ -856,8 +860,10 @@
                 
                   
 
-  
-  
+  <a href="https://github.com/Unity-Technologies/ml-agents/edit/main/docs/ML-Agents-Toolkit-Documentation.md" title="Edit this page" class="md-content__button md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
 
 <h1 id="unity-ml-agents-toolkit-documentation">Unity ML-Agents Toolkit Documentation</h1>
@@ -896,6 +902,10 @@ <h2 id="extending-ml-agents">Extending ML-Agents</h2>
 <li><a href="../Custom-SideChannels/">Creating Custom Side Channels</a></li>
 <li><a href="../Training-ML-Agents/#defining-a-new-sampler-type">Creating Custom Samplers for Environment Parameter Randomization</a></li>
 </ul>
+<h2 id="hugging-face-integration">Hugging Face Integration</h2>
+<ul>
+<li><a href="../Hugging-Face-Integration/">Using Hugging Face to download and upload trained models</a></li>
+</ul>
 <h2 id="python-tutorial-with-google-colab">Python Tutorial with Google Colab</h2>
 <ul>
 <li><a href="https://colab.research.google.com/github/Unity-Technologies/ml-agents/blob/release_20_docs/colab/Colab_UnityEnvironment_1_Run.ipynb">Using a UnityEnvironment</a></li>
@@ -954,6 +964,42 @@ <h2 id="deprecated-docs">Deprecated Docs</h2>
       
         <footer class="md-footer">
   
+    
+    <nav class="md-footer__inner md-grid" aria-label="Footer" >
+      
+        
+        <a href="../ML-Agents-Overview/" class="md-footer__link md-footer__link--prev" aria-label="Previous: ML-Agents Overview" rel="prev">
+          <div class="md-footer__button md-icon">
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
+          </div>
+          <div class="md-footer__title">
+            <div class="md-ellipsis">
+              <span class="md-footer__direction">
+                Previous
+              </span>
+              ML-Agents Overview
+            </div>
+          </div>
+        </a>
+      
+      
+        
+        <a href="../Background-Machine-Learning/" class="md-footer__link md-footer__link--next" aria-label="Next: Machine Learning" rel="next">
+          <div class="md-footer__title">
+            <div class="md-ellipsis">
+              <span class="md-footer__direction">
+                Next
+              </span>
+              Machine Learning
+            </div>
+          </div>
+          <div class="md-footer__button md-icon">
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M4 11v2h12l-5.5 5.5 1.42 1.42L19.84 12l-7.92-7.92L10.5 5.5 16 11H4Z"/></svg>
+          </div>
+        </a>
+      
+    </nav>
+  
   <div class="md-footer-meta md-typeset">
     <div class="md-footer-meta__inner md-grid">
       <div class="md-copyright">
@@ -979,10 +1025,10 @@ <h2 id="deprecated-docs">Deprecated Docs</h2>
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
-    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.208ed371.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.16e2a7d4.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.b4d07000.min.js"></script>
+      <script src="../assets/javascripts/bundle.d6c3db9e.min.js"></script>
       
     
   </body>
diff --git a/Migrating/index.html b/Migrating/index.html
index 7228f41f03..01320b6fa8 100644
--- a/Migrating/index.html
+++ b/Migrating/index.html
@@ -14,14 +14,8 @@
       
         <link rel="canonical" href="https://unity-technologies.github.io/ml-agents/Migrating/">
       
-      
-        <link rel="prev" href="../Limitations/">
-      
-      
-        <link rel="next" href="../Versioning/">
-      
       <link rel="icon" href="../images/unity-logo-black.png">
-      <meta name="generator" content="mkdocs-1.4.3, mkdocs-material-9.1.15">
+      <meta name="generator" content="mkdocs-1.4.2, mkdocs-material-8.5.10">
     
     
       
@@ -29,10 +23,15 @@
       
     
     
-      <link rel="stylesheet" href="../assets/stylesheets/main.26e3688c.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.472b142f.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.ecc896b0.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.08040f6c.min.css">
+        
+          
+          
+          <meta name="theme-color" content="#2094f3">
+        
       
       
 
@@ -87,9 +86,7 @@
     
       
 
-  
-
-<header class="md-header md-header--shadow" data-md-component="header">
+<header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
     <a href=".." title="Unity ML-Agents Toolkit" class="md-header__button md-logo" aria-label="Unity ML-Agents Toolkit" data-md-component="logo">
       
@@ -116,8 +113,6 @@
       </div>
     </div>
     
-      
-    
     
     
       <label class="md-header__button md-icon" for="__search">
@@ -146,7 +141,7 @@
           <div class="md-search-result__meta">
             Initializing search
           </div>
-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
         </div>
       </div>
     </div>
@@ -158,7 +153,7 @@
         <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -202,7 +197,7 @@
       <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -268,18 +263,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_4" type="checkbox" id="__nav_4" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
       
       
-      
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_4">
           Background
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Background" data-md-level="1">
         <label class="md-nav__title" for="__nav_4">
           <span class="md-nav__icon md-icon"></span>
           Background
@@ -345,18 +339,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_5" type="checkbox" id="__nav_5" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
-      
       
       
-        <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_5">
           Interfacing with Unity Builds
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Interfacing with Unity Builds" data-md-level="1">
         <label class="md-nav__title" for="__nav_5">
           <span class="md-nav__icon md-icon"></span>
           Interfacing with Unity Builds
@@ -422,18 +415,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_6" type="checkbox" id="__nav_6" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" >
       
       
-      
-        <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_6">
           Python API Docs
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Python API Docs" data-md-level="1">
         <label class="md-nav__title" for="__nav_6">
           <span class="md-nav__icon md-icon"></span>
           Python API Docs
@@ -513,18 +505,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_7" type="checkbox" id="__nav_7" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_7" >
-      
       
       
-        <label class="md-nav__link" for="__nav_7" id="__nav_7_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_7">
           Tutorials
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_7_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Tutorials" data-md-level="1">
         <label class="md-nav__title" for="__nav_7">
           <span class="md-nav__icon md-icon"></span>
           Tutorials
@@ -564,18 +555,17 @@
     <li class="md-nav__item md-nav__item--active md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_8" type="checkbox" id="__nav_8" checked>
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_8" checked>
       
       
-      
-        <label class="md-nav__link" for="__nav_8" id="__nav_8_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_8">
           About
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="true">
+      <nav class="md-nav" aria-label="About" data-md-level="1">
         <label class="md-nav__title" for="__nav_8">
           <span class="md-nav__icon md-icon"></span>
           About
@@ -619,7 +609,7 @@
   
     <li class="md-nav__item md-nav__item--active">
       
-      <input class="md-nav__toggle md-toggle" type="checkbox" id="__toc">
+      <input class="md-nav__toggle md-toggle" data-md-toggle="toc" type="checkbox" id="__toc">
       
       
         
@@ -686,8 +676,10 @@
                 
                   
 
-  
-  
+  <a href="https://github.com/Unity-Technologies/ml-agents/edit/main/docs/Migrating.md" title="Edit this page" class="md-content__button md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
 
 <h1 id="upgrading">Upgrading</h1>
@@ -1624,6 +1616,42 @@ <h3 id="semantics">Semantics</h3>
       
         <footer class="md-footer">
   
+    
+    <nav class="md-footer__inner md-grid" aria-label="Footer" >
+      
+        
+        <a href="../Limitations/" class="md-footer__link md-footer__link--prev" aria-label="Previous: Limitations" rel="prev">
+          <div class="md-footer__button md-icon">
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
+          </div>
+          <div class="md-footer__title">
+            <div class="md-ellipsis">
+              <span class="md-footer__direction">
+                Previous
+              </span>
+              Limitations
+            </div>
+          </div>
+        </a>
+      
+      
+        
+        <a href="../Versioning/" class="md-footer__link md-footer__link--next" aria-label="Next: Versioning" rel="next">
+          <div class="md-footer__title">
+            <div class="md-ellipsis">
+              <span class="md-footer__direction">
+                Next
+              </span>
+              Versioning
+            </div>
+          </div>
+          <div class="md-footer__button md-icon">
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M4 11v2h12l-5.5 5.5 1.42 1.42L19.84 12l-7.92-7.92L10.5 5.5 16 11H4Z"/></svg>
+          </div>
+        </a>
+      
+    </nav>
+  
   <div class="md-footer-meta md-typeset">
     <div class="md-footer-meta__inner md-grid">
       <div class="md-copyright">
@@ -1649,10 +1677,10 @@ <h3 id="semantics">Semantics</h3>
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
-    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.208ed371.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.16e2a7d4.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.b4d07000.min.js"></script>
+      <script src="../assets/javascripts/bundle.d6c3db9e.min.js"></script>
       
     
   </body>
diff --git a/Package-Settings/index.html b/Package-Settings/index.html
index dd67ca1167..e15a5f74bb 100644
--- a/Package-Settings/index.html
+++ b/Package-Settings/index.html
@@ -14,10 +14,8 @@
       
         <link rel="canonical" href="https://unity-technologies.github.io/ml-agents/Package-Settings/">
       
-      
-      
       <link rel="icon" href="../images/unity-logo-black.png">
-      <meta name="generator" content="mkdocs-1.4.3, mkdocs-material-9.1.15">
+      <meta name="generator" content="mkdocs-1.4.2, mkdocs-material-8.5.10">
     
     
       
@@ -25,10 +23,15 @@
       
     
     
-      <link rel="stylesheet" href="../assets/stylesheets/main.26e3688c.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.472b142f.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.ecc896b0.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.08040f6c.min.css">
+        
+          
+          
+          <meta name="theme-color" content="#2094f3">
+        
       
       
 
@@ -83,9 +86,7 @@
     
       
 
-  
-
-<header class="md-header md-header--shadow" data-md-component="header">
+<header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
     <a href=".." title="Unity ML-Agents Toolkit" class="md-header__button md-logo" aria-label="Unity ML-Agents Toolkit" data-md-component="logo">
       
@@ -112,8 +113,6 @@
       </div>
     </div>
     
-      
-    
     
     
       <label class="md-header__button md-icon" for="__search">
@@ -142,7 +141,7 @@
           <div class="md-search-result__meta">
             Initializing search
           </div>
-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
         </div>
       </div>
     </div>
@@ -154,7 +153,7 @@
         <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -198,7 +197,7 @@
       <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -264,18 +263,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_4" type="checkbox" id="__nav_4" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
       
       
-      
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_4">
           Background
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Background" data-md-level="1">
         <label class="md-nav__title" for="__nav_4">
           <span class="md-nav__icon md-icon"></span>
           Background
@@ -341,18 +339,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_5" type="checkbox" id="__nav_5" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
-      
       
       
-        <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_5">
           Interfacing with Unity Builds
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         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Interfacing with Unity Builds" data-md-level="1">
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           <span class="md-nav__icon md-icon"></span>
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@@ -418,18 +415,17 @@
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-        <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_6">
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Python API Docs" data-md-level="1">
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           <span class="md-nav__icon md-icon"></span>
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@@ -509,18 +505,17 @@
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-      
-        <label class="md-nav__link" for="__nav_7" id="__nav_7_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_7">
           Tutorials
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         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_7_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Tutorials" data-md-level="1">
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           Tutorials
@@ -558,18 +553,17 @@
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-      
       
       
-        <label class="md-nav__link" for="__nav_8" id="__nav_8_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_8">
           About
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="About" data-md-level="1">
         <label class="md-nav__title" for="__nav_8">
           <span class="md-nav__icon md-icon"></span>
           About
@@ -692,8 +686,10 @@
                 
                   
 
-  
-  
+  <a href="https://github.com/Unity-Technologies/ml-agents/edit/main/docs/Package-Settings.md" title="Edit this page" class="md-content__button md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
 
 <h1 id="ml-agents-package-settings">ML-Agents Package Settings</h1>
@@ -753,10 +749,10 @@ <h2 id="multiple-custom-settings-for-different-scenarios">Multiple Custom Settin
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
-    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.208ed371.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.16e2a7d4.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.b4d07000.min.js"></script>
+      <script src="../assets/javascripts/bundle.d6c3db9e.min.js"></script>
       
     
   </body>
diff --git a/Profiling-Python/index.html b/Profiling-Python/index.html
index c7f3cae9d8..7cd49f04d3 100644
--- a/Profiling-Python/index.html
+++ b/Profiling-Python/index.html
@@ -14,10 +14,8 @@
       
         <link rel="canonical" href="https://unity-technologies.github.io/ml-agents/Profiling-Python/">
       
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-      
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-      <meta name="generator" content="mkdocs-1.4.3, mkdocs-material-9.1.15">
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-      <link rel="stylesheet" href="../assets/stylesheets/main.26e3688c.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.472b142f.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.ecc896b0.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.08040f6c.min.css">
+        
+          
+          
+          <meta name="theme-color" content="#2094f3">
+        
       
       
 
@@ -83,9 +86,7 @@
     
       
 
-  
-
-<header class="md-header md-header--shadow" data-md-component="header">
+<header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
     <a href=".." title="Unity ML-Agents Toolkit" class="md-header__button md-logo" aria-label="Unity ML-Agents Toolkit" data-md-component="logo">
       
@@ -112,8 +113,6 @@
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-      
-    
     
     
       <label class="md-header__button md-icon" for="__search">
@@ -142,7 +141,7 @@
           <div class="md-search-result__meta">
             Initializing search
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-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
         </div>
       </div>
     </div>
@@ -154,7 +153,7 @@
         <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -198,7 +197,7 @@
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   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
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   </div>
   <div class="md-source__repository">
     GitHub
@@ -264,18 +263,17 @@
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+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_4" type="checkbox" id="__nav_4" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
       
       
-      
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_4">
           Background
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         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Background" data-md-level="1">
         <label class="md-nav__title" for="__nav_4">
           <span class="md-nav__icon md-icon"></span>
           Background
@@ -341,18 +339,17 @@
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+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_5" type="checkbox" id="__nav_5" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
-      
       
       
-        <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_5">
           Interfacing with Unity Builds
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Interfacing with Unity Builds" data-md-level="1">
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           <span class="md-nav__icon md-icon"></span>
           Interfacing with Unity Builds
@@ -418,18 +415,17 @@
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+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_6" type="checkbox" id="__nav_6" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" >
-      
       
       
-        <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_6">
           Python API Docs
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Python API Docs" data-md-level="1">
         <label class="md-nav__title" for="__nav_6">
           <span class="md-nav__icon md-icon"></span>
           Python API Docs
@@ -509,18 +505,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_7" type="checkbox" id="__nav_7" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_7" >
       
       
-      
-        <label class="md-nav__link" for="__nav_7" id="__nav_7_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_7">
           Tutorials
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_7_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Tutorials" data-md-level="1">
         <label class="md-nav__title" for="__nav_7">
           <span class="md-nav__icon md-icon"></span>
           Tutorials
@@ -558,18 +553,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
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-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_8" >
-      
       
       
-        <label class="md-nav__link" for="__nav_8" id="__nav_8_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_8">
           About
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="About" data-md-level="1">
         <label class="md-nav__title" for="__nav_8">
           <span class="md-nav__icon md-icon"></span>
           About
@@ -725,8 +719,10 @@
                 
                   
 
-  
-  
+  <a href="https://github.com/Unity-Technologies/ml-agents/edit/main/docs/Profiling-Python.md" title="Edit this page" class="md-content__button md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
 
 <h1 id="profiling-in-python">Profiling in Python</h1>
@@ -825,10 +821,10 @@ <h4 id="threads">Threads</h4>
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
-    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.208ed371.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.16e2a7d4.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.b4d07000.min.js"></script>
+      <script src="../assets/javascripts/bundle.d6c3db9e.min.js"></script>
       
     
   </body>
diff --git a/Python-Custom-Trainer-Plugin/index.html b/Python-Custom-Trainer-Plugin/index.html
index 1a780feb7f..c5b865a56b 100644
--- a/Python-Custom-Trainer-Plugin/index.html
+++ b/Python-Custom-Trainer-Plugin/index.html
@@ -14,10 +14,8 @@
       
         <link rel="canonical" href="https://unity-technologies.github.io/ml-agents/Python-Custom-Trainer-Plugin/">
       
-      
-      
       <link rel="icon" href="../images/unity-logo-black.png">
-      <meta name="generator" content="mkdocs-1.4.3, mkdocs-material-9.1.15">
+      <meta name="generator" content="mkdocs-1.4.2, mkdocs-material-8.5.10">
     
     
       
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+      <link rel="stylesheet" href="../assets/stylesheets/main.472b142f.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.ecc896b0.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.08040f6c.min.css">
+        
+          
+          
+          <meta name="theme-color" content="#2094f3">
+        
       
       
 
@@ -83,9 +86,7 @@
     
       
 
-  
-
-<header class="md-header md-header--shadow" data-md-component="header">
+<header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
     <a href=".." title="Unity ML-Agents Toolkit" class="md-header__button md-logo" aria-label="Unity ML-Agents Toolkit" data-md-component="logo">
       
@@ -112,8 +113,6 @@
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     </div>
     
-      
-    
     
     
       <label class="md-header__button md-icon" for="__search">
@@ -142,7 +141,7 @@
           <div class="md-search-result__meta">
             Initializing search
           </div>
-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
         </div>
       </div>
     </div>
@@ -154,7 +153,7 @@
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   <div class="md-source__icon md-icon">
     
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   <div class="md-source__repository">
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@@ -198,7 +197,7 @@
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   <div class="md-source__repository">
     GitHub
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
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           Background
@@ -341,18 +339,17 @@
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+        <label class="md-nav__link" for="__nav_5">
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Interfacing with Unity Builds" data-md-level="1">
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           Interfacing with Unity Builds
@@ -418,18 +415,17 @@
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Python API Docs" data-md-level="1">
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-        <label class="md-nav__link" for="__nav_7" id="__nav_7_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_7">
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_7_label" aria-expanded="false">
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           Tutorials
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-      
       
       
-        <label class="md-nav__link" for="__nav_8" id="__nav_8_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_8">
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         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="About" data-md-level="1">
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           <span class="md-nav__icon md-icon"></span>
           About
@@ -699,8 +693,10 @@
                 
                   
 
-  
-  
+  <a href="https://github.com/Unity-Technologies/ml-agents/edit/main/docs/Python-Custom-Trainer-Plugin.md" title="Edit this page" class="md-content__button md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
 
 <h1 id="unity-ml-agents-custom-trainers-plugin">Unity Ml-Agents Custom trainers Plugin</h1>
@@ -785,10 +781,10 @@ <h2 id="tutorial">Tutorial</h2>
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-    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.208ed371.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.16e2a7d4.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.b4d07000.min.js"></script>
+      <script src="../assets/javascripts/bundle.d6c3db9e.min.js"></script>
       
     
   </body>
diff --git a/Python-Gym-API-Documentation/index.html b/Python-Gym-API-Documentation/index.html
index ea8654dbca..ae01190d91 100644
--- a/Python-Gym-API-Documentation/index.html
+++ b/Python-Gym-API-Documentation/index.html
@@ -14,14 +14,8 @@
       
         <link rel="canonical" href="https://unity-technologies.github.io/ml-agents/Python-Gym-API-Documentation/">
       
-      
-        <link rel="prev" href="../Python-LLAPI/">
-      
-      
-        <link rel="next" href="../Python-PettingZoo-API-Documentation/">
-      
       <link rel="icon" href="../images/unity-logo-black.png">
-      <meta name="generator" content="mkdocs-1.4.3, mkdocs-material-9.1.15">
+      <meta name="generator" content="mkdocs-1.4.2, mkdocs-material-8.5.10">
     
     
       
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-      <link rel="stylesheet" href="../assets/stylesheets/main.26e3688c.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.472b142f.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.ecc896b0.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.08040f6c.min.css">
+        
+          
+          
+          <meta name="theme-color" content="#2094f3">
+        
       
       
 
@@ -87,9 +86,7 @@
     
       
 
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+<header class="md-header" data-md-component="header">
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     <a href=".." title="Unity ML-Agents Toolkit" class="md-header__button md-logo" aria-label="Unity ML-Agents Toolkit" data-md-component="logo">
       
@@ -116,8 +113,6 @@
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-    
     
     
       <label class="md-header__button md-icon" for="__search">
@@ -146,7 +141,7 @@
           <div class="md-search-result__meta">
             Initializing search
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-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
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       </div>
     </div>
@@ -158,7 +153,7 @@
         <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
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+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
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@@ -202,7 +197,7 @@
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   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -268,18 +263,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_4" type="checkbox" id="__nav_4" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
       
       
-      
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_4">
           Background
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Background" data-md-level="1">
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           Background
@@ -345,18 +339,17 @@
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+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_5" type="checkbox" id="__nav_5" >
       
       
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-        <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_5">
           Interfacing with Unity Builds
           <span class="md-nav__icon md-icon"></span>
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Interfacing with Unity Builds" data-md-level="1">
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           <span class="md-nav__icon md-icon"></span>
           Interfacing with Unity Builds
@@ -424,18 +417,17 @@
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-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" checked>
       
       
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-        <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_6">
           Python API Docs
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         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="true">
+      <nav class="md-nav" aria-label="Python API Docs" data-md-level="1">
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           <span class="md-nav__icon md-icon"></span>
           Python API Docs
@@ -451,7 +443,7 @@
   
     <li class="md-nav__item md-nav__item--active">
       
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+      <input class="md-nav__toggle md-toggle" data-md-toggle="toc" type="checkbox" id="__toc">
       
       
         
@@ -525,18 +517,17 @@
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+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_7" type="checkbox" id="__nav_7" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_7" >
-      
       
       
-        <label class="md-nav__link" for="__nav_7" id="__nav_7_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_7">
           Tutorials
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_7_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Tutorials" data-md-level="1">
         <label class="md-nav__title" for="__nav_7">
           <span class="md-nav__icon md-icon"></span>
           Tutorials
@@ -574,18 +565,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
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-      
-        <label class="md-nav__link" for="__nav_8" id="__nav_8_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_8">
           About
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="About" data-md-level="1">
         <label class="md-nav__title" for="__nav_8">
           <span class="md-nav__icon md-icon"></span>
           About
@@ -686,8 +676,10 @@
                 
                   
 
-  
-  
+  <a href="https://github.com/Unity-Technologies/ml-agents/edit/main/docs/Python-Gym-API-Documentation.md" title="Edit this page" class="md-content__button md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
 
 <h1 id="table-of-contents">Table of Contents</h1>
@@ -825,6 +817,42 @@ <h4 id="lookup_action">lookup_action</h4>
       
         <footer class="md-footer">
   
+    
+    <nav class="md-footer__inner md-grid" aria-label="Footer" >
+      
+        
+        <a href="../Python-LLAPI/" class="md-footer__link md-footer__link--prev" aria-label="Previous: Getting started with the LLAPI" rel="prev">
+          <div class="md-footer__button md-icon">
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
+          </div>
+          <div class="md-footer__title">
+            <div class="md-ellipsis">
+              <span class="md-footer__direction">
+                Previous
+              </span>
+              Getting started with the LLAPI
+            </div>
+          </div>
+        </a>
+      
+      
+        
+        <a href="../Python-PettingZoo-API-Documentation/" class="md-footer__link md-footer__link--next" aria-label="Next: Petting Zoo Documentation" rel="next">
+          <div class="md-footer__title">
+            <div class="md-ellipsis">
+              <span class="md-footer__direction">
+                Next
+              </span>
+              Petting Zoo Documentation
+            </div>
+          </div>
+          <div class="md-footer__button md-icon">
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M4 11v2h12l-5.5 5.5 1.42 1.42L19.84 12l-7.92-7.92L10.5 5.5 16 11H4Z"/></svg>
+          </div>
+        </a>
+      
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+  
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-      <script src="../assets/javascripts/bundle.b4d07000.min.js"></script>
+      <script src="../assets/javascripts/bundle.d6c3db9e.min.js"></script>
       
     
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diff --git a/Python-Gym-API/index.html b/Python-Gym-API/index.html
index df37aca47a..0deca0dbee 100644
--- a/Python-Gym-API/index.html
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+        <link rel="stylesheet" href="../assets/stylesheets/palette.08040f6c.min.css">
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+          
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+        
       
       
 
@@ -87,9 +86,7 @@
     
       
 
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@@ -146,7 +141,7 @@
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+          <ol class="md-search-result__list"></ol>
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@@ -268,18 +263,17 @@
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="true">
+      <nav class="md-nav" aria-label="Interfacing with Unity Builds" data-md-level="1">
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     <li class="md-nav__item md-nav__item--active">
       
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@@ -554,18 +547,17 @@
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-        <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_6">
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Python API Docs" data-md-level="1">
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           <span class="md-nav__icon md-icon"></span>
           Python API Docs
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_7_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Tutorials" data-md-level="1">
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           <span class="md-nav__icon md-icon"></span>
           Tutorials
@@ -694,18 +685,17 @@
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-        <label class="md-nav__link" for="__nav_8" id="__nav_8_label" tabindex="0">
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         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="About" data-md-level="1">
         <label class="md-nav__title" for="__nav_8">
           <span class="md-nav__icon md-icon"></span>
           About
@@ -910,8 +900,10 @@
                 
                   
 
-  
-  
+  <a href="https://github.com/Unity-Technologies/ml-agents/edit/main/docs/Python-Gym-API.md" title="Edit this page" class="md-content__button md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
 
 <h1 id="unity-ml-agents-gym-wrapper">Unity ML-Agents Gym Wrapper</h1>
@@ -1229,6 +1221,42 @@ <h3 id="example-gridworld">Example: GridWorld</h3>
       
         <footer class="md-footer">
   
+    
+    <nav class="md-footer__inner md-grid" aria-label="Footer" >
+      
+        
+        <a href="../Background-Unity/" class="md-footer__link md-footer__link--prev" aria-label="Previous: Unity" rel="prev">
+          <div class="md-footer__button md-icon">
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
+          </div>
+          <div class="md-footer__title">
+            <div class="md-ellipsis">
+              <span class="md-footer__direction">
+                Previous
+              </span>
+              Unity
+            </div>
+          </div>
+        </a>
+      
+      
+        
+        <a href="../Python-PettingZoo-API/" class="md-footer__link md-footer__link--next" aria-label="Next: Getting started with the PettingZoo API" rel="next">
+          <div class="md-footer__title">
+            <div class="md-ellipsis">
+              <span class="md-footer__direction">
+                Next
+              </span>
+              Getting started with the PettingZoo API
+            </div>
+          </div>
+          <div class="md-footer__button md-icon">
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M4 11v2h12l-5.5 5.5 1.42 1.42L19.84 12l-7.92-7.92L10.5 5.5 16 11H4Z"/></svg>
+          </div>
+        </a>
+      
+    </nav>
+  
   <div class="md-footer-meta md-typeset">
     <div class="md-footer-meta__inner md-grid">
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@@ -1254,10 +1282,10 @@ <h3 id="example-gridworld">Example: GridWorld</h3>
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+    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.16e2a7d4.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.b4d07000.min.js"></script>
+      <script src="../assets/javascripts/bundle.d6c3db9e.min.js"></script>
       
     
   </body>
diff --git a/Python-LLAPI-Documentation/index.html b/Python-LLAPI-Documentation/index.html
index 5c54da63b9..330dc39585 100644
--- a/Python-LLAPI-Documentation/index.html
+++ b/Python-LLAPI-Documentation/index.html
@@ -14,14 +14,8 @@
       
         <link rel="canonical" href="https://unity-technologies.github.io/ml-agents/Python-LLAPI-Documentation/">
       
-      
-        <link rel="prev" href="../Python-PettingZoo-API-Documentation/">
-      
-      
-        <link rel="next" href="../Python-On-Off-Policy-Trainer-Documentation/">
-      
       <link rel="icon" href="../images/unity-logo-black.png">
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-        <link rel="stylesheet" href="../assets/stylesheets/palette.ecc896b0.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.08040f6c.min.css">
+        
+          
+          
+          <meta name="theme-color" content="#2094f3">
+        
       
       
 
@@ -87,9 +86,7 @@
     
       
 
-  
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-<header class="md-header md-header--shadow" data-md-component="header">
+<header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
     <a href=".." title="Unity ML-Agents Toolkit" class="md-header__button md-logo" aria-label="Unity ML-Agents Toolkit" data-md-component="logo">
       
@@ -116,8 +113,6 @@
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-    
     
     
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@@ -146,7 +141,7 @@
           <div class="md-search-result__meta">
             Initializing search
           </div>
-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
         </div>
       </div>
     </div>
@@ -158,7 +153,7 @@
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   <div class="md-source__icon md-icon">
     
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   <div class="md-source__repository">
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@@ -202,7 +197,7 @@
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           About
@@ -686,8 +676,10 @@
                 
                   
 
-  
-  
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+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
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 <h1 id="table-of-contents">Table of Contents</h1>
@@ -1691,6 +1683,42 @@ <h4 id="channel_id">channel_id</h4>
       
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+    
+    <nav class="md-footer__inner md-grid" aria-label="Footer" >
+      
+        
+        <a href="../Python-PettingZoo-API-Documentation/" class="md-footer__link md-footer__link--prev" aria-label="Previous: Petting Zoo Documentation" rel="prev">
+          <div class="md-footer__button md-icon">
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
+          </div>
+          <div class="md-footer__title">
+            <div class="md-ellipsis">
+              <span class="md-footer__direction">
+                Previous
+              </span>
+              Petting Zoo Documentation
+            </div>
+          </div>
+        </a>
+      
+      
+        
+        <a href="../Python-On-Off-Policy-Trainer-Documentation/" class="md-footer__link md-footer__link--next" aria-label="Next: On/Off Policy Trainer" rel="next">
+          <div class="md-footer__title">
+            <div class="md-ellipsis">
+              <span class="md-footer__direction">
+                Next
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+              On/Off Policy Trainer
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+          </div>
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+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M4 11v2h12l-5.5 5.5 1.42 1.42L19.84 12l-7.92-7.92L10.5 5.5 16 11H4Z"/></svg>
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+      
+    </nav>
+  
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-      <script src="../assets/javascripts/bundle.b4d07000.min.js"></script>
+      <script src="../assets/javascripts/bundle.d6c3db9e.min.js"></script>
       
     
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diff --git a/Python-LLAPI/index.html b/Python-LLAPI/index.html
index 4702f401d8..6f3677ff9b 100644
--- a/Python-LLAPI/index.html
+++ b/Python-LLAPI/index.html
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-      
-        <link rel="prev" href="../Python-PettingZoo-API/">
-      
-      
-        <link rel="next" href="../Python-Gym-API-Documentation/">
-      
       <link rel="icon" href="../images/unity-logo-black.png">
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-      <link rel="stylesheet" href="../assets/stylesheets/main.26e3688c.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.472b142f.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.ecc896b0.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.08040f6c.min.css">
+        
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+          
+          <meta name="theme-color" content="#2094f3">
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@@ -146,7 +141,7 @@
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+          <ol class="md-search-result__list"></ol>
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           Tutorials
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="About" data-md-level="1">
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           About
@@ -908,8 +898,10 @@
                 
                   
 
-  
-  
+  <a href="https://github.com/Unity-Technologies/ml-agents/edit/main/docs/Python-LLAPI.md" title="Edit this page" class="md-content__button md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
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 <h1 id="unity-ml-agents-python-low-level-api">Unity ML-Agents Python Low Level API</h1>
@@ -1241,6 +1233,42 @@ <h4 id="custom-side-channels">Custom side channels</h4>
       
         <footer class="md-footer">
   
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+    <nav class="md-footer__inner md-grid" aria-label="Footer" >
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+    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.16e2a7d4.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.b4d07000.min.js"></script>
+      <script src="../assets/javascripts/bundle.d6c3db9e.min.js"></script>
       
     
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index 56f3889561..97098b1119 100644
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+++ b/Python-On-Off-Policy-Trainer-Documentation/index.html
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+      <link rel="stylesheet" href="../assets/stylesheets/main.472b142f.min.css">
       
         
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+        <link rel="stylesheet" href="../assets/stylesheets/palette.08040f6c.min.css">
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+        
       
       
 
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+<header class="md-header" data-md-component="header">
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-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
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+        <label class="md-nav__link" for="__nav_4">
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Background" data-md-level="1">
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-        <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_5">
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Interfacing with Unity Builds" data-md-level="1">
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           Interfacing with Unity Builds
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-        <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_6">
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         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="true">
+      <nav class="md-nav" aria-label="Python API Docs" data-md-level="1">
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     <li class="md-nav__item md-nav__item--active">
       
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+      <input class="md-nav__toggle md-toggle" data-md-toggle="toc" type="checkbox" id="__toc">
       
       
         
@@ -525,18 +517,17 @@
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_7_label" aria-expanded="false">
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           <span class="md-nav__icon md-icon"></span>
           Tutorials
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-        <label class="md-nav__link" for="__nav_8" id="__nav_8_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_8">
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="About" data-md-level="1">
         <label class="md-nav__title" for="__nav_8">
           <span class="md-nav__icon md-icon"></span>
           About
@@ -686,8 +676,10 @@
                 
                   
 
-  
-  
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+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
 
 <h1 id="table-of-contents">Table of Contents</h1>
@@ -1270,6 +1262,42 @@ <h4 id="from_argparse">from_argparse</h4>
       
         <footer class="md-footer">
   
+    
+    <nav class="md-footer__inner md-grid" aria-label="Footer" >
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+          <div class="md-footer__title">
+            <div class="md-ellipsis">
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+                Previous
+              </span>
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+      
+    </nav>
+  
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@@ -1295,10 +1323,10 @@ <h4 id="from_argparse">from_argparse</h4>
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diff --git a/Python-Optimizer-Documentation/index.html b/Python-Optimizer-Documentation/index.html
index 16f9a965c2..bc67ad0bf5 100644
--- a/Python-Optimizer-Documentation/index.html
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@@ -83,9 +86,7 @@
     
       
 
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+<header class="md-header" data-md-component="header">
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@@ -112,8 +113,6 @@
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@@ -142,7 +141,7 @@
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-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
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           About
@@ -670,8 +664,10 @@
                 
                   
 
-  
-  
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+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
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 <h1 id="table-of-contents">Table of Contents</h1>
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+    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.16e2a7d4.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.b4d07000.min.js"></script>
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diff --git a/Python-PettingZoo-API-Documentation/index.html b/Python-PettingZoo-API-Documentation/index.html
index 2ceffe3d53..ab8fe95e4f 100644
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+++ b/Python-PettingZoo-API-Documentation/index.html
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-      
-        <link rel="prev" href="../Python-Gym-API-Documentation/">
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-      
-        <link rel="next" href="../Python-LLAPI-Documentation/">
-      
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-      <meta name="generator" content="mkdocs-1.4.3, mkdocs-material-9.1.15">
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+      <link rel="stylesheet" href="../assets/stylesheets/main.472b142f.min.css">
       
         
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+        <link rel="stylesheet" href="../assets/stylesheets/palette.08040f6c.min.css">
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@@ -146,7 +141,7 @@
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+          <ol class="md-search-result__list"></ol>
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+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
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@@ -202,7 +197,7 @@
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   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
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+        <label class="md-nav__link" for="__nav_4">
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_7_label" aria-expanded="false">
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="About" data-md-level="1">
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@@ -686,8 +676,10 @@
                 
                   
 
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 <h1 id="table-of-contents">Table of Contents</h1>
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           Tutorials
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="false">
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           About
@@ -760,8 +750,10 @@
                 
                   
 
-  
-  
+  <a href="https://github.com/Unity-Technologies/ml-agents/edit/main/docs/Python-PettingZoo-API.md" title="Edit this page" class="md-content__button md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
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 <h1 id="unity-ml-agents-pettingzoo-wrapper">Unity ML-Agents PettingZoo Wrapper</h1>
@@ -828,6 +820,42 @@ <h2 id="notes">Notes</h2>
       
         <footer class="md-footer">
   
+    
+    <nav class="md-footer__inner md-grid" aria-label="Footer" >
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diff --git a/Readme/index.html b/Readme/index.html
index 7be0524cf2..cea70b981b 100644
--- a/Readme/index.html
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@@ -83,9 +86,7 @@
     
       
 
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@@ -142,7 +141,7 @@
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+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -198,7 +197,7 @@
       <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -264,18 +263,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_4" type="checkbox" id="__nav_4" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
       
       
-      
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_4">
           Background
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Background" data-md-level="1">
         <label class="md-nav__title" for="__nav_4">
           <span class="md-nav__icon md-icon"></span>
           Background
@@ -341,18 +339,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_5" type="checkbox" id="__nav_5" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
-      
       
       
-        <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_5">
           Interfacing with Unity Builds
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Interfacing with Unity Builds" data-md-level="1">
         <label class="md-nav__title" for="__nav_5">
           <span class="md-nav__icon md-icon"></span>
           Interfacing with Unity Builds
@@ -418,18 +415,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_6" type="checkbox" id="__nav_6" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" >
-      
       
       
-        <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_6">
           Python API Docs
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Python API Docs" data-md-level="1">
         <label class="md-nav__title" for="__nav_6">
           <span class="md-nav__icon md-icon"></span>
           Python API Docs
@@ -509,18 +505,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_7" type="checkbox" id="__nav_7" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_7" >
       
       
-      
-        <label class="md-nav__link" for="__nav_7" id="__nav_7_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_7">
           Tutorials
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_7_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Tutorials" data-md-level="1">
         <label class="md-nav__title" for="__nav_7">
           <span class="md-nav__icon md-icon"></span>
           Tutorials
@@ -558,18 +553,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_8" type="checkbox" id="__nav_8" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_8" >
-      
       
       
-        <label class="md-nav__link" for="__nav_8" id="__nav_8_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_8">
           About
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="About" data-md-level="1">
         <label class="md-nav__title" for="__nav_8">
           <span class="md-nav__icon md-icon"></span>
           About
@@ -726,8 +720,10 @@
                 
                   
 
-  
-  
+  <a href="https://github.com/Unity-Technologies/ml-agents/edit/main/docs/Readme.md" title="Edit this page" class="md-content__button md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
 
 <h1 id="unity-ml-agents-toolkit">Unity ML-Agents Toolkit</h1>
@@ -981,10 +977,10 @@ <h2 id="privacy">Privacy</h2>
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
-    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.208ed371.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.16e2a7d4.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.b4d07000.min.js"></script>
+      <script src="../assets/javascripts/bundle.d6c3db9e.min.js"></script>
       
     
   </body>
diff --git a/Training-Configuration-File/index.html b/Training-Configuration-File/index.html
index 87568873d1..6b0286fc96 100644
--- a/Training-Configuration-File/index.html
+++ b/Training-Configuration-File/index.html
@@ -14,10 +14,8 @@
       
         <link rel="canonical" href="https://unity-technologies.github.io/ml-agents/Training-Configuration-File/">
       
-      
-      
       <link rel="icon" href="../images/unity-logo-black.png">
-      <meta name="generator" content="mkdocs-1.4.3, mkdocs-material-9.1.15">
+      <meta name="generator" content="mkdocs-1.4.2, mkdocs-material-8.5.10">
     
     
       
@@ -25,10 +23,15 @@
       
     
     
-      <link rel="stylesheet" href="../assets/stylesheets/main.26e3688c.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.472b142f.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.ecc896b0.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.08040f6c.min.css">
+        
+          
+          
+          <meta name="theme-color" content="#2094f3">
+        
       
       
 
@@ -83,9 +86,7 @@
     
       
 
-  
-
-<header class="md-header md-header--shadow" data-md-component="header">
+<header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
     <a href=".." title="Unity ML-Agents Toolkit" class="md-header__button md-logo" aria-label="Unity ML-Agents Toolkit" data-md-component="logo">
       
@@ -112,8 +113,6 @@
       </div>
     </div>
     
-      
-    
     
     
       <label class="md-header__button md-icon" for="__search">
@@ -142,7 +141,7 @@
           <div class="md-search-result__meta">
             Initializing search
           </div>
-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
         </div>
       </div>
     </div>
@@ -154,7 +153,7 @@
         <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -198,7 +197,7 @@
       <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -264,18 +263,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_4" type="checkbox" id="__nav_4" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
       
       
-      
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_4">
           Background
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Background" data-md-level="1">
         <label class="md-nav__title" for="__nav_4">
           <span class="md-nav__icon md-icon"></span>
           Background
@@ -341,18 +339,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_5" type="checkbox" id="__nav_5" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
-      
       
       
-        <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_5">
           Interfacing with Unity Builds
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Interfacing with Unity Builds" data-md-level="1">
         <label class="md-nav__title" for="__nav_5">
           <span class="md-nav__icon md-icon"></span>
           Interfacing with Unity Builds
@@ -418,18 +415,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_6" type="checkbox" id="__nav_6" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" >
-      
       
       
-        <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_6">
           Python API Docs
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Python API Docs" data-md-level="1">
         <label class="md-nav__title" for="__nav_6">
           <span class="md-nav__icon md-icon"></span>
           Python API Docs
@@ -509,18 +505,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_7" type="checkbox" id="__nav_7" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_7" >
       
       
-      
-        <label class="md-nav__link" for="__nav_7" id="__nav_7_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_7">
           Tutorials
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_7_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Tutorials" data-md-level="1">
         <label class="md-nav__title" for="__nav_7">
           <span class="md-nav__icon md-icon"></span>
           Tutorials
@@ -558,18 +553,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_8" type="checkbox" id="__nav_8" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_8" >
-      
       
       
-        <label class="md-nav__link" for="__nav_8" id="__nav_8_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_8">
           About
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="About" data-md-level="1">
         <label class="md-nav__title" for="__nav_8">
           <span class="md-nav__icon md-icon"></span>
           About
@@ -801,8 +795,10 @@
                 
                   
 
-  
-  
+  <a href="https://github.com/Unity-Technologies/ml-agents/edit/main/docs/Training-Configuration-File.md" title="Edit this page" class="md-content__button md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
 
 <h1 id="training-configuration-file">Training Configuration File</h1>
@@ -1295,10 +1291,10 @@ <h3 id="note-on-swap-steps">Note on Swap Steps</h3>
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+    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.16e2a7d4.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.b4d07000.min.js"></script>
+      <script src="../assets/javascripts/bundle.d6c3db9e.min.js"></script>
       
     
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diff --git a/Training-ML-Agents/index.html b/Training-ML-Agents/index.html
index 1307aaf501..7f954e22e6 100644
--- a/Training-ML-Agents/index.html
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@@ -112,8 +113,6 @@
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@@ -142,7 +141,7 @@
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+          <ol class="md-search-result__list"></ol>
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     GitHub
@@ -264,18 +263,17 @@
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
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           <span class="md-nav__icon md-icon"></span>
           Interfacing with Unity Builds
@@ -418,18 +415,17 @@
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Python API Docs" data-md-level="1">
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           <span class="md-nav__icon md-icon"></span>
           Python API Docs
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_7_label" aria-expanded="false">
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           <span class="md-nav__icon md-icon"></span>
           Tutorials
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-        <label class="md-nav__link" for="__nav_8" id="__nav_8_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_8">
           About
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="About" data-md-level="1">
         <label class="md-nav__title" for="__nav_8">
           <span class="md-nav__icon md-icon"></span>
           About
@@ -847,8 +841,10 @@
                 
                   
 
-  
-  
+  <a href="https://github.com/Unity-Technologies/ml-agents/edit/main/docs/Training-ML-Agents.md" title="Edit this page" class="md-content__button md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
 
 <h1 id="training-ml-agents">Training ML-Agents</h1>
@@ -1089,7 +1085,6 @@ <h3 id="behavior-configurations">Behavior Configurations</h3>
       learning_rate_schedule: linear
 
       # PPO-specific hyperparameters
-      # Replaces the &quot;PPO-specific hyperparameters&quot; section above
       beta: 5.0e-3
       beta_schedule: constant
       epsilon: 0.2
@@ -1475,10 +1470,10 @@ <h3 id="training-using-concurrent-unity-instances">Training Using Concurrent Uni
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+    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.16e2a7d4.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.b4d07000.min.js"></script>
+      <script src="../assets/javascripts/bundle.d6c3db9e.min.js"></script>
       
     
   </body>
diff --git a/Training-Plugins/index.html b/Training-Plugins/index.html
index 91be51efbc..fbf633e8c5 100644
--- a/Training-Plugins/index.html
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-      
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+      <link rel="stylesheet" href="../assets/stylesheets/main.472b142f.min.css">
       
         
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+        <link rel="stylesheet" href="../assets/stylesheets/palette.08040f6c.min.css">
+        
+          
+          
+          <meta name="theme-color" content="#2094f3">
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@@ -83,9 +86,7 @@
     
       
 
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+<header class="md-header" data-md-component="header">
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@@ -112,8 +113,6 @@
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@@ -142,7 +141,7 @@
           <div class="md-search-result__meta">
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-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
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@@ -154,7 +153,7 @@
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   <div class="md-source__repository">
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@@ -198,7 +197,7 @@
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@@ -264,18 +263,17 @@
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-      
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+        <label class="md-nav__link" for="__nav_4">
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+        <label class="md-nav__link" for="__nav_5">
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Interfacing with Unity Builds" data-md-level="1">
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@@ -418,18 +415,17 @@
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+        <label class="md-nav__link" for="__nav_6">
           Python API Docs
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Python API Docs" data-md-level="1">
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-      
-        <label class="md-nav__link" for="__nav_7" id="__nav_7_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_7">
           Tutorials
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         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_7_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Tutorials" data-md-level="1">
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           <span class="md-nav__icon md-icon"></span>
           Tutorials
@@ -558,18 +553,17 @@
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-      
       
       
-        <label class="md-nav__link" for="__nav_8" id="__nav_8_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_8">
           About
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="About" data-md-level="1">
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           <span class="md-nav__icon md-icon"></span>
           About
@@ -745,8 +739,10 @@
                 
                   
 
-  
-  
+  <a href="https://github.com/Unity-Technologies/ml-agents/edit/main/docs/Training-Plugins.md" title="Edit this page" class="md-content__button md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
 
 <h1 id="customizing-training-via-plugins">Customizing Training via Plugins</h1>
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     </div>
     
-    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.208ed371.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.16e2a7d4.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.b4d07000.min.js"></script>
+      <script src="../assets/javascripts/bundle.d6c3db9e.min.js"></script>
       
     
   </body>
diff --git a/Training-on-Amazon-Web-Service/index.html b/Training-on-Amazon-Web-Service/index.html
index f401c52d9d..4e2e39783f 100644
--- a/Training-on-Amazon-Web-Service/index.html
+++ b/Training-on-Amazon-Web-Service/index.html
@@ -14,10 +14,8 @@
       
         <link rel="canonical" href="https://unity-technologies.github.io/ml-agents/Training-on-Amazon-Web-Service/">
       
-      
-      
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-      <meta name="generator" content="mkdocs-1.4.3, mkdocs-material-9.1.15">
+      <meta name="generator" content="mkdocs-1.4.2, mkdocs-material-8.5.10">
     
     
       
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+      <link rel="stylesheet" href="../assets/stylesheets/main.472b142f.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.ecc896b0.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.08040f6c.min.css">
+        
+          
+          
+          <meta name="theme-color" content="#2094f3">
+        
       
       
 
@@ -83,9 +86,7 @@
     
       
 
-  
-
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+<header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
     <a href=".." title="Unity ML-Agents Toolkit" class="md-header__button md-logo" aria-label="Unity ML-Agents Toolkit" data-md-component="logo">
       
@@ -112,8 +113,6 @@
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-    
     
     
       <label class="md-header__button md-icon" for="__search">
@@ -142,7 +141,7 @@
           <div class="md-search-result__meta">
             Initializing search
           </div>
-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
         </div>
       </div>
     </div>
@@ -154,7 +153,7 @@
         <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -198,7 +197,7 @@
       <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -264,18 +263,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_4" type="checkbox" id="__nav_4" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
       
       
-      
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_4">
           Background
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Background" data-md-level="1">
         <label class="md-nav__title" for="__nav_4">
           <span class="md-nav__icon md-icon"></span>
           Background
@@ -341,18 +339,17 @@
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+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_5" type="checkbox" id="__nav_5" >
       
       
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-      
       
       
-        <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_5">
           Interfacing with Unity Builds
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Interfacing with Unity Builds" data-md-level="1">
         <label class="md-nav__title" for="__nav_5">
           <span class="md-nav__icon md-icon"></span>
           Interfacing with Unity Builds
@@ -418,18 +415,17 @@
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+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_6" type="checkbox" id="__nav_6" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" >
-      
       
       
-        <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_6">
           Python API Docs
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Python API Docs" data-md-level="1">
         <label class="md-nav__title" for="__nav_6">
           <span class="md-nav__icon md-icon"></span>
           Python API Docs
@@ -509,18 +505,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_7" type="checkbox" id="__nav_7" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_7" >
       
       
-      
-        <label class="md-nav__link" for="__nav_7" id="__nav_7_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_7">
           Tutorials
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_7_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Tutorials" data-md-level="1">
         <label class="md-nav__title" for="__nav_7">
           <span class="md-nav__icon md-icon"></span>
           Tutorials
@@ -558,18 +553,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
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-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_8" >
-      
       
       
-        <label class="md-nav__link" for="__nav_8" id="__nav_8_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_8">
           About
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="About" data-md-level="1">
         <label class="md-nav__title" for="__nav_8">
           <span class="md-nav__icon md-icon"></span>
           About
@@ -801,8 +795,10 @@
                 
                   
 
-  
-  
+  <a href="https://github.com/Unity-Technologies/ml-agents/edit/main/docs/Training-on-Amazon-Web-Service.md" title="Edit this page" class="md-content__button md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
 
 <h1 id="training-on-amazon-web-service">Training on Amazon Web Service</h1>
@@ -1120,10 +1116,10 @@ <h3 id="could-not-launch-x-server">Could not launch X Server</h3>
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
-    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.208ed371.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.16e2a7d4.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.b4d07000.min.js"></script>
+      <script src="../assets/javascripts/bundle.d6c3db9e.min.js"></script>
       
     
   </body>
diff --git a/Training-on-Microsoft-Azure/index.html b/Training-on-Microsoft-Azure/index.html
index 4a46684b99..f3ed69b8a3 100644
--- a/Training-on-Microsoft-Azure/index.html
+++ b/Training-on-Microsoft-Azure/index.html
@@ -14,10 +14,8 @@
       
         <link rel="canonical" href="https://unity-technologies.github.io/ml-agents/Training-on-Microsoft-Azure/">
       
-      
-      
       <link rel="icon" href="../images/unity-logo-black.png">
-      <meta name="generator" content="mkdocs-1.4.3, mkdocs-material-9.1.15">
+      <meta name="generator" content="mkdocs-1.4.2, mkdocs-material-8.5.10">
     
     
       
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-      <link rel="stylesheet" href="../assets/stylesheets/main.26e3688c.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.472b142f.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.ecc896b0.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.08040f6c.min.css">
+        
+          
+          
+          <meta name="theme-color" content="#2094f3">
+        
       
       
 
@@ -83,9 +86,7 @@
     
       
 
-  
-
-<header class="md-header md-header--shadow" data-md-component="header">
+<header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
     <a href=".." title="Unity ML-Agents Toolkit" class="md-header__button md-logo" aria-label="Unity ML-Agents Toolkit" data-md-component="logo">
       
@@ -112,8 +113,6 @@
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     </div>
     
-      
-    
     
     
       <label class="md-header__button md-icon" for="__search">
@@ -142,7 +141,7 @@
           <div class="md-search-result__meta">
             Initializing search
           </div>
-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
         </div>
       </div>
     </div>
@@ -154,7 +153,7 @@
         <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
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@@ -198,7 +197,7 @@
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
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-        <label class="md-nav__link" for="__nav_8" id="__nav_8_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_8">
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="About" data-md-level="1">
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           About
@@ -734,8 +728,10 @@
                 
                   
 
-  
-  
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+  </a>
 
 
 <h1 id="training-on-microsoft-azure-works-with-ml-agents-toolkit-v03">Training on Microsoft Azure (works with ML-Agents Toolkit v0.3)</h1>
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+    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.16e2a7d4.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
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+      <script src="../assets/javascripts/bundle.d6c3db9e.min.js"></script>
       
     
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diff --git a/Tutorial-Custom-Trainer-Plugin/index.html b/Tutorial-Custom-Trainer-Plugin/index.html
index d08e26ccec..8ad34fcb17 100644
--- a/Tutorial-Custom-Trainer-Plugin/index.html
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-      
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-      
-        <link rel="next" href="../FAQ/">
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+      <link rel="stylesheet" href="../assets/stylesheets/main.472b142f.min.css">
       
         
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+        <link rel="stylesheet" href="../assets/stylesheets/palette.08040f6c.min.css">
+        
+          
+          
+          <meta name="theme-color" content="#2094f3">
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@@ -116,8 +113,6 @@
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@@ -146,7 +141,7 @@
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+          <ol class="md-search-result__list"></ol>
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@@ -158,7 +153,7 @@
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
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+      <nav class="md-nav" aria-label="Interfacing with Unity Builds" data-md-level="1">
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+      <nav class="md-nav" aria-label="Python API Docs" data-md-level="1">
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_7_label" aria-expanded="true">
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     <li class="md-nav__item md-nav__item--active">
       
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-        <label class="md-nav__link" for="__nav_8" id="__nav_8_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_8">
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           <span class="md-nav__icon md-icon"></span>
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="About" data-md-level="1">
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           <span class="md-nav__icon md-icon"></span>
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@@ -782,8 +772,10 @@
                 
                   
 
-  
-  
+  <a href="https://github.com/Unity-Technologies/ml-agents/edit/main/docs/Tutorial-Custom-Trainer-Plugin.md" title="Edit this page" class="md-content__button md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
 
   <h1>Custom Trainer Plugin</h1>
@@ -1028,6 +1020,42 @@ <h3 id="validate-your-implementations">Validate your implementations:</h3>
       
         <footer class="md-footer">
   
+    
+    <nav class="md-footer__inner md-grid" aria-label="Footer" >
+      
+        
+        <a href="../Python-On-Off-Policy-Trainer-Documentation/" class="md-footer__link md-footer__link--prev" aria-label="Previous: On/Off Policy Trainer" rel="prev">
+          <div class="md-footer__button md-icon">
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
+          </div>
+          <div class="md-footer__title">
+            <div class="md-ellipsis">
+              <span class="md-footer__direction">
+                Previous
+              </span>
+              On/Off Policy Trainer
+            </div>
+          </div>
+        </a>
+      
+      
+        
+        <a href="../FAQ/" class="md-footer__link md-footer__link--next" aria-label="Next: FAQs" rel="next">
+          <div class="md-footer__title">
+            <div class="md-ellipsis">
+              <span class="md-footer__direction">
+                Next
+              </span>
+              FAQs
+            </div>
+          </div>
+          <div class="md-footer__button md-icon">
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M4 11v2h12l-5.5 5.5 1.42 1.42L19.84 12l-7.92-7.92L10.5 5.5 16 11H4Z"/></svg>
+          </div>
+        </a>
+      
+    </nav>
+  
   <div class="md-footer-meta md-typeset">
     <div class="md-footer-meta__inner md-grid">
       <div class="md-copyright">
@@ -1053,10 +1081,10 @@ <h3 id="validate-your-implementations">Validate your implementations:</h3>
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
-    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.208ed371.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.16e2a7d4.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.b4d07000.min.js"></script>
+      <script src="../assets/javascripts/bundle.d6c3db9e.min.js"></script>
       
     
   </body>
diff --git a/Unity-Environment-Registry/index.html b/Unity-Environment-Registry/index.html
index 7e72aaa38d..8574956ac2 100644
--- a/Unity-Environment-Registry/index.html
+++ b/Unity-Environment-Registry/index.html
@@ -14,10 +14,8 @@
       
         <link rel="canonical" href="https://unity-technologies.github.io/ml-agents/Unity-Environment-Registry/">
       
-      
-      
       <link rel="icon" href="../images/unity-logo-black.png">
-      <meta name="generator" content="mkdocs-1.4.3, mkdocs-material-9.1.15">
+      <meta name="generator" content="mkdocs-1.4.2, mkdocs-material-8.5.10">
     
     
       
@@ -25,10 +23,15 @@
       
     
     
-      <link rel="stylesheet" href="../assets/stylesheets/main.26e3688c.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.472b142f.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.ecc896b0.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.08040f6c.min.css">
+        
+          
+          
+          <meta name="theme-color" content="#2094f3">
+        
       
       
 
@@ -83,9 +86,7 @@
     
       
 
-  
-
-<header class="md-header md-header--shadow" data-md-component="header">
+<header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
     <a href=".." title="Unity ML-Agents Toolkit" class="md-header__button md-logo" aria-label="Unity ML-Agents Toolkit" data-md-component="logo">
       
@@ -112,8 +113,6 @@
       </div>
     </div>
     
-      
-    
     
     
       <label class="md-header__button md-icon" for="__search">
@@ -142,7 +141,7 @@
           <div class="md-search-result__meta">
             Initializing search
           </div>
-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
         </div>
       </div>
     </div>
@@ -154,7 +153,7 @@
         <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -198,7 +197,7 @@
       <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -264,18 +263,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_4" type="checkbox" id="__nav_4" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
       
       
-      
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_4">
           Background
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Background" data-md-level="1">
         <label class="md-nav__title" for="__nav_4">
           <span class="md-nav__icon md-icon"></span>
           Background
@@ -341,18 +339,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_5" type="checkbox" id="__nav_5" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
-      
       
       
-        <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_5">
           Interfacing with Unity Builds
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Interfacing with Unity Builds" data-md-level="1">
         <label class="md-nav__title" for="__nav_5">
           <span class="md-nav__icon md-icon"></span>
           Interfacing with Unity Builds
@@ -418,18 +415,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_6" type="checkbox" id="__nav_6" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" >
-      
       
       
-        <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_6">
           Python API Docs
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Python API Docs" data-md-level="1">
         <label class="md-nav__title" for="__nav_6">
           <span class="md-nav__icon md-icon"></span>
           Python API Docs
@@ -509,18 +505,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_7" type="checkbox" id="__nav_7" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_7" >
       
       
-      
-        <label class="md-nav__link" for="__nav_7" id="__nav_7_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_7">
           Tutorials
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_7_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Tutorials" data-md-level="1">
         <label class="md-nav__title" for="__nav_7">
           <span class="md-nav__icon md-icon"></span>
           Tutorials
@@ -558,18 +553,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_8" type="checkbox" id="__nav_8" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_8" >
-      
       
       
-        <label class="md-nav__link" for="__nav_8" id="__nav_8_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_8">
           About
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="About" data-md-level="1">
         <label class="md-nav__title" for="__nav_8">
           <span class="md-nav__icon md-icon"></span>
           About
@@ -692,8 +686,10 @@
                 
                   
 
-  
-  
+  <a href="https://github.com/Unity-Technologies/ml-agents/edit/main/docs/Unity-Environment-Registry.md" title="Edit this page" class="md-content__button md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
 
 <h1 id="unity-environment-registry-experimental">Unity Environment Registry [Experimental]</h1>
@@ -782,10 +778,10 @@ <h2 id="create-and-share-your-own-registry">Create and share your own registry</
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
-    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.208ed371.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.16e2a7d4.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.b4d07000.min.js"></script>
+      <script src="../assets/javascripts/bundle.d6c3db9e.min.js"></script>
       
     
   </body>
diff --git a/Unity-Inference-Engine/index.html b/Unity-Inference-Engine/index.html
index 610f2cf6a6..48403a93e0 100644
--- a/Unity-Inference-Engine/index.html
+++ b/Unity-Inference-Engine/index.html
@@ -14,10 +14,8 @@
       
         <link rel="canonical" href="https://unity-technologies.github.io/ml-agents/Unity-Inference-Engine/">
       
-      
-      
       <link rel="icon" href="../images/unity-logo-black.png">
-      <meta name="generator" content="mkdocs-1.4.3, mkdocs-material-9.1.15">
+      <meta name="generator" content="mkdocs-1.4.2, mkdocs-material-8.5.10">
     
     
       
@@ -25,10 +23,15 @@
       
     
     
-      <link rel="stylesheet" href="../assets/stylesheets/main.26e3688c.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.472b142f.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.ecc896b0.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.08040f6c.min.css">
+        
+          
+          
+          <meta name="theme-color" content="#2094f3">
+        
       
       
 
@@ -83,9 +86,7 @@
     
       
 
-  
-
-<header class="md-header md-header--shadow" data-md-component="header">
+<header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
     <a href=".." title="Unity ML-Agents Toolkit" class="md-header__button md-logo" aria-label="Unity ML-Agents Toolkit" data-md-component="logo">
       
@@ -112,8 +113,6 @@
       </div>
     </div>
     
-      
-    
     
     
       <label class="md-header__button md-icon" for="__search">
@@ -142,7 +141,7 @@
           <div class="md-search-result__meta">
             Initializing search
           </div>
-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
         </div>
       </div>
     </div>
@@ -154,7 +153,7 @@
         <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -198,7 +197,7 @@
       <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
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@@ -418,18 +415,17 @@
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_7_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Tutorials" data-md-level="1">
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@@ -558,18 +553,17 @@
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-        <label class="md-nav__link" for="__nav_8" id="__nav_8_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_8">
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="About" data-md-level="1">
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           <span class="md-nav__icon md-icon"></span>
           About
@@ -692,8 +686,10 @@
                 
                   
 
-  
-  
+  <a href="https://github.com/Unity-Technologies/ml-agents/edit/main/docs/Unity-Inference-Engine.md" title="Edit this page" class="md-content__button md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
 
 <h1 id="unity-inference-engine">Unity Inference Engine</h1>
@@ -774,10 +770,10 @@ <h2 id="model-inference-outside-of-unity">Model inference outside of Unity</h2>
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+    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.16e2a7d4.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.b4d07000.min.js"></script>
+      <script src="../assets/javascripts/bundle.d6c3db9e.min.js"></script>
       
     
   </body>
diff --git a/Using-Docker/index.html b/Using-Docker/index.html
index c5f6f4e002..5112624f7d 100644
--- a/Using-Docker/index.html
+++ b/Using-Docker/index.html
@@ -14,10 +14,8 @@
       
         <link rel="canonical" href="https://unity-technologies.github.io/ml-agents/Using-Docker/">
       
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-      
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-      <meta name="generator" content="mkdocs-1.4.3, mkdocs-material-9.1.15">
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+      <link rel="stylesheet" href="../assets/stylesheets/main.472b142f.min.css">
       
         
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+        <link rel="stylesheet" href="../assets/stylesheets/palette.08040f6c.min.css">
+        
+          
+          
+          <meta name="theme-color" content="#2094f3">
+        
       
       
 
@@ -83,9 +86,7 @@
     
       
 
-  
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+<header class="md-header" data-md-component="header">
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     <a href=".." title="Unity ML-Agents Toolkit" class="md-header__button md-logo" aria-label="Unity ML-Agents Toolkit" data-md-component="logo">
       
@@ -112,8 +113,6 @@
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@@ -142,7 +141,7 @@
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-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
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       </div>
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@@ -154,7 +153,7 @@
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   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -198,7 +197,7 @@
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-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
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   </div>
   <div class="md-source__repository">
     GitHub
@@ -264,18 +263,17 @@
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+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_4" type="checkbox" id="__nav_4" >
       
       
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-      
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_4">
           Background
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         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Background" data-md-level="1">
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           <span class="md-nav__icon md-icon"></span>
           Background
@@ -341,18 +339,17 @@
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-      
       
       
-        <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_5">
           Interfacing with Unity Builds
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         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Interfacing with Unity Builds" data-md-level="1">
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           <span class="md-nav__icon md-icon"></span>
           Interfacing with Unity Builds
@@ -418,18 +415,17 @@
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-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" >
-      
       
       
-        <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_6">
           Python API Docs
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Python API Docs" data-md-level="1">
         <label class="md-nav__title" for="__nav_6">
           <span class="md-nav__icon md-icon"></span>
           Python API Docs
@@ -509,18 +505,17 @@
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-      
-        <label class="md-nav__link" for="__nav_7" id="__nav_7_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_7">
           Tutorials
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_7_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Tutorials" data-md-level="1">
         <label class="md-nav__title" for="__nav_7">
           <span class="md-nav__icon md-icon"></span>
           Tutorials
@@ -558,18 +553,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
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-      
       
       
-        <label class="md-nav__link" for="__nav_8" id="__nav_8_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_8">
           About
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="About" data-md-level="1">
         <label class="md-nav__title" for="__nav_8">
           <span class="md-nav__icon md-icon"></span>
           About
@@ -740,8 +734,10 @@
                 
                   
 
-  
-  
+  <a href="https://github.com/Unity-Technologies/ml-agents/edit/main/docs/Using-Docker.md" title="Edit this page" class="md-content__button md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
 
 <h1 id="using-docker-for-ml-agents-deprecated">Using Docker For ML-Agents (Deprecated)</h1>
@@ -931,10 +927,10 @@ <h3 id="stopping-container-and-saving-state">Stopping Container and Saving State
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
-    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.208ed371.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.16e2a7d4.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.b4d07000.min.js"></script>
+      <script src="../assets/javascripts/bundle.d6c3db9e.min.js"></script>
       
     
   </body>
diff --git a/Using-Tensorboard/index.html b/Using-Tensorboard/index.html
index ffe6f158cf..6c6b9c8789 100644
--- a/Using-Tensorboard/index.html
+++ b/Using-Tensorboard/index.html
@@ -14,10 +14,8 @@
       
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-      
-      
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-      <meta name="generator" content="mkdocs-1.4.3, mkdocs-material-9.1.15">
+      <meta name="generator" content="mkdocs-1.4.2, mkdocs-material-8.5.10">
     
     
       
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+      <link rel="stylesheet" href="../assets/stylesheets/main.472b142f.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.ecc896b0.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.08040f6c.min.css">
+        
+          
+          
+          <meta name="theme-color" content="#2094f3">
+        
       
       
 
@@ -83,9 +86,7 @@
     
       
 
-  
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-<header class="md-header md-header--shadow" data-md-component="header">
+<header class="md-header" data-md-component="header">
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     <a href=".." title="Unity ML-Agents Toolkit" class="md-header__button md-logo" aria-label="Unity ML-Agents Toolkit" data-md-component="logo">
       
@@ -112,8 +113,6 @@
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       <label class="md-header__button md-icon" for="__search">
@@ -142,7 +141,7 @@
           <div class="md-search-result__meta">
             Initializing search
           </div>
-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
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       </div>
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@@ -154,7 +153,7 @@
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   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
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   <div class="md-source__repository">
     GitHub
@@ -198,7 +197,7 @@
       <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
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   </div>
   <div class="md-source__repository">
     GitHub
@@ -264,18 +263,17 @@
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-      
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_4">
           Background
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         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Background" data-md-level="1">
         <label class="md-nav__title" for="__nav_4">
           <span class="md-nav__icon md-icon"></span>
           Background
@@ -341,18 +339,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
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-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
-      
       
       
-        <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_5">
           Interfacing with Unity Builds
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Interfacing with Unity Builds" data-md-level="1">
         <label class="md-nav__title" for="__nav_5">
           <span class="md-nav__icon md-icon"></span>
           Interfacing with Unity Builds
@@ -418,18 +415,17 @@
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+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_6" type="checkbox" id="__nav_6" >
       
       
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-        <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_6">
           Python API Docs
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Python API Docs" data-md-level="1">
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           <span class="md-nav__icon md-icon"></span>
           Python API Docs
@@ -509,18 +505,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_7" type="checkbox" id="__nav_7" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_7" >
       
       
-      
-        <label class="md-nav__link" for="__nav_7" id="__nav_7_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_7">
           Tutorials
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_7_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Tutorials" data-md-level="1">
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           <span class="md-nav__icon md-icon"></span>
           Tutorials
@@ -558,18 +553,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
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-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_8" >
-      
       
       
-        <label class="md-nav__link" for="__nav_8" id="__nav_8_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_8">
           About
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="About" data-md-level="1">
         <label class="md-nav__title" for="__nav_8">
           <span class="md-nav__icon md-icon"></span>
           About
@@ -740,8 +734,10 @@
                 
                   
 
-  
-  
+  <a href="https://github.com/Unity-Technologies/ml-agents/edit/main/docs/Using-Tensorboard.md" title="Edit this page" class="md-content__button md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
 
 <h1 id="using-tensorboard-to-observe-training">Using TensorBoard to Observe Training</h1>
@@ -930,10 +926,10 @@ <h2 id="custom-metrics-from-unity">Custom Metrics from Unity</h2>
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
-    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.208ed371.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.16e2a7d4.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.b4d07000.min.js"></script>
+      <script src="../assets/javascripts/bundle.d6c3db9e.min.js"></script>
       
     
   </body>
diff --git a/Using-Virtual-Environment/index.html b/Using-Virtual-Environment/index.html
index 9f883f2b33..6d2edeea42 100644
--- a/Using-Virtual-Environment/index.html
+++ b/Using-Virtual-Environment/index.html
@@ -14,10 +14,8 @@
       
         <link rel="canonical" href="https://unity-technologies.github.io/ml-agents/Using-Virtual-Environment/">
       
-      
-      
       <link rel="icon" href="../images/unity-logo-black.png">
-      <meta name="generator" content="mkdocs-1.4.3, mkdocs-material-9.1.15">
+      <meta name="generator" content="mkdocs-1.4.2, mkdocs-material-8.5.10">
     
     
       
@@ -25,10 +23,15 @@
       
     
     
-      <link rel="stylesheet" href="../assets/stylesheets/main.26e3688c.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.472b142f.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.ecc896b0.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.08040f6c.min.css">
+        
+          
+          
+          <meta name="theme-color" content="#2094f3">
+        
       
       
 
@@ -83,9 +86,7 @@
     
       
 
-  
-
-<header class="md-header md-header--shadow" data-md-component="header">
+<header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
     <a href=".." title="Unity ML-Agents Toolkit" class="md-header__button md-logo" aria-label="Unity ML-Agents Toolkit" data-md-component="logo">
       
@@ -112,8 +113,6 @@
       </div>
     </div>
     
-      
-    
     
     
       <label class="md-header__button md-icon" for="__search">
@@ -142,7 +141,7 @@
           <div class="md-search-result__meta">
             Initializing search
           </div>
-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
         </div>
       </div>
     </div>
@@ -154,7 +153,7 @@
         <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -198,7 +197,7 @@
       <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
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   <div class="md-source__repository">
     GitHub
@@ -264,18 +263,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
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-      
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_4">
           Background
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         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Background" data-md-level="1">
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           <span class="md-nav__icon md-icon"></span>
           Background
@@ -341,18 +339,17 @@
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-      
       
       
-        <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_5">
           Interfacing with Unity Builds
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Interfacing with Unity Builds" data-md-level="1">
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           <span class="md-nav__icon md-icon"></span>
           Interfacing with Unity Builds
@@ -418,18 +415,17 @@
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-      
       
       
-        <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_6">
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         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Python API Docs" data-md-level="1">
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           <span class="md-nav__icon md-icon"></span>
           Python API Docs
@@ -509,18 +505,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
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-      
-        <label class="md-nav__link" for="__nav_7" id="__nav_7_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_7">
           Tutorials
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_7_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Tutorials" data-md-level="1">
         <label class="md-nav__title" for="__nav_7">
           <span class="md-nav__icon md-icon"></span>
           Tutorials
@@ -558,18 +553,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_8" type="checkbox" id="__nav_8" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_8" >
-      
       
       
-        <label class="md-nav__link" for="__nav_8" id="__nav_8_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_8">
           About
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="About" data-md-level="1">
         <label class="md-nav__title" for="__nav_8">
           <span class="md-nav__icon md-icon"></span>
           About
@@ -727,8 +721,10 @@
                 
                   
 
-  
-  
+  <a href="https://github.com/Unity-Technologies/ml-agents/edit/main/docs/Using-Virtual-Environment.md" title="Edit this page" class="md-content__button md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
 
 <h1 id="using-virtual-environment">Using Virtual Environment</h1>
@@ -835,10 +831,10 @@ <h2 id="windows-setup">Windows Setup</h2>
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
-    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.208ed371.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.16e2a7d4.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.b4d07000.min.js"></script>
+      <script src="../assets/javascripts/bundle.d6c3db9e.min.js"></script>
       
     
   </body>
diff --git a/Versioning/index.html b/Versioning/index.html
index 8980b045ac..d8a7d6ba4d 100644
--- a/Versioning/index.html
+++ b/Versioning/index.html
@@ -14,12 +14,8 @@
       
         <link rel="canonical" href="https://unity-technologies.github.io/ml-agents/Versioning/">
       
-      
-        <link rel="prev" href="../Migrating/">
-      
-      
       <link rel="icon" href="../images/unity-logo-black.png">
-      <meta name="generator" content="mkdocs-1.4.3, mkdocs-material-9.1.15">
+      <meta name="generator" content="mkdocs-1.4.2, mkdocs-material-8.5.10">
     
     
       
@@ -27,10 +23,15 @@
       
     
     
-      <link rel="stylesheet" href="../assets/stylesheets/main.26e3688c.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.472b142f.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.ecc896b0.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.08040f6c.min.css">
+        
+          
+          
+          <meta name="theme-color" content="#2094f3">
+        
       
       
 
@@ -85,9 +86,7 @@
     
       
 
-  
-
-<header class="md-header md-header--shadow" data-md-component="header">
+<header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
     <a href=".." title="Unity ML-Agents Toolkit" class="md-header__button md-logo" aria-label="Unity ML-Agents Toolkit" data-md-component="logo">
       
@@ -114,8 +113,6 @@
       </div>
     </div>
     
-      
-    
     
     
       <label class="md-header__button md-icon" for="__search">
@@ -144,7 +141,7 @@
           <div class="md-search-result__meta">
             Initializing search
           </div>
-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
         </div>
       </div>
     </div>
@@ -156,7 +153,7 @@
         <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -200,7 +197,7 @@
       <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -266,18 +263,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_4" type="checkbox" id="__nav_4" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
-      
       
       
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_4">
           Background
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Background" data-md-level="1">
         <label class="md-nav__title" for="__nav_4">
           <span class="md-nav__icon md-icon"></span>
           Background
@@ -343,18 +339,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_5" type="checkbox" id="__nav_5" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
       
       
-      
-        <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_5">
           Interfacing with Unity Builds
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Interfacing with Unity Builds" data-md-level="1">
         <label class="md-nav__title" for="__nav_5">
           <span class="md-nav__icon md-icon"></span>
           Interfacing with Unity Builds
@@ -420,18 +415,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_6" type="checkbox" id="__nav_6" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" >
-      
       
       
-        <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_6">
           Python API Docs
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Python API Docs" data-md-level="1">
         <label class="md-nav__title" for="__nav_6">
           <span class="md-nav__icon md-icon"></span>
           Python API Docs
@@ -511,18 +505,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_7" type="checkbox" id="__nav_7" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_7" >
       
       
-      
-        <label class="md-nav__link" for="__nav_7" id="__nav_7_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_7">
           Tutorials
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_7_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Tutorials" data-md-level="1">
         <label class="md-nav__title" for="__nav_7">
           <span class="md-nav__icon md-icon"></span>
           Tutorials
@@ -562,18 +555,17 @@
     <li class="md-nav__item md-nav__item--active md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_8" type="checkbox" id="__nav_8" checked>
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_8" checked>
-      
       
       
-        <label class="md-nav__link" for="__nav_8" id="__nav_8_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_8">
           About
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="true">
+      <nav class="md-nav" aria-label="About" data-md-level="1">
         <label class="md-nav__title" for="__nav_8">
           <span class="md-nav__icon md-icon"></span>
           About
@@ -631,7 +623,7 @@
   
     <li class="md-nav__item md-nav__item--active">
       
-      <input class="md-nav__toggle md-toggle" type="checkbox" id="__toc">
+      <input class="md-nav__toggle md-toggle" data-md-toggle="toc" type="checkbox" id="__toc">
       
       
         
@@ -826,8 +818,10 @@
                 
                   
 
-  
-  
+  <a href="https://github.com/Unity-Technologies/ml-agents/edit/main/docs/Versioning.md" title="Edit this page" class="md-content__button md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
 
 <h1 id="ml-agents-versioning">ML-Agents Versioning</h1>
@@ -925,6 +919,27 @@ <h2 id="side-channels">Side Channels</h2>
       
         <footer class="md-footer">
   
+    
+    <nav class="md-footer__inner md-grid" aria-label="Footer" >
+      
+        
+        <a href="../Migrating/" class="md-footer__link md-footer__link--prev" aria-label="Previous: Migrating" rel="prev">
+          <div class="md-footer__button md-icon">
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11h12Z"/></svg>
+          </div>
+          <div class="md-footer__title">
+            <div class="md-ellipsis">
+              <span class="md-footer__direction">
+                Previous
+              </span>
+              Migrating
+            </div>
+          </div>
+        </a>
+      
+      
+    </nav>
+  
   <div class="md-footer-meta md-typeset">
     <div class="md-footer-meta__inner md-grid">
       <div class="md-copyright">
@@ -950,10 +965,10 @@ <h2 id="side-channels">Side Channels</h2>
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
-    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.208ed371.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.16e2a7d4.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.b4d07000.min.js"></script>
+      <script src="../assets/javascripts/bundle.d6c3db9e.min.js"></script>
       
     
   </body>
diff --git a/assets/javascripts/bundle.b4d07000.min.js b/assets/javascripts/bundle.b4d07000.min.js
deleted file mode 100644
index 3c0bdad9a7..0000000000
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us(e,{viewport$:t,header$:r}){return ye(document.body).pipe(g(()=>lr(e,{header$:r,viewport$:t})),l(({offset:{y:n}})=>({hidden:n>=10})),ee("hidden"))}function ui(e,t){return $(()=>{let r=new x;return r.subscribe({next({hidden:n}){e.hidden=n},complete(){e.hidden=!1}}),(re("navigation.tabs.sticky")?k({hidden:!1}):us(e,t)).pipe(S(n=>r.next(n)),R(()=>r.complete()),l(n=>P({ref:e},n)))})}function ps(e,{viewport$:t,header$:r}){let n=new Map,o=K("[href^=\\#]",e);for(let a of o){let f=decodeURIComponent(a.hash.substring(1)),c=ce(`[id="${f}"]`);typeof c!="undefined"&&n.set(a,c)}let i=r.pipe(ee("height"),l(({height:a})=>{let f=xe("main"),c=z(":scope > :first-child",f);return a+.8*(c.offsetTop-f.offsetTop)}),pe());return ye(document.body).pipe(ee("height"),g(a=>$(()=>{let f=[];return k([...n].reduce((c,[u,p])=>{for(;f.length&&n.get(f[f.length-1]).tagName>=p.tagName;)f.pop();let m=p.offsetTop;for(;!m&&p.parentElement;)p=p.parentElement,m=p.offsetTop;let d=p.offsetParent;for(;d;d=d.offsetParent)m+=d.offsetTop;return c.set([...f=[...f,u]].reverse(),m)},new Map))}).pipe(l(f=>new Map([...f].sort(([,c],[,u])=>c-u))),Je(i),g(([f,c])=>t.pipe(Pr(([u,p],{offset:{y:m},size:d})=>{let h=m+d.height>=Math.floor(a.height);for(;p.length;){let[,v]=p[0];if(v-c<m||h)u=[...u,p.shift()];else break}for(;u.length;){let[,v]=u[u.length-1];if(v-c>=m&&!h)p=[u.pop(),...p];else break}return[u,p]},[[],[...f]]),J((u,p)=>u[0]===p[0]&&u[1]===p[1])))))).pipe(l(([a,f])=>({prev:a.map(([c])=>c),next:f.map(([c])=>c)})),V({prev:[],next:[]}),Re(2,1),l(([a,f])=>a.prev.length<f.prev.length?{prev:f.prev.slice(Math.max(0,a.prev.length-1),f.prev.length),next:[]}:{prev:f.prev.slice(-1),next:f.next.slice(0,f.next.length-a.next.length)}))}function pi(e,{viewport$:t,header$:r,main$:n,target$:o}){return $(()=>{let i=new x,s=i.pipe(Z(),te(!0));if(i.subscribe(({prev:a,next:f})=>{for(let[c]of f)c.classList.remove("md-nav__link--passed"),c.classList.remove("md-nav__link--active");for(let[c,[u]]of a.entries())u.classList.add("md-nav__link--passed"),u.classList.toggle("md-nav__link--active",c===a.length-1)}),re("toc.follow")){let a=L(t.pipe(ke(1),l(()=>{})),t.pipe(ke(250),l(()=>"smooth")));i.pipe(A(({prev:f})=>f.length>0),Je(n.pipe(Ae(ae))),ne(a)).subscribe(([[{prev:f}],c])=>{let[u]=f[f.length-1];if(u.offsetHeight){let p=ar(u);if(typeof p!="undefined"){let m=u.offsetTop-p.offsetTop,{height:d}=he(p);p.scrollTo({top:m-d/2,behavior:c})}}})}return re("navigation.tracking")&&t.pipe(Q(s),ee("offset"),ke(250),Fe(1),Q(o.pipe(Fe(1))),_t({delay:250}),ne(i)).subscribe(([,{prev:a}])=>{let f=le(),c=a[a.length-1];if(c&&c.length){let[u]=c,{hash:p}=new URL(u.href);f.hash!==p&&(f.hash=p,history.replaceState({},"",`${f}`))}else f.hash="",history.replaceState({},"",`${f}`)}),ps(e,{viewport$:t,header$:r}).pipe(S(a=>i.next(a)),R(()=>i.complete()),l(a=>P({ref:e},a)))})}function ls(e,{viewport$:t,main$:r,target$:n}){let o=t.pipe(l(({offset:{y:s}})=>s),Re(2,1),l(([s,a])=>s>a&&a>0),J()),i=r.pipe(l(({active:s})=>s));return G([i,o]).pipe(l(([s,a])=>!(s&&a)),J(),Q(n.pipe(Fe(1))),te(!0),_t({delay:250}),l(s=>({hidden:s})))}function li(e,{viewport$:t,header$:r,main$:n,target$:o}){let i=new x,s=i.pipe(Z(),te(!0));return i.subscribe({next({hidden:a}){e.hidden=a,a?(e.setAttribute("tabindex","-1"),e.blur()):e.removeAttribute("tabindex")},complete(){e.style.top="",e.hidden=!0,e.removeAttribute("tabindex")}}),r.pipe(Q(s),ee("height")).subscribe(({height:a})=>{e.style.top=`${a+16}px`}),b(e,"click").subscribe(a=>{a.preventDefault(),window.scrollTo({top:0})}),ls(e,{viewport$:t,main$:n,target$:o}).pipe(S(a=>i.next(a)),R(()=>i.complete()),l(a=>P({ref:e},a)))}function mi({document$:e,tablet$:t}){e.pipe(g(()=>K(".md-toggle--indeterminate")),S(r=>{r.indeterminate=!0,r.checked=!1}),se(r=>b(r,"change").pipe(jr(()=>r.classList.contains("md-toggle--indeterminate")),l(()=>r))),ne(t)).subscribe(([r,n])=>{r.classList.remove("md-toggle--indeterminate"),n&&(r.checked=!1)})}function ms(){return/(iPad|iPhone|iPod)/.test(navigator.userAgent)}function di({document$:e}){e.pipe(g(()=>K("[data-md-scrollfix]")),S(t=>t.removeAttribute("data-md-scrollfix")),A(ms),se(t=>b(t,"touchstart").pipe(l(()=>t)))).subscribe(t=>{let r=t.scrollTop;r===0?t.scrollTop=1:r+t.offsetHeight===t.scrollHeight&&(t.scrollTop=r-1)})}function hi({viewport$:e,tablet$:t}){G([Ue("search"),t]).pipe(l(([r,n])=>r&&!n),g(r=>k(r).pipe(Ne(r?400:100))),ne(e)).subscribe(([r,{offset:{y:n}}])=>{if(r)document.body.setAttribute("data-md-scrolllock",""),document.body.style.top=`-${n}px`;else{let o=-1*parseInt(document.body.style.top,10);document.body.removeAttribute("data-md-scrolllock"),document.body.style.top="",o&&window.scrollTo(0,o)}})}Object.entries||(Object.entries=function(e){let t=[];for(let r of Object.keys(e))t.push([r,e[r]]);return t});Object.values||(Object.values=function(e){let t=[];for(let r of Object.keys(e))t.push(e[r]);return t});typeof Element!="undefined"&&(Element.prototype.scrollTo||(Element.prototype.scrollTo=function(e,t){typeof e=="object"?(this.scrollLeft=e.left,this.scrollTop=e.top):(this.scrollLeft=e,this.scrollTop=t)}),Element.prototype.replaceWith||(Element.prototype.replaceWith=function(...e){let t=this.parentNode;if(t){e.length===0&&t.removeChild(this);for(let r=e.length-1;r>=0;r--){let n=e[r];typeof n=="string"?n=document.createTextNode(n):n.parentNode&&n.parentNode.removeChild(n),r?t.insertBefore(this.previousSibling,n):t.replaceChild(n,this)}}}));function ds(){return location.protocol==="file:"?pr(`${new URL("search/search_index.js",Xr.base)}`).pipe(l(()=>__index),X(1)):We(new URL("search/search_index.json",Xr.base))}document.documentElement.classList.remove("no-js");document.documentElement.classList.add("js");var it=qn(),Ct=io(),gt=co(Ct),Zr=oo(),Se=bo(),hr=Vr("(min-width: 960px)"),vi=Vr("(min-width: 1220px)"),gi=fo(),Xr=me(),yi=document.forms.namedItem("search")?ds():ze,en=new x;Ko({alert$:en});re("navigation.instant")&&Qo({location$:Ct,viewport$:Se}).subscribe(it);var bi;((bi=Xr.version)==null?void 0:bi.provider)==="mike"&&Zo({document$:it});L(Ct,gt).pipe(Ne(125)).subscribe(()=>{Ke("drawer",!1),Ke("search",!1)});Zr.pipe(A(({mode:e})=>e==="global")).subscribe(e=>{switch(e.type){case"p":case",":let t=ce("link[rel=prev]");typeof t!="undefined"&&ot(t);break;case"n":case".":let r=ce("link[rel=next]");typeof r!="undefined"&&ot(r);break;case"Enter":let n=_e();n instanceof HTMLLabelElement&&n.click()}});mi({document$:it,tablet$:hr});di({document$:it});hi({viewport$:Se,tablet$:hr});var Ze=Do(xe("header"),{viewport$:Se}),At=it.pipe(l(()=>xe("main")),g(e=>No(e,{viewport$:Se,header$:Ze})),X(1)),hs=L(...oe("consent").map(e=>yo(e,{target$:gt})),...oe("dialog").map(e=>Wo(e,{alert$:en})),...oe("header").map(e=>Vo(e,{viewport$:Se,header$:Ze,main$:At})),...oe("palette").map(e=>qo(e)),...oe("search").map(e=>oi(e,{index$:yi,keyboard$:Zr})),...oe("source").map(e=>fi(e))),bs=$(()=>L(...oe("announce").map(e=>go(e)),...oe("content").map(e=>Uo(e,{viewport$:Se,target$:gt,print$:gi})),...oe("content").map(e=>re("search.highlight")?ii(e,{index$:yi,location$:Ct}):_),...oe("header-title").map(e=>zo(e,{viewport$:Se,header$:Ze})),...oe("sidebar").map(e=>e.getAttribute("data-md-type")==="navigation"?zr(vi,()=>Jr(e,{viewport$:Se,header$:Ze,main$:At})):zr(hr,()=>Jr(e,{viewport$:Se,header$:Ze,main$:At}))),...oe("tabs").map(e=>ui(e,{viewport$:Se,header$:Ze})),...oe("toc").map(e=>pi(e,{viewport$:Se,header$:Ze,main$:At,target$:gt})),...oe("top").map(e=>li(e,{viewport$:Se,header$:Ze,main$:At,target$:gt})))),xi=it.pipe(g(()=>bs),qe(hs),X(1));xi.subscribe();window.document$=it;window.location$=Ct;window.target$=gt;window.keyboard$=Zr;window.viewport$=Se;window.tablet$=hr;window.screen$=vi;window.print$=gi;window.alert$=en;window.component$=xi;})();
-//# sourceMappingURL=bundle.b4d07000.min.js.map
-
diff --git a/assets/javascripts/bundle.b4d07000.min.js.map b/assets/javascripts/bundle.b4d07000.min.js.map
deleted file mode 100644
index 0969edebdf..0000000000
--- a/assets/javascripts/bundle.b4d07000.min.js.map
+++ /dev/null
@@ -1,8 +0,0 @@
-{
-  "version": 3,
-  "sources": ["node_modules/focus-visible/dist/focus-visible.js", "node_modules/url-polyfill/url-polyfill.js", "node_modules/clipboard/dist/clipboard.js", "node_modules/escape-html/index.js", "node_modules/array-flat-polyfill/index.mjs", "src/assets/javascripts/bundle.ts", "node_modules/unfetch/polyfill/index.js", "node_modules/rxjs/node_modules/tslib/tslib.es6.js", "node_modules/rxjs/src/internal/util/isFunction.ts", "node_modules/rxjs/src/internal/util/createErrorClass.ts", "node_modules/rxjs/src/internal/util/UnsubscriptionError.ts", "node_modules/rxjs/src/internal/util/arrRemove.ts", "node_modules/rxjs/src/internal/Subscription.ts", "node_modules/rxjs/src/internal/config.ts", "node_modules/rxjs/src/internal/scheduler/timeoutProvider.ts", "node_modules/rxjs/src/internal/util/reportUnhandledError.ts", "node_modules/rxjs/src/internal/util/noop.ts", "node_modules/rxjs/src/internal/NotificationFactories.ts", "node_modules/rxjs/src/internal/util/errorContext.ts", "node_modules/rxjs/src/internal/Subscriber.ts", "node_modules/rxjs/src/internal/symbol/observable.ts", "node_modules/rxjs/src/internal/util/identity.ts", "node_modules/rxjs/src/internal/util/pipe.ts", "node_modules/rxjs/src/internal/Observable.ts", "node_modules/rxjs/src/internal/util/lift.ts", "node_modules/rxjs/src/internal/operators/OperatorSubscriber.ts", "node_modules/rxjs/src/internal/scheduler/animationFrameProvider.ts", "node_modules/rxjs/src/internal/util/ObjectUnsubscribedError.ts", "node_modules/rxjs/src/internal/Subject.ts", "node_modules/rxjs/src/internal/scheduler/dateTimestampProvider.ts", "node_modules/rxjs/src/internal/ReplaySubject.ts", "node_modules/rxjs/src/internal/scheduler/Action.ts", "node_modules/rxjs/src/internal/scheduler/intervalProvider.ts", "node_modules/rxjs/src/internal/scheduler/AsyncAction.ts", "node_modules/rxjs/src/internal/Scheduler.ts", "node_modules/rxjs/src/internal/scheduler/AsyncScheduler.ts", "node_modules/rxjs/src/internal/scheduler/async.ts", "node_modules/rxjs/src/internal/scheduler/AnimationFrameAction.ts", "node_modules/rxjs/src/internal/scheduler/AnimationFrameScheduler.ts", "node_modules/rxjs/src/internal/scheduler/animationFrame.ts", "node_modules/rxjs/src/internal/observable/empty.ts", "node_modules/rxjs/src/internal/util/isScheduler.ts", "node_modules/rxjs/src/internal/util/args.ts", "node_modules/rxjs/src/internal/util/isArrayLike.ts", "node_modules/rxjs/src/internal/util/isPromise.ts", "node_modules/rxjs/src/internal/util/isInteropObservable.ts", "node_modules/rxjs/src/internal/util/isAsyncIterable.ts", "node_modules/rxjs/src/internal/util/throwUnobservableError.ts", "node_modules/rxjs/src/internal/symbol/iterator.ts", "node_modules/rxjs/src/internal/util/isIterable.ts", "node_modules/rxjs/src/internal/util/isReadableStreamLike.ts", "node_modules/rxjs/src/internal/observable/innerFrom.ts", "node_modules/rxjs/src/internal/util/executeSchedule.ts", "node_modules/rxjs/src/internal/operators/observeOn.ts", "node_modules/rxjs/src/internal/operators/subscribeOn.ts", "node_modules/rxjs/src/internal/scheduled/scheduleObservable.ts", "node_modules/rxjs/src/internal/scheduled/schedulePromise.ts", "node_modules/rxjs/src/internal/scheduled/scheduleArray.ts", "node_modules/rxjs/src/internal/scheduled/scheduleIterable.ts", "node_modules/rxjs/src/internal/scheduled/scheduleAsyncIterable.ts", "node_modules/rxjs/src/internal/scheduled/scheduleReadableStreamLike.ts", "node_modules/rxjs/src/internal/scheduled/scheduled.ts", "node_modules/rxjs/src/internal/observable/from.ts", "node_modules/rxjs/src/internal/observable/of.ts", "node_modules/rxjs/src/internal/observable/throwError.ts", "node_modules/rxjs/src/internal/util/EmptyError.ts", "node_modules/rxjs/src/internal/util/isDate.ts", "node_modules/rxjs/src/internal/operators/map.ts", "node_modules/rxjs/src/internal/util/mapOneOrManyArgs.ts", "node_modules/rxjs/src/internal/util/argsArgArrayOrObject.ts", "node_modules/rxjs/src/internal/util/createObject.ts", "node_modules/rxjs/src/internal/observable/combineLatest.ts", "node_modules/rxjs/src/internal/operators/mergeInternals.ts", "node_modules/rxjs/src/internal/operators/mergeMap.ts", "node_modules/rxjs/src/internal/operators/mergeAll.ts", "node_modules/rxjs/src/internal/operators/concatAll.ts", "node_modules/rxjs/src/internal/observable/concat.ts", "node_modules/rxjs/src/internal/observable/defer.ts", "node_modules/rxjs/src/internal/observable/fromEvent.ts", "node_modules/rxjs/src/internal/observable/fromEventPattern.ts", "node_modules/rxjs/src/internal/observable/timer.ts", "node_modules/rxjs/src/internal/observable/merge.ts", "node_modules/rxjs/src/internal/observable/never.ts", "node_modules/rxjs/src/internal/util/argsOrArgArray.ts", "node_modules/rxjs/src/internal/operators/filter.ts", "node_modules/rxjs/src/internal/observable/zip.ts", "node_modules/rxjs/src/internal/operators/audit.ts", "node_modules/rxjs/src/internal/operators/auditTime.ts", "node_modules/rxjs/src/internal/operators/bufferCount.ts", "node_modules/rxjs/src/internal/operators/catchError.ts", "node_modules/rxjs/src/internal/operators/scanInternals.ts", "node_modules/rxjs/src/internal/operators/combineLatest.ts", "node_modules/rxjs/src/internal/operators/combineLatestWith.ts", "node_modules/rxjs/src/internal/operators/debounceTime.ts", "node_modules/rxjs/src/internal/operators/defaultIfEmpty.ts", "node_modules/rxjs/src/internal/operators/take.ts", "node_modules/rxjs/src/internal/operators/ignoreElements.ts", "node_modules/rxjs/src/internal/operators/mapTo.ts", "node_modules/rxjs/src/internal/operators/delayWhen.ts", "node_modules/rxjs/src/internal/operators/delay.ts", "node_modules/rxjs/src/internal/operators/distinctUntilChanged.ts", "node_modules/rxjs/src/internal/operators/distinctUntilKeyChanged.ts", "node_modules/rxjs/src/internal/operators/throwIfEmpty.ts", "node_modules/rxjs/src/internal/operators/endWith.ts", "node_modules/rxjs/src/internal/operators/finalize.ts", "node_modules/rxjs/src/internal/operators/first.ts", "node_modules/rxjs/src/internal/operators/merge.ts", "node_modules/rxjs/src/internal/operators/mergeWith.ts", "node_modules/rxjs/src/internal/operators/repeat.ts", "node_modules/rxjs/src/internal/operators/scan.ts", "node_modules/rxjs/src/internal/operators/share.ts", "node_modules/rxjs/src/internal/operators/shareReplay.ts", "node_modules/rxjs/src/internal/operators/skip.ts", "node_modules/rxjs/src/internal/operators/skipUntil.ts", "node_modules/rxjs/src/internal/operators/startWith.ts", "node_modules/rxjs/src/internal/operators/switchMap.ts", "node_modules/rxjs/src/internal/operators/takeUntil.ts", "node_modules/rxjs/src/internal/operators/takeWhile.ts", "node_modules/rxjs/src/internal/operators/tap.ts", "node_modules/rxjs/src/internal/operators/throttle.ts", "node_modules/rxjs/src/internal/operators/throttleTime.ts", "node_modules/rxjs/src/internal/operators/withLatestFrom.ts", "node_modules/rxjs/src/internal/operators/zip.ts", "node_modules/rxjs/src/internal/operators/zipWith.ts", "src/assets/javascripts/browser/document/index.ts", "src/assets/javascripts/browser/element/_/index.ts", "src/assets/javascripts/browser/element/focus/index.ts", "src/assets/javascripts/browser/element/offset/_/index.ts", "src/assets/javascripts/browser/element/offset/content/index.ts", "node_modules/resize-observer-polyfill/dist/ResizeObserver.es.js", "src/assets/javascripts/browser/element/size/_/index.ts", "src/assets/javascripts/browser/element/size/content/index.ts", "src/assets/javascripts/browser/element/visibility/index.ts", "src/assets/javascripts/browser/toggle/index.ts", "src/assets/javascripts/browser/keyboard/index.ts", "src/assets/javascripts/browser/location/_/index.ts", "src/assets/javascripts/utilities/h/index.ts", "src/assets/javascripts/utilities/round/index.ts", "src/assets/javascripts/browser/location/hash/index.ts", "src/assets/javascripts/browser/media/index.ts", "src/assets/javascripts/browser/request/index.ts", "src/assets/javascripts/browser/script/index.ts", "src/assets/javascripts/browser/viewport/offset/index.ts", "src/assets/javascripts/browser/viewport/size/index.ts", "src/assets/javascripts/browser/viewport/_/index.ts", "src/assets/javascripts/browser/viewport/at/index.ts", "node_modules/iframe-worker/shim/index.js", "src/assets/javascripts/browser/worker/index.ts", "src/assets/javascripts/_/index.ts", "src/assets/javascripts/components/_/index.ts", "src/assets/javascripts/components/announce/index.ts", "src/assets/javascripts/components/consent/index.ts", "src/assets/javascripts/components/content/code/_/index.ts", "src/assets/javascripts/templates/tooltip/index.tsx", "src/assets/javascripts/templates/annotation/index.tsx", "src/assets/javascripts/templates/clipboard/index.tsx", "src/assets/javascripts/templates/search/index.tsx", "src/assets/javascripts/templates/source/index.tsx", "src/assets/javascripts/templates/tabbed/index.tsx", "src/assets/javascripts/templates/table/index.tsx", "src/assets/javascripts/templates/version/index.tsx", "src/assets/javascripts/components/content/annotation/_/index.ts", "src/assets/javascripts/components/content/annotation/list/index.ts", "src/assets/javascripts/components/content/code/mermaid/index.ts", "src/assets/javascripts/components/content/details/index.ts", "src/assets/javascripts/components/content/table/index.ts", "src/assets/javascripts/components/content/tabs/index.ts", "src/assets/javascripts/components/content/_/index.ts", "src/assets/javascripts/components/dialog/index.ts", "src/assets/javascripts/components/header/_/index.ts", "src/assets/javascripts/components/header/title/index.ts", "src/assets/javascripts/components/main/index.ts", "src/assets/javascripts/components/palette/index.ts", "src/assets/javascripts/integrations/clipboard/index.ts", "src/assets/javascripts/integrations/sitemap/index.ts", "src/assets/javascripts/integrations/instant/index.ts", "src/assets/javascripts/integrations/search/highlighter/index.ts", "src/assets/javascripts/integrations/search/worker/message/index.ts", "src/assets/javascripts/integrations/search/worker/_/index.ts", "src/assets/javascripts/integrations/version/index.ts", "src/assets/javascripts/components/search/query/index.ts", "src/assets/javascripts/components/search/result/index.ts", "src/assets/javascripts/components/search/share/index.ts", "src/assets/javascripts/components/search/suggest/index.ts", "src/assets/javascripts/components/search/_/index.ts", "src/assets/javascripts/components/search/highlight/index.ts", "src/assets/javascripts/components/sidebar/index.ts", "src/assets/javascripts/components/source/facts/github/index.ts", "src/assets/javascripts/components/source/facts/gitlab/index.ts", "src/assets/javascripts/components/source/facts/_/index.ts", "src/assets/javascripts/components/source/_/index.ts", "src/assets/javascripts/components/tabs/index.ts", "src/assets/javascripts/components/toc/index.ts", "src/assets/javascripts/components/top/index.ts", "src/assets/javascripts/patches/indeterminate/index.ts", "src/assets/javascripts/patches/scrollfix/index.ts", "src/assets/javascripts/patches/scrolllock/index.ts", "src/assets/javascripts/polyfills/index.ts"],
-  "sourceRoot": "../../..",
-  "sourcesContent": ["(function (global, factory) {\n  typeof exports === 'object' && typeof module !== 'undefined' ? factory() :\n  typeof define === 'function' && define.amd ? define(factory) :\n  (factory());\n}(this, (function () { 'use strict';\n\n  /**\n   * Applies the :focus-visible polyfill at the given scope.\n   * A scope in this case is either the top-level Document or a Shadow Root.\n   *\n   * @param {(Document|ShadowRoot)} scope\n   * @see https://github.com/WICG/focus-visible\n   */\n  function applyFocusVisiblePolyfill(scope) {\n    var hadKeyboardEvent = true;\n    var hadFocusVisibleRecently = false;\n    var hadFocusVisibleRecentlyTimeout = null;\n\n    var inputTypesAllowlist = {\n      text: true,\n      search: true,\n      url: true,\n      tel: true,\n      email: true,\n      password: true,\n      number: true,\n      date: true,\n      month: true,\n      week: true,\n      time: true,\n      datetime: true,\n      'datetime-local': true\n    };\n\n    /**\n     * Helper function for legacy browsers and iframes which sometimes focus\n     * elements like document, body, and non-interactive SVG.\n     * @param {Element} el\n     */\n    function isValidFocusTarget(el) {\n      if (\n        el &&\n        el !== document &&\n        el.nodeName !== 'HTML' &&\n        el.nodeName !== 'BODY' &&\n        'classList' in el &&\n        'contains' in el.classList\n      ) {\n        return true;\n      }\n      return false;\n    }\n\n    /**\n     * Computes whether the given element should automatically trigger the\n     * `focus-visible` class being added, i.e. whether it should always match\n     * `:focus-visible` when focused.\n     * @param {Element} el\n     * @return {boolean}\n     */\n    function focusTriggersKeyboardModality(el) {\n      var type = el.type;\n      var tagName = el.tagName;\n\n      if (tagName === 'INPUT' && inputTypesAllowlist[type] && !el.readOnly) {\n        return true;\n      }\n\n      if (tagName === 'TEXTAREA' && !el.readOnly) {\n        return true;\n      }\n\n      if (el.isContentEditable) {\n        return true;\n      }\n\n      return false;\n    }\n\n    /**\n     * Add the `focus-visible` class to the given element if it was not added by\n     * the author.\n     * @param {Element} el\n     */\n    function addFocusVisibleClass(el) {\n      if (el.classList.contains('focus-visible')) {\n        return;\n      }\n      el.classList.add('focus-visible');\n      el.setAttribute('data-focus-visible-added', '');\n    }\n\n    /**\n     * Remove the `focus-visible` class from the given element if it was not\n     * originally added by the author.\n     * @param {Element} el\n     */\n    function removeFocusVisibleClass(el) {\n      if (!el.hasAttribute('data-focus-visible-added')) {\n        return;\n      }\n      el.classList.remove('focus-visible');\n      el.removeAttribute('data-focus-visible-added');\n    }\n\n    /**\n     * If the most recent user interaction was via the keyboard;\n     * and the key press did not include a meta, alt/option, or control key;\n     * then the modality is keyboard. Otherwise, the modality is not keyboard.\n     * Apply `focus-visible` to any current active element and keep track\n     * of our keyboard modality state with `hadKeyboardEvent`.\n     * @param {KeyboardEvent} e\n     */\n    function onKeyDown(e) {\n      if (e.metaKey || e.altKey || e.ctrlKey) {\n        return;\n      }\n\n      if (isValidFocusTarget(scope.activeElement)) {\n        addFocusVisibleClass(scope.activeElement);\n      }\n\n      hadKeyboardEvent = true;\n    }\n\n    /**\n     * If at any point a user clicks with a pointing device, ensure that we change\n     * the modality away from keyboard.\n     * This avoids the situation where a user presses a key on an already focused\n     * element, and then clicks on a different element, focusing it with a\n     * pointing device, while we still think we're in keyboard modality.\n     * @param {Event} e\n     */\n    function onPointerDown(e) {\n      hadKeyboardEvent = false;\n    }\n\n    /**\n     * On `focus`, add the `focus-visible` class to the target if:\n     * - the target received focus as a result of keyboard navigation, or\n     * - the event target is an element that will likely require interaction\n     *   via the keyboard (e.g. a text box)\n     * @param {Event} e\n     */\n    function onFocus(e) {\n      // Prevent IE from focusing the document or HTML element.\n      if (!isValidFocusTarget(e.target)) {\n        return;\n      }\n\n      if (hadKeyboardEvent || focusTriggersKeyboardModality(e.target)) {\n        addFocusVisibleClass(e.target);\n      }\n    }\n\n    /**\n     * On `blur`, remove the `focus-visible` class from the target.\n     * @param {Event} e\n     */\n    function onBlur(e) {\n      if (!isValidFocusTarget(e.target)) {\n        return;\n      }\n\n      if (\n        e.target.classList.contains('focus-visible') ||\n        e.target.hasAttribute('data-focus-visible-added')\n      ) {\n        // To detect a tab/window switch, we look for a blur event followed\n        // rapidly by a visibility change.\n        // If we don't see a visibility change within 100ms, it's probably a\n        // regular focus change.\n        hadFocusVisibleRecently = true;\n        window.clearTimeout(hadFocusVisibleRecentlyTimeout);\n        hadFocusVisibleRecentlyTimeout = window.setTimeout(function() {\n          hadFocusVisibleRecently = false;\n        }, 100);\n        removeFocusVisibleClass(e.target);\n      }\n    }\n\n    /**\n     * If the user changes tabs, keep track of whether or not the previously\n     * focused element had .focus-visible.\n     * @param {Event} e\n     */\n    function onVisibilityChange(e) {\n      if (document.visibilityState === 'hidden') {\n        // If the tab becomes active again, the browser will handle calling focus\n        // on the element (Safari actually calls it twice).\n        // If this tab change caused a blur on an element with focus-visible,\n        // re-apply the class when the user switches back to the tab.\n        if (hadFocusVisibleRecently) {\n          hadKeyboardEvent = true;\n        }\n        addInitialPointerMoveListeners();\n      }\n    }\n\n    /**\n     * Add a group of listeners to detect usage of any pointing devices.\n     * These listeners will be added when the polyfill first loads, and anytime\n     * the window is blurred, so that they are active when the window regains\n     * focus.\n     */\n    function addInitialPointerMoveListeners() {\n      document.addEventListener('mousemove', onInitialPointerMove);\n      document.addEventListener('mousedown', onInitialPointerMove);\n      document.addEventListener('mouseup', onInitialPointerMove);\n      document.addEventListener('pointermove', onInitialPointerMove);\n      document.addEventListener('pointerdown', onInitialPointerMove);\n      document.addEventListener('pointerup', onInitialPointerMove);\n      document.addEventListener('touchmove', onInitialPointerMove);\n      document.addEventListener('touchstart', onInitialPointerMove);\n      document.addEventListener('touchend', onInitialPointerMove);\n    }\n\n    function removeInitialPointerMoveListeners() {\n      document.removeEventListener('mousemove', onInitialPointerMove);\n      document.removeEventListener('mousedown', onInitialPointerMove);\n      document.removeEventListener('mouseup', onInitialPointerMove);\n      document.removeEventListener('pointermove', onInitialPointerMove);\n      document.removeEventListener('pointerdown', onInitialPointerMove);\n      document.removeEventListener('pointerup', onInitialPointerMove);\n      document.removeEventListener('touchmove', onInitialPointerMove);\n      document.removeEventListener('touchstart', onInitialPointerMove);\n      document.removeEventListener('touchend', onInitialPointerMove);\n    }\n\n    /**\n     * When the polfyill first loads, assume the user is in keyboard modality.\n     * If any event is received from a pointing device (e.g. mouse, pointer,\n     * touch), turn off keyboard modality.\n     * This accounts for situations where focus enters the page from the URL bar.\n     * @param {Event} e\n     */\n    function onInitialPointerMove(e) {\n      // Work around a Safari quirk that fires a mousemove on <html> whenever the\n      // window blurs, even if you're tabbing out of the page. \u00AF\\_(\u30C4)_/\u00AF\n      if (e.target.nodeName && e.target.nodeName.toLowerCase() === 'html') {\n        return;\n      }\n\n      hadKeyboardEvent = false;\n      removeInitialPointerMoveListeners();\n    }\n\n    // For some kinds of state, we are interested in changes at the global scope\n    // only. For example, global pointer input, global key presses and global\n    // visibility change should affect the state at every scope:\n    document.addEventListener('keydown', onKeyDown, true);\n    document.addEventListener('mousedown', onPointerDown, true);\n    document.addEventListener('pointerdown', onPointerDown, true);\n    document.addEventListener('touchstart', onPointerDown, true);\n    document.addEventListener('visibilitychange', onVisibilityChange, true);\n\n    addInitialPointerMoveListeners();\n\n    // For focus and blur, we specifically care about state changes in the local\n    // scope. This is because focus / blur events that originate from within a\n    // shadow root are not re-dispatched from the host element if it was already\n    // the active element in its own scope:\n    scope.addEventListener('focus', onFocus, true);\n    scope.addEventListener('blur', onBlur, true);\n\n    // We detect that a node is a ShadowRoot by ensuring that it is a\n    // DocumentFragment and also has a host property. This check covers native\n    // implementation and polyfill implementation transparently. If we only cared\n    // about the native implementation, we could just check if the scope was\n    // an instance of a ShadowRoot.\n    if (scope.nodeType === Node.DOCUMENT_FRAGMENT_NODE && scope.host) {\n      // Since a ShadowRoot is a special kind of DocumentFragment, it does not\n      // have a root element to add a class to. So, we add this attribute to the\n      // host element instead:\n      scope.host.setAttribute('data-js-focus-visible', '');\n    } else if (scope.nodeType === Node.DOCUMENT_NODE) {\n      document.documentElement.classList.add('js-focus-visible');\n      document.documentElement.setAttribute('data-js-focus-visible', '');\n    }\n  }\n\n  // It is important to wrap all references to global window and document in\n  // these checks to support server-side rendering use cases\n  // @see https://github.com/WICG/focus-visible/issues/199\n  if (typeof window !== 'undefined' && typeof document !== 'undefined') {\n    // Make the polyfill helper globally available. This can be used as a signal\n    // to interested libraries that wish to coordinate with the polyfill for e.g.,\n    // applying the polyfill to a shadow root:\n    window.applyFocusVisiblePolyfill = applyFocusVisiblePolyfill;\n\n    // Notify interested libraries of the polyfill's presence, in case the\n    // polyfill was loaded lazily:\n    var event;\n\n    try {\n      event = new CustomEvent('focus-visible-polyfill-ready');\n    } catch (error) {\n      // IE11 does not support using CustomEvent as a constructor directly:\n      event = document.createEvent('CustomEvent');\n      event.initCustomEvent('focus-visible-polyfill-ready', false, false, {});\n    }\n\n    window.dispatchEvent(event);\n  }\n\n  if (typeof document !== 'undefined') {\n    // Apply the polyfill to the global document, so that no JavaScript\n    // coordination is required to use the polyfill in the top-level document:\n    applyFocusVisiblePolyfill(document);\n  }\n\n})));\n", "(function(global) {\r\n  /**\r\n   * Polyfill URLSearchParams\r\n   *\r\n   * Inspired from : https://github.com/WebReflection/url-search-params/blob/master/src/url-search-params.js\r\n   */\r\n\r\n  var checkIfIteratorIsSupported = function() {\r\n    try {\r\n      return !!Symbol.iterator;\r\n    } catch (error) {\r\n      return false;\r\n    }\r\n  };\r\n\r\n\r\n  var iteratorSupported = checkIfIteratorIsSupported();\r\n\r\n  var createIterator = function(items) {\r\n    var iterator = {\r\n      next: function() {\r\n        var value = items.shift();\r\n        return { done: value === void 0, value: value };\r\n      }\r\n    };\r\n\r\n    if (iteratorSupported) {\r\n      iterator[Symbol.iterator] = function() {\r\n        return iterator;\r\n      };\r\n    }\r\n\r\n    return iterator;\r\n  };\r\n\r\n  /**\r\n   * Search param name and values should be encoded according to https://url.spec.whatwg.org/#urlencoded-serializing\r\n   * encodeURIComponent() produces the same result except encoding spaces as `%20` instead of `+`.\r\n   */\r\n  var serializeParam = function(value) {\r\n    return encodeURIComponent(value).replace(/%20/g, '+');\r\n  };\r\n\r\n  var deserializeParam = function(value) {\r\n    return decodeURIComponent(String(value).replace(/\\+/g, ' '));\r\n  };\r\n\r\n  var polyfillURLSearchParams = function() {\r\n\r\n    var URLSearchParams = function(searchString) {\r\n      Object.defineProperty(this, '_entries', { writable: true, value: {} });\r\n      var typeofSearchString = typeof searchString;\r\n\r\n      if (typeofSearchString === 'undefined') {\r\n        // do nothing\r\n      } else if (typeofSearchString === 'string') {\r\n        if (searchString !== '') {\r\n          this._fromString(searchString);\r\n        }\r\n      } else if (searchString instanceof URLSearchParams) {\r\n        var _this = this;\r\n        searchString.forEach(function(value, name) {\r\n          _this.append(name, value);\r\n        });\r\n      } else if ((searchString !== null) && (typeofSearchString === 'object')) {\r\n        if (Object.prototype.toString.call(searchString) === '[object Array]') {\r\n          for (var i = 0; i < searchString.length; i++) {\r\n            var entry = searchString[i];\r\n            if ((Object.prototype.toString.call(entry) === '[object Array]') || (entry.length !== 2)) {\r\n              this.append(entry[0], entry[1]);\r\n            } else {\r\n              throw new TypeError('Expected [string, any] as entry at index ' + i + ' of URLSearchParams\\'s input');\r\n            }\r\n          }\r\n        } else {\r\n          for (var key in searchString) {\r\n            if (searchString.hasOwnProperty(key)) {\r\n              this.append(key, searchString[key]);\r\n            }\r\n          }\r\n        }\r\n      } else {\r\n        throw new TypeError('Unsupported input\\'s type for URLSearchParams');\r\n      }\r\n    };\r\n\r\n    var proto = URLSearchParams.prototype;\r\n\r\n    proto.append = function(name, value) {\r\n      if (name in this._entries) {\r\n        this._entries[name].push(String(value));\r\n      } else {\r\n        this._entries[name] = [String(value)];\r\n      }\r\n    };\r\n\r\n    proto.delete = function(name) {\r\n      delete this._entries[name];\r\n    };\r\n\r\n    proto.get = function(name) {\r\n      return (name in this._entries) ? this._entries[name][0] : null;\r\n    };\r\n\r\n    proto.getAll = function(name) {\r\n      return (name in this._entries) ? this._entries[name].slice(0) : [];\r\n    };\r\n\r\n    proto.has = function(name) {\r\n      return (name in this._entries);\r\n    };\r\n\r\n    proto.set = function(name, value) {\r\n      this._entries[name] = [String(value)];\r\n    };\r\n\r\n    proto.forEach = function(callback, thisArg) {\r\n      var entries;\r\n      for (var name in this._entries) {\r\n        if (this._entries.hasOwnProperty(name)) {\r\n          entries = this._entries[name];\r\n          for (var i = 0; i < entries.length; i++) {\r\n            callback.call(thisArg, entries[i], name, this);\r\n          }\r\n        }\r\n      }\r\n    };\r\n\r\n    proto.keys = function() {\r\n      var items = [];\r\n      this.forEach(function(value, name) {\r\n        items.push(name);\r\n      });\r\n      return createIterator(items);\r\n    };\r\n\r\n    proto.values = function() {\r\n      var items = [];\r\n      this.forEach(function(value) {\r\n        items.push(value);\r\n      });\r\n      return createIterator(items);\r\n    };\r\n\r\n    proto.entries = function() {\r\n      var items = [];\r\n      this.forEach(function(value, name) {\r\n        items.push([name, value]);\r\n      });\r\n      return createIterator(items);\r\n    };\r\n\r\n    if (iteratorSupported) {\r\n      proto[Symbol.iterator] = proto.entries;\r\n    }\r\n\r\n    proto.toString = function() {\r\n      var searchArray = [];\r\n      this.forEach(function(value, name) {\r\n        searchArray.push(serializeParam(name) + '=' + serializeParam(value));\r\n      });\r\n      return searchArray.join('&');\r\n    };\r\n\r\n\r\n    global.URLSearchParams = URLSearchParams;\r\n  };\r\n\r\n  var checkIfURLSearchParamsSupported = function() {\r\n    try {\r\n      var URLSearchParams = global.URLSearchParams;\r\n\r\n      return (\r\n        (new URLSearchParams('?a=1').toString() === 'a=1') &&\r\n        (typeof URLSearchParams.prototype.set === 'function') &&\r\n        (typeof URLSearchParams.prototype.entries === 'function')\r\n      );\r\n    } catch (e) {\r\n      return false;\r\n    }\r\n  };\r\n\r\n  if (!checkIfURLSearchParamsSupported()) {\r\n    polyfillURLSearchParams();\r\n  }\r\n\r\n  var proto = global.URLSearchParams.prototype;\r\n\r\n  if (typeof proto.sort !== 'function') {\r\n    proto.sort = function() {\r\n      var _this = this;\r\n      var items = [];\r\n      this.forEach(function(value, name) {\r\n        items.push([name, value]);\r\n        if (!_this._entries) {\r\n          _this.delete(name);\r\n        }\r\n      });\r\n      items.sort(function(a, b) {\r\n        if (a[0] < b[0]) {\r\n          return -1;\r\n        } else if (a[0] > b[0]) {\r\n          return +1;\r\n        } else {\r\n          return 0;\r\n        }\r\n      });\r\n      if (_this._entries) { // force reset because IE keeps keys index\r\n        _this._entries = {};\r\n      }\r\n      for (var i = 0; i < items.length; i++) {\r\n        this.append(items[i][0], items[i][1]);\r\n      }\r\n    };\r\n  }\r\n\r\n  if (typeof proto._fromString !== 'function') {\r\n    Object.defineProperty(proto, '_fromString', {\r\n      enumerable: false,\r\n      configurable: false,\r\n      writable: false,\r\n      value: function(searchString) {\r\n        if (this._entries) {\r\n          this._entries = {};\r\n        } else {\r\n          var keys = [];\r\n          this.forEach(function(value, name) {\r\n            keys.push(name);\r\n          });\r\n          for (var i = 0; i < keys.length; i++) {\r\n            this.delete(keys[i]);\r\n          }\r\n        }\r\n\r\n        searchString = searchString.replace(/^\\?/, '');\r\n        var attributes = searchString.split('&');\r\n        var attribute;\r\n        for (var i = 0; i < attributes.length; i++) {\r\n          attribute = attributes[i].split('=');\r\n          this.append(\r\n            deserializeParam(attribute[0]),\r\n            (attribute.length > 1) ? deserializeParam(attribute[1]) : ''\r\n          );\r\n        }\r\n      }\r\n    });\r\n  }\r\n\r\n  // HTMLAnchorElement\r\n\r\n})(\r\n  (typeof global !== 'undefined') ? global\r\n    : ((typeof window !== 'undefined') ? window\r\n    : ((typeof self !== 'undefined') ? self : this))\r\n);\r\n\r\n(function(global) {\r\n  /**\r\n   * Polyfill URL\r\n   *\r\n   * Inspired from : https://github.com/arv/DOM-URL-Polyfill/blob/master/src/url.js\r\n   */\r\n\r\n  var checkIfURLIsSupported = function() {\r\n    try {\r\n      var u = new global.URL('b', 'http://a');\r\n      u.pathname = 'c d';\r\n      return (u.href === 'http://a/c%20d') && u.searchParams;\r\n    } catch (e) {\r\n      return false;\r\n    }\r\n  };\r\n\r\n\r\n  var polyfillURL = function() {\r\n    var _URL = global.URL;\r\n\r\n    var URL = function(url, base) {\r\n      if (typeof url !== 'string') url = String(url);\r\n      if (base && typeof base !== 'string') base = String(base);\r\n\r\n      // Only create another document if the base is different from current location.\r\n      var doc = document, baseElement;\r\n      if (base && (global.location === void 0 || base !== global.location.href)) {\r\n        base = base.toLowerCase();\r\n        doc = document.implementation.createHTMLDocument('');\r\n        baseElement = doc.createElement('base');\r\n        baseElement.href = base;\r\n        doc.head.appendChild(baseElement);\r\n        try {\r\n          if (baseElement.href.indexOf(base) !== 0) throw new Error(baseElement.href);\r\n        } catch (err) {\r\n          throw new Error('URL unable to set base ' + base + ' due to ' + err);\r\n        }\r\n      }\r\n\r\n      var anchorElement = doc.createElement('a');\r\n      anchorElement.href = url;\r\n      if (baseElement) {\r\n        doc.body.appendChild(anchorElement);\r\n        anchorElement.href = anchorElement.href; // force href to refresh\r\n      }\r\n\r\n      var inputElement = doc.createElement('input');\r\n      inputElement.type = 'url';\r\n      inputElement.value = url;\r\n\r\n      if (anchorElement.protocol === ':' || !/:/.test(anchorElement.href) || (!inputElement.checkValidity() && !base)) {\r\n        throw new TypeError('Invalid URL');\r\n      }\r\n\r\n      Object.defineProperty(this, '_anchorElement', {\r\n        value: anchorElement\r\n      });\r\n\r\n\r\n      // create a linked searchParams which reflect its changes on URL\r\n      var searchParams = new global.URLSearchParams(this.search);\r\n      var enableSearchUpdate = true;\r\n      var enableSearchParamsUpdate = true;\r\n      var _this = this;\r\n      ['append', 'delete', 'set'].forEach(function(methodName) {\r\n        var method = searchParams[methodName];\r\n        searchParams[methodName] = function() {\r\n          method.apply(searchParams, arguments);\r\n          if (enableSearchUpdate) {\r\n            enableSearchParamsUpdate = false;\r\n            _this.search = searchParams.toString();\r\n            enableSearchParamsUpdate = true;\r\n          }\r\n        };\r\n      });\r\n\r\n      Object.defineProperty(this, 'searchParams', {\r\n        value: searchParams,\r\n        enumerable: true\r\n      });\r\n\r\n      var search = void 0;\r\n      Object.defineProperty(this, '_updateSearchParams', {\r\n        enumerable: false,\r\n        configurable: false,\r\n        writable: false,\r\n        value: function() {\r\n          if (this.search !== search) {\r\n            search = this.search;\r\n            if (enableSearchParamsUpdate) {\r\n              enableSearchUpdate = false;\r\n              this.searchParams._fromString(this.search);\r\n              enableSearchUpdate = true;\r\n            }\r\n          }\r\n        }\r\n      });\r\n    };\r\n\r\n    var proto = URL.prototype;\r\n\r\n    var linkURLWithAnchorAttribute = function(attributeName) {\r\n      Object.defineProperty(proto, attributeName, {\r\n        get: function() {\r\n          return this._anchorElement[attributeName];\r\n        },\r\n        set: function(value) {\r\n          this._anchorElement[attributeName] = value;\r\n        },\r\n        enumerable: true\r\n      });\r\n    };\r\n\r\n    ['hash', 'host', 'hostname', 'port', 'protocol']\r\n      .forEach(function(attributeName) {\r\n        linkURLWithAnchorAttribute(attributeName);\r\n      });\r\n\r\n    Object.defineProperty(proto, 'search', {\r\n      get: function() {\r\n        return this._anchorElement['search'];\r\n      },\r\n      set: function(value) {\r\n        this._anchorElement['search'] = value;\r\n        this._updateSearchParams();\r\n      },\r\n      enumerable: true\r\n    });\r\n\r\n    Object.defineProperties(proto, {\r\n\r\n      'toString': {\r\n        get: function() {\r\n          var _this = this;\r\n          return function() {\r\n            return _this.href;\r\n          };\r\n        }\r\n      },\r\n\r\n      'href': {\r\n        get: function() {\r\n          return this._anchorElement.href.replace(/\\?$/, '');\r\n        },\r\n        set: function(value) {\r\n          this._anchorElement.href = value;\r\n          this._updateSearchParams();\r\n        },\r\n        enumerable: true\r\n      },\r\n\r\n      'pathname': {\r\n        get: function() {\r\n          return this._anchorElement.pathname.replace(/(^\\/?)/, '/');\r\n        },\r\n        set: function(value) {\r\n          this._anchorElement.pathname = value;\r\n        },\r\n        enumerable: true\r\n      },\r\n\r\n      'origin': {\r\n        get: function() {\r\n          // get expected port from protocol\r\n          var expectedPort = { 'http:': 80, 'https:': 443, 'ftp:': 21 }[this._anchorElement.protocol];\r\n          // add port to origin if, expected port is different than actual port\r\n          // and it is not empty f.e http://foo:8080\r\n          // 8080 != 80 && 8080 != ''\r\n          var addPortToOrigin = this._anchorElement.port != expectedPort &&\r\n            this._anchorElement.port !== '';\r\n\r\n          return this._anchorElement.protocol +\r\n            '//' +\r\n            this._anchorElement.hostname +\r\n            (addPortToOrigin ? (':' + this._anchorElement.port) : '');\r\n        },\r\n        enumerable: true\r\n      },\r\n\r\n      'password': { // TODO\r\n        get: function() {\r\n          return '';\r\n        },\r\n        set: function(value) {\r\n        },\r\n        enumerable: true\r\n      },\r\n\r\n      'username': { // TODO\r\n        get: function() {\r\n          return '';\r\n        },\r\n        set: function(value) {\r\n        },\r\n        enumerable: true\r\n      },\r\n    });\r\n\r\n    URL.createObjectURL = function(blob) {\r\n      return _URL.createObjectURL.apply(_URL, arguments);\r\n    };\r\n\r\n    URL.revokeObjectURL = function(url) {\r\n      return _URL.revokeObjectURL.apply(_URL, arguments);\r\n    };\r\n\r\n    global.URL = URL;\r\n\r\n  };\r\n\r\n  if (!checkIfURLIsSupported()) {\r\n    polyfillURL();\r\n  }\r\n\r\n  if ((global.location !== void 0) && !('origin' in global.location)) {\r\n    var getOrigin = function() {\r\n      return global.location.protocol + '//' + global.location.hostname + (global.location.port ? (':' + global.location.port) : '');\r\n    };\r\n\r\n    try {\r\n      Object.defineProperty(global.location, 'origin', {\r\n        get: getOrigin,\r\n        enumerable: true\r\n      });\r\n    } catch (e) {\r\n      setInterval(function() {\r\n        global.location.origin = getOrigin();\r\n      }, 100);\r\n    }\r\n  }\r\n\r\n})(\r\n  (typeof global !== 'undefined') ? global\r\n    : ((typeof window !== 'undefined') ? window\r\n    : ((typeof self !== 'undefined') ? self : this))\r\n);\r\n", "/*!\n * clipboard.js v2.0.11\n * https://clipboardjs.com/\n *\n * Licensed MIT \u00A9 Zeno Rocha\n */\n(function webpackUniversalModuleDefinition(root, factory) {\n\tif(typeof exports === 'object' && typeof module === 'object')\n\t\tmodule.exports = factory();\n\telse if(typeof define === 'function' && define.amd)\n\t\tdefine([], factory);\n\telse if(typeof exports === 'object')\n\t\texports[\"ClipboardJS\"] = factory();\n\telse\n\t\troot[\"ClipboardJS\"] = factory();\n})(this, function() {\nreturn /******/ (function() { // webpackBootstrap\n/******/ \tvar __webpack_modules__ = ({\n\n/***/ 686:\n/***/ (function(__unused_webpack_module, __webpack_exports__, __webpack_require__) {\n\n\"use strict\";\n\n// EXPORTS\n__webpack_require__.d(__webpack_exports__, {\n  \"default\": function() { return /* binding */ clipboard; }\n});\n\n// EXTERNAL MODULE: ./node_modules/tiny-emitter/index.js\nvar tiny_emitter = __webpack_require__(279);\nvar tiny_emitter_default = /*#__PURE__*/__webpack_require__.n(tiny_emitter);\n// EXTERNAL MODULE: ./node_modules/good-listener/src/listen.js\nvar listen = __webpack_require__(370);\nvar listen_default = /*#__PURE__*/__webpack_require__.n(listen);\n// EXTERNAL MODULE: ./node_modules/select/src/select.js\nvar src_select = __webpack_require__(817);\nvar select_default = /*#__PURE__*/__webpack_require__.n(src_select);\n;// CONCATENATED MODULE: ./src/common/command.js\n/**\n * Executes a given operation type.\n * @param {String} type\n * @return {Boolean}\n */\nfunction command(type) {\n  try {\n    return document.execCommand(type);\n  } catch (err) {\n    return false;\n  }\n}\n;// CONCATENATED MODULE: ./src/actions/cut.js\n\n\n/**\n * Cut action wrapper.\n * @param {String|HTMLElement} target\n * @return {String}\n */\n\nvar ClipboardActionCut = function ClipboardActionCut(target) {\n  var selectedText = select_default()(target);\n  command('cut');\n  return selectedText;\n};\n\n/* harmony default export */ var actions_cut = (ClipboardActionCut);\n;// CONCATENATED MODULE: ./src/common/create-fake-element.js\n/**\n * Creates a fake textarea element with a value.\n * @param {String} value\n * @return {HTMLElement}\n */\nfunction createFakeElement(value) {\n  var isRTL = document.documentElement.getAttribute('dir') === 'rtl';\n  var fakeElement = document.createElement('textarea'); // Prevent zooming on iOS\n\n  fakeElement.style.fontSize = '12pt'; // Reset box model\n\n  fakeElement.style.border = '0';\n  fakeElement.style.padding = '0';\n  fakeElement.style.margin = '0'; // Move element out of screen horizontally\n\n  fakeElement.style.position = 'absolute';\n  fakeElement.style[isRTL ? 'right' : 'left'] = '-9999px'; // Move element to the same position vertically\n\n  var yPosition = window.pageYOffset || document.documentElement.scrollTop;\n  fakeElement.style.top = \"\".concat(yPosition, \"px\");\n  fakeElement.setAttribute('readonly', '');\n  fakeElement.value = value;\n  return fakeElement;\n}\n;// CONCATENATED MODULE: ./src/actions/copy.js\n\n\n\n/**\n * Create fake copy action wrapper using a fake element.\n * @param {String} target\n * @param {Object} options\n * @return {String}\n */\n\nvar fakeCopyAction = function fakeCopyAction(value, options) {\n  var fakeElement = createFakeElement(value);\n  options.container.appendChild(fakeElement);\n  var selectedText = select_default()(fakeElement);\n  command('copy');\n  fakeElement.remove();\n  return selectedText;\n};\n/**\n * Copy action wrapper.\n * @param {String|HTMLElement} target\n * @param {Object} options\n * @return {String}\n */\n\n\nvar ClipboardActionCopy = function ClipboardActionCopy(target) {\n  var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {\n    container: document.body\n  };\n  var selectedText = '';\n\n  if (typeof target === 'string') {\n    selectedText = fakeCopyAction(target, options);\n  } else if (target instanceof HTMLInputElement && !['text', 'search', 'url', 'tel', 'password'].includes(target === null || target === void 0 ? void 0 : target.type)) {\n    // If input type doesn't support `setSelectionRange`. Simulate it. https://developer.mozilla.org/en-US/docs/Web/API/HTMLInputElement/setSelectionRange\n    selectedText = fakeCopyAction(target.value, options);\n  } else {\n    selectedText = select_default()(target);\n    command('copy');\n  }\n\n  return selectedText;\n};\n\n/* harmony default export */ var actions_copy = (ClipboardActionCopy);\n;// CONCATENATED MODULE: ./src/actions/default.js\nfunction _typeof(obj) { \"@babel/helpers - typeof\"; if (typeof Symbol === \"function\" && typeof Symbol.iterator === \"symbol\") { _typeof = function _typeof(obj) { return typeof obj; }; } else { _typeof = function _typeof(obj) { return obj && typeof Symbol === \"function\" && obj.constructor === Symbol && obj !== Symbol.prototype ? \"symbol\" : typeof obj; }; } return _typeof(obj); }\n\n\n\n/**\n * Inner function which performs selection from either `text` or `target`\n * properties and then executes copy or cut operations.\n * @param {Object} options\n */\n\nvar ClipboardActionDefault = function ClipboardActionDefault() {\n  var options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};\n  // Defines base properties passed from constructor.\n  var _options$action = options.action,\n      action = _options$action === void 0 ? 'copy' : _options$action,\n      container = options.container,\n      target = options.target,\n      text = options.text; // Sets the `action` to be performed which can be either 'copy' or 'cut'.\n\n  if (action !== 'copy' && action !== 'cut') {\n    throw new Error('Invalid \"action\" value, use either \"copy\" or \"cut\"');\n  } // Sets the `target` property using an element that will be have its content copied.\n\n\n  if (target !== undefined) {\n    if (target && _typeof(target) === 'object' && target.nodeType === 1) {\n      if (action === 'copy' && target.hasAttribute('disabled')) {\n        throw new Error('Invalid \"target\" attribute. Please use \"readonly\" instead of \"disabled\" attribute');\n      }\n\n      if (action === 'cut' && (target.hasAttribute('readonly') || target.hasAttribute('disabled'))) {\n        throw new Error('Invalid \"target\" attribute. You can\\'t cut text from elements with \"readonly\" or \"disabled\" attributes');\n      }\n    } else {\n      throw new Error('Invalid \"target\" value, use a valid Element');\n    }\n  } // Define selection strategy based on `text` property.\n\n\n  if (text) {\n    return actions_copy(text, {\n      container: container\n    });\n  } // Defines which selection strategy based on `target` property.\n\n\n  if (target) {\n    return action === 'cut' ? actions_cut(target) : actions_copy(target, {\n      container: container\n    });\n  }\n};\n\n/* harmony default export */ var actions_default = (ClipboardActionDefault);\n;// CONCATENATED MODULE: ./src/clipboard.js\nfunction clipboard_typeof(obj) { \"@babel/helpers - typeof\"; if (typeof Symbol === \"function\" && typeof Symbol.iterator === \"symbol\") { clipboard_typeof = function _typeof(obj) { return typeof obj; }; } else { clipboard_typeof = function _typeof(obj) { return obj && typeof Symbol === \"function\" && obj.constructor === Symbol && obj !== Symbol.prototype ? \"symbol\" : typeof obj; }; } return clipboard_typeof(obj); }\n\nfunction _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError(\"Cannot call a class as a function\"); } }\n\nfunction _defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if (\"value\" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } }\n\nfunction _createClass(Constructor, protoProps, staticProps) { if (protoProps) _defineProperties(Constructor.prototype, protoProps); if (staticProps) _defineProperties(Constructor, staticProps); return Constructor; }\n\nfunction _inherits(subClass, superClass) { if (typeof superClass !== \"function\" && superClass !== null) { throw new TypeError(\"Super expression must either be null or a function\"); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, writable: true, configurable: true } }); if (superClass) _setPrototypeOf(subClass, superClass); }\n\nfunction _setPrototypeOf(o, p) { _setPrototypeOf = Object.setPrototypeOf || function _setPrototypeOf(o, p) { o.__proto__ = p; return o; }; return _setPrototypeOf(o, p); }\n\nfunction _createSuper(Derived) { var hasNativeReflectConstruct = _isNativeReflectConstruct(); return function _createSuperInternal() { var Super = _getPrototypeOf(Derived), result; if (hasNativeReflectConstruct) { var NewTarget = _getPrototypeOf(this).constructor; result = Reflect.construct(Super, arguments, NewTarget); } else { result = Super.apply(this, arguments); } return _possibleConstructorReturn(this, result); }; }\n\nfunction _possibleConstructorReturn(self, call) { if (call && (clipboard_typeof(call) === \"object\" || typeof call === \"function\")) { return call; } return _assertThisInitialized(self); }\n\nfunction _assertThisInitialized(self) { if (self === void 0) { throw new ReferenceError(\"this hasn't been initialised - super() hasn't been called\"); } return self; }\n\nfunction _isNativeReflectConstruct() { if (typeof Reflect === \"undefined\" || !Reflect.construct) return false; if (Reflect.construct.sham) return false; if (typeof Proxy === \"function\") return true; try { Date.prototype.toString.call(Reflect.construct(Date, [], function () {})); return true; } catch (e) { return false; } }\n\nfunction _getPrototypeOf(o) { _getPrototypeOf = Object.setPrototypeOf ? Object.getPrototypeOf : function _getPrototypeOf(o) { return o.__proto__ || Object.getPrototypeOf(o); }; return _getPrototypeOf(o); }\n\n\n\n\n\n\n/**\n * Helper function to retrieve attribute value.\n * @param {String} suffix\n * @param {Element} element\n */\n\nfunction getAttributeValue(suffix, element) {\n  var attribute = \"data-clipboard-\".concat(suffix);\n\n  if (!element.hasAttribute(attribute)) {\n    return;\n  }\n\n  return element.getAttribute(attribute);\n}\n/**\n * Base class which takes one or more elements, adds event listeners to them,\n * and instantiates a new `ClipboardAction` on each click.\n */\n\n\nvar Clipboard = /*#__PURE__*/function (_Emitter) {\n  _inherits(Clipboard, _Emitter);\n\n  var _super = _createSuper(Clipboard);\n\n  /**\n   * @param {String|HTMLElement|HTMLCollection|NodeList} trigger\n   * @param {Object} options\n   */\n  function Clipboard(trigger, options) {\n    var _this;\n\n    _classCallCheck(this, Clipboard);\n\n    _this = _super.call(this);\n\n    _this.resolveOptions(options);\n\n    _this.listenClick(trigger);\n\n    return _this;\n  }\n  /**\n   * Defines if attributes would be resolved using internal setter functions\n   * or custom functions that were passed in the constructor.\n   * @param {Object} options\n   */\n\n\n  _createClass(Clipboard, [{\n    key: \"resolveOptions\",\n    value: function resolveOptions() {\n      var options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};\n      this.action = typeof options.action === 'function' ? options.action : this.defaultAction;\n      this.target = typeof options.target === 'function' ? options.target : this.defaultTarget;\n      this.text = typeof options.text === 'function' ? options.text : this.defaultText;\n      this.container = clipboard_typeof(options.container) === 'object' ? options.container : document.body;\n    }\n    /**\n     * Adds a click event listener to the passed trigger.\n     * @param {String|HTMLElement|HTMLCollection|NodeList} trigger\n     */\n\n  }, {\n    key: \"listenClick\",\n    value: function listenClick(trigger) {\n      var _this2 = this;\n\n      this.listener = listen_default()(trigger, 'click', function (e) {\n        return _this2.onClick(e);\n      });\n    }\n    /**\n     * Defines a new `ClipboardAction` on each click event.\n     * @param {Event} e\n     */\n\n  }, {\n    key: \"onClick\",\n    value: function onClick(e) {\n      var trigger = e.delegateTarget || e.currentTarget;\n      var action = this.action(trigger) || 'copy';\n      var text = actions_default({\n        action: action,\n        container: this.container,\n        target: this.target(trigger),\n        text: this.text(trigger)\n      }); // Fires an event based on the copy operation result.\n\n      this.emit(text ? 'success' : 'error', {\n        action: action,\n        text: text,\n        trigger: trigger,\n        clearSelection: function clearSelection() {\n          if (trigger) {\n            trigger.focus();\n          }\n\n          window.getSelection().removeAllRanges();\n        }\n      });\n    }\n    /**\n     * Default `action` lookup function.\n     * @param {Element} trigger\n     */\n\n  }, {\n    key: \"defaultAction\",\n    value: function defaultAction(trigger) {\n      return getAttributeValue('action', trigger);\n    }\n    /**\n     * Default `target` lookup function.\n     * @param {Element} trigger\n     */\n\n  }, {\n    key: \"defaultTarget\",\n    value: function defaultTarget(trigger) {\n      var selector = getAttributeValue('target', trigger);\n\n      if (selector) {\n        return document.querySelector(selector);\n      }\n    }\n    /**\n     * Allow fire programmatically a copy action\n     * @param {String|HTMLElement} target\n     * @param {Object} options\n     * @returns Text copied.\n     */\n\n  }, {\n    key: \"defaultText\",\n\n    /**\n     * Default `text` lookup function.\n     * @param {Element} trigger\n     */\n    value: function defaultText(trigger) {\n      return getAttributeValue('text', trigger);\n    }\n    /**\n     * Destroy lifecycle.\n     */\n\n  }, {\n    key: \"destroy\",\n    value: function destroy() {\n      this.listener.destroy();\n    }\n  }], [{\n    key: \"copy\",\n    value: function copy(target) {\n      var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {\n        container: document.body\n      };\n      return actions_copy(target, options);\n    }\n    /**\n     * Allow fire programmatically a cut action\n     * @param {String|HTMLElement} target\n     * @returns Text cutted.\n     */\n\n  }, {\n    key: \"cut\",\n    value: function cut(target) {\n      return actions_cut(target);\n    }\n    /**\n     * Returns the support of the given action, or all actions if no action is\n     * given.\n     * @param {String} [action]\n     */\n\n  }, {\n    key: \"isSupported\",\n    value: function isSupported() {\n      var action = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : ['copy', 'cut'];\n      var actions = typeof action === 'string' ? [action] : action;\n      var support = !!document.queryCommandSupported;\n      actions.forEach(function (action) {\n        support = support && !!document.queryCommandSupported(action);\n      });\n      return support;\n    }\n  }]);\n\n  return Clipboard;\n}((tiny_emitter_default()));\n\n/* harmony default export */ var clipboard = (Clipboard);\n\n/***/ }),\n\n/***/ 828:\n/***/ (function(module) {\n\nvar DOCUMENT_NODE_TYPE = 9;\n\n/**\n * A polyfill for Element.matches()\n */\nif (typeof Element !== 'undefined' && !Element.prototype.matches) {\n    var proto = Element.prototype;\n\n    proto.matches = proto.matchesSelector ||\n                    proto.mozMatchesSelector ||\n                    proto.msMatchesSelector ||\n                    proto.oMatchesSelector ||\n                    proto.webkitMatchesSelector;\n}\n\n/**\n * Finds the closest parent that matches a selector.\n *\n * @param {Element} element\n * @param {String} selector\n * @return {Function}\n */\nfunction closest (element, selector) {\n    while (element && element.nodeType !== DOCUMENT_NODE_TYPE) {\n        if (typeof element.matches === 'function' &&\n            element.matches(selector)) {\n          return element;\n        }\n        element = element.parentNode;\n    }\n}\n\nmodule.exports = closest;\n\n\n/***/ }),\n\n/***/ 438:\n/***/ (function(module, __unused_webpack_exports, __webpack_require__) {\n\nvar closest = __webpack_require__(828);\n\n/**\n * Delegates event to a selector.\n *\n * @param {Element} element\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @param {Boolean} useCapture\n * @return {Object}\n */\nfunction _delegate(element, selector, type, callback, useCapture) {\n    var listenerFn = listener.apply(this, arguments);\n\n    element.addEventListener(type, listenerFn, useCapture);\n\n    return {\n        destroy: function() {\n            element.removeEventListener(type, listenerFn, useCapture);\n        }\n    }\n}\n\n/**\n * Delegates event to a selector.\n *\n * @param {Element|String|Array} [elements]\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @param {Boolean} useCapture\n * @return {Object}\n */\nfunction delegate(elements, selector, type, callback, useCapture) {\n    // Handle the regular Element usage\n    if (typeof elements.addEventListener === 'function') {\n        return _delegate.apply(null, arguments);\n    }\n\n    // Handle Element-less usage, it defaults to global delegation\n    if (typeof type === 'function') {\n        // Use `document` as the first parameter, then apply arguments\n        // This is a short way to .unshift `arguments` without running into deoptimizations\n        return _delegate.bind(null, document).apply(null, arguments);\n    }\n\n    // Handle Selector-based usage\n    if (typeof elements === 'string') {\n        elements = document.querySelectorAll(elements);\n    }\n\n    // Handle Array-like based usage\n    return Array.prototype.map.call(elements, function (element) {\n        return _delegate(element, selector, type, callback, useCapture);\n    });\n}\n\n/**\n * Finds closest match and invokes callback.\n *\n * @param {Element} element\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @return {Function}\n */\nfunction listener(element, selector, type, callback) {\n    return function(e) {\n        e.delegateTarget = closest(e.target, selector);\n\n        if (e.delegateTarget) {\n            callback.call(element, e);\n        }\n    }\n}\n\nmodule.exports = delegate;\n\n\n/***/ }),\n\n/***/ 879:\n/***/ (function(__unused_webpack_module, exports) {\n\n/**\n * Check if argument is a HTML element.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.node = function(value) {\n    return value !== undefined\n        && value instanceof HTMLElement\n        && value.nodeType === 1;\n};\n\n/**\n * Check if argument is a list of HTML elements.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.nodeList = function(value) {\n    var type = Object.prototype.toString.call(value);\n\n    return value !== undefined\n        && (type === '[object NodeList]' || type === '[object HTMLCollection]')\n        && ('length' in value)\n        && (value.length === 0 || exports.node(value[0]));\n};\n\n/**\n * Check if argument is a string.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.string = function(value) {\n    return typeof value === 'string'\n        || value instanceof String;\n};\n\n/**\n * Check if argument is a function.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.fn = function(value) {\n    var type = Object.prototype.toString.call(value);\n\n    return type === '[object Function]';\n};\n\n\n/***/ }),\n\n/***/ 370:\n/***/ (function(module, __unused_webpack_exports, __webpack_require__) {\n\nvar is = __webpack_require__(879);\nvar delegate = __webpack_require__(438);\n\n/**\n * Validates all params and calls the right\n * listener function based on its target type.\n *\n * @param {String|HTMLElement|HTMLCollection|NodeList} target\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listen(target, type, callback) {\n    if (!target && !type && !callback) {\n        throw new Error('Missing required arguments');\n    }\n\n    if (!is.string(type)) {\n        throw new TypeError('Second argument must be a String');\n    }\n\n    if (!is.fn(callback)) {\n        throw new TypeError('Third argument must be a Function');\n    }\n\n    if (is.node(target)) {\n        return listenNode(target, type, callback);\n    }\n    else if (is.nodeList(target)) {\n        return listenNodeList(target, type, callback);\n    }\n    else if (is.string(target)) {\n        return listenSelector(target, type, callback);\n    }\n    else {\n        throw new TypeError('First argument must be a String, HTMLElement, HTMLCollection, or NodeList');\n    }\n}\n\n/**\n * Adds an event listener to a HTML element\n * and returns a remove listener function.\n *\n * @param {HTMLElement} node\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenNode(node, type, callback) {\n    node.addEventListener(type, callback);\n\n    return {\n        destroy: function() {\n            node.removeEventListener(type, callback);\n        }\n    }\n}\n\n/**\n * Add an event listener to a list of HTML elements\n * and returns a remove listener function.\n *\n * @param {NodeList|HTMLCollection} nodeList\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenNodeList(nodeList, type, callback) {\n    Array.prototype.forEach.call(nodeList, function(node) {\n        node.addEventListener(type, callback);\n    });\n\n    return {\n        destroy: function() {\n            Array.prototype.forEach.call(nodeList, function(node) {\n                node.removeEventListener(type, callback);\n            });\n        }\n    }\n}\n\n/**\n * Add an event listener to a selector\n * and returns a remove listener function.\n *\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenSelector(selector, type, callback) {\n    return delegate(document.body, selector, type, callback);\n}\n\nmodule.exports = listen;\n\n\n/***/ }),\n\n/***/ 817:\n/***/ (function(module) {\n\nfunction select(element) {\n    var selectedText;\n\n    if (element.nodeName === 'SELECT') {\n        element.focus();\n\n        selectedText = element.value;\n    }\n    else if (element.nodeName === 'INPUT' || element.nodeName === 'TEXTAREA') {\n        var isReadOnly = element.hasAttribute('readonly');\n\n        if (!isReadOnly) {\n            element.setAttribute('readonly', '');\n        }\n\n        element.select();\n        element.setSelectionRange(0, element.value.length);\n\n        if (!isReadOnly) {\n            element.removeAttribute('readonly');\n        }\n\n        selectedText = element.value;\n    }\n    else {\n        if (element.hasAttribute('contenteditable')) {\n            element.focus();\n        }\n\n        var selection = window.getSelection();\n        var range = document.createRange();\n\n        range.selectNodeContents(element);\n        selection.removeAllRanges();\n        selection.addRange(range);\n\n        selectedText = selection.toString();\n    }\n\n    return selectedText;\n}\n\nmodule.exports = select;\n\n\n/***/ }),\n\n/***/ 279:\n/***/ (function(module) {\n\nfunction E () {\n  // Keep this empty so it's easier to inherit from\n  // (via https://github.com/lipsmack from https://github.com/scottcorgan/tiny-emitter/issues/3)\n}\n\nE.prototype = {\n  on: function (name, callback, ctx) {\n    var e = this.e || (this.e = {});\n\n    (e[name] || (e[name] = [])).push({\n      fn: callback,\n      ctx: ctx\n    });\n\n    return this;\n  },\n\n  once: function (name, callback, ctx) {\n    var self = this;\n    function listener () {\n      self.off(name, listener);\n      callback.apply(ctx, arguments);\n    };\n\n    listener._ = callback\n    return this.on(name, listener, ctx);\n  },\n\n  emit: function (name) {\n    var data = [].slice.call(arguments, 1);\n    var evtArr = ((this.e || (this.e = {}))[name] || []).slice();\n    var i = 0;\n    var len = evtArr.length;\n\n    for (i; i < len; i++) {\n      evtArr[i].fn.apply(evtArr[i].ctx, data);\n    }\n\n    return this;\n  },\n\n  off: function (name, callback) {\n    var e = this.e || (this.e = {});\n    var evts = e[name];\n    var liveEvents = [];\n\n    if (evts && callback) {\n      for (var i = 0, len = evts.length; i < len; i++) {\n        if (evts[i].fn !== callback && evts[i].fn._ !== callback)\n          liveEvents.push(evts[i]);\n      }\n    }\n\n    // Remove event from queue to prevent memory leak\n    // Suggested by https://github.com/lazd\n    // Ref: https://github.com/scottcorgan/tiny-emitter/commit/c6ebfaa9bc973b33d110a84a307742b7cf94c953#commitcomment-5024910\n\n    (liveEvents.length)\n      ? e[name] = liveEvents\n      : delete e[name];\n\n    return this;\n  }\n};\n\nmodule.exports = E;\nmodule.exports.TinyEmitter = E;\n\n\n/***/ })\n\n/******/ \t});\n/************************************************************************/\n/******/ \t// The module cache\n/******/ \tvar __webpack_module_cache__ = {};\n/******/ \t\n/******/ \t// The require function\n/******/ \tfunction __webpack_require__(moduleId) {\n/******/ \t\t// Check if module is in cache\n/******/ \t\tif(__webpack_module_cache__[moduleId]) {\n/******/ \t\t\treturn __webpack_module_cache__[moduleId].exports;\n/******/ \t\t}\n/******/ \t\t// Create a new module (and put it into the cache)\n/******/ \t\tvar module = __webpack_module_cache__[moduleId] = {\n/******/ \t\t\t// no module.id needed\n/******/ \t\t\t// no module.loaded needed\n/******/ \t\t\texports: {}\n/******/ \t\t};\n/******/ \t\n/******/ \t\t// Execute the module function\n/******/ \t\t__webpack_modules__[moduleId](module, module.exports, __webpack_require__);\n/******/ \t\n/******/ \t\t// Return the exports of the module\n/******/ \t\treturn module.exports;\n/******/ \t}\n/******/ \t\n/************************************************************************/\n/******/ \t/* webpack/runtime/compat get default export */\n/******/ \t!function() {\n/******/ \t\t// getDefaultExport function for compatibility with non-harmony modules\n/******/ \t\t__webpack_require__.n = function(module) {\n/******/ \t\t\tvar getter = module && module.__esModule ?\n/******/ \t\t\t\tfunction() { return module['default']; } :\n/******/ \t\t\t\tfunction() { return module; };\n/******/ \t\t\t__webpack_require__.d(getter, { a: getter });\n/******/ \t\t\treturn getter;\n/******/ \t\t};\n/******/ \t}();\n/******/ \t\n/******/ \t/* webpack/runtime/define property getters */\n/******/ \t!function() {\n/******/ \t\t// define getter functions for harmony exports\n/******/ \t\t__webpack_require__.d = function(exports, definition) {\n/******/ \t\t\tfor(var key in definition) {\n/******/ \t\t\t\tif(__webpack_require__.o(definition, key) && !__webpack_require__.o(exports, key)) {\n/******/ \t\t\t\t\tObject.defineProperty(exports, key, { enumerable: true, get: definition[key] });\n/******/ \t\t\t\t}\n/******/ \t\t\t}\n/******/ \t\t};\n/******/ \t}();\n/******/ \t\n/******/ \t/* webpack/runtime/hasOwnProperty shorthand */\n/******/ \t!function() {\n/******/ \t\t__webpack_require__.o = function(obj, prop) { return Object.prototype.hasOwnProperty.call(obj, prop); }\n/******/ \t}();\n/******/ \t\n/************************************************************************/\n/******/ \t// module exports must be returned from runtime so entry inlining is disabled\n/******/ \t// startup\n/******/ \t// Load entry module and return exports\n/******/ \treturn __webpack_require__(686);\n/******/ })()\n.default;\n});", "/*!\n * escape-html\n * Copyright(c) 2012-2013 TJ Holowaychuk\n * Copyright(c) 2015 Andreas Lubbe\n * Copyright(c) 2015 Tiancheng \"Timothy\" Gu\n * MIT Licensed\n */\n\n'use strict';\n\n/**\n * Module variables.\n * @private\n */\n\nvar matchHtmlRegExp = /[\"'&<>]/;\n\n/**\n * Module exports.\n * @public\n */\n\nmodule.exports = escapeHtml;\n\n/**\n * Escape special characters in the given string of html.\n *\n * @param  {string} string The string to escape for inserting into HTML\n * @return {string}\n * @public\n */\n\nfunction escapeHtml(string) {\n  var str = '' + string;\n  var match = matchHtmlRegExp.exec(str);\n\n  if (!match) {\n    return str;\n  }\n\n  var escape;\n  var html = '';\n  var index = 0;\n  var lastIndex = 0;\n\n  for (index = match.index; index < str.length; index++) {\n    switch (str.charCodeAt(index)) {\n      case 34: // \"\n        escape = '&quot;';\n        break;\n      case 38: // &\n        escape = '&amp;';\n        break;\n      case 39: // '\n        escape = '&#39;';\n        break;\n      case 60: // <\n        escape = '&lt;';\n        break;\n      case 62: // >\n        escape = '&gt;';\n        break;\n      default:\n        continue;\n    }\n\n    if (lastIndex !== index) {\n      html += str.substring(lastIndex, index);\n    }\n\n    lastIndex = index + 1;\n    html += escape;\n  }\n\n  return lastIndex !== index\n    ? html + str.substring(lastIndex, index)\n    : html;\n}\n", "Array.prototype.flat||Object.defineProperty(Array.prototype,\"flat\",{configurable:!0,value:function r(){var t=isNaN(arguments[0])?1:Number(arguments[0]);return t?Array.prototype.reduce.call(this,function(a,e){return Array.isArray(e)?a.push.apply(a,r.call(e,t-1)):a.push(e),a},[]):Array.prototype.slice.call(this)},writable:!0}),Array.prototype.flatMap||Object.defineProperty(Array.prototype,\"flatMap\",{configurable:!0,value:function(r){return Array.prototype.map.apply(this,arguments).flat()},writable:!0})\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport \"array-flat-polyfill\"\nimport \"focus-visible\"\nimport \"unfetch/polyfill\"\nimport \"url-polyfill\"\n\nimport {\n  EMPTY,\n  NEVER,\n  Observable,\n  Subject,\n  defer,\n  delay,\n  filter,\n  map,\n  merge,\n  mergeWith,\n  shareReplay,\n  switchMap\n} from \"rxjs\"\n\nimport { configuration, feature } from \"./_\"\nimport {\n  at,\n  getActiveElement,\n  getOptionalElement,\n  requestJSON,\n  setLocation,\n  setToggle,\n  watchDocument,\n  watchKeyboard,\n  watchLocation,\n  watchLocationTarget,\n  watchMedia,\n  watchPrint,\n  watchScript,\n  watchViewport\n} from \"./browser\"\nimport {\n  getComponentElement,\n  getComponentElements,\n  mountAnnounce,\n  mountBackToTop,\n  mountConsent,\n  mountContent,\n  mountDialog,\n  mountHeader,\n  mountHeaderTitle,\n  mountPalette,\n  mountSearch,\n  mountSearchHiglight,\n  mountSidebar,\n  mountSource,\n  mountTableOfContents,\n  mountTabs,\n  watchHeader,\n  watchMain\n} from \"./components\"\nimport {\n  SearchIndex,\n  setupClipboardJS,\n  setupInstantLoading,\n  setupVersionSelector\n} from \"./integrations\"\nimport {\n  patchIndeterminate,\n  patchScrollfix,\n  patchScrolllock\n} from \"./patches\"\nimport \"./polyfills\"\n\n/* ----------------------------------------------------------------------------\n * Functions - @todo refactor\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch search index\n *\n * @returns Search index observable\n */\nfunction fetchSearchIndex(): Observable<SearchIndex> {\n  if (location.protocol === \"file:\") {\n    return watchScript(\n      `${new URL(\"search/search_index.js\", config.base)}`\n    )\n      .pipe(\n        // @ts-ignore - @todo fix typings\n        map(() => __index),\n        shareReplay(1)\n      )\n  } else {\n    return requestJSON<SearchIndex>(\n      new URL(\"search/search_index.json\", config.base)\n    )\n  }\n}\n\n/* ----------------------------------------------------------------------------\n * Application\n * ------------------------------------------------------------------------- */\n\n/* Yay, JavaScript is available */\ndocument.documentElement.classList.remove(\"no-js\")\ndocument.documentElement.classList.add(\"js\")\n\n/* Set up navigation observables and subjects */\nconst document$ = watchDocument()\nconst location$ = watchLocation()\nconst target$   = watchLocationTarget(location$)\nconst keyboard$ = watchKeyboard()\n\n/* Set up media observables */\nconst viewport$ = watchViewport()\nconst tablet$   = watchMedia(\"(min-width: 960px)\")\nconst screen$   = watchMedia(\"(min-width: 1220px)\")\nconst print$    = watchPrint()\n\n/* Retrieve search index, if search is enabled */\nconst config = configuration()\nconst index$ = document.forms.namedItem(\"search\")\n  ? fetchSearchIndex()\n  : NEVER\n\n/* Set up Clipboard.js integration */\nconst alert$ = new Subject<string>()\nsetupClipboardJS({ alert$ })\n\n/* Set up instant loading, if enabled */\nif (feature(\"navigation.instant\"))\n  setupInstantLoading({ location$, viewport$ })\n    .subscribe(document$)\n\n/* Set up version selector */\nif (config.version?.provider === \"mike\")\n  setupVersionSelector({ document$ })\n\n/* Always close drawer and search on navigation */\nmerge(location$, target$)\n  .pipe(\n    delay(125)\n  )\n    .subscribe(() => {\n      setToggle(\"drawer\", false)\n      setToggle(\"search\", false)\n    })\n\n/* Set up global keyboard handlers */\nkeyboard$\n  .pipe(\n    filter(({ mode }) => mode === \"global\")\n  )\n    .subscribe(key => {\n      switch (key.type) {\n\n        /* Go to previous page */\n        case \"p\":\n        case \",\":\n          const prev = getOptionalElement<HTMLLinkElement>(\"link[rel=prev]\")\n          if (typeof prev !== \"undefined\")\n            setLocation(prev)\n          break\n\n        /* Go to next page */\n        case \"n\":\n        case \".\":\n          const next = getOptionalElement<HTMLLinkElement>(\"link[rel=next]\")\n          if (typeof next !== \"undefined\")\n            setLocation(next)\n          break\n\n        /* Expand navigation, see https://bit.ly/3ZjG5io */\n        case \"Enter\":\n          const active = getActiveElement()\n          if (active instanceof HTMLLabelElement)\n            active.click()\n      }\n    })\n\n/* Set up patches */\npatchIndeterminate({ document$, tablet$ })\npatchScrollfix({ document$ })\npatchScrolllock({ viewport$, tablet$ })\n\n/* Set up header and main area observable */\nconst header$ = watchHeader(getComponentElement(\"header\"), { viewport$ })\nconst main$ = document$\n  .pipe(\n    map(() => getComponentElement(\"main\")),\n    switchMap(el => watchMain(el, { viewport$, header$ })),\n    shareReplay(1)\n  )\n\n/* Set up control component observables */\nconst control$ = merge(\n\n  /* Consent */\n  ...getComponentElements(\"consent\")\n    .map(el => mountConsent(el, { target$ })),\n\n  /* Dialog */\n  ...getComponentElements(\"dialog\")\n    .map(el => mountDialog(el, { alert$ })),\n\n  /* Header */\n  ...getComponentElements(\"header\")\n    .map(el => mountHeader(el, { viewport$, header$, main$ })),\n\n  /* Color palette */\n  ...getComponentElements(\"palette\")\n    .map(el => mountPalette(el)),\n\n  /* Search */\n  ...getComponentElements(\"search\")\n    .map(el => mountSearch(el, { index$, keyboard$ })),\n\n  /* Repository information */\n  ...getComponentElements(\"source\")\n    .map(el => mountSource(el))\n)\n\n/* Set up content component observables */\nconst content$ = defer(() => merge(\n\n  /* Announcement bar */\n  ...getComponentElements(\"announce\")\n    .map(el => mountAnnounce(el)),\n\n  /* Content */\n  ...getComponentElements(\"content\")\n    .map(el => mountContent(el, { viewport$, target$, print$ })),\n\n  /* Search highlighting */\n  ...getComponentElements(\"content\")\n    .map(el => feature(\"search.highlight\")\n      ? mountSearchHiglight(el, { index$, location$ })\n      : EMPTY\n    ),\n\n  /* Header title */\n  ...getComponentElements(\"header-title\")\n    .map(el => mountHeaderTitle(el, { viewport$, header$ })),\n\n  /* Sidebar */\n  ...getComponentElements(\"sidebar\")\n    .map(el => el.getAttribute(\"data-md-type\") === \"navigation\"\n      ? at(screen$, () => mountSidebar(el, { viewport$, header$, main$ }))\n      : at(tablet$, () => mountSidebar(el, { viewport$, header$, main$ }))\n    ),\n\n  /* Navigation tabs */\n  ...getComponentElements(\"tabs\")\n    .map(el => mountTabs(el, { viewport$, header$ })),\n\n  /* Table of contents */\n  ...getComponentElements(\"toc\")\n    .map(el => mountTableOfContents(el, {\n      viewport$, header$, main$, target$\n    })),\n\n  /* Back-to-top button */\n  ...getComponentElements(\"top\")\n    .map(el => mountBackToTop(el, { viewport$, header$, main$, target$ }))\n))\n\n/* Set up component observables */\nconst component$ = document$\n  .pipe(\n    switchMap(() => content$),\n    mergeWith(control$),\n    shareReplay(1)\n  )\n\n/* Subscribe to all components */\ncomponent$.subscribe()\n\n/* ----------------------------------------------------------------------------\n * Exports\n * ------------------------------------------------------------------------- */\n\nwindow.document$  = document$          /* Document observable */\nwindow.location$  = location$          /* Location subject */\nwindow.target$    = target$            /* Location target observable */\nwindow.keyboard$  = keyboard$          /* Keyboard observable */\nwindow.viewport$  = viewport$          /* Viewport observable */\nwindow.tablet$    = tablet$            /* Media tablet observable */\nwindow.screen$    = screen$            /* Media screen observable */\nwindow.print$     = print$             /* Media print observable */\nwindow.alert$     = alert$             /* Alert subject */\nwindow.component$ = component$         /* Component observable */\n", "self.fetch||(self.fetch=function(e,n){return n=n||{},new Promise(function(t,r){var s=new XMLHttpRequest,o=[],u={},a=function e(){return{ok:2==(s.status/100|0),statusText:s.statusText,status:s.status,url:s.responseURL,text:function(){return Promise.resolve(s.responseText)},json:function(){return Promise.resolve(s.responseText).then(JSON.parse)},blob:function(){return Promise.resolve(new Blob([s.response]))},clone:e,headers:{keys:function(){return o},entries:function(){return o.map(function(e){return[e,s.getResponseHeader(e)]})},get:function(e){return s.getResponseHeader(e)},has:function(e){return null!=s.getResponseHeader(e)}}}};for(var i in s.open(n.method||\"get\",e,!0),s.onload=function(){s.getAllResponseHeaders().toLowerCase().replace(/^(.+?):/gm,function(e,n){u[n]||o.push(u[n]=n)}),t(a())},s.onerror=r,s.withCredentials=\"include\"==n.credentials,n.headers)s.setRequestHeader(i,n.headers[i]);s.send(n.body||null)})});\n", "/*! *****************************************************************************\r\nCopyright (c) Microsoft Corporation.\r\n\r\nPermission to use, copy, modify, and/or distribute this software for any\r\npurpose with or without fee is hereby granted.\r\n\r\nTHE SOFTWARE IS PROVIDED \"AS IS\" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH\r\nREGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY\r\nAND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT,\r\nINDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM\r\nLOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR\r\nOTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR\r\nPERFORMANCE OF THIS SOFTWARE.\r\n***************************************************************************** */\r\n/* global Reflect, Promise */\r\n\r\nvar extendStatics = function(d, b) {\r\n    extendStatics = Object.setPrototypeOf ||\r\n        ({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) ||\r\n        function (d, b) { for (var p in b) if (Object.prototype.hasOwnProperty.call(b, p)) d[p] = b[p]; };\r\n    return extendStatics(d, b);\r\n};\r\n\r\nexport function __extends(d, b) {\r\n    if (typeof b !== \"function\" && b !== null)\r\n        throw new TypeError(\"Class extends value \" + String(b) + \" is not a constructor or null\");\r\n    extendStatics(d, b);\r\n    function __() { this.constructor = d; }\r\n    d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());\r\n}\r\n\r\nexport var __assign = function() {\r\n    __assign = Object.assign || function __assign(t) {\r\n        for (var s, i = 1, n = arguments.length; i < n; i++) {\r\n            s = arguments[i];\r\n            for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p)) t[p] = s[p];\r\n        }\r\n        return t;\r\n    }\r\n    return __assign.apply(this, arguments);\r\n}\r\n\r\nexport function __rest(s, e) {\r\n    var t = {};\r\n    for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p) && e.indexOf(p) < 0)\r\n        t[p] = s[p];\r\n    if (s != null && typeof Object.getOwnPropertySymbols === \"function\")\r\n        for (var i = 0, p = Object.getOwnPropertySymbols(s); i < p.length; i++) {\r\n            if (e.indexOf(p[i]) < 0 && Object.prototype.propertyIsEnumerable.call(s, p[i]))\r\n                t[p[i]] = s[p[i]];\r\n        }\r\n    return t;\r\n}\r\n\r\nexport function __decorate(decorators, target, key, desc) {\r\n    var c = arguments.length, r = c < 3 ? target : desc === null ? desc = Object.getOwnPropertyDescriptor(target, key) : desc, d;\r\n    if (typeof Reflect === \"object\" && typeof Reflect.decorate === \"function\") r = Reflect.decorate(decorators, target, key, desc);\r\n    else for (var i = decorators.length - 1; i >= 0; i--) if (d = decorators[i]) r = (c < 3 ? d(r) : c > 3 ? d(target, key, r) : d(target, key)) || r;\r\n    return c > 3 && r && Object.defineProperty(target, key, r), r;\r\n}\r\n\r\nexport function __param(paramIndex, decorator) {\r\n    return function (target, key) { decorator(target, key, paramIndex); }\r\n}\r\n\r\nexport function __metadata(metadataKey, metadataValue) {\r\n    if (typeof Reflect === \"object\" && typeof Reflect.metadata === \"function\") return Reflect.metadata(metadataKey, metadataValue);\r\n}\r\n\r\nexport function __awaiter(thisArg, _arguments, P, generator) {\r\n    function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); }\r\n    return new (P || (P = Promise))(function (resolve, reject) {\r\n        function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }\r\n        function rejected(value) { try { step(generator[\"throw\"](value)); } catch (e) { reject(e); } }\r\n        function step(result) { result.done ? resolve(result.value) : adopt(result.value).then(fulfilled, rejected); }\r\n        step((generator = generator.apply(thisArg, _arguments || [])).next());\r\n    });\r\n}\r\n\r\nexport function __generator(thisArg, body) {\r\n    var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;\r\n    return g = { next: verb(0), \"throw\": verb(1), \"return\": verb(2) }, typeof Symbol === \"function\" && (g[Symbol.iterator] = function() { return this; }), g;\r\n    function verb(n) { return function (v) { return step([n, v]); }; }\r\n    function step(op) {\r\n        if (f) throw new TypeError(\"Generator is already executing.\");\r\n        while (_) try {\r\n            if (f = 1, y && (t = op[0] & 2 ? y[\"return\"] : op[0] ? y[\"throw\"] || ((t = y[\"return\"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;\r\n            if (y = 0, t) op = [op[0] & 2, t.value];\r\n            switch (op[0]) {\r\n                case 0: case 1: t = op; break;\r\n                case 4: _.label++; return { value: op[1], done: false };\r\n                case 5: _.label++; y = op[1]; op = [0]; continue;\r\n                case 7: op = _.ops.pop(); _.trys.pop(); continue;\r\n                default:\r\n                    if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }\r\n                    if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }\r\n                    if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }\r\n                    if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }\r\n                    if (t[2]) _.ops.pop();\r\n                    _.trys.pop(); continue;\r\n            }\r\n            op = body.call(thisArg, _);\r\n        } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }\r\n        if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };\r\n    }\r\n}\r\n\r\nexport var __createBinding = Object.create ? (function(o, m, k, k2) {\r\n    if (k2 === undefined) k2 = k;\r\n    Object.defineProperty(o, k2, { enumerable: true, get: function() { return m[k]; } });\r\n}) : (function(o, m, k, k2) {\r\n    if (k2 === undefined) k2 = k;\r\n    o[k2] = m[k];\r\n});\r\n\r\nexport function __exportStar(m, o) {\r\n    for (var p in m) if (p !== \"default\" && !Object.prototype.hasOwnProperty.call(o, p)) __createBinding(o, m, p);\r\n}\r\n\r\nexport function __values(o) {\r\n    var s = typeof Symbol === \"function\" && Symbol.iterator, m = s && o[s], i = 0;\r\n    if (m) return m.call(o);\r\n    if (o && typeof o.length === \"number\") return {\r\n        next: function () {\r\n            if (o && i >= o.length) o = void 0;\r\n            return { value: o && o[i++], done: !o };\r\n        }\r\n    };\r\n    throw new TypeError(s ? \"Object is not iterable.\" : \"Symbol.iterator is not defined.\");\r\n}\r\n\r\nexport function __read(o, n) {\r\n    var m = typeof Symbol === \"function\" && o[Symbol.iterator];\r\n    if (!m) return o;\r\n    var i = m.call(o), r, ar = [], e;\r\n    try {\r\n        while ((n === void 0 || n-- > 0) && !(r = i.next()).done) ar.push(r.value);\r\n    }\r\n    catch (error) { e = { error: error }; }\r\n    finally {\r\n        try {\r\n            if (r && !r.done && (m = i[\"return\"])) m.call(i);\r\n        }\r\n        finally { if (e) throw e.error; }\r\n    }\r\n    return ar;\r\n}\r\n\r\n/** @deprecated */\r\nexport function __spread() {\r\n    for (var ar = [], i = 0; i < arguments.length; i++)\r\n        ar = ar.concat(__read(arguments[i]));\r\n    return ar;\r\n}\r\n\r\n/** @deprecated */\r\nexport function __spreadArrays() {\r\n    for (var s = 0, i = 0, il = arguments.length; i < il; i++) s += arguments[i].length;\r\n    for (var r = Array(s), k = 0, i = 0; i < il; i++)\r\n        for (var a = arguments[i], j = 0, jl = a.length; j < jl; j++, k++)\r\n            r[k] = a[j];\r\n    return r;\r\n}\r\n\r\nexport function __spreadArray(to, from, pack) {\r\n    if (pack || arguments.length === 2) for (var i = 0, l = from.length, ar; i < l; i++) {\r\n        if (ar || !(i in from)) {\r\n            if (!ar) ar = Array.prototype.slice.call(from, 0, i);\r\n            ar[i] = from[i];\r\n        }\r\n    }\r\n    return to.concat(ar || Array.prototype.slice.call(from));\r\n}\r\n\r\nexport function __await(v) {\r\n    return this instanceof __await ? (this.v = v, this) : new __await(v);\r\n}\r\n\r\nexport function __asyncGenerator(thisArg, _arguments, generator) {\r\n    if (!Symbol.asyncIterator) throw new TypeError(\"Symbol.asyncIterator is not defined.\");\r\n    var g = generator.apply(thisArg, _arguments || []), i, q = [];\r\n    return i = {}, verb(\"next\"), verb(\"throw\"), verb(\"return\"), i[Symbol.asyncIterator] = function () { return this; }, i;\r\n    function verb(n) { if (g[n]) i[n] = function (v) { return new Promise(function (a, b) { q.push([n, v, a, b]) > 1 || resume(n, v); }); }; }\r\n    function resume(n, v) { try { step(g[n](v)); } catch (e) { settle(q[0][3], e); } }\r\n    function step(r) { r.value instanceof __await ? Promise.resolve(r.value.v).then(fulfill, reject) : settle(q[0][2], r); }\r\n    function fulfill(value) { resume(\"next\", value); }\r\n    function reject(value) { resume(\"throw\", value); }\r\n    function settle(f, v) { if (f(v), q.shift(), q.length) resume(q[0][0], q[0][1]); }\r\n}\r\n\r\nexport function __asyncDelegator(o) {\r\n    var i, p;\r\n    return i = {}, verb(\"next\"), verb(\"throw\", function (e) { throw e; }), verb(\"return\"), i[Symbol.iterator] = function () { return this; }, i;\r\n    function verb(n, f) { i[n] = o[n] ? function (v) { return (p = !p) ? { value: __await(o[n](v)), done: n === \"return\" } : f ? f(v) : v; } : f; }\r\n}\r\n\r\nexport function __asyncValues(o) {\r\n    if (!Symbol.asyncIterator) throw new TypeError(\"Symbol.asyncIterator is not defined.\");\r\n    var m = o[Symbol.asyncIterator], i;\r\n    return m ? m.call(o) : (o = typeof __values === \"function\" ? __values(o) : o[Symbol.iterator](), i = {}, verb(\"next\"), verb(\"throw\"), verb(\"return\"), i[Symbol.asyncIterator] = function () { return this; }, i);\r\n    function verb(n) { i[n] = o[n] && function (v) { return new Promise(function (resolve, reject) { v = o[n](v), settle(resolve, reject, v.done, v.value); }); }; }\r\n    function settle(resolve, reject, d, v) { Promise.resolve(v).then(function(v) { resolve({ value: v, done: d }); }, reject); }\r\n}\r\n\r\nexport function __makeTemplateObject(cooked, raw) {\r\n    if (Object.defineProperty) { Object.defineProperty(cooked, \"raw\", { value: raw }); } else { cooked.raw = raw; }\r\n    return cooked;\r\n};\r\n\r\nvar __setModuleDefault = Object.create ? (function(o, v) {\r\n    Object.defineProperty(o, \"default\", { enumerable: true, value: v });\r\n}) : function(o, v) {\r\n    o[\"default\"] = v;\r\n};\r\n\r\nexport function __importStar(mod) {\r\n    if (mod && mod.__esModule) return mod;\r\n    var result = {};\r\n    if (mod != null) for (var k in mod) if (k !== \"default\" && Object.prototype.hasOwnProperty.call(mod, k)) __createBinding(result, mod, k);\r\n    __setModuleDefault(result, mod);\r\n    return result;\r\n}\r\n\r\nexport function __importDefault(mod) {\r\n    return (mod && mod.__esModule) ? mod : { default: mod };\r\n}\r\n\r\nexport function __classPrivateFieldGet(receiver, state, kind, f) {\r\n    if (kind === \"a\" && !f) throw new TypeError(\"Private accessor was defined without a getter\");\r\n    if (typeof state === \"function\" ? receiver !== state || !f : !state.has(receiver)) throw new TypeError(\"Cannot read private member from an object whose class did not declare it\");\r\n    return kind === \"m\" ? f : kind === \"a\" ? f.call(receiver) : f ? f.value : state.get(receiver);\r\n}\r\n\r\nexport function __classPrivateFieldSet(receiver, state, value, kind, f) {\r\n    if (kind === \"m\") throw new TypeError(\"Private method is not writable\");\r\n    if (kind === \"a\" && !f) throw new TypeError(\"Private accessor was defined without a setter\");\r\n    if (typeof state === \"function\" ? receiver !== state || !f : !state.has(receiver)) throw new TypeError(\"Cannot write private member to an object whose class did not declare it\");\r\n    return (kind === \"a\" ? f.call(receiver, value) : f ? f.value = value : state.set(receiver, value)), value;\r\n}\r\n", "/**\n * Returns true if the object is a function.\n * @param value The value to check\n */\nexport function isFunction(value: any): value is (...args: any[]) => any {\n  return typeof value === 'function';\n}\n", "/**\n * Used to create Error subclasses until the community moves away from ES5.\n *\n * This is because compiling from TypeScript down to ES5 has issues with subclassing Errors\n * as well as other built-in types: https://github.com/Microsoft/TypeScript/issues/12123\n *\n * @param createImpl A factory function to create the actual constructor implementation. The returned\n * function should be a named function that calls `_super` internally.\n */\nexport function createErrorClass<T>(createImpl: (_super: any) => any): T {\n  const _super = (instance: any) => {\n    Error.call(instance);\n    instance.stack = new Error().stack;\n  };\n\n  const ctorFunc = createImpl(_super);\n  ctorFunc.prototype = Object.create(Error.prototype);\n  ctorFunc.prototype.constructor = ctorFunc;\n  return ctorFunc;\n}\n", "import { createErrorClass } from './createErrorClass';\n\nexport interface UnsubscriptionError extends Error {\n  readonly errors: any[];\n}\n\nexport interface UnsubscriptionErrorCtor {\n  /**\n   * @deprecated Internal implementation detail. Do not construct error instances.\n   * Cannot be tagged as internal: https://github.com/ReactiveX/rxjs/issues/6269\n   */\n  new (errors: any[]): UnsubscriptionError;\n}\n\n/**\n * An error thrown when one or more errors have occurred during the\n * `unsubscribe` of a {@link Subscription}.\n */\nexport const UnsubscriptionError: UnsubscriptionErrorCtor = createErrorClass(\n  (_super) =>\n    function UnsubscriptionErrorImpl(this: any, errors: (Error | string)[]) {\n      _super(this);\n      this.message = errors\n        ? `${errors.length} errors occurred during unsubscription:\n${errors.map((err, i) => `${i + 1}) ${err.toString()}`).join('\\n  ')}`\n        : '';\n      this.name = 'UnsubscriptionError';\n      this.errors = errors;\n    }\n);\n", "/**\n * Removes an item from an array, mutating it.\n * @param arr The array to remove the item from\n * @param item The item to remove\n */\nexport function arrRemove<T>(arr: T[] | undefined | null, item: T) {\n  if (arr) {\n    const index = arr.indexOf(item);\n    0 <= index && arr.splice(index, 1);\n  }\n}\n", "import { isFunction } from './util/isFunction';\nimport { UnsubscriptionError } from './util/UnsubscriptionError';\nimport { SubscriptionLike, TeardownLogic, Unsubscribable } from './types';\nimport { arrRemove } from './util/arrRemove';\n\n/**\n * Represents a disposable resource, such as the execution of an Observable. A\n * Subscription has one important method, `unsubscribe`, that takes no argument\n * and just disposes the resource held by the subscription.\n *\n * Additionally, subscriptions may be grouped together through the `add()`\n * method, which will attach a child Subscription to the current Subscription.\n * When a Subscription is unsubscribed, all its children (and its grandchildren)\n * will be unsubscribed as well.\n *\n * @class Subscription\n */\nexport class Subscription implements SubscriptionLike {\n  /** @nocollapse */\n  public static EMPTY = (() => {\n    const empty = new Subscription();\n    empty.closed = true;\n    return empty;\n  })();\n\n  /**\n   * A flag to indicate whether this Subscription has already been unsubscribed.\n   */\n  public closed = false;\n\n  private _parentage: Subscription[] | Subscription | null = null;\n\n  /**\n   * The list of registered finalizers to execute upon unsubscription. Adding and removing from this\n   * list occurs in the {@link #add} and {@link #remove} methods.\n   */\n  private _finalizers: Exclude<TeardownLogic, void>[] | null = null;\n\n  /**\n   * @param initialTeardown A function executed first as part of the finalization\n   * process that is kicked off when {@link #unsubscribe} is called.\n   */\n  constructor(private initialTeardown?: () => void) {}\n\n  /**\n   * Disposes the resources held by the subscription. May, for instance, cancel\n   * an ongoing Observable execution or cancel any other type of work that\n   * started when the Subscription was created.\n   * @return {void}\n   */\n  unsubscribe(): void {\n    let errors: any[] | undefined;\n\n    if (!this.closed) {\n      this.closed = true;\n\n      // Remove this from it's parents.\n      const { _parentage } = this;\n      if (_parentage) {\n        this._parentage = null;\n        if (Array.isArray(_parentage)) {\n          for (const parent of _parentage) {\n            parent.remove(this);\n          }\n        } else {\n          _parentage.remove(this);\n        }\n      }\n\n      const { initialTeardown: initialFinalizer } = this;\n      if (isFunction(initialFinalizer)) {\n        try {\n          initialFinalizer();\n        } catch (e) {\n          errors = e instanceof UnsubscriptionError ? e.errors : [e];\n        }\n      }\n\n      const { _finalizers } = this;\n      if (_finalizers) {\n        this._finalizers = null;\n        for (const finalizer of _finalizers) {\n          try {\n            execFinalizer(finalizer);\n          } catch (err) {\n            errors = errors ?? [];\n            if (err instanceof UnsubscriptionError) {\n              errors = [...errors, ...err.errors];\n            } else {\n              errors.push(err);\n            }\n          }\n        }\n      }\n\n      if (errors) {\n        throw new UnsubscriptionError(errors);\n      }\n    }\n  }\n\n  /**\n   * Adds a finalizer to this subscription, so that finalization will be unsubscribed/called\n   * when this subscription is unsubscribed. If this subscription is already {@link #closed},\n   * because it has already been unsubscribed, then whatever finalizer is passed to it\n   * will automatically be executed (unless the finalizer itself is also a closed subscription).\n   *\n   * Closed Subscriptions cannot be added as finalizers to any subscription. Adding a closed\n   * subscription to a any subscription will result in no operation. (A noop).\n   *\n   * Adding a subscription to itself, or adding `null` or `undefined` will not perform any\n   * operation at all. (A noop).\n   *\n   * `Subscription` instances that are added to this instance will automatically remove themselves\n   * if they are unsubscribed. Functions and {@link Unsubscribable} objects that you wish to remove\n   * will need to be removed manually with {@link #remove}\n   *\n   * @param teardown The finalization logic to add to this subscription.\n   */\n  add(teardown: TeardownLogic): void {\n    // Only add the finalizer if it's not undefined\n    // and don't add a subscription to itself.\n    if (teardown && teardown !== this) {\n      if (this.closed) {\n        // If this subscription is already closed,\n        // execute whatever finalizer is handed to it automatically.\n        execFinalizer(teardown);\n      } else {\n        if (teardown instanceof Subscription) {\n          // We don't add closed subscriptions, and we don't add the same subscription\n          // twice. Subscription unsubscribe is idempotent.\n          if (teardown.closed || teardown._hasParent(this)) {\n            return;\n          }\n          teardown._addParent(this);\n        }\n        (this._finalizers = this._finalizers ?? []).push(teardown);\n      }\n    }\n  }\n\n  /**\n   * Checks to see if a this subscription already has a particular parent.\n   * This will signal that this subscription has already been added to the parent in question.\n   * @param parent the parent to check for\n   */\n  private _hasParent(parent: Subscription) {\n    const { _parentage } = this;\n    return _parentage === parent || (Array.isArray(_parentage) && _parentage.includes(parent));\n  }\n\n  /**\n   * Adds a parent to this subscription so it can be removed from the parent if it\n   * unsubscribes on it's own.\n   *\n   * NOTE: THIS ASSUMES THAT {@link _hasParent} HAS ALREADY BEEN CHECKED.\n   * @param parent The parent subscription to add\n   */\n  private _addParent(parent: Subscription) {\n    const { _parentage } = this;\n    this._parentage = Array.isArray(_parentage) ? (_parentage.push(parent), _parentage) : _parentage ? [_parentage, parent] : parent;\n  }\n\n  /**\n   * Called on a child when it is removed via {@link #remove}.\n   * @param parent The parent to remove\n   */\n  private _removeParent(parent: Subscription) {\n    const { _parentage } = this;\n    if (_parentage === parent) {\n      this._parentage = null;\n    } else if (Array.isArray(_parentage)) {\n      arrRemove(_parentage, parent);\n    }\n  }\n\n  /**\n   * Removes a finalizer from this subscription that was previously added with the {@link #add} method.\n   *\n   * Note that `Subscription` instances, when unsubscribed, will automatically remove themselves\n   * from every other `Subscription` they have been added to. This means that using the `remove` method\n   * is not a common thing and should be used thoughtfully.\n   *\n   * If you add the same finalizer instance of a function or an unsubscribable object to a `Subscription` instance\n   * more than once, you will need to call `remove` the same number of times to remove all instances.\n   *\n   * All finalizer instances are removed to free up memory upon unsubscription.\n   *\n   * @param teardown The finalizer to remove from this subscription\n   */\n  remove(teardown: Exclude<TeardownLogic, void>): void {\n    const { _finalizers } = this;\n    _finalizers && arrRemove(_finalizers, teardown);\n\n    if (teardown instanceof Subscription) {\n      teardown._removeParent(this);\n    }\n  }\n}\n\nexport const EMPTY_SUBSCRIPTION = Subscription.EMPTY;\n\nexport function isSubscription(value: any): value is Subscription {\n  return (\n    value instanceof Subscription ||\n    (value && 'closed' in value && isFunction(value.remove) && isFunction(value.add) && isFunction(value.unsubscribe))\n  );\n}\n\nfunction execFinalizer(finalizer: Unsubscribable | (() => void)) {\n  if (isFunction(finalizer)) {\n    finalizer();\n  } else {\n    finalizer.unsubscribe();\n  }\n}\n", "import { Subscriber } from './Subscriber';\nimport { ObservableNotification } from './types';\n\n/**\n * The {@link GlobalConfig} object for RxJS. It is used to configure things\n * like how to react on unhandled errors.\n */\nexport const config: GlobalConfig = {\n  onUnhandledError: null,\n  onStoppedNotification: null,\n  Promise: undefined,\n  useDeprecatedSynchronousErrorHandling: false,\n  useDeprecatedNextContext: false,\n};\n\n/**\n * The global configuration object for RxJS, used to configure things\n * like how to react on unhandled errors. Accessible via {@link config}\n * object.\n */\nexport interface GlobalConfig {\n  /**\n   * A registration point for unhandled errors from RxJS. These are errors that\n   * cannot were not handled by consuming code in the usual subscription path. For\n   * example, if you have this configured, and you subscribe to an observable without\n   * providing an error handler, errors from that subscription will end up here. This\n   * will _always_ be called asynchronously on another job in the runtime. This is because\n   * we do not want errors thrown in this user-configured handler to interfere with the\n   * behavior of the library.\n   */\n  onUnhandledError: ((err: any) => void) | null;\n\n  /**\n   * A registration point for notifications that cannot be sent to subscribers because they\n   * have completed, errored or have been explicitly unsubscribed. By default, next, complete\n   * and error notifications sent to stopped subscribers are noops. However, sometimes callers\n   * might want a different behavior. For example, with sources that attempt to report errors\n   * to stopped subscribers, a caller can configure RxJS to throw an unhandled error instead.\n   * This will _always_ be called asynchronously on another job in the runtime. This is because\n   * we do not want errors thrown in this user-configured handler to interfere with the\n   * behavior of the library.\n   */\n  onStoppedNotification: ((notification: ObservableNotification<any>, subscriber: Subscriber<any>) => void) | null;\n\n  /**\n   * The promise constructor used by default for {@link Observable#toPromise toPromise} and {@link Observable#forEach forEach}\n   * methods.\n   *\n   * @deprecated As of version 8, RxJS will no longer support this sort of injection of a\n   * Promise constructor. If you need a Promise implementation other than native promises,\n   * please polyfill/patch Promise as you see appropriate. Will be removed in v8.\n   */\n  Promise?: PromiseConstructorLike;\n\n  /**\n   * If true, turns on synchronous error rethrowing, which is a deprecated behavior\n   * in v6 and higher. This behavior enables bad patterns like wrapping a subscribe\n   * call in a try/catch block. It also enables producer interference, a nasty bug\n   * where a multicast can be broken for all observers by a downstream consumer with\n   * an unhandled error. DO NOT USE THIS FLAG UNLESS IT'S NEEDED TO BUY TIME\n   * FOR MIGRATION REASONS.\n   *\n   * @deprecated As of version 8, RxJS will no longer support synchronous throwing\n   * of unhandled errors. All errors will be thrown on a separate call stack to prevent bad\n   * behaviors described above. Will be removed in v8.\n   */\n  useDeprecatedSynchronousErrorHandling: boolean;\n\n  /**\n   * If true, enables an as-of-yet undocumented feature from v5: The ability to access\n   * `unsubscribe()` via `this` context in `next` functions created in observers passed\n   * to `subscribe`.\n   *\n   * This is being removed because the performance was severely problematic, and it could also cause\n   * issues when types other than POJOs are passed to subscribe as subscribers, as they will likely have\n   * their `this` context overwritten.\n   *\n   * @deprecated As of version 8, RxJS will no longer support altering the\n   * context of next functions provided as part of an observer to Subscribe. Instead,\n   * you will have access to a subscription or a signal or token that will allow you to do things like\n   * unsubscribe and test closed status. Will be removed in v8.\n   */\n  useDeprecatedNextContext: boolean;\n}\n", "import type { TimerHandle } from './timerHandle';\ntype SetTimeoutFunction = (handler: () => void, timeout?: number, ...args: any[]) => TimerHandle;\ntype ClearTimeoutFunction = (handle: TimerHandle) => void;\n\ninterface TimeoutProvider {\n  setTimeout: SetTimeoutFunction;\n  clearTimeout: ClearTimeoutFunction;\n  delegate:\n    | {\n        setTimeout: SetTimeoutFunction;\n        clearTimeout: ClearTimeoutFunction;\n      }\n    | undefined;\n}\n\nexport const timeoutProvider: TimeoutProvider = {\n  // When accessing the delegate, use the variable rather than `this` so that\n  // the functions can be called without being bound to the provider.\n  setTimeout(handler: () => void, timeout?: number, ...args) {\n    const { delegate } = timeoutProvider;\n    if (delegate?.setTimeout) {\n      return delegate.setTimeout(handler, timeout, ...args);\n    }\n    return setTimeout(handler, timeout, ...args);\n  },\n  clearTimeout(handle) {\n    const { delegate } = timeoutProvider;\n    return (delegate?.clearTimeout || clearTimeout)(handle as any);\n  },\n  delegate: undefined,\n};\n", "import { config } from '../config';\nimport { timeoutProvider } from '../scheduler/timeoutProvider';\n\n/**\n * Handles an error on another job either with the user-configured {@link onUnhandledError},\n * or by throwing it on that new job so it can be picked up by `window.onerror`, `process.on('error')`, etc.\n *\n * This should be called whenever there is an error that is out-of-band with the subscription\n * or when an error hits a terminal boundary of the subscription and no error handler was provided.\n *\n * @param err the error to report\n */\nexport function reportUnhandledError(err: any) {\n  timeoutProvider.setTimeout(() => {\n    const { onUnhandledError } = config;\n    if (onUnhandledError) {\n      // Execute the user-configured error handler.\n      onUnhandledError(err);\n    } else {\n      // Throw so it is picked up by the runtime's uncaught error mechanism.\n      throw err;\n    }\n  });\n}\n", "/* tslint:disable:no-empty */\nexport function noop() { }\n", "import { CompleteNotification, NextNotification, ErrorNotification } from './types';\n\n/**\n * A completion object optimized for memory use and created to be the\n * same \"shape\" as other notifications in v8.\n * @internal\n */\nexport const COMPLETE_NOTIFICATION = (() => createNotification('C', undefined, undefined) as CompleteNotification)();\n\n/**\n * Internal use only. Creates an optimized error notification that is the same \"shape\"\n * as other notifications.\n * @internal\n */\nexport function errorNotification(error: any): ErrorNotification {\n  return createNotification('E', undefined, error) as any;\n}\n\n/**\n * Internal use only. Creates an optimized next notification that is the same \"shape\"\n * as other notifications.\n * @internal\n */\nexport function nextNotification<T>(value: T) {\n  return createNotification('N', value, undefined) as NextNotification<T>;\n}\n\n/**\n * Ensures that all notifications created internally have the same \"shape\" in v8.\n *\n * TODO: This is only exported to support a crazy legacy test in `groupBy`.\n * @internal\n */\nexport function createNotification(kind: 'N' | 'E' | 'C', value: any, error: any) {\n  return {\n    kind,\n    value,\n    error,\n  };\n}\n", "import { config } from '../config';\n\nlet context: { errorThrown: boolean; error: any } | null = null;\n\n/**\n * Handles dealing with errors for super-gross mode. Creates a context, in which\n * any synchronously thrown errors will be passed to {@link captureError}. Which\n * will record the error such that it will be rethrown after the call back is complete.\n * TODO: Remove in v8\n * @param cb An immediately executed function.\n */\nexport function errorContext(cb: () => void) {\n  if (config.useDeprecatedSynchronousErrorHandling) {\n    const isRoot = !context;\n    if (isRoot) {\n      context = { errorThrown: false, error: null };\n    }\n    cb();\n    if (isRoot) {\n      const { errorThrown, error } = context!;\n      context = null;\n      if (errorThrown) {\n        throw error;\n      }\n    }\n  } else {\n    // This is the general non-deprecated path for everyone that\n    // isn't crazy enough to use super-gross mode (useDeprecatedSynchronousErrorHandling)\n    cb();\n  }\n}\n\n/**\n * Captures errors only in super-gross mode.\n * @param err the error to capture\n */\nexport function captureError(err: any) {\n  if (config.useDeprecatedSynchronousErrorHandling && context) {\n    context.errorThrown = true;\n    context.error = err;\n  }\n}\n", "import { isFunction } from './util/isFunction';\nimport { Observer, ObservableNotification } from './types';\nimport { isSubscription, Subscription } from './Subscription';\nimport { config } from './config';\nimport { reportUnhandledError } from './util/reportUnhandledError';\nimport { noop } from './util/noop';\nimport { nextNotification, errorNotification, COMPLETE_NOTIFICATION } from './NotificationFactories';\nimport { timeoutProvider } from './scheduler/timeoutProvider';\nimport { captureError } from './util/errorContext';\n\n/**\n * Implements the {@link Observer} interface and extends the\n * {@link Subscription} class. While the {@link Observer} is the public API for\n * consuming the values of an {@link Observable}, all Observers get converted to\n * a Subscriber, in order to provide Subscription-like capabilities such as\n * `unsubscribe`. Subscriber is a common type in RxJS, and crucial for\n * implementing operators, but it is rarely used as a public API.\n *\n * @class Subscriber<T>\n */\nexport class Subscriber<T> extends Subscription implements Observer<T> {\n  /**\n   * A static factory for a Subscriber, given a (potentially partial) definition\n   * of an Observer.\n   * @param next The `next` callback of an Observer.\n   * @param error The `error` callback of an\n   * Observer.\n   * @param complete The `complete` callback of an\n   * Observer.\n   * @return A Subscriber wrapping the (partially defined)\n   * Observer represented by the given arguments.\n   * @nocollapse\n   * @deprecated Do not use. Will be removed in v8. There is no replacement for this\n   * method, and there is no reason to be creating instances of `Subscriber` directly.\n   * If you have a specific use case, please file an issue.\n   */\n  static create<T>(next?: (x?: T) => void, error?: (e?: any) => void, complete?: () => void): Subscriber<T> {\n    return new SafeSubscriber(next, error, complete);\n  }\n\n  /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n  protected isStopped: boolean = false;\n  /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n  protected destination: Subscriber<any> | Observer<any>; // this `any` is the escape hatch to erase extra type param (e.g. R)\n\n  /**\n   * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n   * There is no reason to directly create an instance of Subscriber. This type is exported for typings reasons.\n   */\n  constructor(destination?: Subscriber<any> | Observer<any>) {\n    super();\n    if (destination) {\n      this.destination = destination;\n      // Automatically chain subscriptions together here.\n      // if destination is a Subscription, then it is a Subscriber.\n      if (isSubscription(destination)) {\n        destination.add(this);\n      }\n    } else {\n      this.destination = EMPTY_OBSERVER;\n    }\n  }\n\n  /**\n   * The {@link Observer} callback to receive notifications of type `next` from\n   * the Observable, with a value. The Observable may call this method 0 or more\n   * times.\n   * @param {T} [value] The `next` value.\n   * @return {void}\n   */\n  next(value?: T): void {\n    if (this.isStopped) {\n      handleStoppedNotification(nextNotification(value), this);\n    } else {\n      this._next(value!);\n    }\n  }\n\n  /**\n   * The {@link Observer} callback to receive notifications of type `error` from\n   * the Observable, with an attached `Error`. Notifies the Observer that\n   * the Observable has experienced an error condition.\n   * @param {any} [err] The `error` exception.\n   * @return {void}\n   */\n  error(err?: any): void {\n    if (this.isStopped) {\n      handleStoppedNotification(errorNotification(err), this);\n    } else {\n      this.isStopped = true;\n      this._error(err);\n    }\n  }\n\n  /**\n   * The {@link Observer} callback to receive a valueless notification of type\n   * `complete` from the Observable. Notifies the Observer that the Observable\n   * has finished sending push-based notifications.\n   * @return {void}\n   */\n  complete(): void {\n    if (this.isStopped) {\n      handleStoppedNotification(COMPLETE_NOTIFICATION, this);\n    } else {\n      this.isStopped = true;\n      this._complete();\n    }\n  }\n\n  unsubscribe(): void {\n    if (!this.closed) {\n      this.isStopped = true;\n      super.unsubscribe();\n      this.destination = null!;\n    }\n  }\n\n  protected _next(value: T): void {\n    this.destination.next(value);\n  }\n\n  protected _error(err: any): void {\n    try {\n      this.destination.error(err);\n    } finally {\n      this.unsubscribe();\n    }\n  }\n\n  protected _complete(): void {\n    try {\n      this.destination.complete();\n    } finally {\n      this.unsubscribe();\n    }\n  }\n}\n\n/**\n * This bind is captured here because we want to be able to have\n * compatibility with monoid libraries that tend to use a method named\n * `bind`. In particular, a library called Monio requires this.\n */\nconst _bind = Function.prototype.bind;\n\nfunction bind<Fn extends (...args: any[]) => any>(fn: Fn, thisArg: any): Fn {\n  return _bind.call(fn, thisArg);\n}\n\n/**\n * Internal optimization only, DO NOT EXPOSE.\n * @internal\n */\nclass ConsumerObserver<T> implements Observer<T> {\n  constructor(private partialObserver: Partial<Observer<T>>) {}\n\n  next(value: T): void {\n    const { partialObserver } = this;\n    if (partialObserver.next) {\n      try {\n        partialObserver.next(value);\n      } catch (error) {\n        handleUnhandledError(error);\n      }\n    }\n  }\n\n  error(err: any): void {\n    const { partialObserver } = this;\n    if (partialObserver.error) {\n      try {\n        partialObserver.error(err);\n      } catch (error) {\n        handleUnhandledError(error);\n      }\n    } else {\n      handleUnhandledError(err);\n    }\n  }\n\n  complete(): void {\n    const { partialObserver } = this;\n    if (partialObserver.complete) {\n      try {\n        partialObserver.complete();\n      } catch (error) {\n        handleUnhandledError(error);\n      }\n    }\n  }\n}\n\nexport class SafeSubscriber<T> extends Subscriber<T> {\n  constructor(\n    observerOrNext?: Partial<Observer<T>> | ((value: T) => void) | null,\n    error?: ((e?: any) => void) | null,\n    complete?: (() => void) | null\n  ) {\n    super();\n\n    let partialObserver: Partial<Observer<T>>;\n    if (isFunction(observerOrNext) || !observerOrNext) {\n      // The first argument is a function, not an observer. The next\n      // two arguments *could* be observers, or they could be empty.\n      partialObserver = {\n        next: (observerOrNext ?? undefined) as (((value: T) => void) | undefined),\n        error: error ?? undefined,\n        complete: complete ?? undefined,\n      };\n    } else {\n      // The first argument is a partial observer.\n      let context: any;\n      if (this && config.useDeprecatedNextContext) {\n        // This is a deprecated path that made `this.unsubscribe()` available in\n        // next handler functions passed to subscribe. This only exists behind a flag\n        // now, as it is *very* slow.\n        context = Object.create(observerOrNext);\n        context.unsubscribe = () => this.unsubscribe();\n        partialObserver = {\n          next: observerOrNext.next && bind(observerOrNext.next, context),\n          error: observerOrNext.error && bind(observerOrNext.error, context),\n          complete: observerOrNext.complete && bind(observerOrNext.complete, context),\n        };\n      } else {\n        // The \"normal\" path. Just use the partial observer directly.\n        partialObserver = observerOrNext;\n      }\n    }\n\n    // Wrap the partial observer to ensure it's a full observer, and\n    // make sure proper error handling is accounted for.\n    this.destination = new ConsumerObserver(partialObserver);\n  }\n}\n\nfunction handleUnhandledError(error: any) {\n  if (config.useDeprecatedSynchronousErrorHandling) {\n    captureError(error);\n  } else {\n    // Ideal path, we report this as an unhandled error,\n    // which is thrown on a new call stack.\n    reportUnhandledError(error);\n  }\n}\n\n/**\n * An error handler used when no error handler was supplied\n * to the SafeSubscriber -- meaning no error handler was supplied\n * do the `subscribe` call on our observable.\n * @param err The error to handle\n */\nfunction defaultErrorHandler(err: any) {\n  throw err;\n}\n\n/**\n * A handler for notifications that cannot be sent to a stopped subscriber.\n * @param notification The notification being sent\n * @param subscriber The stopped subscriber\n */\nfunction handleStoppedNotification(notification: ObservableNotification<any>, subscriber: Subscriber<any>) {\n  const { onStoppedNotification } = config;\n  onStoppedNotification && timeoutProvider.setTimeout(() => onStoppedNotification(notification, subscriber));\n}\n\n/**\n * The observer used as a stub for subscriptions where the user did not\n * pass any arguments to `subscribe`. Comes with the default error handling\n * behavior.\n */\nexport const EMPTY_OBSERVER: Readonly<Observer<any>> & { closed: true } = {\n  closed: true,\n  next: noop,\n  error: defaultErrorHandler,\n  complete: noop,\n};\n", "/**\n * Symbol.observable or a string \"@@observable\". Used for interop\n *\n * @deprecated We will no longer be exporting this symbol in upcoming versions of RxJS.\n * Instead polyfill and use Symbol.observable directly *or* use https://www.npmjs.com/package/symbol-observable\n */\nexport const observable: string | symbol = (() => (typeof Symbol === 'function' && Symbol.observable) || '@@observable')();\n", "/**\n * This function takes one parameter and just returns it. Simply put,\n * this is like `<T>(x: T): T => x`.\n *\n * ## Examples\n *\n * This is useful in some cases when using things like `mergeMap`\n *\n * ```ts\n * import { interval, take, map, range, mergeMap, identity } from 'rxjs';\n *\n * const source$ = interval(1000).pipe(take(5));\n *\n * const result$ = source$.pipe(\n *   map(i => range(i)),\n *   mergeMap(identity) // same as mergeMap(x => x)\n * );\n *\n * result$.subscribe({\n *   next: console.log\n * });\n * ```\n *\n * Or when you want to selectively apply an operator\n *\n * ```ts\n * import { interval, take, identity } from 'rxjs';\n *\n * const shouldLimit = () => Math.random() < 0.5;\n *\n * const source$ = interval(1000);\n *\n * const result$ = source$.pipe(shouldLimit() ? take(5) : identity);\n *\n * result$.subscribe({\n *   next: console.log\n * });\n * ```\n *\n * @param x Any value that is returned by this function\n * @returns The value passed as the first parameter to this function\n */\nexport function identity<T>(x: T): T {\n  return x;\n}\n", "import { identity } from './identity';\nimport { UnaryFunction } from '../types';\n\nexport function pipe(): typeof identity;\nexport function pipe<T, A>(fn1: UnaryFunction<T, A>): UnaryFunction<T, A>;\nexport function pipe<T, A, B>(fn1: UnaryFunction<T, A>, fn2: UnaryFunction<A, B>): UnaryFunction<T, B>;\nexport function pipe<T, A, B, C>(fn1: UnaryFunction<T, A>, fn2: UnaryFunction<A, B>, fn3: UnaryFunction<B, C>): UnaryFunction<T, C>;\nexport function pipe<T, A, B, C, D>(\n  fn1: UnaryFunction<T, A>,\n  fn2: UnaryFunction<A, B>,\n  fn3: UnaryFunction<B, C>,\n  fn4: UnaryFunction<C, D>\n): UnaryFunction<T, D>;\nexport function pipe<T, A, B, C, D, E>(\n  fn1: UnaryFunction<T, A>,\n  fn2: UnaryFunction<A, B>,\n  fn3: UnaryFunction<B, C>,\n  fn4: UnaryFunction<C, D>,\n  fn5: UnaryFunction<D, E>\n): UnaryFunction<T, E>;\nexport function pipe<T, A, B, C, D, E, F>(\n  fn1: UnaryFunction<T, A>,\n  fn2: UnaryFunction<A, B>,\n  fn3: UnaryFunction<B, C>,\n  fn4: UnaryFunction<C, D>,\n  fn5: UnaryFunction<D, E>,\n  fn6: UnaryFunction<E, F>\n): UnaryFunction<T, F>;\nexport function pipe<T, A, B, C, D, E, F, G>(\n  fn1: UnaryFunction<T, A>,\n  fn2: UnaryFunction<A, B>,\n  fn3: UnaryFunction<B, C>,\n  fn4: UnaryFunction<C, D>,\n  fn5: UnaryFunction<D, E>,\n  fn6: UnaryFunction<E, F>,\n  fn7: UnaryFunction<F, G>\n): UnaryFunction<T, G>;\nexport function pipe<T, A, B, C, D, E, F, G, H>(\n  fn1: UnaryFunction<T, A>,\n  fn2: UnaryFunction<A, B>,\n  fn3: UnaryFunction<B, C>,\n  fn4: UnaryFunction<C, D>,\n  fn5: UnaryFunction<D, E>,\n  fn6: UnaryFunction<E, F>,\n  fn7: UnaryFunction<F, G>,\n  fn8: UnaryFunction<G, H>\n): UnaryFunction<T, H>;\nexport function pipe<T, A, B, C, D, E, F, G, H, I>(\n  fn1: UnaryFunction<T, A>,\n  fn2: UnaryFunction<A, B>,\n  fn3: UnaryFunction<B, C>,\n  fn4: UnaryFunction<C, D>,\n  fn5: UnaryFunction<D, E>,\n  fn6: UnaryFunction<E, F>,\n  fn7: UnaryFunction<F, G>,\n  fn8: UnaryFunction<G, H>,\n  fn9: UnaryFunction<H, I>\n): UnaryFunction<T, I>;\nexport function pipe<T, A, B, C, D, E, F, G, H, I>(\n  fn1: UnaryFunction<T, A>,\n  fn2: UnaryFunction<A, B>,\n  fn3: UnaryFunction<B, C>,\n  fn4: UnaryFunction<C, D>,\n  fn5: UnaryFunction<D, E>,\n  fn6: UnaryFunction<E, F>,\n  fn7: UnaryFunction<F, G>,\n  fn8: UnaryFunction<G, H>,\n  fn9: UnaryFunction<H, I>,\n  ...fns: UnaryFunction<any, any>[]\n): UnaryFunction<T, unknown>;\n\n/**\n * pipe() can be called on one or more functions, each of which can take one argument (\"UnaryFunction\")\n * and uses it to return a value.\n * It returns a function that takes one argument, passes it to the first UnaryFunction, and then\n * passes the result to the next one, passes that result to the next one, and so on.  \n */\nexport function pipe(...fns: Array<UnaryFunction<any, any>>): UnaryFunction<any, any> {\n  return pipeFromArray(fns);\n}\n\n/** @internal */\nexport function pipeFromArray<T, R>(fns: Array<UnaryFunction<T, R>>): UnaryFunction<T, R> {\n  if (fns.length === 0) {\n    return identity as UnaryFunction<any, any>;\n  }\n\n  if (fns.length === 1) {\n    return fns[0];\n  }\n\n  return function piped(input: T): R {\n    return fns.reduce((prev: any, fn: UnaryFunction<T, R>) => fn(prev), input as any);\n  };\n}\n", "import { Operator } from './Operator';\nimport { SafeSubscriber, Subscriber } from './Subscriber';\nimport { isSubscription, Subscription } from './Subscription';\nimport { TeardownLogic, OperatorFunction, Subscribable, Observer } from './types';\nimport { observable as Symbol_observable } from './symbol/observable';\nimport { pipeFromArray } from './util/pipe';\nimport { config } from './config';\nimport { isFunction } from './util/isFunction';\nimport { errorContext } from './util/errorContext';\n\n/**\n * A representation of any set of values over any amount of time. This is the most basic building block\n * of RxJS.\n *\n * @class Observable<T>\n */\nexport class Observable<T> implements Subscribable<T> {\n  /**\n   * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n   */\n  source: Observable<any> | undefined;\n\n  /**\n   * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n   */\n  operator: Operator<any, T> | undefined;\n\n  /**\n   * @constructor\n   * @param {Function} subscribe the function that is called when the Observable is\n   * initially subscribed to. This function is given a Subscriber, to which new values\n   * can be `next`ed, or an `error` method can be called to raise an error, or\n   * `complete` can be called to notify of a successful completion.\n   */\n  constructor(subscribe?: (this: Observable<T>, subscriber: Subscriber<T>) => TeardownLogic) {\n    if (subscribe) {\n      this._subscribe = subscribe;\n    }\n  }\n\n  // HACK: Since TypeScript inherits static properties too, we have to\n  // fight against TypeScript here so Subject can have a different static create signature\n  /**\n   * Creates a new Observable by calling the Observable constructor\n   * @owner Observable\n   * @method create\n   * @param {Function} subscribe? the subscriber function to be passed to the Observable constructor\n   * @return {Observable} a new observable\n   * @nocollapse\n   * @deprecated Use `new Observable()` instead. Will be removed in v8.\n   */\n  static create: (...args: any[]) => any = <T>(subscribe?: (subscriber: Subscriber<T>) => TeardownLogic) => {\n    return new Observable<T>(subscribe);\n  };\n\n  /**\n   * Creates a new Observable, with this Observable instance as the source, and the passed\n   * operator defined as the new observable's operator.\n   * @method lift\n   * @param operator the operator defining the operation to take on the observable\n   * @return a new observable with the Operator applied\n   * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n   * If you have implemented an operator using `lift`, it is recommended that you create an\n   * operator by simply returning `new Observable()` directly. See \"Creating new operators from\n   * scratch\" section here: https://rxjs.dev/guide/operators\n   */\n  lift<R>(operator?: Operator<T, R>): Observable<R> {\n    const observable = new Observable<R>();\n    observable.source = this;\n    observable.operator = operator;\n    return observable;\n  }\n\n  subscribe(observerOrNext?: Partial<Observer<T>> | ((value: T) => void)): Subscription;\n  /** @deprecated Instead of passing separate callback arguments, use an observer argument. Signatures taking separate callback arguments will be removed in v8. Details: https://rxjs.dev/deprecations/subscribe-arguments */\n  subscribe(next?: ((value: T) => void) | null, error?: ((error: any) => void) | null, complete?: (() => void) | null): Subscription;\n  /**\n   * Invokes an execution of an Observable and registers Observer handlers for notifications it will emit.\n   *\n   * <span class=\"informal\">Use it when you have all these Observables, but still nothing is happening.</span>\n   *\n   * `subscribe` is not a regular operator, but a method that calls Observable's internal `subscribe` function. It\n   * might be for example a function that you passed to Observable's constructor, but most of the time it is\n   * a library implementation, which defines what will be emitted by an Observable, and when it be will emitted. This means\n   * that calling `subscribe` is actually the moment when Observable starts its work, not when it is created, as it is often\n   * the thought.\n   *\n   * Apart from starting the execution of an Observable, this method allows you to listen for values\n   * that an Observable emits, as well as for when it completes or errors. You can achieve this in two\n   * of the following ways.\n   *\n   * The first way is creating an object that implements {@link Observer} interface. It should have methods\n   * defined by that interface, but note that it should be just a regular JavaScript object, which you can create\n   * yourself in any way you want (ES6 class, classic function constructor, object literal etc.). In particular, do\n   * not attempt to use any RxJS implementation details to create Observers - you don't need them. Remember also\n   * that your object does not have to implement all methods. If you find yourself creating a method that doesn't\n   * do anything, you can simply omit it. Note however, if the `error` method is not provided and an error happens,\n   * it will be thrown asynchronously. Errors thrown asynchronously cannot be caught using `try`/`catch`. Instead,\n   * use the {@link onUnhandledError} configuration option or use a runtime handler (like `window.onerror` or\n   * `process.on('error)`) to be notified of unhandled errors. Because of this, it's recommended that you provide\n   * an `error` method to avoid missing thrown errors.\n   *\n   * The second way is to give up on Observer object altogether and simply provide callback functions in place of its methods.\n   * This means you can provide three functions as arguments to `subscribe`, where the first function is equivalent\n   * of a `next` method, the second of an `error` method and the third of a `complete` method. Just as in case of an Observer,\n   * if you do not need to listen for something, you can omit a function by passing `undefined` or `null`,\n   * since `subscribe` recognizes these functions by where they were placed in function call. When it comes\n   * to the `error` function, as with an Observer, if not provided, errors emitted by an Observable will be thrown asynchronously.\n   *\n   * You can, however, subscribe with no parameters at all. This may be the case where you're not interested in terminal events\n   * and you also handled emissions internally by using operators (e.g. using `tap`).\n   *\n   * Whichever style of calling `subscribe` you use, in both cases it returns a Subscription object.\n   * This object allows you to call `unsubscribe` on it, which in turn will stop the work that an Observable does and will clean\n   * up all resources that an Observable used. Note that cancelling a subscription will not call `complete` callback\n   * provided to `subscribe` function, which is reserved for a regular completion signal that comes from an Observable.\n   *\n   * Remember that callbacks provided to `subscribe` are not guaranteed to be called asynchronously.\n   * It is an Observable itself that decides when these functions will be called. For example {@link of}\n   * by default emits all its values synchronously. Always check documentation for how given Observable\n   * will behave when subscribed and if its default behavior can be modified with a `scheduler`.\n   *\n   * #### Examples\n   *\n   * Subscribe with an {@link guide/observer Observer}\n   *\n   * ```ts\n   * import { of } from 'rxjs';\n   *\n   * const sumObserver = {\n   *   sum: 0,\n   *   next(value) {\n   *     console.log('Adding: ' + value);\n   *     this.sum = this.sum + value;\n   *   },\n   *   error() {\n   *     // We actually could just remove this method,\n   *     // since we do not really care about errors right now.\n   *   },\n   *   complete() {\n   *     console.log('Sum equals: ' + this.sum);\n   *   }\n   * };\n   *\n   * of(1, 2, 3) // Synchronously emits 1, 2, 3 and then completes.\n   *   .subscribe(sumObserver);\n   *\n   * // Logs:\n   * // 'Adding: 1'\n   * // 'Adding: 2'\n   * // 'Adding: 3'\n   * // 'Sum equals: 6'\n   * ```\n   *\n   * Subscribe with functions ({@link deprecations/subscribe-arguments deprecated})\n   *\n   * ```ts\n   * import { of } from 'rxjs'\n   *\n   * let sum = 0;\n   *\n   * of(1, 2, 3).subscribe(\n   *   value => {\n   *     console.log('Adding: ' + value);\n   *     sum = sum + value;\n   *   },\n   *   undefined,\n   *   () => console.log('Sum equals: ' + sum)\n   * );\n   *\n   * // Logs:\n   * // 'Adding: 1'\n   * // 'Adding: 2'\n   * // 'Adding: 3'\n   * // 'Sum equals: 6'\n   * ```\n   *\n   * Cancel a subscription\n   *\n   * ```ts\n   * import { interval } from 'rxjs';\n   *\n   * const subscription = interval(1000).subscribe({\n   *   next(num) {\n   *     console.log(num)\n   *   },\n   *   complete() {\n   *     // Will not be called, even when cancelling subscription.\n   *     console.log('completed!');\n   *   }\n   * });\n   *\n   * setTimeout(() => {\n   *   subscription.unsubscribe();\n   *   console.log('unsubscribed!');\n   * }, 2500);\n   *\n   * // Logs:\n   * // 0 after 1s\n   * // 1 after 2s\n   * // 'unsubscribed!' after 2.5s\n   * ```\n   *\n   * @param {Observer|Function} observerOrNext (optional) Either an observer with methods to be called,\n   * or the first of three possible handlers, which is the handler for each value emitted from the subscribed\n   * Observable.\n   * @param {Function} error (optional) A handler for a terminal event resulting from an error. If no error handler is provided,\n   * the error will be thrown asynchronously as unhandled.\n   * @param {Function} complete (optional) A handler for a terminal event resulting from successful completion.\n   * @return {Subscription} a subscription reference to the registered handlers\n   * @method subscribe\n   */\n  subscribe(\n    observerOrNext?: Partial<Observer<T>> | ((value: T) => void) | null,\n    error?: ((error: any) => void) | null,\n    complete?: (() => void) | null\n  ): Subscription {\n    const subscriber = isSubscriber(observerOrNext) ? observerOrNext : new SafeSubscriber(observerOrNext, error, complete);\n\n    errorContext(() => {\n      const { operator, source } = this;\n      subscriber.add(\n        operator\n          ? // We're dealing with a subscription in the\n            // operator chain to one of our lifted operators.\n            operator.call(subscriber, source)\n          : source\n          ? // If `source` has a value, but `operator` does not, something that\n            // had intimate knowledge of our API, like our `Subject`, must have\n            // set it. We're going to just call `_subscribe` directly.\n            this._subscribe(subscriber)\n          : // In all other cases, we're likely wrapping a user-provided initializer\n            // function, so we need to catch errors and handle them appropriately.\n            this._trySubscribe(subscriber)\n      );\n    });\n\n    return subscriber;\n  }\n\n  /** @internal */\n  protected _trySubscribe(sink: Subscriber<T>): TeardownLogic {\n    try {\n      return this._subscribe(sink);\n    } catch (err) {\n      // We don't need to return anything in this case,\n      // because it's just going to try to `add()` to a subscription\n      // above.\n      sink.error(err);\n    }\n  }\n\n  /**\n   * Used as a NON-CANCELLABLE means of subscribing to an observable, for use with\n   * APIs that expect promises, like `async/await`. You cannot unsubscribe from this.\n   *\n   * **WARNING**: Only use this with observables you *know* will complete. If the source\n   * observable does not complete, you will end up with a promise that is hung up, and\n   * potentially all of the state of an async function hanging out in memory. To avoid\n   * this situation, look into adding something like {@link timeout}, {@link take},\n   * {@link takeWhile}, or {@link takeUntil} amongst others.\n   *\n   * #### Example\n   *\n   * ```ts\n   * import { interval, take } from 'rxjs';\n   *\n   * const source$ = interval(1000).pipe(take(4));\n   *\n   * async function getTotal() {\n   *   let total = 0;\n   *\n   *   await source$.forEach(value => {\n   *     total += value;\n   *     console.log('observable -> ' + value);\n   *   });\n   *\n   *   return total;\n   * }\n   *\n   * getTotal().then(\n   *   total => console.log('Total: ' + total)\n   * );\n   *\n   * // Expected:\n   * // 'observable -> 0'\n   * // 'observable -> 1'\n   * // 'observable -> 2'\n   * // 'observable -> 3'\n   * // 'Total: 6'\n   * ```\n   *\n   * @param next a handler for each value emitted by the observable\n   * @return a promise that either resolves on observable completion or\n   *  rejects with the handled error\n   */\n  forEach(next: (value: T) => void): Promise<void>;\n\n  /**\n   * @param next a handler for each value emitted by the observable\n   * @param promiseCtor a constructor function used to instantiate the Promise\n   * @return a promise that either resolves on observable completion or\n   *  rejects with the handled error\n   * @deprecated Passing a Promise constructor will no longer be available\n   * in upcoming versions of RxJS. This is because it adds weight to the library, for very\n   * little benefit. If you need this functionality, it is recommended that you either\n   * polyfill Promise, or you create an adapter to convert the returned native promise\n   * to whatever promise implementation you wanted. Will be removed in v8.\n   */\n  forEach(next: (value: T) => void, promiseCtor: PromiseConstructorLike): Promise<void>;\n\n  forEach(next: (value: T) => void, promiseCtor?: PromiseConstructorLike): Promise<void> {\n    promiseCtor = getPromiseCtor(promiseCtor);\n\n    return new promiseCtor<void>((resolve, reject) => {\n      const subscriber = new SafeSubscriber<T>({\n        next: (value) => {\n          try {\n            next(value);\n          } catch (err) {\n            reject(err);\n            subscriber.unsubscribe();\n          }\n        },\n        error: reject,\n        complete: resolve,\n      });\n      this.subscribe(subscriber);\n    }) as Promise<void>;\n  }\n\n  /** @internal */\n  protected _subscribe(subscriber: Subscriber<any>): TeardownLogic {\n    return this.source?.subscribe(subscriber);\n  }\n\n  /**\n   * An interop point defined by the es7-observable spec https://github.com/zenparsing/es-observable\n   * @method Symbol.observable\n   * @return {Observable} this instance of the observable\n   */\n  [Symbol_observable]() {\n    return this;\n  }\n\n  /* tslint:disable:max-line-length */\n  pipe(): Observable<T>;\n  pipe<A>(op1: OperatorFunction<T, A>): Observable<A>;\n  pipe<A, B>(op1: OperatorFunction<T, A>, op2: OperatorFunction<A, B>): Observable<B>;\n  pipe<A, B, C>(op1: OperatorFunction<T, A>, op2: OperatorFunction<A, B>, op3: OperatorFunction<B, C>): Observable<C>;\n  pipe<A, B, C, D>(\n    op1: OperatorFunction<T, A>,\n    op2: OperatorFunction<A, B>,\n    op3: OperatorFunction<B, C>,\n    op4: OperatorFunction<C, D>\n  ): Observable<D>;\n  pipe<A, B, C, D, E>(\n    op1: OperatorFunction<T, A>,\n    op2: OperatorFunction<A, B>,\n    op3: OperatorFunction<B, C>,\n    op4: OperatorFunction<C, D>,\n    op5: OperatorFunction<D, E>\n  ): Observable<E>;\n  pipe<A, B, C, D, E, F>(\n    op1: OperatorFunction<T, A>,\n    op2: OperatorFunction<A, B>,\n    op3: OperatorFunction<B, C>,\n    op4: OperatorFunction<C, D>,\n    op5: OperatorFunction<D, E>,\n    op6: OperatorFunction<E, F>\n  ): Observable<F>;\n  pipe<A, B, C, D, E, F, G>(\n    op1: OperatorFunction<T, A>,\n    op2: OperatorFunction<A, B>,\n    op3: OperatorFunction<B, C>,\n    op4: OperatorFunction<C, D>,\n    op5: OperatorFunction<D, E>,\n    op6: OperatorFunction<E, F>,\n    op7: OperatorFunction<F, G>\n  ): Observable<G>;\n  pipe<A, B, C, D, E, F, G, H>(\n    op1: OperatorFunction<T, A>,\n    op2: OperatorFunction<A, B>,\n    op3: OperatorFunction<B, C>,\n    op4: OperatorFunction<C, D>,\n    op5: OperatorFunction<D, E>,\n    op6: OperatorFunction<E, F>,\n    op7: OperatorFunction<F, G>,\n    op8: OperatorFunction<G, H>\n  ): Observable<H>;\n  pipe<A, B, C, D, E, F, G, H, I>(\n    op1: OperatorFunction<T, A>,\n    op2: OperatorFunction<A, B>,\n    op3: OperatorFunction<B, C>,\n    op4: OperatorFunction<C, D>,\n    op5: OperatorFunction<D, E>,\n    op6: OperatorFunction<E, F>,\n    op7: OperatorFunction<F, G>,\n    op8: OperatorFunction<G, H>,\n    op9: OperatorFunction<H, I>\n  ): Observable<I>;\n  pipe<A, B, C, D, E, F, G, H, I>(\n    op1: OperatorFunction<T, A>,\n    op2: OperatorFunction<A, B>,\n    op3: OperatorFunction<B, C>,\n    op4: OperatorFunction<C, D>,\n    op5: OperatorFunction<D, E>,\n    op6: OperatorFunction<E, F>,\n    op7: OperatorFunction<F, G>,\n    op8: OperatorFunction<G, H>,\n    op9: OperatorFunction<H, I>,\n    ...operations: OperatorFunction<any, any>[]\n  ): Observable<unknown>;\n  /* tslint:enable:max-line-length */\n\n  /**\n   * Used to stitch together functional operators into a chain.\n   * @method pipe\n   * @return {Observable} the Observable result of all of the operators having\n   * been called in the order they were passed in.\n   *\n   * ## Example\n   *\n   * ```ts\n   * import { interval, filter, map, scan } from 'rxjs';\n   *\n   * interval(1000)\n   *   .pipe(\n   *     filter(x => x % 2 === 0),\n   *     map(x => x + x),\n   *     scan((acc, x) => acc + x)\n   *   )\n   *   .subscribe(x => console.log(x));\n   * ```\n   */\n  pipe(...operations: OperatorFunction<any, any>[]): Observable<any> {\n    return pipeFromArray(operations)(this);\n  }\n\n  /* tslint:disable:max-line-length */\n  /** @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise */\n  toPromise(): Promise<T | undefined>;\n  /** @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise */\n  toPromise(PromiseCtor: typeof Promise): Promise<T | undefined>;\n  /** @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise */\n  toPromise(PromiseCtor: PromiseConstructorLike): Promise<T | undefined>;\n  /* tslint:enable:max-line-length */\n\n  /**\n   * Subscribe to this Observable and get a Promise resolving on\n   * `complete` with the last emission (if any).\n   *\n   * **WARNING**: Only use this with observables you *know* will complete. If the source\n   * observable does not complete, you will end up with a promise that is hung up, and\n   * potentially all of the state of an async function hanging out in memory. To avoid\n   * this situation, look into adding something like {@link timeout}, {@link take},\n   * {@link takeWhile}, or {@link takeUntil} amongst others.\n   *\n   * @method toPromise\n   * @param [promiseCtor] a constructor function used to instantiate\n   * the Promise\n   * @return A Promise that resolves with the last value emit, or\n   * rejects on an error. If there were no emissions, Promise\n   * resolves with undefined.\n   * @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise\n   */\n  toPromise(promiseCtor?: PromiseConstructorLike): Promise<T | undefined> {\n    promiseCtor = getPromiseCtor(promiseCtor);\n\n    return new promiseCtor((resolve, reject) => {\n      let value: T | undefined;\n      this.subscribe(\n        (x: T) => (value = x),\n        (err: any) => reject(err),\n        () => resolve(value)\n      );\n    }) as Promise<T | undefined>;\n  }\n}\n\n/**\n * Decides between a passed promise constructor from consuming code,\n * A default configured promise constructor, and the native promise\n * constructor and returns it. If nothing can be found, it will throw\n * an error.\n * @param promiseCtor The optional promise constructor to passed by consuming code\n */\nfunction getPromiseCtor(promiseCtor: PromiseConstructorLike | undefined) {\n  return promiseCtor ?? config.Promise ?? Promise;\n}\n\nfunction isObserver<T>(value: any): value is Observer<T> {\n  return value && isFunction(value.next) && isFunction(value.error) && isFunction(value.complete);\n}\n\nfunction isSubscriber<T>(value: any): value is Subscriber<T> {\n  return (value && value instanceof Subscriber) || (isObserver(value) && isSubscription(value));\n}\n", "import { Observable } from '../Observable';\nimport { Subscriber } from '../Subscriber';\nimport { OperatorFunction } from '../types';\nimport { isFunction } from './isFunction';\n\n/**\n * Used to determine if an object is an Observable with a lift function.\n */\nexport function hasLift(source: any): source is { lift: InstanceType<typeof Observable>['lift'] } {\n  return isFunction(source?.lift);\n}\n\n/**\n * Creates an `OperatorFunction`. Used to define operators throughout the library in a concise way.\n * @param init The logic to connect the liftedSource to the subscriber at the moment of subscription.\n */\nexport function operate<T, R>(\n  init: (liftedSource: Observable<T>, subscriber: Subscriber<R>) => (() => void) | void\n): OperatorFunction<T, R> {\n  return (source: Observable<T>) => {\n    if (hasLift(source)) {\n      return source.lift(function (this: Subscriber<R>, liftedSource: Observable<T>) {\n        try {\n          return init(liftedSource, this);\n        } catch (err) {\n          this.error(err);\n        }\n      });\n    }\n    throw new TypeError('Unable to lift unknown Observable type');\n  };\n}\n", "import { Subscriber } from '../Subscriber';\n\n/**\n * Creates an instance of an `OperatorSubscriber`.\n * @param destination The downstream subscriber.\n * @param onNext Handles next values, only called if this subscriber is not stopped or closed. Any\n * error that occurs in this function is caught and sent to the `error` method of this subscriber.\n * @param onError Handles errors from the subscription, any errors that occur in this handler are caught\n * and send to the `destination` error handler.\n * @param onComplete Handles completion notification from the subscription. Any errors that occur in\n * this handler are sent to the `destination` error handler.\n * @param onFinalize Additional teardown logic here. This will only be called on teardown if the\n * subscriber itself is not already closed. This is called after all other teardown logic is executed.\n */\nexport function createOperatorSubscriber<T>(\n  destination: Subscriber<any>,\n  onNext?: (value: T) => void,\n  onComplete?: () => void,\n  onError?: (err: any) => void,\n  onFinalize?: () => void\n): Subscriber<T> {\n  return new OperatorSubscriber(destination, onNext, onComplete, onError, onFinalize);\n}\n\n/**\n * A generic helper for allowing operators to be created with a Subscriber and\n * use closures to capture necessary state from the operator function itself.\n */\nexport class OperatorSubscriber<T> extends Subscriber<T> {\n  /**\n   * Creates an instance of an `OperatorSubscriber`.\n   * @param destination The downstream subscriber.\n   * @param onNext Handles next values, only called if this subscriber is not stopped or closed. Any\n   * error that occurs in this function is caught and sent to the `error` method of this subscriber.\n   * @param onError Handles errors from the subscription, any errors that occur in this handler are caught\n   * and send to the `destination` error handler.\n   * @param onComplete Handles completion notification from the subscription. Any errors that occur in\n   * this handler are sent to the `destination` error handler.\n   * @param onFinalize Additional finalization logic here. This will only be called on finalization if the\n   * subscriber itself is not already closed. This is called after all other finalization logic is executed.\n   * @param shouldUnsubscribe An optional check to see if an unsubscribe call should truly unsubscribe.\n   * NOTE: This currently **ONLY** exists to support the strange behavior of {@link groupBy}, where unsubscription\n   * to the resulting observable does not actually disconnect from the source if there are active subscriptions\n   * to any grouped observable. (DO NOT EXPOSE OR USE EXTERNALLY!!!)\n   */\n  constructor(\n    destination: Subscriber<any>,\n    onNext?: (value: T) => void,\n    onComplete?: () => void,\n    onError?: (err: any) => void,\n    private onFinalize?: () => void,\n    private shouldUnsubscribe?: () => boolean\n  ) {\n    // It's important - for performance reasons - that all of this class's\n    // members are initialized and that they are always initialized in the same\n    // order. This will ensure that all OperatorSubscriber instances have the\n    // same hidden class in V8. This, in turn, will help keep the number of\n    // hidden classes involved in property accesses within the base class as\n    // low as possible. If the number of hidden classes involved exceeds four,\n    // the property accesses will become megamorphic and performance penalties\n    // will be incurred - i.e. inline caches won't be used.\n    //\n    // The reasons for ensuring all instances have the same hidden class are\n    // further discussed in this blog post from Benedikt Meurer:\n    // https://benediktmeurer.de/2018/03/23/impact-of-polymorphism-on-component-based-frameworks-like-react/\n    super(destination);\n    this._next = onNext\n      ? function (this: OperatorSubscriber<T>, value: T) {\n          try {\n            onNext(value);\n          } catch (err) {\n            destination.error(err);\n          }\n        }\n      : super._next;\n    this._error = onError\n      ? function (this: OperatorSubscriber<T>, err: any) {\n          try {\n            onError(err);\n          } catch (err) {\n            // Send any errors that occur down stream.\n            destination.error(err);\n          } finally {\n            // Ensure finalization.\n            this.unsubscribe();\n          }\n        }\n      : super._error;\n    this._complete = onComplete\n      ? function (this: OperatorSubscriber<T>) {\n          try {\n            onComplete();\n          } catch (err) {\n            // Send any errors that occur down stream.\n            destination.error(err);\n          } finally {\n            // Ensure finalization.\n            this.unsubscribe();\n          }\n        }\n      : super._complete;\n  }\n\n  unsubscribe() {\n    if (!this.shouldUnsubscribe || this.shouldUnsubscribe()) {\n      const { closed } = this;\n      super.unsubscribe();\n      // Execute additional teardown if we have any and we didn't already do so.\n      !closed && this.onFinalize?.();\n    }\n  }\n}\n", "import { Subscription } from '../Subscription';\n\ninterface AnimationFrameProvider {\n  schedule(callback: FrameRequestCallback): Subscription;\n  requestAnimationFrame: typeof requestAnimationFrame;\n  cancelAnimationFrame: typeof cancelAnimationFrame;\n  delegate:\n    | {\n        requestAnimationFrame: typeof requestAnimationFrame;\n        cancelAnimationFrame: typeof cancelAnimationFrame;\n      }\n    | undefined;\n}\n\nexport const animationFrameProvider: AnimationFrameProvider = {\n  // When accessing the delegate, use the variable rather than `this` so that\n  // the functions can be called without being bound to the provider.\n  schedule(callback) {\n    let request = requestAnimationFrame;\n    let cancel: typeof cancelAnimationFrame | undefined = cancelAnimationFrame;\n    const { delegate } = animationFrameProvider;\n    if (delegate) {\n      request = delegate.requestAnimationFrame;\n      cancel = delegate.cancelAnimationFrame;\n    }\n    const handle = request((timestamp) => {\n      // Clear the cancel function. The request has been fulfilled, so\n      // attempting to cancel the request upon unsubscription would be\n      // pointless.\n      cancel = undefined;\n      callback(timestamp);\n    });\n    return new Subscription(() => cancel?.(handle));\n  },\n  requestAnimationFrame(...args) {\n    const { delegate } = animationFrameProvider;\n    return (delegate?.requestAnimationFrame || requestAnimationFrame)(...args);\n  },\n  cancelAnimationFrame(...args) {\n    const { delegate } = animationFrameProvider;\n    return (delegate?.cancelAnimationFrame || cancelAnimationFrame)(...args);\n  },\n  delegate: undefined,\n};\n", "import { createErrorClass } from './createErrorClass';\n\nexport interface ObjectUnsubscribedError extends Error {}\n\nexport interface ObjectUnsubscribedErrorCtor {\n  /**\n   * @deprecated Internal implementation detail. Do not construct error instances.\n   * Cannot be tagged as internal: https://github.com/ReactiveX/rxjs/issues/6269\n   */\n  new (): ObjectUnsubscribedError;\n}\n\n/**\n * An error thrown when an action is invalid because the object has been\n * unsubscribed.\n *\n * @see {@link Subject}\n * @see {@link BehaviorSubject}\n *\n * @class ObjectUnsubscribedError\n */\nexport const ObjectUnsubscribedError: ObjectUnsubscribedErrorCtor = createErrorClass(\n  (_super) =>\n    function ObjectUnsubscribedErrorImpl(this: any) {\n      _super(this);\n      this.name = 'ObjectUnsubscribedError';\n      this.message = 'object unsubscribed';\n    }\n);\n", "import { Operator } from './Operator';\nimport { Observable } from './Observable';\nimport { Subscriber } from './Subscriber';\nimport { Subscription, EMPTY_SUBSCRIPTION } from './Subscription';\nimport { Observer, SubscriptionLike, TeardownLogic } from './types';\nimport { ObjectUnsubscribedError } from './util/ObjectUnsubscribedError';\nimport { arrRemove } from './util/arrRemove';\nimport { errorContext } from './util/errorContext';\n\n/**\n * A Subject is a special type of Observable that allows values to be\n * multicasted to many Observers. Subjects are like EventEmitters.\n *\n * Every Subject is an Observable and an Observer. You can subscribe to a\n * Subject, and you can call next to feed values as well as error and complete.\n */\nexport class Subject<T> extends Observable<T> implements SubscriptionLike {\n  closed = false;\n\n  private currentObservers: Observer<T>[] | null = null;\n\n  /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n  observers: Observer<T>[] = [];\n  /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n  isStopped = false;\n  /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n  hasError = false;\n  /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n  thrownError: any = null;\n\n  /**\n   * Creates a \"subject\" by basically gluing an observer to an observable.\n   *\n   * @nocollapse\n   * @deprecated Recommended you do not use. Will be removed at some point in the future. Plans for replacement still under discussion.\n   */\n  static create: (...args: any[]) => any = <T>(destination: Observer<T>, source: Observable<T>): AnonymousSubject<T> => {\n    return new AnonymousSubject<T>(destination, source);\n  };\n\n  constructor() {\n    // NOTE: This must be here to obscure Observable's constructor.\n    super();\n  }\n\n  /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n  lift<R>(operator: Operator<T, R>): Observable<R> {\n    const subject = new AnonymousSubject(this, this);\n    subject.operator = operator as any;\n    return subject as any;\n  }\n\n  /** @internal */\n  protected _throwIfClosed() {\n    if (this.closed) {\n      throw new ObjectUnsubscribedError();\n    }\n  }\n\n  next(value: T) {\n    errorContext(() => {\n      this._throwIfClosed();\n      if (!this.isStopped) {\n        if (!this.currentObservers) {\n          this.currentObservers = Array.from(this.observers);\n        }\n        for (const observer of this.currentObservers) {\n          observer.next(value);\n        }\n      }\n    });\n  }\n\n  error(err: any) {\n    errorContext(() => {\n      this._throwIfClosed();\n      if (!this.isStopped) {\n        this.hasError = this.isStopped = true;\n        this.thrownError = err;\n        const { observers } = this;\n        while (observers.length) {\n          observers.shift()!.error(err);\n        }\n      }\n    });\n  }\n\n  complete() {\n    errorContext(() => {\n      this._throwIfClosed();\n      if (!this.isStopped) {\n        this.isStopped = true;\n        const { observers } = this;\n        while (observers.length) {\n          observers.shift()!.complete();\n        }\n      }\n    });\n  }\n\n  unsubscribe() {\n    this.isStopped = this.closed = true;\n    this.observers = this.currentObservers = null!;\n  }\n\n  get observed() {\n    return this.observers?.length > 0;\n  }\n\n  /** @internal */\n  protected _trySubscribe(subscriber: Subscriber<T>): TeardownLogic {\n    this._throwIfClosed();\n    return super._trySubscribe(subscriber);\n  }\n\n  /** @internal */\n  protected _subscribe(subscriber: Subscriber<T>): Subscription {\n    this._throwIfClosed();\n    this._checkFinalizedStatuses(subscriber);\n    return this._innerSubscribe(subscriber);\n  }\n\n  /** @internal */\n  protected _innerSubscribe(subscriber: Subscriber<any>) {\n    const { hasError, isStopped, observers } = this;\n    if (hasError || isStopped) {\n      return EMPTY_SUBSCRIPTION;\n    }\n    this.currentObservers = null;\n    observers.push(subscriber);\n    return new Subscription(() => {\n      this.currentObservers = null;\n      arrRemove(observers, subscriber);\n    });\n  }\n\n  /** @internal */\n  protected _checkFinalizedStatuses(subscriber: Subscriber<any>) {\n    const { hasError, thrownError, isStopped } = this;\n    if (hasError) {\n      subscriber.error(thrownError);\n    } else if (isStopped) {\n      subscriber.complete();\n    }\n  }\n\n  /**\n   * Creates a new Observable with this Subject as the source. You can do this\n   * to create custom Observer-side logic of the Subject and conceal it from\n   * code that uses the Observable.\n   * @return {Observable} Observable that the Subject casts to\n   */\n  asObservable(): Observable<T> {\n    const observable: any = new Observable<T>();\n    observable.source = this;\n    return observable;\n  }\n}\n\n/**\n * @class AnonymousSubject<T>\n */\nexport class AnonymousSubject<T> extends Subject<T> {\n  constructor(\n    /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n    public destination?: Observer<T>,\n    source?: Observable<T>\n  ) {\n    super();\n    this.source = source;\n  }\n\n  next(value: T) {\n    this.destination?.next?.(value);\n  }\n\n  error(err: any) {\n    this.destination?.error?.(err);\n  }\n\n  complete() {\n    this.destination?.complete?.();\n  }\n\n  /** @internal */\n  protected _subscribe(subscriber: Subscriber<T>): Subscription {\n    return this.source?.subscribe(subscriber) ?? EMPTY_SUBSCRIPTION;\n  }\n}\n", "import { TimestampProvider } from '../types';\n\ninterface DateTimestampProvider extends TimestampProvider {\n  delegate: TimestampProvider | undefined;\n}\n\nexport const dateTimestampProvider: DateTimestampProvider = {\n  now() {\n    // Use the variable rather than `this` so that the function can be called\n    // without being bound to the provider.\n    return (dateTimestampProvider.delegate || Date).now();\n  },\n  delegate: undefined,\n};\n", "import { Subject } from './Subject';\nimport { TimestampProvider } from './types';\nimport { Subscriber } from './Subscriber';\nimport { Subscription } from './Subscription';\nimport { dateTimestampProvider } from './scheduler/dateTimestampProvider';\n\n/**\n * A variant of {@link Subject} that \"replays\" old values to new subscribers by emitting them when they first subscribe.\n *\n * `ReplaySubject` has an internal buffer that will store a specified number of values that it has observed. Like `Subject`,\n * `ReplaySubject` \"observes\" values by having them passed to its `next` method. When it observes a value, it will store that\n * value for a time determined by the configuration of the `ReplaySubject`, as passed to its constructor.\n *\n * When a new subscriber subscribes to the `ReplaySubject` instance, it will synchronously emit all values in its buffer in\n * a First-In-First-Out (FIFO) manner. The `ReplaySubject` will also complete, if it has observed completion; and it will\n * error if it has observed an error.\n *\n * There are two main configuration items to be concerned with:\n *\n * 1. `bufferSize` - This will determine how many items are stored in the buffer, defaults to infinite.\n * 2. `windowTime` - The amount of time to hold a value in the buffer before removing it from the buffer.\n *\n * Both configurations may exist simultaneously. So if you would like to buffer a maximum of 3 values, as long as the values\n * are less than 2 seconds old, you could do so with a `new ReplaySubject(3, 2000)`.\n *\n * ### Differences with BehaviorSubject\n *\n * `BehaviorSubject` is similar to `new ReplaySubject(1)`, with a couple of exceptions:\n *\n * 1. `BehaviorSubject` comes \"primed\" with a single value upon construction.\n * 2. `ReplaySubject` will replay values, even after observing an error, where `BehaviorSubject` will not.\n *\n * @see {@link Subject}\n * @see {@link BehaviorSubject}\n * @see {@link shareReplay}\n */\nexport class ReplaySubject<T> extends Subject<T> {\n  private _buffer: (T | number)[] = [];\n  private _infiniteTimeWindow = true;\n\n  /**\n   * @param bufferSize The size of the buffer to replay on subscription\n   * @param windowTime The amount of time the buffered items will stay buffered\n   * @param timestampProvider An object with a `now()` method that provides the current timestamp. This is used to\n   * calculate the amount of time something has been buffered.\n   */\n  constructor(\n    private _bufferSize = Infinity,\n    private _windowTime = Infinity,\n    private _timestampProvider: TimestampProvider = dateTimestampProvider\n  ) {\n    super();\n    this._infiniteTimeWindow = _windowTime === Infinity;\n    this._bufferSize = Math.max(1, _bufferSize);\n    this._windowTime = Math.max(1, _windowTime);\n  }\n\n  next(value: T): void {\n    const { isStopped, _buffer, _infiniteTimeWindow, _timestampProvider, _windowTime } = this;\n    if (!isStopped) {\n      _buffer.push(value);\n      !_infiniteTimeWindow && _buffer.push(_timestampProvider.now() + _windowTime);\n    }\n    this._trimBuffer();\n    super.next(value);\n  }\n\n  /** @internal */\n  protected _subscribe(subscriber: Subscriber<T>): Subscription {\n    this._throwIfClosed();\n    this._trimBuffer();\n\n    const subscription = this._innerSubscribe(subscriber);\n\n    const { _infiniteTimeWindow, _buffer } = this;\n    // We use a copy here, so reentrant code does not mutate our array while we're\n    // emitting it to a new subscriber.\n    const copy = _buffer.slice();\n    for (let i = 0; i < copy.length && !subscriber.closed; i += _infiniteTimeWindow ? 1 : 2) {\n      subscriber.next(copy[i] as T);\n    }\n\n    this._checkFinalizedStatuses(subscriber);\n\n    return subscription;\n  }\n\n  private _trimBuffer() {\n    const { _bufferSize, _timestampProvider, _buffer, _infiniteTimeWindow } = this;\n    // If we don't have an infinite buffer size, and we're over the length,\n    // use splice to truncate the old buffer values off. Note that we have to\n    // double the size for instances where we're not using an infinite time window\n    // because we're storing the values and the timestamps in the same array.\n    const adjustedBufferSize = (_infiniteTimeWindow ? 1 : 2) * _bufferSize;\n    _bufferSize < Infinity && adjustedBufferSize < _buffer.length && _buffer.splice(0, _buffer.length - adjustedBufferSize);\n\n    // Now, if we're not in an infinite time window, remove all values where the time is\n    // older than what is allowed.\n    if (!_infiniteTimeWindow) {\n      const now = _timestampProvider.now();\n      let last = 0;\n      // Search the array for the first timestamp that isn't expired and\n      // truncate the buffer up to that point.\n      for (let i = 1; i < _buffer.length && (_buffer[i] as number) <= now; i += 2) {\n        last = i;\n      }\n      last && _buffer.splice(0, last + 1);\n    }\n  }\n}\n", "import { Scheduler } from '../Scheduler';\nimport { Subscription } from '../Subscription';\nimport { SchedulerAction } from '../types';\n\n/**\n * A unit of work to be executed in a `scheduler`. An action is typically\n * created from within a {@link SchedulerLike} and an RxJS user does not need to concern\n * themselves about creating and manipulating an Action.\n *\n * ```ts\n * class Action<T> extends Subscription {\n *   new (scheduler: Scheduler, work: (state?: T) => void);\n *   schedule(state?: T, delay: number = 0): Subscription;\n * }\n * ```\n *\n * @class Action<T>\n */\nexport class Action<T> extends Subscription {\n  constructor(scheduler: Scheduler, work: (this: SchedulerAction<T>, state?: T) => void) {\n    super();\n  }\n  /**\n   * Schedules this action on its parent {@link SchedulerLike} for execution. May be passed\n   * some context object, `state`. May happen at some point in the future,\n   * according to the `delay` parameter, if specified.\n   * @param {T} [state] Some contextual data that the `work` function uses when\n   * called by the Scheduler.\n   * @param {number} [delay] Time to wait before executing the work, where the\n   * time unit is implicit and defined by the Scheduler.\n   * @return {void}\n   */\n  public schedule(state?: T, delay: number = 0): Subscription {\n    return this;\n  }\n}\n", "import type { TimerHandle } from './timerHandle';\ntype SetIntervalFunction = (handler: () => void, timeout?: number, ...args: any[]) => TimerHandle;\ntype ClearIntervalFunction = (handle: TimerHandle) => void;\n\ninterface IntervalProvider {\n  setInterval: SetIntervalFunction;\n  clearInterval: ClearIntervalFunction;\n  delegate:\n    | {\n        setInterval: SetIntervalFunction;\n        clearInterval: ClearIntervalFunction;\n      }\n    | undefined;\n}\n\nexport const intervalProvider: IntervalProvider = {\n  // When accessing the delegate, use the variable rather than `this` so that\n  // the functions can be called without being bound to the provider.\n  setInterval(handler: () => void, timeout?: number, ...args) {\n    const { delegate } = intervalProvider;\n    if (delegate?.setInterval) {\n      return delegate.setInterval(handler, timeout, ...args);\n    }\n    return setInterval(handler, timeout, ...args);\n  },\n  clearInterval(handle) {\n    const { delegate } = intervalProvider;\n    return (delegate?.clearInterval || clearInterval)(handle as any);\n  },\n  delegate: undefined,\n};\n", "import { Action } from './Action';\nimport { SchedulerAction } from '../types';\nimport { Subscription } from '../Subscription';\nimport { AsyncScheduler } from './AsyncScheduler';\nimport { intervalProvider } from './intervalProvider';\nimport { arrRemove } from '../util/arrRemove';\nimport { TimerHandle } from './timerHandle';\n\nexport class AsyncAction<T> extends Action<T> {\n  public id: TimerHandle | undefined;\n  public state?: T;\n  // @ts-ignore: Property has no initializer and is not definitely assigned\n  public delay: number;\n  protected pending: boolean = false;\n\n  constructor(protected scheduler: AsyncScheduler, protected work: (this: SchedulerAction<T>, state?: T) => void) {\n    super(scheduler, work);\n  }\n\n  public schedule(state?: T, delay: number = 0): Subscription {\n    if (this.closed) {\n      return this;\n    }\n\n    // Always replace the current state with the new state.\n    this.state = state;\n\n    const id = this.id;\n    const scheduler = this.scheduler;\n\n    //\n    // Important implementation note:\n    //\n    // Actions only execute once by default, unless rescheduled from within the\n    // scheduled callback. This allows us to implement single and repeat\n    // actions via the same code path, without adding API surface area, as well\n    // as mimic traditional recursion but across asynchronous boundaries.\n    //\n    // However, JS runtimes and timers distinguish between intervals achieved by\n    // serial `setTimeout` calls vs. a single `setInterval` call. An interval of\n    // serial `setTimeout` calls can be individually delayed, which delays\n    // scheduling the next `setTimeout`, and so on. `setInterval` attempts to\n    // guarantee the interval callback will be invoked more precisely to the\n    // interval period, regardless of load.\n    //\n    // Therefore, we use `setInterval` to schedule single and repeat actions.\n    // If the action reschedules itself with the same delay, the interval is not\n    // canceled. If the action doesn't reschedule, or reschedules with a\n    // different delay, the interval will be canceled after scheduled callback\n    // execution.\n    //\n    if (id != null) {\n      this.id = this.recycleAsyncId(scheduler, id, delay);\n    }\n\n    // Set the pending flag indicating that this action has been scheduled, or\n    // has recursively rescheduled itself.\n    this.pending = true;\n\n    this.delay = delay;\n    // If this action has already an async Id, don't request a new one.\n    this.id = this.id ?? this.requestAsyncId(scheduler, this.id, delay);\n\n    return this;\n  }\n\n  protected requestAsyncId(scheduler: AsyncScheduler, _id?: TimerHandle, delay: number = 0): TimerHandle {\n    return intervalProvider.setInterval(scheduler.flush.bind(scheduler, this), delay);\n  }\n\n  protected recycleAsyncId(_scheduler: AsyncScheduler, id?: TimerHandle, delay: number | null = 0): TimerHandle | undefined {\n    // If this action is rescheduled with the same delay time, don't clear the interval id.\n    if (delay != null && this.delay === delay && this.pending === false) {\n      return id;\n    }\n    // Otherwise, if the action's delay time is different from the current delay,\n    // or the action has been rescheduled before it's executed, clear the interval id\n    if (id != null) {\n      intervalProvider.clearInterval(id);\n    }\n\n    return undefined;\n  }\n\n  /**\n   * Immediately executes this action and the `work` it contains.\n   * @return {any}\n   */\n  public execute(state: T, delay: number): any {\n    if (this.closed) {\n      return new Error('executing a cancelled action');\n    }\n\n    this.pending = false;\n    const error = this._execute(state, delay);\n    if (error) {\n      return error;\n    } else if (this.pending === false && this.id != null) {\n      // Dequeue if the action didn't reschedule itself. Don't call\n      // unsubscribe(), because the action could reschedule later.\n      // For example:\n      // ```\n      // scheduler.schedule(function doWork(counter) {\n      //   /* ... I'm a busy worker bee ... */\n      //   var originalAction = this;\n      //   /* wait 100ms before rescheduling the action */\n      //   setTimeout(function () {\n      //     originalAction.schedule(counter + 1);\n      //   }, 100);\n      // }, 1000);\n      // ```\n      this.id = this.recycleAsyncId(this.scheduler, this.id, null);\n    }\n  }\n\n  protected _execute(state: T, _delay: number): any {\n    let errored: boolean = false;\n    let errorValue: any;\n    try {\n      this.work(state);\n    } catch (e) {\n      errored = true;\n      // HACK: Since code elsewhere is relying on the \"truthiness\" of the\n      // return here, we can't have it return \"\" or 0 or false.\n      // TODO: Clean this up when we refactor schedulers mid-version-8 or so.\n      errorValue = e ? e : new Error('Scheduled action threw falsy error');\n    }\n    if (errored) {\n      this.unsubscribe();\n      return errorValue;\n    }\n  }\n\n  unsubscribe() {\n    if (!this.closed) {\n      const { id, scheduler } = this;\n      const { actions } = scheduler;\n\n      this.work = this.state = this.scheduler = null!;\n      this.pending = false;\n\n      arrRemove(actions, this);\n      if (id != null) {\n        this.id = this.recycleAsyncId(scheduler, id, null);\n      }\n\n      this.delay = null!;\n      super.unsubscribe();\n    }\n  }\n}\n", "import { Action } from './scheduler/Action';\nimport { Subscription } from './Subscription';\nimport { SchedulerLike, SchedulerAction } from './types';\nimport { dateTimestampProvider } from './scheduler/dateTimestampProvider';\n\n/**\n * An execution context and a data structure to order tasks and schedule their\n * execution. Provides a notion of (potentially virtual) time, through the\n * `now()` getter method.\n *\n * Each unit of work in a Scheduler is called an `Action`.\n *\n * ```ts\n * class Scheduler {\n *   now(): number;\n *   schedule(work, delay?, state?): Subscription;\n * }\n * ```\n *\n * @class Scheduler\n * @deprecated Scheduler is an internal implementation detail of RxJS, and\n * should not be used directly. Rather, create your own class and implement\n * {@link SchedulerLike}. Will be made internal in v8.\n */\nexport class Scheduler implements SchedulerLike {\n  public static now: () => number = dateTimestampProvider.now;\n\n  constructor(private schedulerActionCtor: typeof Action, now: () => number = Scheduler.now) {\n    this.now = now;\n  }\n\n  /**\n   * A getter method that returns a number representing the current time\n   * (at the time this function was called) according to the scheduler's own\n   * internal clock.\n   * @return {number} A number that represents the current time. May or may not\n   * have a relation to wall-clock time. May or may not refer to a time unit\n   * (e.g. milliseconds).\n   */\n  public now: () => number;\n\n  /**\n   * Schedules a function, `work`, for execution. May happen at some point in\n   * the future, according to the `delay` parameter, if specified. May be passed\n   * some context object, `state`, which will be passed to the `work` function.\n   *\n   * The given arguments will be processed an stored as an Action object in a\n   * queue of actions.\n   *\n   * @param {function(state: ?T): ?Subscription} work A function representing a\n   * task, or some unit of work to be executed by the Scheduler.\n   * @param {number} [delay] Time to wait before executing the work, where the\n   * time unit is implicit and defined by the Scheduler itself.\n   * @param {T} [state] Some contextual data that the `work` function uses when\n   * called by the Scheduler.\n   * @return {Subscription} A subscription in order to be able to unsubscribe\n   * the scheduled work.\n   */\n  public schedule<T>(work: (this: SchedulerAction<T>, state?: T) => void, delay: number = 0, state?: T): Subscription {\n    return new this.schedulerActionCtor<T>(this, work).schedule(state, delay);\n  }\n}\n", "import { Scheduler } from '../Scheduler';\nimport { Action } from './Action';\nimport { AsyncAction } from './AsyncAction';\nimport { TimerHandle } from './timerHandle';\n\nexport class AsyncScheduler extends Scheduler {\n  public actions: Array<AsyncAction<any>> = [];\n  /**\n   * A flag to indicate whether the Scheduler is currently executing a batch of\n   * queued actions.\n   * @type {boolean}\n   * @internal\n   */\n  public _active: boolean = false;\n  /**\n   * An internal ID used to track the latest asynchronous task such as those\n   * coming from `setTimeout`, `setInterval`, `requestAnimationFrame`, and\n   * others.\n   * @type {any}\n   * @internal\n   */\n  public _scheduled: TimerHandle | undefined;\n\n  constructor(SchedulerAction: typeof Action, now: () => number = Scheduler.now) {\n    super(SchedulerAction, now);\n  }\n\n  public flush(action: AsyncAction<any>): void {\n    const { actions } = this;\n\n    if (this._active) {\n      actions.push(action);\n      return;\n    }\n\n    let error: any;\n    this._active = true;\n\n    do {\n      if ((error = action.execute(action.state, action.delay))) {\n        break;\n      }\n    } while ((action = actions.shift()!)); // exhaust the scheduler queue\n\n    this._active = false;\n\n    if (error) {\n      while ((action = actions.shift()!)) {\n        action.unsubscribe();\n      }\n      throw error;\n    }\n  }\n}\n", "import { AsyncAction } from './AsyncAction';\nimport { AsyncScheduler } from './AsyncScheduler';\n\n/**\n *\n * Async Scheduler\n *\n * <span class=\"informal\">Schedule task as if you used setTimeout(task, duration)</span>\n *\n * `async` scheduler schedules tasks asynchronously, by putting them on the JavaScript\n * event loop queue. It is best used to delay tasks in time or to schedule tasks repeating\n * in intervals.\n *\n * If you just want to \"defer\" task, that is to perform it right after currently\n * executing synchronous code ends (commonly achieved by `setTimeout(deferredTask, 0)`),\n * better choice will be the {@link asapScheduler} scheduler.\n *\n * ## Examples\n * Use async scheduler to delay task\n * ```ts\n * import { asyncScheduler } from 'rxjs';\n *\n * const task = () => console.log('it works!');\n *\n * asyncScheduler.schedule(task, 2000);\n *\n * // After 2 seconds logs:\n * // \"it works!\"\n * ```\n *\n * Use async scheduler to repeat task in intervals\n * ```ts\n * import { asyncScheduler } from 'rxjs';\n *\n * function task(state) {\n *   console.log(state);\n *   this.schedule(state + 1, 1000); // `this` references currently executing Action,\n *                                   // which we reschedule with new state and delay\n * }\n *\n * asyncScheduler.schedule(task, 3000, 0);\n *\n * // Logs:\n * // 0 after 3s\n * // 1 after 4s\n * // 2 after 5s\n * // 3 after 6s\n * ```\n */\n\nexport const asyncScheduler = new AsyncScheduler(AsyncAction);\n\n/**\n * @deprecated Renamed to {@link asyncScheduler}. Will be removed in v8.\n */\nexport const async = asyncScheduler;\n", "import { AsyncAction } from './AsyncAction';\nimport { AnimationFrameScheduler } from './AnimationFrameScheduler';\nimport { SchedulerAction } from '../types';\nimport { animationFrameProvider } from './animationFrameProvider';\nimport { TimerHandle } from './timerHandle';\n\nexport class AnimationFrameAction<T> extends AsyncAction<T> {\n  constructor(protected scheduler: AnimationFrameScheduler, protected work: (this: SchedulerAction<T>, state?: T) => void) {\n    super(scheduler, work);\n  }\n\n  protected requestAsyncId(scheduler: AnimationFrameScheduler, id?: TimerHandle, delay: number = 0): TimerHandle {\n    // If delay is greater than 0, request as an async action.\n    if (delay !== null && delay > 0) {\n      return super.requestAsyncId(scheduler, id, delay);\n    }\n    // Push the action to the end of the scheduler queue.\n    scheduler.actions.push(this);\n    // If an animation frame has already been requested, don't request another\n    // one. If an animation frame hasn't been requested yet, request one. Return\n    // the current animation frame request id.\n    return scheduler._scheduled || (scheduler._scheduled = animationFrameProvider.requestAnimationFrame(() => scheduler.flush(undefined)));\n  }\n\n  protected recycleAsyncId(scheduler: AnimationFrameScheduler, id?: TimerHandle, delay: number = 0): TimerHandle | undefined {\n    // If delay exists and is greater than 0, or if the delay is null (the\n    // action wasn't rescheduled) but was originally scheduled as an async\n    // action, then recycle as an async action.\n    if (delay != null ? delay > 0 : this.delay > 0) {\n      return super.recycleAsyncId(scheduler, id, delay);\n    }\n    // If the scheduler queue has no remaining actions with the same async id,\n    // cancel the requested animation frame and set the scheduled flag to\n    // undefined so the next AnimationFrameAction will request its own.\n    const { actions } = scheduler;\n    if (id != null && actions[actions.length - 1]?.id !== id) {\n      animationFrameProvider.cancelAnimationFrame(id as number);\n      scheduler._scheduled = undefined;\n    }\n    // Return undefined so the action knows to request a new async id if it's rescheduled.\n    return undefined;\n  }\n}\n", "import { AsyncAction } from './AsyncAction';\nimport { AsyncScheduler } from './AsyncScheduler';\n\nexport class AnimationFrameScheduler extends AsyncScheduler {\n  public flush(action?: AsyncAction<any>): void {\n    this._active = true;\n    // The async id that effects a call to flush is stored in _scheduled.\n    // Before executing an action, it's necessary to check the action's async\n    // id to determine whether it's supposed to be executed in the current\n    // flush.\n    // Previous implementations of this method used a count to determine this,\n    // but that was unsound, as actions that are unsubscribed - i.e. cancelled -\n    // are removed from the actions array and that can shift actions that are\n    // scheduled to be executed in a subsequent flush into positions at which\n    // they are executed within the current flush.\n    const flushId = this._scheduled;\n    this._scheduled = undefined;\n\n    const { actions } = this;\n    let error: any;\n    action = action || actions.shift()!;\n\n    do {\n      if ((error = action.execute(action.state, action.delay))) {\n        break;\n      }\n    } while ((action = actions[0]) && action.id === flushId && actions.shift());\n\n    this._active = false;\n\n    if (error) {\n      while ((action = actions[0]) && action.id === flushId && actions.shift()) {\n        action.unsubscribe();\n      }\n      throw error;\n    }\n  }\n}\n", "import { AnimationFrameAction } from './AnimationFrameAction';\nimport { AnimationFrameScheduler } from './AnimationFrameScheduler';\n\n/**\n *\n * Animation Frame Scheduler\n *\n * <span class=\"informal\">Perform task when `window.requestAnimationFrame` would fire</span>\n *\n * When `animationFrame` scheduler is used with delay, it will fall back to {@link asyncScheduler} scheduler\n * behaviour.\n *\n * Without delay, `animationFrame` scheduler can be used to create smooth browser animations.\n * It makes sure scheduled task will happen just before next browser content repaint,\n * thus performing animations as efficiently as possible.\n *\n * ## Example\n * Schedule div height animation\n * ```ts\n * // html: <div style=\"background: #0ff;\"></div>\n * import { animationFrameScheduler } from 'rxjs';\n *\n * const div = document.querySelector('div');\n *\n * animationFrameScheduler.schedule(function(height) {\n *   div.style.height = height + \"px\";\n *\n *   this.schedule(height + 1);  // `this` references currently executing Action,\n *                               // which we reschedule with new state\n * }, 0, 0);\n *\n * // You will see a div element growing in height\n * ```\n */\n\nexport const animationFrameScheduler = new AnimationFrameScheduler(AnimationFrameAction);\n\n/**\n * @deprecated Renamed to {@link animationFrameScheduler}. Will be removed in v8.\n */\nexport const animationFrame = animationFrameScheduler;\n", "import { Observable } from '../Observable';\nimport { SchedulerLike } from '../types';\n\n/**\n * A simple Observable that emits no items to the Observer and immediately\n * emits a complete notification.\n *\n * <span class=\"informal\">Just emits 'complete', and nothing else.</span>\n *\n * ![](empty.png)\n *\n * A simple Observable that only emits the complete notification. It can be used\n * for composing with other Observables, such as in a {@link mergeMap}.\n *\n * ## Examples\n *\n * Log complete notification\n *\n * ```ts\n * import { EMPTY } from 'rxjs';\n *\n * EMPTY.subscribe({\n *   next: () => console.log('Next'),\n *   complete: () => console.log('Complete!')\n * });\n *\n * // Outputs\n * // Complete!\n * ```\n *\n * Emit the number 7, then complete\n *\n * ```ts\n * import { EMPTY, startWith } from 'rxjs';\n *\n * const result = EMPTY.pipe(startWith(7));\n * result.subscribe(x => console.log(x));\n *\n * // Outputs\n * // 7\n * ```\n *\n * Map and flatten only odd numbers to the sequence `'a'`, `'b'`, `'c'`\n *\n * ```ts\n * import { interval, mergeMap, of, EMPTY } from 'rxjs';\n *\n * const interval$ = interval(1000);\n * const result = interval$.pipe(\n *   mergeMap(x => x % 2 === 1 ? of('a', 'b', 'c') : EMPTY),\n * );\n * result.subscribe(x => console.log(x));\n *\n * // Results in the following to the console:\n * // x is equal to the count on the interval, e.g. (0, 1, 2, 3, ...)\n * // x will occur every 1000ms\n * // if x % 2 is equal to 1, print a, b, c (each on its own)\n * // if x % 2 is not equal to 1, nothing will be output\n * ```\n *\n * @see {@link Observable}\n * @see {@link NEVER}\n * @see {@link of}\n * @see {@link throwError}\n */\nexport const EMPTY = new Observable<never>((subscriber) => subscriber.complete());\n\n/**\n * @param scheduler A {@link SchedulerLike} to use for scheduling\n * the emission of the complete notification.\n * @deprecated Replaced with the {@link EMPTY} constant or {@link scheduled} (e.g. `scheduled([], scheduler)`). Will be removed in v8.\n */\nexport function empty(scheduler?: SchedulerLike) {\n  return scheduler ? emptyScheduled(scheduler) : EMPTY;\n}\n\nfunction emptyScheduled(scheduler: SchedulerLike) {\n  return new Observable<never>((subscriber) => scheduler.schedule(() => subscriber.complete()));\n}\n", "import { SchedulerLike } from '../types';\nimport { isFunction } from './isFunction';\n\nexport function isScheduler(value: any): value is SchedulerLike {\n  return value && isFunction(value.schedule);\n}\n", "import { SchedulerLike } from '../types';\nimport { isFunction } from './isFunction';\nimport { isScheduler } from './isScheduler';\n\nfunction last<T>(arr: T[]): T | undefined {\n  return arr[arr.length - 1];\n}\n\nexport function popResultSelector(args: any[]): ((...args: unknown[]) => unknown) | undefined {\n  return isFunction(last(args)) ? args.pop() : undefined;\n}\n\nexport function popScheduler(args: any[]): SchedulerLike | undefined {\n  return isScheduler(last(args)) ? args.pop() : undefined;\n}\n\nexport function popNumber(args: any[], defaultValue: number): number {\n  return typeof last(args) === 'number' ? args.pop()! : defaultValue;\n}\n", "export const isArrayLike = (<T>(x: any): x is ArrayLike<T> => x && typeof x.length === 'number' && typeof x !== 'function');", "import { isFunction } from \"./isFunction\";\n\n/**\n * Tests to see if the object is \"thennable\".\n * @param value the object to test\n */\nexport function isPromise(value: any): value is PromiseLike<any> {\n  return isFunction(value?.then);\n}\n", "import { InteropObservable } from '../types';\nimport { observable as Symbol_observable } from '../symbol/observable';\nimport { isFunction } from './isFunction';\n\n/** Identifies an input as being Observable (but not necessary an Rx Observable) */\nexport function isInteropObservable(input: any): input is InteropObservable<any> {\n  return isFunction(input[Symbol_observable]);\n}\n", "import { isFunction } from './isFunction';\n\nexport function isAsyncIterable<T>(obj: any): obj is AsyncIterable<T> {\n  return Symbol.asyncIterator && isFunction(obj?.[Symbol.asyncIterator]);\n}\n", "/**\n * Creates the TypeError to throw if an invalid object is passed to `from` or `scheduled`.\n * @param input The object that was passed.\n */\nexport function createInvalidObservableTypeError(input: any) {\n  // TODO: We should create error codes that can be looked up, so this can be less verbose.\n  return new TypeError(\n    `You provided ${\n      input !== null && typeof input === 'object' ? 'an invalid object' : `'${input}'`\n    } where a stream was expected. You can provide an Observable, Promise, ReadableStream, Array, AsyncIterable, or Iterable.`\n  );\n}\n", "export function getSymbolIterator(): symbol {\n  if (typeof Symbol !== 'function' || !Symbol.iterator) {\n    return '@@iterator' as any;\n  }\n\n  return Symbol.iterator;\n}\n\nexport const iterator = getSymbolIterator();\n", "import { iterator as Symbol_iterator } from '../symbol/iterator';\nimport { isFunction } from './isFunction';\n\n/** Identifies an input as being an Iterable */\nexport function isIterable(input: any): input is Iterable<any> {\n  return isFunction(input?.[Symbol_iterator]);\n}\n", "import { ReadableStreamLike } from '../types';\nimport { isFunction } from './isFunction';\n\nexport async function* readableStreamLikeToAsyncGenerator<T>(readableStream: ReadableStreamLike<T>): AsyncGenerator<T> {\n  const reader = readableStream.getReader();\n  try {\n    while (true) {\n      const { value, done } = await reader.read();\n      if (done) {\n        return;\n      }\n      yield value!;\n    }\n  } finally {\n    reader.releaseLock();\n  }\n}\n\nexport function isReadableStreamLike<T>(obj: any): obj is ReadableStreamLike<T> {\n  // We don't want to use instanceof checks because they would return\n  // false for instances from another Realm, like an <iframe>.\n  return isFunction(obj?.getReader);\n}\n", "import { isArrayLike } from '../util/isArrayLike';\nimport { isPromise } from '../util/isPromise';\nimport { Observable } from '../Observable';\nimport { ObservableInput, ObservedValueOf, ReadableStreamLike } from '../types';\nimport { isInteropObservable } from '../util/isInteropObservable';\nimport { isAsyncIterable } from '../util/isAsyncIterable';\nimport { createInvalidObservableTypeError } from '../util/throwUnobservableError';\nimport { isIterable } from '../util/isIterable';\nimport { isReadableStreamLike, readableStreamLikeToAsyncGenerator } from '../util/isReadableStreamLike';\nimport { Subscriber } from '../Subscriber';\nimport { isFunction } from '../util/isFunction';\nimport { reportUnhandledError } from '../util/reportUnhandledError';\nimport { observable as Symbol_observable } from '../symbol/observable';\n\nexport function innerFrom<O extends ObservableInput<any>>(input: O): Observable<ObservedValueOf<O>>;\nexport function innerFrom<T>(input: ObservableInput<T>): Observable<T> {\n  if (input instanceof Observable) {\n    return input;\n  }\n  if (input != null) {\n    if (isInteropObservable(input)) {\n      return fromInteropObservable(input);\n    }\n    if (isArrayLike(input)) {\n      return fromArrayLike(input);\n    }\n    if (isPromise(input)) {\n      return fromPromise(input);\n    }\n    if (isAsyncIterable(input)) {\n      return fromAsyncIterable(input);\n    }\n    if (isIterable(input)) {\n      return fromIterable(input);\n    }\n    if (isReadableStreamLike(input)) {\n      return fromReadableStreamLike(input);\n    }\n  }\n\n  throw createInvalidObservableTypeError(input);\n}\n\n/**\n * Creates an RxJS Observable from an object that implements `Symbol.observable`.\n * @param obj An object that properly implements `Symbol.observable`.\n */\nexport function fromInteropObservable<T>(obj: any) {\n  return new Observable((subscriber: Subscriber<T>) => {\n    const obs = obj[Symbol_observable]();\n    if (isFunction(obs.subscribe)) {\n      return obs.subscribe(subscriber);\n    }\n    // Should be caught by observable subscribe function error handling.\n    throw new TypeError('Provided object does not correctly implement Symbol.observable');\n  });\n}\n\n/**\n * Synchronously emits the values of an array like and completes.\n * This is exported because there are creation functions and operators that need to\n * make direct use of the same logic, and there's no reason to make them run through\n * `from` conditionals because we *know* they're dealing with an array.\n * @param array The array to emit values from\n */\nexport function fromArrayLike<T>(array: ArrayLike<T>) {\n  return new Observable((subscriber: Subscriber<T>) => {\n    // Loop over the array and emit each value. Note two things here:\n    // 1. We're making sure that the subscriber is not closed on each loop.\n    //    This is so we don't continue looping over a very large array after\n    //    something like a `take`, `takeWhile`, or other synchronous unsubscription\n    //    has already unsubscribed.\n    // 2. In this form, reentrant code can alter that array we're looping over.\n    //    This is a known issue, but considered an edge case. The alternative would\n    //    be to copy the array before executing the loop, but this has\n    //    performance implications.\n    for (let i = 0; i < array.length && !subscriber.closed; i++) {\n      subscriber.next(array[i]);\n    }\n    subscriber.complete();\n  });\n}\n\nexport function fromPromise<T>(promise: PromiseLike<T>) {\n  return new Observable((subscriber: Subscriber<T>) => {\n    promise\n      .then(\n        (value) => {\n          if (!subscriber.closed) {\n            subscriber.next(value);\n            subscriber.complete();\n          }\n        },\n        (err: any) => subscriber.error(err)\n      )\n      .then(null, reportUnhandledError);\n  });\n}\n\nexport function fromIterable<T>(iterable: Iterable<T>) {\n  return new Observable((subscriber: Subscriber<T>) => {\n    for (const value of iterable) {\n      subscriber.next(value);\n      if (subscriber.closed) {\n        return;\n      }\n    }\n    subscriber.complete();\n  });\n}\n\nexport function fromAsyncIterable<T>(asyncIterable: AsyncIterable<T>) {\n  return new Observable((subscriber: Subscriber<T>) => {\n    process(asyncIterable, subscriber).catch((err) => subscriber.error(err));\n  });\n}\n\nexport function fromReadableStreamLike<T>(readableStream: ReadableStreamLike<T>) {\n  return fromAsyncIterable(readableStreamLikeToAsyncGenerator(readableStream));\n}\n\nasync function process<T>(asyncIterable: AsyncIterable<T>, subscriber: Subscriber<T>) {\n  for await (const value of asyncIterable) {\n    subscriber.next(value);\n    // A side-effect may have closed our subscriber,\n    // check before the next iteration.\n    if (subscriber.closed) {\n      return;\n    }\n  }\n  subscriber.complete();\n}\n", "import { Subscription } from '../Subscription';\nimport { SchedulerAction, SchedulerLike } from '../types';\n\nexport function executeSchedule(\n  parentSubscription: Subscription,\n  scheduler: SchedulerLike,\n  work: () => void,\n  delay: number,\n  repeat: true\n): void;\nexport function executeSchedule(\n  parentSubscription: Subscription,\n  scheduler: SchedulerLike,\n  work: () => void,\n  delay?: number,\n  repeat?: false\n): Subscription;\n\nexport function executeSchedule(\n  parentSubscription: Subscription,\n  scheduler: SchedulerLike,\n  work: () => void,\n  delay = 0,\n  repeat = false\n): Subscription | void {\n  const scheduleSubscription = scheduler.schedule(function (this: SchedulerAction<any>) {\n    work();\n    if (repeat) {\n      parentSubscription.add(this.schedule(null, delay));\n    } else {\n      this.unsubscribe();\n    }\n  }, delay);\n\n  parentSubscription.add(scheduleSubscription);\n\n  if (!repeat) {\n    // Because user-land scheduler implementations are unlikely to properly reuse\n    // Actions for repeat scheduling, we can't trust that the returned subscription\n    // will control repeat subscription scenarios. So we're trying to avoid using them\n    // incorrectly within this library.\n    return scheduleSubscription;\n  }\n}\n", "/** @prettier */\nimport { MonoTypeOperatorFunction, SchedulerLike } from '../types';\nimport { executeSchedule } from '../util/executeSchedule';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\n/**\n * Re-emits all notifications from source Observable with specified scheduler.\n *\n * <span class=\"informal\">Ensure a specific scheduler is used, from outside of an Observable.</span>\n *\n * `observeOn` is an operator that accepts a scheduler as a first parameter, which will be used to reschedule\n * notifications emitted by the source Observable. It might be useful, if you do not have control over\n * internal scheduler of a given Observable, but want to control when its values are emitted nevertheless.\n *\n * Returned Observable emits the same notifications (nexted values, complete and error events) as the source Observable,\n * but rescheduled with provided scheduler. Note that this doesn't mean that source Observables internal\n * scheduler will be replaced in any way. Original scheduler still will be used, but when the source Observable emits\n * notification, it will be immediately scheduled again - this time with scheduler passed to `observeOn`.\n * An anti-pattern would be calling `observeOn` on Observable that emits lots of values synchronously, to split\n * that emissions into asynchronous chunks. For this to happen, scheduler would have to be passed into the source\n * Observable directly (usually into the operator that creates it). `observeOn` simply delays notifications a\n * little bit more, to ensure that they are emitted at expected moments.\n *\n * As a matter of fact, `observeOn` accepts second parameter, which specifies in milliseconds with what delay notifications\n * will be emitted. The main difference between {@link delay} operator and `observeOn` is that `observeOn`\n * will delay all notifications - including error notifications - while `delay` will pass through error\n * from source Observable immediately when it is emitted. In general it is highly recommended to use `delay` operator\n * for any kind of delaying of values in the stream, while using `observeOn` to specify which scheduler should be used\n * for notification emissions in general.\n *\n * ## Example\n *\n * Ensure values in subscribe are called just before browser repaint\n *\n * ```ts\n * import { interval, observeOn, animationFrameScheduler } from 'rxjs';\n *\n * const someDiv = document.createElement('div');\n * someDiv.style.cssText = 'width: 200px;background: #09c';\n * document.body.appendChild(someDiv);\n * const intervals = interval(10);      // Intervals are scheduled\n *                                      // with async scheduler by default...\n * intervals.pipe(\n *   observeOn(animationFrameScheduler) // ...but we will observe on animationFrame\n * )                                    // scheduler to ensure smooth animation.\n * .subscribe(val => {\n *   someDiv.style.height = val + 'px';\n * });\n * ```\n *\n * @see {@link delay}\n *\n * @param scheduler Scheduler that will be used to reschedule notifications from source Observable.\n * @param delay Number of milliseconds that states with what delay every notification should be rescheduled.\n * @return A function that returns an Observable that emits the same\n * notifications as the source Observable, but with provided scheduler.\n */\nexport function observeOn<T>(scheduler: SchedulerLike, delay = 0): MonoTypeOperatorFunction<T> {\n  return operate((source, subscriber) => {\n    source.subscribe(\n      createOperatorSubscriber(\n        subscriber,\n        (value) => executeSchedule(subscriber, scheduler, () => subscriber.next(value), delay),\n        () => executeSchedule(subscriber, scheduler, () => subscriber.complete(), delay),\n        (err) => executeSchedule(subscriber, scheduler, () => subscriber.error(err), delay)\n      )\n    );\n  });\n}\n", "import { MonoTypeOperatorFunction, SchedulerLike } from '../types';\nimport { operate } from '../util/lift';\n\n/**\n * Asynchronously subscribes Observers to this Observable on the specified {@link SchedulerLike}.\n *\n * With `subscribeOn` you can decide what type of scheduler a specific Observable will be using when it is subscribed to.\n *\n * Schedulers control the speed and order of emissions to observers from an Observable stream.\n *\n * ![](subscribeOn.png)\n *\n * ## Example\n *\n * Given the following code:\n *\n * ```ts\n * import { of, merge } from 'rxjs';\n *\n * const a = of(1, 2, 3);\n * const b = of(4, 5, 6);\n *\n * merge(a, b).subscribe(console.log);\n *\n * // Outputs\n * // 1\n * // 2\n * // 3\n * // 4\n * // 5\n * // 6\n * ```\n *\n * Both Observable `a` and `b` will emit their values directly and synchronously once they are subscribed to.\n *\n * If we instead use the `subscribeOn` operator declaring that we want to use the {@link asyncScheduler} for values emitted by Observable `a`:\n *\n * ```ts\n * import { of, subscribeOn, asyncScheduler, merge } from 'rxjs';\n *\n * const a = of(1, 2, 3).pipe(subscribeOn(asyncScheduler));\n * const b = of(4, 5, 6);\n *\n * merge(a, b).subscribe(console.log);\n *\n * // Outputs\n * // 4\n * // 5\n * // 6\n * // 1\n * // 2\n * // 3\n * ```\n *\n * The reason for this is that Observable `b` emits its values directly and synchronously like before\n * but the emissions from `a` are scheduled on the event loop because we are now using the {@link asyncScheduler} for that specific Observable.\n *\n * @param scheduler The {@link SchedulerLike} to perform subscription actions on.\n * @param delay A delay to pass to the scheduler to delay subscriptions\n * @return A function that returns an Observable modified so that its\n * subscriptions happen on the specified {@link SchedulerLike}.\n */\nexport function subscribeOn<T>(scheduler: SchedulerLike, delay: number = 0): MonoTypeOperatorFunction<T> {\n  return operate((source, subscriber) => {\n    subscriber.add(scheduler.schedule(() => source.subscribe(subscriber), delay));\n  });\n}\n", "import { innerFrom } from '../observable/innerFrom';\nimport { observeOn } from '../operators/observeOn';\nimport { subscribeOn } from '../operators/subscribeOn';\nimport { InteropObservable, SchedulerLike } from '../types';\n\nexport function scheduleObservable<T>(input: InteropObservable<T>, scheduler: SchedulerLike) {\n  return innerFrom(input).pipe(subscribeOn(scheduler), observeOn(scheduler));\n}\n", "import { innerFrom } from '../observable/innerFrom';\nimport { observeOn } from '../operators/observeOn';\nimport { subscribeOn } from '../operators/subscribeOn';\nimport { SchedulerLike } from '../types';\n\nexport function schedulePromise<T>(input: PromiseLike<T>, scheduler: SchedulerLike) {\n  return innerFrom(input).pipe(subscribeOn(scheduler), observeOn(scheduler));\n}\n", "import { Observable } from '../Observable';\nimport { SchedulerLike } from '../types';\n\nexport function scheduleArray<T>(input: ArrayLike<T>, scheduler: SchedulerLike) {\n  return new Observable<T>((subscriber) => {\n    // The current array index.\n    let i = 0;\n    // Start iterating over the array like on a schedule.\n    return scheduler.schedule(function () {\n      if (i === input.length) {\n        // If we have hit the end of the array like in the\n        // previous job, we can complete.\n        subscriber.complete();\n      } else {\n        // Otherwise let's next the value at the current index,\n        // then increment our index.\n        subscriber.next(input[i++]);\n        // If the last emission didn't cause us to close the subscriber\n        // (via take or some side effect), reschedule the job and we'll\n        // make another pass.\n        if (!subscriber.closed) {\n          this.schedule();\n        }\n      }\n    });\n  });\n}\n", "import { Observable } from '../Observable';\nimport { SchedulerLike } from '../types';\nimport { iterator as Symbol_iterator } from '../symbol/iterator';\nimport { isFunction } from '../util/isFunction';\nimport { executeSchedule } from '../util/executeSchedule';\n\n/**\n * Used in {@link scheduled} to create an observable from an Iterable.\n * @param input The iterable to create an observable from\n * @param scheduler The scheduler to use\n */\nexport function scheduleIterable<T>(input: Iterable<T>, scheduler: SchedulerLike) {\n  return new Observable<T>((subscriber) => {\n    let iterator: Iterator<T, T>;\n\n    // Schedule the initial creation of the iterator from\n    // the iterable. This is so the code in the iterable is\n    // not called until the scheduled job fires.\n    executeSchedule(subscriber, scheduler, () => {\n      // Create the iterator.\n      iterator = (input as any)[Symbol_iterator]();\n\n      executeSchedule(\n        subscriber,\n        scheduler,\n        () => {\n          let value: T;\n          let done: boolean | undefined;\n          try {\n            // Pull the value out of the iterator\n            ({ value, done } = iterator.next());\n          } catch (err) {\n            // We got an error while pulling from the iterator\n            subscriber.error(err);\n            return;\n          }\n\n          if (done) {\n            // If it is \"done\" we just complete. This mimics the\n            // behavior of JavaScript's `for..of` consumption of\n            // iterables, which will not emit the value from an iterator\n            // result of `{ done: true: value: 'here' }`.\n            subscriber.complete();\n          } else {\n            // The iterable is not done, emit the value.\n            subscriber.next(value);\n          }\n        },\n        0,\n        true\n      );\n    });\n\n    // During finalization, if we see this iterator has a `return` method,\n    // then we know it is a Generator, and not just an Iterator. So we call\n    // the `return()` function. This will ensure that any `finally { }` blocks\n    // inside of the generator we can hit will be hit properly.\n    return () => isFunction(iterator?.return) && iterator.return();\n  });\n}\n", "import { SchedulerLike } from '../types';\nimport { Observable } from '../Observable';\nimport { executeSchedule } from '../util/executeSchedule';\n\nexport function scheduleAsyncIterable<T>(input: AsyncIterable<T>, scheduler: SchedulerLike) {\n  if (!input) {\n    throw new Error('Iterable cannot be null');\n  }\n  return new Observable<T>((subscriber) => {\n    executeSchedule(subscriber, scheduler, () => {\n      const iterator = input[Symbol.asyncIterator]();\n      executeSchedule(\n        subscriber,\n        scheduler,\n        () => {\n          iterator.next().then((result) => {\n            if (result.done) {\n              // This will remove the subscriptions from\n              // the parent subscription.\n              subscriber.complete();\n            } else {\n              subscriber.next(result.value);\n            }\n          });\n        },\n        0,\n        true\n      );\n    });\n  });\n}\n", "import { SchedulerLike, ReadableStreamLike } from '../types';\nimport { Observable } from '../Observable';\nimport { scheduleAsyncIterable } from './scheduleAsyncIterable';\nimport { readableStreamLikeToAsyncGenerator } from '../util/isReadableStreamLike';\n\nexport function scheduleReadableStreamLike<T>(input: ReadableStreamLike<T>, scheduler: SchedulerLike): Observable<T> {\n  return scheduleAsyncIterable(readableStreamLikeToAsyncGenerator(input), scheduler);\n}\n", "import { scheduleObservable } from './scheduleObservable';\nimport { schedulePromise } from './schedulePromise';\nimport { scheduleArray } from './scheduleArray';\nimport { scheduleIterable } from './scheduleIterable';\nimport { scheduleAsyncIterable } from './scheduleAsyncIterable';\nimport { isInteropObservable } from '../util/isInteropObservable';\nimport { isPromise } from '../util/isPromise';\nimport { isArrayLike } from '../util/isArrayLike';\nimport { isIterable } from '../util/isIterable';\nimport { ObservableInput, SchedulerLike } from '../types';\nimport { Observable } from '../Observable';\nimport { isAsyncIterable } from '../util/isAsyncIterable';\nimport { createInvalidObservableTypeError } from '../util/throwUnobservableError';\nimport { isReadableStreamLike } from '../util/isReadableStreamLike';\nimport { scheduleReadableStreamLike } from './scheduleReadableStreamLike';\n\n/**\n * Converts from a common {@link ObservableInput} type to an observable where subscription and emissions\n * are scheduled on the provided scheduler.\n *\n * @see {@link from}\n * @see {@link of}\n *\n * @param input The observable, array, promise, iterable, etc you would like to schedule\n * @param scheduler The scheduler to use to schedule the subscription and emissions from\n * the returned observable.\n */\nexport function scheduled<T>(input: ObservableInput<T>, scheduler: SchedulerLike): Observable<T> {\n  if (input != null) {\n    if (isInteropObservable(input)) {\n      return scheduleObservable(input, scheduler);\n    }\n    if (isArrayLike(input)) {\n      return scheduleArray(input, scheduler);\n    }\n    if (isPromise(input)) {\n      return schedulePromise(input, scheduler);\n    }\n    if (isAsyncIterable(input)) {\n      return scheduleAsyncIterable(input, scheduler);\n    }\n    if (isIterable(input)) {\n      return scheduleIterable(input, scheduler);\n    }\n    if (isReadableStreamLike(input)) {\n      return scheduleReadableStreamLike(input, scheduler);\n    }\n  }\n  throw createInvalidObservableTypeError(input);\n}\n", "import { Observable } from '../Observable';\nimport { ObservableInput, SchedulerLike, ObservedValueOf } from '../types';\nimport { scheduled } from '../scheduled/scheduled';\nimport { innerFrom } from './innerFrom';\n\nexport function from<O extends ObservableInput<any>>(input: O): Observable<ObservedValueOf<O>>;\n/** @deprecated The `scheduler` parameter will be removed in v8. Use `scheduled`. Details: https://rxjs.dev/deprecations/scheduler-argument */\nexport function from<O extends ObservableInput<any>>(input: O, scheduler: SchedulerLike | undefined): Observable<ObservedValueOf<O>>;\n\n/**\n * Creates an Observable from an Array, an array-like object, a Promise, an iterable object, or an Observable-like object.\n *\n * <span class=\"informal\">Converts almost anything to an Observable.</span>\n *\n * ![](from.png)\n *\n * `from` converts various other objects and data types into Observables. It also converts a Promise, an array-like, or an\n * <a href=\"https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Iteration_protocols#iterable\" target=\"_blank\">iterable</a>\n * object into an Observable that emits the items in that promise, array, or iterable. A String, in this context, is treated\n * as an array of characters. Observable-like objects (contains a function named with the ES2015 Symbol for Observable) can also be\n * converted through this operator.\n *\n * ## Examples\n *\n * Converts an array to an Observable\n *\n * ```ts\n * import { from } from 'rxjs';\n *\n * const array = [10, 20, 30];\n * const result = from(array);\n *\n * result.subscribe(x => console.log(x));\n *\n * // Logs:\n * // 10\n * // 20\n * // 30\n * ```\n *\n * Convert an infinite iterable (from a generator) to an Observable\n *\n * ```ts\n * import { from, take } from 'rxjs';\n *\n * function* generateDoubles(seed) {\n *    let i = seed;\n *    while (true) {\n *      yield i;\n *      i = 2 * i; // double it\n *    }\n * }\n *\n * const iterator = generateDoubles(3);\n * const result = from(iterator).pipe(take(10));\n *\n * result.subscribe(x => console.log(x));\n *\n * // Logs:\n * // 3\n * // 6\n * // 12\n * // 24\n * // 48\n * // 96\n * // 192\n * // 384\n * // 768\n * // 1536\n * ```\n *\n * With `asyncScheduler`\n *\n * ```ts\n * import { from, asyncScheduler } from 'rxjs';\n *\n * console.log('start');\n *\n * const array = [10, 20, 30];\n * const result = from(array, asyncScheduler);\n *\n * result.subscribe(x => console.log(x));\n *\n * console.log('end');\n *\n * // Logs:\n * // 'start'\n * // 'end'\n * // 10\n * // 20\n * // 30\n * ```\n *\n * @see {@link fromEvent}\n * @see {@link fromEventPattern}\n *\n * @param {ObservableInput<T>} A subscription object, a Promise, an Observable-like,\n * an Array, an iterable, or an array-like object to be converted.\n * @param {SchedulerLike} An optional {@link SchedulerLike} on which to schedule the emission of values.\n * @return {Observable<T>}\n */\nexport function from<T>(input: ObservableInput<T>, scheduler?: SchedulerLike): Observable<T> {\n  return scheduler ? scheduled(input, scheduler) : innerFrom(input);\n}\n", "import { SchedulerLike, ValueFromArray } from '../types';\nimport { Observable } from '../Observable';\nimport { popScheduler } from '../util/args';\nimport { from } from './from';\n\n// Devs are more likely to pass null or undefined than they are a scheduler\n// without accompanying values. To make things easier for (naughty) devs who\n// use the `strictNullChecks: false` TypeScript compiler option, these\n// overloads with explicit null and undefined values are included.\n\nexport function of(value: null): Observable<null>;\nexport function of(value: undefined): Observable<undefined>;\n\n/** @deprecated The `scheduler` parameter will be removed in v8. Use `scheduled`. Details: https://rxjs.dev/deprecations/scheduler-argument */\nexport function of(scheduler: SchedulerLike): Observable<never>;\n/** @deprecated The `scheduler` parameter will be removed in v8. Use `scheduled`. Details: https://rxjs.dev/deprecations/scheduler-argument */\nexport function of<A extends readonly unknown[]>(...valuesAndScheduler: [...A, SchedulerLike]): Observable<ValueFromArray<A>>;\n\nexport function of(): Observable<never>;\n/** @deprecated Do not specify explicit type parameters. Signatures with type parameters that cannot be inferred will be removed in v8. */\nexport function of<T>(): Observable<T>;\nexport function of<T>(value: T): Observable<T>;\nexport function of<A extends readonly unknown[]>(...values: A): Observable<ValueFromArray<A>>;\n\n/**\n * Converts the arguments to an observable sequence.\n *\n * <span class=\"informal\">Each argument becomes a `next` notification.</span>\n *\n * ![](of.png)\n *\n * Unlike {@link from}, it does not do any flattening and emits each argument in whole\n * as a separate `next` notification.\n *\n * ## Examples\n *\n * Emit the values `10, 20, 30`\n *\n * ```ts\n * import { of } from 'rxjs';\n *\n * of(10, 20, 30)\n *   .subscribe({\n *     next: value => console.log('next:', value),\n *     error: err => console.log('error:', err),\n *     complete: () => console.log('the end'),\n *   });\n *\n * // Outputs\n * // next: 10\n * // next: 20\n * // next: 30\n * // the end\n * ```\n *\n * Emit the array `[1, 2, 3]`\n *\n * ```ts\n * import { of } from 'rxjs';\n *\n * of([1, 2, 3])\n *   .subscribe({\n *     next: value => console.log('next:', value),\n *     error: err => console.log('error:', err),\n *     complete: () => console.log('the end'),\n *   });\n *\n * // Outputs\n * // next: [1, 2, 3]\n * // the end\n * ```\n *\n * @see {@link from}\n * @see {@link range}\n *\n * @param {...T} values A comma separated list of arguments you want to be emitted\n * @return {Observable} An Observable that emits the arguments\n * described above and then completes.\n */\nexport function of<T>(...args: Array<T | SchedulerLike>): Observable<T> {\n  const scheduler = popScheduler(args);\n  return from(args as T[], scheduler);\n}\n", "import { Observable } from '../Observable';\nimport { Subscriber } from '../Subscriber';\nimport { SchedulerLike } from '../types';\nimport { isFunction } from '../util/isFunction';\n\n/**\n * Creates an observable that will create an error instance and push it to the consumer as an error\n * immediately upon subscription.\n *\n * <span class=\"informal\">Just errors and does nothing else</span>\n *\n * ![](throw.png)\n *\n * This creation function is useful for creating an observable that will create an error and error every\n * time it is subscribed to. Generally, inside of most operators when you might want to return an errored\n * observable, this is unnecessary. In most cases, such as in the inner return of {@link concatMap},\n * {@link mergeMap}, {@link defer}, and many others, you can simply throw the error, and RxJS will pick\n * that up and notify the consumer of the error.\n *\n * ## Example\n *\n * Create a simple observable that will create a new error with a timestamp and log it\n * and the message every time you subscribe to it\n *\n * ```ts\n * import { throwError } from 'rxjs';\n *\n * let errorCount = 0;\n *\n * const errorWithTimestamp$ = throwError(() => {\n *   const error: any = new Error(`This is error number ${ ++errorCount }`);\n *   error.timestamp = Date.now();\n *   return error;\n * });\n *\n * errorWithTimestamp$.subscribe({\n *   error: err => console.log(err.timestamp, err.message)\n * });\n *\n * errorWithTimestamp$.subscribe({\n *   error: err => console.log(err.timestamp, err.message)\n * });\n *\n * // Logs the timestamp and a new error message for each subscription\n * ```\n *\n * ### Unnecessary usage\n *\n * Using `throwError` inside of an operator or creation function\n * with a callback, is usually not necessary\n *\n * ```ts\n * import { of, concatMap, timer, throwError } from 'rxjs';\n *\n * const delays$ = of(1000, 2000, Infinity, 3000);\n *\n * delays$.pipe(\n *   concatMap(ms => {\n *     if (ms < 10000) {\n *       return timer(ms);\n *     } else {\n *       // This is probably overkill.\n *       return throwError(() => new Error(`Invalid time ${ ms }`));\n *     }\n *   })\n * )\n * .subscribe({\n *   next: console.log,\n *   error: console.error\n * });\n * ```\n *\n * You can just throw the error instead\n *\n * ```ts\n * import { of, concatMap, timer } from 'rxjs';\n *\n * const delays$ = of(1000, 2000, Infinity, 3000);\n *\n * delays$.pipe(\n *   concatMap(ms => {\n *     if (ms < 10000) {\n *       return timer(ms);\n *     } else {\n *       // Cleaner and easier to read for most folks.\n *       throw new Error(`Invalid time ${ ms }`);\n *     }\n *   })\n * )\n * .subscribe({\n *   next: console.log,\n *   error: console.error\n * });\n * ```\n *\n * @param errorFactory A factory function that will create the error instance that is pushed.\n */\nexport function throwError(errorFactory: () => any): Observable<never>;\n\n/**\n * Returns an observable that will error with the specified error immediately upon subscription.\n *\n * @param error The error instance to emit\n * @deprecated Support for passing an error value will be removed in v8. Instead, pass a factory function to `throwError(() => new Error('test'))`. This is\n * because it will create the error at the moment it should be created and capture a more appropriate stack trace. If\n * for some reason you need to create the error ahead of time, you can still do that: `const err = new Error('test'); throwError(() => err);`.\n */\nexport function throwError(error: any): Observable<never>;\n\n/**\n * Notifies the consumer of an error using a given scheduler by scheduling it at delay `0` upon subscription.\n *\n * @param errorOrErrorFactory An error instance or error factory\n * @param scheduler A scheduler to use to schedule the error notification\n * @deprecated The `scheduler` parameter will be removed in v8.\n * Use `throwError` in combination with {@link observeOn}: `throwError(() => new Error('test')).pipe(observeOn(scheduler));`.\n * Details: https://rxjs.dev/deprecations/scheduler-argument\n */\nexport function throwError(errorOrErrorFactory: any, scheduler: SchedulerLike): Observable<never>;\n\nexport function throwError(errorOrErrorFactory: any, scheduler?: SchedulerLike): Observable<never> {\n  const errorFactory = isFunction(errorOrErrorFactory) ? errorOrErrorFactory : () => errorOrErrorFactory;\n  const init = (subscriber: Subscriber<never>) => subscriber.error(errorFactory());\n  return new Observable(scheduler ? (subscriber) => scheduler.schedule(init as any, 0, subscriber) : init);\n}\n", "import { createErrorClass } from './createErrorClass';\n\nexport interface EmptyError extends Error {}\n\nexport interface EmptyErrorCtor {\n  /**\n   * @deprecated Internal implementation detail. Do not construct error instances.\n   * Cannot be tagged as internal: https://github.com/ReactiveX/rxjs/issues/6269\n   */\n  new (): EmptyError;\n}\n\n/**\n * An error thrown when an Observable or a sequence was queried but has no\n * elements.\n *\n * @see {@link first}\n * @see {@link last}\n * @see {@link single}\n * @see {@link firstValueFrom}\n * @see {@link lastValueFrom}\n *\n * @class EmptyError\n */\nexport const EmptyError: EmptyErrorCtor = createErrorClass((_super) => function EmptyErrorImpl(this: any) {\n  _super(this);\n  this.name = 'EmptyError';\n  this.message = 'no elements in sequence';\n});\n", "/**\n * Checks to see if a value is not only a `Date` object,\n * but a *valid* `Date` object that can be converted to a\n * number. For example, `new Date('blah')` is indeed an\n * `instanceof Date`, however it cannot be converted to a\n * number.\n */\nexport function isValidDate(value: any): value is Date {\n  return value instanceof Date && !isNaN(value as any);\n}\n", "import { OperatorFunction } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\nexport function map<T, R>(project: (value: T, index: number) => R): OperatorFunction<T, R>;\n/** @deprecated Use a closure instead of a `thisArg`. Signatures accepting a `thisArg` will be removed in v8. */\nexport function map<T, R, A>(project: (this: A, value: T, index: number) => R, thisArg: A): OperatorFunction<T, R>;\n\n/**\n * Applies a given `project` function to each value emitted by the source\n * Observable, and emits the resulting values as an Observable.\n *\n * <span class=\"informal\">Like [Array.prototype.map()](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Array/map),\n * it passes each source value through a transformation function to get\n * corresponding output values.</span>\n *\n * ![](map.png)\n *\n * Similar to the well known `Array.prototype.map` function, this operator\n * applies a projection to each value and emits that projection in the output\n * Observable.\n *\n * ## Example\n *\n * Map every click to the `clientX` position of that click\n *\n * ```ts\n * import { fromEvent, map } from 'rxjs';\n *\n * const clicks = fromEvent<PointerEvent>(document, 'click');\n * const positions = clicks.pipe(map(ev => ev.clientX));\n *\n * positions.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link mapTo}\n * @see {@link pluck}\n *\n * @param {function(value: T, index: number): R} project The function to apply\n * to each `value` emitted by the source Observable. The `index` parameter is\n * the number `i` for the i-th emission that has happened since the\n * subscription, starting from the number `0`.\n * @param {any} [thisArg] An optional argument to define what `this` is in the\n * `project` function.\n * @return A function that returns an Observable that emits the values from the\n * source Observable transformed by the given `project` function.\n */\nexport function map<T, R>(project: (value: T, index: number) => R, thisArg?: any): OperatorFunction<T, R> {\n  return operate((source, subscriber) => {\n    // The index of the value from the source. Used with projection.\n    let index = 0;\n    // Subscribe to the source, all errors and completions are sent along\n    // to the consumer.\n    source.subscribe(\n      createOperatorSubscriber(subscriber, (value: T) => {\n        // Call the projection function with the appropriate this context,\n        // and send the resulting value to the consumer.\n        subscriber.next(project.call(thisArg, value, index++));\n      })\n    );\n  });\n}\n", "import { OperatorFunction } from \"../types\";\nimport { map } from \"../operators/map\";\n\nconst { isArray } = Array;\n\nfunction callOrApply<T, R>(fn: ((...values: T[]) => R), args: T|T[]): R {\n    return isArray(args) ? fn(...args) : fn(args);\n}\n\n/**\n * Used in several -- mostly deprecated -- situations where we need to \n * apply a list of arguments or a single argument to a result selector.\n */\nexport function mapOneOrManyArgs<T, R>(fn: ((...values: T[]) => R)): OperatorFunction<T|T[], R> {\n    return map(args => callOrApply(fn, args))\n}", "const { isArray } = Array;\nconst { getPrototypeOf, prototype: objectProto, keys: getKeys } = Object;\n\n/**\n * Used in functions where either a list of arguments, a single array of arguments, or a\n * dictionary of arguments can be returned. Returns an object with an `args` property with\n * the arguments in an array, if it is a dictionary, it will also return the `keys` in another\n * property.\n */\nexport function argsArgArrayOrObject<T, O extends Record<string, T>>(args: T[] | [O] | [T[]]): { args: T[]; keys: string[] | null } {\n  if (args.length === 1) {\n    const first = args[0];\n    if (isArray(first)) {\n      return { args: first, keys: null };\n    }\n    if (isPOJO(first)) {\n      const keys = getKeys(first);\n      return {\n        args: keys.map((key) => first[key]),\n        keys,\n      };\n    }\n  }\n\n  return { args: args as T[], keys: null };\n}\n\nfunction isPOJO(obj: any): obj is object {\n  return obj && typeof obj === 'object' && getPrototypeOf(obj) === objectProto;\n}\n", "export function createObject(keys: string[], values: any[]) {\n  return keys.reduce((result, key, i) => ((result[key] = values[i]), result), {} as any);\n}\n", "import { Observable } from '../Observable';\nimport { ObservableInput, SchedulerLike, ObservedValueOf, ObservableInputTuple } from '../types';\nimport { argsArgArrayOrObject } from '../util/argsArgArrayOrObject';\nimport { Subscriber } from '../Subscriber';\nimport { from } from './from';\nimport { identity } from '../util/identity';\nimport { Subscription } from '../Subscription';\nimport { mapOneOrManyArgs } from '../util/mapOneOrManyArgs';\nimport { popResultSelector, popScheduler } from '../util/args';\nimport { createObject } from '../util/createObject';\nimport { createOperatorSubscriber } from '../operators/OperatorSubscriber';\nimport { AnyCatcher } from '../AnyCatcher';\nimport { executeSchedule } from '../util/executeSchedule';\n\n// combineLatest(any)\n// We put this first because we need to catch cases where the user has supplied\n// _exactly `any`_ as the argument. Since `any` literally matches _anything_,\n// we don't want it to randomly hit one of the other type signatures below,\n// as we have no idea at build-time what type we should be returning when given an any.\n\n/**\n * You have passed `any` here, we can't figure out if it is\n * an array or an object, so you're getting `unknown`. Use better types.\n * @param arg Something typed as `any`\n */\nexport function combineLatest<T extends AnyCatcher>(arg: T): Observable<unknown>;\n\n// combineLatest([a, b, c])\nexport function combineLatest(sources: []): Observable<never>;\nexport function combineLatest<A extends readonly unknown[]>(sources: readonly [...ObservableInputTuple<A>]): Observable<A>;\n/** @deprecated The `scheduler` parameter will be removed in v8. Use `scheduled` and `combineLatestAll`. Details: https://rxjs.dev/deprecations/scheduler-argument */\nexport function combineLatest<A extends readonly unknown[], R>(\n  sources: readonly [...ObservableInputTuple<A>],\n  resultSelector: (...values: A) => R,\n  scheduler: SchedulerLike\n): Observable<R>;\nexport function combineLatest<A extends readonly unknown[], R>(\n  sources: readonly [...ObservableInputTuple<A>],\n  resultSelector: (...values: A) => R\n): Observable<R>;\n/** @deprecated The `scheduler` parameter will be removed in v8. Use `scheduled` and `combineLatestAll`. Details: https://rxjs.dev/deprecations/scheduler-argument */\nexport function combineLatest<A extends readonly unknown[]>(\n  sources: readonly [...ObservableInputTuple<A>],\n  scheduler: SchedulerLike\n): Observable<A>;\n\n// combineLatest(a, b, c)\n/** @deprecated Pass an array of sources instead. The rest-parameters signature will be removed in v8. Details: https://rxjs.dev/deprecations/array-argument */\nexport function combineLatest<A extends readonly unknown[]>(...sources: [...ObservableInputTuple<A>]): Observable<A>;\n/** @deprecated The `scheduler` parameter will be removed in v8. Use `scheduled` and `combineLatestAll`. Details: https://rxjs.dev/deprecations/scheduler-argument */\nexport function combineLatest<A extends readonly unknown[], R>(\n  ...sourcesAndResultSelectorAndScheduler: [...ObservableInputTuple<A>, (...values: A) => R, SchedulerLike]\n): Observable<R>;\n/** @deprecated Pass an array of sources instead. The rest-parameters signature will be removed in v8. Details: https://rxjs.dev/deprecations/array-argument */\nexport function combineLatest<A extends readonly unknown[], R>(\n  ...sourcesAndResultSelector: [...ObservableInputTuple<A>, (...values: A) => R]\n): Observable<R>;\n/** @deprecated The `scheduler` parameter will be removed in v8. Use `scheduled` and `combineLatestAll`. Details: https://rxjs.dev/deprecations/scheduler-argument */\nexport function combineLatest<A extends readonly unknown[]>(\n  ...sourcesAndScheduler: [...ObservableInputTuple<A>, SchedulerLike]\n): Observable<A>;\n\n// combineLatest({a, b, c})\nexport function combineLatest(sourcesObject: { [K in any]: never }): Observable<never>;\nexport function combineLatest<T extends Record<string, ObservableInput<any>>>(\n  sourcesObject: T\n): Observable<{ [K in keyof T]: ObservedValueOf<T[K]> }>;\n\n/**\n * Combines multiple Observables to create an Observable whose values are\n * calculated from the latest values of each of its input Observables.\n *\n * <span class=\"informal\">Whenever any input Observable emits a value, it\n * computes a formula using the latest values from all the inputs, then emits\n * the output of that formula.</span>\n *\n * ![](combineLatest.png)\n *\n * `combineLatest` combines the values from all the Observables passed in the\n * observables array. This is done by subscribing to each Observable in order and,\n * whenever any Observable emits, collecting an array of the most recent\n * values from each Observable. So if you pass `n` Observables to this operator,\n * the returned Observable will always emit an array of `n` values, in an order\n * corresponding to the order of the passed Observables (the value from the first Observable\n * will be at index 0 of the array and so on).\n *\n * Static version of `combineLatest` accepts an array of Observables. Note that an array of\n * Observables is a good choice, if you don't know beforehand how many Observables\n * you will combine. Passing an empty array will result in an Observable that\n * completes immediately.\n *\n * To ensure the output array always has the same length, `combineLatest` will\n * actually wait for all input Observables to emit at least once,\n * before it starts emitting results. This means if some Observable emits\n * values before other Observables started emitting, all these values but the last\n * will be lost. On the other hand, if some Observable does not emit a value but\n * completes, resulting Observable will complete at the same moment without\n * emitting anything, since it will now be impossible to include a value from the\n * completed Observable in the resulting array. Also, if some input Observable does\n * not emit any value and never completes, `combineLatest` will also never emit\n * and never complete, since, again, it will wait for all streams to emit some\n * value.\n *\n * If at least one Observable was passed to `combineLatest` and all passed Observables\n * emitted something, the resulting Observable will complete when all combined\n * streams complete. So even if some Observable completes, the result of\n * `combineLatest` will still emit values when other Observables do. In case\n * of a completed Observable, its value from now on will always be the last\n * emitted value. On the other hand, if any Observable errors, `combineLatest`\n * will error immediately as well, and all other Observables will be unsubscribed.\n *\n * ## Examples\n *\n * Combine two timer Observables\n *\n * ```ts\n * import { timer, combineLatest } from 'rxjs';\n *\n * const firstTimer = timer(0, 1000); // emit 0, 1, 2... after every second, starting from now\n * const secondTimer = timer(500, 1000); // emit 0, 1, 2... after every second, starting 0,5s from now\n * const combinedTimers = combineLatest([firstTimer, secondTimer]);\n * combinedTimers.subscribe(value => console.log(value));\n * // Logs\n * // [0, 0] after 0.5s\n * // [1, 0] after 1s\n * // [1, 1] after 1.5s\n * // [2, 1] after 2s\n * ```\n *\n * Combine a dictionary of Observables\n *\n * ```ts\n * import { of, delay, startWith, combineLatest } from 'rxjs';\n *\n * const observables = {\n *   a: of(1).pipe(delay(1000), startWith(0)),\n *   b: of(5).pipe(delay(5000), startWith(0)),\n *   c: of(10).pipe(delay(10000), startWith(0))\n * };\n * const combined = combineLatest(observables);\n * combined.subscribe(value => console.log(value));\n * // Logs\n * // { a: 0, b: 0, c: 0 } immediately\n * // { a: 1, b: 0, c: 0 } after 1s\n * // { a: 1, b: 5, c: 0 } after 5s\n * // { a: 1, b: 5, c: 10 } after 10s\n * ```\n *\n * Combine an array of Observables\n *\n * ```ts\n * import { of, delay, startWith, combineLatest } from 'rxjs';\n *\n * const observables = [1, 5, 10].map(\n *   n => of(n).pipe(\n *     delay(n * 1000), // emit 0 and then emit n after n seconds\n *     startWith(0)\n *   )\n * );\n * const combined = combineLatest(observables);\n * combined.subscribe(value => console.log(value));\n * // Logs\n * // [0, 0, 0] immediately\n * // [1, 0, 0] after 1s\n * // [1, 5, 0] after 5s\n * // [1, 5, 10] after 10s\n * ```\n *\n * Use map operator to dynamically calculate the Body-Mass Index\n *\n * ```ts\n * import { of, combineLatest, map } from 'rxjs';\n *\n * const weight = of(70, 72, 76, 79, 75);\n * const height = of(1.76, 1.77, 1.78);\n * const bmi = combineLatest([weight, height]).pipe(\n *   map(([w, h]) => w / (h * h)),\n * );\n * bmi.subscribe(x => console.log('BMI is ' + x));\n *\n * // With output to console:\n * // BMI is 24.212293388429753\n * // BMI is 23.93948099205209\n * // BMI is 23.671253629592222\n * ```\n *\n * @see {@link combineLatestAll}\n * @see {@link merge}\n * @see {@link withLatestFrom}\n *\n * @param {ObservableInput} [observables] An array of input Observables to combine with each other.\n * An array of Observables must be given as the first argument.\n * @param {function} [project] An optional function to project the values from\n * the combined latest values into a new value on the output Observable.\n * @param {SchedulerLike} [scheduler=null] The {@link SchedulerLike} to use for subscribing to\n * each input Observable.\n * @return {Observable} An Observable of projected values from the most recent\n * values from each input Observable, or an array of the most recent values from\n * each input Observable.\n */\nexport function combineLatest<O extends ObservableInput<any>, R>(...args: any[]): Observable<R> | Observable<ObservedValueOf<O>[]> {\n  const scheduler = popScheduler(args);\n  const resultSelector = popResultSelector(args);\n\n  const { args: observables, keys } = argsArgArrayOrObject(args);\n\n  if (observables.length === 0) {\n    // If no observables are passed, or someone has passed an empty array\n    // of observables, or even an empty object POJO, we need to just\n    // complete (EMPTY), but we have to honor the scheduler provided if any.\n    return from([], scheduler as any);\n  }\n\n  const result = new Observable<ObservedValueOf<O>[]>(\n    combineLatestInit(\n      observables as ObservableInput<ObservedValueOf<O>>[],\n      scheduler,\n      keys\n        ? // A handler for scrubbing the array of args into a dictionary.\n          (values) => createObject(keys, values)\n        : // A passthrough to just return the array\n          identity\n    )\n  );\n\n  return resultSelector ? (result.pipe(mapOneOrManyArgs(resultSelector)) as Observable<R>) : result;\n}\n\nexport function combineLatestInit(\n  observables: ObservableInput<any>[],\n  scheduler?: SchedulerLike,\n  valueTransform: (values: any[]) => any = identity\n) {\n  return (subscriber: Subscriber<any>) => {\n    // The outer subscription. We're capturing this in a function\n    // because we may have to schedule it.\n    maybeSchedule(\n      scheduler,\n      () => {\n        const { length } = observables;\n        // A store for the values each observable has emitted so far. We match observable to value on index.\n        const values = new Array(length);\n        // The number of currently active subscriptions, as they complete, we decrement this number to see if\n        // we are all done combining values, so we can complete the result.\n        let active = length;\n        // The number of inner sources that still haven't emitted the first value\n        // We need to track this because all sources need to emit one value in order\n        // to start emitting values.\n        let remainingFirstValues = length;\n        // The loop to kick off subscription. We're keying everything on index `i` to relate the observables passed\n        // in to the slot in the output array or the key in the array of keys in the output dictionary.\n        for (let i = 0; i < length; i++) {\n          maybeSchedule(\n            scheduler,\n            () => {\n              const source = from(observables[i], scheduler as any);\n              let hasFirstValue = false;\n              source.subscribe(\n                createOperatorSubscriber(\n                  subscriber,\n                  (value) => {\n                    // When we get a value, record it in our set of values.\n                    values[i] = value;\n                    if (!hasFirstValue) {\n                      // If this is our first value, record that.\n                      hasFirstValue = true;\n                      remainingFirstValues--;\n                    }\n                    if (!remainingFirstValues) {\n                      // We're not waiting for any more\n                      // first values, so we can emit!\n                      subscriber.next(valueTransform(values.slice()));\n                    }\n                  },\n                  () => {\n                    if (!--active) {\n                      // We only complete the result if we have no more active\n                      // inner observables.\n                      subscriber.complete();\n                    }\n                  }\n                )\n              );\n            },\n            subscriber\n          );\n        }\n      },\n      subscriber\n    );\n  };\n}\n\n/**\n * A small utility to handle the couple of locations where we want to schedule if a scheduler was provided,\n * but we don't if there was no scheduler.\n */\nfunction maybeSchedule(scheduler: SchedulerLike | undefined, execute: () => void, subscription: Subscription) {\n  if (scheduler) {\n    executeSchedule(subscription, scheduler, execute);\n  } else {\n    execute();\n  }\n}\n", "import { Observable } from '../Observable';\nimport { innerFrom } from '../observable/innerFrom';\nimport { Subscriber } from '../Subscriber';\nimport { ObservableInput, SchedulerLike } from '../types';\nimport { executeSchedule } from '../util/executeSchedule';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\n/**\n * A process embodying the general \"merge\" strategy. This is used in\n * `mergeMap` and `mergeScan` because the logic is otherwise nearly identical.\n * @param source The original source observable\n * @param subscriber The consumer subscriber\n * @param project The projection function to get our inner sources\n * @param concurrent The number of concurrent inner subscriptions\n * @param onBeforeNext Additional logic to apply before nexting to our consumer\n * @param expand If `true` this will perform an \"expand\" strategy, which differs only\n * in that it recurses, and the inner subscription must be schedule-able.\n * @param innerSubScheduler A scheduler to use to schedule inner subscriptions,\n * this is to support the expand strategy, mostly, and should be deprecated\n */\nexport function mergeInternals<T, R>(\n  source: Observable<T>,\n  subscriber: Subscriber<R>,\n  project: (value: T, index: number) => ObservableInput<R>,\n  concurrent: number,\n  onBeforeNext?: (innerValue: R) => void,\n  expand?: boolean,\n  innerSubScheduler?: SchedulerLike,\n  additionalFinalizer?: () => void\n) {\n  // Buffered values, in the event of going over our concurrency limit\n  const buffer: T[] = [];\n  // The number of active inner subscriptions.\n  let active = 0;\n  // An index to pass to our accumulator function\n  let index = 0;\n  // Whether or not the outer source has completed.\n  let isComplete = false;\n\n  /**\n   * Checks to see if we can complete our result or not.\n   */\n  const checkComplete = () => {\n    // If the outer has completed, and nothing is left in the buffer,\n    // and we don't have any active inner subscriptions, then we can\n    // Emit the state and complete.\n    if (isComplete && !buffer.length && !active) {\n      subscriber.complete();\n    }\n  };\n\n  // If we're under our concurrency limit, just start the inner subscription, otherwise buffer and wait.\n  const outerNext = (value: T) => (active < concurrent ? doInnerSub(value) : buffer.push(value));\n\n  const doInnerSub = (value: T) => {\n    // If we're expanding, we need to emit the outer values and the inner values\n    // as the inners will \"become outers\" in a way as they are recursively fed\n    // back to the projection mechanism.\n    expand && subscriber.next(value as any);\n\n    // Increment the number of active subscriptions so we can track it\n    // against our concurrency limit later.\n    active++;\n\n    // A flag used to show that the inner observable completed.\n    // This is checked during finalization to see if we should\n    // move to the next item in the buffer, if there is on.\n    let innerComplete = false;\n\n    // Start our inner subscription.\n    innerFrom(project(value, index++)).subscribe(\n      createOperatorSubscriber(\n        subscriber,\n        (innerValue) => {\n          // `mergeScan` has additional handling here. For example\n          // taking the inner value and updating state.\n          onBeforeNext?.(innerValue);\n\n          if (expand) {\n            // If we're expanding, then just recurse back to our outer\n            // handler. It will emit the value first thing.\n            outerNext(innerValue as any);\n          } else {\n            // Otherwise, emit the inner value.\n            subscriber.next(innerValue);\n          }\n        },\n        () => {\n          // Flag that we have completed, so we know to check the buffer\n          // during finalization.\n          innerComplete = true;\n        },\n        // Errors are passed to the destination.\n        undefined,\n        () => {\n          // During finalization, if the inner completed (it wasn't errored or\n          // cancelled), then we want to try the next item in the buffer if\n          // there is one.\n          if (innerComplete) {\n            // We have to wrap this in a try/catch because it happens during\n            // finalization, possibly asynchronously, and we want to pass\n            // any errors that happen (like in a projection function) to\n            // the outer Subscriber.\n            try {\n              // INNER SOURCE COMPLETE\n              // Decrement the active count to ensure that the next time\n              // we try to call `doInnerSub`, the number is accurate.\n              active--;\n              // If we have more values in the buffer, try to process those\n              // Note that this call will increment `active` ahead of the\n              // next conditional, if there were any more inner subscriptions\n              // to start.\n              while (buffer.length && active < concurrent) {\n                const bufferedValue = buffer.shift()!;\n                // Particularly for `expand`, we need to check to see if a scheduler was provided\n                // for when we want to start our inner subscription. Otherwise, we just start\n                // are next inner subscription.\n                if (innerSubScheduler) {\n                  executeSchedule(subscriber, innerSubScheduler, () => doInnerSub(bufferedValue));\n                } else {\n                  doInnerSub(bufferedValue);\n                }\n              }\n              // Check to see if we can complete, and complete if so.\n              checkComplete();\n            } catch (err) {\n              subscriber.error(err);\n            }\n          }\n        }\n      )\n    );\n  };\n\n  // Subscribe to our source observable.\n  source.subscribe(\n    createOperatorSubscriber(subscriber, outerNext, () => {\n      // Outer completed, make a note of it, and check to see if we can complete everything.\n      isComplete = true;\n      checkComplete();\n    })\n  );\n\n  // Additional finalization (for when the destination is torn down).\n  // Other finalization is added implicitly via subscription above.\n  return () => {\n    additionalFinalizer?.();\n  };\n}\n", "import { ObservableInput, OperatorFunction, ObservedValueOf } from '../types';\nimport { map } from './map';\nimport { innerFrom } from '../observable/innerFrom';\nimport { operate } from '../util/lift';\nimport { mergeInternals } from './mergeInternals';\nimport { isFunction } from '../util/isFunction';\n\n/* tslint:disable:max-line-length */\nexport function mergeMap<T, O extends ObservableInput<any>>(\n  project: (value: T, index: number) => O,\n  concurrent?: number\n): OperatorFunction<T, ObservedValueOf<O>>;\n/** @deprecated The `resultSelector` parameter will be removed in v8. Use an inner `map` instead. Details: https://rxjs.dev/deprecations/resultSelector */\nexport function mergeMap<T, O extends ObservableInput<any>>(\n  project: (value: T, index: number) => O,\n  resultSelector: undefined,\n  concurrent?: number\n): OperatorFunction<T, ObservedValueOf<O>>;\n/** @deprecated The `resultSelector` parameter will be removed in v8. Use an inner `map` instead. Details: https://rxjs.dev/deprecations/resultSelector */\nexport function mergeMap<T, R, O extends ObservableInput<any>>(\n  project: (value: T, index: number) => O,\n  resultSelector: (outerValue: T, innerValue: ObservedValueOf<O>, outerIndex: number, innerIndex: number) => R,\n  concurrent?: number\n): OperatorFunction<T, R>;\n/* tslint:enable:max-line-length */\n\n/**\n * Projects each source value to an Observable which is merged in the output\n * Observable.\n *\n * <span class=\"informal\">Maps each value to an Observable, then flattens all of\n * these inner Observables using {@link mergeAll}.</span>\n *\n * ![](mergeMap.png)\n *\n * Returns an Observable that emits items based on applying a function that you\n * supply to each item emitted by the source Observable, where that function\n * returns an Observable, and then merging those resulting Observables and\n * emitting the results of this merger.\n *\n * ## Example\n *\n * Map and flatten each letter to an Observable ticking every 1 second\n *\n * ```ts\n * import { of, mergeMap, interval, map } from 'rxjs';\n *\n * const letters = of('a', 'b', 'c');\n * const result = letters.pipe(\n *   mergeMap(x => interval(1000).pipe(map(i => x + i)))\n * );\n *\n * result.subscribe(x => console.log(x));\n *\n * // Results in the following:\n * // a0\n * // b0\n * // c0\n * // a1\n * // b1\n * // c1\n * // continues to list a, b, c every second with respective ascending integers\n * ```\n *\n * @see {@link concatMap}\n * @see {@link exhaustMap}\n * @see {@link merge}\n * @see {@link mergeAll}\n * @see {@link mergeMapTo}\n * @see {@link mergeScan}\n * @see {@link switchMap}\n *\n * @param {function(value: T, ?index: number): ObservableInput} project A function\n * that, when applied to an item emitted by the source Observable, returns an\n * Observable.\n * @param {number} [concurrent=Infinity] Maximum number of input\n * Observables being subscribed to concurrently.\n * @return A function that returns an Observable that emits the result of\n * applying the projection function (and the optional deprecated\n * `resultSelector`) to each item emitted by the source Observable and merging\n * the results of the Observables obtained from this transformation.\n */\nexport function mergeMap<T, R, O extends ObservableInput<any>>(\n  project: (value: T, index: number) => O,\n  resultSelector?: ((outerValue: T, innerValue: ObservedValueOf<O>, outerIndex: number, innerIndex: number) => R) | number,\n  concurrent: number = Infinity\n): OperatorFunction<T, ObservedValueOf<O> | R> {\n  if (isFunction(resultSelector)) {\n    // DEPRECATED PATH\n    return mergeMap((a, i) => map((b: any, ii: number) => resultSelector(a, b, i, ii))(innerFrom(project(a, i))), concurrent);\n  } else if (typeof resultSelector === 'number') {\n    concurrent = resultSelector;\n  }\n\n  return operate((source, subscriber) => mergeInternals(source, subscriber, project, concurrent));\n}\n", "import { mergeMap } from './mergeMap';\nimport { identity } from '../util/identity';\nimport { OperatorFunction, ObservableInput, ObservedValueOf } from '../types';\n\n/**\n * Converts a higher-order Observable into a first-order Observable which\n * concurrently delivers all values that are emitted on the inner Observables.\n *\n * <span class=\"informal\">Flattens an Observable-of-Observables.</span>\n *\n * ![](mergeAll.png)\n *\n * `mergeAll` subscribes to an Observable that emits Observables, also known as\n * a higher-order Observable. Each time it observes one of these emitted inner\n * Observables, it subscribes to that and delivers all the values from the\n * inner Observable on the output Observable. The output Observable only\n * completes once all inner Observables have completed. Any error delivered by\n * a inner Observable will be immediately emitted on the output Observable.\n *\n * ## Examples\n *\n * Spawn a new interval Observable for each click event, and blend their outputs as one Observable\n *\n * ```ts\n * import { fromEvent, map, interval, mergeAll } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const higherOrder = clicks.pipe(map(() => interval(1000)));\n * const firstOrder = higherOrder.pipe(mergeAll());\n *\n * firstOrder.subscribe(x => console.log(x));\n * ```\n *\n * Count from 0 to 9 every second for each click, but only allow 2 concurrent timers\n *\n * ```ts\n * import { fromEvent, map, interval, take, mergeAll } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const higherOrder = clicks.pipe(\n *   map(() => interval(1000).pipe(take(10)))\n * );\n * const firstOrder = higherOrder.pipe(mergeAll(2));\n *\n * firstOrder.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link combineLatestAll}\n * @see {@link concatAll}\n * @see {@link exhaustAll}\n * @see {@link merge}\n * @see {@link mergeMap}\n * @see {@link mergeMapTo}\n * @see {@link mergeScan}\n * @see {@link switchAll}\n * @see {@link switchMap}\n * @see {@link zipAll}\n *\n * @param {number} [concurrent=Infinity] Maximum number of inner\n * Observables being subscribed to concurrently.\n * @return A function that returns an Observable that emits values coming from\n * all the inner Observables emitted by the source Observable.\n */\nexport function mergeAll<O extends ObservableInput<any>>(concurrent: number = Infinity): OperatorFunction<O, ObservedValueOf<O>> {\n  return mergeMap(identity, concurrent);\n}\n", "import { mergeAll } from './mergeAll';\nimport { OperatorFunction, ObservableInput, ObservedValueOf } from '../types';\n\n/**\n * Converts a higher-order Observable into a first-order Observable by\n * concatenating the inner Observables in order.\n *\n * <span class=\"informal\">Flattens an Observable-of-Observables by putting one\n * inner Observable after the other.</span>\n *\n * ![](concatAll.svg)\n *\n * Joins every Observable emitted by the source (a higher-order Observable), in\n * a serial fashion. It subscribes to each inner Observable only after the\n * previous inner Observable has completed, and merges all of their values into\n * the returned observable.\n *\n * __Warning:__ If the source Observable emits Observables quickly and\n * endlessly, and the inner Observables it emits generally complete slower than\n * the source emits, you can run into memory issues as the incoming Observables\n * collect in an unbounded buffer.\n *\n * Note: `concatAll` is equivalent to `mergeAll` with concurrency parameter set\n * to `1`.\n *\n * ## Example\n *\n * For each click event, tick every second from 0 to 3, with no concurrency\n *\n * ```ts\n * import { fromEvent, map, interval, take, concatAll } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const higherOrder = clicks.pipe(\n *   map(() => interval(1000).pipe(take(4)))\n * );\n * const firstOrder = higherOrder.pipe(concatAll());\n * firstOrder.subscribe(x => console.log(x));\n *\n * // Results in the following:\n * // (results are not concurrent)\n * // For every click on the \"document\" it will emit values 0 to 3 spaced\n * // on a 1000ms interval\n * // one click = 1000ms-> 0 -1000ms-> 1 -1000ms-> 2 -1000ms-> 3\n * ```\n *\n * @see {@link combineLatestAll}\n * @see {@link concat}\n * @see {@link concatMap}\n * @see {@link concatMapTo}\n * @see {@link exhaustAll}\n * @see {@link mergeAll}\n * @see {@link switchAll}\n * @see {@link switchMap}\n * @see {@link zipAll}\n *\n * @return A function that returns an Observable emitting values from all the\n * inner Observables concatenated.\n */\nexport function concatAll<O extends ObservableInput<any>>(): OperatorFunction<O, ObservedValueOf<O>> {\n  return mergeAll(1);\n}\n", "import { Observable } from '../Observable';\nimport { ObservableInputTuple, SchedulerLike } from '../types';\nimport { concatAll } from '../operators/concatAll';\nimport { popScheduler } from '../util/args';\nimport { from } from './from';\n\nexport function concat<T extends readonly unknown[]>(...inputs: [...ObservableInputTuple<T>]): Observable<T[number]>;\nexport function concat<T extends readonly unknown[]>(\n  ...inputsAndScheduler: [...ObservableInputTuple<T>, SchedulerLike]\n): Observable<T[number]>;\n\n/**\n * Creates an output Observable which sequentially emits all values from the first given\n * Observable and then moves on to the next.\n *\n * <span class=\"informal\">Concatenates multiple Observables together by\n * sequentially emitting their values, one Observable after the other.</span>\n *\n * ![](concat.png)\n *\n * `concat` joins multiple Observables together, by subscribing to them one at a time and\n * merging their results into the output Observable. You can pass either an array of\n * Observables, or put them directly as arguments. Passing an empty array will result\n * in Observable that completes immediately.\n *\n * `concat` will subscribe to first input Observable and emit all its values, without\n * changing or affecting them in any way. When that Observable completes, it will\n * subscribe to then next Observable passed and, again, emit its values. This will be\n * repeated, until the operator runs out of Observables. When last input Observable completes,\n * `concat` will complete as well. At any given moment only one Observable passed to operator\n * emits values. If you would like to emit values from passed Observables concurrently, check out\n * {@link merge} instead, especially with optional `concurrent` parameter. As a matter of fact,\n * `concat` is an equivalent of `merge` operator with `concurrent` parameter set to `1`.\n *\n * Note that if some input Observable never completes, `concat` will also never complete\n * and Observables following the one that did not complete will never be subscribed. On the other\n * hand, if some Observable simply completes immediately after it is subscribed, it will be\n * invisible for `concat`, which will just move on to the next Observable.\n *\n * If any Observable in chain errors, instead of passing control to the next Observable,\n * `concat` will error immediately as well. Observables that would be subscribed after\n * the one that emitted error, never will.\n *\n * If you pass to `concat` the same Observable many times, its stream of values\n * will be \"replayed\" on every subscription, which means you can repeat given Observable\n * as many times as you like. If passing the same Observable to `concat` 1000 times becomes tedious,\n * you can always use {@link repeat}.\n *\n * ## Examples\n *\n * Concatenate a timer counting from 0 to 3 with a synchronous sequence from 1 to 10\n *\n * ```ts\n * import { interval, take, range, concat } from 'rxjs';\n *\n * const timer = interval(1000).pipe(take(4));\n * const sequence = range(1, 10);\n * const result = concat(timer, sequence);\n * result.subscribe(x => console.log(x));\n *\n * // results in:\n * // 0 -1000ms-> 1 -1000ms-> 2 -1000ms-> 3 -immediate-> 1 ... 10\n * ```\n *\n * Concatenate 3 Observables\n *\n * ```ts\n * import { interval, take, concat } from 'rxjs';\n *\n * const timer1 = interval(1000).pipe(take(10));\n * const timer2 = interval(2000).pipe(take(6));\n * const timer3 = interval(500).pipe(take(10));\n *\n * const result = concat(timer1, timer2, timer3);\n * result.subscribe(x => console.log(x));\n *\n * // results in the following:\n * // (Prints to console sequentially)\n * // -1000ms-> 0 -1000ms-> 1 -1000ms-> ... 9\n * // -2000ms-> 0 -2000ms-> 1 -2000ms-> ... 5\n * // -500ms-> 0 -500ms-> 1 -500ms-> ... 9\n * ```\n *\n * Concatenate the same Observable to repeat it\n *\n * ```ts\n * import { interval, take, concat } from 'rxjs';\n *\n * const timer = interval(1000).pipe(take(2));\n *\n * concat(timer, timer) // concatenating the same Observable!\n *   .subscribe({\n *     next: value => console.log(value),\n *     complete: () => console.log('...and it is done!')\n *   });\n *\n * // Logs:\n * // 0 after 1s\n * // 1 after 2s\n * // 0 after 3s\n * // 1 after 4s\n * // '...and it is done!' also after 4s\n * ```\n *\n * @see {@link concatAll}\n * @see {@link concatMap}\n * @see {@link concatMapTo}\n * @see {@link startWith}\n * @see {@link endWith}\n *\n * @param args Input Observables to concatenate.\n */\nexport function concat(...args: any[]): Observable<unknown> {\n  return concatAll()(from(args, popScheduler(args)));\n}\n", "import { Observable } from '../Observable';\nimport { ObservedValueOf, ObservableInput } from '../types';\nimport { innerFrom } from './innerFrom';\n\n/**\n * Creates an Observable that, on subscribe, calls an Observable factory to\n * make an Observable for each new Observer.\n *\n * <span class=\"informal\">Creates the Observable lazily, that is, only when it\n * is subscribed.\n * </span>\n *\n * ![](defer.png)\n *\n * `defer` allows you to create an Observable only when the Observer\n * subscribes. It waits until an Observer subscribes to it, calls the given\n * factory function to get an Observable -- where a factory function typically\n * generates a new Observable -- and subscribes the Observer to this Observable.\n * In case the factory function returns a falsy value, then EMPTY is used as\n * Observable instead. Last but not least, an exception during the factory\n * function call is transferred to the Observer by calling `error`.\n *\n * ## Example\n *\n * Subscribe to either an Observable of clicks or an Observable of interval, at random\n *\n * ```ts\n * import { defer, fromEvent, interval } from 'rxjs';\n *\n * const clicksOrInterval = defer(() => {\n *   return Math.random() > 0.5\n *     ? fromEvent(document, 'click')\n *     : interval(1000);\n * });\n * clicksOrInterval.subscribe(x => console.log(x));\n *\n * // Results in the following behavior:\n * // If the result of Math.random() is greater than 0.5 it will listen\n * // for clicks anywhere on the \"document\"; when document is clicked it\n * // will log a MouseEvent object to the console. If the result is less\n * // than 0.5 it will emit ascending numbers, one every second(1000ms).\n * ```\n *\n * @see {@link Observable}\n *\n * @param {function(): ObservableInput} observableFactory The Observable\n * factory function to invoke for each Observer that subscribes to the output\n * Observable. May also return a Promise, which will be converted on the fly\n * to an Observable.\n * @return {Observable} An Observable whose Observers' subscriptions trigger\n * an invocation of the given Observable factory function.\n */\nexport function defer<R extends ObservableInput<any>>(observableFactory: () => R): Observable<ObservedValueOf<R>> {\n  return new Observable<ObservedValueOf<R>>((subscriber) => {\n    innerFrom(observableFactory()).subscribe(subscriber);\n  });\n}\n", "import { innerFrom } from '../observable/innerFrom';\nimport { Observable } from '../Observable';\nimport { mergeMap } from '../operators/mergeMap';\nimport { isArrayLike } from '../util/isArrayLike';\nimport { isFunction } from '../util/isFunction';\nimport { mapOneOrManyArgs } from '../util/mapOneOrManyArgs';\n\n// These constants are used to create handler registry functions using array mapping below.\nconst nodeEventEmitterMethods = ['addListener', 'removeListener'] as const;\nconst eventTargetMethods = ['addEventListener', 'removeEventListener'] as const;\nconst jqueryMethods = ['on', 'off'] as const;\n\nexport interface NodeStyleEventEmitter {\n  addListener(eventName: string | symbol, handler: NodeEventHandler): this;\n  removeListener(eventName: string | symbol, handler: NodeEventHandler): this;\n}\n\nexport type NodeEventHandler = (...args: any[]) => void;\n\n// For APIs that implement `addListener` and `removeListener` methods that may\n// not use the same arguments or return EventEmitter values\n// such as React Native\nexport interface NodeCompatibleEventEmitter {\n  addListener(eventName: string, handler: NodeEventHandler): void | {};\n  removeListener(eventName: string, handler: NodeEventHandler): void | {};\n}\n\n// Use handler types like those in @types/jquery. See:\n// https://github.com/DefinitelyTyped/DefinitelyTyped/blob/847731ba1d7fa6db6b911c0e43aa0afe596e7723/types/jquery/misc.d.ts#L6395\nexport interface JQueryStyleEventEmitter<TContext, T> {\n  on(eventName: string, handler: (this: TContext, t: T, ...args: any[]) => any): void;\n  off(eventName: string, handler: (this: TContext, t: T, ...args: any[]) => any): void;\n}\n\nexport interface EventListenerObject<E> {\n  handleEvent(evt: E): void;\n}\n\nexport interface HasEventTargetAddRemove<E> {\n  addEventListener(\n    type: string,\n    listener: ((evt: E) => void) | EventListenerObject<E> | null,\n    options?: boolean | AddEventListenerOptions\n  ): void;\n  removeEventListener(\n    type: string,\n    listener: ((evt: E) => void) | EventListenerObject<E> | null,\n    options?: EventListenerOptions | boolean\n  ): void;\n}\n\nexport interface EventListenerOptions {\n  capture?: boolean;\n  passive?: boolean;\n  once?: boolean;\n}\n\nexport interface AddEventListenerOptions extends EventListenerOptions {\n  once?: boolean;\n  passive?: boolean;\n}\n\nexport function fromEvent<T>(target: HasEventTargetAddRemove<T> | ArrayLike<HasEventTargetAddRemove<T>>, eventName: string): Observable<T>;\nexport function fromEvent<T, R>(\n  target: HasEventTargetAddRemove<T> | ArrayLike<HasEventTargetAddRemove<T>>,\n  eventName: string,\n  resultSelector: (event: T) => R\n): Observable<R>;\nexport function fromEvent<T>(\n  target: HasEventTargetAddRemove<T> | ArrayLike<HasEventTargetAddRemove<T>>,\n  eventName: string,\n  options: EventListenerOptions\n): Observable<T>;\nexport function fromEvent<T, R>(\n  target: HasEventTargetAddRemove<T> | ArrayLike<HasEventTargetAddRemove<T>>,\n  eventName: string,\n  options: EventListenerOptions,\n  resultSelector: (event: T) => R\n): Observable<R>;\n\nexport function fromEvent(target: NodeStyleEventEmitter | ArrayLike<NodeStyleEventEmitter>, eventName: string): Observable<unknown>;\n/** @deprecated Do not specify explicit type parameters. Signatures with type parameters that cannot be inferred will be removed in v8. */\nexport function fromEvent<T>(target: NodeStyleEventEmitter | ArrayLike<NodeStyleEventEmitter>, eventName: string): Observable<T>;\nexport function fromEvent<R>(\n  target: NodeStyleEventEmitter | ArrayLike<NodeStyleEventEmitter>,\n  eventName: string,\n  resultSelector: (...args: any[]) => R\n): Observable<R>;\n\nexport function fromEvent(\n  target: NodeCompatibleEventEmitter | ArrayLike<NodeCompatibleEventEmitter>,\n  eventName: string\n): Observable<unknown>;\n/** @deprecated Do not specify explicit type parameters. Signatures with type parameters that cannot be inferred will be removed in v8. */\nexport function fromEvent<T>(target: NodeCompatibleEventEmitter | ArrayLike<NodeCompatibleEventEmitter>, eventName: string): Observable<T>;\nexport function fromEvent<R>(\n  target: NodeCompatibleEventEmitter | ArrayLike<NodeCompatibleEventEmitter>,\n  eventName: string,\n  resultSelector: (...args: any[]) => R\n): Observable<R>;\n\nexport function fromEvent<T>(\n  target: JQueryStyleEventEmitter<any, T> | ArrayLike<JQueryStyleEventEmitter<any, T>>,\n  eventName: string\n): Observable<T>;\nexport function fromEvent<T, R>(\n  target: JQueryStyleEventEmitter<any, T> | ArrayLike<JQueryStyleEventEmitter<any, T>>,\n  eventName: string,\n  resultSelector: (value: T, ...args: any[]) => R\n): Observable<R>;\n\n/**\n * Creates an Observable that emits events of a specific type coming from the\n * given event target.\n *\n * <span class=\"informal\">Creates an Observable from DOM events, or Node.js\n * EventEmitter events or others.</span>\n *\n * ![](fromEvent.png)\n *\n * `fromEvent` accepts as a first argument event target, which is an object with methods\n * for registering event handler functions. As a second argument it takes string that indicates\n * type of event we want to listen for. `fromEvent` supports selected types of event targets,\n * which are described in detail below. If your event target does not match any of the ones listed,\n * you should use {@link fromEventPattern}, which can be used on arbitrary APIs.\n * When it comes to APIs supported by `fromEvent`, their methods for adding and removing event\n * handler functions have different names, but they all accept a string describing event type\n * and function itself, which will be called whenever said event happens.\n *\n * Every time resulting Observable is subscribed, event handler function will be registered\n * to event target on given event type. When that event fires, value\n * passed as a first argument to registered function will be emitted by output Observable.\n * When Observable is unsubscribed, function will be unregistered from event target.\n *\n * Note that if event target calls registered function with more than one argument, second\n * and following arguments will not appear in resulting stream. In order to get access to them,\n * you can pass to `fromEvent` optional project function, which will be called with all arguments\n * passed to event handler. Output Observable will then emit value returned by project function,\n * instead of the usual value.\n *\n * Remember that event targets listed below are checked via duck typing. It means that\n * no matter what kind of object you have and no matter what environment you work in,\n * you can safely use `fromEvent` on that object if it exposes described methods (provided\n * of course they behave as was described above). So for example if Node.js library exposes\n * event target which has the same method names as DOM EventTarget, `fromEvent` is still\n * a good choice.\n *\n * If the API you use is more callback then event handler oriented (subscribed\n * callback function fires only once and thus there is no need to manually\n * unregister it), you should use {@link bindCallback} or {@link bindNodeCallback}\n * instead.\n *\n * `fromEvent` supports following types of event targets:\n *\n * **DOM EventTarget**\n *\n * This is an object with `addEventListener` and `removeEventListener` methods.\n *\n * In the browser, `addEventListener` accepts - apart from event type string and event\n * handler function arguments - optional third parameter, which is either an object or boolean,\n * both used for additional configuration how and when passed function will be called. When\n * `fromEvent` is used with event target of that type, you can provide this values\n * as third parameter as well.\n *\n * **Node.js EventEmitter**\n *\n * An object with `addListener` and `removeListener` methods.\n *\n * **JQuery-style event target**\n *\n * An object with `on` and `off` methods\n *\n * **DOM NodeList**\n *\n * List of DOM Nodes, returned for example by `document.querySelectorAll` or `Node.childNodes`.\n *\n * Although this collection is not event target in itself, `fromEvent` will iterate over all Nodes\n * it contains and install event handler function in every of them. When returned Observable\n * is unsubscribed, function will be removed from all Nodes.\n *\n * **DOM HtmlCollection**\n *\n * Just as in case of NodeList it is a collection of DOM nodes. Here as well event handler function is\n * installed and removed in each of elements.\n *\n *\n * ## Examples\n *\n * Emit clicks happening on the DOM document\n *\n * ```ts\n * import { fromEvent } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * clicks.subscribe(x => console.log(x));\n *\n * // Results in:\n * // MouseEvent object logged to console every time a click\n * // occurs on the document.\n * ```\n *\n * Use `addEventListener` with capture option\n *\n * ```ts\n * import { fromEvent } from 'rxjs';\n *\n * const div = document.createElement('div');\n * div.style.cssText = 'width: 200px; height: 200px; background: #09c;';\n * document.body.appendChild(div);\n *\n * // note optional configuration parameter which will be passed to addEventListener\n * const clicksInDocument = fromEvent(document, 'click', { capture: true });\n * const clicksInDiv = fromEvent(div, 'click');\n *\n * clicksInDocument.subscribe(() => console.log('document'));\n * clicksInDiv.subscribe(() => console.log('div'));\n *\n * // By default events bubble UP in DOM tree, so normally\n * // when we would click on div in document\n * // \"div\" would be logged first and then \"document\".\n * // Since we specified optional `capture` option, document\n * // will catch event when it goes DOWN DOM tree, so console\n * // will log \"document\" and then \"div\".\n * ```\n *\n * @see {@link bindCallback}\n * @see {@link bindNodeCallback}\n * @see {@link fromEventPattern}\n *\n * @param {FromEventTarget<T>} target The DOM EventTarget, Node.js\n * EventEmitter, JQuery-like event target, NodeList or HTMLCollection to attach the event handler to.\n * @param {string} eventName The event name of interest, being emitted by the\n * `target`.\n * @param {EventListenerOptions} [options] Options to pass through to addEventListener\n * @return {Observable<T>}\n */\nexport function fromEvent<T>(\n  target: any,\n  eventName: string,\n  options?: EventListenerOptions | ((...args: any[]) => T),\n  resultSelector?: (...args: any[]) => T\n): Observable<T> {\n  if (isFunction(options)) {\n    resultSelector = options;\n    options = undefined;\n  }\n  if (resultSelector) {\n    return fromEvent<T>(target, eventName, options as EventListenerOptions).pipe(mapOneOrManyArgs(resultSelector));\n  }\n\n  // Figure out our add and remove methods. In order to do this,\n  // we are going to analyze the target in a preferred order, if\n  // the target matches a given signature, we take the two \"add\" and \"remove\"\n  // method names and apply them to a map to create opposite versions of the\n  // same function. This is because they all operate in duplicate pairs,\n  // `addListener(name, handler)`, `removeListener(name, handler)`, for example.\n  // The call only differs by method name, as to whether or not you're adding or removing.\n  const [add, remove] =\n    // If it is an EventTarget, we need to use a slightly different method than the other two patterns.\n    isEventTarget(target)\n      ? eventTargetMethods.map((methodName) => (handler: any) => target[methodName](eventName, handler, options as EventListenerOptions))\n      : // In all other cases, the call pattern is identical with the exception of the method names.\n      isNodeStyleEventEmitter(target)\n      ? nodeEventEmitterMethods.map(toCommonHandlerRegistry(target, eventName))\n      : isJQueryStyleEventEmitter(target)\n      ? jqueryMethods.map(toCommonHandlerRegistry(target, eventName))\n      : [];\n\n  // If add is falsy, it's because we didn't match a pattern above.\n  // Check to see if it is an ArrayLike, because if it is, we want to\n  // try to apply fromEvent to all of it's items. We do this check last,\n  // because there are may be some types that are both ArrayLike *and* implement\n  // event registry points, and we'd rather delegate to that when possible.\n  if (!add) {\n    if (isArrayLike(target)) {\n      return mergeMap((subTarget: any) => fromEvent(subTarget, eventName, options as EventListenerOptions))(\n        innerFrom(target)\n      ) as Observable<T>;\n    }\n  }\n\n  // If add is falsy and we made it here, it's because we didn't\n  // match any valid target objects above.\n  if (!add) {\n    throw new TypeError('Invalid event target');\n  }\n\n  return new Observable<T>((subscriber) => {\n    // The handler we are going to register. Forwards the event object, by itself, or\n    // an array of arguments to the event handler, if there is more than one argument,\n    // to the consumer.\n    const handler = (...args: any[]) => subscriber.next(1 < args.length ? args : args[0]);\n    // Do the work of adding the handler to the target.\n    add(handler);\n    // When we finalize, we want to remove the handler and free up memory.\n    return () => remove!(handler);\n  });\n}\n\n/**\n * Used to create `add` and `remove` functions to register and unregister event handlers\n * from a target in the most common handler pattern, where there are only two arguments.\n * (e.g.  `on(name, fn)`, `off(name, fn)`, `addListener(name, fn)`, or `removeListener(name, fn)`)\n * @param target The target we're calling methods on\n * @param eventName The event name for the event we're creating register or unregister functions for\n */\nfunction toCommonHandlerRegistry(target: any, eventName: string) {\n  return (methodName: string) => (handler: any) => target[methodName](eventName, handler);\n}\n\n/**\n * Checks to see if the target implements the required node-style EventEmitter methods\n * for adding and removing event handlers.\n * @param target the object to check\n */\nfunction isNodeStyleEventEmitter(target: any): target is NodeStyleEventEmitter {\n  return isFunction(target.addListener) && isFunction(target.removeListener);\n}\n\n/**\n * Checks to see if the target implements the required jQuery-style EventEmitter methods\n * for adding and removing event handlers.\n * @param target the object to check\n */\nfunction isJQueryStyleEventEmitter(target: any): target is JQueryStyleEventEmitter<any, any> {\n  return isFunction(target.on) && isFunction(target.off);\n}\n\n/**\n * Checks to see if the target implements the required EventTarget methods\n * for adding and removing event handlers.\n * @param target the object to check\n */\nfunction isEventTarget(target: any): target is HasEventTargetAddRemove<any> {\n  return isFunction(target.addEventListener) && isFunction(target.removeEventListener);\n}\n", "import { Observable } from '../Observable';\nimport { isFunction } from '../util/isFunction';\nimport { NodeEventHandler } from './fromEvent';\nimport { mapOneOrManyArgs } from '../util/mapOneOrManyArgs';\n\n/* tslint:disable:max-line-length */\nexport function fromEventPattern<T>(\n  addHandler: (handler: NodeEventHandler) => any,\n  removeHandler?: (handler: NodeEventHandler, signal?: any) => void\n): Observable<T>;\nexport function fromEventPattern<T>(\n  addHandler: (handler: NodeEventHandler) => any,\n  removeHandler?: (handler: NodeEventHandler, signal?: any) => void,\n  resultSelector?: (...args: any[]) => T\n): Observable<T>;\n/* tslint:enable:max-line-length */\n\n/**\n * Creates an Observable from an arbitrary API for registering event handlers.\n *\n * <span class=\"informal\">When that method for adding event handler was something {@link fromEvent}\n * was not prepared for.</span>\n *\n * ![](fromEventPattern.png)\n *\n * `fromEventPattern` allows you to convert into an Observable any API that supports registering handler functions\n * for events. It is similar to {@link fromEvent}, but far\n * more flexible. In fact, all use cases of {@link fromEvent} could be easily handled by\n * `fromEventPattern` (although in slightly more verbose way).\n *\n * This operator accepts as a first argument an `addHandler` function, which will be injected with\n * handler parameter. That handler is actually an event handler function that you now can pass\n * to API expecting it. `addHandler` will be called whenever Observable\n * returned by the operator is subscribed, so registering handler in API will not\n * necessarily happen when `fromEventPattern` is called.\n *\n * After registration, every time an event that we listen to happens,\n * Observable returned by `fromEventPattern` will emit value that event handler\n * function was called with. Note that if event handler was called with more\n * than one argument, second and following arguments will not appear in the Observable.\n *\n * If API you are using allows to unregister event handlers as well, you can pass to `fromEventPattern`\n * another function - `removeHandler` - as a second parameter. It will be injected\n * with the same handler function as before, which now you can use to unregister\n * it from the API. `removeHandler` will be called when consumer of resulting Observable\n * unsubscribes from it.\n *\n * In some APIs unregistering is actually handled differently. Method registering an event handler\n * returns some kind of token, which is later used to identify which function should\n * be unregistered or it itself has method that unregisters event handler.\n * If that is the case with your API, make sure token returned\n * by registering method is returned by `addHandler`. Then it will be passed\n * as a second argument to `removeHandler`, where you will be able to use it.\n *\n * If you need access to all event handler parameters (not only the first one),\n * or you need to transform them in any way, you can call `fromEventPattern` with optional\n * third parameter - project function which will accept all arguments passed to\n * event handler when it is called. Whatever is returned from project function will appear on\n * resulting stream instead of usual event handlers first argument. This means\n * that default project can be thought of as function that takes its first parameter\n * and ignores the rest.\n *\n * ## Examples\n *\n * Emits clicks happening on the DOM document\n *\n * ```ts\n * import { fromEventPattern } from 'rxjs';\n *\n * function addClickHandler(handler) {\n *   document.addEventListener('click', handler);\n * }\n *\n * function removeClickHandler(handler) {\n *   document.removeEventListener('click', handler);\n * }\n *\n * const clicks = fromEventPattern(\n *   addClickHandler,\n *   removeClickHandler\n * );\n * clicks.subscribe(x => console.log(x));\n *\n * // Whenever you click anywhere in the browser, DOM MouseEvent\n * // object will be logged.\n * ```\n *\n * Use with API that returns cancellation token\n *\n * ```ts\n * import { fromEventPattern } from 'rxjs';\n *\n * const token = someAPI.registerEventHandler(function() {});\n * someAPI.unregisterEventHandler(token); // this APIs cancellation method accepts\n *                                        // not handler itself, but special token.\n *\n * const someAPIObservable = fromEventPattern(\n *   function(handler) { return someAPI.registerEventHandler(handler); }, // Note that we return the token here...\n *   function(handler, token) { someAPI.unregisterEventHandler(token); }  // ...to then use it here.\n * );\n * ```\n *\n * Use with project function\n *\n * ```ts\n * import { fromEventPattern } from 'rxjs';\n *\n * someAPI.registerEventHandler((eventType, eventMessage) => {\n *   console.log(eventType, eventMessage); // Logs 'EVENT_TYPE' 'EVENT_MESSAGE' to console.\n * });\n *\n * const someAPIObservable = fromEventPattern(\n *   handler => someAPI.registerEventHandler(handler),\n *   handler => someAPI.unregisterEventHandler(handler)\n *   (eventType, eventMessage) => eventType + ' --- ' + eventMessage // without that function only 'EVENT_TYPE'\n * );                                                                // would be emitted by the Observable\n *\n * someAPIObservable.subscribe(value => console.log(value));\n *\n * // Logs:\n * // 'EVENT_TYPE --- EVENT_MESSAGE'\n * ```\n *\n * @see {@link fromEvent}\n * @see {@link bindCallback}\n * @see {@link bindNodeCallback}\n *\n * @param {function(handler: Function): any} addHandler A function that takes\n * a `handler` function as argument and attaches it somehow to the actual\n * source of events.\n * @param {function(handler: Function, token?: any): void} [removeHandler] A function that\n * takes a `handler` function as an argument and removes it from the event source. If `addHandler`\n * returns some kind of token, `removeHandler` function will have it as a second parameter.\n * @param {function(...args: any): T} [project] A function to\n * transform results. It takes the arguments from the event handler and\n * should return a single value.\n * @return {Observable<T>} Observable which, when an event happens, emits first parameter\n * passed to registered event handler. Alternatively it emits whatever project function returns\n * at that moment.\n */\nexport function fromEventPattern<T>(\n  addHandler: (handler: NodeEventHandler) => any,\n  removeHandler?: (handler: NodeEventHandler, signal?: any) => void,\n  resultSelector?: (...args: any[]) => T\n): Observable<T | T[]> {\n  if (resultSelector) {\n    return fromEventPattern<T>(addHandler, removeHandler).pipe(mapOneOrManyArgs(resultSelector));\n  }\n\n  return new Observable<T | T[]>((subscriber) => {\n    const handler = (...e: T[]) => subscriber.next(e.length === 1 ? e[0] : e);\n    const retValue = addHandler(handler);\n    return isFunction(removeHandler) ? () => removeHandler(handler, retValue) : undefined;\n  });\n}\n", "import { Observable } from '../Observable';\nimport { SchedulerLike } from '../types';\nimport { async as asyncScheduler } from '../scheduler/async';\nimport { isScheduler } from '../util/isScheduler';\nimport { isValidDate } from '../util/isDate';\n\n/**\n * Creates an observable that will wait for a specified time period, or exact date, before\n * emitting the number 0.\n *\n * <span class=\"informal\">Used to emit a notification after a delay.</span>\n *\n * This observable is useful for creating delays in code, or racing against other values\n * for ad-hoc timeouts.\n *\n * The `delay` is specified by default in milliseconds, however providing a custom scheduler could\n * create a different behavior.\n *\n * ## Examples\n *\n * Wait 3 seconds and start another observable\n *\n * You might want to use `timer` to delay subscription to an\n * observable by a set amount of time. Here we use a timer with\n * {@link concatMapTo} or {@link concatMap} in order to wait\n * a few seconds and start a subscription to a source.\n *\n * ```ts\n * import { of, timer, concatMap } from 'rxjs';\n *\n * // This could be any observable\n * const source = of(1, 2, 3);\n *\n * timer(3000)\n *   .pipe(concatMap(() => source))\n *   .subscribe(console.log);\n * ```\n *\n * Take all values until the start of the next minute\n *\n * Using a `Date` as the trigger for the first emission, you can\n * do things like wait until midnight to fire an event, or in this case,\n * wait until a new minute starts (chosen so the example wouldn't take\n * too long to run) in order to stop watching a stream. Leveraging\n * {@link takeUntil}.\n *\n * ```ts\n * import { interval, takeUntil, timer } from 'rxjs';\n *\n * // Build a Date object that marks the\n * // next minute.\n * const currentDate = new Date();\n * const startOfNextMinute = new Date(\n *   currentDate.getFullYear(),\n *   currentDate.getMonth(),\n *   currentDate.getDate(),\n *   currentDate.getHours(),\n *   currentDate.getMinutes() + 1\n * );\n *\n * // This could be any observable stream\n * const source = interval(1000);\n *\n * const result = source.pipe(\n *   takeUntil(timer(startOfNextMinute))\n * );\n *\n * result.subscribe(console.log);\n * ```\n *\n * ### Known Limitations\n *\n * - The {@link asyncScheduler} uses `setTimeout` which has limitations for how far in the future it can be scheduled.\n *\n * - If a `scheduler` is provided that returns a timestamp other than an epoch from `now()`, and\n * a `Date` object is passed to the `dueTime` argument, the calculation for when the first emission\n * should occur will be incorrect. In this case, it would be best to do your own calculations\n * ahead of time, and pass a `number` in as the `dueTime`.\n *\n * @param due If a `number`, the amount of time in milliseconds to wait before emitting.\n * If a `Date`, the exact time at which to emit.\n * @param scheduler The scheduler to use to schedule the delay. Defaults to {@link asyncScheduler}.\n */\nexport function timer(due: number | Date, scheduler?: SchedulerLike): Observable<0>;\n\n/**\n * Creates an observable that starts an interval after a specified delay, emitting incrementing numbers -- starting at `0` --\n * on each interval after words.\n *\n * The `delay` and `intervalDuration` are specified by default in milliseconds, however providing a custom scheduler could\n * create a different behavior.\n *\n * ## Example\n *\n * ### Start an interval that starts right away\n *\n * Since {@link interval} waits for the passed delay before starting,\n * sometimes that's not ideal. You may want to start an interval immediately.\n * `timer` works well for this. Here we have both side-by-side so you can\n * see them in comparison.\n *\n * Note that this observable will never complete.\n *\n * ```ts\n * import { timer, interval } from 'rxjs';\n *\n * timer(0, 1000).subscribe(n => console.log('timer', n));\n * interval(1000).subscribe(n => console.log('interval', n));\n * ```\n *\n * ### Known Limitations\n *\n * - The {@link asyncScheduler} uses `setTimeout` which has limitations for how far in the future it can be scheduled.\n *\n * - If a `scheduler` is provided that returns a timestamp other than an epoch from `now()`, and\n * a `Date` object is passed to the `dueTime` argument, the calculation for when the first emission\n * should occur will be incorrect. In this case, it would be best to do your own calculations\n * ahead of time, and pass a `number` in as the `startDue`.\n * @param startDue If a `number`, is the time to wait before starting the interval.\n * If a `Date`, is the exact time at which to start the interval.\n * @param intervalDuration The delay between each value emitted in the interval. Passing a\n * negative number here will result in immediate completion after the first value is emitted, as though\n * no `intervalDuration` was passed at all.\n * @param scheduler The scheduler to use to schedule the delay. Defaults to {@link asyncScheduler}.\n */\nexport function timer(startDue: number | Date, intervalDuration: number, scheduler?: SchedulerLike): Observable<number>;\n\n/**\n * @deprecated The signature allowing `undefined` to be passed for `intervalDuration` will be removed in v8. Use the `timer(dueTime, scheduler?)` signature instead.\n */\nexport function timer(dueTime: number | Date, unused: undefined, scheduler?: SchedulerLike): Observable<0>;\n\nexport function timer(\n  dueTime: number | Date = 0,\n  intervalOrScheduler?: number | SchedulerLike,\n  scheduler: SchedulerLike = asyncScheduler\n): Observable<number> {\n  // Since negative intervalDuration is treated as though no\n  // interval was specified at all, we start with a negative number.\n  let intervalDuration = -1;\n\n  if (intervalOrScheduler != null) {\n    // If we have a second argument, and it's a scheduler,\n    // override the scheduler we had defaulted. Otherwise,\n    // it must be an interval.\n    if (isScheduler(intervalOrScheduler)) {\n      scheduler = intervalOrScheduler;\n    } else {\n      // Note that this *could* be negative, in which case\n      // it's like not passing an intervalDuration at all.\n      intervalDuration = intervalOrScheduler;\n    }\n  }\n\n  return new Observable((subscriber) => {\n    // If a valid date is passed, calculate how long to wait before\n    // executing the first value... otherwise, if it's a number just schedule\n    // that many milliseconds (or scheduler-specified unit size) in the future.\n    let due = isValidDate(dueTime) ? +dueTime - scheduler!.now() : dueTime;\n\n    if (due < 0) {\n      // Ensure we don't schedule in the future.\n      due = 0;\n    }\n\n    // The incrementing value we emit.\n    let n = 0;\n\n    // Start the timer.\n    return scheduler.schedule(function () {\n      if (!subscriber.closed) {\n        // Emit the next value and increment.\n        subscriber.next(n++);\n\n        if (0 <= intervalDuration) {\n          // If we have a interval after the initial timer,\n          // reschedule with the period.\n          this.schedule(undefined, intervalDuration);\n        } else {\n          // We didn't have an interval. So just complete.\n          subscriber.complete();\n        }\n      }\n    }, due);\n  });\n}\n", "import { Observable } from '../Observable';\nimport { ObservableInput, ObservableInputTuple, SchedulerLike } from '../types';\nimport { mergeAll } from '../operators/mergeAll';\nimport { innerFrom } from './innerFrom';\nimport { EMPTY } from './empty';\nimport { popNumber, popScheduler } from '../util/args';\nimport { from } from './from';\n\nexport function merge<A extends readonly unknown[]>(...sources: [...ObservableInputTuple<A>]): Observable<A[number]>;\nexport function merge<A extends readonly unknown[]>(...sourcesAndConcurrency: [...ObservableInputTuple<A>, number?]): Observable<A[number]>;\n/** @deprecated The `scheduler` parameter will be removed in v8. Use `scheduled` and `mergeAll`. Details: https://rxjs.dev/deprecations/scheduler-argument */\nexport function merge<A extends readonly unknown[]>(\n  ...sourcesAndScheduler: [...ObservableInputTuple<A>, SchedulerLike?]\n): Observable<A[number]>;\n/** @deprecated The `scheduler` parameter will be removed in v8. Use `scheduled` and `mergeAll`. Details: https://rxjs.dev/deprecations/scheduler-argument */\nexport function merge<A extends readonly unknown[]>(\n  ...sourcesAndConcurrencyAndScheduler: [...ObservableInputTuple<A>, number?, SchedulerLike?]\n): Observable<A[number]>;\n\n/**\n * Creates an output Observable which concurrently emits all values from every\n * given input Observable.\n *\n * <span class=\"informal\">Flattens multiple Observables together by blending\n * their values into one Observable.</span>\n *\n * ![](merge.png)\n *\n * `merge` subscribes to each given input Observable (as arguments), and simply\n * forwards (without doing any transformation) all the values from all the input\n * Observables to the output Observable. The output Observable only completes\n * once all input Observables have completed. Any error delivered by an input\n * Observable will be immediately emitted on the output Observable.\n *\n * ## Examples\n *\n * Merge together two Observables: 1s interval and clicks\n *\n * ```ts\n * import { merge, fromEvent, interval } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const timer = interval(1000);\n * const clicksOrTimer = merge(clicks, timer);\n * clicksOrTimer.subscribe(x => console.log(x));\n *\n * // Results in the following:\n * // timer will emit ascending values, one every second(1000ms) to console\n * // clicks logs MouseEvents to console every time the \"document\" is clicked\n * // Since the two streams are merged you see these happening\n * // as they occur.\n * ```\n *\n * Merge together 3 Observables, but run only 2 concurrently\n *\n * ```ts\n * import { interval, take, merge } from 'rxjs';\n *\n * const timer1 = interval(1000).pipe(take(10));\n * const timer2 = interval(2000).pipe(take(6));\n * const timer3 = interval(500).pipe(take(10));\n *\n * const concurrent = 2; // the argument\n * const merged = merge(timer1, timer2, timer3, concurrent);\n * merged.subscribe(x => console.log(x));\n *\n * // Results in the following:\n * // - First timer1 and timer2 will run concurrently\n * // - timer1 will emit a value every 1000ms for 10 iterations\n * // - timer2 will emit a value every 2000ms for 6 iterations\n * // - after timer1 hits its max iteration, timer2 will\n * //   continue, and timer3 will start to run concurrently with timer2\n * // - when timer2 hits its max iteration it terminates, and\n * //   timer3 will continue to emit a value every 500ms until it is complete\n * ```\n *\n * @see {@link mergeAll}\n * @see {@link mergeMap}\n * @see {@link mergeMapTo}\n * @see {@link mergeScan}\n *\n * @param {...ObservableInput} observables Input Observables to merge together.\n * @param {number} [concurrent=Infinity] Maximum number of input\n * Observables being subscribed to concurrently.\n * @param {SchedulerLike} [scheduler=null] The {@link SchedulerLike} to use for managing\n * concurrency of input Observables.\n * @return {Observable} an Observable that emits items that are the result of\n * every input Observable.\n */\nexport function merge(...args: (ObservableInput<unknown> | number | SchedulerLike)[]): Observable<unknown> {\n  const scheduler = popScheduler(args);\n  const concurrent = popNumber(args, Infinity);\n  const sources = args as ObservableInput<unknown>[];\n  return !sources.length\n    ? // No source provided\n      EMPTY\n    : sources.length === 1\n    ? // One source? Just return it.\n      innerFrom(sources[0])\n    : // Merge all sources\n      mergeAll(concurrent)(from(sources, scheduler));\n}\n", "import { Observable } from '../Observable';\nimport { noop } from '../util/noop';\n\n/**\n * An Observable that emits no items to the Observer and never completes.\n *\n * ![](never.png)\n *\n * A simple Observable that emits neither values nor errors nor the completion\n * notification. It can be used for testing purposes or for composing with other\n * Observables. Please note that by never emitting a complete notification, this\n * Observable keeps the subscription from being disposed automatically.\n * Subscriptions need to be manually disposed.\n *\n * ##  Example\n *\n * Emit the number 7, then never emit anything else (not even complete)\n *\n * ```ts\n * import { NEVER, startWith } from 'rxjs';\n *\n * const info = () => console.log('Will not be called');\n *\n * const result = NEVER.pipe(startWith(7));\n * result.subscribe({\n *   next: x => console.log(x),\n *   error: info,\n *   complete: info\n * });\n * ```\n *\n * @see {@link Observable}\n * @see {@link EMPTY}\n * @see {@link of}\n * @see {@link throwError}\n */\nexport const NEVER = new Observable<never>(noop);\n\n/**\n * @deprecated Replaced with the {@link NEVER} constant. Will be removed in v8.\n */\nexport function never() {\n  return NEVER;\n}\n", "const { isArray } = Array;\n\n/**\n * Used in operators and functions that accept either a list of arguments, or an array of arguments\n * as a single argument.\n */\nexport function argsOrArgArray<T>(args: (T | T[])[]): T[] {\n  return args.length === 1 && isArray(args[0]) ? args[0] : (args as T[]);\n}\n", "import { OperatorFunction, MonoTypeOperatorFunction, TruthyTypesOf } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\n/** @deprecated Use a closure instead of a `thisArg`. Signatures accepting a `thisArg` will be removed in v8. */\nexport function filter<T, S extends T, A>(predicate: (this: A, value: T, index: number) => value is S, thisArg: A): OperatorFunction<T, S>;\nexport function filter<T, S extends T>(predicate: (value: T, index: number) => value is S): OperatorFunction<T, S>;\nexport function filter<T>(predicate: BooleanConstructor): OperatorFunction<T, TruthyTypesOf<T>>;\n/** @deprecated Use a closure instead of a `thisArg`. Signatures accepting a `thisArg` will be removed in v8. */\nexport function filter<T, A>(predicate: (this: A, value: T, index: number) => boolean, thisArg: A): MonoTypeOperatorFunction<T>;\nexport function filter<T>(predicate: (value: T, index: number) => boolean): MonoTypeOperatorFunction<T>;\n\n/**\n * Filter items emitted by the source Observable by only emitting those that\n * satisfy a specified predicate.\n *\n * <span class=\"informal\">Like\n * [Array.prototype.filter()](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Array/filter),\n * it only emits a value from the source if it passes a criterion function.</span>\n *\n * ![](filter.png)\n *\n * Similar to the well-known `Array.prototype.filter` method, this operator\n * takes values from the source Observable, passes them through a `predicate`\n * function and only emits those values that yielded `true`.\n *\n * ## Example\n *\n * Emit only click events whose target was a DIV element\n *\n * ```ts\n * import { fromEvent, filter } from 'rxjs';\n *\n * const div = document.createElement('div');\n * div.style.cssText = 'width: 200px; height: 200px; background: #09c;';\n * document.body.appendChild(div);\n *\n * const clicks = fromEvent(document, 'click');\n * const clicksOnDivs = clicks.pipe(filter(ev => (<HTMLElement>ev.target).tagName === 'DIV'));\n * clicksOnDivs.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link distinct}\n * @see {@link distinctUntilChanged}\n * @see {@link distinctUntilKeyChanged}\n * @see {@link ignoreElements}\n * @see {@link partition}\n * @see {@link skip}\n *\n * @param predicate A function that\n * evaluates each value emitted by the source Observable. If it returns `true`,\n * the value is emitted, if `false` the value is not passed to the output\n * Observable. The `index` parameter is the number `i` for the i-th source\n * emission that has happened since the subscription, starting from the number\n * `0`.\n * @param thisArg An optional argument to determine the value of `this`\n * in the `predicate` function.\n * @return A function that returns an Observable that emits items from the\n * source Observable that satisfy the specified `predicate`.\n */\nexport function filter<T>(predicate: (value: T, index: number) => boolean, thisArg?: any): MonoTypeOperatorFunction<T> {\n  return operate((source, subscriber) => {\n    // An index passed to our predicate function on each call.\n    let index = 0;\n\n    // Subscribe to the source, all errors and completions are\n    // forwarded to the consumer.\n    source.subscribe(\n      // Call the predicate with the appropriate `this` context,\n      // if the predicate returns `true`, then send the value\n      // to the consumer.\n      createOperatorSubscriber(subscriber, (value) => predicate.call(thisArg, value, index++) && subscriber.next(value))\n    );\n  });\n}\n", "import { Observable } from '../Observable';\nimport { ObservableInputTuple } from '../types';\nimport { innerFrom } from './innerFrom';\nimport { argsOrArgArray } from '../util/argsOrArgArray';\nimport { EMPTY } from './empty';\nimport { createOperatorSubscriber } from '../operators/OperatorSubscriber';\nimport { popResultSelector } from '../util/args';\n\nexport function zip<A extends readonly unknown[]>(sources: [...ObservableInputTuple<A>]): Observable<A>;\nexport function zip<A extends readonly unknown[], R>(\n  sources: [...ObservableInputTuple<A>],\n  resultSelector: (...values: A) => R\n): Observable<R>;\nexport function zip<A extends readonly unknown[]>(...sources: [...ObservableInputTuple<A>]): Observable<A>;\nexport function zip<A extends readonly unknown[], R>(\n  ...sourcesAndResultSelector: [...ObservableInputTuple<A>, (...values: A) => R]\n): Observable<R>;\n\n/**\n * Combines multiple Observables to create an Observable whose values are calculated from the values, in order, of each\n * of its input Observables.\n *\n * If the last parameter is a function, this function is used to compute the created value from the input values.\n * Otherwise, an array of the input values is returned.\n *\n * ## Example\n *\n * Combine age and name from different sources\n *\n * ```ts\n * import { of, zip, map } from 'rxjs';\n *\n * const age$ = of(27, 25, 29);\n * const name$ = of('Foo', 'Bar', 'Beer');\n * const isDev$ = of(true, true, false);\n *\n * zip(age$, name$, isDev$).pipe(\n *   map(([age, name, isDev]) => ({ age, name, isDev }))\n * )\n * .subscribe(x => console.log(x));\n *\n * // Outputs\n * // { age: 27, name: 'Foo', isDev: true }\n * // { age: 25, name: 'Bar', isDev: true }\n * // { age: 29, name: 'Beer', isDev: false }\n * ```\n *\n * @param sources\n * @return {Observable<R>}\n */\nexport function zip(...args: unknown[]): Observable<unknown> {\n  const resultSelector = popResultSelector(args);\n\n  const sources = argsOrArgArray(args) as Observable<unknown>[];\n\n  return sources.length\n    ? new Observable<unknown[]>((subscriber) => {\n        // A collection of buffers of values from each source.\n        // Keyed by the same index with which the sources were passed in.\n        let buffers: unknown[][] = sources.map(() => []);\n\n        // An array of flags of whether or not the sources have completed.\n        // This is used to check to see if we should complete the result.\n        // Keyed by the same index with which the sources were passed in.\n        let completed = sources.map(() => false);\n\n        // When everything is done, release the arrays above.\n        subscriber.add(() => {\n          buffers = completed = null!;\n        });\n\n        // Loop over our sources and subscribe to each one. The index `i` is\n        // especially important here, because we use it in closures below to\n        // access the related buffers and completion properties\n        for (let sourceIndex = 0; !subscriber.closed && sourceIndex < sources.length; sourceIndex++) {\n          innerFrom(sources[sourceIndex]).subscribe(\n            createOperatorSubscriber(\n              subscriber,\n              (value) => {\n                buffers[sourceIndex].push(value);\n                // if every buffer has at least one value in it, then we\n                // can shift out the oldest value from each buffer and emit\n                // them as an array.\n                if (buffers.every((buffer) => buffer.length)) {\n                  const result: any = buffers.map((buffer) => buffer.shift()!);\n                  // Emit the array. If theres' a result selector, use that.\n                  subscriber.next(resultSelector ? resultSelector(...result) : result);\n                  // If any one of the sources is both complete and has an empty buffer\n                  // then we complete the result. This is because we cannot possibly have\n                  // any more values to zip together.\n                  if (buffers.some((buffer, i) => !buffer.length && completed[i])) {\n                    subscriber.complete();\n                  }\n                }\n              },\n              () => {\n                // This source completed. Mark it as complete so we can check it later\n                // if we have to.\n                completed[sourceIndex] = true;\n                // But, if this complete source has nothing in its buffer, then we\n                // can complete the result, because we can't possibly have any more\n                // values from this to zip together with the other values.\n                !buffers[sourceIndex].length && subscriber.complete();\n              }\n            )\n          );\n        }\n\n        // When everything is done, release the arrays above.\n        return () => {\n          buffers = completed = null!;\n        };\n      })\n    : EMPTY;\n}\n", "import { Subscriber } from '../Subscriber';\nimport { MonoTypeOperatorFunction, ObservableInput } from '../types';\n\nimport { operate } from '../util/lift';\nimport { innerFrom } from '../observable/innerFrom';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\n/**\n * Ignores source values for a duration determined by another Observable, then\n * emits the most recent value from the source Observable, then repeats this\n * process.\n *\n * <span class=\"informal\">It's like {@link auditTime}, but the silencing\n * duration is determined by a second Observable.</span>\n *\n * ![](audit.svg)\n *\n * `audit` is similar to `throttle`, but emits the last value from the silenced\n * time window, instead of the first value. `audit` emits the most recent value\n * from the source Observable on the output Observable as soon as its internal\n * timer becomes disabled, and ignores source values while the timer is enabled.\n * Initially, the timer is disabled. As soon as the first source value arrives,\n * the timer is enabled by calling the `durationSelector` function with the\n * source value, which returns the \"duration\" Observable. When the duration\n * Observable emits a value, the timer is disabled, then the most\n * recent source value is emitted on the output Observable, and this process\n * repeats for the next source value.\n *\n * ## Example\n *\n * Emit clicks at a rate of at most one click per second\n *\n * ```ts\n * import { fromEvent, audit, interval } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const result = clicks.pipe(audit(ev => interval(1000)));\n * result.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link auditTime}\n * @see {@link debounce}\n * @see {@link delayWhen}\n * @see {@link sample}\n * @see {@link throttle}\n *\n * @param durationSelector A function\n * that receives a value from the source Observable, for computing the silencing\n * duration, returned as an Observable or a Promise.\n * @return A function that returns an Observable that performs rate-limiting of\n * emissions from the source Observable.\n */\nexport function audit<T>(durationSelector: (value: T) => ObservableInput<any>): MonoTypeOperatorFunction<T> {\n  return operate((source, subscriber) => {\n    let hasValue = false;\n    let lastValue: T | null = null;\n    let durationSubscriber: Subscriber<any> | null = null;\n    let isComplete = false;\n\n    const endDuration = () => {\n      durationSubscriber?.unsubscribe();\n      durationSubscriber = null;\n      if (hasValue) {\n        hasValue = false;\n        const value = lastValue!;\n        lastValue = null;\n        subscriber.next(value);\n      }\n      isComplete && subscriber.complete();\n    };\n\n    const cleanupDuration = () => {\n      durationSubscriber = null;\n      isComplete && subscriber.complete();\n    };\n\n    source.subscribe(\n      createOperatorSubscriber(\n        subscriber,\n        (value) => {\n          hasValue = true;\n          lastValue = value;\n          if (!durationSubscriber) {\n            innerFrom(durationSelector(value)).subscribe(\n              (durationSubscriber = createOperatorSubscriber(subscriber, endDuration, cleanupDuration))\n            );\n          }\n        },\n        () => {\n          isComplete = true;\n          (!hasValue || !durationSubscriber || durationSubscriber.closed) && subscriber.complete();\n        }\n      )\n    );\n  });\n}\n", "import { asyncScheduler } from '../scheduler/async';\nimport { audit } from './audit';\nimport { timer } from '../observable/timer';\nimport { MonoTypeOperatorFunction, SchedulerLike } from '../types';\n\n/**\n * Ignores source values for `duration` milliseconds, then emits the most recent\n * value from the source Observable, then repeats this process.\n *\n * <span class=\"informal\">When it sees a source value, it ignores that plus\n * the next ones for `duration` milliseconds, and then it emits the most recent\n * value from the source.</span>\n *\n * ![](auditTime.png)\n *\n * `auditTime` is similar to `throttleTime`, but emits the last value from the\n * silenced time window, instead of the first value. `auditTime` emits the most\n * recent value from the source Observable on the output Observable as soon as\n * its internal timer becomes disabled, and ignores source values while the\n * timer is enabled. Initially, the timer is disabled. As soon as the first\n * source value arrives, the timer is enabled. After `duration` milliseconds (or\n * the time unit determined internally by the optional `scheduler`) has passed,\n * the timer is disabled, then the most recent source value is emitted on the\n * output Observable, and this process repeats for the next source value.\n * Optionally takes a {@link SchedulerLike} for managing timers.\n *\n * ## Example\n *\n * Emit clicks at a rate of at most one click per second\n *\n * ```ts\n * import { fromEvent, auditTime } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const result = clicks.pipe(auditTime(1000));\n * result.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link audit}\n * @see {@link debounceTime}\n * @see {@link delay}\n * @see {@link sampleTime}\n * @see {@link throttleTime}\n *\n * @param {number} duration Time to wait before emitting the most recent source\n * value, measured in milliseconds or the time unit determined internally\n * by the optional `scheduler`.\n * @param {SchedulerLike} [scheduler=async] The {@link SchedulerLike} to use for\n * managing the timers that handle the rate-limiting behavior.\n * @return A function that returns an Observable that performs rate-limiting of\n * emissions from the source Observable.\n */\nexport function auditTime<T>(duration: number, scheduler: SchedulerLike = asyncScheduler): MonoTypeOperatorFunction<T> {\n  return audit(() => timer(duration, scheduler));\n}\n", "import { OperatorFunction } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\nimport { arrRemove } from '../util/arrRemove';\n\n/**\n * Buffers the source Observable values until the size hits the maximum\n * `bufferSize` given.\n *\n * <span class=\"informal\">Collects values from the past as an array, and emits\n * that array only when its size reaches `bufferSize`.</span>\n *\n * ![](bufferCount.png)\n *\n * Buffers a number of values from the source Observable by `bufferSize` then\n * emits the buffer and clears it, and starts a new buffer each\n * `startBufferEvery` values. If `startBufferEvery` is not provided or is\n * `null`, then new buffers are started immediately at the start of the source\n * and when each buffer closes and is emitted.\n *\n * ## Examples\n *\n * Emit the last two click events as an array\n *\n * ```ts\n * import { fromEvent, bufferCount } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const buffered = clicks.pipe(bufferCount(2));\n * buffered.subscribe(x => console.log(x));\n * ```\n *\n * On every click, emit the last two click events as an array\n *\n * ```ts\n * import { fromEvent, bufferCount } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const buffered = clicks.pipe(bufferCount(2, 1));\n * buffered.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link buffer}\n * @see {@link bufferTime}\n * @see {@link bufferToggle}\n * @see {@link bufferWhen}\n * @see {@link pairwise}\n * @see {@link windowCount}\n *\n * @param {number} bufferSize The maximum size of the buffer emitted.\n * @param {number} [startBufferEvery] Interval at which to start a new buffer.\n * For example if `startBufferEvery` is `2`, then a new buffer will be started\n * on every other value from the source. A new buffer is started at the\n * beginning of the source by default.\n * @return A function that returns an Observable of arrays of buffered values.\n */\nexport function bufferCount<T>(bufferSize: number, startBufferEvery: number | null = null): OperatorFunction<T, T[]> {\n  // If no `startBufferEvery` value was supplied, then we're\n  // opening and closing on the bufferSize itself.\n  startBufferEvery = startBufferEvery ?? bufferSize;\n\n  return operate((source, subscriber) => {\n    let buffers: T[][] = [];\n    let count = 0;\n\n    source.subscribe(\n      createOperatorSubscriber(\n        subscriber,\n        (value) => {\n          let toEmit: T[][] | null = null;\n\n          // Check to see if we need to start a buffer.\n          // This will start one at the first value, and then\n          // a new one every N after that.\n          if (count++ % startBufferEvery! === 0) {\n            buffers.push([]);\n          }\n\n          // Push our value into our active buffers.\n          for (const buffer of buffers) {\n            buffer.push(value);\n            // Check to see if we're over the bufferSize\n            // if we are, record it so we can emit it later.\n            // If we emitted it now and removed it, it would\n            // mutate the `buffers` array while we're looping\n            // over it.\n            if (bufferSize <= buffer.length) {\n              toEmit = toEmit ?? [];\n              toEmit.push(buffer);\n            }\n          }\n\n          if (toEmit) {\n            // We have found some buffers that are over the\n            // `bufferSize`. Emit them, and remove them from our\n            // buffers list.\n            for (const buffer of toEmit) {\n              arrRemove(buffers, buffer);\n              subscriber.next(buffer);\n            }\n          }\n        },\n        () => {\n          // When the source completes, emit all of our\n          // active buffers.\n          for (const buffer of buffers) {\n            subscriber.next(buffer);\n          }\n          subscriber.complete();\n        },\n        // Pass all errors through to consumer.\n        undefined,\n        () => {\n          // Clean up our memory when we finalize\n          buffers = null!;\n        }\n      )\n    );\n  });\n}\n", "import { Observable } from '../Observable';\n\nimport { ObservableInput, OperatorFunction, ObservedValueOf } from '../types';\nimport { Subscription } from '../Subscription';\nimport { innerFrom } from '../observable/innerFrom';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\nimport { operate } from '../util/lift';\n\n/* tslint:disable:max-line-length */\nexport function catchError<T, O extends ObservableInput<any>>(\n  selector: (err: any, caught: Observable<T>) => O\n): OperatorFunction<T, T | ObservedValueOf<O>>;\n/* tslint:enable:max-line-length */\n\n/**\n * Catches errors on the observable to be handled by returning a new observable or throwing an error.\n *\n * <span class=\"informal\">\n * It only listens to the error channel and ignores notifications.\n * Handles errors from the source observable, and maps them to a new observable.\n * The error may also be rethrown, or a new error can be thrown to emit an error from the result.\n * </span>\n *\n * ![](catch.png)\n *\n * This operator handles errors, but forwards along all other events to the resulting observable.\n * If the source observable terminates with an error, it will map that error to a new observable,\n * subscribe to it, and forward all of its events to the resulting observable.\n *\n * ## Examples\n *\n * Continue with a different Observable when there's an error\n *\n * ```ts\n * import { of, map, catchError } from 'rxjs';\n *\n * of(1, 2, 3, 4, 5)\n *   .pipe(\n *     map(n => {\n *       if (n === 4) {\n *         throw 'four!';\n *       }\n *       return n;\n *     }),\n *     catchError(err => of('I', 'II', 'III', 'IV', 'V'))\n *   )\n *   .subscribe(x => console.log(x));\n *   // 1, 2, 3, I, II, III, IV, V\n * ```\n *\n * Retry the caught source Observable again in case of error, similar to `retry()` operator\n *\n * ```ts\n * import { of, map, catchError, take } from 'rxjs';\n *\n * of(1, 2, 3, 4, 5)\n *   .pipe(\n *     map(n => {\n *       if (n === 4) {\n *         throw 'four!';\n *       }\n *       return n;\n *     }),\n *     catchError((err, caught) => caught),\n *     take(30)\n *   )\n *   .subscribe(x => console.log(x));\n *   // 1, 2, 3, 1, 2, 3, ...\n * ```\n *\n * Throw a new error when the source Observable throws an error\n *\n * ```ts\n * import { of, map, catchError } from 'rxjs';\n *\n * of(1, 2, 3, 4, 5)\n *   .pipe(\n *     map(n => {\n *       if (n === 4) {\n *         throw 'four!';\n *       }\n *       return n;\n *     }),\n *     catchError(err => {\n *       throw 'error in source. Details: ' + err;\n *     })\n *   )\n *   .subscribe({\n *     next: x => console.log(x),\n *     error: err => console.log(err)\n *   });\n *   // 1, 2, 3, error in source. Details: four!\n * ```\n *\n * @see {@link onErrorResumeNext}\n * @see {@link repeat}\n * @see {@link repeatWhen}\n * @see {@link retry }\n * @see {@link retryWhen}\n *\n * @param {function} selector a function that takes as arguments `err`, which is the error, and `caught`, which\n * is the source observable, in case you'd like to \"retry\" that observable by returning it again. Whatever observable\n * is returned by the `selector` will be used to continue the observable chain.\n * @return A function that returns an Observable that originates from either\n * the source or the Observable returned by the `selector` function.\n */\nexport function catchError<T, O extends ObservableInput<any>>(\n  selector: (err: any, caught: Observable<T>) => O\n): OperatorFunction<T, T | ObservedValueOf<O>> {\n  return operate((source, subscriber) => {\n    let innerSub: Subscription | null = null;\n    let syncUnsub = false;\n    let handledResult: Observable<ObservedValueOf<O>>;\n\n    innerSub = source.subscribe(\n      createOperatorSubscriber(subscriber, undefined, undefined, (err) => {\n        handledResult = innerFrom(selector(err, catchError(selector)(source)));\n        if (innerSub) {\n          innerSub.unsubscribe();\n          innerSub = null;\n          handledResult.subscribe(subscriber);\n        } else {\n          // We don't have an innerSub yet, that means the error was synchronous\n          // because the subscribe call hasn't returned yet.\n          syncUnsub = true;\n        }\n      })\n    );\n\n    if (syncUnsub) {\n      // We have a synchronous error, we need to make sure to\n      // finalize right away. This ensures that callbacks in the `finalize` operator are called\n      // at the right time, and that finalization occurs at the expected\n      // time between the source error and the subscription to the\n      // next observable.\n      innerSub.unsubscribe();\n      innerSub = null;\n      handledResult!.subscribe(subscriber);\n    }\n  });\n}\n", "import { Observable } from '../Observable';\nimport { Subscriber } from '../Subscriber';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\n/**\n * A basic scan operation. This is used for `scan` and `reduce`.\n * @param accumulator The accumulator to use\n * @param seed The seed value for the state to accumulate\n * @param hasSeed Whether or not a seed was provided\n * @param emitOnNext Whether or not to emit the state on next\n * @param emitBeforeComplete Whether or not to emit the before completion\n */\n\nexport function scanInternals<V, A, S>(\n  accumulator: (acc: V | A | S, value: V, index: number) => A,\n  seed: S,\n  hasSeed: boolean,\n  emitOnNext: boolean,\n  emitBeforeComplete?: undefined | true\n) {\n  return (source: Observable<V>, subscriber: Subscriber<any>) => {\n    // Whether or not we have state yet. This will only be\n    // false before the first value arrives if we didn't get\n    // a seed value.\n    let hasState = hasSeed;\n    // The state that we're tracking, starting with the seed,\n    // if there is one, and then updated by the return value\n    // from the accumulator on each emission.\n    let state: any = seed;\n    // An index to pass to the accumulator function.\n    let index = 0;\n\n    // Subscribe to our source. All errors and completions are passed through.\n    source.subscribe(\n      createOperatorSubscriber(\n        subscriber,\n        (value) => {\n          // Always increment the index.\n          const i = index++;\n          // Set the state\n          state = hasState\n            ? // We already have state, so we can get the new state from the accumulator\n              accumulator(state, value, i)\n            : // We didn't have state yet, a seed value was not provided, so\n\n              // we set the state to the first value, and mark that we have state now\n              ((hasState = true), value);\n\n          // Maybe send it to the consumer.\n          emitOnNext && subscriber.next(state);\n        },\n        // If an onComplete was given, call it, otherwise\n        // just pass through the complete notification to the consumer.\n        emitBeforeComplete &&\n          (() => {\n            hasState && subscriber.next(state);\n            subscriber.complete();\n          })\n      )\n    );\n  };\n}\n", "import { combineLatestInit } from '../observable/combineLatest';\nimport { ObservableInput, ObservableInputTuple, OperatorFunction } from '../types';\nimport { operate } from '../util/lift';\nimport { argsOrArgArray } from '../util/argsOrArgArray';\nimport { mapOneOrManyArgs } from '../util/mapOneOrManyArgs';\nimport { pipe } from '../util/pipe';\nimport { popResultSelector } from '../util/args';\n\n/** @deprecated Replaced with {@link combineLatestWith}. Will be removed in v8. */\nexport function combineLatest<T, A extends readonly unknown[], R>(\n  sources: [...ObservableInputTuple<A>],\n  project: (...values: [T, ...A]) => R\n): OperatorFunction<T, R>;\n/** @deprecated Replaced with {@link combineLatestWith}. Will be removed in v8. */\nexport function combineLatest<T, A extends readonly unknown[], R>(sources: [...ObservableInputTuple<A>]): OperatorFunction<T, [T, ...A]>;\n\n/** @deprecated Replaced with {@link combineLatestWith}. Will be removed in v8. */\nexport function combineLatest<T, A extends readonly unknown[], R>(\n  ...sourcesAndProject: [...ObservableInputTuple<A>, (...values: [T, ...A]) => R]\n): OperatorFunction<T, R>;\n/** @deprecated Replaced with {@link combineLatestWith}. Will be removed in v8. */\nexport function combineLatest<T, A extends readonly unknown[], R>(...sources: [...ObservableInputTuple<A>]): OperatorFunction<T, [T, ...A]>;\n\n/**\n * @deprecated Replaced with {@link combineLatestWith}. Will be removed in v8.\n */\nexport function combineLatest<T, R>(...args: (ObservableInput<any> | ((...values: any[]) => R))[]): OperatorFunction<T, unknown> {\n  const resultSelector = popResultSelector(args);\n  return resultSelector\n    ? pipe(combineLatest(...(args as Array<ObservableInput<any>>)), mapOneOrManyArgs(resultSelector))\n    : operate((source, subscriber) => {\n        combineLatestInit([source, ...argsOrArgArray(args)])(subscriber);\n      });\n}\n", "import { ObservableInputTuple, OperatorFunction, Cons } from '../types';\nimport { combineLatest } from './combineLatest';\n\n/**\n * Create an observable that combines the latest values from all passed observables and the source\n * into arrays and emits them.\n *\n * Returns an observable, that when subscribed to, will subscribe to the source observable and all\n * sources provided as arguments. Once all sources emit at least one value, all of the latest values\n * will be emitted as an array. After that, every time any source emits a value, all of the latest values\n * will be emitted as an array.\n *\n * This is a useful operator for eagerly calculating values based off of changed inputs.\n *\n * ## Example\n *\n * Simple concatenation of values from two inputs\n *\n * ```ts\n * import { fromEvent, combineLatestWith, map } from 'rxjs';\n *\n * // Setup: Add two inputs to the page\n * const input1 = document.createElement('input');\n * document.body.appendChild(input1);\n * const input2 = document.createElement('input');\n * document.body.appendChild(input2);\n *\n * // Get streams of changes\n * const input1Changes$ = fromEvent(input1, 'change');\n * const input2Changes$ = fromEvent(input2, 'change');\n *\n * // Combine the changes by adding them together\n * input1Changes$.pipe(\n *   combineLatestWith(input2Changes$),\n *   map(([e1, e2]) => (<HTMLInputElement>e1.target).value + ' - ' + (<HTMLInputElement>e2.target).value)\n * )\n * .subscribe(x => console.log(x));\n * ```\n *\n * @param otherSources the other sources to subscribe to.\n * @return A function that returns an Observable that emits the latest\n * emissions from both source and provided Observables.\n */\nexport function combineLatestWith<T, A extends readonly unknown[]>(\n  ...otherSources: [...ObservableInputTuple<A>]\n): OperatorFunction<T, Cons<T, A>> {\n  return combineLatest(...otherSources);\n}\n", "import { asyncScheduler } from '../scheduler/async';\nimport { Subscription } from '../Subscription';\nimport { MonoTypeOperatorFunction, SchedulerAction, SchedulerLike } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\n/**\n * Emits a notification from the source Observable only after a particular time span\n * has passed without another source emission.\n *\n * <span class=\"informal\">It's like {@link delay}, but passes only the most\n * recent notification from each burst of emissions.</span>\n *\n * ![](debounceTime.png)\n *\n * `debounceTime` delays notifications emitted by the source Observable, but drops\n * previous pending delayed emissions if a new notification arrives on the source\n * Observable. This operator keeps track of the most recent notification from the\n * source Observable, and emits that only when `dueTime` has passed\n * without any other notification appearing on the source Observable. If a new value\n * appears before `dueTime` silence occurs, the previous notification will be dropped\n * and will not be emitted and a new `dueTime` is scheduled.\n * If the completing event happens during `dueTime` the last cached notification\n * is emitted before the completion event is forwarded to the output observable.\n * If the error event happens during `dueTime` or after it only the error event is\n * forwarded to the output observable. The cache notification is not emitted in this case.\n *\n * This is a rate-limiting operator, because it is impossible for more than one\n * notification to be emitted in any time window of duration `dueTime`, but it is also\n * a delay-like operator since output emissions do not occur at the same time as\n * they did on the source Observable. Optionally takes a {@link SchedulerLike} for\n * managing timers.\n *\n * ## Example\n *\n * Emit the most recent click after a burst of clicks\n *\n * ```ts\n * import { fromEvent, debounceTime } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const result = clicks.pipe(debounceTime(1000));\n * result.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link audit}\n * @see {@link auditTime}\n * @see {@link debounce}\n * @see {@link sample}\n * @see {@link sampleTime}\n * @see {@link throttle}\n * @see {@link throttleTime}\n *\n * @param {number} dueTime The timeout duration in milliseconds (or the time\n * unit determined internally by the optional `scheduler`) for the window of\n * time required to wait for emission silence before emitting the most recent\n * source value.\n * @param {SchedulerLike} [scheduler=async] The {@link SchedulerLike} to use for\n * managing the timers that handle the timeout for each value.\n * @return A function that returns an Observable that delays the emissions of\n * the source Observable by the specified `dueTime`, and may drop some values\n * if they occur too frequently.\n */\nexport function debounceTime<T>(dueTime: number, scheduler: SchedulerLike = asyncScheduler): MonoTypeOperatorFunction<T> {\n  return operate((source, subscriber) => {\n    let activeTask: Subscription | null = null;\n    let lastValue: T | null = null;\n    let lastTime: number | null = null;\n\n    const emit = () => {\n      if (activeTask) {\n        // We have a value! Free up memory first, then emit the value.\n        activeTask.unsubscribe();\n        activeTask = null;\n        const value = lastValue!;\n        lastValue = null;\n        subscriber.next(value);\n      }\n    };\n    function emitWhenIdle(this: SchedulerAction<unknown>) {\n      // This is called `dueTime` after the first value\n      // but we might have received new values during this window!\n\n      const targetTime = lastTime! + dueTime;\n      const now = scheduler.now();\n      if (now < targetTime) {\n        // On that case, re-schedule to the new target\n        activeTask = this.schedule(undefined, targetTime - now);\n        subscriber.add(activeTask);\n        return;\n      }\n\n      emit();\n    }\n\n    source.subscribe(\n      createOperatorSubscriber(\n        subscriber,\n        (value: T) => {\n          lastValue = value;\n          lastTime = scheduler.now();\n\n          // Only set up a task if it's not already up\n          if (!activeTask) {\n            activeTask = scheduler.schedule(emitWhenIdle, dueTime);\n            subscriber.add(activeTask);\n          }\n        },\n        () => {\n          // Source completed.\n          // Emit any pending debounced values then complete\n          emit();\n          subscriber.complete();\n        },\n        // Pass all errors through to consumer.\n        undefined,\n        () => {\n          // Finalization.\n          lastValue = activeTask = null;\n        }\n      )\n    );\n  });\n}\n", "import { OperatorFunction } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\n/**\n * Emits a given value if the source Observable completes without emitting any\n * `next` value, otherwise mirrors the source Observable.\n *\n * <span class=\"informal\">If the source Observable turns out to be empty, then\n * this operator will emit a default value.</span>\n *\n * ![](defaultIfEmpty.png)\n *\n * `defaultIfEmpty` emits the values emitted by the source Observable or a\n * specified default value if the source Observable is empty (completes without\n * having emitted any `next` value).\n *\n * ## Example\n *\n * If no clicks happen in 5 seconds, then emit 'no clicks'\n *\n * ```ts\n * import { fromEvent, takeUntil, interval, defaultIfEmpty } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const clicksBeforeFive = clicks.pipe(takeUntil(interval(5000)));\n * const result = clicksBeforeFive.pipe(defaultIfEmpty('no clicks'));\n * result.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link empty}\n * @see {@link last}\n *\n * @param defaultValue The default value used if the source\n * Observable is empty.\n * @return A function that returns an Observable that emits either the\n * specified `defaultValue` if the source Observable emits no items, or the\n * values emitted by the source Observable.\n */\nexport function defaultIfEmpty<T, R>(defaultValue: R): OperatorFunction<T, T | R> {\n  return operate((source, subscriber) => {\n    let hasValue = false;\n    source.subscribe(\n      createOperatorSubscriber(\n        subscriber,\n        (value) => {\n          hasValue = true;\n          subscriber.next(value);\n        },\n        () => {\n          if (!hasValue) {\n            subscriber.next(defaultValue!);\n          }\n          subscriber.complete();\n        }\n      )\n    );\n  });\n}\n", "import { MonoTypeOperatorFunction } from '../types';\nimport { EMPTY } from '../observable/empty';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\n/**\n * Emits only the first `count` values emitted by the source Observable.\n *\n * <span class=\"informal\">Takes the first `count` values from the source, then\n * completes.</span>\n *\n * ![](take.png)\n *\n * `take` returns an Observable that emits only the first `count` values emitted\n * by the source Observable. If the source emits fewer than `count` values then\n * all of its values are emitted. After that, it completes, regardless if the\n * source completes.\n *\n * ## Example\n *\n * Take the first 5 seconds of an infinite 1-second interval Observable\n *\n * ```ts\n * import { interval, take } from 'rxjs';\n *\n * const intervalCount = interval(1000);\n * const takeFive = intervalCount.pipe(take(5));\n * takeFive.subscribe(x => console.log(x));\n *\n * // Logs:\n * // 0\n * // 1\n * // 2\n * // 3\n * // 4\n * ```\n *\n * @see {@link takeLast}\n * @see {@link takeUntil}\n * @see {@link takeWhile}\n * @see {@link skip}\n *\n * @param count The maximum number of `next` values to emit.\n * @return A function that returns an Observable that emits only the first\n * `count` values emitted by the source Observable, or all of the values from\n * the source if the source emits fewer than `count` values.\n */\nexport function take<T>(count: number): MonoTypeOperatorFunction<T> {\n  return count <= 0\n    ? // If we are taking no values, that's empty.\n      () => EMPTY\n    : operate((source, subscriber) => {\n        let seen = 0;\n        source.subscribe(\n          createOperatorSubscriber(subscriber, (value) => {\n            // Increment the number of values we have seen,\n            // then check it against the allowed count to see\n            // if we are still letting values through.\n            if (++seen <= count) {\n              subscriber.next(value);\n              // If we have met or passed our allowed count,\n              // we need to complete. We have to do <= here,\n              // because re-entrant code will increment `seen` twice.\n              if (count <= seen) {\n                subscriber.complete();\n              }\n            }\n          })\n        );\n      });\n}\n", "import { OperatorFunction } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\nimport { noop } from '../util/noop';\n\n/**\n * Ignores all items emitted by the source Observable and only passes calls of `complete` or `error`.\n *\n * ![](ignoreElements.png)\n *\n * The `ignoreElements` operator suppresses all items emitted by the source Observable,\n * but allows its termination notification (either `error` or `complete`) to pass through unchanged.\n *\n * If you do not care about the items being emitted by an Observable, but you do want to be notified\n * when it completes or when it terminates with an error, you can apply the `ignoreElements` operator\n * to the Observable, which will ensure that it will never call its observers\u2019 `next` handlers.\n *\n * ## Example\n *\n * Ignore all `next` emissions from the source\n *\n * ```ts\n * import { of, ignoreElements } from 'rxjs';\n *\n * of('you', 'talking', 'to', 'me')\n *   .pipe(ignoreElements())\n *   .subscribe({\n *     next: word => console.log(word),\n *     error: err => console.log('error:', err),\n *     complete: () => console.log('the end'),\n *   });\n *\n * // result:\n * // 'the end'\n * ```\n *\n * @return A function that returns an empty Observable that only calls\n * `complete` or `error`, based on which one is called by the source\n * Observable.\n */\nexport function ignoreElements(): OperatorFunction<unknown, never> {\n  return operate((source, subscriber) => {\n    source.subscribe(createOperatorSubscriber(subscriber, noop));\n  });\n}\n", "import { OperatorFunction } from '../types';\nimport { map } from './map';\n\n/** @deprecated To be removed in v9. Use {@link map} instead: `map(() => value)`. */\nexport function mapTo<R>(value: R): OperatorFunction<unknown, R>;\n/**\n * @deprecated Do not specify explicit type parameters. Signatures with type parameters\n * that cannot be inferred will be removed in v8. `mapTo` itself will be removed in v9,\n * use {@link map} instead: `map(() => value)`.\n * */\nexport function mapTo<T, R>(value: R): OperatorFunction<T, R>;\n\n/**\n * Emits the given constant value on the output Observable every time the source\n * Observable emits a value.\n *\n * <span class=\"informal\">Like {@link map}, but it maps every source value to\n * the same output value every time.</span>\n *\n * ![](mapTo.png)\n *\n * Takes a constant `value` as argument, and emits that whenever the source\n * Observable emits a value. In other words, ignores the actual source value,\n * and simply uses the emission moment to know when to emit the given `value`.\n *\n * ## Example\n *\n * Map every click to the string `'Hi'`\n *\n * ```ts\n * import { fromEvent, mapTo } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const greetings = clicks.pipe(mapTo('Hi'));\n *\n * greetings.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link map}\n *\n * @param value The value to map each source value to.\n * @return A function that returns an Observable that emits the given `value`\n * every time the source Observable emits.\n * @deprecated To be removed in v9. Use {@link map} instead: `map(() => value)`.\n */\nexport function mapTo<R>(value: R): OperatorFunction<unknown, R> {\n  return map(() => value);\n}\n", "import { Observable } from '../Observable';\nimport { MonoTypeOperatorFunction, ObservableInput } from '../types';\nimport { concat } from '../observable/concat';\nimport { take } from './take';\nimport { ignoreElements } from './ignoreElements';\nimport { mapTo } from './mapTo';\nimport { mergeMap } from './mergeMap';\nimport { innerFrom } from '../observable/innerFrom';\n\n/** @deprecated The `subscriptionDelay` parameter will be removed in v8. */\nexport function delayWhen<T>(\n  delayDurationSelector: (value: T, index: number) => ObservableInput<any>,\n  subscriptionDelay: Observable<any>\n): MonoTypeOperatorFunction<T>;\nexport function delayWhen<T>(delayDurationSelector: (value: T, index: number) => ObservableInput<any>): MonoTypeOperatorFunction<T>;\n\n/**\n * Delays the emission of items from the source Observable by a given time span\n * determined by the emissions of another Observable.\n *\n * <span class=\"informal\">It's like {@link delay}, but the time span of the\n * delay duration is determined by a second Observable.</span>\n *\n * ![](delayWhen.png)\n *\n * `delayWhen` operator shifts each emitted value from the source Observable by\n * a time span determined by another Observable. When the source emits a value,\n * the `delayDurationSelector` function is called with the value emitted from\n * the source Observable as the first argument to the `delayDurationSelector`.\n * The `delayDurationSelector` function should return an {@link ObservableInput},\n * that is internally converted to an Observable that is called the \"duration\"\n * Observable.\n *\n * The source value is emitted on the output Observable only when the \"duration\"\n * Observable emits ({@link guide/glossary-and-semantics#next next}s) any value.\n * Upon that, the \"duration\" Observable gets unsubscribed.\n *\n * Before RxJS V7, the {@link guide/glossary-and-semantics#complete completion}\n * of the \"duration\" Observable would have been triggering the emission of the\n * source value to the output Observable, but with RxJS V7, this is not the case\n * anymore.\n *\n * Only next notifications (from the \"duration\" Observable) trigger values from\n * the source Observable to be passed to the output Observable. If the \"duration\"\n * Observable only emits the complete notification (without next), the value\n * emitted by the source Observable will never get to the output Observable - it\n * will be swallowed. If the \"duration\" Observable errors, the error will be\n * propagated to the output Observable.\n *\n * Optionally, `delayWhen` takes a second argument, `subscriptionDelay`, which\n * is an Observable. When `subscriptionDelay` emits its first value or\n * completes, the source Observable is subscribed to and starts behaving like\n * described in the previous paragraph. If `subscriptionDelay` is not provided,\n * `delayWhen` will subscribe to the source Observable as soon as the output\n * Observable is subscribed.\n *\n * ## Example\n *\n * Delay each click by a random amount of time, between 0 and 5 seconds\n *\n * ```ts\n * import { fromEvent, delayWhen, interval } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const delayedClicks = clicks.pipe(\n *   delayWhen(() => interval(Math.random() * 5000))\n * );\n * delayedClicks.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link delay}\n * @see {@link throttle}\n * @see {@link throttleTime}\n * @see {@link debounce}\n * @see {@link debounceTime}\n * @see {@link sample}\n * @see {@link sampleTime}\n * @see {@link audit}\n * @see {@link auditTime}\n *\n * @param delayDurationSelector A function that returns an `ObservableInput` for\n * each `value` emitted by the source Observable, which is then used to delay the\n * emission of that `value` on the output Observable until the `ObservableInput`\n * returned from this function emits a next value. When called, beside `value`,\n * this function receives a zero-based `index` of the emission order.\n * @param subscriptionDelay An Observable that triggers the subscription to the\n * source Observable once it emits any value.\n * @return A function that returns an Observable that delays the emissions of\n * the source Observable by an amount of time specified by the Observable\n * returned by `delayDurationSelector`.\n */\nexport function delayWhen<T>(\n  delayDurationSelector: (value: T, index: number) => ObservableInput<any>,\n  subscriptionDelay?: Observable<any>\n): MonoTypeOperatorFunction<T> {\n  if (subscriptionDelay) {\n    // DEPRECATED PATH\n    return (source: Observable<T>) =>\n      concat(subscriptionDelay.pipe(take(1), ignoreElements()), source.pipe(delayWhen(delayDurationSelector)));\n  }\n\n  return mergeMap((value, index) => innerFrom(delayDurationSelector(value, index)).pipe(take(1), mapTo(value)));\n}\n", "import { asyncScheduler } from '../scheduler/async';\nimport { MonoTypeOperatorFunction, SchedulerLike } from '../types';\nimport { delayWhen } from './delayWhen';\nimport { timer } from '../observable/timer';\n\n/**\n * Delays the emission of items from the source Observable by a given timeout or\n * until a given Date.\n *\n * <span class=\"informal\">Time shifts each item by some specified amount of\n * milliseconds.</span>\n *\n * ![](delay.svg)\n *\n * If the delay argument is a Number, this operator time shifts the source\n * Observable by that amount of time expressed in milliseconds. The relative\n * time intervals between the values are preserved.\n *\n * If the delay argument is a Date, this operator time shifts the start of the\n * Observable execution until the given date occurs.\n *\n * ## Examples\n *\n * Delay each click by one second\n *\n * ```ts\n * import { fromEvent, delay } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const delayedClicks = clicks.pipe(delay(1000)); // each click emitted after 1 second\n * delayedClicks.subscribe(x => console.log(x));\n * ```\n *\n * Delay all clicks until a future date happens\n *\n * ```ts\n * import { fromEvent, delay } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const date = new Date('March 15, 2050 12:00:00'); // in the future\n * const delayedClicks = clicks.pipe(delay(date)); // click emitted only after that date\n * delayedClicks.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link delayWhen}\n * @see {@link throttle}\n * @see {@link throttleTime}\n * @see {@link debounce}\n * @see {@link debounceTime}\n * @see {@link sample}\n * @see {@link sampleTime}\n * @see {@link audit}\n * @see {@link auditTime}\n *\n * @param {number|Date} due The delay duration in milliseconds (a `number`) or\n * a `Date` until which the emission of the source items is delayed.\n * @param {SchedulerLike} [scheduler=async] The {@link SchedulerLike} to use for\n * managing the timers that handle the time-shift for each item.\n * @return A function that returns an Observable that delays the emissions of\n * the source Observable by the specified timeout or Date.\n */\nexport function delay<T>(due: number | Date, scheduler: SchedulerLike = asyncScheduler): MonoTypeOperatorFunction<T> {\n  const duration = timer(due, scheduler);\n  return delayWhen(() => duration);\n}\n", "import { MonoTypeOperatorFunction } from '../types';\nimport { identity } from '../util/identity';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\nexport function distinctUntilChanged<T>(comparator?: (previous: T, current: T) => boolean): MonoTypeOperatorFunction<T>;\nexport function distinctUntilChanged<T, K>(\n  comparator: (previous: K, current: K) => boolean,\n  keySelector: (value: T) => K\n): MonoTypeOperatorFunction<T>;\n\n/**\n * Returns a result {@link Observable} that emits all values pushed by the source observable if they\n * are distinct in comparison to the last value the result observable emitted.\n *\n * When provided without parameters or with the first parameter (`{@link distinctUntilChanged#comparator comparator}`),\n * it behaves like this:\n *\n * 1. It will always emit the first value from the source.\n * 2. For all subsequent values pushed by the source, they will be compared to the previously emitted values\n *    using the provided `comparator` or an `===` equality check.\n * 3. If the value pushed by the source is determined to be unequal by this check, that value is emitted and\n *    becomes the new \"previously emitted value\" internally.\n *\n * When the second parameter (`{@link distinctUntilChanged#keySelector keySelector}`) is provided, the behavior\n * changes:\n *\n * 1. It will always emit the first value from the source.\n * 2. The `keySelector` will be run against all values, including the first value.\n * 3. For all values after the first, the selected key will be compared against the key selected from\n *    the previously emitted value using the `comparator`.\n * 4. If the keys are determined to be unequal by this check, the value (not the key), is emitted\n *    and the selected key from that value is saved for future comparisons against other keys.\n *\n * ## Examples\n *\n * A very basic example with no `{@link distinctUntilChanged#comparator comparator}`. Note that `1` is emitted more than once,\n * because it's distinct in comparison to the _previously emitted_ value,\n * not in comparison to _all other emitted values_.\n *\n * ```ts\n * import { of, distinctUntilChanged } from 'rxjs';\n *\n * of(1, 1, 1, 2, 2, 2, 1, 1, 3, 3)\n *   .pipe(distinctUntilChanged())\n *   .subscribe(console.log);\n * // Logs: 1, 2, 1, 3\n * ```\n *\n * With a `{@link distinctUntilChanged#comparator comparator}`, you can do custom comparisons. Let's say\n * you only want to emit a value when all of its components have\n * changed:\n *\n * ```ts\n * import { of, distinctUntilChanged } from 'rxjs';\n *\n * const totallyDifferentBuilds$ = of(\n *   { engineVersion: '1.1.0', transmissionVersion: '1.2.0' },\n *   { engineVersion: '1.1.0', transmissionVersion: '1.4.0' },\n *   { engineVersion: '1.3.0', transmissionVersion: '1.4.0' },\n *   { engineVersion: '1.3.0', transmissionVersion: '1.5.0' },\n *   { engineVersion: '2.0.0', transmissionVersion: '1.5.0' }\n * ).pipe(\n *   distinctUntilChanged((prev, curr) => {\n *     return (\n *       prev.engineVersion === curr.engineVersion ||\n *       prev.transmissionVersion === curr.transmissionVersion\n *     );\n *   })\n * );\n *\n * totallyDifferentBuilds$.subscribe(console.log);\n *\n * // Logs:\n * // { engineVersion: '1.1.0', transmissionVersion: '1.2.0' }\n * // { engineVersion: '1.3.0', transmissionVersion: '1.4.0' }\n * // { engineVersion: '2.0.0', transmissionVersion: '1.5.0' }\n * ```\n *\n * You can also provide a custom `{@link distinctUntilChanged#comparator comparator}` to check that emitted\n * changes are only in one direction. Let's say you only want to get\n * the next record temperature:\n *\n * ```ts\n * import { of, distinctUntilChanged } from 'rxjs';\n *\n * const temps$ = of(30, 31, 20, 34, 33, 29, 35, 20);\n *\n * const recordHighs$ = temps$.pipe(\n *   distinctUntilChanged((prevHigh, temp) => {\n *     // If the current temp is less than\n *     // or the same as the previous record,\n *     // the record hasn't changed.\n *     return temp <= prevHigh;\n *   })\n * );\n *\n * recordHighs$.subscribe(console.log);\n * // Logs: 30, 31, 34, 35\n * ```\n *\n * Selecting update events only when the `updatedBy` field shows\n * the account changed hands.\n *\n * ```ts\n * import { of, distinctUntilChanged } from 'rxjs';\n *\n * // A stream of updates to a given account\n * const accountUpdates$ = of(\n *   { updatedBy: 'blesh', data: [] },\n *   { updatedBy: 'blesh', data: [] },\n *   { updatedBy: 'ncjamieson', data: [] },\n *   { updatedBy: 'ncjamieson', data: [] },\n *   { updatedBy: 'blesh', data: [] }\n * );\n *\n * // We only want the events where it changed hands\n * const changedHands$ = accountUpdates$.pipe(\n *   distinctUntilChanged(undefined, update => update.updatedBy)\n * );\n *\n * changedHands$.subscribe(console.log);\n * // Logs:\n * // { updatedBy: 'blesh', data: Array[0] }\n * // { updatedBy: 'ncjamieson', data: Array[0] }\n * // { updatedBy: 'blesh', data: Array[0] }\n * ```\n *\n * @see {@link distinct}\n * @see {@link distinctUntilKeyChanged}\n *\n * @param comparator A function used to compare the previous and current keys for\n * equality. Defaults to a `===` check.\n * @param keySelector Used to select a key value to be passed to the `comparator`.\n *\n * @return A function that returns an Observable that emits items from the\n * source Observable with distinct values.\n */\nexport function distinctUntilChanged<T, K>(\n  comparator?: (previous: K, current: K) => boolean,\n  keySelector: (value: T) => K = identity as (value: T) => K\n): MonoTypeOperatorFunction<T> {\n  // We've been allowing `null` do be passed as the `compare`, so we can't do\n  // a default value for the parameter, because that will only work\n  // for `undefined`.\n  comparator = comparator ?? defaultCompare;\n\n  return operate((source, subscriber) => {\n    // The previous key, used to compare against keys selected\n    // from new arrivals to determine \"distinctiveness\".\n    let previousKey: K;\n    // Whether or not this is the first value we've gotten.\n    let first = true;\n\n    source.subscribe(\n      createOperatorSubscriber(subscriber, (value) => {\n        // We always call the key selector.\n        const currentKey = keySelector(value);\n\n        // If it's the first value, we always emit it.\n        // Otherwise, we compare this key to the previous key, and\n        // if the comparer returns false, we emit.\n        if (first || !comparator!(previousKey, currentKey)) {\n          // Update our state *before* we emit the value\n          // as emission can be the source of re-entrant code\n          // in functional libraries like this. We only really\n          // need to do this if it's the first value, or if the\n          // key we're tracking in previous needs to change.\n          first = false;\n          previousKey = currentKey;\n\n          // Emit the value!\n          subscriber.next(value);\n        }\n      })\n    );\n  });\n}\n\nfunction defaultCompare(a: any, b: any) {\n  return a === b;\n}\n", "import { distinctUntilChanged } from './distinctUntilChanged';\nimport { MonoTypeOperatorFunction } from '../types';\n\n/* tslint:disable:max-line-length */\nexport function distinctUntilKeyChanged<T>(key: keyof T): MonoTypeOperatorFunction<T>;\nexport function distinctUntilKeyChanged<T, K extends keyof T>(key: K, compare: (x: T[K], y: T[K]) => boolean): MonoTypeOperatorFunction<T>;\n/* tslint:enable:max-line-length */\n\n/**\n * Returns an Observable that emits all items emitted by the source Observable that are distinct by comparison from the previous item,\n * using a property accessed by using the key provided to check if the two items are distinct.\n *\n * If a comparator function is provided, then it will be called for each item to test for whether or not that value should be emitted.\n *\n * If a comparator function is not provided, an equality check is used by default.\n *\n * ## Examples\n *\n * An example comparing the name of persons\n *\n * ```ts\n * import { of, distinctUntilKeyChanged } from 'rxjs';\n *\n * of(\n *   { age: 4, name: 'Foo' },\n *   { age: 7, name: 'Bar' },\n *   { age: 5, name: 'Foo' },\n *   { age: 6, name: 'Foo' }\n * ).pipe(\n *   distinctUntilKeyChanged('name')\n * )\n * .subscribe(x => console.log(x));\n *\n * // displays:\n * // { age: 4, name: 'Foo' }\n * // { age: 7, name: 'Bar' }\n * // { age: 5, name: 'Foo' }\n * ```\n *\n * An example comparing the first letters of the name\n *\n * ```ts\n * import { of, distinctUntilKeyChanged } from 'rxjs';\n *\n * of(\n *   { age: 4, name: 'Foo1' },\n *   { age: 7, name: 'Bar' },\n *   { age: 5, name: 'Foo2' },\n *   { age: 6, name: 'Foo3' }\n * ).pipe(\n *   distinctUntilKeyChanged('name', (x, y) => x.substring(0, 3) === y.substring(0, 3))\n * )\n * .subscribe(x => console.log(x));\n *\n * // displays:\n * // { age: 4, name: 'Foo1' }\n * // { age: 7, name: 'Bar' }\n * // { age: 5, name: 'Foo2' }\n * ```\n *\n * @see {@link distinct}\n * @see {@link distinctUntilChanged}\n *\n * @param {string} key String key for object property lookup on each item.\n * @param {function} [compare] Optional comparison function called to test if an item is distinct from the previous item in the source.\n * @return A function that returns an Observable that emits items from the\n * source Observable with distinct values based on the key specified.\n */\nexport function distinctUntilKeyChanged<T, K extends keyof T>(key: K, compare?: (x: T[K], y: T[K]) => boolean): MonoTypeOperatorFunction<T> {\n  return distinctUntilChanged((x: T, y: T) => compare ? compare(x[key], y[key]) : x[key] === y[key]);\n}\n", "import { EmptyError } from '../util/EmptyError';\nimport { MonoTypeOperatorFunction } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\n/**\n * If the source observable completes without emitting a value, it will emit\n * an error. The error will be created at that time by the optional\n * `errorFactory` argument, otherwise, the error will be {@link EmptyError}.\n *\n * ![](throwIfEmpty.png)\n *\n * ## Example\n *\n * Throw an error if the document wasn't clicked within 1 second\n *\n * ```ts\n * import { fromEvent, takeUntil, timer, throwIfEmpty } from 'rxjs';\n *\n * const click$ = fromEvent(document, 'click');\n *\n * click$.pipe(\n *   takeUntil(timer(1000)),\n *   throwIfEmpty(() => new Error('The document was not clicked within 1 second'))\n * )\n * .subscribe({\n *   next() {\n *    console.log('The document was clicked');\n *   },\n *   error(err) {\n *     console.error(err.message);\n *   }\n * });\n * ```\n *\n * @param errorFactory A factory function called to produce the\n * error to be thrown when the source observable completes without emitting a\n * value.\n * @return A function that returns an Observable that throws an error if the\n * source Observable completed without emitting.\n */\nexport function throwIfEmpty<T>(errorFactory: () => any = defaultErrorFactory): MonoTypeOperatorFunction<T> {\n  return operate((source, subscriber) => {\n    let hasValue = false;\n    source.subscribe(\n      createOperatorSubscriber(\n        subscriber,\n        (value) => {\n          hasValue = true;\n          subscriber.next(value);\n        },\n        () => (hasValue ? subscriber.complete() : subscriber.error(errorFactory()))\n      )\n    );\n  });\n}\n\nfunction defaultErrorFactory() {\n  return new EmptyError();\n}\n", "/** prettier */\nimport { Observable } from '../Observable';\nimport { concat } from '../observable/concat';\nimport { of } from '../observable/of';\nimport { MonoTypeOperatorFunction, SchedulerLike, OperatorFunction, ValueFromArray } from '../types';\n\n/** @deprecated The `scheduler` parameter will be removed in v8. Use `scheduled` and `concatAll`. Details: https://rxjs.dev/deprecations/scheduler-argument */\nexport function endWith<T>(scheduler: SchedulerLike): MonoTypeOperatorFunction<T>;\n/** @deprecated The `scheduler` parameter will be removed in v8. Use `scheduled` and `concatAll`. Details: https://rxjs.dev/deprecations/scheduler-argument */\nexport function endWith<T, A extends unknown[] = T[]>(\n  ...valuesAndScheduler: [...A, SchedulerLike]\n): OperatorFunction<T, T | ValueFromArray<A>>;\n\nexport function endWith<T, A extends unknown[] = T[]>(...values: A): OperatorFunction<T, T | ValueFromArray<A>>;\n\n/**\n * Returns an observable that will emit all values from the source, then synchronously emit\n * the provided value(s) immediately after the source completes.\n *\n * NOTE: Passing a last argument of a Scheduler is _deprecated_, and may result in incorrect\n * types in TypeScript.\n *\n * This is useful for knowing when an observable ends. Particularly when paired with an\n * operator like {@link takeUntil}\n *\n * ![](endWith.png)\n *\n * ## Example\n *\n * Emit values to know when an interval starts and stops. The interval will\n * stop when a user clicks anywhere on the document.\n *\n * ```ts\n * import { interval, map, fromEvent, startWith, takeUntil, endWith } from 'rxjs';\n *\n * const ticker$ = interval(5000).pipe(\n *   map(() => 'tick')\n * );\n *\n * const documentClicks$ = fromEvent(document, 'click');\n *\n * ticker$.pipe(\n *   startWith('interval started'),\n *   takeUntil(documentClicks$),\n *   endWith('interval ended by click')\n * )\n * .subscribe(x => console.log(x));\n *\n * // Result (assuming a user clicks after 15 seconds)\n * // 'interval started'\n * // 'tick'\n * // 'tick'\n * // 'tick'\n * // 'interval ended by click'\n * ```\n *\n * @see {@link startWith}\n * @see {@link concat}\n * @see {@link takeUntil}\n *\n * @param values Items you want the modified Observable to emit last.\n * @return A function that returns an Observable that emits all values from the\n * source, then synchronously emits the provided value(s) immediately after the\n * source completes.\n */\nexport function endWith<T>(...values: Array<T | SchedulerLike>): MonoTypeOperatorFunction<T> {\n  return (source: Observable<T>) => concat(source, of(...values)) as Observable<T>;\n}\n", "import { MonoTypeOperatorFunction } from '../types';\nimport { operate } from '../util/lift';\n\n/**\n * Returns an Observable that mirrors the source Observable, but will call a specified function when\n * the source terminates on complete or error.\n * The specified function will also be called when the subscriber explicitly unsubscribes.\n *\n * ## Examples\n *\n * Execute callback function when the observable completes\n *\n * ```ts\n * import { interval, take, finalize } from 'rxjs';\n *\n * // emit value in sequence every 1 second\n * const source = interval(1000);\n * const example = source.pipe(\n *   take(5), //take only the first 5 values\n *   finalize(() => console.log('Sequence complete')) // Execute when the observable completes\n * );\n * const subscribe = example.subscribe(val => console.log(val));\n *\n * // results:\n * // 0\n * // 1\n * // 2\n * // 3\n * // 4\n * // 'Sequence complete'\n * ```\n *\n * Execute callback function when the subscriber explicitly unsubscribes\n *\n * ```ts\n * import { interval, finalize, tap, noop, timer } from 'rxjs';\n *\n * const source = interval(100).pipe(\n *   finalize(() => console.log('[finalize] Called')),\n *   tap({\n *     next: () => console.log('[next] Called'),\n *     error: () => console.log('[error] Not called'),\n *     complete: () => console.log('[tap complete] Not called')\n *   })\n * );\n *\n * const sub = source.subscribe({\n *   next: x => console.log(x),\n *   error: noop,\n *   complete: () => console.log('[complete] Not called')\n * });\n *\n * timer(150).subscribe(() => sub.unsubscribe());\n *\n * // results:\n * // '[next] Called'\n * // 0\n * // '[finalize] Called'\n * ```\n *\n * @param {function} callback Function to be called when source terminates.\n * @return A function that returns an Observable that mirrors the source, but\n * will call the specified function on termination.\n */\nexport function finalize<T>(callback: () => void): MonoTypeOperatorFunction<T> {\n  return operate((source, subscriber) => {\n    // TODO: This try/finally was only added for `useDeprecatedSynchronousErrorHandling`.\n    // REMOVE THIS WHEN THAT HOT GARBAGE IS REMOVED IN V8.\n    try {\n      source.subscribe(subscriber);\n    } finally {\n      subscriber.add(callback);\n    }\n  });\n}\n", "import { Observable } from '../Observable';\nimport { EmptyError } from '../util/EmptyError';\nimport { OperatorFunction, TruthyTypesOf } from '../types';\nimport { filter } from './filter';\nimport { take } from './take';\nimport { defaultIfEmpty } from './defaultIfEmpty';\nimport { throwIfEmpty } from './throwIfEmpty';\nimport { identity } from '../util/identity';\n\nexport function first<T, D = T>(predicate?: null, defaultValue?: D): OperatorFunction<T, T | D>;\nexport function first<T>(predicate: BooleanConstructor): OperatorFunction<T, TruthyTypesOf<T>>;\nexport function first<T, D>(predicate: BooleanConstructor, defaultValue: D): OperatorFunction<T, TruthyTypesOf<T> | D>;\nexport function first<T, S extends T>(\n  predicate: (value: T, index: number, source: Observable<T>) => value is S,\n  defaultValue?: S\n): OperatorFunction<T, S>;\nexport function first<T, S extends T, D>(\n  predicate: (value: T, index: number, source: Observable<T>) => value is S,\n  defaultValue: D\n): OperatorFunction<T, S | D>;\nexport function first<T, D = T>(\n  predicate: (value: T, index: number, source: Observable<T>) => boolean,\n  defaultValue?: D\n): OperatorFunction<T, T | D>;\n\n/**\n * Emits only the first value (or the first value that meets some condition)\n * emitted by the source Observable.\n *\n * <span class=\"informal\">Emits only the first value. Or emits only the first\n * value that passes some test.</span>\n *\n * ![](first.png)\n *\n * If called with no arguments, `first` emits the first value of the source\n * Observable, then completes. If called with a `predicate` function, `first`\n * emits the first value of the source that matches the specified condition. Throws an error if\n * `defaultValue` was not provided and a matching element is not found.\n *\n * ## Examples\n *\n * Emit only the first click that happens on the DOM\n *\n * ```ts\n * import { fromEvent, first } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const result = clicks.pipe(first());\n * result.subscribe(x => console.log(x));\n * ```\n *\n * Emits the first click that happens on a DIV\n *\n * ```ts\n * import { fromEvent, first } from 'rxjs';\n *\n * const div = document.createElement('div');\n * div.style.cssText = 'width: 200px; height: 200px; background: #09c;';\n * document.body.appendChild(div);\n *\n * const clicks = fromEvent(document, 'click');\n * const result = clicks.pipe(first(ev => (<HTMLElement>ev.target).tagName === 'DIV'));\n * result.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link filter}\n * @see {@link find}\n * @see {@link take}\n *\n * @throws {EmptyError} Delivers an EmptyError to the Observer's `error`\n * callback if the Observable completes before any `next` notification was sent.\n * This is how `first()` is different from {@link take}(1) which completes instead.\n *\n * @param {function(value: T, index: number, source: Observable<T>): boolean} [predicate]\n * An optional function called with each item to test for condition matching.\n * @param {D} [defaultValue] The default value emitted in case no valid value\n * was found on the source.\n * @return A function that returns an Observable that emits the first item that\n * matches the condition.\n */\nexport function first<T, D>(\n  predicate?: ((value: T, index: number, source: Observable<T>) => boolean) | null,\n  defaultValue?: D\n): OperatorFunction<T, T | D> {\n  const hasDefaultValue = arguments.length >= 2;\n  return (source: Observable<T>) =>\n    source.pipe(\n      predicate ? filter((v, i) => predicate(v, i, source)) : identity,\n      take(1),\n      hasDefaultValue ? defaultIfEmpty(defaultValue!) : throwIfEmpty(() => new EmptyError())\n    );\n}\n", "import { ObservableInput, ObservableInputTuple, OperatorFunction, SchedulerLike } from '../types';\nimport { operate } from '../util/lift';\nimport { argsOrArgArray } from '../util/argsOrArgArray';\nimport { mergeAll } from './mergeAll';\nimport { popNumber, popScheduler } from '../util/args';\nimport { from } from '../observable/from';\n\n/** @deprecated Replaced with {@link mergeWith}. Will be removed in v8. */\nexport function merge<T, A extends readonly unknown[]>(...sources: [...ObservableInputTuple<A>]): OperatorFunction<T, T | A[number]>;\n/** @deprecated Replaced with {@link mergeWith}. Will be removed in v8. */\nexport function merge<T, A extends readonly unknown[]>(\n  ...sourcesAndConcurrency: [...ObservableInputTuple<A>, number]\n): OperatorFunction<T, T | A[number]>;\n/** @deprecated Replaced with {@link mergeWith}. Will be removed in v8. */\nexport function merge<T, A extends readonly unknown[]>(\n  ...sourcesAndScheduler: [...ObservableInputTuple<A>, SchedulerLike]\n): OperatorFunction<T, T | A[number]>;\n/** @deprecated Replaced with {@link mergeWith}. Will be removed in v8. */\nexport function merge<T, A extends readonly unknown[]>(\n  ...sourcesAndConcurrencyAndScheduler: [...ObservableInputTuple<A>, number, SchedulerLike]\n): OperatorFunction<T, T | A[number]>;\n\nexport function merge<T>(...args: unknown[]): OperatorFunction<T, unknown> {\n  const scheduler = popScheduler(args);\n  const concurrent = popNumber(args, Infinity);\n  args = argsOrArgArray(args);\n\n  return operate((source, subscriber) => {\n    mergeAll(concurrent)(from([source, ...(args as ObservableInput<T>[])], scheduler)).subscribe(subscriber);\n  });\n}\n", "import { ObservableInputTuple, OperatorFunction } from '../types';\nimport { merge } from './merge';\n\n/**\n * Merge the values from all observables to a single observable result.\n *\n * Creates an observable, that when subscribed to, subscribes to the source\n * observable, and all other sources provided as arguments. All values from\n * every source are emitted from the resulting subscription.\n *\n * When all sources complete, the resulting observable will complete.\n *\n * When any source errors, the resulting observable will error.\n *\n * ## Example\n *\n * Joining all outputs from multiple user input event streams\n *\n * ```ts\n * import { fromEvent, map, mergeWith } from 'rxjs';\n *\n * const clicks$ = fromEvent(document, 'click').pipe(map(() => 'click'));\n * const mousemoves$ = fromEvent(document, 'mousemove').pipe(map(() => 'mousemove'));\n * const dblclicks$ = fromEvent(document, 'dblclick').pipe(map(() => 'dblclick'));\n *\n * mousemoves$\n *   .pipe(mergeWith(clicks$, dblclicks$))\n *   .subscribe(x => console.log(x));\n *\n * // result (assuming user interactions)\n * // 'mousemove'\n * // 'mousemove'\n * // 'mousemove'\n * // 'click'\n * // 'click'\n * // 'dblclick'\n * ```\n *\n * @see {@link merge}\n *\n * @param otherSources the sources to combine the current source with.\n * @return A function that returns an Observable that merges the values from\n * all given Observables.\n */\nexport function mergeWith<T, A extends readonly unknown[]>(\n  ...otherSources: [...ObservableInputTuple<A>]\n): OperatorFunction<T, T | A[number]> {\n  return merge(...otherSources);\n}\n", "import { Subscription } from '../Subscription';\nimport { EMPTY } from '../observable/empty';\nimport { operate } from '../util/lift';\nimport { MonoTypeOperatorFunction, ObservableInput } from '../types';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\nimport { innerFrom } from '../observable/innerFrom';\nimport { timer } from '../observable/timer';\n\nexport interface RepeatConfig {\n  /**\n   * The number of times to repeat the source. Defaults to `Infinity`.\n   */\n  count?: number;\n\n  /**\n   * If a `number`, will delay the repeat of the source by that number of milliseconds.\n   * If a function, it will provide the number of times the source has been subscribed to,\n   * and the return value should be a valid observable input that will notify when the source\n   * should be repeated. If the notifier observable is empty, the result will complete.\n   */\n  delay?: number | ((count: number) => ObservableInput<any>);\n}\n\n/**\n * Returns an Observable that will resubscribe to the source stream when the source stream completes.\n *\n * <span class=\"informal\">Repeats all values emitted on the source. It's like {@link retry}, but for non error cases.</span>\n *\n * ![](repeat.png)\n *\n * Repeat will output values from a source until the source completes, then it will resubscribe to the\n * source a specified number of times, with a specified delay. Repeat can be particularly useful in\n * combination with closing operators like {@link take}, {@link takeUntil}, {@link first}, or {@link takeWhile},\n * as it can be used to restart a source again from scratch.\n *\n * Repeat is very similar to {@link retry}, where {@link retry} will resubscribe to the source in the error case, but\n * `repeat` will resubscribe if the source completes.\n *\n * Note that `repeat` will _not_ catch errors. Use {@link retry} for that.\n *\n * - `repeat(0)` returns an empty observable\n * - `repeat()` will repeat forever\n * - `repeat({ delay: 200 })` will repeat forever, with a delay of 200ms between repetitions.\n * - `repeat({ count: 2, delay: 400 })` will repeat twice, with a delay of 400ms between repetitions.\n * - `repeat({ delay: (count) => timer(count * 1000) })` will repeat forever, but will have a delay that grows by one second for each repetition.\n *\n * ## Example\n *\n * Repeat a message stream\n *\n * ```ts\n * import { of, repeat } from 'rxjs';\n *\n * const source = of('Repeat message');\n * const result = source.pipe(repeat(3));\n *\n * result.subscribe(x => console.log(x));\n *\n * // Results\n * // 'Repeat message'\n * // 'Repeat message'\n * // 'Repeat message'\n * ```\n *\n * Repeat 3 values, 2 times\n *\n * ```ts\n * import { interval, take, repeat } from 'rxjs';\n *\n * const source = interval(1000);\n * const result = source.pipe(take(3), repeat(2));\n *\n * result.subscribe(x => console.log(x));\n *\n * // Results every second\n * // 0\n * // 1\n * // 2\n * // 0\n * // 1\n * // 2\n * ```\n *\n * Defining two complex repeats with delays on the same source.\n * Note that the second repeat cannot be called until the first\n * repeat as exhausted it's count.\n *\n * ```ts\n * import { defer, of, repeat } from 'rxjs';\n *\n * const source = defer(() => {\n *    return of(`Hello, it is ${new Date()}`)\n * });\n *\n * source.pipe(\n *    // Repeat 3 times with a delay of 1 second between repetitions\n *    repeat({\n *      count: 3,\n *      delay: 1000,\n *    }),\n *\n *    // *Then* repeat forever, but with an exponential step-back\n *    // maxing out at 1 minute.\n *    repeat({\n *      delay: (count) => timer(Math.min(60000, 2 ^ count * 1000))\n *    })\n * )\n * ```\n *\n * @see {@link repeatWhen}\n * @see {@link retry}\n *\n * @param count The number of times the source Observable items are repeated, a count of 0 will yield\n * an empty Observable.\n */\nexport function repeat<T>(countOrConfig?: number | RepeatConfig): MonoTypeOperatorFunction<T> {\n  let count = Infinity;\n  let delay: RepeatConfig['delay'];\n\n  if (countOrConfig != null) {\n    if (typeof countOrConfig === 'object') {\n      ({ count = Infinity, delay } = countOrConfig);\n    } else {\n      count = countOrConfig;\n    }\n  }\n\n  return count <= 0\n    ? () => EMPTY\n    : operate((source, subscriber) => {\n        let soFar = 0;\n        let sourceSub: Subscription | null;\n\n        const resubscribe = () => {\n          sourceSub?.unsubscribe();\n          sourceSub = null;\n          if (delay != null) {\n            const notifier = typeof delay === 'number' ? timer(delay) : innerFrom(delay(soFar));\n            const notifierSubscriber = createOperatorSubscriber(subscriber, () => {\n              notifierSubscriber.unsubscribe();\n              subscribeToSource();\n            });\n            notifier.subscribe(notifierSubscriber);\n          } else {\n            subscribeToSource();\n          }\n        };\n\n        const subscribeToSource = () => {\n          let syncUnsub = false;\n          sourceSub = source.subscribe(\n            createOperatorSubscriber(subscriber, undefined, () => {\n              if (++soFar < count) {\n                if (sourceSub) {\n                  resubscribe();\n                } else {\n                  syncUnsub = true;\n                }\n              } else {\n                subscriber.complete();\n              }\n            })\n          );\n\n          if (syncUnsub) {\n            resubscribe();\n          }\n        };\n\n        subscribeToSource();\n      });\n}\n", "import { OperatorFunction } from '../types';\nimport { operate } from '../util/lift';\nimport { scanInternals } from './scanInternals';\n\nexport function scan<V, A = V>(accumulator: (acc: A | V, value: V, index: number) => A): OperatorFunction<V, V | A>;\nexport function scan<V, A>(accumulator: (acc: A, value: V, index: number) => A, seed: A): OperatorFunction<V, A>;\nexport function scan<V, A, S>(accumulator: (acc: A | S, value: V, index: number) => A, seed: S): OperatorFunction<V, A>;\n\n// TODO: link to a \"redux pattern\" section in the guide (location TBD)\n\n/**\n * Useful for encapsulating and managing state. Applies an accumulator (or \"reducer function\")\n * to each value from the source after an initial state is established -- either via\n * a `seed` value (second argument), or from the first value from the source.\n *\n * <span class=\"informal\">It's like {@link reduce}, but emits the current\n * accumulation state after each update</span>\n *\n * ![](scan.png)\n *\n * This operator maintains an internal state and emits it after processing each value as follows:\n *\n * 1. First value arrives\n *   - If a `seed` value was supplied (as the second argument to `scan`), let `state = seed` and `value = firstValue`.\n *   - If NO `seed` value was supplied (no second argument), let `state = firstValue` and go to 3.\n * 2. Let `state = accumulator(state, value)`.\n *   - If an error is thrown by `accumulator`, notify the consumer of an error. The process ends.\n * 3. Emit `state`.\n * 4. Next value arrives, let `value = nextValue`, go to 2.\n *\n * ## Examples\n *\n * An average of previous numbers. This example shows how\n * not providing a `seed` can prime the stream with the\n * first value from the source.\n *\n * ```ts\n * import { of, scan, map } from 'rxjs';\n *\n * const numbers$ = of(1, 2, 3);\n *\n * numbers$\n *   .pipe(\n *     // Get the sum of the numbers coming in.\n *     scan((total, n) => total + n),\n *     // Get the average by dividing the sum by the total number\n *     // received so far (which is 1 more than the zero-based index).\n *     map((sum, index) => sum / (index + 1))\n *   )\n *   .subscribe(console.log);\n * ```\n *\n * The Fibonacci sequence. This example shows how you can use\n * a seed to prime accumulation process. Also... you know... Fibonacci.\n * So important to like, computers and stuff that its whiteboarded\n * in job interviews. Now you can show them the Rx version! (Please don't, haha)\n *\n * ```ts\n * import { interval, scan, map, startWith } from 'rxjs';\n *\n * const firstTwoFibs = [0, 1];\n * // An endless stream of Fibonacci numbers.\n * const fibonacci$ = interval(1000).pipe(\n *   // Scan to get the fibonacci numbers (after 0, 1)\n *   scan(([a, b]) => [b, a + b], firstTwoFibs),\n *   // Get the second number in the tuple, it's the one you calculated\n *   map(([, n]) => n),\n *   // Start with our first two digits :)\n *   startWith(...firstTwoFibs)\n * );\n *\n * fibonacci$.subscribe(console.log);\n * ```\n *\n * @see {@link expand}\n * @see {@link mergeScan}\n * @see {@link reduce}\n * @see {@link switchScan}\n *\n * @param accumulator A \"reducer function\". This will be called for each value after an initial state is\n * acquired.\n * @param seed The initial state. If this is not provided, the first value from the source will\n * be used as the initial state, and emitted without going through the accumulator. All subsequent values\n * will be processed by the accumulator function. If this is provided, all values will go through\n * the accumulator function.\n * @return A function that returns an Observable of the accumulated values.\n */\nexport function scan<V, A, S>(accumulator: (acc: V | A | S, value: V, index: number) => A, seed?: S): OperatorFunction<V, V | A> {\n  // providing a seed of `undefined` *should* be valid and trigger\n  // hasSeed! so don't use `seed !== undefined` checks!\n  // For this reason, we have to check it here at the original call site\n  // otherwise inside Operator/Subscriber we won't know if `undefined`\n  // means they didn't provide anything or if they literally provided `undefined`\n  return operate(scanInternals(accumulator, seed as S, arguments.length >= 2, true));\n}\n", "import { innerFrom } from '../observable/innerFrom';\nimport { Subject } from '../Subject';\nimport { SafeSubscriber } from '../Subscriber';\nimport { Subscription } from '../Subscription';\nimport { MonoTypeOperatorFunction, SubjectLike, ObservableInput } from '../types';\nimport { operate } from '../util/lift';\n\nexport interface ShareConfig<T> {\n  /**\n   * The factory used to create the subject that will connect the source observable to\n   * multicast consumers.\n   */\n  connector?: () => SubjectLike<T>;\n  /**\n   * If `true`, the resulting observable will reset internal state on error from source and return to a \"cold\" state. This\n   * allows the resulting observable to be \"retried\" in the event of an error.\n   * If `false`, when an error comes from the source it will push the error into the connecting subject, and the subject\n   * will remain the connecting subject, meaning the resulting observable will not go \"cold\" again, and subsequent retries\n   * or resubscriptions will resubscribe to that same subject. In all cases, RxJS subjects will emit the same error again, however\n   * {@link ReplaySubject} will also push its buffered values before pushing the error.\n   * It is also possible to pass a notifier factory returning an `ObservableInput` instead which grants more fine-grained\n   * control over how and when the reset should happen. This allows behaviors like conditional or delayed resets.\n   */\n  resetOnError?: boolean | ((error: any) => ObservableInput<any>);\n  /**\n   * If `true`, the resulting observable will reset internal state on completion from source and return to a \"cold\" state. This\n   * allows the resulting observable to be \"repeated\" after it is done.\n   * If `false`, when the source completes, it will push the completion through the connecting subject, and the subject\n   * will remain the connecting subject, meaning the resulting observable will not go \"cold\" again, and subsequent repeats\n   * or resubscriptions will resubscribe to that same subject.\n   * It is also possible to pass a notifier factory returning an `ObservableInput` instead which grants more fine-grained\n   * control over how and when the reset should happen. This allows behaviors like conditional or delayed resets.\n   */\n  resetOnComplete?: boolean | (() => ObservableInput<any>);\n  /**\n   * If `true`, when the number of subscribers to the resulting observable reaches zero due to those subscribers unsubscribing, the\n   * internal state will be reset and the resulting observable will return to a \"cold\" state. This means that the next\n   * time the resulting observable is subscribed to, a new subject will be created and the source will be subscribed to\n   * again.\n   * If `false`, when the number of subscribers to the resulting observable reaches zero due to unsubscription, the subject\n   * will remain connected to the source, and new subscriptions to the result will be connected through that same subject.\n   * It is also possible to pass a notifier factory returning an `ObservableInput` instead which grants more fine-grained\n   * control over how and when the reset should happen. This allows behaviors like conditional or delayed resets.\n   */\n  resetOnRefCountZero?: boolean | (() => ObservableInput<any>);\n}\n\nexport function share<T>(): MonoTypeOperatorFunction<T>;\n\nexport function share<T>(options: ShareConfig<T>): MonoTypeOperatorFunction<T>;\n\n/**\n * Returns a new Observable that multicasts (shares) the original Observable. As long as there is at least one\n * Subscriber this Observable will be subscribed and emitting data. When all subscribers have unsubscribed it will\n * unsubscribe from the source Observable. Because the Observable is multicasting it makes the stream `hot`.\n * This is an alias for `multicast(() => new Subject()), refCount()`.\n *\n * The subscription to the underlying source Observable can be reset (unsubscribe and resubscribe for new subscribers),\n * if the subscriber count to the shared observable drops to 0, or if the source Observable errors or completes. It is\n * possible to use notifier factories for the resets to allow for behaviors like conditional or delayed resets. Please\n * note that resetting on error or complete of the source Observable does not behave like a transparent retry or restart\n * of the source because the error or complete will be forwarded to all subscribers and their subscription will be\n * closed. Only new subscribers after a reset on error or complete happened will cause a fresh subscription to the\n * source. To achieve transparent retries or restarts pipe the source through appropriate operators before sharing.\n *\n * ![](share.png)\n *\n * ## Example\n *\n * Generate new multicast Observable from the `source` Observable value\n *\n * ```ts\n * import { interval, tap, map, take, share } from 'rxjs';\n *\n * const source = interval(1000).pipe(\n *   tap(x => console.log('Processing: ', x)),\n *   map(x => x * x),\n *   take(6),\n *   share()\n * );\n *\n * source.subscribe(x => console.log('subscription 1: ', x));\n * source.subscribe(x => console.log('subscription 2: ', x));\n *\n * // Logs:\n * // Processing: 0\n * // subscription 1: 0\n * // subscription 2: 0\n * // Processing: 1\n * // subscription 1: 1\n * // subscription 2: 1\n * // Processing: 2\n * // subscription 1: 4\n * // subscription 2: 4\n * // Processing: 3\n * // subscription 1: 9\n * // subscription 2: 9\n * // Processing: 4\n * // subscription 1: 16\n * // subscription 2: 16\n * // Processing: 5\n * // subscription 1: 25\n * // subscription 2: 25\n * ```\n *\n * ## Example with notifier factory: Delayed reset\n *\n * ```ts\n * import { interval, take, share, timer } from 'rxjs';\n *\n * const source = interval(1000).pipe(\n *   take(3),\n *   share({\n *     resetOnRefCountZero: () => timer(1000)\n *   })\n * );\n *\n * const subscriptionOne = source.subscribe(x => console.log('subscription 1: ', x));\n * setTimeout(() => subscriptionOne.unsubscribe(), 1300);\n *\n * setTimeout(() => source.subscribe(x => console.log('subscription 2: ', x)), 1700);\n *\n * setTimeout(() => source.subscribe(x => console.log('subscription 3: ', x)), 5000);\n *\n * // Logs:\n * // subscription 1:  0\n * // (subscription 1 unsubscribes here)\n * // (subscription 2 subscribes here ~400ms later, source was not reset)\n * // subscription 2:  1\n * // subscription 2:  2\n * // (subscription 2 unsubscribes here)\n * // (subscription 3 subscribes here ~2000ms later, source did reset before)\n * // subscription 3:  0\n * // subscription 3:  1\n * // subscription 3:  2\n * ```\n *\n * @see {@link shareReplay}\n *\n * @return A function that returns an Observable that mirrors the source.\n */\nexport function share<T>(options: ShareConfig<T> = {}): MonoTypeOperatorFunction<T> {\n  const { connector = () => new Subject<T>(), resetOnError = true, resetOnComplete = true, resetOnRefCountZero = true } = options;\n  // It's necessary to use a wrapper here, as the _operator_ must be\n  // referentially transparent. Otherwise, it cannot be used in calls to the\n  // static `pipe` function - to create a partial pipeline.\n  //\n  // The _operator function_ - the function returned by the _operator_ - will\n  // not be referentially transparent - as it shares its source - but the\n  // _operator function_ is called when the complete pipeline is composed via a\n  // call to a source observable's `pipe` method - not when the static `pipe`\n  // function is called.\n  return (wrapperSource) => {\n    let connection: SafeSubscriber<T> | undefined;\n    let resetConnection: Subscription | undefined;\n    let subject: SubjectLike<T> | undefined;\n    let refCount = 0;\n    let hasCompleted = false;\n    let hasErrored = false;\n\n    const cancelReset = () => {\n      resetConnection?.unsubscribe();\n      resetConnection = undefined;\n    };\n    // Used to reset the internal state to a \"cold\"\n    // state, as though it had never been subscribed to.\n    const reset = () => {\n      cancelReset();\n      connection = subject = undefined;\n      hasCompleted = hasErrored = false;\n    };\n    const resetAndUnsubscribe = () => {\n      // We need to capture the connection before\n      // we reset (if we need to reset).\n      const conn = connection;\n      reset();\n      conn?.unsubscribe();\n    };\n\n    return operate<T, T>((source, subscriber) => {\n      refCount++;\n      if (!hasErrored && !hasCompleted) {\n        cancelReset();\n      }\n\n      // Create the subject if we don't have one yet. Grab a local reference to\n      // it as well, which avoids non-null assertions when using it and, if we\n      // connect to it now, then error/complete need a reference after it was\n      // reset.\n      const dest = (subject = subject ?? connector());\n\n      // Add the finalization directly to the subscriber - instead of returning it -\n      // so that the handling of the subscriber's unsubscription will be wired\n      // up _before_ the subscription to the source occurs. This is done so that\n      // the assignment to the source connection's `closed` property will be seen\n      // by synchronous firehose sources.\n      subscriber.add(() => {\n        refCount--;\n\n        // If we're resetting on refCount === 0, and it's 0, we only want to do\n        // that on \"unsubscribe\", really. Resetting on error or completion is a different\n        // configuration.\n        if (refCount === 0 && !hasErrored && !hasCompleted) {\n          resetConnection = handleReset(resetAndUnsubscribe, resetOnRefCountZero);\n        }\n      });\n\n      // The following line adds the subscription to the subscriber passed.\n      // Basically, `subscriber === dest.subscribe(subscriber)` is `true`.\n      dest.subscribe(subscriber);\n\n      if (\n        !connection &&\n        // Check this shareReplay is still activate - it can be reset to 0\n        // and be \"unsubscribed\" _before_ it actually subscribes.\n        // If we were to subscribe then, it'd leak and get stuck.\n        refCount > 0\n      ) {\n        // We need to create a subscriber here - rather than pass an observer and\n        // assign the returned subscription to connection - because it's possible\n        // for reentrant subscriptions to the shared observable to occur and in\n        // those situations we want connection to be already-assigned so that we\n        // don't create another connection to the source.\n        connection = new SafeSubscriber({\n          next: (value) => dest.next(value),\n          error: (err) => {\n            hasErrored = true;\n            cancelReset();\n            resetConnection = handleReset(reset, resetOnError, err);\n            dest.error(err);\n          },\n          complete: () => {\n            hasCompleted = true;\n            cancelReset();\n            resetConnection = handleReset(reset, resetOnComplete);\n            dest.complete();\n          },\n        });\n        innerFrom(source).subscribe(connection);\n      }\n    })(wrapperSource);\n  };\n}\n\nfunction handleReset<T extends unknown[] = never[]>(\n  reset: () => void,\n  on: boolean | ((...args: T) => ObservableInput<any>),\n  ...args: T\n): Subscription | undefined {\n  if (on === true) {\n    reset();\n    return;\n  }\n\n  if (on === false) {\n    return;\n  }\n\n  const onSubscriber = new SafeSubscriber({\n    next: () => {\n      onSubscriber.unsubscribe();\n      reset();\n    },\n  });\n\n  return innerFrom(on(...args)).subscribe(onSubscriber);\n}\n", "import { ReplaySubject } from '../ReplaySubject';\nimport { MonoTypeOperatorFunction, SchedulerLike } from '../types';\nimport { share } from './share';\n\nexport interface ShareReplayConfig {\n  bufferSize?: number;\n  windowTime?: number;\n  refCount: boolean;\n  scheduler?: SchedulerLike;\n}\n\nexport function shareReplay<T>(config: ShareReplayConfig): MonoTypeOperatorFunction<T>;\nexport function shareReplay<T>(bufferSize?: number, windowTime?: number, scheduler?: SchedulerLike): MonoTypeOperatorFunction<T>;\n\n/**\n * Share source and replay specified number of emissions on subscription.\n *\n * This operator is a specialization of `replay` that connects to a source observable\n * and multicasts through a `ReplaySubject` constructed with the specified arguments.\n * A successfully completed source will stay cached in the `shareReplay`ed observable forever,\n * but an errored source can be retried.\n *\n * ## Why use `shareReplay`?\n *\n * You generally want to use `shareReplay` when you have side-effects or taxing computations\n * that you do not wish to be executed amongst multiple subscribers.\n * It may also be valuable in situations where you know you will have late subscribers to\n * a stream that need access to previously emitted values.\n * This ability to replay values on subscription is what differentiates {@link share} and `shareReplay`.\n *\n * ## Reference counting\n *\n * By default `shareReplay` will use `refCount` of false, meaning that it will _not_ unsubscribe the\n * source when the reference counter drops to zero, i.e. the inner `ReplaySubject` will _not_ be unsubscribed\n * (and potentially run for ever).\n * This is the default as it is expected that `shareReplay` is often used to keep around expensive to setup\n * observables which we want to keep running instead of having to do the expensive setup again.\n *\n * As of RXJS version 6.4.0 a new overload signature was added to allow for manual control over what\n * happens when the operators internal reference counter drops to zero.\n * If `refCount` is true, the source will be unsubscribed from once the reference count drops to zero, i.e.\n * the inner `ReplaySubject` will be unsubscribed. All new subscribers will receive value emissions from a\n * new `ReplaySubject` which in turn will cause a new subscription to the source observable.\n *\n * ## Examples\n *\n * Example with a third subscriber coming late to the party\n *\n * ```ts\n * import { interval, take, shareReplay } from 'rxjs';\n *\n * const shared$ = interval(2000).pipe(\n *   take(6),\n *   shareReplay(3)\n * );\n *\n * shared$.subscribe(x => console.log('sub A: ', x));\n * shared$.subscribe(y => console.log('sub B: ', y));\n *\n * setTimeout(() => {\n *   shared$.subscribe(y => console.log('sub C: ', y));\n * }, 11000);\n *\n * // Logs:\n * // (after ~2000 ms)\n * // sub A: 0\n * // sub B: 0\n * // (after ~4000 ms)\n * // sub A: 1\n * // sub B: 1\n * // (after ~6000 ms)\n * // sub A: 2\n * // sub B: 2\n * // (after ~8000 ms)\n * // sub A: 3\n * // sub B: 3\n * // (after ~10000 ms)\n * // sub A: 4\n * // sub B: 4\n * // (after ~11000 ms, sub C gets the last 3 values)\n * // sub C: 2\n * // sub C: 3\n * // sub C: 4\n * // (after ~12000 ms)\n * // sub A: 5\n * // sub B: 5\n * // sub C: 5\n * ```\n *\n * Example for `refCount` usage\n *\n * ```ts\n * import { Observable, tap, interval, shareReplay, take } from 'rxjs';\n *\n * const log = <T>(name: string, source: Observable<T>) => source.pipe(\n *   tap({\n *     subscribe: () => console.log(`${ name }: subscribed`),\n *     next: value => console.log(`${ name }: ${ value }`),\n *     complete: () => console.log(`${ name }: completed`),\n *     finalize: () => console.log(`${ name }: unsubscribed`)\n *   })\n * );\n *\n * const obs$ = log('source', interval(1000));\n *\n * const shared$ = log('shared', obs$.pipe(\n *   shareReplay({ bufferSize: 1, refCount: true }),\n *   take(2)\n * ));\n *\n * shared$.subscribe(x => console.log('sub A: ', x));\n * shared$.subscribe(y => console.log('sub B: ', y));\n *\n * // PRINTS:\n * // shared: subscribed <-- reference count = 1\n * // source: subscribed\n * // shared: subscribed <-- reference count = 2\n * // source: 0\n * // shared: 0\n * // sub A: 0\n * // shared: 0\n * // sub B: 0\n * // source: 1\n * // shared: 1\n * // sub A: 1\n * // shared: completed <-- take(2) completes the subscription for sub A\n * // shared: unsubscribed <-- reference count = 1\n * // shared: 1\n * // sub B: 1\n * // shared: completed <-- take(2) completes the subscription for sub B\n * // shared: unsubscribed <-- reference count = 0\n * // source: unsubscribed <-- replaySubject unsubscribes from source observable because the reference count dropped to 0 and refCount is true\n *\n * // In case of refCount being false, the unsubscribe is never called on the source and the source would keep on emitting, even if no subscribers\n * // are listening.\n * // source: 2\n * // source: 3\n * // source: 4\n * // ...\n * ```\n *\n * @see {@link publish}\n * @see {@link share}\n * @see {@link publishReplay}\n *\n * @param configOrBufferSize Maximum element count of the replay buffer or {@link ShareReplayConfig configuration}\n * object.\n * @param windowTime Maximum time length of the replay buffer in milliseconds.\n * @param scheduler Scheduler where connected observers within the selector function\n * will be invoked on.\n * @return A function that returns an Observable sequence that contains the\n * elements of a sequence produced by multicasting the source sequence within a\n * selector function.\n */\nexport function shareReplay<T>(\n  configOrBufferSize?: ShareReplayConfig | number,\n  windowTime?: number,\n  scheduler?: SchedulerLike\n): MonoTypeOperatorFunction<T> {\n  let bufferSize: number;\n  let refCount = false;\n  if (configOrBufferSize && typeof configOrBufferSize === 'object') {\n    ({ bufferSize = Infinity, windowTime = Infinity, refCount = false, scheduler } = configOrBufferSize);\n  } else {\n    bufferSize = (configOrBufferSize ?? Infinity) as number;\n  }\n  return share<T>({\n    connector: () => new ReplaySubject(bufferSize, windowTime, scheduler),\n    resetOnError: true,\n    resetOnComplete: false,\n    resetOnRefCountZero: refCount,\n  });\n}\n", "import { MonoTypeOperatorFunction } from '../types';\nimport { filter } from './filter';\n\n/**\n * Returns an Observable that skips the first `count` items emitted by the source Observable.\n *\n * ![](skip.png)\n *\n * Skips the values until the sent notifications are equal or less than provided skip count. It raises\n * an error if skip count is equal or more than the actual number of emits and source raises an error.\n *\n * ## Example\n *\n * Skip the values before the emission\n *\n * ```ts\n * import { interval, skip } from 'rxjs';\n *\n * // emit every half second\n * const source = interval(500);\n * // skip the first 10 emitted values\n * const result = source.pipe(skip(10));\n *\n * result.subscribe(value => console.log(value));\n * // output: 10...11...12...13...\n * ```\n *\n * @see {@link last}\n * @see {@link skipWhile}\n * @see {@link skipUntil}\n * @see {@link skipLast}\n *\n * @param {Number} count - The number of times, items emitted by source Observable should be skipped.\n * @return A function that returns an Observable that skips the first `count`\n * values emitted by the source Observable.\n */\nexport function skip<T>(count: number): MonoTypeOperatorFunction<T> {\n  return filter((_, index) => count <= index);\n}\n", "import { MonoTypeOperatorFunction, ObservableInput } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\nimport { innerFrom } from '../observable/innerFrom';\nimport { noop } from '../util/noop';\n\n/**\n * Returns an Observable that skips items emitted by the source Observable until a second Observable emits an item.\n *\n * The `skipUntil` operator causes the observable stream to skip the emission of values until the passed in observable\n * emits the first value. This can be particularly useful in combination with user interactions, responses of HTTP\n * requests or waiting for specific times to pass by.\n *\n * ![](skipUntil.png)\n *\n * Internally, the `skipUntil` operator subscribes to the passed in `notifier` `ObservableInput` (which gets converted\n * to an Observable) in order to recognize the emission of its first value. When `notifier` emits next, the operator\n * unsubscribes from it and starts emitting the values of the *source* observable until it completes or errors. It\n * will never let the *source* observable emit any values if the `notifier` completes or throws an error without\n * emitting a value before.\n *\n * ## Example\n *\n * In the following example, all emitted values of the interval observable are skipped until the user clicks anywhere\n * within the page\n *\n * ```ts\n * import { interval, fromEvent, skipUntil } from 'rxjs';\n *\n * const intervalObservable = interval(1000);\n * const click = fromEvent(document, 'click');\n *\n * const emitAfterClick = intervalObservable.pipe(\n *   skipUntil(click)\n * );\n * // clicked at 4.6s. output: 5...6...7...8........ or\n * // clicked at 7.3s. output: 8...9...10..11.......\n * emitAfterClick.subscribe(value => console.log(value));\n * ```\n *\n * @see {@link last}\n * @see {@link skip}\n * @see {@link skipWhile}\n * @see {@link skipLast}\n *\n * @param notifier An `ObservableInput` that has to emit an item before the source Observable elements begin to\n * be mirrored by the resulting Observable.\n * @return A function that returns an Observable that skips items from the\n * source Observable until the `notifier` Observable emits an item, then emits the\n * remaining items.\n */\nexport function skipUntil<T>(notifier: ObservableInput<any>): MonoTypeOperatorFunction<T> {\n  return operate((source, subscriber) => {\n    let taking = false;\n\n    const skipSubscriber = createOperatorSubscriber(\n      subscriber,\n      () => {\n        skipSubscriber?.unsubscribe();\n        taking = true;\n      },\n      noop\n    );\n\n    innerFrom(notifier).subscribe(skipSubscriber);\n\n    source.subscribe(createOperatorSubscriber(subscriber, (value) => taking && subscriber.next(value)));\n  });\n}\n", "import { concat } from '../observable/concat';\nimport { OperatorFunction, SchedulerLike, ValueFromArray } from '../types';\nimport { popScheduler } from '../util/args';\nimport { operate } from '../util/lift';\n\n// Devs are more likely to pass null or undefined than they are a scheduler\n// without accompanying values. To make things easier for (naughty) devs who\n// use the `strictNullChecks: false` TypeScript compiler option, these\n// overloads with explicit null and undefined values are included.\n\nexport function startWith<T>(value: null): OperatorFunction<T, T | null>;\nexport function startWith<T>(value: undefined): OperatorFunction<T, T | undefined>;\n\n/** @deprecated The `scheduler` parameter will be removed in v8. Use `scheduled` and `concatAll`. Details: https://rxjs.dev/deprecations/scheduler-argument */\nexport function startWith<T, A extends readonly unknown[] = T[]>(\n  ...valuesAndScheduler: [...A, SchedulerLike]\n): OperatorFunction<T, T | ValueFromArray<A>>;\nexport function startWith<T, A extends readonly unknown[] = T[]>(...values: A): OperatorFunction<T, T | ValueFromArray<A>>;\n\n/**\n * Returns an observable that, at the moment of subscription, will synchronously emit all\n * values provided to this operator, then subscribe to the source and mirror all of its emissions\n * to subscribers.\n *\n * This is a useful way to know when subscription has occurred on an existing observable.\n *\n * <span class=\"informal\">First emits its arguments in order, and then any\n * emissions from the source.</span>\n *\n * ![](startWith.png)\n *\n * ## Examples\n *\n * Emit a value when a timer starts.\n *\n * ```ts\n * import { timer, map, startWith } from 'rxjs';\n *\n * timer(1000)\n *   .pipe(\n *     map(() => 'timer emit'),\n *     startWith('timer start')\n *   )\n *   .subscribe(x => console.log(x));\n *\n * // results:\n * // 'timer start'\n * // 'timer emit'\n * ```\n *\n * @param values Items you want the modified Observable to emit first.\n * @return A function that returns an Observable that synchronously emits\n * provided values before subscribing to the source Observable.\n *\n * @see {@link endWith}\n * @see {@link finalize}\n * @see {@link concat}\n */\nexport function startWith<T, D>(...values: D[]): OperatorFunction<T, T | D> {\n  const scheduler = popScheduler(values);\n  return operate((source, subscriber) => {\n    // Here we can't pass `undefined` as a scheduler, because if we did, the\n    // code inside of `concat` would be confused by the `undefined`, and treat it\n    // like an invalid observable. So we have to split it two different ways.\n    (scheduler ? concat(values, source, scheduler) : concat(values, source)).subscribe(subscriber);\n  });\n}\n", "import { Subscriber } from '../Subscriber';\nimport { ObservableInput, OperatorFunction, ObservedValueOf } from '../types';\nimport { innerFrom } from '../observable/innerFrom';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\n/* tslint:disable:max-line-length */\nexport function switchMap<T, O extends ObservableInput<any>>(\n  project: (value: T, index: number) => O\n): OperatorFunction<T, ObservedValueOf<O>>;\n/** @deprecated The `resultSelector` parameter will be removed in v8. Use an inner `map` instead. Details: https://rxjs.dev/deprecations/resultSelector */\nexport function switchMap<T, O extends ObservableInput<any>>(\n  project: (value: T, index: number) => O,\n  resultSelector: undefined\n): OperatorFunction<T, ObservedValueOf<O>>;\n/** @deprecated The `resultSelector` parameter will be removed in v8. Use an inner `map` instead. Details: https://rxjs.dev/deprecations/resultSelector */\nexport function switchMap<T, R, O extends ObservableInput<any>>(\n  project: (value: T, index: number) => O,\n  resultSelector: (outerValue: T, innerValue: ObservedValueOf<O>, outerIndex: number, innerIndex: number) => R\n): OperatorFunction<T, R>;\n/* tslint:enable:max-line-length */\n\n/**\n * Projects each source value to an Observable which is merged in the output\n * Observable, emitting values only from the most recently projected Observable.\n *\n * <span class=\"informal\">Maps each value to an Observable, then flattens all of\n * these inner Observables using {@link switchAll}.</span>\n *\n * ![](switchMap.png)\n *\n * Returns an Observable that emits items based on applying a function that you\n * supply to each item emitted by the source Observable, where that function\n * returns an (so-called \"inner\") Observable. Each time it observes one of these\n * inner Observables, the output Observable begins emitting the items emitted by\n * that inner Observable. When a new inner Observable is emitted, `switchMap`\n * stops emitting items from the earlier-emitted inner Observable and begins\n * emitting items from the new one. It continues to behave like this for\n * subsequent inner Observables.\n *\n * ## Example\n *\n * Generate new Observable according to source Observable values\n *\n * ```ts\n * import { of, switchMap } from 'rxjs';\n *\n * const switched = of(1, 2, 3).pipe(switchMap(x => of(x, x ** 2, x ** 3)));\n * switched.subscribe(x => console.log(x));\n * // outputs\n * // 1\n * // 1\n * // 1\n * // 2\n * // 4\n * // 8\n * // 3\n * // 9\n * // 27\n * ```\n *\n * Restart an interval Observable on every click event\n *\n * ```ts\n * import { fromEvent, switchMap, interval } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const result = clicks.pipe(switchMap(() => interval(1000)));\n * result.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link concatMap}\n * @see {@link exhaustMap}\n * @see {@link mergeMap}\n * @see {@link switchAll}\n * @see {@link switchMapTo}\n *\n * @param {function(value: T, index: number): ObservableInput} project A function\n * that, when applied to an item emitted by the source Observable, returns an\n * Observable.\n * @return A function that returns an Observable that emits the result of\n * applying the projection function (and the optional deprecated\n * `resultSelector`) to each item emitted by the source Observable and taking\n * only the values from the most recently projected inner Observable.\n */\nexport function switchMap<T, R, O extends ObservableInput<any>>(\n  project: (value: T, index: number) => O,\n  resultSelector?: (outerValue: T, innerValue: ObservedValueOf<O>, outerIndex: number, innerIndex: number) => R\n): OperatorFunction<T, ObservedValueOf<O> | R> {\n  return operate((source, subscriber) => {\n    let innerSubscriber: Subscriber<ObservedValueOf<O>> | null = null;\n    let index = 0;\n    // Whether or not the source subscription has completed\n    let isComplete = false;\n\n    // We only complete the result if the source is complete AND we don't have an active inner subscription.\n    // This is called both when the source completes and when the inners complete.\n    const checkComplete = () => isComplete && !innerSubscriber && subscriber.complete();\n\n    source.subscribe(\n      createOperatorSubscriber(\n        subscriber,\n        (value) => {\n          // Cancel the previous inner subscription if there was one\n          innerSubscriber?.unsubscribe();\n          let innerIndex = 0;\n          const outerIndex = index++;\n          // Start the next inner subscription\n          innerFrom(project(value, outerIndex)).subscribe(\n            (innerSubscriber = createOperatorSubscriber(\n              subscriber,\n              // When we get a new inner value, next it through. Note that this is\n              // handling the deprecate result selector here. This is because with this architecture\n              // it ends up being smaller than using the map operator.\n              (innerValue) => subscriber.next(resultSelector ? resultSelector(value, innerValue, outerIndex, innerIndex++) : innerValue),\n              () => {\n                // The inner has completed. Null out the inner subscriber to\n                // free up memory and to signal that we have no inner subscription\n                // currently.\n                innerSubscriber = null!;\n                checkComplete();\n              }\n            ))\n          );\n        },\n        () => {\n          isComplete = true;\n          checkComplete();\n        }\n      )\n    );\n  });\n}\n", "import { MonoTypeOperatorFunction, ObservableInput } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\nimport { innerFrom } from '../observable/innerFrom';\nimport { noop } from '../util/noop';\n\n/**\n * Emits the values emitted by the source Observable until a `notifier`\n * Observable emits a value.\n *\n * <span class=\"informal\">Lets values pass until a second Observable,\n * `notifier`, emits a value. Then, it completes.</span>\n *\n * ![](takeUntil.png)\n *\n * `takeUntil` subscribes and begins mirroring the source Observable. It also\n * monitors a second Observable, `notifier` that you provide. If the `notifier`\n * emits a value, the output Observable stops mirroring the source Observable\n * and completes. If the `notifier` doesn't emit any value and completes\n * then `takeUntil` will pass all values.\n *\n * ## Example\n *\n * Tick every second until the first click happens\n *\n * ```ts\n * import { interval, fromEvent, takeUntil } from 'rxjs';\n *\n * const source = interval(1000);\n * const clicks = fromEvent(document, 'click');\n * const result = source.pipe(takeUntil(clicks));\n * result.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link take}\n * @see {@link takeLast}\n * @see {@link takeWhile}\n * @see {@link skip}\n *\n * @param {Observable} notifier The Observable whose first emitted value will\n * cause the output Observable of `takeUntil` to stop emitting values from the\n * source Observable.\n * @return A function that returns an Observable that emits the values from the\n * source Observable until `notifier` emits its first value.\n */\nexport function takeUntil<T>(notifier: ObservableInput<any>): MonoTypeOperatorFunction<T> {\n  return operate((source, subscriber) => {\n    innerFrom(notifier).subscribe(createOperatorSubscriber(subscriber, () => subscriber.complete(), noop));\n    !subscriber.closed && source.subscribe(subscriber);\n  });\n}\n", "import { OperatorFunction, MonoTypeOperatorFunction, TruthyTypesOf } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\n\nexport function takeWhile<T>(predicate: BooleanConstructor, inclusive: true): MonoTypeOperatorFunction<T>;\nexport function takeWhile<T>(predicate: BooleanConstructor, inclusive: false): OperatorFunction<T, TruthyTypesOf<T>>;\nexport function takeWhile<T>(predicate: BooleanConstructor): OperatorFunction<T, TruthyTypesOf<T>>;\nexport function takeWhile<T, S extends T>(predicate: (value: T, index: number) => value is S): OperatorFunction<T, S>;\nexport function takeWhile<T, S extends T>(predicate: (value: T, index: number) => value is S, inclusive: false): OperatorFunction<T, S>;\nexport function takeWhile<T>(predicate: (value: T, index: number) => boolean, inclusive?: boolean): MonoTypeOperatorFunction<T>;\n\n/**\n * Emits values emitted by the source Observable so long as each value satisfies\n * the given `predicate`, and then completes as soon as this `predicate` is not\n * satisfied.\n *\n * <span class=\"informal\">Takes values from the source only while they pass the\n * condition given. When the first value does not satisfy, it completes.</span>\n *\n * ![](takeWhile.png)\n *\n * `takeWhile` subscribes and begins mirroring the source Observable. Each value\n * emitted on the source is given to the `predicate` function which returns a\n * boolean, representing a condition to be satisfied by the source values. The\n * output Observable emits the source values until such time as the `predicate`\n * returns false, at which point `takeWhile` stops mirroring the source\n * Observable and completes the output Observable.\n *\n * ## Example\n *\n * Emit click events only while the clientX property is greater than 200\n *\n * ```ts\n * import { fromEvent, takeWhile } from 'rxjs';\n *\n * const clicks = fromEvent<PointerEvent>(document, 'click');\n * const result = clicks.pipe(takeWhile(ev => ev.clientX > 200));\n * result.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link take}\n * @see {@link takeLast}\n * @see {@link takeUntil}\n * @see {@link skip}\n *\n * @param {function(value: T, index: number): boolean} predicate A function that\n * evaluates a value emitted by the source Observable and returns a boolean.\n * Also takes the (zero-based) index as the second argument.\n * @param {boolean} inclusive When set to `true` the value that caused\n * `predicate` to return `false` will also be emitted.\n * @return A function that returns an Observable that emits values from the\n * source Observable so long as each value satisfies the condition defined by\n * the `predicate`, then completes.\n */\nexport function takeWhile<T>(predicate: (value: T, index: number) => boolean, inclusive = false): MonoTypeOperatorFunction<T> {\n  return operate((source, subscriber) => {\n    let index = 0;\n    source.subscribe(\n      createOperatorSubscriber(subscriber, (value) => {\n        const result = predicate(value, index++);\n        (result || inclusive) && subscriber.next(value);\n        !result && subscriber.complete();\n      })\n    );\n  });\n}\n", "import { MonoTypeOperatorFunction, Observer } from '../types';\nimport { isFunction } from '../util/isFunction';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\nimport { identity } from '../util/identity';\n\n/**\n * An extension to the {@link Observer} interface used only by the {@link tap} operator.\n *\n * It provides a useful set of callbacks a user can register to do side-effects in\n * cases other than what the usual {@link Observer} callbacks are\n * ({@link guide/glossary-and-semantics#next next},\n * {@link guide/glossary-and-semantics#error error} and/or\n * {@link guide/glossary-and-semantics#complete complete}).\n *\n * ## Example\n *\n * ```ts\n * import { fromEvent, switchMap, tap, interval, take } from 'rxjs';\n *\n * const source$ = fromEvent(document, 'click');\n * const result$ = source$.pipe(\n *   switchMap((_, i) => i % 2 === 0\n *     ? fromEvent(document, 'mousemove').pipe(\n *         tap({\n *           subscribe: () => console.log('Subscribed to the mouse move events after click #' + i),\n *           unsubscribe: () => console.log('Mouse move events #' + i + ' unsubscribed'),\n *           finalize: () => console.log('Mouse move events #' + i + ' finalized')\n *         })\n *       )\n *     : interval(1_000).pipe(\n *         take(5),\n *         tap({\n *           subscribe: () => console.log('Subscribed to the 1-second interval events after click #' + i),\n *           unsubscribe: () => console.log('1-second interval events #' + i + ' unsubscribed'),\n *           finalize: () => console.log('1-second interval events #' + i + ' finalized')\n *         })\n *       )\n *   )\n * );\n *\n * const subscription = result$.subscribe({\n *   next: console.log\n * });\n *\n * setTimeout(() => {\n *   console.log('Unsubscribe after 60 seconds');\n *   subscription.unsubscribe();\n * }, 60_000);\n * ```\n */\nexport interface TapObserver<T> extends Observer<T> {\n  /**\n   * The callback that `tap` operator invokes at the moment when the source Observable\n   * gets subscribed to.\n   */\n  subscribe: () => void;\n  /**\n   * The callback that `tap` operator invokes when an explicit\n   * {@link guide/glossary-and-semantics#unsubscription unsubscribe} happens. It won't get invoked on\n   * `error` or `complete` events.\n   */\n  unsubscribe: () => void;\n  /**\n   * The callback that `tap` operator invokes when any kind of\n   * {@link guide/glossary-and-semantics#finalization finalization} happens - either when\n   * the source Observable `error`s or `complete`s or when it gets explicitly unsubscribed\n   * by the user. There is no difference in using this callback or the {@link finalize}\n   * operator, but if you're already using `tap` operator, you can use this callback\n   * instead. You'd get the same result in either case.\n   */\n  finalize: () => void;\n}\nexport function tap<T>(observerOrNext?: Partial<TapObserver<T>> | ((value: T) => void)): MonoTypeOperatorFunction<T>;\n/** @deprecated Instead of passing separate callback arguments, use an observer argument. Signatures taking separate callback arguments will be removed in v8. Details: https://rxjs.dev/deprecations/subscribe-arguments */\nexport function tap<T>(\n  next?: ((value: T) => void) | null,\n  error?: ((error: any) => void) | null,\n  complete?: (() => void) | null\n): MonoTypeOperatorFunction<T>;\n\n/**\n * Used to perform side-effects for notifications from the source observable\n *\n * <span class=\"informal\">Used when you want to affect outside state with a notification without altering the notification</span>\n *\n * ![](tap.png)\n *\n * Tap is designed to allow the developer a designated place to perform side effects. While you _could_ perform side-effects\n * inside of a `map` or a `mergeMap`, that would make their mapping functions impure, which isn't always a big deal, but will\n * make it so you can't do things like memoize those functions. The `tap` operator is designed solely for such side-effects to\n * help you remove side-effects from other operations.\n *\n * For any notification, next, error, or complete, `tap` will call the appropriate callback you have provided to it, via a function\n * reference, or a partial observer, then pass that notification down the stream.\n *\n * The observable returned by `tap` is an exact mirror of the source, with one exception: Any error that occurs -- synchronously -- in a handler\n * provided to `tap` will be emitted as an error from the returned observable.\n *\n * > Be careful! You can mutate objects as they pass through the `tap` operator's handlers.\n *\n * The most common use of `tap` is actually for debugging. You can place a `tap(console.log)` anywhere\n * in your observable `pipe`, log out the notifications as they are emitted by the source returned by the previous\n * operation.\n *\n * ## Examples\n *\n * Check a random number before it is handled. Below is an observable that will use a random number between 0 and 1,\n * and emit `'big'` or `'small'` depending on the size of that number. But we wanted to log what the original number\n * was, so we have added a `tap(console.log)`.\n *\n * ```ts\n * import { of, tap, map } from 'rxjs';\n *\n * of(Math.random()).pipe(\n *   tap(console.log),\n *   map(n => n > 0.5 ? 'big' : 'small')\n * ).subscribe(console.log);\n * ```\n *\n * Using `tap` to analyze a value and force an error. Below is an observable where in our system we only\n * want to emit numbers 3 or less we get from another source. We can force our observable to error\n * using `tap`.\n *\n * ```ts\n * import { of, tap } from 'rxjs';\n *\n * const source = of(1, 2, 3, 4, 5);\n *\n * source.pipe(\n *   tap(n => {\n *     if (n > 3) {\n *       throw new TypeError(`Value ${ n } is greater than 3`);\n *     }\n *   })\n * )\n * .subscribe({ next: console.log, error: err => console.log(err.message) });\n * ```\n *\n * We want to know when an observable completes before moving on to the next observable. The system\n * below will emit a random series of `'X'` characters from 3 different observables in sequence. The\n * only way we know when one observable completes and moves to the next one, in this case, is because\n * we have added a `tap` with the side effect of logging to console.\n *\n * ```ts\n * import { of, concatMap, interval, take, map, tap } from 'rxjs';\n *\n * of(1, 2, 3).pipe(\n *   concatMap(n => interval(1000).pipe(\n *     take(Math.round(Math.random() * 10)),\n *     map(() => 'X'),\n *     tap({ complete: () => console.log(`Done with ${ n }`) })\n *   ))\n * )\n * .subscribe(console.log);\n * ```\n *\n * @see {@link finalize}\n * @see {@link TapObserver}\n *\n * @param observerOrNext A next handler or partial observer\n * @param error An error handler\n * @param complete A completion handler\n * @return A function that returns an Observable identical to the source, but\n * runs the specified Observer or callback(s) for each item.\n */\nexport function tap<T>(\n  observerOrNext?: Partial<TapObserver<T>> | ((value: T) => void) | null,\n  error?: ((e: any) => void) | null,\n  complete?: (() => void) | null\n): MonoTypeOperatorFunction<T> {\n  // We have to check to see not only if next is a function,\n  // but if error or complete were passed. This is because someone\n  // could technically call tap like `tap(null, fn)` or `tap(null, null, fn)`.\n  const tapObserver =\n    isFunction(observerOrNext) || error || complete\n      ? // tslint:disable-next-line: no-object-literal-type-assertion\n        ({ next: observerOrNext as Exclude<typeof observerOrNext, Partial<TapObserver<T>>>, error, complete } as Partial<TapObserver<T>>)\n      : observerOrNext;\n\n  return tapObserver\n    ? operate((source, subscriber) => {\n        tapObserver.subscribe?.();\n        let isUnsub = true;\n        source.subscribe(\n          createOperatorSubscriber(\n            subscriber,\n            (value) => {\n              tapObserver.next?.(value);\n              subscriber.next(value);\n            },\n            () => {\n              isUnsub = false;\n              tapObserver.complete?.();\n              subscriber.complete();\n            },\n            (err) => {\n              isUnsub = false;\n              tapObserver.error?.(err);\n              subscriber.error(err);\n            },\n            () => {\n              if (isUnsub) {\n                tapObserver.unsubscribe?.();\n              }\n              tapObserver.finalize?.();\n            }\n          )\n        );\n      })\n    : // Tap was called with no valid tap observer or handler\n      // (e.g. `tap(null, null, null)` or `tap(null)` or `tap()`)\n      // so we're going to just mirror the source.\n      identity;\n}\n", "import { Subscription } from '../Subscription';\n\nimport { MonoTypeOperatorFunction, ObservableInput } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\nimport { innerFrom } from '../observable/innerFrom';\n\n/**\n * An object interface used by {@link throttle} or {@link throttleTime} that ensure\n * configuration options of these operators.\n *\n * @see {@link throttle}\n * @see {@link throttleTime}\n */\nexport interface ThrottleConfig {\n  /**\n   * If `true`, the resulting Observable will emit the first value from the source\n   * Observable at the **start** of the \"throttling\" process (when starting an\n   * internal timer that prevents other emissions from the source to pass through).\n   * If `false`, it will not emit the first value from the source Observable at the\n   * start of the \"throttling\" process.\n   *\n   * If not provided, defaults to: `true`.\n   */\n  leading?: boolean;\n  /**\n   * If `true`, the resulting Observable will emit the last value from the source\n   * Observable at the **end** of the \"throttling\" process (when ending an internal\n   * timer that prevents other emissions from the source to pass through).\n   * If `false`, it will not emit the last value from the source Observable at the\n   * end of the \"throttling\" process.\n   *\n   * If not provided, defaults to: `false`.\n   */\n  trailing?: boolean;\n}\n\n/**\n * Emits a value from the source Observable, then ignores subsequent source\n * values for a duration determined by another Observable, then repeats this\n * process.\n *\n * <span class=\"informal\">It's like {@link throttleTime}, but the silencing\n * duration is determined by a second Observable.</span>\n *\n * ![](throttle.svg)\n *\n * `throttle` emits the source Observable values on the output Observable\n * when its internal timer is disabled, and ignores source values when the timer\n * is enabled. Initially, the timer is disabled. As soon as the first source\n * value arrives, it is forwarded to the output Observable, and then the timer\n * is enabled by calling the `durationSelector` function with the source value,\n * which returns the \"duration\" Observable. When the duration Observable emits a\n * value, the timer is disabled, and this process repeats for the\n * next source value.\n *\n * ## Example\n *\n * Emit clicks at a rate of at most one click per second\n *\n * ```ts\n * import { fromEvent, throttle, interval } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const result = clicks.pipe(throttle(() => interval(1000)));\n *\n * result.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link audit}\n * @see {@link debounce}\n * @see {@link delayWhen}\n * @see {@link sample}\n * @see {@link throttleTime}\n *\n * @param durationSelector A function that receives a value from the source\n * Observable, for computing the silencing duration for each source value,\n * returned as an `ObservableInput`.\n * @param config A configuration object to define `leading` and `trailing`\n * behavior. Defaults to `{ leading: true, trailing: false }`.\n * @return A function that returns an Observable that performs the throttle\n * operation to limit the rate of emissions from the source.\n */\nexport function throttle<T>(durationSelector: (value: T) => ObservableInput<any>, config?: ThrottleConfig): MonoTypeOperatorFunction<T> {\n  return operate((source, subscriber) => {\n    const { leading = true, trailing = false } = config ?? {};\n    let hasValue = false;\n    let sendValue: T | null = null;\n    let throttled: Subscription | null = null;\n    let isComplete = false;\n\n    const endThrottling = () => {\n      throttled?.unsubscribe();\n      throttled = null;\n      if (trailing) {\n        send();\n        isComplete && subscriber.complete();\n      }\n    };\n\n    const cleanupThrottling = () => {\n      throttled = null;\n      isComplete && subscriber.complete();\n    };\n\n    const startThrottle = (value: T) =>\n      (throttled = innerFrom(durationSelector(value)).subscribe(createOperatorSubscriber(subscriber, endThrottling, cleanupThrottling)));\n\n    const send = () => {\n      if (hasValue) {\n        // Ensure we clear out our value and hasValue flag\n        // before we emit, otherwise reentrant code can cause\n        // issues here.\n        hasValue = false;\n        const value = sendValue!;\n        sendValue = null;\n        // Emit the value.\n        subscriber.next(value);\n        !isComplete && startThrottle(value);\n      }\n    };\n\n    source.subscribe(\n      createOperatorSubscriber(\n        subscriber,\n        // Regarding the presence of throttled.closed in the following\n        // conditions, if a synchronous duration selector is specified - weird,\n        // but legal - an already-closed subscription will be assigned to\n        // throttled, so the subscription's closed property needs to be checked,\n        // too.\n        (value) => {\n          hasValue = true;\n          sendValue = value;\n          !(throttled && !throttled.closed) && (leading ? send() : startThrottle(value));\n        },\n        () => {\n          isComplete = true;\n          !(trailing && hasValue && throttled && !throttled.closed) && subscriber.complete();\n        }\n      )\n    );\n  });\n}\n", "import { asyncScheduler } from '../scheduler/async';\nimport { throttle, ThrottleConfig } from './throttle';\nimport { MonoTypeOperatorFunction, SchedulerLike } from '../types';\nimport { timer } from '../observable/timer';\n\n/**\n * Emits a value from the source Observable, then ignores subsequent source\n * values for `duration` milliseconds, then repeats this process.\n *\n * <span class=\"informal\">Lets a value pass, then ignores source values for the\n * next `duration` milliseconds.</span>\n *\n * ![](throttleTime.png)\n *\n * `throttleTime` emits the source Observable values on the output Observable\n * when its internal timer is disabled, and ignores source values when the timer\n * is enabled. Initially, the timer is disabled. As soon as the first source\n * value arrives, it is forwarded to the output Observable, and then the timer\n * is enabled. After `duration` milliseconds (or the time unit determined\n * internally by the optional `scheduler`) has passed, the timer is disabled,\n * and this process repeats for the next source value. Optionally takes a\n * {@link SchedulerLike} for managing timers.\n *\n * ## Examples\n *\n * ### Limit click rate\n *\n * Emit clicks at a rate of at most one click per second\n *\n * ```ts\n * import { fromEvent, throttleTime } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const result = clicks.pipe(throttleTime(1000));\n *\n * result.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link auditTime}\n * @see {@link debounceTime}\n * @see {@link delay}\n * @see {@link sampleTime}\n * @see {@link throttle}\n *\n * @param duration Time to wait before emitting another value after\n * emitting the last value, measured in milliseconds or the time unit determined\n * internally by the optional `scheduler`.\n * @param scheduler The {@link SchedulerLike} to use for\n * managing the timers that handle the throttling. Defaults to {@link asyncScheduler}.\n * @param config A configuration object to define `leading` and\n * `trailing` behavior. Defaults to `{ leading: true, trailing: false }`.\n * @return A function that returns an Observable that performs the throttle\n * operation to limit the rate of emissions from the source.\n */\nexport function throttleTime<T>(\n  duration: number,\n  scheduler: SchedulerLike = asyncScheduler,\n  config?: ThrottleConfig\n): MonoTypeOperatorFunction<T> {\n  const duration$ = timer(duration, scheduler);\n  return throttle(() => duration$, config);\n}\n", "import { OperatorFunction, ObservableInputTuple } from '../types';\nimport { operate } from '../util/lift';\nimport { createOperatorSubscriber } from './OperatorSubscriber';\nimport { innerFrom } from '../observable/innerFrom';\nimport { identity } from '../util/identity';\nimport { noop } from '../util/noop';\nimport { popResultSelector } from '../util/args';\n\nexport function withLatestFrom<T, O extends unknown[]>(...inputs: [...ObservableInputTuple<O>]): OperatorFunction<T, [T, ...O]>;\n\nexport function withLatestFrom<T, O extends unknown[], R>(\n  ...inputs: [...ObservableInputTuple<O>, (...value: [T, ...O]) => R]\n): OperatorFunction<T, R>;\n\n/**\n * Combines the source Observable with other Observables to create an Observable\n * whose values are calculated from the latest values of each, only when the\n * source emits.\n *\n * <span class=\"informal\">Whenever the source Observable emits a value, it\n * computes a formula using that value plus the latest values from other input\n * Observables, then emits the output of that formula.</span>\n *\n * ![](withLatestFrom.png)\n *\n * `withLatestFrom` combines each value from the source Observable (the\n * instance) with the latest values from the other input Observables only when\n * the source emits a value, optionally using a `project` function to determine\n * the value to be emitted on the output Observable. All input Observables must\n * emit at least one value before the output Observable will emit a value.\n *\n * ## Example\n *\n * On every click event, emit an array with the latest timer event plus the click event\n *\n * ```ts\n * import { fromEvent, interval, withLatestFrom } from 'rxjs';\n *\n * const clicks = fromEvent(document, 'click');\n * const timer = interval(1000);\n * const result = clicks.pipe(withLatestFrom(timer));\n * result.subscribe(x => console.log(x));\n * ```\n *\n * @see {@link combineLatest}\n *\n * @param {ObservableInput} other An input Observable to combine with the source\n * Observable. More than one input Observables may be given as argument.\n * @param {Function} [project] Projection function for combining values\n * together. Receives all values in order of the Observables passed, where the\n * first parameter is a value from the source Observable. (e.g.\n * `a.pipe(withLatestFrom(b, c), map(([a1, b1, c1]) => a1 + b1 + c1))`). If this is not\n * passed, arrays will be emitted on the output Observable.\n * @return A function that returns an Observable of projected values from the\n * most recent values from each input Observable, or an array of the most\n * recent values from each input Observable.\n */\nexport function withLatestFrom<T, R>(...inputs: any[]): OperatorFunction<T, R | any[]> {\n  const project = popResultSelector(inputs) as ((...args: any[]) => R) | undefined;\n\n  return operate((source, subscriber) => {\n    const len = inputs.length;\n    const otherValues = new Array(len);\n    // An array of whether or not the other sources have emitted. Matched with them by index.\n    // TODO: At somepoint, we should investigate the performance implications here, and look\n    // into using a `Set()` and checking the `size` to see if we're ready.\n    let hasValue = inputs.map(() => false);\n    // Flipped true when we have at least one value from all other sources and\n    // we are ready to start emitting values.\n    let ready = false;\n\n    // Other sources. Note that here we are not checking `subscriber.closed`,\n    // this causes all inputs to be subscribed to, even if nothing can be emitted\n    // from them. This is an important distinction because subscription constitutes\n    // a side-effect.\n    for (let i = 0; i < len; i++) {\n      innerFrom(inputs[i]).subscribe(\n        createOperatorSubscriber(\n          subscriber,\n          (value) => {\n            otherValues[i] = value;\n            if (!ready && !hasValue[i]) {\n              // If we're not ready yet, flag to show this observable has emitted.\n              hasValue[i] = true;\n              // Intentionally terse code.\n              // If all of our other observables have emitted, set `ready` to `true`,\n              // so we know we can start emitting values, then clean up the `hasValue` array,\n              // because we don't need it anymore.\n              (ready = hasValue.every(identity)) && (hasValue = null!);\n            }\n          },\n          // Completing one of the other sources has\n          // no bearing on the completion of our result.\n          noop\n        )\n      );\n    }\n\n    // Source subscription\n    source.subscribe(\n      createOperatorSubscriber(subscriber, (value) => {\n        if (ready) {\n          // We have at least one value from the other sources. Go ahead and emit.\n          const values = [value, ...otherValues];\n          subscriber.next(project ? project(...values) : values);\n        }\n      })\n    );\n  });\n}\n", "import { zip as zipStatic } from '../observable/zip';\nimport { ObservableInput, ObservableInputTuple, OperatorFunction, Cons } from '../types';\nimport { operate } from '../util/lift';\n\n/** @deprecated Replaced with {@link zipWith}. Will be removed in v8. */\nexport function zip<T, A extends readonly unknown[]>(otherInputs: [...ObservableInputTuple<A>]): OperatorFunction<T, Cons<T, A>>;\n/** @deprecated Replaced with {@link zipWith}. Will be removed in v8. */\nexport function zip<T, A extends readonly unknown[], R>(\n  otherInputsAndProject: [...ObservableInputTuple<A>],\n  project: (...values: Cons<T, A>) => R\n): OperatorFunction<T, R>;\n/** @deprecated Replaced with {@link zipWith}. Will be removed in v8. */\nexport function zip<T, A extends readonly unknown[]>(...otherInputs: [...ObservableInputTuple<A>]): OperatorFunction<T, Cons<T, A>>;\n/** @deprecated Replaced with {@link zipWith}. Will be removed in v8. */\nexport function zip<T, A extends readonly unknown[], R>(\n  ...otherInputsAndProject: [...ObservableInputTuple<A>, (...values: Cons<T, A>) => R]\n): OperatorFunction<T, R>;\n\n/**\n * @deprecated Replaced with {@link zipWith}. Will be removed in v8.\n */\nexport function zip<T, R>(...sources: Array<ObservableInput<any> | ((...values: Array<any>) => R)>): OperatorFunction<T, any> {\n  return operate((source, subscriber) => {\n    zipStatic(source as ObservableInput<any>, ...(sources as Array<ObservableInput<any>>)).subscribe(subscriber);\n  });\n}\n", "import { ObservableInputTuple, OperatorFunction, Cons } from '../types';\nimport { zip } from './zip';\n\n/**\n * Subscribes to the source, and the observable inputs provided as arguments, and combines their values, by index, into arrays.\n *\n * What is meant by \"combine by index\": The first value from each will be made into a single array, then emitted,\n * then the second value from each will be combined into a single array and emitted, then the third value\n * from each will be combined into a single array and emitted, and so on.\n *\n * This will continue until it is no longer able to combine values of the same index into an array.\n *\n * After the last value from any one completed source is emitted in an array, the resulting observable will complete,\n * as there is no way to continue \"zipping\" values together by index.\n *\n * Use-cases for this operator are limited. There are memory concerns if one of the streams is emitting\n * values at a much faster rate than the others. Usage should likely be limited to streams that emit\n * at a similar pace, or finite streams of known length.\n *\n * In many cases, authors want `combineLatestWith` and not `zipWith`.\n *\n * @param otherInputs other observable inputs to collate values from.\n * @return A function that returns an Observable that emits items by index\n * combined from the source Observable and provided Observables, in form of an\n * array.\n */\nexport function zipWith<T, A extends readonly unknown[]>(...otherInputs: [...ObservableInputTuple<A>]): OperatorFunction<T, Cons<T, A>> {\n  return zip(...otherInputs);\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  ReplaySubject,\n  Subject,\n  fromEvent\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch document\n *\n * Documents are implemented as subjects, so all downstream observables are\n * automatically updated when a new document is emitted.\n *\n * @returns Document subject\n */\nexport function watchDocument(): Subject<Document> {\n  const document$ = new ReplaySubject<Document>(1)\n  fromEvent(document, \"DOMContentLoaded\", { once: true })\n    .subscribe(() => document$.next(document))\n\n  /* Return document */\n  return document$\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve all elements matching the query selector\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Elements\n */\nexport function getElements<T extends keyof HTMLElementTagNameMap>(\n  selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T][]\n\nexport function getElements<T extends HTMLElement>(\n  selector: string, node?: ParentNode\n): T[]\n\nexport function getElements<T extends HTMLElement>(\n  selector: string, node: ParentNode = document\n): T[] {\n  return Array.from(node.querySelectorAll<T>(selector))\n}\n\n/**\n * Retrieve an element matching a query selector or throw a reference error\n *\n * Note that this function assumes that the element is present. If unsure if an\n * element is existent, use the `getOptionalElement` function instead.\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Element\n */\nexport function getElement<T extends keyof HTMLElementTagNameMap>(\n  selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T]\n\nexport function getElement<T extends HTMLElement>(\n  selector: string, node?: ParentNode\n): T\n\nexport function getElement<T extends HTMLElement>(\n  selector: string, node: ParentNode = document\n): T {\n  const el = getOptionalElement<T>(selector, node)\n  if (typeof el === \"undefined\")\n    throw new ReferenceError(\n      `Missing element: expected \"${selector}\" to be present`\n    )\n\n  /* Return element */\n  return el\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Retrieve an optional element matching the query selector\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Element or nothing\n */\nexport function getOptionalElement<T extends keyof HTMLElementTagNameMap>(\n  selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T] | undefined\n\nexport function getOptionalElement<T extends HTMLElement>(\n  selector: string, node?: ParentNode\n): T | undefined\n\nexport function getOptionalElement<T extends HTMLElement>(\n  selector: string, node: ParentNode = document\n): T | undefined {\n  return node.querySelector<T>(selector) || undefined\n}\n\n/**\n * Retrieve the currently active element\n *\n * @returns Element or nothing\n */\nexport function getActiveElement(): HTMLElement | undefined {\n  return document.activeElement instanceof HTMLElement\n    ? document.activeElement || undefined\n    : undefined\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  debounceTime,\n  distinctUntilChanged,\n  fromEvent,\n  map,\n  merge,\n  startWith\n} from \"rxjs\"\n\nimport { getActiveElement } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch element focus\n *\n * Previously, this function used `focus` and `blur` events to determine whether\n * an element is focused, but this doesn't work if there are focusable elements\n * within the elements itself. A better solutions are `focusin` and `focusout`\n * events, which bubble up the tree and allow for more fine-grained control.\n *\n * `debounceTime` is necessary, because when a focus change happens inside an\n * element, the observable would first emit `false` and then `true` again.\n *\n * @param el - Element\n *\n * @returns Element focus observable\n */\nexport function watchElementFocus(\n  el: HTMLElement\n): Observable<boolean> {\n  return merge(\n    fromEvent(document.body, \"focusin\"),\n    fromEvent(document.body, \"focusout\")\n  )\n    .pipe(\n      debounceTime(1),\n      map(() => {\n        const active = getActiveElement()\n        return typeof active !== \"undefined\"\n          ? el.contains(active)\n          : false\n      }),\n      startWith(el === getActiveElement()),\n      distinctUntilChanged()\n    )\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  animationFrameScheduler,\n  auditTime,\n  fromEvent,\n  map,\n  merge,\n  startWith\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Element offset\n */\nexport interface ElementOffset {\n  x: number                            /* Horizontal offset */\n  y: number                            /* Vertical offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element offset\n *\n * @param el - Element\n *\n * @returns Element offset\n */\nexport function getElementOffset(\n  el: HTMLElement\n): ElementOffset {\n  return {\n    x: el.offsetLeft,\n    y: el.offsetTop\n  }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch element offset\n *\n * @param el - Element\n *\n * @returns Element offset observable\n */\nexport function watchElementOffset(\n  el: HTMLElement\n): Observable<ElementOffset> {\n  return merge(\n    fromEvent(window, \"load\"),\n    fromEvent(window, \"resize\")\n  )\n    .pipe(\n      auditTime(0, animationFrameScheduler),\n      map(() => getElementOffset(el)),\n      startWith(getElementOffset(el))\n    )\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  animationFrameScheduler,\n  auditTime,\n  fromEvent,\n  map,\n  merge,\n  startWith\n} from \"rxjs\"\n\nimport { ElementOffset } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element content offset (= scroll offset)\n *\n * @param el - Element\n *\n * @returns Element content offset\n */\nexport function getElementContentOffset(\n  el: HTMLElement\n): ElementOffset {\n  return {\n    x: el.scrollLeft,\n    y: el.scrollTop\n  }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch element content offset\n *\n * @param el - Element\n *\n * @returns Element content offset observable\n */\nexport function watchElementContentOffset(\n  el: HTMLElement\n): Observable<ElementOffset> {\n  return merge(\n    fromEvent(el, \"scroll\"),\n    fromEvent(window, \"resize\")\n  )\n    .pipe(\n      auditTime(0, animationFrameScheduler),\n      map(() => getElementContentOffset(el)),\n      startWith(getElementContentOffset(el))\n    )\n}\n", "/**\r\n * A collection of shims that provide minimal functionality of the ES6 collections.\r\n *\r\n * These implementations are not meant to be used outside of the ResizeObserver\r\n * modules as they cover only a limited range of use cases.\r\n */\r\n/* eslint-disable require-jsdoc, valid-jsdoc */\r\nvar MapShim = (function () {\r\n    if (typeof Map !== 'undefined') {\r\n        return Map;\r\n    }\r\n    /**\r\n     * Returns index in provided array that matches the specified key.\r\n     *\r\n     * @param {Array<Array>} arr\r\n     * @param {*} key\r\n     * @returns {number}\r\n     */\r\n    function getIndex(arr, key) {\r\n        var result = -1;\r\n        arr.some(function (entry, index) {\r\n            if (entry[0] === key) {\r\n                result = index;\r\n                return true;\r\n            }\r\n            return false;\r\n        });\r\n        return result;\r\n    }\r\n    return /** @class */ (function () {\r\n        function class_1() {\r\n            this.__entries__ = [];\r\n        }\r\n        Object.defineProperty(class_1.prototype, \"size\", {\r\n            /**\r\n             * @returns {boolean}\r\n             */\r\n            get: function () {\r\n                return this.__entries__.length;\r\n            },\r\n            enumerable: true,\r\n            configurable: true\r\n        });\r\n        /**\r\n         * @param {*} key\r\n         * @returns {*}\r\n         */\r\n        class_1.prototype.get = function (key) {\r\n            var index = getIndex(this.__entries__, key);\r\n            var entry = this.__entries__[index];\r\n            return entry && entry[1];\r\n        };\r\n        /**\r\n         * @param {*} key\r\n         * @param {*} value\r\n         * @returns {void}\r\n         */\r\n        class_1.prototype.set = function (key, value) {\r\n            var index = getIndex(this.__entries__, key);\r\n            if (~index) {\r\n                this.__entries__[index][1] = value;\r\n            }\r\n            else {\r\n                this.__entries__.push([key, value]);\r\n            }\r\n        };\r\n        /**\r\n         * @param {*} key\r\n         * @returns {void}\r\n         */\r\n        class_1.prototype.delete = function (key) {\r\n            var entries = this.__entries__;\r\n            var index = getIndex(entries, key);\r\n            if (~index) {\r\n                entries.splice(index, 1);\r\n            }\r\n        };\r\n        /**\r\n         * @param {*} key\r\n         * @returns {void}\r\n         */\r\n        class_1.prototype.has = function (key) {\r\n            return !!~getIndex(this.__entries__, key);\r\n        };\r\n        /**\r\n         * @returns {void}\r\n         */\r\n        class_1.prototype.clear = function () {\r\n            this.__entries__.splice(0);\r\n        };\r\n        /**\r\n         * @param {Function} callback\r\n         * @param {*} [ctx=null]\r\n         * @returns {void}\r\n         */\r\n        class_1.prototype.forEach = function (callback, ctx) {\r\n            if (ctx === void 0) { ctx = null; }\r\n            for (var _i = 0, _a = this.__entries__; _i < _a.length; _i++) {\r\n                var entry = _a[_i];\r\n                callback.call(ctx, entry[1], entry[0]);\r\n            }\r\n        };\r\n        return class_1;\r\n    }());\r\n})();\n\n/**\r\n * Detects whether window and document objects are available in current environment.\r\n */\r\nvar isBrowser = typeof window !== 'undefined' && typeof document !== 'undefined' && window.document === document;\n\n// Returns global object of a current environment.\r\nvar global$1 = (function () {\r\n    if (typeof global !== 'undefined' && global.Math === Math) {\r\n        return global;\r\n    }\r\n    if (typeof self !== 'undefined' && self.Math === Math) {\r\n        return self;\r\n    }\r\n    if (typeof window !== 'undefined' && window.Math === Math) {\r\n        return window;\r\n    }\r\n    // eslint-disable-next-line no-new-func\r\n    return Function('return this')();\r\n})();\n\n/**\r\n * A shim for the requestAnimationFrame which falls back to the setTimeout if\r\n * first one is not supported.\r\n *\r\n * @returns {number} Requests' identifier.\r\n */\r\nvar requestAnimationFrame$1 = (function () {\r\n    if (typeof requestAnimationFrame === 'function') {\r\n        // It's required to use a bounded function because IE sometimes throws\r\n        // an \"Invalid calling object\" error if rAF is invoked without the global\r\n        // object on the left hand side.\r\n        return requestAnimationFrame.bind(global$1);\r\n    }\r\n    return function (callback) { return setTimeout(function () { return callback(Date.now()); }, 1000 / 60); };\r\n})();\n\n// Defines minimum timeout before adding a trailing call.\r\nvar trailingTimeout = 2;\r\n/**\r\n * Creates a wrapper function which ensures that provided callback will be\r\n * invoked only once during the specified delay period.\r\n *\r\n * @param {Function} callback - Function to be invoked after the delay period.\r\n * @param {number} delay - Delay after which to invoke callback.\r\n * @returns {Function}\r\n */\r\nfunction throttle (callback, delay) {\r\n    var leadingCall = false, trailingCall = false, lastCallTime = 0;\r\n    /**\r\n     * Invokes the original callback function and schedules new invocation if\r\n     * the \"proxy\" was called during current request.\r\n     *\r\n     * @returns {void}\r\n     */\r\n    function resolvePending() {\r\n        if (leadingCall) {\r\n            leadingCall = false;\r\n            callback();\r\n        }\r\n        if (trailingCall) {\r\n            proxy();\r\n        }\r\n    }\r\n    /**\r\n     * Callback invoked after the specified delay. It will further postpone\r\n     * invocation of the original function delegating it to the\r\n     * requestAnimationFrame.\r\n     *\r\n     * @returns {void}\r\n     */\r\n    function timeoutCallback() {\r\n        requestAnimationFrame$1(resolvePending);\r\n    }\r\n    /**\r\n     * Schedules invocation of the original function.\r\n     *\r\n     * @returns {void}\r\n     */\r\n    function proxy() {\r\n        var timeStamp = Date.now();\r\n        if (leadingCall) {\r\n            // Reject immediately following calls.\r\n            if (timeStamp - lastCallTime < trailingTimeout) {\r\n                return;\r\n            }\r\n            // Schedule new call to be in invoked when the pending one is resolved.\r\n            // This is important for \"transitions\" which never actually start\r\n            // immediately so there is a chance that we might miss one if change\r\n            // happens amids the pending invocation.\r\n            trailingCall = true;\r\n        }\r\n        else {\r\n            leadingCall = true;\r\n            trailingCall = false;\r\n            setTimeout(timeoutCallback, delay);\r\n        }\r\n        lastCallTime = timeStamp;\r\n    }\r\n    return proxy;\r\n}\n\n// Minimum delay before invoking the update of observers.\r\nvar REFRESH_DELAY = 20;\r\n// A list of substrings of CSS properties used to find transition events that\r\n// might affect dimensions of observed elements.\r\nvar transitionKeys = ['top', 'right', 'bottom', 'left', 'width', 'height', 'size', 'weight'];\r\n// Check if MutationObserver is available.\r\nvar mutationObserverSupported = typeof MutationObserver !== 'undefined';\r\n/**\r\n * Singleton controller class which handles updates of ResizeObserver instances.\r\n */\r\nvar ResizeObserverController = /** @class */ (function () {\r\n    /**\r\n     * Creates a new instance of ResizeObserverController.\r\n     *\r\n     * @private\r\n     */\r\n    function ResizeObserverController() {\r\n        /**\r\n         * Indicates whether DOM listeners have been added.\r\n         *\r\n         * @private {boolean}\r\n         */\r\n        this.connected_ = false;\r\n        /**\r\n         * Tells that controller has subscribed for Mutation Events.\r\n         *\r\n         * @private {boolean}\r\n         */\r\n        this.mutationEventsAdded_ = false;\r\n        /**\r\n         * Keeps reference to the instance of MutationObserver.\r\n         *\r\n         * @private {MutationObserver}\r\n         */\r\n        this.mutationsObserver_ = null;\r\n        /**\r\n         * A list of connected observers.\r\n         *\r\n         * @private {Array<ResizeObserverSPI>}\r\n         */\r\n        this.observers_ = [];\r\n        this.onTransitionEnd_ = this.onTransitionEnd_.bind(this);\r\n        this.refresh = throttle(this.refresh.bind(this), REFRESH_DELAY);\r\n    }\r\n    /**\r\n     * Adds observer to observers list.\r\n     *\r\n     * @param {ResizeObserverSPI} observer - Observer to be added.\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverController.prototype.addObserver = function (observer) {\r\n        if (!~this.observers_.indexOf(observer)) {\r\n            this.observers_.push(observer);\r\n        }\r\n        // Add listeners if they haven't been added yet.\r\n        if (!this.connected_) {\r\n            this.connect_();\r\n        }\r\n    };\r\n    /**\r\n     * Removes observer from observers list.\r\n     *\r\n     * @param {ResizeObserverSPI} observer - Observer to be removed.\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverController.prototype.removeObserver = function (observer) {\r\n        var observers = this.observers_;\r\n        var index = observers.indexOf(observer);\r\n        // Remove observer if it's present in registry.\r\n        if (~index) {\r\n            observers.splice(index, 1);\r\n        }\r\n        // Remove listeners if controller has no connected observers.\r\n        if (!observers.length && this.connected_) {\r\n            this.disconnect_();\r\n        }\r\n    };\r\n    /**\r\n     * Invokes the update of observers. It will continue running updates insofar\r\n     * it detects changes.\r\n     *\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverController.prototype.refresh = function () {\r\n        var changesDetected = this.updateObservers_();\r\n        // Continue running updates if changes have been detected as there might\r\n        // be future ones caused by CSS transitions.\r\n        if (changesDetected) {\r\n            this.refresh();\r\n        }\r\n    };\r\n    /**\r\n     * Updates every observer from observers list and notifies them of queued\r\n     * entries.\r\n     *\r\n     * @private\r\n     * @returns {boolean} Returns \"true\" if any observer has detected changes in\r\n     *      dimensions of it's elements.\r\n     */\r\n    ResizeObserverController.prototype.updateObservers_ = function () {\r\n        // Collect observers that have active observations.\r\n        var activeObservers = this.observers_.filter(function (observer) {\r\n            return observer.gatherActive(), observer.hasActive();\r\n        });\r\n        // Deliver notifications in a separate cycle in order to avoid any\r\n        // collisions between observers, e.g. when multiple instances of\r\n        // ResizeObserver are tracking the same element and the callback of one\r\n        // of them changes content dimensions of the observed target. Sometimes\r\n        // this may result in notifications being blocked for the rest of observers.\r\n        activeObservers.forEach(function (observer) { return observer.broadcastActive(); });\r\n        return activeObservers.length > 0;\r\n    };\r\n    /**\r\n     * Initializes DOM listeners.\r\n     *\r\n     * @private\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverController.prototype.connect_ = function () {\r\n        // Do nothing if running in a non-browser environment or if listeners\r\n        // have been already added.\r\n        if (!isBrowser || this.connected_) {\r\n            return;\r\n        }\r\n        // Subscription to the \"Transitionend\" event is used as a workaround for\r\n        // delayed transitions. This way it's possible to capture at least the\r\n        // final state of an element.\r\n        document.addEventListener('transitionend', this.onTransitionEnd_);\r\n        window.addEventListener('resize', this.refresh);\r\n        if (mutationObserverSupported) {\r\n            this.mutationsObserver_ = new MutationObserver(this.refresh);\r\n            this.mutationsObserver_.observe(document, {\r\n                attributes: true,\r\n                childList: true,\r\n                characterData: true,\r\n                subtree: true\r\n            });\r\n        }\r\n        else {\r\n            document.addEventListener('DOMSubtreeModified', this.refresh);\r\n            this.mutationEventsAdded_ = true;\r\n        }\r\n        this.connected_ = true;\r\n    };\r\n    /**\r\n     * Removes DOM listeners.\r\n     *\r\n     * @private\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverController.prototype.disconnect_ = function () {\r\n        // Do nothing if running in a non-browser environment or if listeners\r\n        // have been already removed.\r\n        if (!isBrowser || !this.connected_) {\r\n            return;\r\n        }\r\n        document.removeEventListener('transitionend', this.onTransitionEnd_);\r\n        window.removeEventListener('resize', this.refresh);\r\n        if (this.mutationsObserver_) {\r\n            this.mutationsObserver_.disconnect();\r\n        }\r\n        if (this.mutationEventsAdded_) {\r\n            document.removeEventListener('DOMSubtreeModified', this.refresh);\r\n        }\r\n        this.mutationsObserver_ = null;\r\n        this.mutationEventsAdded_ = false;\r\n        this.connected_ = false;\r\n    };\r\n    /**\r\n     * \"Transitionend\" event handler.\r\n     *\r\n     * @private\r\n     * @param {TransitionEvent} event\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverController.prototype.onTransitionEnd_ = function (_a) {\r\n        var _b = _a.propertyName, propertyName = _b === void 0 ? '' : _b;\r\n        // Detect whether transition may affect dimensions of an element.\r\n        var isReflowProperty = transitionKeys.some(function (key) {\r\n            return !!~propertyName.indexOf(key);\r\n        });\r\n        if (isReflowProperty) {\r\n            this.refresh();\r\n        }\r\n    };\r\n    /**\r\n     * Returns instance of the ResizeObserverController.\r\n     *\r\n     * @returns {ResizeObserverController}\r\n     */\r\n    ResizeObserverController.getInstance = function () {\r\n        if (!this.instance_) {\r\n            this.instance_ = new ResizeObserverController();\r\n        }\r\n        return this.instance_;\r\n    };\r\n    /**\r\n     * Holds reference to the controller's instance.\r\n     *\r\n     * @private {ResizeObserverController}\r\n     */\r\n    ResizeObserverController.instance_ = null;\r\n    return ResizeObserverController;\r\n}());\n\n/**\r\n * Defines non-writable/enumerable properties of the provided target object.\r\n *\r\n * @param {Object} target - Object for which to define properties.\r\n * @param {Object} props - Properties to be defined.\r\n * @returns {Object} Target object.\r\n */\r\nvar defineConfigurable = (function (target, props) {\r\n    for (var _i = 0, _a = Object.keys(props); _i < _a.length; _i++) {\r\n        var key = _a[_i];\r\n        Object.defineProperty(target, key, {\r\n            value: props[key],\r\n            enumerable: false,\r\n            writable: false,\r\n            configurable: true\r\n        });\r\n    }\r\n    return target;\r\n});\n\n/**\r\n * Returns the global object associated with provided element.\r\n *\r\n * @param {Object} target\r\n * @returns {Object}\r\n */\r\nvar getWindowOf = (function (target) {\r\n    // Assume that the element is an instance of Node, which means that it\r\n    // has the \"ownerDocument\" property from which we can retrieve a\r\n    // corresponding global object.\r\n    var ownerGlobal = target && target.ownerDocument && target.ownerDocument.defaultView;\r\n    // Return the local global object if it's not possible extract one from\r\n    // provided element.\r\n    return ownerGlobal || global$1;\r\n});\n\n// Placeholder of an empty content rectangle.\r\nvar emptyRect = createRectInit(0, 0, 0, 0);\r\n/**\r\n * Converts provided string to a number.\r\n *\r\n * @param {number|string} value\r\n * @returns {number}\r\n */\r\nfunction toFloat(value) {\r\n    return parseFloat(value) || 0;\r\n}\r\n/**\r\n * Extracts borders size from provided styles.\r\n *\r\n * @param {CSSStyleDeclaration} styles\r\n * @param {...string} positions - Borders positions (top, right, ...)\r\n * @returns {number}\r\n */\r\nfunction getBordersSize(styles) {\r\n    var positions = [];\r\n    for (var _i = 1; _i < arguments.length; _i++) {\r\n        positions[_i - 1] = arguments[_i];\r\n    }\r\n    return positions.reduce(function (size, position) {\r\n        var value = styles['border-' + position + '-width'];\r\n        return size + toFloat(value);\r\n    }, 0);\r\n}\r\n/**\r\n * Extracts paddings sizes from provided styles.\r\n *\r\n * @param {CSSStyleDeclaration} styles\r\n * @returns {Object} Paddings box.\r\n */\r\nfunction getPaddings(styles) {\r\n    var positions = ['top', 'right', 'bottom', 'left'];\r\n    var paddings = {};\r\n    for (var _i = 0, positions_1 = positions; _i < positions_1.length; _i++) {\r\n        var position = positions_1[_i];\r\n        var value = styles['padding-' + position];\r\n        paddings[position] = toFloat(value);\r\n    }\r\n    return paddings;\r\n}\r\n/**\r\n * Calculates content rectangle of provided SVG element.\r\n *\r\n * @param {SVGGraphicsElement} target - Element content rectangle of which needs\r\n *      to be calculated.\r\n * @returns {DOMRectInit}\r\n */\r\nfunction getSVGContentRect(target) {\r\n    var bbox = target.getBBox();\r\n    return createRectInit(0, 0, bbox.width, bbox.height);\r\n}\r\n/**\r\n * Calculates content rectangle of provided HTMLElement.\r\n *\r\n * @param {HTMLElement} target - Element for which to calculate the content rectangle.\r\n * @returns {DOMRectInit}\r\n */\r\nfunction getHTMLElementContentRect(target) {\r\n    // Client width & height properties can't be\r\n    // used exclusively as they provide rounded values.\r\n    var clientWidth = target.clientWidth, clientHeight = target.clientHeight;\r\n    // By this condition we can catch all non-replaced inline, hidden and\r\n    // detached elements. Though elements with width & height properties less\r\n    // than 0.5 will be discarded as well.\r\n    //\r\n    // Without it we would need to implement separate methods for each of\r\n    // those cases and it's not possible to perform a precise and performance\r\n    // effective test for hidden elements. E.g. even jQuery's ':visible' filter\r\n    // gives wrong results for elements with width & height less than 0.5.\r\n    if (!clientWidth && !clientHeight) {\r\n        return emptyRect;\r\n    }\r\n    var styles = getWindowOf(target).getComputedStyle(target);\r\n    var paddings = getPaddings(styles);\r\n    var horizPad = paddings.left + paddings.right;\r\n    var vertPad = paddings.top + paddings.bottom;\r\n    // Computed styles of width & height are being used because they are the\r\n    // only dimensions available to JS that contain non-rounded values. It could\r\n    // be possible to utilize the getBoundingClientRect if only it's data wasn't\r\n    // affected by CSS transformations let alone paddings, borders and scroll bars.\r\n    var width = toFloat(styles.width), height = toFloat(styles.height);\r\n    // Width & height include paddings and borders when the 'border-box' box\r\n    // model is applied (except for IE).\r\n    if (styles.boxSizing === 'border-box') {\r\n        // Following conditions are required to handle Internet Explorer which\r\n        // doesn't include paddings and borders to computed CSS dimensions.\r\n        //\r\n        // We can say that if CSS dimensions + paddings are equal to the \"client\"\r\n        // properties then it's either IE, and thus we don't need to subtract\r\n        // anything, or an element merely doesn't have paddings/borders styles.\r\n        if (Math.round(width + horizPad) !== clientWidth) {\r\n            width -= getBordersSize(styles, 'left', 'right') + horizPad;\r\n        }\r\n        if (Math.round(height + vertPad) !== clientHeight) {\r\n            height -= getBordersSize(styles, 'top', 'bottom') + vertPad;\r\n        }\r\n    }\r\n    // Following steps can't be applied to the document's root element as its\r\n    // client[Width/Height] properties represent viewport area of the window.\r\n    // Besides, it's as well not necessary as the <html> itself neither has\r\n    // rendered scroll bars nor it can be clipped.\r\n    if (!isDocumentElement(target)) {\r\n        // In some browsers (only in Firefox, actually) CSS width & height\r\n        // include scroll bars size which can be removed at this step as scroll\r\n        // bars are the only difference between rounded dimensions + paddings\r\n        // and \"client\" properties, though that is not always true in Chrome.\r\n        var vertScrollbar = Math.round(width + horizPad) - clientWidth;\r\n        var horizScrollbar = Math.round(height + vertPad) - clientHeight;\r\n        // Chrome has a rather weird rounding of \"client\" properties.\r\n        // E.g. for an element with content width of 314.2px it sometimes gives\r\n        // the client width of 315px and for the width of 314.7px it may give\r\n        // 314px. And it doesn't happen all the time. So just ignore this delta\r\n        // as a non-relevant.\r\n        if (Math.abs(vertScrollbar) !== 1) {\r\n            width -= vertScrollbar;\r\n        }\r\n        if (Math.abs(horizScrollbar) !== 1) {\r\n            height -= horizScrollbar;\r\n        }\r\n    }\r\n    return createRectInit(paddings.left, paddings.top, width, height);\r\n}\r\n/**\r\n * Checks whether provided element is an instance of the SVGGraphicsElement.\r\n *\r\n * @param {Element} target - Element to be checked.\r\n * @returns {boolean}\r\n */\r\nvar isSVGGraphicsElement = (function () {\r\n    // Some browsers, namely IE and Edge, don't have the SVGGraphicsElement\r\n    // interface.\r\n    if (typeof SVGGraphicsElement !== 'undefined') {\r\n        return function (target) { return target instanceof getWindowOf(target).SVGGraphicsElement; };\r\n    }\r\n    // If it's so, then check that element is at least an instance of the\r\n    // SVGElement and that it has the \"getBBox\" method.\r\n    // eslint-disable-next-line no-extra-parens\r\n    return function (target) { return (target instanceof getWindowOf(target).SVGElement &&\r\n        typeof target.getBBox === 'function'); };\r\n})();\r\n/**\r\n * Checks whether provided element is a document element (<html>).\r\n *\r\n * @param {Element} target - Element to be checked.\r\n * @returns {boolean}\r\n */\r\nfunction isDocumentElement(target) {\r\n    return target === getWindowOf(target).document.documentElement;\r\n}\r\n/**\r\n * Calculates an appropriate content rectangle for provided html or svg element.\r\n *\r\n * @param {Element} target - Element content rectangle of which needs to be calculated.\r\n * @returns {DOMRectInit}\r\n */\r\nfunction getContentRect(target) {\r\n    if (!isBrowser) {\r\n        return emptyRect;\r\n    }\r\n    if (isSVGGraphicsElement(target)) {\r\n        return getSVGContentRect(target);\r\n    }\r\n    return getHTMLElementContentRect(target);\r\n}\r\n/**\r\n * Creates rectangle with an interface of the DOMRectReadOnly.\r\n * Spec: https://drafts.fxtf.org/geometry/#domrectreadonly\r\n *\r\n * @param {DOMRectInit} rectInit - Object with rectangle's x/y coordinates and dimensions.\r\n * @returns {DOMRectReadOnly}\r\n */\r\nfunction createReadOnlyRect(_a) {\r\n    var x = _a.x, y = _a.y, width = _a.width, height = _a.height;\r\n    // If DOMRectReadOnly is available use it as a prototype for the rectangle.\r\n    var Constr = typeof DOMRectReadOnly !== 'undefined' ? DOMRectReadOnly : Object;\r\n    var rect = Object.create(Constr.prototype);\r\n    // Rectangle's properties are not writable and non-enumerable.\r\n    defineConfigurable(rect, {\r\n        x: x, y: y, width: width, height: height,\r\n        top: y,\r\n        right: x + width,\r\n        bottom: height + y,\r\n        left: x\r\n    });\r\n    return rect;\r\n}\r\n/**\r\n * Creates DOMRectInit object based on the provided dimensions and the x/y coordinates.\r\n * Spec: https://drafts.fxtf.org/geometry/#dictdef-domrectinit\r\n *\r\n * @param {number} x - X coordinate.\r\n * @param {number} y - Y coordinate.\r\n * @param {number} width - Rectangle's width.\r\n * @param {number} height - Rectangle's height.\r\n * @returns {DOMRectInit}\r\n */\r\nfunction createRectInit(x, y, width, height) {\r\n    return { x: x, y: y, width: width, height: height };\r\n}\n\n/**\r\n * Class that is responsible for computations of the content rectangle of\r\n * provided DOM element and for keeping track of it's changes.\r\n */\r\nvar ResizeObservation = /** @class */ (function () {\r\n    /**\r\n     * Creates an instance of ResizeObservation.\r\n     *\r\n     * @param {Element} target - Element to be observed.\r\n     */\r\n    function ResizeObservation(target) {\r\n        /**\r\n         * Broadcasted width of content rectangle.\r\n         *\r\n         * @type {number}\r\n         */\r\n        this.broadcastWidth = 0;\r\n        /**\r\n         * Broadcasted height of content rectangle.\r\n         *\r\n         * @type {number}\r\n         */\r\n        this.broadcastHeight = 0;\r\n        /**\r\n         * Reference to the last observed content rectangle.\r\n         *\r\n         * @private {DOMRectInit}\r\n         */\r\n        this.contentRect_ = createRectInit(0, 0, 0, 0);\r\n        this.target = target;\r\n    }\r\n    /**\r\n     * Updates content rectangle and tells whether it's width or height properties\r\n     * have changed since the last broadcast.\r\n     *\r\n     * @returns {boolean}\r\n     */\r\n    ResizeObservation.prototype.isActive = function () {\r\n        var rect = getContentRect(this.target);\r\n        this.contentRect_ = rect;\r\n        return (rect.width !== this.broadcastWidth ||\r\n            rect.height !== this.broadcastHeight);\r\n    };\r\n    /**\r\n     * Updates 'broadcastWidth' and 'broadcastHeight' properties with a data\r\n     * from the corresponding properties of the last observed content rectangle.\r\n     *\r\n     * @returns {DOMRectInit} Last observed content rectangle.\r\n     */\r\n    ResizeObservation.prototype.broadcastRect = function () {\r\n        var rect = this.contentRect_;\r\n        this.broadcastWidth = rect.width;\r\n        this.broadcastHeight = rect.height;\r\n        return rect;\r\n    };\r\n    return ResizeObservation;\r\n}());\n\nvar ResizeObserverEntry = /** @class */ (function () {\r\n    /**\r\n     * Creates an instance of ResizeObserverEntry.\r\n     *\r\n     * @param {Element} target - Element that is being observed.\r\n     * @param {DOMRectInit} rectInit - Data of the element's content rectangle.\r\n     */\r\n    function ResizeObserverEntry(target, rectInit) {\r\n        var contentRect = createReadOnlyRect(rectInit);\r\n        // According to the specification following properties are not writable\r\n        // and are also not enumerable in the native implementation.\r\n        //\r\n        // Property accessors are not being used as they'd require to define a\r\n        // private WeakMap storage which may cause memory leaks in browsers that\r\n        // don't support this type of collections.\r\n        defineConfigurable(this, { target: target, contentRect: contentRect });\r\n    }\r\n    return ResizeObserverEntry;\r\n}());\n\nvar ResizeObserverSPI = /** @class */ (function () {\r\n    /**\r\n     * Creates a new instance of ResizeObserver.\r\n     *\r\n     * @param {ResizeObserverCallback} callback - Callback function that is invoked\r\n     *      when one of the observed elements changes it's content dimensions.\r\n     * @param {ResizeObserverController} controller - Controller instance which\r\n     *      is responsible for the updates of observer.\r\n     * @param {ResizeObserver} callbackCtx - Reference to the public\r\n     *      ResizeObserver instance which will be passed to callback function.\r\n     */\r\n    function ResizeObserverSPI(callback, controller, callbackCtx) {\r\n        /**\r\n         * Collection of resize observations that have detected changes in dimensions\r\n         * of elements.\r\n         *\r\n         * @private {Array<ResizeObservation>}\r\n         */\r\n        this.activeObservations_ = [];\r\n        /**\r\n         * Registry of the ResizeObservation instances.\r\n         *\r\n         * @private {Map<Element, ResizeObservation>}\r\n         */\r\n        this.observations_ = new MapShim();\r\n        if (typeof callback !== 'function') {\r\n            throw new TypeError('The callback provided as parameter 1 is not a function.');\r\n        }\r\n        this.callback_ = callback;\r\n        this.controller_ = controller;\r\n        this.callbackCtx_ = callbackCtx;\r\n    }\r\n    /**\r\n     * Starts observing provided element.\r\n     *\r\n     * @param {Element} target - Element to be observed.\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverSPI.prototype.observe = function (target) {\r\n        if (!arguments.length) {\r\n            throw new TypeError('1 argument required, but only 0 present.');\r\n        }\r\n        // Do nothing if current environment doesn't have the Element interface.\r\n        if (typeof Element === 'undefined' || !(Element instanceof Object)) {\r\n            return;\r\n        }\r\n        if (!(target instanceof getWindowOf(target).Element)) {\r\n            throw new TypeError('parameter 1 is not of type \"Element\".');\r\n        }\r\n        var observations = this.observations_;\r\n        // Do nothing if element is already being observed.\r\n        if (observations.has(target)) {\r\n            return;\r\n        }\r\n        observations.set(target, new ResizeObservation(target));\r\n        this.controller_.addObserver(this);\r\n        // Force the update of observations.\r\n        this.controller_.refresh();\r\n    };\r\n    /**\r\n     * Stops observing provided element.\r\n     *\r\n     * @param {Element} target - Element to stop observing.\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverSPI.prototype.unobserve = function (target) {\r\n        if (!arguments.length) {\r\n            throw new TypeError('1 argument required, but only 0 present.');\r\n        }\r\n        // Do nothing if current environment doesn't have the Element interface.\r\n        if (typeof Element === 'undefined' || !(Element instanceof Object)) {\r\n            return;\r\n        }\r\n        if (!(target instanceof getWindowOf(target).Element)) {\r\n            throw new TypeError('parameter 1 is not of type \"Element\".');\r\n        }\r\n        var observations = this.observations_;\r\n        // Do nothing if element is not being observed.\r\n        if (!observations.has(target)) {\r\n            return;\r\n        }\r\n        observations.delete(target);\r\n        if (!observations.size) {\r\n            this.controller_.removeObserver(this);\r\n        }\r\n    };\r\n    /**\r\n     * Stops observing all elements.\r\n     *\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverSPI.prototype.disconnect = function () {\r\n        this.clearActive();\r\n        this.observations_.clear();\r\n        this.controller_.removeObserver(this);\r\n    };\r\n    /**\r\n     * Collects observation instances the associated element of which has changed\r\n     * it's content rectangle.\r\n     *\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverSPI.prototype.gatherActive = function () {\r\n        var _this = this;\r\n        this.clearActive();\r\n        this.observations_.forEach(function (observation) {\r\n            if (observation.isActive()) {\r\n                _this.activeObservations_.push(observation);\r\n            }\r\n        });\r\n    };\r\n    /**\r\n     * Invokes initial callback function with a list of ResizeObserverEntry\r\n     * instances collected from active resize observations.\r\n     *\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverSPI.prototype.broadcastActive = function () {\r\n        // Do nothing if observer doesn't have active observations.\r\n        if (!this.hasActive()) {\r\n            return;\r\n        }\r\n        var ctx = this.callbackCtx_;\r\n        // Create ResizeObserverEntry instance for every active observation.\r\n        var entries = this.activeObservations_.map(function (observation) {\r\n            return new ResizeObserverEntry(observation.target, observation.broadcastRect());\r\n        });\r\n        this.callback_.call(ctx, entries, ctx);\r\n        this.clearActive();\r\n    };\r\n    /**\r\n     * Clears the collection of active observations.\r\n     *\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverSPI.prototype.clearActive = function () {\r\n        this.activeObservations_.splice(0);\r\n    };\r\n    /**\r\n     * Tells whether observer has active observations.\r\n     *\r\n     * @returns {boolean}\r\n     */\r\n    ResizeObserverSPI.prototype.hasActive = function () {\r\n        return this.activeObservations_.length > 0;\r\n    };\r\n    return ResizeObserverSPI;\r\n}());\n\n// Registry of internal observers. If WeakMap is not available use current shim\r\n// for the Map collection as it has all required methods and because WeakMap\r\n// can't be fully polyfilled anyway.\r\nvar observers = typeof WeakMap !== 'undefined' ? new WeakMap() : new MapShim();\r\n/**\r\n * ResizeObserver API. Encapsulates the ResizeObserver SPI implementation\r\n * exposing only those methods and properties that are defined in the spec.\r\n */\r\nvar ResizeObserver = /** @class */ (function () {\r\n    /**\r\n     * Creates a new instance of ResizeObserver.\r\n     *\r\n     * @param {ResizeObserverCallback} callback - Callback that is invoked when\r\n     *      dimensions of the observed elements change.\r\n     */\r\n    function ResizeObserver(callback) {\r\n        if (!(this instanceof ResizeObserver)) {\r\n            throw new TypeError('Cannot call a class as a function.');\r\n        }\r\n        if (!arguments.length) {\r\n            throw new TypeError('1 argument required, but only 0 present.');\r\n        }\r\n        var controller = ResizeObserverController.getInstance();\r\n        var observer = new ResizeObserverSPI(callback, controller, this);\r\n        observers.set(this, observer);\r\n    }\r\n    return ResizeObserver;\r\n}());\r\n// Expose public methods of ResizeObserver.\r\n[\r\n    'observe',\r\n    'unobserve',\r\n    'disconnect'\r\n].forEach(function (method) {\r\n    ResizeObserver.prototype[method] = function () {\r\n        var _a;\r\n        return (_a = observers.get(this))[method].apply(_a, arguments);\r\n    };\r\n});\n\nvar index = (function () {\r\n    // Export existing implementation if available.\r\n    if (typeof global$1.ResizeObserver !== 'undefined') {\r\n        return global$1.ResizeObserver;\r\n    }\r\n    return ResizeObserver;\r\n})();\n\nexport default index;\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport ResizeObserver from \"resize-observer-polyfill\"\nimport {\n  NEVER,\n  Observable,\n  Subject,\n  defer,\n  filter,\n  finalize,\n  map,\n  merge,\n  of,\n  shareReplay,\n  startWith,\n  switchMap,\n  tap\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Element offset\n */\nexport interface ElementSize {\n  width: number                        /* Element width */\n  height: number                       /* Element height */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Resize observer entry subject\n */\nconst entry$ = new Subject<ResizeObserverEntry>()\n\n/**\n * Resize observer observable\n *\n * This observable will create a `ResizeObserver` on the first subscription\n * and will automatically terminate it when there are no more subscribers.\n * It's quite important to centralize observation in a single `ResizeObserver`,\n * as the performance difference can be quite dramatic, as the link shows.\n *\n * @see https://bit.ly/3iIYfEm - Google Groups on performance\n */\nconst observer$ = defer(() => of(\n  new ResizeObserver(entries => {\n    for (const entry of entries)\n      entry$.next(entry)\n  })\n))\n  .pipe(\n    switchMap(observer => merge(NEVER, of(observer))\n      .pipe(\n        finalize(() => observer.disconnect())\n      )\n    ),\n    shareReplay(1)\n  )\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element size\n *\n * @param el - Element\n *\n * @returns Element size\n */\nexport function getElementSize(\n  el: HTMLElement\n): ElementSize {\n  return {\n    width:  el.offsetWidth,\n    height: el.offsetHeight\n  }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch element size\n *\n * This function returns an observable that subscribes to a single internal\n * instance of `ResizeObserver` upon subscription, and emit resize events until\n * termination. Note that this function should not be called with the same\n * element twice, as the first unsubscription will terminate observation.\n *\n * Sadly, we can't use the `DOMRect` objects returned by the observer, because\n * we need the emitted values to be consistent with `getElementSize`, which will\n * return the used values (rounded) and not actual values (unrounded). Thus, we\n * use the `offset*` properties. See the linked GitHub issue.\n *\n * @see https://bit.ly/3m0k3he - GitHub issue\n *\n * @param el - Element\n *\n * @returns Element size observable\n */\nexport function watchElementSize(\n  el: HTMLElement\n): Observable<ElementSize> {\n  return observer$\n    .pipe(\n      tap(observer => observer.observe(el)),\n      switchMap(observer => entry$\n        .pipe(\n          filter(({ target }) => target === el),\n          finalize(() => observer.unobserve(el)),\n          map(() => getElementSize(el))\n        )\n      ),\n      startWith(getElementSize(el))\n    )\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { ElementSize } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element content size (= scroll width and height)\n *\n * @param el - Element\n *\n * @returns Element content size\n */\nexport function getElementContentSize(\n  el: HTMLElement\n): ElementSize {\n  return {\n    width:  el.scrollWidth,\n    height: el.scrollHeight\n  }\n}\n\n/**\n * Retrieve the overflowing container of an element, if any\n *\n * @param el - Element\n *\n * @returns Overflowing container or nothing\n */\nexport function getElementContainer(\n  el: HTMLElement\n): HTMLElement | undefined {\n  let parent = el.parentElement\n  while (parent)\n    if (\n      el.scrollWidth  <= parent.scrollWidth &&\n      el.scrollHeight <= parent.scrollHeight\n    )\n      parent = (el = parent).parentElement\n    else\n      break\n\n  /* Return overflowing container */\n  return parent ? el : undefined\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  NEVER,\n  Observable,\n  Subject,\n  defer,\n  distinctUntilChanged,\n  filter,\n  finalize,\n  map,\n  merge,\n  of,\n  shareReplay,\n  switchMap,\n  tap\n} from \"rxjs\"\n\nimport {\n  getElementContentSize,\n  getElementSize,\n  watchElementContentOffset\n} from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Intersection observer entry subject\n */\nconst entry$ = new Subject<IntersectionObserverEntry>()\n\n/**\n * Intersection observer observable\n *\n * This observable will create an `IntersectionObserver` on first subscription\n * and will automatically terminate it when there are no more subscribers.\n *\n * @see https://bit.ly/3iIYfEm - Google Groups on performance\n */\nconst observer$ = defer(() => of(\n  new IntersectionObserver(entries => {\n    for (const entry of entries)\n      entry$.next(entry)\n  }, {\n    threshold: 0\n  })\n))\n  .pipe(\n    switchMap(observer => merge(NEVER, of(observer))\n      .pipe(\n        finalize(() => observer.disconnect())\n      )\n    ),\n    shareReplay(1)\n  )\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch element visibility\n *\n * @param el - Element\n *\n * @returns Element visibility observable\n */\nexport function watchElementVisibility(\n  el: HTMLElement\n): Observable<boolean> {\n  return observer$\n    .pipe(\n      tap(observer => observer.observe(el)),\n      switchMap(observer => entry$\n        .pipe(\n          filter(({ target }) => target === el),\n          finalize(() => observer.unobserve(el)),\n          map(({ isIntersecting }) => isIntersecting)\n        )\n      )\n    )\n}\n\n/**\n * Watch element boundary\n *\n * This function returns an observable which emits whether the bottom content\n * boundary (= scroll offset) of an element is within a certain threshold.\n *\n * @param el - Element\n * @param threshold - Threshold\n *\n * @returns Element boundary observable\n */\nexport function watchElementBoundary(\n  el: HTMLElement, threshold = 16\n): Observable<boolean> {\n  return watchElementContentOffset(el)\n    .pipe(\n      map(({ y }) => {\n        const visible = getElementSize(el)\n        const content = getElementContentSize(el)\n        return y >= (\n          content.height - visible.height - threshold\n        )\n      }),\n      distinctUntilChanged()\n    )\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  fromEvent,\n  map,\n  startWith\n} from \"rxjs\"\n\nimport { getElement } from \"../element\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Toggle\n */\nexport type Toggle =\n  | \"drawer\"                           /* Toggle for drawer */\n  | \"search\"                           /* Toggle for search */\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Toggle map\n */\nconst toggles: Record<Toggle, HTMLInputElement> = {\n  drawer: getElement(\"[data-md-toggle=drawer]\"),\n  search: getElement(\"[data-md-toggle=search]\")\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve the value of a toggle\n *\n * @param name - Toggle\n *\n * @returns Toggle value\n */\nexport function getToggle(name: Toggle): boolean {\n  return toggles[name].checked\n}\n\n/**\n * Set toggle\n *\n * Simulating a click event seems to be the most cross-browser compatible way\n * of changing the value while also emitting a `change` event. Before, Material\n * used `CustomEvent` to programmatically change the value of a toggle, but this\n * is a much simpler and cleaner solution which doesn't require a polyfill.\n *\n * @param name - Toggle\n * @param value - Toggle value\n */\nexport function setToggle(name: Toggle, value: boolean): void {\n  if (toggles[name].checked !== value)\n    toggles[name].click()\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch toggle\n *\n * @param name - Toggle\n *\n * @returns Toggle value observable\n */\nexport function watchToggle(name: Toggle): Observable<boolean> {\n  const el = toggles[name]\n  return fromEvent(el, \"change\")\n    .pipe(\n      map(() => el.checked),\n      startWith(el.checked)\n    )\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  filter,\n  fromEvent,\n  map,\n  merge,\n  share,\n  startWith,\n  switchMap\n} from \"rxjs\"\n\nimport { getActiveElement } from \"../element\"\nimport { getToggle } from \"../toggle\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Keyboard mode\n */\nexport type KeyboardMode =\n  | \"global\"                           /* Global */\n  | \"search\"                           /* Search is open */\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Keyboard\n */\nexport interface Keyboard {\n  mode: KeyboardMode                   /* Keyboard mode */\n  type: string                         /* Key type */\n  claim(): void                        /* Key claim */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Check whether an element may receive keyboard input\n *\n * @param el - Element\n * @param type - Key type\n *\n * @returns Test result\n */\nfunction isSusceptibleToKeyboard(\n  el: HTMLElement, type: string\n): boolean {\n  switch (el.constructor) {\n\n    /* Input elements */\n    case HTMLInputElement:\n      /* @ts-expect-error - omit unnecessary type cast */\n      if (el.type === \"radio\")\n        return /^Arrow/.test(type)\n      else\n        return true\n\n    /* Select element and textarea */\n    case HTMLSelectElement:\n    case HTMLTextAreaElement:\n      return true\n\n    /* Everything else */\n    default:\n      return el.isContentEditable\n  }\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch composition events\n *\n * @returns Composition observable\n */\nexport function watchComposition(): Observable<boolean> {\n  return merge(\n    fromEvent(window, \"compositionstart\").pipe(map(() => true)),\n    fromEvent(window, \"compositionend\").pipe(map(() => false))\n  )\n    .pipe(\n      startWith(false)\n    )\n}\n\n/**\n * Watch keyboard\n *\n * @returns Keyboard observable\n */\nexport function watchKeyboard(): Observable<Keyboard> {\n  const keyboard$ = fromEvent<KeyboardEvent>(window, \"keydown\")\n    .pipe(\n      filter(ev => !(ev.metaKey || ev.ctrlKey)),\n      map(ev => ({\n        mode: getToggle(\"search\") ? \"search\" : \"global\",\n        type: ev.key,\n        claim() {\n          ev.preventDefault()\n          ev.stopPropagation()\n        }\n      } as Keyboard)),\n      filter(({ mode, type }) => {\n        if (mode === \"global\") {\n          const active = getActiveElement()\n          if (typeof active !== \"undefined\")\n            return !isSusceptibleToKeyboard(active, type)\n        }\n        return true\n      }),\n      share()\n    )\n\n  /* Don't emit during composition events - see https://bit.ly/3te3Wl8 */\n  return watchComposition()\n    .pipe(\n      switchMap(active => !active ? keyboard$ : EMPTY)\n    )\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Subject } from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve location\n *\n * This function returns a `URL` object (and not `Location`) to normalize the\n * typings across the application. Furthermore, locations need to be tracked\n * without setting them and `Location` is a singleton which represents the\n * current location.\n *\n * @returns URL\n */\nexport function getLocation(): URL {\n  return new URL(location.href)\n}\n\n/**\n * Set location\n *\n * @param url - URL to change to\n */\nexport function setLocation(url: URL | HTMLLinkElement): void {\n  location.href = url.href\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch location\n *\n * @returns Location subject\n */\nexport function watchLocation(): Subject<URL> {\n  return new Subject<URL>()\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { JSX as JSXInternal } from \"preact\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * HTML attributes\n */\ntype Attributes =\n  & JSXInternal.HTMLAttributes\n  & JSXInternal.SVGAttributes\n  & Record<string, any>\n\n/**\n * Child element\n */\ntype Child =\n  | ChildNode\n  | HTMLElement\n  | Text\n  | string\n  | number\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Append a child node to an element\n *\n * @param el - Element\n * @param child - Child node(s)\n */\nfunction appendChild(el: HTMLElement, child: Child | Child[]): void {\n\n  /* Handle primitive types (including raw HTML) */\n  if (typeof child === \"string\" || typeof child === \"number\") {\n    el.innerHTML += child.toString()\n\n  /* Handle nodes */\n  } else if (child instanceof Node) {\n    el.appendChild(child)\n\n  /* Handle nested children */\n  } else if (Array.isArray(child)) {\n    for (const node of child)\n      appendChild(el, node)\n  }\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * JSX factory\n *\n * @template T - Element type\n *\n * @param tag - HTML tag\n * @param attributes - HTML attributes\n * @param children - Child elements\n *\n * @returns Element\n */\nexport function h<T extends keyof HTMLElementTagNameMap>(\n  tag: T, attributes?: Attributes | null, ...children: Child[]\n): HTMLElementTagNameMap[T]\n\nexport function h<T extends h.JSX.Element>(\n  tag: string, attributes?: Attributes | null, ...children: Child[]\n): T\n\nexport function h<T extends h.JSX.Element>(\n  tag: string, attributes?: Attributes | null, ...children: Child[]\n): T {\n  const el = document.createElement(tag)\n\n  /* Set attributes, if any */\n  if (attributes)\n    for (const attr of Object.keys(attributes)) {\n      if (typeof attributes[attr] === \"undefined\")\n        continue\n\n      /* Set default attribute or boolean */\n      if (typeof attributes[attr] !== \"boolean\")\n        el.setAttribute(attr, attributes[attr])\n      else\n        el.setAttribute(attr, \"\")\n    }\n\n  /* Append child nodes */\n  for (const child of children)\n    appendChild(el, child)\n\n  /* Return element */\n  return el as T\n}\n\n/* ----------------------------------------------------------------------------\n * Namespace\n * ------------------------------------------------------------------------- */\n\nexport declare namespace h {\n  namespace JSX {\n    type Element = HTMLElement\n    type IntrinsicElements = JSXInternal.IntrinsicElements\n  }\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Round a number for display with repository facts\n *\n * This is a reverse-engineered version of GitHub's weird rounding algorithm\n * for stars, forks and all other numbers. While all numbers below `1,000` are\n * returned as-is, bigger numbers are converted to fixed numbers:\n *\n * - `1,049` => `1k`\n * - `1,050` => `1.1k`\n * - `1,949` => `1.9k`\n * - `1,950` => `2k`\n *\n * @param value - Original value\n *\n * @returns Rounded value\n */\nexport function round(value: number): string {\n  if (value > 999) {\n    const digits = +((value - 950) % 1000 > 99)\n    return `${((value + 0.000001) / 1000).toFixed(digits)}k`\n  } else {\n    return value.toString()\n  }\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  filter,\n  fromEvent,\n  map,\n  merge,\n  shareReplay,\n  startWith\n} from \"rxjs\"\n\nimport { getOptionalElement } from \"~/browser\"\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve location hash\n *\n * @returns Location hash\n */\nexport function getLocationHash(): string {\n  return location.hash.substring(1)\n}\n\n/**\n * Set location hash\n *\n * Setting a new fragment identifier via `location.hash` will have no effect\n * if the value doesn't change. When a new fragment identifier is set, we want\n * the browser to target the respective element at all times, which is why we\n * use this dirty little trick.\n *\n * @param hash - Location hash\n */\nexport function setLocationHash(hash: string): void {\n  const el = h(\"a\", { href: hash })\n  el.addEventListener(\"click\", ev => ev.stopPropagation())\n  el.click()\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch location hash\n *\n * @param location$ - Location observable\n *\n * @returns Location hash observable\n */\nexport function watchLocationHash(\n  location$: Observable<URL>\n): Observable<string> {\n  return merge(\n    fromEvent<HashChangeEvent>(window, \"hashchange\"),\n    location$\n  )\n    .pipe(\n      map(getLocationHash),\n      startWith(getLocationHash()),\n      filter(hash => hash.length > 0),\n      shareReplay(1)\n    )\n}\n\n/**\n * Watch location target\n *\n * @param location$ - Location observable\n *\n * @returns Location target observable\n */\nexport function watchLocationTarget(\n  location$: Observable<URL>\n): Observable<HTMLElement> {\n  return watchLocationHash(location$)\n    .pipe(\n      map(id => getOptionalElement(`[id=\"${id}\"]`)!),\n      filter(el => typeof el !== \"undefined\")\n    )\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  fromEvent,\n  fromEventPattern,\n  map,\n  merge,\n  startWith,\n  switchMap\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch media query\n *\n * Note that although `MediaQueryList.addListener` is deprecated we have to\n * use it, because it's the only way to ensure proper downward compatibility.\n *\n * @see https://bit.ly/3dUBH2m - GitHub issue\n *\n * @param query - Media query\n *\n * @returns Media observable\n */\nexport function watchMedia(query: string): Observable<boolean> {\n  const media = matchMedia(query)\n  return fromEventPattern<boolean>(next => (\n    media.addListener(() => next(media.matches))\n  ))\n    .pipe(\n      startWith(media.matches)\n    )\n}\n\n/**\n * Watch print mode\n *\n * @returns Print observable\n */\nexport function watchPrint(): Observable<boolean> {\n  const media = matchMedia(\"print\")\n  return merge(\n    fromEvent(window, \"beforeprint\").pipe(map(() => true)),\n    fromEvent(window, \"afterprint\").pipe(map(() => false))\n  )\n    .pipe(\n      startWith(media.matches)\n    )\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Toggle an observable with a media observable\n *\n * @template T - Data type\n *\n * @param query$ - Media observable\n * @param factory - Observable factory\n *\n * @returns Toggled observable\n */\nexport function at<T>(\n  query$: Observable<boolean>, factory: () => Observable<T>\n): Observable<T> {\n  return query$\n    .pipe(\n      switchMap(active => active ? factory() : EMPTY)\n    )\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  catchError,\n  from,\n  map,\n  of,\n  shareReplay,\n  switchMap,\n  throwError\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch the given URL\n *\n * If the request fails (e.g. when dispatched from `file://` locations), the\n * observable will complete without emitting a value.\n *\n * @param url - Request URL\n * @param options - Options\n *\n * @returns Response observable\n */\nexport function request(\n  url: URL | string, options: RequestInit = { credentials: \"same-origin\" }\n): Observable<Response> {\n  return from(fetch(`${url}`, options))\n    .pipe(\n      catchError(() => EMPTY),\n      switchMap(res => res.status !== 200\n        ? throwError(() => new Error(res.statusText))\n        : of(res)\n      )\n    )\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Fetch JSON from the given URL\n *\n * @template T - Data type\n *\n * @param url - Request URL\n * @param options - Options\n *\n * @returns Data observable\n */\nexport function requestJSON<T>(\n  url: URL | string, options?: RequestInit\n): Observable<T> {\n  return request(url, options)\n    .pipe(\n      switchMap(res => res.json()),\n      shareReplay(1)\n    )\n}\n\n/**\n * Fetch XML from the given URL\n *\n * @param url - Request URL\n * @param options - Options\n *\n * @returns Data observable\n */\nexport function requestXML(\n  url: URL | string, options?: RequestInit\n): Observable<Document> {\n  const dom = new DOMParser()\n  return request(url, options)\n    .pipe(\n      switchMap(res => res.text()),\n      map(res => dom.parseFromString(res, \"text/xml\")),\n      shareReplay(1)\n    )\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  defer,\n  finalize,\n  fromEvent,\n  map,\n  merge,\n  switchMap,\n  take,\n  throwError\n} from \"rxjs\"\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create and load a `script` element\n *\n * This function returns an observable that will emit when the script was\n * successfully loaded, or throw an error if it wasn't.\n *\n * @param src - Script URL\n *\n * @returns Script observable\n */\nexport function watchScript(src: string): Observable<void> {\n  const script = h(\"script\", { src })\n  return defer(() => {\n    document.head.appendChild(script)\n    return merge(\n      fromEvent(script, \"load\"),\n      fromEvent(script, \"error\")\n        .pipe(\n          switchMap(() => (\n            throwError(() => new ReferenceError(`Invalid script: ${src}`))\n          ))\n        )\n    )\n      .pipe(\n        map(() => undefined),\n        finalize(() => document.head.removeChild(script)),\n        take(1)\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  fromEvent,\n  map,\n  merge,\n  startWith\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Viewport offset\n */\nexport interface ViewportOffset {\n  x: number                            /* Horizontal offset */\n  y: number                            /* Vertical offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve viewport offset\n *\n * On iOS Safari, viewport offset can be negative due to overflow scrolling.\n * As this may induce strange behaviors downstream, we'll just limit it to 0.\n *\n * @returns Viewport offset\n */\nexport function getViewportOffset(): ViewportOffset {\n  return {\n    x: Math.max(0, scrollX),\n    y: Math.max(0, scrollY)\n  }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport offset\n *\n * @returns Viewport offset observable\n */\nexport function watchViewportOffset(): Observable<ViewportOffset> {\n  return merge(\n    fromEvent(window, \"scroll\", { passive: true }),\n    fromEvent(window, \"resize\", { passive: true })\n  )\n    .pipe(\n      map(getViewportOffset),\n      startWith(getViewportOffset())\n    )\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  fromEvent,\n  map,\n  startWith\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Viewport size\n */\nexport interface ViewportSize {\n  width: number                        /* Viewport width */\n  height: number                       /* Viewport height */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve viewport size\n *\n * @returns Viewport size\n */\nexport function getViewportSize(): ViewportSize {\n  return {\n    width:  innerWidth,\n    height: innerHeight\n  }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport size\n *\n * @returns Viewport size observable\n */\nexport function watchViewportSize(): Observable<ViewportSize> {\n  return fromEvent(window, \"resize\", { passive: true })\n    .pipe(\n      map(getViewportSize),\n      startWith(getViewportSize())\n    )\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  combineLatest,\n  map,\n  shareReplay\n} from \"rxjs\"\n\nimport {\n  ViewportOffset,\n  watchViewportOffset\n} from \"../offset\"\nimport {\n  ViewportSize,\n  watchViewportSize\n} from \"../size\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Viewport\n */\nexport interface Viewport {\n  offset: ViewportOffset               /* Viewport offset */\n  size: ViewportSize                   /* Viewport size */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport\n *\n * @returns Viewport observable\n */\nexport function watchViewport(): Observable<Viewport> {\n  return combineLatest([\n    watchViewportOffset(),\n    watchViewportSize()\n  ])\n    .pipe(\n      map(([offset, size]) => ({ offset, size })),\n      shareReplay(1)\n    )\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  combineLatest,\n  distinctUntilKeyChanged,\n  map\n} from \"rxjs\"\n\nimport { Header } from \"~/components\"\n\nimport { getElementOffset } from \"../../element\"\nimport { Viewport } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport relative to element\n *\n * @param el - Element\n * @param options - Options\n *\n * @returns Viewport observable\n */\nexport function watchViewportAt(\n  el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable<Viewport> {\n  const size$ = viewport$\n    .pipe(\n      distinctUntilKeyChanged(\"size\")\n    )\n\n  /* Compute element offset */\n  const offset$ = combineLatest([size$, header$])\n    .pipe(\n      map(() => getElementOffset(el))\n    )\n\n  /* Compute relative viewport, return hot observable */\n  return combineLatest([header$, viewport$, offset$])\n    .pipe(\n      map(([{ height }, { offset, size }, { x, y }]) => ({\n        offset: {\n          x: offset.x - x,\n          y: offset.y - y + height\n        },\n        size\n      }))\n    )\n}\n", "\"use strict\";(()=>{function c(s,n){parent.postMessage(s,n||\"*\")}function d(...s){return s.reduce((n,e)=>n.then(()=>new Promise(r=>{let t=document.createElement(\"script\");t.src=e,t.onload=r,document.body.appendChild(t)})),Promise.resolve())}var o=class extends EventTarget{constructor(e){super();this.url=e;this.m=e=>{e.source===this.w&&(this.dispatchEvent(new MessageEvent(\"message\",{data:e.data})),this.onmessage&&this.onmessage(e))};this.e=(e,r,t,i,m)=>{if(r===`${this.url}`){let a=new ErrorEvent(\"error\",{message:e,filename:r,lineno:t,colno:i,error:m});this.dispatchEvent(a),this.onerror&&this.onerror(a)}};let r=document.createElement(\"iframe\");r.hidden=!0,document.body.appendChild(this.iframe=r),this.w.document.open(),this.w.document.write(`<html><body><script>postMessage=${c};importScripts=${d};addEventListener(\"error\",ev=>{parent.dispatchEvent(new ErrorEvent(\"error\",{filename:\"${e}\",error:ev.error}))})<\\/script><script src=${e}?${+Date.now()}><\\/script></body></html>`),this.w.document.close(),onmessage=this.m,onerror=this.e,this.r=new Promise((t,i)=>{this.w.onload=t,this.w.onerror=i})}terminate(){document.body.removeChild(this.iframe),onmessage=onerror=null}postMessage(e){this.r.catch().then(()=>{this.w.dispatchEvent(new MessageEvent(\"message\",{data:e}))})}get w(){return this.iframe.contentWindow}};window.IFrameWorker=o;location.protocol===\"file:\"&&(window.Worker=o);})();\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport \"iframe-worker/shim\"\nimport {\n  Observable,\n  Subject,\n  endWith,\n  fromEvent,\n  ignoreElements,\n  mergeWith,\n  share,\n  takeUntil\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Worker message\n */\nexport interface WorkerMessage {\n  type: unknown                        /* Message type */\n  data?: unknown                       /* Message data */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create an observable for receiving from a web worker\n *\n * @template T - Data type\n *\n * @param worker - Web worker\n *\n * @returns Message observable\n */\nfunction recv<T>(worker: Worker): Observable<T> {\n  return fromEvent<MessageEvent<T>, T>(worker, \"message\", ev => ev.data)\n}\n\n/**\n * Create a subject for sending to a web worker\n *\n * @template T - Data type\n *\n * @param worker - Web worker\n *\n * @returns Message subject\n */\nfunction send<T>(worker: Worker): Subject<T> {\n  const send$ = new Subject<T>()\n  send$.subscribe(data => worker.postMessage(data))\n\n  /* Return message subject */\n  return send$\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create a bidirectional communication channel to a web worker\n *\n * @template T - Data type\n *\n * @param url - Worker URL\n * @param worker - Worker\n *\n * @returns Worker subject\n */\nexport function watchWorker<T extends WorkerMessage>(\n  url: string, worker = new Worker(url)\n): Subject<T> {\n  const recv$ = recv<T>(worker)\n  const send$ = send<T>(worker)\n\n  /* Create worker subject and forward messages */\n  const worker$ = new Subject<T>()\n  worker$.subscribe(send$)\n\n  /* Return worker subject */\n  const done$ = send$.pipe(ignoreElements(), endWith(true))\n  return worker$\n    .pipe(\n      ignoreElements(),\n      mergeWith(recv$.pipe(takeUntil(done$))),\n      share()\n    ) as Subject<T>\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { getElement, getLocation } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Feature flag\n */\nexport type Flag =\n  | \"announce.dismiss\"                 /* Dismissable announcement bar */\n  | \"content.code.annotate\"            /* Code annotations */\n  | \"content.code.copy\"                /* Code copy button */\n  | \"content.lazy\"                     /* Lazy content elements */\n  | \"content.tabs.link\"                /* Link content tabs */\n  | \"header.autohide\"                  /* Hide header */\n  | \"navigation.expand\"                /* Automatic expansion */\n  | \"navigation.indexes\"               /* Section pages */\n  | \"navigation.instant\"               /* Instant loading */\n  | \"navigation.sections\"              /* Section navigation */\n  | \"navigation.tabs\"                  /* Tabs navigation */\n  | \"navigation.tabs.sticky\"           /* Tabs navigation (sticky) */\n  | \"navigation.top\"                   /* Back-to-top button */\n  | \"navigation.tracking\"              /* Anchor tracking */\n  | \"search.highlight\"                 /* Search highlighting */\n  | \"search.share\"                     /* Search sharing */\n  | \"search.suggest\"                   /* Search suggestions */\n  | \"toc.follow\"                       /* Following table of contents */\n  | \"toc.integrate\"                    /* Integrated table of contents */\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Translation\n */\nexport type Translation =\n  | \"clipboard.copy\"                   /* Copy to clipboard */\n  | \"clipboard.copied\"                 /* Copied to clipboard */\n  | \"search.result.placeholder\"        /* Type to start searching */\n  | \"search.result.none\"               /* No matching documents */\n  | \"search.result.one\"                /* 1 matching document */\n  | \"search.result.other\"              /* # matching documents */\n  | \"search.result.more.one\"           /* 1 more on this page */\n  | \"search.result.more.other\"         /* # more on this page */\n  | \"search.result.term.missing\"       /* Missing */\n  | \"select.version\"                   /* Version selector */\n\n/**\n * Translations\n */\nexport type Translations =\n  Record<Translation, string>\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Versioning\n */\nexport interface Versioning {\n  provider: \"mike\"                     /* Version provider */\n  default?: string                     /* Default version */\n}\n\n/**\n * Configuration\n */\nexport interface Config {\n  base: string                         /* Base URL */\n  features: Flag[]                     /* Feature flags */\n  translations: Translations           /* Translations */\n  search: string                       /* Search worker URL */\n  tags?: Record<string, string>        /* Tags mapping */\n  version?: Versioning                 /* Versioning */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve global configuration and make base URL absolute\n */\nconst script = getElement(\"#__config\")\nconst config: Config = JSON.parse(script.textContent!)\nconfig.base = `${new URL(config.base, getLocation())}`\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve global configuration\n *\n * @returns Global configuration\n */\nexport function configuration(): Config {\n  return config\n}\n\n/**\n * Check whether a feature flag is enabled\n *\n * @param flag - Feature flag\n *\n * @returns Test result\n */\nexport function feature(flag: Flag): boolean {\n  return config.features.includes(flag)\n}\n\n/**\n * Retrieve the translation for the given key\n *\n * @param key - Key to be translated\n * @param value - Positional value, if any\n *\n * @returns Translation\n */\nexport function translation(\n  key: Translation, value?: string | number\n): string {\n  return typeof value !== \"undefined\"\n    ? config.translations[key].replace(\"#\", value.toString())\n    : config.translations[key]\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { getElement, getElements } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Component type\n */\nexport type ComponentType =\n  | \"announce\"                         /* Announcement bar */\n  | \"container\"                        /* Container */\n  | \"consent\"                          /* Consent */\n  | \"content\"                          /* Content */\n  | \"dialog\"                           /* Dialog */\n  | \"header\"                           /* Header */\n  | \"header-title\"                     /* Header title */\n  | \"header-topic\"                     /* Header topic */\n  | \"main\"                             /* Main area */\n  | \"outdated\"                         /* Version warning */\n  | \"palette\"                          /* Color palette */\n  | \"search\"                           /* Search */\n  | \"search-query\"                     /* Search input */\n  | \"search-result\"                    /* Search results */\n  | \"search-share\"                     /* Search sharing */\n  | \"search-suggest\"                   /* Search suggestions */\n  | \"sidebar\"                          /* Sidebar */\n  | \"skip\"                             /* Skip link */\n  | \"source\"                           /* Repository information */\n  | \"tabs\"                             /* Navigation tabs */\n  | \"toc\"                              /* Table of contents */\n  | \"top\"                              /* Back-to-top button */\n\n/**\n * Component\n *\n * @template T - Component type\n * @template U - Reference type\n */\nexport type Component<\n  T extends {} = {},\n  U extends HTMLElement = HTMLElement\n> =\n  T & {\n    ref: U                             /* Component reference */\n  }\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Component type map\n */\ninterface ComponentTypeMap {\n  \"announce\": HTMLElement              /* Announcement bar */\n  \"container\": HTMLElement             /* Container */\n  \"consent\": HTMLElement               /* Consent */\n  \"content\": HTMLElement               /* Content */\n  \"dialog\": HTMLElement                /* Dialog */\n  \"header\": HTMLElement                /* Header */\n  \"header-title\": HTMLElement          /* Header title */\n  \"header-topic\": HTMLElement          /* Header topic */\n  \"main\": HTMLElement                  /* Main area */\n  \"outdated\": HTMLElement              /* Version warning */\n  \"palette\": HTMLElement               /* Color palette */\n  \"search\": HTMLElement                /* Search */\n  \"search-query\": HTMLInputElement     /* Search input */\n  \"search-result\": HTMLElement         /* Search results */\n  \"search-share\": HTMLAnchorElement    /* Search sharing */\n  \"search-suggest\": HTMLElement        /* Search suggestions */\n  \"sidebar\": HTMLElement               /* Sidebar */\n  \"skip\": HTMLAnchorElement            /* Skip link */\n  \"source\": HTMLAnchorElement          /* Repository information */\n  \"tabs\": HTMLElement                  /* Navigation tabs */\n  \"toc\": HTMLElement                   /* Table of contents */\n  \"top\": HTMLAnchorElement             /* Back-to-top button */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve the element for a given component or throw a reference error\n *\n * @template T - Component type\n *\n * @param type - Component type\n * @param node - Node of reference\n *\n * @returns Element\n */\nexport function getComponentElement<T extends ComponentType>(\n  type: T, node: ParentNode = document\n): ComponentTypeMap[T] {\n  return getElement(`[data-md-component=${type}]`, node)\n}\n\n/**\n * Retrieve all elements for a given component\n *\n * @template T - Component type\n *\n * @param type - Component type\n * @param node - Node of reference\n *\n * @returns Elements\n */\nexport function getComponentElements<T extends ComponentType>(\n  type: T, node: ParentNode = document\n): ComponentTypeMap[T][] {\n  return getElements(`[data-md-component=${type}]`, node)\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  Subject,\n  defer,\n  finalize,\n  fromEvent,\n  map,\n  tap\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport { getElement } from \"~/browser\"\n\nimport { Component } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Announcement bar\n */\nexport interface Announce {\n  hash: number                        /* Content hash */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch announcement bar\n *\n * @param el - Announcement bar element\n *\n * @returns Announcement bar observable\n */\nexport function watchAnnounce(\n  el: HTMLElement\n): Observable<Announce> {\n  const button = getElement(\".md-typeset > :first-child\", el)\n  return fromEvent(button, \"click\", { once: true })\n    .pipe(\n      map(() => getElement(\".md-typeset\", el)),\n      map(content => ({ hash: __md_hash(content.innerHTML) }))\n    )\n}\n\n/**\n * Mount announcement bar\n *\n * @param el - Announcement bar element\n *\n * @returns Announcement bar component observable\n */\nexport function mountAnnounce(\n  el: HTMLElement\n): Observable<Component<Announce>> {\n  if (!feature(\"announce.dismiss\") || !el.childElementCount)\n    return EMPTY\n\n  /* Mount component on subscription */\n  return defer(() => {\n    const push$ = new Subject<Announce>()\n    push$.subscribe(({ hash }) => {\n      el.hidden = true\n\n      /* Persist preference in local storage */\n      __md_set<number>(\"__announce\", hash)\n    })\n\n    /* Create and return component */\n    return watchAnnounce(el)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  finalize,\n  map,\n  tap\n} from \"rxjs\"\n\nimport { Component } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Consent\n */\nexport interface Consent {\n  hidden: boolean                      /* Consent is hidden */\n}\n\n/**\n * Consent defaults\n */\nexport interface ConsentDefaults {\n  analytics?: boolean                  /* Consent for Analytics */\n  github?: boolean                     /* Consent for GitHub */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  target$: Observable<HTMLElement>     /* Target observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  target$: Observable<HTMLElement>     /* Target observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch consent\n *\n * @param el - Consent element\n * @param options - Options\n *\n * @returns Consent observable\n */\nexport function watchConsent(\n  el: HTMLElement, { target$ }: WatchOptions\n): Observable<Consent> {\n  return target$\n    .pipe(\n      map(target => ({ hidden: target !== el }))\n    )\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Mount consent\n *\n * @param el - Consent element\n * @param options - Options\n *\n * @returns Consent component observable\n */\nexport function mountConsent(\n  el: HTMLElement, options: MountOptions\n): Observable<Component<Consent>> {\n  const internal$ = new Subject<Consent>()\n  internal$.subscribe(({ hidden }) => {\n    el.hidden = hidden\n  })\n\n  /* Create and return component */\n  return watchConsent(el, options)\n    .pipe(\n      tap(state => internal$.next(state)),\n      finalize(() => internal$.complete()),\n      map(state => ({ ref: el, ...state }))\n    )\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport ClipboardJS from \"clipboard\"\nimport {\n  EMPTY,\n  Observable,\n  Subject,\n  defer,\n  distinctUntilChanged,\n  distinctUntilKeyChanged,\n  filter,\n  finalize,\n  map,\n  mergeWith,\n  switchMap,\n  take,\n  tap\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n  getElementContentSize,\n  watchElementSize,\n  watchElementVisibility\n} from \"~/browser\"\nimport { renderClipboardButton } from \"~/templates\"\n\nimport { Component } from \"../../../_\"\nimport {\n  Annotation,\n  mountAnnotationList\n} from \"../../annotation\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Code block\n */\nexport interface CodeBlock {\n  scrollable: boolean                  /* Code block overflows */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  target$: Observable<HTMLElement>     /* Location target observable */\n  print$: Observable<boolean>          /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Global sequence number for code blocks\n */\nlet sequence = 0\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Find candidate list element directly following a code block\n *\n * @param el - Code block element\n *\n * @returns List element or nothing\n */\nfunction findCandidateList(el: HTMLElement): HTMLElement | undefined {\n  if (el.nextElementSibling) {\n    const sibling = el.nextElementSibling as HTMLElement\n    if (sibling.tagName === \"OL\")\n      return sibling\n\n    /* Skip empty paragraphs - see https://bit.ly/3r4ZJ2O */\n    else if (sibling.tagName === \"P\" && !sibling.children.length)\n      return findCandidateList(sibling)\n  }\n\n  /* Everything else */\n  return undefined\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch code block\n *\n * This function monitors size changes of the viewport, as well as switches of\n * content tabs with embedded code blocks, as both may trigger overflow.\n *\n * @param el - Code block element\n *\n * @returns Code block observable\n */\nexport function watchCodeBlock(\n  el: HTMLElement\n): Observable<CodeBlock> {\n  return watchElementSize(el)\n    .pipe(\n      map(({ width }) => {\n        const content = getElementContentSize(el)\n        return {\n          scrollable: content.width > width\n        }\n      }),\n      distinctUntilKeyChanged(\"scrollable\")\n    )\n}\n\n/**\n * Mount code block\n *\n * This function ensures that an overflowing code block is focusable through\n * keyboard, so it can be scrolled without a mouse to improve on accessibility.\n * Furthermore, if code annotations are enabled, they are mounted if and only\n * if the code block is currently visible, e.g., not in a hidden content tab.\n *\n * Note that code blocks may be mounted eagerly or lazily. If they're mounted\n * lazily (on first visibility), code annotation anchor links will not work,\n * as they are evaluated on initial page load, and code annotations in general\n * might feel a little bumpier.\n *\n * @param el - Code block element\n * @param options - Options\n *\n * @returns Code block and annotation component observable\n */\nexport function mountCodeBlock(\n  el: HTMLElement, options: MountOptions\n): Observable<Component<CodeBlock | Annotation>> {\n  const { matches: hover } = matchMedia(\"(hover)\")\n\n  /* Defer mounting of code block - see https://bit.ly/3vHVoVD */\n  const factory$ = defer(() => {\n    const push$ = new Subject<CodeBlock>()\n    push$.subscribe(({ scrollable }) => {\n      if (scrollable && hover)\n        el.setAttribute(\"tabindex\", \"0\")\n      else\n        el.removeAttribute(\"tabindex\")\n    })\n\n    /* Render button for Clipboard.js integration */\n    if (ClipboardJS.isSupported()) {\n      if (el.closest(\".copy\") || (\n        feature(\"content.code.copy\") && !el.closest(\".no-copy\")\n      )) {\n        const parent = el.closest(\"pre\")!\n        parent.id = `__code_${sequence++}`\n        parent.insertBefore(\n          renderClipboardButton(parent.id),\n          el\n        )\n      }\n    }\n\n    /* Handle code annotations */\n    const container = el.closest(\".highlight\")\n    if (container instanceof HTMLElement) {\n      const list = findCandidateList(container)\n\n      /* Mount code annotations, if enabled */\n      if (typeof list !== \"undefined\" && (\n        container.classList.contains(\"annotate\") ||\n        feature(\"content.code.annotate\")\n      )) {\n        const annotations$ = mountAnnotationList(list, el, options)\n\n        /* Create and return component */\n        return watchCodeBlock(el)\n          .pipe(\n            tap(state => push$.next(state)),\n            finalize(() => push$.complete()),\n            map(state => ({ ref: el, ...state })),\n            mergeWith(\n              watchElementSize(container)\n                .pipe(\n                  map(({ width, height }) => width && height),\n                  distinctUntilChanged(),\n                  switchMap(active => active ? annotations$ : EMPTY)\n                )\n            )\n          )\n      }\n    }\n\n    /* Create and return component */\n    return watchCodeBlock(el)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n\n  /* Mount code block lazily */\n  if (feature(\"content.lazy\"))\n    return watchElementVisibility(el)\n      .pipe(\n        filter(visible => visible),\n        take(1),\n        switchMap(() => factory$)\n      )\n\n  /* Mount code block */\n  return factory$\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a tooltip\n *\n * @param id - Tooltip identifier\n *\n * @returns Element\n */\nexport function renderTooltip(id?: string): HTMLElement {\n  return (\n    <div class=\"md-tooltip\" id={id}>\n      <div class=\"md-tooltip__inner md-typeset\"></div>\n    </div>\n  )\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { h } from \"~/utilities\"\n\nimport { renderTooltip } from \"../tooltip\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render an annotation\n *\n * @param id - Annotation identifier\n * @param prefix - Tooltip identifier prefix\n *\n * @returns Element\n */\nexport function renderAnnotation(\n  id: string | number, prefix?: string\n): HTMLElement {\n  prefix = prefix ? `${prefix}_annotation_${id}` : undefined\n\n  /* Render tooltip with anchor, if given */\n  if (prefix) {\n    const anchor = prefix ? `#${prefix}` : undefined\n    return (\n      <aside class=\"md-annotation\" tabIndex={0}>\n        {renderTooltip(prefix)}\n        <a href={anchor} class=\"md-annotation__index\" tabIndex={-1}>\n          <span data-md-annotation-id={id}></span>\n        </a>\n      </aside>\n    )\n  } else {\n    return (\n      <aside class=\"md-annotation\" tabIndex={0}>\n        {renderTooltip(prefix)}\n        <span class=\"md-annotation__index\" tabIndex={-1}>\n          <span data-md-annotation-id={id}></span>\n        </span>\n      </aside>\n    )\n  }\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { translation } from \"~/_\"\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a 'copy-to-clipboard' button\n *\n * @param id - Unique identifier\n *\n * @returns Element\n */\nexport function renderClipboardButton(id: string): HTMLElement {\n  return (\n    <button\n      class=\"md-clipboard md-icon\"\n      title={translation(\"clipboard.copy\")}\n      data-clipboard-target={`#${id} > code`}\n    ></button>\n  )\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { ComponentChild } from \"preact\"\n\nimport { configuration, feature, translation } from \"~/_\"\nimport { SearchItem } from \"~/integrations/search\"\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Render flag\n */\nconst enum Flag {\n  TEASER = 1,                          /* Render teaser */\n  PARENT = 2                           /* Render as parent */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper function\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a search document\n *\n * @param document - Search document\n * @param flag - Render flags\n *\n * @returns Element\n */\nfunction renderSearchDocument(\n  document: SearchItem, flag: Flag\n): HTMLElement {\n  const parent = flag & Flag.PARENT\n  const teaser = flag & Flag.TEASER\n\n  /* Render missing query terms */\n  const missing = Object.keys(document.terms)\n    .filter(key => !document.terms[key])\n    .reduce<ComponentChild[]>((list, key) => [\n      ...list, <del>{key}</del>, \" \"\n    ], [])\n    .slice(0, -1)\n\n  /* Assemble query string for highlighting */\n  const config = configuration()\n  const url = new URL(document.location, config.base)\n  if (feature(\"search.highlight\"))\n    url.searchParams.set(\"h\", Object.entries(document.terms)\n      .filter(([, match]) => match)\n      .reduce((highlight, [value]) => `${highlight} ${value}`.trim(), \"\")\n    )\n\n  /* Render article or section, depending on flags */\n  const { tags } = configuration()\n  return (\n    <a href={`${url}`} class=\"md-search-result__link\" tabIndex={-1}>\n      <article\n        class=\"md-search-result__article md-typeset\"\n        data-md-score={document.score.toFixed(2)}\n      >\n        {parent > 0 && <div class=\"md-search-result__icon md-icon\"></div>}\n        {parent > 0 && <h1>{document.title}</h1>}\n        {parent <= 0 && <h2>{document.title}</h2>}\n        {teaser > 0 && document.text.length > 0 &&\n          document.text\n        }\n        {document.tags && document.tags.map(tag => {\n          const type = tags\n            ? tag in tags\n              ? `md-tag-icon md-tag--${tags[tag]}`\n              : \"md-tag-icon\"\n            : \"\"\n          return (\n            <span class={`md-tag ${type}`}>{tag}</span>\n          )\n        })}\n        {teaser > 0 && missing.length > 0 &&\n          <p class=\"md-search-result__terms\">\n            {translation(\"search.result.term.missing\")}: {...missing}\n          </p>\n        }\n      </article>\n    </a>\n  )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a search result\n *\n * @param result - Search result\n *\n * @returns Element\n */\nexport function renderSearchResultItem(\n  result: SearchItem[]\n): HTMLElement {\n  const threshold = result[0].score\n  const docs = [...result]\n\n  const config = configuration()\n\n  /* Find and extract parent article */\n  const parent = docs.findIndex(doc => {\n    const l = `${new URL(doc.location, config.base)}` // @todo hacky\n    return !l.includes(\"#\")\n  })\n  const [article] = docs.splice(parent, 1)\n\n  /* Determine last index above threshold */\n  let index = docs.findIndex(doc => doc.score < threshold)\n  if (index === -1)\n    index = docs.length\n\n  /* Partition sections */\n  const best = docs.slice(0, index)\n  const more = docs.slice(index)\n\n  /* Render children */\n  const children = [\n    renderSearchDocument(article, Flag.PARENT | +(!parent && index === 0)),\n    ...best.map(section => renderSearchDocument(section, Flag.TEASER)),\n    ...more.length ? [\n      <details class=\"md-search-result__more\">\n        <summary tabIndex={-1}>\n          <div>\n            {more.length > 0 && more.length === 1\n              ? translation(\"search.result.more.one\")\n              : translation(\"search.result.more.other\", more.length)\n            }\n          </div>\n        </summary>\n        {...more.map(section => renderSearchDocument(section, Flag.TEASER))}\n      </details>\n    ] : []\n  ]\n\n  /* Render search result */\n  return (\n    <li class=\"md-search-result__item\">\n      {children}\n    </li>\n  )\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { SourceFacts } from \"~/components\"\nimport { h, round } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render repository facts\n *\n * @param facts - Repository facts\n *\n * @returns Element\n */\nexport function renderSourceFacts(facts: SourceFacts): HTMLElement {\n  return (\n    <ul class=\"md-source__facts\">\n      {Object.entries(facts).map(([key, value]) => (\n        <li class={`md-source__fact md-source__fact--${key}`}>\n          {typeof value === \"number\" ? round(value) : value}\n        </li>\n      ))}\n    </ul>\n  )\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Tabbed control type\n */\ntype TabbedControlType =\n  | \"prev\"\n  | \"next\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render control for content tabs\n *\n * @param type - Control type\n *\n * @returns Element\n */\nexport function renderTabbedControl(\n  type: TabbedControlType\n): HTMLElement {\n  const classes = `tabbed-control tabbed-control--${type}`\n  return (\n    <div class={classes} hidden>\n      <button class=\"tabbed-button\" tabIndex={-1} aria-hidden=\"true\"></button>\n    </div>\n  )\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a table inside a wrapper to improve scrolling on mobile\n *\n * @param table - Table element\n *\n * @returns Element\n */\nexport function renderTable(table: HTMLElement): HTMLElement {\n  return (\n    <div class=\"md-typeset__scrollwrap\">\n      <div class=\"md-typeset__table\">\n        {table}\n      </div>\n    </div>\n  )\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { configuration, translation } from \"~/_\"\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Version\n */\nexport interface Version {\n  version: string                      /* Version identifier */\n  title: string                        /* Version title */\n  aliases: string[]                    /* Version aliases */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a version\n *\n * @param version - Version\n *\n * @returns Element\n */\nfunction renderVersion(version: Version): HTMLElement {\n  const config = configuration()\n\n  /* Ensure trailing slash - see https://bit.ly/3rL5u3f */\n  const url = new URL(`../${version.version}/`, config.base)\n  return (\n    <li class=\"md-version__item\">\n      <a href={`${url}`} class=\"md-version__link\">\n        {version.title}\n      </a>\n    </li>\n  )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a version selector\n *\n * @param versions - Versions\n * @param active - Active version\n *\n * @returns Element\n */\nexport function renderVersionSelector(\n  versions: Version[], active: Version\n): HTMLElement {\n  return (\n    <div class=\"md-version\">\n      <button\n        class=\"md-version__current\"\n        aria-label={translation(\"select.version\")}\n      >\n        {active.title}\n      </button>\n      <ul class=\"md-version__list\">\n        {versions.map(renderVersion)}\n      </ul>\n    </div>\n  )\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  animationFrameScheduler,\n  auditTime,\n  combineLatest,\n  debounceTime,\n  defer,\n  delay,\n  endWith,\n  filter,\n  finalize,\n  fromEvent,\n  ignoreElements,\n  map,\n  merge,\n  switchMap,\n  take,\n  takeUntil,\n  tap,\n  throttleTime,\n  withLatestFrom\n} from \"rxjs\"\n\nimport {\n  ElementOffset,\n  getActiveElement,\n  getElementSize,\n  watchElementContentOffset,\n  watchElementFocus,\n  watchElementOffset,\n  watchElementVisibility\n} from \"~/browser\"\n\nimport { Component } from \"../../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Annotation\n */\nexport interface Annotation {\n  active: boolean                      /* Annotation is active */\n  offset: ElementOffset                /* Annotation offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  target$: Observable<HTMLElement>     /* Location target observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch annotation\n *\n * @param el - Annotation element\n * @param container - Containing element\n *\n * @returns Annotation observable\n */\nexport function watchAnnotation(\n  el: HTMLElement, container: HTMLElement\n): Observable<Annotation> {\n  const offset$ = defer(() => combineLatest([\n    watchElementOffset(el),\n    watchElementContentOffset(container)\n  ]))\n    .pipe(\n      map(([{ x, y }, scroll]): ElementOffset => {\n        const { width, height } = getElementSize(el)\n        return ({\n          x: x - scroll.x + width / 2,\n          y: y - scroll.y + height / 2\n        })\n      })\n    )\n\n  /* Actively watch annotation on focus */\n  return watchElementFocus(el)\n    .pipe(\n      switchMap(active => offset$\n        .pipe(\n          map(offset => ({ active, offset })),\n          take(+!active || Infinity)\n        )\n      )\n    )\n}\n\n/**\n * Mount annotation\n *\n * @param el - Annotation element\n * @param container - Containing element\n * @param options - Options\n *\n * @returns Annotation component observable\n */\nexport function mountAnnotation(\n  el: HTMLElement, container: HTMLElement, { target$ }: MountOptions\n): Observable<Component<Annotation>> {\n  const [tooltip, index] = Array.from(el.children)\n\n  /* Mount component on subscription */\n  return defer(() => {\n    const push$ = new Subject<Annotation>()\n    const done$ = push$.pipe(ignoreElements(), endWith(true))\n    push$.subscribe({\n\n      /* Handle emission */\n      next({ offset }) {\n        el.style.setProperty(\"--md-tooltip-x\", `${offset.x}px`)\n        el.style.setProperty(\"--md-tooltip-y\", `${offset.y}px`)\n      },\n\n      /* Handle complete */\n      complete() {\n        el.style.removeProperty(\"--md-tooltip-x\")\n        el.style.removeProperty(\"--md-tooltip-y\")\n      }\n    })\n\n    /* Start animation only when annotation is visible */\n    watchElementVisibility(el)\n      .pipe(\n        takeUntil(done$)\n      )\n        .subscribe(visible => {\n          el.toggleAttribute(\"data-md-visible\", visible)\n        })\n\n    /* Toggle tooltip presence to mitigate empty lines when copying */\n    merge(\n      push$.pipe(filter(({ active }) => active)),\n      push$.pipe(debounceTime(250), filter(({ active }) => !active))\n    )\n      .subscribe({\n\n        /* Handle emission */\n        next({ active }) {\n          if (active)\n            el.prepend(tooltip)\n          else\n            tooltip.remove()\n        },\n\n        /* Handle complete */\n        complete() {\n          el.prepend(tooltip)\n        }\n      })\n\n    /* Toggle tooltip visibility */\n    push$\n      .pipe(\n        auditTime(16, animationFrameScheduler)\n      )\n        .subscribe(({ active }) => {\n          tooltip.classList.toggle(\"md-tooltip--active\", active)\n        })\n\n    /* Track relative origin of tooltip */\n    push$\n      .pipe(\n        throttleTime(125, animationFrameScheduler),\n        filter(() => !!el.offsetParent),\n        map(() => el.offsetParent!.getBoundingClientRect()),\n        map(({ x }) => x)\n      )\n        .subscribe({\n\n          /* Handle emission */\n          next(origin) {\n            if (origin)\n              el.style.setProperty(\"--md-tooltip-0\", `${-origin}px`)\n            else\n              el.style.removeProperty(\"--md-tooltip-0\")\n          },\n\n          /* Handle complete */\n          complete() {\n            el.style.removeProperty(\"--md-tooltip-0\")\n          }\n        })\n\n    /* Allow to copy link without scrolling to anchor */\n    fromEvent<MouseEvent>(index, \"click\")\n      .pipe(\n        takeUntil(done$),\n        filter(ev => !(ev.metaKey || ev.ctrlKey))\n      )\n        .subscribe(ev => {\n          ev.stopPropagation()\n          ev.preventDefault()\n        })\n\n    /* Allow to open link in new tab or blur on close */\n    fromEvent<MouseEvent>(index, \"mousedown\")\n      .pipe(\n        takeUntil(done$),\n        withLatestFrom(push$)\n      )\n        .subscribe(([ev, { active }]) => {\n\n          /* Open in new tab */\n          if (ev.button !== 0 || ev.metaKey || ev.ctrlKey) {\n            ev.preventDefault()\n\n          /* Close annotation */\n          } else if (active) {\n            ev.preventDefault()\n\n            /* Focus parent annotation, if any */\n            const parent = el.parentElement!.closest(\".md-annotation\")\n            if (parent instanceof HTMLElement)\n              parent.focus()\n            else\n              getActiveElement()?.blur()\n          }\n        })\n\n    /* Open and focus annotation on location target */\n    target$\n      .pipe(\n        takeUntil(done$),\n        filter(target => target === tooltip),\n        delay(125)\n      )\n        .subscribe(() => el.focus())\n\n    /* Create and return component */\n    return watchAnnotation(el, container)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  Subject,\n  defer,\n  endWith,\n  finalize,\n  ignoreElements,\n  merge,\n  share,\n  takeUntil\n} from \"rxjs\"\n\nimport {\n  getElement,\n  getElements,\n  getOptionalElement\n} from \"~/browser\"\nimport { renderAnnotation } from \"~/templates\"\n\nimport { Component } from \"../../../_\"\nimport {\n  Annotation,\n  mountAnnotation\n} from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  target$: Observable<HTMLElement>     /* Location target observable */\n  print$: Observable<boolean>          /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Find all annotation markers in the given code block\n *\n * @param container - Containing element\n *\n * @returns Annotation markers\n */\nfunction findAnnotationMarkers(container: HTMLElement): Text[] {\n  const markers: Text[] = []\n  for (const el of getElements(\".c, .c1, .cm\", container)) {\n    const nodes: Text[] = []\n\n    /* Find all text nodes in current element */\n    const it = document.createNodeIterator(el, NodeFilter.SHOW_TEXT)\n    for (let node = it.nextNode(); node; node = it.nextNode())\n      nodes.push(node as Text)\n\n    /* Find all markers in each text node */\n    for (let text of nodes) {\n      let match: RegExpExecArray | null\n\n      /* Split text at marker and add to list */\n      while ((match = /(\\(\\d+\\))(!)?/.exec(text.textContent!))) {\n        const [, id, force] = match\n        if (typeof force === \"undefined\") {\n          const marker = text.splitText(match.index)\n          text = marker.splitText(id.length)\n          markers.push(marker)\n\n        /* Replace entire text with marker */\n        } else {\n          text.textContent = id\n          markers.push(text)\n          break\n        }\n      }\n    }\n  }\n  return markers\n}\n\n/**\n * Swap the child nodes of two elements\n *\n * @param source - Source element\n * @param target - Target element\n */\nfunction swap(source: HTMLElement, target: HTMLElement): void {\n  target.append(...Array.from(source.childNodes))\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount annotation list\n *\n * This function analyzes the containing code block and checks for markers\n * referring to elements in the given annotation list. If no markers are found,\n * the list is left untouched. Otherwise, list elements are rendered as\n * annotations inside the code block.\n *\n * @param el - Annotation list element\n * @param container - Containing element\n * @param options - Options\n *\n * @returns Annotation component observable\n */\nexport function mountAnnotationList(\n  el: HTMLElement, container: HTMLElement, { target$, print$ }: MountOptions\n): Observable<Component<Annotation>> {\n\n  /* Compute prefix for tooltip anchors */\n  const parent = container.closest(\"[id]\")\n  const prefix = parent?.id\n\n  /* Find and replace all markers with empty annotations */\n  const annotations = new Map<string, HTMLElement>()\n  for (const marker of findAnnotationMarkers(container)) {\n    const [, id] = marker.textContent!.match(/\\((\\d+)\\)/)!\n    if (getOptionalElement(`li:nth-child(${id})`, el)) {\n      annotations.set(id, renderAnnotation(id, prefix))\n      marker.replaceWith(annotations.get(id)!)\n    }\n  }\n\n  /* Keep list if there are no annotations to render */\n  if (annotations.size === 0)\n    return EMPTY\n\n  /* Mount component on subscription */\n  return defer(() => {\n    const done$ = new Subject()\n\n    /* Retrieve container pairs for swapping */\n    const pairs: [HTMLElement, HTMLElement][] = []\n    for (const [id, annotation] of annotations)\n      pairs.push([\n        getElement(\".md-typeset\", annotation),\n        getElement(`li:nth-child(${id})`, el)\n      ])\n\n    /* Handle print mode - see https://bit.ly/3rgPdpt */\n    print$\n      .pipe(\n        takeUntil(done$.pipe(ignoreElements(), endWith(true)))\n      )\n        .subscribe(active => {\n          el.hidden = !active\n\n          /* Show annotations in code block or list (print) */\n          for (const [inner, child] of pairs)\n            if (!active)\n              swap(child, inner)\n            else\n              swap(inner, child)\n        })\n\n    /* Create and return component */\n    return merge(...[...annotations]\n      .map(([, annotation]) => (\n        mountAnnotation(annotation, container, { target$ })\n      ))\n    )\n      .pipe(\n        finalize(() => done$.complete()),\n        share()\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  map,\n  of,\n  shareReplay,\n  tap\n} from \"rxjs\"\n\nimport { watchScript } from \"~/browser\"\nimport { h } from \"~/utilities\"\n\nimport { Component } from \"../../../_\"\n\nimport themeCSS from \"./index.css\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mermaid diagram\n */\nexport interface Mermaid {}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Mermaid instance observable\n */\nlet mermaid$: Observable<void>\n\n/**\n * Global sequence number for diagrams\n */\nlet sequence = 0\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch Mermaid script\n *\n * @returns Mermaid scripts observable\n */\nfunction fetchScripts(): Observable<void> {\n  return typeof mermaid === \"undefined\" || mermaid instanceof Element\n    ? watchScript(\"https://unpkg.com/mermaid@9.4.3/dist/mermaid.min.js\")\n    : of(undefined)\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount Mermaid diagram\n *\n * @param el - Code block element\n *\n * @returns Mermaid diagram component observable\n */\nexport function mountMermaid(\n  el: HTMLElement\n): Observable<Component<Mermaid>> {\n  el.classList.remove(\"mermaid\") // Hack: mitigate https://bit.ly/3CiN6Du\n  mermaid$ ||= fetchScripts()\n    .pipe(\n      tap(() => mermaid.initialize({\n        startOnLoad: false,\n        themeCSS,\n        sequence: {\n          actorFontSize: \"16px\", // Hack: mitigate https://bit.ly/3y0NEi3\n          messageFontSize: \"16px\",\n          noteFontSize: \"16px\"\n        }\n      })),\n      map(() => undefined),\n      shareReplay(1)\n    )\n\n  /* Render diagram */\n  mermaid$.subscribe(() => {\n    el.classList.add(\"mermaid\") // Hack: mitigate https://bit.ly/3CiN6Du\n    const id = `__mermaid_${sequence++}`\n\n    /* Create host element to replace code block */\n    const host = h(\"div\", { class: \"mermaid\" })\n    const text = el.textContent\n\n    /* Render and inject diagram */\n    mermaid.mermaidAPI.render(id, text, (svg: string, fn: Function) => {\n\n      /* Create a shadow root and inject diagram */\n      const shadow = host.attachShadow({ mode: \"closed\" })\n      shadow.innerHTML = svg\n\n      /* Replace code block with diagram and bind functions */\n      el.replaceWith(host)\n      fn?.(shadow)\n    })\n  })\n\n  /* Create and return component */\n  return mermaid$\n    .pipe(\n      map(() => ({ ref: el }))\n    )\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  defer,\n  filter,\n  finalize,\n  map,\n  merge,\n  tap\n} from \"rxjs\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Details\n */\nexport interface Details {\n  action: \"open\" | \"close\"             /* Details state */\n  reveal?: boolean                     /* Details is revealed */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  target$: Observable<HTMLElement>     /* Location target observable */\n  print$: Observable<boolean>          /* Media print observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  target$: Observable<HTMLElement>     /* Location target observable */\n  print$: Observable<boolean>          /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch details\n *\n * @param el - Details element\n * @param options - Options\n *\n * @returns Details observable\n */\nexport function watchDetails(\n  el: HTMLDetailsElement, { target$, print$ }: WatchOptions\n): Observable<Details> {\n  let open = true\n  return merge(\n\n    /* Open and focus details on location target */\n    target$\n      .pipe(\n        map(target => target.closest(\"details:not([open])\")!),\n        filter(details => el === details),\n        map(() => ({\n          action: \"open\", reveal: true\n        }) as Details)\n      ),\n\n    /* Open details on print and close afterwards */\n    print$\n      .pipe(\n        filter(active => active || !open),\n        tap(() => open = el.open),\n        map(active => ({\n          action: active ? \"open\" : \"close\"\n        }) as Details)\n      )\n  )\n}\n\n/**\n * Mount details\n *\n * This function ensures that `details` tags are opened on anchor jumps and\n * prior to printing, so the whole content of the page is visible.\n *\n * @param el - Details element\n * @param options - Options\n *\n * @returns Details component observable\n */\nexport function mountDetails(\n  el: HTMLDetailsElement, options: MountOptions\n): Observable<Component<Details>> {\n  return defer(() => {\n    const push$ = new Subject<Details>()\n    push$.subscribe(({ action, reveal }) => {\n      el.toggleAttribute(\"open\", action === \"open\")\n      if (reveal)\n        el.scrollIntoView()\n    })\n\n    /* Create and return component */\n    return watchDetails(el, options)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Observable, of } from \"rxjs\"\n\nimport { renderTable } from \"~/templates\"\nimport { h } from \"~/utilities\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Data table\n */\nexport interface DataTable {}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Sentinel for replacement\n */\nconst sentinel = h(\"table\")\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount data table\n *\n * This function wraps a data table in another scrollable container, so it can\n * be smoothly scrolled on smaller screen sizes and won't break the layout.\n *\n * @param el - Data table element\n *\n * @returns Data table component observable\n */\nexport function mountDataTable(\n  el: HTMLElement\n): Observable<Component<DataTable>> {\n  el.replaceWith(sentinel)\n  sentinel.replaceWith(renderTable(el))\n\n  /* Create and return component */\n  return of({ ref: el })\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  animationFrameScheduler,\n  asyncScheduler,\n  auditTime,\n  combineLatest,\n  defer,\n  endWith,\n  finalize,\n  fromEvent,\n  ignoreElements,\n  map,\n  merge,\n  skip,\n  startWith,\n  subscribeOn,\n  takeUntil,\n  tap,\n  withLatestFrom\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n  Viewport,\n  getElement,\n  getElementContentOffset,\n  getElementContentSize,\n  getElementOffset,\n  getElementSize,\n  getElements,\n  watchElementContentOffset,\n  watchElementSize\n} from \"~/browser\"\nimport { renderTabbedControl } from \"~/templates\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Content tabs\n */\nexport interface ContentTabs {\n  active: HTMLLabelElement             /* Active tab label */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch content tabs\n *\n * @param el - Content tabs element\n *\n * @returns Content tabs observable\n */\nexport function watchContentTabs(\n  el: HTMLElement\n): Observable<ContentTabs> {\n  const inputs = getElements<HTMLInputElement>(\":scope > input\", el)\n  const initial = inputs.find(input => input.checked) || inputs[0]\n  return merge(...inputs.map(input => fromEvent(input, \"change\")\n    .pipe(\n      map(() => getElement<HTMLLabelElement>(`label[for=\"${input.id}\"]`))\n    )\n  ))\n    .pipe(\n      startWith(getElement<HTMLLabelElement>(`label[for=\"${initial.id}\"]`)),\n      map(active => ({ active }))\n    )\n}\n\n/**\n * Mount content tabs\n *\n * This function scrolls the active tab into view. While this functionality is\n * provided by browsers as part of `scrollInfoView`, browsers will always also\n * scroll the vertical axis, which we do not want. Thus, we decided to provide\n * this functionality ourselves.\n *\n * @param el - Content tabs element\n * @param options - Options\n *\n * @returns Content tabs component observable\n */\nexport function mountContentTabs(\n  el: HTMLElement, { viewport$ }: MountOptions\n): Observable<Component<ContentTabs>> {\n\n  /* Render content tab previous button for pagination */\n  const prev = renderTabbedControl(\"prev\")\n  el.append(prev)\n\n  /* Render content tab next button for pagination */\n  const next = renderTabbedControl(\"next\")\n  el.append(next)\n\n  /* Mount component on subscription */\n  const container = getElement(\".tabbed-labels\", el)\n  return defer(() => {\n    const push$ = new Subject<ContentTabs>()\n    const done$ = push$.pipe(ignoreElements(), endWith(true))\n    combineLatest([push$, watchElementSize(el)])\n      .pipe(\n        auditTime(1, animationFrameScheduler),\n        takeUntil(done$)\n      )\n        .subscribe({\n\n          /* Handle emission */\n          next([{ active }, size]) {\n            const offset = getElementOffset(active)\n            const { width } = getElementSize(active)\n\n            /* Set tab indicator offset and width */\n            el.style.setProperty(\"--md-indicator-x\", `${offset.x}px`)\n            el.style.setProperty(\"--md-indicator-width\", `${width}px`)\n\n            /* Scroll container to active content tab */\n            const content = getElementContentOffset(container)\n            if (\n              offset.x         < content.x              ||\n              offset.x + width > content.x + size.width\n            )\n              container.scrollTo({\n                left: Math.max(0, offset.x - 16),\n                behavior: \"smooth\"\n              })\n          },\n\n          /* Handle complete */\n          complete() {\n            el.style.removeProperty(\"--md-indicator-x\")\n            el.style.removeProperty(\"--md-indicator-width\")\n          }\n        })\n\n    /* Hide content tab buttons on borders */\n    combineLatest([\n      watchElementContentOffset(container),\n      watchElementSize(container)\n    ])\n      .pipe(\n        takeUntil(done$)\n      )\n        .subscribe(([offset, size]) => {\n          const content = getElementContentSize(container)\n          prev.hidden = offset.x < 16\n          next.hidden = offset.x > content.width - size.width - 16\n        })\n\n    /* Paginate content tab container on click */\n    merge(\n      fromEvent(prev, \"click\").pipe(map(() => -1)),\n      fromEvent(next, \"click\").pipe(map(() => +1))\n    )\n      .pipe(\n        takeUntil(done$)\n      )\n        .subscribe(direction => {\n          const { width } = getElementSize(container)\n          container.scrollBy({\n            left: width * direction,\n            behavior: \"smooth\"\n          })\n        })\n\n    /* Set up linking of content tabs, if enabled */\n    if (feature(\"content.tabs.link\"))\n      push$.pipe(\n        skip(1),\n        withLatestFrom(viewport$)\n      )\n        .subscribe(([{ active }, { offset }]) => {\n          const tab = active.innerText.trim()\n          if (active.hasAttribute(\"data-md-switching\")) {\n            active.removeAttribute(\"data-md-switching\")\n\n          /* Determine viewport offset of active tab */\n          } else {\n            const y = el.offsetTop - offset.y\n\n            /* Passively activate other tabs */\n            for (const set of getElements(\"[data-tabs]\"))\n              for (const input of getElements<HTMLInputElement>(\n                \":scope > input\", set\n              )) {\n                const label = getElement(`label[for=\"${input.id}\"]`)\n                if (\n                  label !== active &&\n                  label.innerText.trim() === tab\n                ) {\n                  label.setAttribute(\"data-md-switching\", \"\")\n                  input.click()\n                  break\n                }\n              }\n\n            /* Bring active tab into view */\n            window.scrollTo({\n              top: el.offsetTop - y\n            })\n\n            /* Persist active tabs in local storage */\n            const tabs = __md_get<string[]>(\"__tabs\") || []\n            __md_set(\"__tabs\", [...new Set([tab, ...tabs])])\n          }\n        })\n\n    /* Pause media (audio, video) on switch - see https://bit.ly/3Bk6cel */\n    push$.pipe(takeUntil(done$))\n      .subscribe(() => {\n        for (const media of getElements<HTMLAudioElement>(\"audio, video\", el))\n          media.pause()\n      })\n\n    /* Create and return component */\n    return watchContentTabs(el)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n    .pipe(\n      subscribeOn(asyncScheduler)\n    )\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Observable, merge } from \"rxjs\"\n\nimport { Viewport, getElements } from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { Annotation } from \"../annotation\"\nimport {\n  CodeBlock,\n  Mermaid,\n  mountCodeBlock,\n  mountMermaid\n} from \"../code\"\nimport {\n  Details,\n  mountDetails\n} from \"../details\"\nimport {\n  DataTable,\n  mountDataTable\n} from \"../table\"\nimport {\n  ContentTabs,\n  mountContentTabs\n} from \"../tabs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Content\n */\nexport type Content =\n  | Annotation\n  | ContentTabs\n  | CodeBlock\n  | Mermaid\n  | DataTable\n  | Details\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  target$: Observable<HTMLElement>     /* Location target observable */\n  print$: Observable<boolean>          /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount content\n *\n * This function mounts all components that are found in the content of the\n * actual article, including code blocks, data tables and details.\n *\n * @param el - Content element\n * @param options - Options\n *\n * @returns Content component observable\n */\nexport function mountContent(\n  el: HTMLElement, { viewport$, target$, print$ }: MountOptions\n): Observable<Component<Content>> {\n  return merge(\n\n    /* Code blocks */\n    ...getElements(\"pre:not(.mermaid) > code\", el)\n      .map(child => mountCodeBlock(child, { target$, print$ })),\n\n    /* Mermaid diagrams */\n    ...getElements(\"pre.mermaid\", el)\n      .map(child => mountMermaid(child)),\n\n    /* Data tables */\n    ...getElements(\"table:not([class])\", el)\n      .map(child => mountDataTable(child)),\n\n    /* Details */\n    ...getElements(\"details\", el)\n      .map(child => mountDetails(child, { target$, print$ })),\n\n    /* Content tabs */\n    ...getElements(\"[data-tabs]\", el)\n      .map(child => mountContentTabs(child, { viewport$ }))\n  )\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  defer,\n  delay,\n  finalize,\n  map,\n  merge,\n  of,\n  switchMap,\n  tap\n} from \"rxjs\"\n\nimport { getElement } from \"~/browser\"\n\nimport { Component } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Dialog\n */\nexport interface Dialog {\n  message: string                      /* Dialog message */\n  active: boolean                      /* Dialog is active */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  alert$: Subject<string>              /* Alert subject */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  alert$: Subject<string>              /* Alert subject */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch dialog\n *\n * @param _el - Dialog element\n * @param options - Options\n *\n * @returns Dialog observable\n */\nexport function watchDialog(\n  _el: HTMLElement, { alert$ }: WatchOptions\n): Observable<Dialog> {\n  return alert$\n    .pipe(\n      switchMap(message => merge(\n        of(true),\n        of(false).pipe(delay(2000))\n      )\n        .pipe(\n          map(active => ({ message, active }))\n        )\n      )\n    )\n}\n\n/**\n * Mount dialog\n *\n * This function reveals the dialog in the right corner when a new alert is\n * emitted through the subject that is passed as part of the options.\n *\n * @param el - Dialog element\n * @param options - Options\n *\n * @returns Dialog component observable\n */\nexport function mountDialog(\n  el: HTMLElement, options: MountOptions\n): Observable<Component<Dialog>> {\n  const inner = getElement(\".md-typeset\", el)\n  return defer(() => {\n    const push$ = new Subject<Dialog>()\n    push$.subscribe(({ message, active }) => {\n      el.classList.toggle(\"md-dialog--active\", active)\n      inner.textContent = message\n    })\n\n    /* Create and return component */\n    return watchDialog(el, options)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  bufferCount,\n  combineLatest,\n  combineLatestWith,\n  defer,\n  distinctUntilChanged,\n  distinctUntilKeyChanged,\n  endWith,\n  filter,\n  ignoreElements,\n  map,\n  of,\n  shareReplay,\n  startWith,\n  switchMap,\n  takeUntil\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n  Viewport,\n  watchElementSize,\n  watchToggle\n} from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { Main } from \"../../main\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Header\n */\nexport interface Header {\n  height: number                       /* Header visible height */\n  hidden: boolean                      /* Header is hidden */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n  main$: Observable<Main>              /* Main area observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Compute whether the header is hidden\n *\n * If the user scrolls past a certain threshold, the header can be hidden when\n * scrolling down, and shown when scrolling up.\n *\n * @param options - Options\n *\n * @returns Toggle observable\n */\nfunction isHidden({ viewport$ }: WatchOptions): Observable<boolean> {\n  if (!feature(\"header.autohide\"))\n    return of(false)\n\n  /* Compute direction and turning point */\n  const direction$ = viewport$\n    .pipe(\n      map(({ offset: { y } }) => y),\n      bufferCount(2, 1),\n      map(([a, b]) => [a < b, b] as const),\n      distinctUntilKeyChanged(0)\n    )\n\n  /* Compute whether header should be hidden */\n  const hidden$ = combineLatest([viewport$, direction$])\n    .pipe(\n      filter(([{ offset }, [, y]]) => Math.abs(y - offset.y) > 100),\n      map(([, [direction]]) => direction),\n      distinctUntilChanged()\n    )\n\n  /* Compute threshold for hiding */\n  const search$ = watchToggle(\"search\")\n  return combineLatest([viewport$, search$])\n    .pipe(\n      map(([{ offset }, search]) => offset.y > 400 && !search),\n      distinctUntilChanged(),\n      switchMap(active => active ? hidden$ : of(false)),\n      startWith(false)\n    )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch header\n *\n * @param el - Header element\n * @param options - Options\n *\n * @returns Header observable\n */\nexport function watchHeader(\n  el: HTMLElement, options: WatchOptions\n): Observable<Header> {\n  return defer(() => combineLatest([\n    watchElementSize(el),\n    isHidden(options)\n  ]))\n    .pipe(\n      map(([{ height }, hidden]) => ({\n        height,\n        hidden\n      })),\n      distinctUntilChanged((a, b) => (\n        a.height === b.height &&\n        a.hidden === b.hidden\n      )),\n      shareReplay(1)\n    )\n}\n\n/**\n * Mount header\n *\n * This function manages the different states of the header, i.e. whether it's\n * hidden or rendered with a shadow. This depends heavily on the main area.\n *\n * @param el - Header element\n * @param options - Options\n *\n * @returns Header component observable\n */\nexport function mountHeader(\n  el: HTMLElement, { header$, main$ }: MountOptions\n): Observable<Component<Header>> {\n  return defer(() => {\n    const push$ = new Subject<Main>()\n    const done$ = push$.pipe(ignoreElements(), endWith(true))\n    push$\n      .pipe(\n        distinctUntilKeyChanged(\"active\"),\n        combineLatestWith(header$)\n      )\n        .subscribe(([{ active }, { hidden }]) => {\n          el.classList.toggle(\"md-header--shadow\", active && !hidden)\n          el.hidden = hidden\n        })\n\n    /* Link to main area */\n    main$.subscribe(push$)\n\n    /* Create and return component */\n    return header$\n      .pipe(\n        takeUntil(done$),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  Subject,\n  defer,\n  distinctUntilKeyChanged,\n  finalize,\n  map,\n  tap\n} from \"rxjs\"\n\nimport {\n  Viewport,\n  getElementSize,\n  getOptionalElement,\n  watchViewportAt\n} from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { Header } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Header\n */\nexport interface HeaderTitle {\n  active: boolean                      /* Header title is active */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch header title\n *\n * @param el - Heading element\n * @param options - Options\n *\n * @returns Header title observable\n */\nexport function watchHeaderTitle(\n  el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable<HeaderTitle> {\n  return watchViewportAt(el, { viewport$, header$ })\n    .pipe(\n      map(({ offset: { y } }) => {\n        const { height } = getElementSize(el)\n        return {\n          active: y >= height\n        }\n      }),\n      distinctUntilKeyChanged(\"active\")\n    )\n}\n\n/**\n * Mount header title\n *\n * This function swaps the header title from the site title to the title of the\n * current page when the user scrolls past the first headline.\n *\n * @param el - Header title element\n * @param options - Options\n *\n * @returns Header title component observable\n */\nexport function mountHeaderTitle(\n  el: HTMLElement, options: MountOptions\n): Observable<Component<HeaderTitle>> {\n  return defer(() => {\n    const push$ = new Subject<HeaderTitle>()\n    push$.subscribe({\n\n      /* Handle emission */\n      next({ active }) {\n        el.classList.toggle(\"md-header__title--active\", active)\n      },\n\n      /* Handle complete */\n      complete() {\n        el.classList.remove(\"md-header__title--active\")\n      }\n    })\n\n    /* Obtain headline, if any */\n    const heading = getOptionalElement(\"article h1\")\n    if (typeof heading === \"undefined\")\n      return EMPTY\n\n    /* Create and return component */\n    return watchHeaderTitle(heading, options)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  combineLatest,\n  distinctUntilChanged,\n  distinctUntilKeyChanged,\n  map,\n  switchMap\n} from \"rxjs\"\n\nimport {\n  Viewport,\n  watchElementSize\n} from \"~/browser\"\n\nimport { Header } from \"../header\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Main area\n */\nexport interface Main {\n  offset: number                       /* Main area top offset */\n  height: number                       /* Main area visible height */\n  active: boolean                      /* Main area is active */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch main area\n *\n * This function returns an observable that computes the visual parameters of\n * the main area which depends on the viewport vertical offset and height, as\n * well as the height of the header element, if the header is fixed.\n *\n * @param el - Main area element\n * @param options - Options\n *\n * @returns Main area observable\n */\nexport function watchMain(\n  el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable<Main> {\n\n  /* Compute necessary adjustment for header */\n  const adjust$ = header$\n    .pipe(\n      map(({ height }) => height),\n      distinctUntilChanged()\n    )\n\n  /* Compute the main area's top and bottom borders */\n  const border$ = adjust$\n    .pipe(\n      switchMap(() => watchElementSize(el)\n        .pipe(\n          map(({ height }) => ({\n            top:    el.offsetTop,\n            bottom: el.offsetTop + height\n          })),\n          distinctUntilKeyChanged(\"bottom\")\n        )\n      )\n    )\n\n  /* Compute the main area's offset, visible height and if we scrolled past */\n  return combineLatest([adjust$, border$, viewport$])\n    .pipe(\n      map(([header, { top, bottom }, { offset: { y }, size: { height } }]) => {\n        height = Math.max(0, height\n          - Math.max(0, top    - y,  header)\n          - Math.max(0, height + y - bottom)\n        )\n        return {\n          offset: top - header,\n          height,\n          active: top - header <= y\n        }\n      }),\n      distinctUntilChanged((a, b) => (\n        a.offset === b.offset &&\n        a.height === b.height &&\n        a.active === b.active\n      ))\n    )\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  asyncScheduler,\n  defer,\n  finalize,\n  fromEvent,\n  map,\n  mergeMap,\n  observeOn,\n  of,\n  shareReplay,\n  startWith,\n  tap\n} from \"rxjs\"\n\nimport { getElements } from \"~/browser\"\nimport { h } from \"~/utilities\"\n\nimport {\n  Component,\n  getComponentElement\n} from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Palette colors\n */\nexport interface PaletteColor {\n  scheme?: string                      /* Color scheme */\n  primary?: string                     /* Primary color */\n  accent?: string                      /* Accent color */\n}\n\n/**\n * Palette\n */\nexport interface Palette {\n  index: number                        /* Palette index */\n  color: PaletteColor                  /* Palette colors */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch color palette\n *\n * @param inputs - Color palette element\n *\n * @returns Color palette observable\n */\nexport function watchPalette(\n  inputs: HTMLInputElement[]\n): Observable<Palette> {\n  const current = __md_get<Palette>(\"__palette\") || {\n    index: inputs.findIndex(input => matchMedia(\n      input.getAttribute(\"data-md-color-media\")!\n    ).matches)\n  }\n\n  /* Emit changes in color palette */\n  return of(...inputs)\n    .pipe(\n      mergeMap(input => fromEvent(input, \"change\")\n        .pipe(\n          map(() => input)\n        )\n      ),\n      startWith(inputs[Math.max(0, current.index)]),\n      map(input => ({\n        index: inputs.indexOf(input),\n        color: {\n          scheme:  input.getAttribute(\"data-md-color-scheme\"),\n          primary: input.getAttribute(\"data-md-color-primary\"),\n          accent:  input.getAttribute(\"data-md-color-accent\")\n        }\n      } as Palette)),\n      shareReplay(1)\n    )\n}\n\n/**\n * Mount color palette\n *\n * @param el - Color palette element\n *\n * @returns Color palette component observable\n */\nexport function mountPalette(\n  el: HTMLElement\n): Observable<Component<Palette>> {\n  const meta = h(\"meta\", { name: \"theme-color\" })\n  document.head.appendChild(meta)\n\n  /* Mount component on subscription */\n  return defer(() => {\n    const push$ = new Subject<Palette>()\n    push$.subscribe(palette => {\n      document.body.setAttribute(\"data-md-color-switching\", \"\")\n\n      /* Set color palette */\n      for (const [key, value] of Object.entries(palette.color))\n        document.body.setAttribute(`data-md-color-${key}`, value)\n\n      /* Toggle visibility */\n      for (let index = 0; index < inputs.length; index++) {\n        const label = inputs[index].nextElementSibling\n        if (label instanceof HTMLElement)\n          label.hidden = palette.index !== index\n      }\n\n      /* Persist preference in local storage */\n      __md_set(\"__palette\", palette)\n    })\n\n    /* Update theme-color meta tag */\n    push$\n      .pipe(\n        map(() => {\n          const header = getComponentElement(\"header\")\n          const { backgroundColor } = window.getComputedStyle(header)\n\n          /* Return color in hexadecimal format */\n          return backgroundColor.match(/\\d+/g)!\n            .map(value => (+value).toString(16).padStart(2, \"0\"))\n            .join(\"\")\n        })\n      )\n        .subscribe(color => meta.content = `#${color}`)\n\n    /* Revert transition durations after color switch */\n    push$.pipe(observeOn(asyncScheduler))\n      .subscribe(() => {\n        document.body.removeAttribute(\"data-md-color-switching\")\n      })\n\n    /* Create and return component */\n    const inputs = getElements<HTMLInputElement>(\"input\", el)\n    return watchPalette(inputs)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport ClipboardJS from \"clipboard\"\nimport {\n  Observable,\n  Subject,\n  map,\n  tap\n} from \"rxjs\"\n\nimport { translation } from \"~/_\"\nimport { getElement } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Setup options\n */\ninterface SetupOptions {\n  alert$: Subject<string>              /* Alert subject */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Extract text to copy\n *\n * @param el - HTML element\n *\n * @returns Extracted text\n */\nfunction extract(el: HTMLElement): string {\n  el.setAttribute(\"data-md-copying\", \"\")\n  const text = el.innerText\n  el.removeAttribute(\"data-md-copying\")\n  return text\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up Clipboard.js integration\n *\n * @param options - Options\n */\nexport function setupClipboardJS(\n  { alert$ }: SetupOptions\n): void {\n  if (ClipboardJS.isSupported()) {\n    new Observable<ClipboardJS.Event>(subscriber => {\n      new ClipboardJS(\"[data-clipboard-target], [data-clipboard-text]\", {\n        text: el => (\n          el.getAttribute(\"data-clipboard-text\")! ||\n          extract(getElement(\n            el.getAttribute(\"data-clipboard-target\")!\n          ))\n        )\n      })\n        .on(\"success\", ev => subscriber.next(ev))\n    })\n      .pipe(\n        tap(ev => {\n          const trigger = ev.trigger as HTMLElement\n          trigger.focus()\n        }),\n        map(() => translation(\"clipboard.copied\"))\n      )\n        .subscribe(alert$)\n  }\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  catchError,\n  defaultIfEmpty,\n  map,\n  of,\n  tap\n} from \"rxjs\"\n\nimport { configuration } from \"~/_\"\nimport { getElements, requestXML } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Sitemap, i.e. a list of URLs\n */\nexport type Sitemap = string[]\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Preprocess a list of URLs\n *\n * This function replaces the `site_url` in the sitemap with the actual base\n * URL, to allow instant loading to work in occasions like Netlify previews.\n *\n * @param urls - URLs\n *\n * @returns URL path parts\n */\nfunction preprocess(urls: Sitemap): Sitemap {\n  if (urls.length < 2)\n    return [\"\"]\n\n  /* Take the first two URLs and remove everything after the last slash */\n  const [root, next] = [...urls]\n    .sort((a, b) => a.length - b.length)\n    .map(url => url.replace(/[^/]+$/, \"\"))\n\n  /* Compute common prefix */\n  let index = 0\n  if (root === next)\n    index = root.length\n  else\n    while (root.charCodeAt(index) === next.charCodeAt(index))\n      index++\n\n  /* Remove common prefix and return in original order */\n  return urls.map(url => url.replace(root.slice(0, index), \"\"))\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch the sitemap for the given base URL\n *\n * @param base - Base URL\n *\n * @returns Sitemap observable\n */\nexport function fetchSitemap(base?: URL): Observable<Sitemap> {\n  const cached = __md_get<Sitemap>(\"__sitemap\", sessionStorage, base)\n  if (cached) {\n    return of(cached)\n  } else {\n    const config = configuration()\n    return requestXML(new URL(\"sitemap.xml\", base || config.base))\n      .pipe(\n        map(sitemap => preprocess(getElements(\"loc\", sitemap)\n          .map(node => node.textContent!)\n        )),\n        catchError(() => EMPTY), // @todo refactor instant loading\n        defaultIfEmpty([]),\n        tap(sitemap => __md_set(\"__sitemap\", sitemap, sessionStorage, base))\n      )\n  }\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  Subject,\n  bufferCount,\n  catchError,\n  concat,\n  debounceTime,\n  distinctUntilKeyChanged,\n  endWith,\n  fromEvent,\n  ignoreElements,\n  map,\n  of,\n  share,\n  skip,\n  startWith,\n  switchMap,\n  take,\n  withLatestFrom\n} from \"rxjs\"\n\nimport { configuration, feature } from \"~/_\"\nimport {\n  Viewport,\n  getElements,\n  getLocation,\n  getOptionalElement,\n  request,\n  setLocation,\n  setLocationHash\n} from \"~/browser\"\nimport { getComponentElement } from \"~/components\"\n\nimport { fetchSitemap } from \"../sitemap\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Setup options\n */\ninterface SetupOptions {\n  location$: Subject<URL>              /* Location subject */\n  viewport$: Observable<Viewport>      /* Viewport observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up instant navigation\n *\n * This is a heavily orchestrated operation - see inline comments to learn how\n * this works with Material for MkDocs, and how you can hook into it.\n *\n * @param options - Options\n *\n * @returns Document observable\n */\nexport function setupInstantLoading(\n  { location$, viewport$ }: SetupOptions\n): Observable<Document> {\n  const config = configuration()\n  if (location.protocol === \"file:\")\n    return EMPTY\n\n  // Load sitemap immediately, so we have it available when the user initiates\n  // the first instant navigation request, and canonicalize URLs to the current\n  // base URL. The base URL will remain stable in between loads, as it's only\n  // read at the first initialization of the application.\n  const sitemap$ = fetchSitemap()\n    .pipe(\n      map(paths => paths.map(path => `${new URL(path, config.base)}`))\n    )\n\n  // Intercept inter-site navigation - to keep the number of event listeners\n  // low we use the fact that uncaptured events bubble up to the body. This also\n  // has the nice property that we don't need to detach and then again attach\n  // event listeners when instant navigation occurs.\n  const instant$ = fromEvent<MouseEvent>(document.body, \"click\")\n    .pipe(\n      withLatestFrom(sitemap$),\n      switchMap(([ev, sitemap]) => {\n        if (!(ev.target instanceof Element))\n          return EMPTY\n\n        // Skip, as target is not within a link - clicks on non-link elements\n        // are also captured, which we need to exclude from processing.\n        const el = ev.target.closest(\"a\")\n        if (el === null)\n          return EMPTY\n\n        // Skip, as link opens in new window - we now know we have captured a\n        // click on a link, but the link either has a `target` property defined,\n        // or the user pressed the `meta` or `ctrl` key to open it in a new\n        // window. Thus, we need to filter those events, too.\n        if (el.target || ev.metaKey || ev.ctrlKey)\n          return EMPTY\n\n        // Next, we must check if the URL is relevant for us, i.e., if it's an\n        // internal link to a page that is managed by MkDocs. Only then we can\n        // be sure that the structure of the page to be loaded adheres to the\n        // current document structure and can subsequently be injected into it\n        // without doing a full reload. For this reason, we must canonicalize\n        // the URL by removing all search parameters and hash fragments.\n        const url = new URL(el.href)\n        url.search = url.hash = \"\"\n\n        // Skip, if URL is not included in the sitemap - this could be the case\n        // when linking between versions or languages, or to another page that\n        // the author included as part of the build, but that is not managed by\n        // MkDocs. In that case we must not continue with instant navigation.\n        if (!sitemap.includes(`${url}`))\n          return EMPTY\n\n        // We now know that we have a link to an internal page, so we prevent\n        // the browser from navigation and emit the URL for instant navigation.\n        // Note that this also includes anchor links, which means we need to\n        // implement anchor positioning ourselves. The reason for this is that\n        // if we wouldn't manage anchor links as well, scroll restoration will\n        // not work correctly (e.g. following an anchor link and scrolling).\n        ev.preventDefault()\n        return of(new URL(el.href))\n      }),\n      share()\n    )\n\n  // Before fetching for the first time, resolve the absolute favicon position,\n  // as the browser will try to fetch the icon immediately.\n  instant$.pipe(take(1))\n    .subscribe(() => {\n      const favicon = getOptionalElement<HTMLLinkElement>(\"link[rel=icon]\")\n      if (typeof favicon !== \"undefined\")\n        favicon.href = favicon.href\n    })\n\n  // Enable scroll restoration before window unloads - this is essential to\n  // ensure that full reloads (F5) restore the viewport offset correctly. If\n  // only popstate events wouldn't reset the scroll position prior to their\n  // emission, we could just reset this in popstate. Meh.\n  fromEvent(window, \"beforeunload\")\n    .subscribe(() => {\n      history.scrollRestoration = \"auto\"\n    })\n\n  // When an instant navigation event occurs, disable scroll restoration, since\n  // we must normalize and synchronize the behavior across all browsers. For\n  // instance, when the user clicks the back or forward button, the browser\n  // would immediately jump to the position of the previous document.\n  instant$.pipe(withLatestFrom(viewport$))\n    .subscribe(([url, { offset }]) => {\n      history.scrollRestoration = \"manual\"\n\n      // While it would be better UX to defer the history state change until the\n      // document was fully fetched and parsed, we must schedule it here, since\n      // popstate events are emitted when history state changes happen. Moreover\n      // we need to back up the current viewport offset, so we can restore it\n      // when popstate events occur, e.g., when the browser's back and forward\n      // buttons are used for navigation.\n      history.replaceState(offset, \"\")\n      history.pushState(null, \"\", url)\n    })\n\n  // Emit URL that should be fetched via instant loading on location subject,\n  // which was passed into this function. The idea is that instant loading can\n  // be intercepted by other parts of the application, which can synchronously\n  // back up or restore state before instant loading happens.\n  instant$.subscribe(location$)\n\n  // Fetch document - when fetching, we could use `responseType: document`, but\n  // since all MkDocs links are relative, we need to make sure that the current\n  // location matches the document we just loaded. Otherwise any relative links\n  // in the document might use the old location. If the request fails for some\n  // reason, we fall back to regular navigation and set the location explicitly,\n  // which will force-load the page. Furthermore, we must pre-warm the buffer\n  // for the duplicate check, or the first click on an anchor link will also\n  // trigger an instant loading event, which doesn't make sense.\n  const response$ = location$\n    .pipe(\n      startWith(getLocation()),\n      distinctUntilKeyChanged(\"pathname\"),\n      skip(1),\n      switchMap(url => request(url)\n        .pipe(\n          catchError(() => {\n            setLocation(url)\n            return EMPTY\n          })\n        )\n      )\n    )\n\n  // Initialize the DOM parser, parse the returned HTML, and replace selected\n  // meta tags and components before handing control down to the application.\n  const dom = new DOMParser()\n  const document$ = response$\n    .pipe(\n      switchMap(res => res.text()),\n      switchMap(res => {\n        const document = dom.parseFromString(res, \"text/html\")\n        for (const selector of [\n\n          // Meta tags\n          \"title\",\n          \"link[rel=canonical]\",\n          \"meta[name=author]\",\n          \"meta[name=description]\",\n\n          // Components\n          \"[data-md-component=announce]\",\n          \"[data-md-component=container]\",\n          \"[data-md-component=header-topic]\",\n          \"[data-md-component=outdated]\",\n          \"[data-md-component=logo]\",\n          \"[data-md-component=skip]\",\n          ...feature(\"navigation.tabs.sticky\")\n            ? [\"[data-md-component=tabs]\"]\n            : []\n        ]) {\n          const source = getOptionalElement(selector)\n          const target = getOptionalElement(selector, document)\n          if (\n            typeof source !== \"undefined\" &&\n            typeof target !== \"undefined\"\n          ) {\n            source.replaceWith(target)\n          }\n        }\n\n        // After meta tags and components were replaced, re-evaluate scripts\n        // that were provided by the author as part of Markdown files.\n        const container = getComponentElement(\"container\")\n        return concat(getElements(\"script\", container))\n          .pipe(\n            switchMap(el => {\n              const script = document.createElement(\"script\")\n              if (el.src) {\n                for (const name of el.getAttributeNames())\n                  script.setAttribute(name, el.getAttribute(name)!)\n                el.replaceWith(script)\n\n                // Complete when script is loaded\n                return new Observable(observer => {\n                  script.onload = () => observer.complete()\n                })\n\n              // Complete immediately\n              } else {\n                script.textContent = el.textContent\n                el.replaceWith(script)\n                return EMPTY\n              }\n            }),\n            ignoreElements(),\n            endWith(document)\n          )\n      }),\n      share()\n    )\n\n  // Intercept popstate events, e.g. when using the browser's back and forward\n  // buttons, and emit new location for fetching and parsing.\n  const popstate$ = fromEvent<PopStateEvent>(window, \"popstate\")\n  popstate$.pipe(map(getLocation))\n    .subscribe(location$)\n\n  // Intercept clicks on anchor links, and scroll document into position. As\n  // we disabled scroll restoration, we need to do this manually here.\n  location$\n    .pipe(\n      startWith(getLocation()),\n      bufferCount(2, 1),\n      switchMap(([prev, next]) => (\n        prev.pathname === next.pathname &&\n        prev.hash     !== next.hash\n      )\n        ? of(next)\n        : EMPTY\n      )\n    )\n      .subscribe(url => {\n        if (history.state !== null || !url.hash) {\n          window.scrollTo(0, history.state?.y ?? 0)\n        } else {\n          history.scrollRestoration = \"auto\"\n          setLocationHash(url.hash)\n          history.scrollRestoration = \"manual\"\n        }\n      })\n\n  // After parsing the document, check if the current history entry has a state.\n  // This may happen when users press the back or forward button to visit a page\n  // that was already seen. If there's no state, it means a new page was visited\n  // and we should scroll to the top, unless an anchor is given.\n  document$.pipe(withLatestFrom(location$))\n    .subscribe(([, url]) => {\n      if (history.state !== null || !url.hash) {\n        window.scrollTo(0, history.state?.y ?? 0)\n      } else {\n        setLocationHash(url.hash)\n      }\n    })\n\n  // If the current history is not empty, register an event listener updating\n  // the current history state whenever the scroll position changes. This must\n  // be debounced and cannot be done in popstate, as popstate has already\n  // removed the entry from the history.\n  document$\n    .pipe(\n      switchMap(() => viewport$),\n      distinctUntilKeyChanged(\"offset\"),\n      debounceTime(100)\n    )\n      .subscribe(({ offset }) => {\n        history.replaceState(offset, \"\")\n      })\n\n  // Return document\n  return document$\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport escapeHTML from \"escape-html\"\n\nimport { SearchConfig } from \"../config\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search highlight function\n *\n * @param value - Value\n *\n * @returns Highlighted value\n */\nexport type SearchHighlightFn = (value: string) => string\n\n/**\n * Search highlight factory function\n *\n * @param query - Query value\n *\n * @returns Search highlight function\n */\nexport type SearchHighlightFactoryFn = (query: string) => SearchHighlightFn\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create a search highlighter\n *\n * @param config - Search configuration\n *\n * @returns Search highlight factory function\n */\nexport function setupSearchHighlighter(\n  config: SearchConfig\n): SearchHighlightFactoryFn {\n  // Hack: temporarily remove pure lookaheads\n  const regex = config.separator.split(\"|\").map(term => {\n    const temp = term.replace(/(\\(\\?[!=][^)]+\\))/g, \"\")\n    return temp.length === 0 ? \"\uFFFD\" : term\n  })\n    .join(\"|\")\n\n  const separator = new RegExp(regex, \"img\")\n  const highlight = (_: unknown, data: string, term: string) => {\n    return `${data}<mark data-md-highlight>${term}</mark>`\n  }\n\n  /* Return factory function */\n  return (query: string) => {\n    query = query\n      .replace(/[\\s*+\\-:~^]+/g, \" \")\n      .trim()\n\n    /* Create search term match expression */\n    const match = new RegExp(`(^|${config.separator}|)(${\n      query\n        .replace(/[|\\\\{}()[\\]^$+*?.-]/g, \"\\\\$&\")\n        .replace(separator, \"|\")\n    })`, \"img\")\n\n    /* Highlight string value */\n    return value => escapeHTML(value)\n      .replace(match, highlight)\n      .replace(/<\\/mark>(\\s+)<mark[^>]*>/img, \"$1\")\n  }\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A RTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { SearchResult } from \"../../_\"\nimport { SearchIndex } from \"../../config\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search message type\n */\nexport const enum SearchMessageType {\n  SETUP,                               /* Search index setup */\n  READY,                               /* Search index ready */\n  QUERY,                               /* Search query */\n  RESULT                               /* Search results */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Message containing the data necessary to setup the search index\n */\nexport interface SearchSetupMessage {\n  type: SearchMessageType.SETUP        /* Message type */\n  data: SearchIndex                    /* Message data */\n}\n\n/**\n * Message indicating the search index is ready\n */\nexport interface SearchReadyMessage {\n  type: SearchMessageType.READY        /* Message type */\n}\n\n/**\n * Message containing a search query\n */\nexport interface SearchQueryMessage {\n  type: SearchMessageType.QUERY        /* Message type */\n  data: string                         /* Message data */\n}\n\n/**\n * Message containing results for a search query\n */\nexport interface SearchResultMessage {\n  type: SearchMessageType.RESULT       /* Message type */\n  data: SearchResult                   /* Message data */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Message exchanged with the search worker\n */\nexport type SearchMessage =\n  | SearchSetupMessage\n  | SearchReadyMessage\n  | SearchQueryMessage\n  | SearchResultMessage\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Type guard for search ready messages\n *\n * @param message - Search worker message\n *\n * @returns Test result\n */\nexport function isSearchReadyMessage(\n  message: SearchMessage\n): message is SearchReadyMessage {\n  return message.type === SearchMessageType.READY\n}\n\n/**\n * Type guard for search result messages\n *\n * @param message - Search worker message\n *\n * @returns Test result\n */\nexport function isSearchResultMessage(\n  message: SearchMessage\n): message is SearchResultMessage {\n  return message.type === SearchMessageType.RESULT\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A RTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  ObservableInput,\n  Subject,\n  first,\n  merge,\n  of,\n  switchMap\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport { watchToggle, watchWorker } from \"~/browser\"\n\nimport { SearchIndex } from \"../../config\"\nimport {\n  SearchMessage,\n  SearchMessageType\n} from \"../message\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up search worker\n *\n * This function creates and initializes a web worker that is used for search,\n * so that the user interface doesn't freeze. In general, the application does\n * not care how search is implemented, as long as the web worker conforms to\n * the format expected by the application as defined in `SearchMessage`. This\n * allows the author to implement custom search functionality, by providing a\n * custom web worker via configuration.\n *\n * Material for MkDocs' built-in search implementation makes use of Lunr.js, an\n * efficient and fast implementation for client-side search. Leveraging a tiny\n * iframe-based web worker shim, search is even supported for the `file://`\n * protocol, enabling search for local non-hosted builds.\n *\n * If the protocol is `file://`, search initialization is deferred to mitigate\n * freezing, as it's now synchronous by design - see https://bit.ly/3C521EO\n *\n * @see https://bit.ly/3igvtQv - How to implement custom search\n *\n * @param url - Worker URL\n * @param index$ - Search index observable input\n *\n * @returns Search worker\n */\nexport function setupSearchWorker(\n  url: string, index$: ObservableInput<SearchIndex>\n): Subject<SearchMessage> {\n  const worker$ = watchWorker<SearchMessage>(url)\n  merge(\n    of(location.protocol !== \"file:\"),\n    watchToggle(\"search\")\n  )\n    .pipe(\n      first(active => active),\n      switchMap(() => index$)\n    )\n      .subscribe(({ config, docs }) => worker$.next({\n        type: SearchMessageType.SETUP,\n        data: {\n          config,\n          docs,\n          options: {\n            suggest: feature(\"search.suggest\")\n          }\n        }\n      }))\n\n  /* Return search worker */\n  return worker$\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Subject,\n  catchError,\n  combineLatest,\n  filter,\n  fromEvent,\n  map,\n  of,\n  switchMap,\n  withLatestFrom\n} from \"rxjs\"\n\nimport { configuration } from \"~/_\"\nimport {\n  getElement,\n  getLocation,\n  requestJSON,\n  setLocation\n} from \"~/browser\"\nimport { getComponentElements } from \"~/components\"\nimport {\n  Version,\n  renderVersionSelector\n} from \"~/templates\"\n\nimport { fetchSitemap } from \"../sitemap\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Setup options\n */\ninterface SetupOptions {\n  document$: Subject<Document>         /* Document subject */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up version selector\n *\n * @param options - Options\n */\nexport function setupVersionSelector(\n  { document$ }: SetupOptions\n): void {\n  const config = configuration()\n  const versions$ = requestJSON<Version[]>(\n    new URL(\"../versions.json\", config.base)\n  )\n    .pipe(\n      catchError(() => EMPTY) // @todo refactor instant loading\n    )\n\n  /* Determine current version */\n  const current$ = versions$\n    .pipe(\n      map(versions => {\n        const [, current] = config.base.match(/([^/]+)\\/?$/)!\n        return versions.find(({ version, aliases }) => (\n          version === current || aliases.includes(current)\n        )) || versions[0]\n      })\n    )\n\n  /* Intercept inter-version navigation */\n  versions$\n    .pipe(\n      map(versions => new Map(versions.map(version => [\n        `${new URL(`../${version.version}/`, config.base)}`,\n        version\n      ]))),\n      switchMap(urls => fromEvent<MouseEvent>(document.body, \"click\")\n        .pipe(\n          filter(ev => !ev.metaKey && !ev.ctrlKey),\n          withLatestFrom(current$),\n          switchMap(([ev, current]) => {\n            if (ev.target instanceof Element) {\n              const el = ev.target.closest(\"a\")\n              if (el && !el.target && urls.has(el.href)) {\n                const url = el.href\n                // This is a temporary hack to detect if a version inside the\n                // version selector or on another part of the site was clicked.\n                // If we're inside the version selector, we definitely want to\n                // find the same page, as we might have different deployments\n                // due to aliases. However, if we're outside the version\n                // selector, we must abort here, because we might otherwise\n                // interfere with instant loading. We need to refactor this\n                // at some point together with instant loading.\n                //\n                // See https://github.com/squidfunk/mkdocs-material/issues/4012\n                if (!ev.target.closest(\".md-version\")) {\n                  const version = urls.get(url)!\n                  if (version === current)\n                    return EMPTY\n                }\n                ev.preventDefault()\n                return of(url)\n              }\n            }\n            return EMPTY\n          }),\n          switchMap(url => {\n            const { version } = urls.get(url)!\n            return fetchSitemap(new URL(url))\n              .pipe(\n                map(sitemap => {\n                  const location = getLocation()\n                  const path = location.href.replace(config.base, \"\")\n                  return sitemap.includes(path.split(\"#\")[0])\n                    ? new URL(`../${version}/${path}`, config.base)\n                    : new URL(url)\n                })\n              )\n          })\n        )\n      )\n    )\n      .subscribe(url => setLocation(url))\n\n  /* Render version selector and warning */\n  combineLatest([versions$, current$])\n    .subscribe(([versions, current]) => {\n      const topic = getElement(\".md-header__topic\")\n      topic.appendChild(renderVersionSelector(versions, current))\n    })\n\n  /* Integrate outdated version banner with instant loading */\n  document$.pipe(switchMap(() => current$))\n    .subscribe(current => {\n\n      /* Check if version state was already determined */\n      let outdated = __md_get(\"__outdated\", sessionStorage)\n      if (outdated === null) {\n        outdated = true\n\n        /* Check if version is considered a default */\n        const ignored = [config.version?.default || [\"latest\"]].flat()\n        main: for (const ignore of ignored)\n          for (const alias of current.aliases)\n            if (new RegExp(ignore, \"i\").test(alias)) {\n              outdated = false\n              break main\n            }\n\n        /* Persist version state in session storage */\n        __md_set(\"__outdated\", outdated, sessionStorage)\n      }\n\n      /* Unhide outdated version banner */\n      if (outdated)\n        for (const warning of getComponentElements(\"outdated\"))\n          warning.hidden = false\n    })\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  combineLatest,\n  distinctUntilChanged,\n  distinctUntilKeyChanged,\n  endWith,\n  finalize,\n  first,\n  fromEvent,\n  ignoreElements,\n  map,\n  merge,\n  shareReplay,\n  takeUntil,\n  tap\n} from \"rxjs\"\n\nimport {\n  getLocation,\n  setToggle,\n  watchElementFocus,\n  watchToggle\n} from \"~/browser\"\nimport {\n  SearchMessage,\n  SearchMessageType,\n  isSearchReadyMessage\n} from \"~/integrations\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search query\n */\nexport interface SearchQuery {\n  value: string                        /* Query value */\n  focus: boolean                       /* Query focus */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  worker$: Subject<SearchMessage>      /* Search worker */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  worker$: Subject<SearchMessage>      /* Search worker */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch search query\n *\n * Note that the focus event which triggers re-reading the current query value\n * is delayed by `1ms` so the input's empty state is allowed to propagate.\n *\n * @param el - Search query element\n * @param options - Options\n *\n * @returns Search query observable\n */\nexport function watchSearchQuery(\n  el: HTMLInputElement, { worker$ }: WatchOptions\n): Observable<SearchQuery> {\n\n  /* Support search deep linking */\n  const { searchParams } = getLocation()\n  if (searchParams.has(\"q\")) {\n    setToggle(\"search\", true)\n\n    /* Set query from parameter */\n    el.value = searchParams.get(\"q\")!\n    el.focus()\n\n    /* Remove query parameter on close */\n    watchToggle(\"search\")\n      .pipe(\n        first(active => !active)\n      )\n        .subscribe(() => {\n          const url = new URL(location.href)\n          url.searchParams.delete(\"q\")\n          history.replaceState({}, \"\", `${url}`)\n        })\n  }\n\n  /* Intercept focus and input events */\n  const focus$ = watchElementFocus(el)\n  const value$ = merge(\n    worker$.pipe(first(isSearchReadyMessage)),\n    fromEvent(el, \"keyup\"),\n    focus$\n  )\n    .pipe(\n      map(() => el.value),\n      distinctUntilChanged()\n    )\n\n  /* Combine into single observable */\n  return combineLatest([value$, focus$])\n    .pipe(\n      map(([value, focus]) => ({ value, focus })),\n      shareReplay(1)\n    )\n}\n\n/**\n * Mount search query\n *\n * @param el - Search query element\n * @param options - Options\n *\n * @returns Search query component observable\n */\nexport function mountSearchQuery(\n  el: HTMLInputElement, { worker$ }: MountOptions\n): Observable<Component<SearchQuery, HTMLInputElement>> {\n  const push$ = new Subject<SearchQuery>()\n  const done$ = push$.pipe(ignoreElements(), endWith(true))\n\n  /* Handle value change */\n  combineLatest([\n    worker$.pipe(first(isSearchReadyMessage)),\n    push$\n  ], (_, query) => query)\n    .pipe(\n      distinctUntilKeyChanged(\"value\")\n    )\n      .subscribe(({ value }) => worker$.next({\n        type: SearchMessageType.QUERY,\n        data: value\n      }))\n\n  /* Handle focus change */\n  push$\n    .pipe(\n      distinctUntilKeyChanged(\"focus\")\n    )\n      .subscribe(({ focus }) => {\n        if (focus)\n          setToggle(\"search\", focus)\n      })\n\n  /* Handle reset */\n  fromEvent(el.form!, \"reset\")\n    .pipe(\n      takeUntil(done$)\n    )\n      .subscribe(() => el.focus())\n\n  /* Create and return component */\n  return watchSearchQuery(el, { worker$ })\n    .pipe(\n      tap(state => push$.next(state)),\n      finalize(() => push$.complete()),\n      map(state => ({ ref: el, ...state })),\n      shareReplay(1)\n    )\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  Subject,\n  bufferCount,\n  filter,\n  finalize,\n  first,\n  fromEvent,\n  map,\n  merge,\n  mergeMap,\n  of,\n  share,\n  skipUntil,\n  switchMap,\n  takeUntil,\n  tap,\n  withLatestFrom,\n  zipWith\n} from \"rxjs\"\n\nimport { translation } from \"~/_\"\nimport {\n  getElement,\n  getOptionalElement,\n  watchElementBoundary,\n  watchToggle\n} from \"~/browser\"\nimport {\n  SearchMessage,\n  SearchResult,\n  isSearchReadyMessage,\n  isSearchResultMessage\n} from \"~/integrations\"\nimport { renderSearchResultItem } from \"~/templates\"\nimport { round } from \"~/utilities\"\n\nimport { Component } from \"../../_\"\nimport { SearchQuery } from \"../query\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  query$: Observable<SearchQuery>      /* Search query observable */\n  worker$: Subject<SearchMessage>      /* Search worker */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search result list\n *\n * This function performs a lazy rendering of the search results, depending on\n * the vertical offset of the search result container.\n *\n * @param el - Search result list element\n * @param options - Options\n *\n * @returns Search result list component observable\n */\nexport function mountSearchResult(\n  el: HTMLElement, { worker$, query$ }: MountOptions\n): Observable<Component<SearchResult>> {\n  const push$ = new Subject<SearchResult>()\n  const boundary$ = watchElementBoundary(el.parentElement!)\n    .pipe(\n      filter(Boolean)\n    )\n\n  /* Retrieve container */\n  const container = el.parentElement!\n\n  /* Retrieve nested components */\n  const meta = getElement(\":scope > :first-child\", el)\n  const list = getElement(\":scope > :last-child\", el)\n\n  /* Reveal to accessibility tree \u2013 see https://bit.ly/3iAA7t8 */\n  watchToggle(\"search\")\n    .subscribe(active => list.setAttribute(\n      \"role\", active ? \"list\" : \"presentation\"\n    ))\n\n  /* Update search result metadata */\n  push$\n    .pipe(\n      withLatestFrom(query$),\n      skipUntil(worker$.pipe(first(isSearchReadyMessage)))\n    )\n      .subscribe(([{ items }, { value }]) => {\n        switch (items.length) {\n\n          /* No results */\n          case 0:\n            meta.textContent = value.length\n              ? translation(\"search.result.none\")\n              : translation(\"search.result.placeholder\")\n            break\n\n          /* One result */\n          case 1:\n            meta.textContent = translation(\"search.result.one\")\n            break\n\n          /* Multiple result */\n          default:\n            const count = round(items.length)\n            meta.textContent = translation(\"search.result.other\", count)\n        }\n      })\n\n  /* Render search result item */\n  const render$ = push$\n    .pipe(\n      tap(() => list.innerHTML = \"\"),\n      switchMap(({ items }) => merge(\n        of(...items.slice(0, 10)),\n        of(...items.slice(10))\n          .pipe(\n            bufferCount(4),\n            zipWith(boundary$),\n            switchMap(([chunk]) => chunk)\n          )\n      )),\n      map(renderSearchResultItem),\n      share()\n    )\n\n  /* Update search result list */\n  render$.subscribe(item => list.appendChild(item))\n  render$\n    .pipe(\n      mergeMap(item => {\n        const details = getOptionalElement(\"details\", item)\n        if (typeof details === \"undefined\")\n          return EMPTY\n\n        /* Keep position of details element stable */\n        return fromEvent(details, \"toggle\")\n          .pipe(\n            takeUntil(push$),\n            map(() => details)\n          )\n      })\n    )\n      .subscribe(details => {\n        if (\n          details.open === false &&\n          details.offsetTop <= container.scrollTop\n        )\n          container.scrollTo({ top: details.offsetTop })\n      })\n\n  /* Filter search result message */\n  const result$ = worker$\n    .pipe(\n      filter(isSearchResultMessage),\n      map(({ data }) => data)\n    )\n\n  /* Create and return component */\n  return result$\n    .pipe(\n      tap(state => push$.next(state)),\n      finalize(() => push$.complete()),\n      map(state => ({ ref: el, ...state }))\n    )\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  endWith,\n  finalize,\n  fromEvent,\n  ignoreElements,\n  map,\n  takeUntil,\n  tap\n} from \"rxjs\"\n\nimport { getLocation } from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { SearchQuery } from \"../query\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search sharing\n */\nexport interface SearchShare {\n  url: URL                             /* Deep link for sharing */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  query$: Observable<SearchQuery>      /* Search query observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  query$: Observable<SearchQuery>      /* Search query observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search sharing\n *\n * @param _el - Search sharing element\n * @param options - Options\n *\n * @returns Search sharing observable\n */\nexport function watchSearchShare(\n  _el: HTMLElement, { query$ }: WatchOptions\n): Observable<SearchShare> {\n  return query$\n    .pipe(\n      map(({ value }) => {\n        const url = getLocation()\n        url.hash = \"\"\n\n        /* Compute readable query strings */\n        value = value\n          .replace(/\\s+/g, \"+\")        /* Collapse whitespace */\n          .replace(/&/g, \"%26\")        /* Escape '&' character */\n          .replace(/=/g, \"%3D\")        /* Escape '=' character */\n\n        /* Replace query string */\n        url.search = `q=${value}`\n        return { url }\n      })\n    )\n}\n\n/**\n * Mount search sharing\n *\n * @param el - Search sharing element\n * @param options - Options\n *\n * @returns Search sharing component observable\n */\nexport function mountSearchShare(\n  el: HTMLAnchorElement, options: MountOptions\n): Observable<Component<SearchShare>> {\n  const push$ = new Subject<SearchShare>()\n  const done$ = push$.pipe(ignoreElements(), endWith(true))\n  push$.subscribe(({ url }) => {\n    el.setAttribute(\"data-clipboard-text\", el.href)\n    el.href = `${url}`\n  })\n\n  /* Prevent following of link */\n  fromEvent(el, \"click\")\n    .pipe(\n      takeUntil(done$)\n    )\n      .subscribe(ev => ev.preventDefault())\n\n  /* Create and return component */\n  return watchSearchShare(el, options)\n    .pipe(\n      tap(state => push$.next(state)),\n      finalize(() => push$.complete()),\n      map(state => ({ ref: el, ...state }))\n    )\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  asyncScheduler,\n  combineLatestWith,\n  distinctUntilChanged,\n  filter,\n  finalize,\n  fromEvent,\n  map,\n  merge,\n  observeOn,\n  tap\n} from \"rxjs\"\n\nimport { Keyboard } from \"~/browser\"\nimport {\n  SearchMessage,\n  SearchResult,\n  isSearchResultMessage\n} from \"~/integrations\"\n\nimport { Component, getComponentElement } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search suggestions\n */\nexport interface SearchSuggest {}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  keyboard$: Observable<Keyboard>      /* Keyboard observable */\n  worker$: Subject<SearchMessage>      /* Search worker */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search suggestions\n *\n * This function will perform a lazy rendering of the search results, depending\n * on the vertical offset of the search result container.\n *\n * @param el - Search result list element\n * @param options - Options\n *\n * @returns Search result list component observable\n */\nexport function mountSearchSuggest(\n  el: HTMLElement, { worker$, keyboard$ }: MountOptions\n): Observable<Component<SearchSuggest>> {\n  const push$ = new Subject<SearchResult>()\n\n  /* Retrieve query component and track all changes */\n  const query  = getComponentElement(\"search-query\")\n  const query$ = merge(\n    fromEvent(query, \"keydown\"),\n    fromEvent(query, \"focus\")\n  )\n    .pipe(\n      observeOn(asyncScheduler),\n      map(() => query.value),\n      distinctUntilChanged(),\n    )\n\n  /* Update search suggestions */\n  push$\n    .pipe(\n      combineLatestWith(query$),\n      map(([{ suggest }, value]) => {\n        const words = value.split(/([\\s-]+)/)\n        if (suggest?.length && words[words.length - 1]) {\n          const last = suggest[suggest.length - 1]\n          if (last.startsWith(words[words.length - 1]))\n            words[words.length - 1] = last\n        } else {\n          words.length = 0\n        }\n        return words\n      })\n    )\n      .subscribe(words => el.innerHTML = words\n        .join(\"\")\n        .replace(/\\s/g, \"&nbsp;\")\n      )\n\n  /* Set up search keyboard handlers */\n  keyboard$\n    .pipe(\n      filter(({ mode }) => mode === \"search\")\n    )\n      .subscribe(key => {\n        switch (key.type) {\n\n          /* Right arrow: accept current suggestion */\n          case \"ArrowRight\":\n            if (\n              el.innerText.length &&\n              query.selectionStart === query.value.length\n            )\n              query.value = el.innerText\n            break\n        }\n      })\n\n  /* Filter search result message */\n  const result$ = worker$\n    .pipe(\n      filter(isSearchResultMessage),\n      map(({ data }) => data)\n    )\n\n  /* Create and return component */\n  return result$\n    .pipe(\n      tap(state => push$.next(state)),\n      finalize(() => push$.complete()),\n      map(() => ({ ref: el }))\n    )\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  NEVER,\n  Observable,\n  ObservableInput,\n  filter,\n  fromEvent,\n  merge,\n  mergeWith\n} from \"rxjs\"\n\nimport { configuration } from \"~/_\"\nimport {\n  Keyboard,\n  getActiveElement,\n  getElements,\n  setToggle\n} from \"~/browser\"\nimport {\n  SearchIndex,\n  SearchResult,\n  setupSearchWorker\n} from \"~/integrations\"\n\nimport {\n  Component,\n  getComponentElement,\n  getComponentElements\n} from \"../../_\"\nimport {\n  SearchQuery,\n  mountSearchQuery\n} from \"../query\"\nimport { mountSearchResult } from \"../result\"\nimport {\n  SearchShare,\n  mountSearchShare\n} from \"../share\"\nimport {\n  SearchSuggest,\n  mountSearchSuggest\n} from \"../suggest\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search\n */\nexport type Search =\n  | SearchQuery\n  | SearchResult\n  | SearchShare\n  | SearchSuggest\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  index$: ObservableInput<SearchIndex> /* Search index observable */\n  keyboard$: Observable<Keyboard>      /* Keyboard observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search\n *\n * This function sets up the search functionality, including the underlying\n * web worker and all keyboard bindings.\n *\n * @param el - Search element\n * @param options - Options\n *\n * @returns Search component observable\n */\nexport function mountSearch(\n  el: HTMLElement, { index$, keyboard$ }: MountOptions\n): Observable<Component<Search>> {\n  const config = configuration()\n  try {\n    const worker$ = setupSearchWorker(config.search, index$)\n\n    /* Retrieve query and result components */\n    const query  = getComponentElement(\"search-query\", el)\n    const result = getComponentElement(\"search-result\", el)\n\n    /* Always close search on result selection */\n    fromEvent<PointerEvent>(el, \"click\")\n      .pipe(\n        filter(({ target }) => (\n          target instanceof Element && !!target.closest(\"a\")\n        ))\n      )\n        .subscribe(() => setToggle(\"search\", false))\n\n    /* Set up search keyboard handlers */\n    keyboard$\n      .pipe(\n        filter(({ mode }) => mode === \"search\")\n      )\n        .subscribe(key => {\n          const active = getActiveElement()\n          switch (key.type) {\n\n            /* Enter: go to first (best) result */\n            case \"Enter\":\n              if (active === query) {\n                const anchors = new Map<HTMLAnchorElement, number>()\n                for (const anchor of getElements<HTMLAnchorElement>(\n                  \":first-child [href]\", result\n                )) {\n                  const article = anchor.firstElementChild!\n                  anchors.set(anchor, parseFloat(\n                    article.getAttribute(\"data-md-score\")!\n                  ))\n                }\n\n                /* Go to result with highest score, if any */\n                if (anchors.size) {\n                  const [[best]] = [...anchors].sort(([, a], [, b]) => b - a)\n                  best.click()\n                }\n\n                /* Otherwise omit form submission */\n                key.claim()\n              }\n              break\n\n            /* Escape or Tab: close search */\n            case \"Escape\":\n            case \"Tab\":\n              setToggle(\"search\", false)\n              query.blur()\n              break\n\n            /* Vertical arrows: select previous or next search result */\n            case \"ArrowUp\":\n            case \"ArrowDown\":\n              if (typeof active === \"undefined\") {\n                query.focus()\n              } else {\n                const els = [query, ...getElements(\n                  \":not(details) > [href], summary, details[open] [href]\",\n                  result\n                )]\n                const i = Math.max(0, (\n                  Math.max(0, els.indexOf(active)) + els.length + (\n                    key.type === \"ArrowUp\" ? -1 : +1\n                  )\n                ) % els.length)\n                els[i].focus()\n              }\n\n              /* Prevent scrolling of page */\n              key.claim()\n              break\n\n            /* All other keys: hand to search query */\n            default:\n              if (query !== getActiveElement())\n                query.focus()\n          }\n        })\n\n    /* Set up global keyboard handlers */\n    keyboard$\n      .pipe(\n        filter(({ mode }) => mode === \"global\")\n      )\n        .subscribe(key => {\n          switch (key.type) {\n\n            /* Open search and select query */\n            case \"f\":\n            case \"s\":\n            case \"/\":\n              query.focus()\n              query.select()\n\n              /* Prevent scrolling of page */\n              key.claim()\n              break\n          }\n        })\n\n    /* Create and return component */\n    const query$ = mountSearchQuery(query, { worker$ })\n    return merge(\n      query$,\n      mountSearchResult(result, { worker$, query$ })\n    )\n      .pipe(\n        mergeWith(\n\n          /* Search sharing */\n          ...getComponentElements(\"search-share\", el)\n            .map(child => mountSearchShare(child, { query$ })),\n\n          /* Search suggestions */\n          ...getComponentElements(\"search-suggest\", el)\n            .map(child => mountSearchSuggest(child, { worker$, keyboard$ }))\n        )\n      )\n\n  /* Gracefully handle broken search */\n  } catch (err) {\n    el.hidden = true\n    return NEVER\n  }\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  ObservableInput,\n  combineLatest,\n  filter,\n  map,\n  startWith\n} from \"rxjs\"\n\nimport { getLocation } from \"~/browser\"\nimport {\n  SearchIndex,\n  setupSearchHighlighter\n} from \"~/integrations\"\nimport { h } from \"~/utilities\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search highlighting\n */\nexport interface SearchHighlight {\n  nodes: Map<ChildNode, string>        /* Map of replacements */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  index$: ObservableInput<SearchIndex> /* Search index observable */\n  location$: Observable<URL>           /* Location observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search highlighting\n *\n * @param el - Content element\n * @param options - Options\n *\n * @returns Search highlighting component observable\n */\nexport function mountSearchHiglight(\n  el: HTMLElement, { index$, location$ }: MountOptions\n): Observable<Component<SearchHighlight>> {\n  return combineLatest([\n    index$,\n    location$\n      .pipe(\n        startWith(getLocation()),\n        filter(url => !!url.searchParams.get(\"h\"))\n      )\n  ])\n    .pipe(\n      map(([index, url]) => setupSearchHighlighter(index.config)(\n        url.searchParams.get(\"h\")!\n      )),\n      map(fn => {\n        const nodes = new Map<ChildNode, string>()\n\n        /* Traverse text nodes and collect matches */\n        const it = document.createNodeIterator(el, NodeFilter.SHOW_TEXT)\n        for (let node = it.nextNode(); node; node = it.nextNode()) {\n          if (node.parentElement?.offsetHeight) {\n            const original = node.textContent!\n            const replaced = fn(original)\n            if (replaced.length > original.length)\n              nodes.set(node as ChildNode, replaced)\n          }\n        }\n\n        /* Replace original nodes with matches */\n        for (const [node, text] of nodes) {\n          const { childNodes } = h(\"span\", null, text)\n          node.replaceWith(...Array.from(childNodes))\n        }\n\n        /* Return component */\n        return { ref: el, nodes }\n      })\n    )\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  animationFrameScheduler,\n  auditTime,\n  combineLatest,\n  defer,\n  distinctUntilChanged,\n  endWith,\n  finalize,\n  first,\n  from,\n  fromEvent,\n  ignoreElements,\n  map,\n  mergeMap,\n  takeUntil,\n  tap,\n  withLatestFrom\n} from \"rxjs\"\n\nimport {\n  Viewport,\n  getElement,\n  getElementContainer,\n  getElementOffset,\n  getElementSize,\n  getElements\n} from \"~/browser\"\n\nimport { Component } from \"../_\"\nimport { Header } from \"../header\"\nimport { Main } from \"../main\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Sidebar\n */\nexport interface Sidebar {\n  height: number                       /* Sidebar height */\n  locked: boolean                      /* Sidebar is locked */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  main$: Observable<Main>              /* Main area observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n  main$: Observable<Main>              /* Main area observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch sidebar\n *\n * This function returns an observable that computes the visual parameters of\n * the sidebar which depends on the vertical viewport offset, as well as the\n * height of the main area. When the page is scrolled beyond the header, the\n * sidebar is locked and fills the remaining space.\n *\n * @param el - Sidebar element\n * @param options - Options\n *\n * @returns Sidebar observable\n */\nexport function watchSidebar(\n  el: HTMLElement, { viewport$, main$ }: WatchOptions\n): Observable<Sidebar> {\n  const parent = el.parentElement!\n  const adjust =\n    parent.offsetTop -\n    parent.parentElement!.offsetTop\n\n  /* Compute the sidebar's available height and if it should be locked */\n  return combineLatest([main$, viewport$])\n    .pipe(\n      map(([{ offset, height }, { offset: { y } }]) => {\n        height = height\n          + Math.min(adjust, Math.max(0, y - offset))\n          - adjust\n        return {\n          height,\n          locked: y >= offset + adjust\n        }\n      }),\n      distinctUntilChanged((a, b) => (\n        a.height === b.height &&\n        a.locked === b.locked\n      ))\n    )\n}\n\n/**\n * Mount sidebar\n *\n * This function doesn't set the height of the actual sidebar, but of its first\n * child \u2013 the `.md-sidebar__scrollwrap` element in order to mitigiate jittery\n * sidebars when the footer is scrolled into view. At some point we switched\n * from `absolute` / `fixed` positioning to `sticky` positioning, significantly\n * reducing jitter in some browsers (respectively Firefox and Safari) when\n * scrolling from the top. However, top-aligned sticky positioning means that\n * the sidebar snaps to the bottom when the end of the container is reached.\n * This is what leads to the mentioned jitter, as the sidebar's height may be\n * updated too slowly.\n *\n * This behaviour can be mitigiated by setting the height of the sidebar to `0`\n * while preserving the padding, and the height on its first element.\n *\n * @param el - Sidebar element\n * @param options - Options\n *\n * @returns Sidebar component observable\n */\nexport function mountSidebar(\n  el: HTMLElement, { header$, ...options }: MountOptions\n): Observable<Component<Sidebar>> {\n  const inner = getElement(\".md-sidebar__scrollwrap\", el)\n  const { y } = getElementOffset(inner)\n  return defer(() => {\n    const push$ = new Subject<Sidebar>()\n    const done$ = push$.pipe(ignoreElements(), endWith(true))\n    const next$ = push$\n      .pipe(\n        auditTime(0, animationFrameScheduler)\n      )\n\n    /* Update sidebar height and offset */\n    next$.pipe(withLatestFrom(header$))\n      .subscribe({\n\n        /* Handle emission */\n        next([{ height }, { height: offset }]) {\n          inner.style.height = `${height - 2 * y}px`\n          el.style.top       = `${offset}px`\n        },\n\n        /* Handle complete */\n        complete() {\n          inner.style.height = \"\"\n          el.style.top       = \"\"\n        }\n      })\n\n    /* Bring active item into view on initial load */\n    next$.pipe(first())\n      .subscribe(() => {\n        for (const item of getElements(\".md-nav__link--active[href]\", el)) {\n          const container = getElementContainer(item)\n          if (typeof container !== \"undefined\") {\n            const offset = item.offsetTop - container.offsetTop\n            const { height } = getElementSize(container)\n            container.scrollTo({\n              top: offset - height / 2\n            })\n          }\n        }\n      })\n\n    /* Handle accessibility for expandable items, see https://bit.ly/3jaod9p */\n    from(getElements<HTMLLabelElement>(\"label[tabindex]\", el))\n      .pipe(\n        mergeMap(label => fromEvent(label, \"click\")\n          .pipe(\n            map(() => label),\n            takeUntil(done$)\n          )\n        )\n      )\n        .subscribe(label => {\n          const input = getElement<HTMLInputElement>(`[id=\"${label.htmlFor}\"]`)\n          const nav = getElement(`[aria-labelledby=\"${label.id}\"]`)\n          nav.setAttribute(\"aria-expanded\", `${input.checked}`)\n        })\n\n    /* Create and return component */\n    return watchSidebar(el, options)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Repo, User } from \"github-types\"\nimport {\n  EMPTY,\n  Observable,\n  catchError,\n  defaultIfEmpty,\n  map,\n  zip\n} from \"rxjs\"\n\nimport { requestJSON } from \"~/browser\"\n\nimport { SourceFacts } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * GitHub release (partial)\n */\ninterface Release {\n  tag_name: string                     /* Tag name */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch GitHub repository facts\n *\n * @param user - GitHub user or organization\n * @param repo - GitHub repository\n *\n * @returns Repository facts observable\n */\nexport function fetchSourceFactsFromGitHub(\n  user: string, repo?: string\n): Observable<SourceFacts> {\n  if (typeof repo !== \"undefined\") {\n    const url = `https://api.github.com/repos/${user}/${repo}`\n    return zip(\n\n      /* Fetch version */\n      requestJSON<Release>(`${url}/releases/latest`)\n        .pipe(\n          catchError(() => EMPTY), // @todo refactor instant loading\n          map(release => ({\n            version: release.tag_name\n          })),\n          defaultIfEmpty({})\n        ),\n\n      /* Fetch stars and forks */\n      requestJSON<Repo>(url)\n        .pipe(\n          catchError(() => EMPTY), // @todo refactor instant loading\n          map(info => ({\n            stars: info.stargazers_count,\n            forks: info.forks_count\n          })),\n          defaultIfEmpty({})\n        )\n    )\n      .pipe(\n        map(([release, info]) => ({ ...release, ...info }))\n      )\n\n  /* User or organization */\n  } else {\n    const url = `https://api.github.com/users/${user}`\n    return requestJSON<User>(url)\n      .pipe(\n        map(info => ({\n          repositories: info.public_repos\n        })),\n        defaultIfEmpty({})\n      )\n  }\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { ProjectSchema } from \"gitlab\"\nimport {\n  EMPTY,\n  Observable,\n  catchError,\n  defaultIfEmpty,\n  map\n} from \"rxjs\"\n\nimport { requestJSON } from \"~/browser\"\n\nimport { SourceFacts } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch GitLab repository facts\n *\n * @param base - GitLab base\n * @param project - GitLab project\n *\n * @returns Repository facts observable\n */\nexport function fetchSourceFactsFromGitLab(\n  base: string, project: string\n): Observable<SourceFacts> {\n  const url = `https://${base}/api/v4/projects/${encodeURIComponent(project)}`\n  return requestJSON<ProjectSchema>(url)\n    .pipe(\n      catchError(() => EMPTY), // @todo refactor instant loading\n      map(({ star_count, forks_count }) => ({\n        stars: star_count,\n        forks: forks_count\n      })),\n      defaultIfEmpty({})\n    )\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { EMPTY, Observable } from \"rxjs\"\n\nimport { fetchSourceFactsFromGitHub } from \"../github\"\nimport { fetchSourceFactsFromGitLab } from \"../gitlab\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Repository facts for repositories\n */\nexport interface RepositoryFacts {\n  stars?: number                       /* Number of stars */\n  forks?: number                       /* Number of forks */\n  version?: string                     /* Latest version */\n}\n\n/**\n * Repository facts for organizations\n */\nexport interface OrganizationFacts {\n  repositories?: number                /* Number of repositories */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Repository facts\n */\nexport type SourceFacts =\n  | RepositoryFacts\n  | OrganizationFacts\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch repository facts\n *\n * @param url - Repository URL\n *\n * @returns Repository facts observable\n */\nexport function fetchSourceFacts(\n  url: string\n): Observable<SourceFacts> {\n\n  /* Try to match GitHub repository */\n  let match = url.match(/^.+github\\.com\\/([^/]+)\\/?([^/]+)?/i)\n  if (match) {\n    const [, user, repo] = match\n    return fetchSourceFactsFromGitHub(user, repo)\n  }\n\n  /* Try to match GitLab repository */\n  match = url.match(/^.+?([^/]*gitlab[^/]+)\\/(.+?)\\/?$/i)\n  if (match) {\n    const [, base, slug] = match\n    return fetchSourceFactsFromGitLab(base, slug)\n  }\n\n  /* Fallback */\n  return EMPTY\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  Subject,\n  catchError,\n  defer,\n  filter,\n  finalize,\n  map,\n  of,\n  shareReplay,\n  tap\n} from \"rxjs\"\n\nimport { getElement } from \"~/browser\"\nimport { ConsentDefaults } from \"~/components/consent\"\nimport { renderSourceFacts } from \"~/templates\"\n\nimport {\n  Component,\n  getComponentElements\n} from \"../../_\"\nimport {\n  SourceFacts,\n  fetchSourceFacts\n} from \"../facts\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Repository information\n */\nexport interface Source {\n  facts: SourceFacts                   /* Repository facts */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Repository information observable\n */\nlet fetch$: Observable<Source>\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch repository information\n *\n * This function tries to read the repository facts from session storage, and\n * if unsuccessful, fetches them from the underlying provider.\n *\n * @param el - Repository information element\n *\n * @returns Repository information observable\n */\nexport function watchSource(\n  el: HTMLAnchorElement\n): Observable<Source> {\n  return fetch$ ||= defer(() => {\n    const cached = __md_get<SourceFacts>(\"__source\", sessionStorage)\n    if (cached) {\n      return of(cached)\n    } else {\n\n      /* Check if consent is configured and was given */\n      const els = getComponentElements(\"consent\")\n      if (els.length) {\n        const consent = __md_get<ConsentDefaults>(\"__consent\")\n        if (!(consent && consent.github))\n          return EMPTY\n      }\n\n      /* Fetch repository facts */\n      return fetchSourceFacts(el.href)\n        .pipe(\n          tap(facts => __md_set(\"__source\", facts, sessionStorage))\n        )\n    }\n  })\n    .pipe(\n      catchError(() => EMPTY),\n      filter(facts => Object.keys(facts).length > 0),\n      map(facts => ({ facts })),\n      shareReplay(1)\n    )\n}\n\n/**\n * Mount repository information\n *\n * @param el - Repository information element\n *\n * @returns Repository information component observable\n */\nexport function mountSource(\n  el: HTMLAnchorElement\n): Observable<Component<Source>> {\n  const inner = getElement(\":scope > :last-child\", el)\n  return defer(() => {\n    const push$ = new Subject<Source>()\n    push$.subscribe(({ facts }) => {\n      inner.appendChild(renderSourceFacts(facts))\n      inner.classList.add(\"md-source__repository--active\")\n    })\n\n    /* Create and return component */\n    return watchSource(el)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  defer,\n  distinctUntilKeyChanged,\n  finalize,\n  map,\n  of,\n  switchMap,\n  tap\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n  Viewport,\n  watchElementSize,\n  watchViewportAt\n} from \"~/browser\"\n\nimport { Component } from \"../_\"\nimport { Header } from \"../header\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Navigation tabs\n */\nexport interface Tabs {\n  hidden: boolean                      /* Navigation tabs are hidden */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch navigation tabs\n *\n * @param el - Navigation tabs element\n * @param options - Options\n *\n * @returns Navigation tabs observable\n */\nexport function watchTabs(\n  el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable<Tabs> {\n  return watchElementSize(document.body)\n    .pipe(\n      switchMap(() => watchViewportAt(el, { header$, viewport$ })),\n      map(({ offset: { y } }) => {\n        return {\n          hidden: y >= 10\n        }\n      }),\n      distinctUntilKeyChanged(\"hidden\")\n    )\n}\n\n/**\n * Mount navigation tabs\n *\n * This function hides the navigation tabs when scrolling past the threshold\n * and makes them reappear in a nice CSS animation when scrolling back up.\n *\n * @param el - Navigation tabs element\n * @param options - Options\n *\n * @returns Navigation tabs component observable\n */\nexport function mountTabs(\n  el: HTMLElement, options: MountOptions\n): Observable<Component<Tabs>> {\n  return defer(() => {\n    const push$ = new Subject<Tabs>()\n    push$.subscribe({\n\n      /* Handle emission */\n      next({ hidden }) {\n        el.hidden = hidden\n      },\n\n      /* Handle complete */\n      complete() {\n        el.hidden = false\n      }\n    })\n\n    /* Create and return component */\n    return (\n      feature(\"navigation.tabs.sticky\")\n        ? of({ hidden: false })\n        : watchTabs(el, options)\n    )\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  asyncScheduler,\n  bufferCount,\n  combineLatestWith,\n  debounceTime,\n  defer,\n  distinctUntilChanged,\n  distinctUntilKeyChanged,\n  endWith,\n  filter,\n  finalize,\n  ignoreElements,\n  map,\n  merge,\n  observeOn,\n  of,\n  repeat,\n  scan,\n  share,\n  skip,\n  startWith,\n  switchMap,\n  takeUntil,\n  tap,\n  withLatestFrom\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n  Viewport,\n  getElement,\n  getElementContainer,\n  getElementSize,\n  getElements,\n  getLocation,\n  getOptionalElement,\n  watchElementSize\n} from \"~/browser\"\n\nimport {\n  Component,\n  getComponentElement\n} from \"../_\"\nimport { Header } from \"../header\"\nimport { Main } from \"../main\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Table of contents\n */\nexport interface TableOfContents {\n  prev: HTMLAnchorElement[][]          /* Anchors (previous) */\n  next: HTMLAnchorElement[][]          /* Anchors (next) */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n  main$: Observable<Main>              /* Main area observable */\n  target$: Observable<HTMLElement>     /* Location target observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch table of contents\n *\n * This is effectively a scroll spy implementation which will account for the\n * fixed header and automatically re-calculate anchor offsets when the viewport\n * is resized. The returned observable will only emit if the table of contents\n * needs to be repainted.\n *\n * This implementation tracks an anchor element's entire path starting from its\n * level up to the top-most anchor element, e.g. `[h3, h2, h1]`. Although the\n * Material theme currently doesn't make use of this information, it enables\n * the styling of the entire hierarchy through customization.\n *\n * Note that the current anchor is the last item of the `prev` anchor list.\n *\n * @param el - Table of contents element\n * @param options - Options\n *\n * @returns Table of contents observable\n */\nexport function watchTableOfContents(\n  el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable<TableOfContents> {\n  const table = new Map<HTMLAnchorElement, HTMLElement>()\n\n  /* Compute anchor-to-target mapping */\n  const anchors = getElements<HTMLAnchorElement>(\"[href^=\\\\#]\", el)\n  for (const anchor of anchors) {\n    const id = decodeURIComponent(anchor.hash.substring(1))\n    const target = getOptionalElement(`[id=\"${id}\"]`)\n    if (typeof target !== \"undefined\")\n      table.set(anchor, target)\n  }\n\n  /* Compute necessary adjustment for header */\n  const adjust$ = header$\n    .pipe(\n      distinctUntilKeyChanged(\"height\"),\n      map(({ height }) => {\n        const main = getComponentElement(\"main\")\n        const grid = getElement(\":scope > :first-child\", main)\n        return height + 0.8 * (\n          grid.offsetTop -\n          main.offsetTop\n        )\n      }),\n      share()\n    )\n\n  /* Compute partition of previous and next anchors */\n  const partition$ = watchElementSize(document.body)\n    .pipe(\n      distinctUntilKeyChanged(\"height\"),\n\n      /* Build index to map anchor paths to vertical offsets */\n      switchMap(body => defer(() => {\n        let path: HTMLAnchorElement[] = []\n        return of([...table].reduce((index, [anchor, target]) => {\n          while (path.length) {\n            const last = table.get(path[path.length - 1])!\n            if (last.tagName >= target.tagName) {\n              path.pop()\n            } else {\n              break\n            }\n          }\n\n          /* If the current anchor is hidden, continue with its parent */\n          let offset = target.offsetTop\n          while (!offset && target.parentElement) {\n            target = target.parentElement\n            offset = target.offsetTop\n          }\n\n          /* Fix anchor offsets in tables - see https://bit.ly/3CUFOcn */\n          let parent = target.offsetParent as HTMLElement\n          for (; parent; parent = parent.offsetParent as HTMLElement)\n            offset += parent.offsetTop\n\n          /* Map reversed anchor path to vertical offset */\n          return index.set(\n            [...path = [...path, anchor]].reverse(),\n            offset\n          )\n        }, new Map<HTMLAnchorElement[], number>()))\n      })\n        .pipe(\n\n          /* Sort index by vertical offset (see https://bit.ly/30z6QSO) */\n          map(index => new Map([...index].sort(([, a], [, b]) => a - b))),\n          combineLatestWith(adjust$),\n\n          /* Re-compute partition when viewport offset changes */\n          switchMap(([index, adjust]) => viewport$\n            .pipe(\n              scan(([prev, next], { offset: { y }, size }) => {\n                const last = y + size.height >= Math.floor(body.height)\n\n                /* Look forward */\n                while (next.length) {\n                  const [, offset] = next[0]\n                  if (offset - adjust < y || last) {\n                    prev = [...prev, next.shift()!]\n                  } else {\n                    break\n                  }\n                }\n\n                /* Look backward */\n                while (prev.length) {\n                  const [, offset] = prev[prev.length - 1]\n                  if (offset - adjust >= y && !last) {\n                    next = [prev.pop()!, ...next]\n                  } else {\n                    break\n                  }\n                }\n\n                /* Return partition */\n                return [prev, next]\n              }, [[], [...index]]),\n              distinctUntilChanged((a, b) => (\n                a[0] === b[0] &&\n                a[1] === b[1]\n              ))\n            )\n          )\n        )\n      )\n    )\n\n  /* Compute and return anchor list migrations */\n  return partition$\n    .pipe(\n      map(([prev, next]) => ({\n        prev: prev.map(([path]) => path),\n        next: next.map(([path]) => path)\n      })),\n\n      /* Extract anchor list migrations */\n      startWith({ prev: [], next: [] }),\n      bufferCount(2, 1),\n      map(([a, b]) => {\n\n        /* Moving down */\n        if (a.prev.length < b.prev.length) {\n          return {\n            prev: b.prev.slice(Math.max(0, a.prev.length - 1), b.prev.length),\n            next: []\n          }\n\n        /* Moving up */\n        } else {\n          return {\n            prev: b.prev.slice(-1),\n            next: b.next.slice(0, b.next.length - a.next.length)\n          }\n        }\n      })\n    )\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Mount table of contents\n *\n * @param el - Table of contents element\n * @param options - Options\n *\n * @returns Table of contents component observable\n */\nexport function mountTableOfContents(\n  el: HTMLElement, { viewport$, header$, main$, target$ }: MountOptions\n): Observable<Component<TableOfContents>> {\n  return defer(() => {\n    const push$ = new Subject<TableOfContents>()\n    const done$ = push$.pipe(ignoreElements(), endWith(true))\n    push$.subscribe(({ prev, next }) => {\n\n      /* Look forward */\n      for (const [anchor] of next) {\n        anchor.classList.remove(\"md-nav__link--passed\")\n        anchor.classList.remove(\"md-nav__link--active\")\n      }\n\n      /* Look backward */\n      for (const [index, [anchor]] of prev.entries()) {\n        anchor.classList.add(\"md-nav__link--passed\")\n        anchor.classList.toggle(\n          \"md-nav__link--active\",\n          index === prev.length - 1\n        )\n      }\n    })\n\n    /* Set up following, if enabled */\n    if (feature(\"toc.follow\")) {\n\n      /* Toggle smooth scrolling only for anchor clicks */\n      const smooth$ = merge(\n        viewport$.pipe(debounceTime(1), map(() => undefined)),\n        viewport$.pipe(debounceTime(250), map(() => \"smooth\" as const))\n      )\n\n      /* Bring active anchor into view */ // @todo: refactor\n      push$\n        .pipe(\n          filter(({ prev }) => prev.length > 0),\n          combineLatestWith(main$.pipe(observeOn(asyncScheduler))),\n          withLatestFrom(smooth$)\n        )\n          .subscribe(([[{ prev }], behavior]) => {\n            const [anchor] = prev[prev.length - 1]\n            if (anchor.offsetHeight) {\n\n              /* Retrieve overflowing container and scroll */\n              const container = getElementContainer(anchor)\n              if (typeof container !== \"undefined\") {\n                const offset = anchor.offsetTop - container.offsetTop\n                const { height } = getElementSize(container)\n                container.scrollTo({\n                  top: offset - height / 2,\n                  behavior\n                })\n              }\n            }\n          })\n    }\n\n    /* Set up anchor tracking, if enabled */\n    if (feature(\"navigation.tracking\"))\n      viewport$\n        .pipe(\n          takeUntil(done$),\n          distinctUntilKeyChanged(\"offset\"),\n          debounceTime(250),\n          skip(1),\n          takeUntil(target$.pipe(skip(1))),\n          repeat({ delay: 250 }),\n          withLatestFrom(push$)\n        )\n          .subscribe(([, { prev }]) => {\n            const url = getLocation()\n\n            /* Set hash fragment to active anchor */\n            const anchor = prev[prev.length - 1]\n            if (anchor && anchor.length) {\n              const [active] = anchor\n              const { hash } = new URL(active.href)\n              if (url.hash !== hash) {\n                url.hash = hash\n                history.replaceState({}, \"\", `${url}`)\n              }\n\n            /* Reset anchor when at the top */\n            } else {\n              url.hash = \"\"\n              history.replaceState({}, \"\", `${url}`)\n            }\n          })\n\n    /* Create and return component */\n    return watchTableOfContents(el, { viewport$, header$ })\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  bufferCount,\n  combineLatest,\n  distinctUntilChanged,\n  distinctUntilKeyChanged,\n  endWith,\n  finalize,\n  fromEvent,\n  ignoreElements,\n  map,\n  repeat,\n  skip,\n  takeUntil,\n  tap\n} from \"rxjs\"\n\nimport { Viewport } from \"~/browser\"\n\nimport { Component } from \"../_\"\nimport { Header } from \"../header\"\nimport { Main } from \"../main\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Back-to-top button\n */\nexport interface BackToTop {\n  hidden: boolean                      /* Back-to-top button is hidden */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  main$: Observable<Main>              /* Main area observable */\n  target$: Observable<HTMLElement>     /* Location target observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n  main$: Observable<Main>              /* Main area observable */\n  target$: Observable<HTMLElement>     /* Location target observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch back-to-top\n *\n * @param _el - Back-to-top element\n * @param options - Options\n *\n * @returns Back-to-top observable\n */\nexport function watchBackToTop(\n  _el: HTMLElement, { viewport$, main$, target$ }: WatchOptions\n): Observable<BackToTop> {\n\n  /* Compute direction */\n  const direction$ = viewport$\n    .pipe(\n      map(({ offset: { y } }) => y),\n      bufferCount(2, 1),\n      map(([a, b]) => a > b && b > 0),\n      distinctUntilChanged()\n    )\n\n  /* Compute whether main area is active */\n  const active$ = main$\n    .pipe(\n      map(({ active }) => active)\n    )\n\n  /* Compute threshold for hiding */\n  return combineLatest([active$, direction$])\n    .pipe(\n      map(([active, direction]) => !(active && direction)),\n      distinctUntilChanged(),\n      takeUntil(target$.pipe(skip(1))),\n      endWith(true),\n      repeat({ delay: 250 }),\n      map(hidden => ({ hidden }))\n    )\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Mount back-to-top\n *\n * @param el - Back-to-top element\n * @param options - Options\n *\n * @returns Back-to-top component observable\n */\nexport function mountBackToTop(\n  el: HTMLElement, { viewport$, header$, main$, target$ }: MountOptions\n): Observable<Component<BackToTop>> {\n  const push$ = new Subject<BackToTop>()\n  const done$ = push$.pipe(ignoreElements(), endWith(true))\n  push$.subscribe({\n\n    /* Handle emission */\n    next({ hidden }) {\n      el.hidden = hidden\n      if (hidden) {\n        el.setAttribute(\"tabindex\", \"-1\")\n        el.blur()\n      } else {\n        el.removeAttribute(\"tabindex\")\n      }\n    },\n\n    /* Handle complete */\n    complete() {\n      el.style.top = \"\"\n      el.hidden = true\n      el.removeAttribute(\"tabindex\")\n    }\n  })\n\n  /* Watch header height */\n  header$\n    .pipe(\n      takeUntil(done$),\n      distinctUntilKeyChanged(\"height\")\n    )\n      .subscribe(({ height }) => {\n        el.style.top = `${height + 16}px`\n      })\n\n  /* Go back to top */\n  fromEvent(el, \"click\")\n    .subscribe(ev => {\n      ev.preventDefault()\n      window.scrollTo({ top: 0 })\n    })\n\n  /* Create and return component */\n  return watchBackToTop(el, { viewport$, main$, target$ })\n    .pipe(\n      tap(state => push$.next(state)),\n      finalize(() => push$.complete()),\n      map(state => ({ ref: el, ...state }))\n    )\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  fromEvent,\n  map,\n  mergeMap,\n  switchMap,\n  takeWhile,\n  tap,\n  withLatestFrom\n} from \"rxjs\"\n\nimport { getElements } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch options\n */\ninterface PatchOptions {\n  document$: Observable<Document>      /* Document observable */\n  tablet$: Observable<boolean>         /* Media tablet observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch indeterminate checkboxes\n *\n * This function replaces the indeterminate \"pseudo state\" with the actual\n * indeterminate state, which is used to keep navigation always expanded.\n *\n * @param options - Options\n */\nexport function patchIndeterminate(\n  { document$, tablet$ }: PatchOptions\n): void {\n  document$\n    .pipe(\n      switchMap(() => getElements<HTMLInputElement>(\n        \".md-toggle--indeterminate\"\n      )),\n      tap(el => {\n        el.indeterminate = true\n        el.checked = false\n      }),\n      mergeMap(el => fromEvent(el, \"change\")\n        .pipe(\n          takeWhile(() => el.classList.contains(\"md-toggle--indeterminate\")),\n          map(() => el)\n        )\n      ),\n      withLatestFrom(tablet$)\n    )\n      .subscribe(([el, tablet]) => {\n        el.classList.remove(\"md-toggle--indeterminate\")\n        if (tablet)\n          el.checked = false\n      })\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  filter,\n  fromEvent,\n  map,\n  mergeMap,\n  switchMap,\n  tap\n} from \"rxjs\"\n\nimport { getElements } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch options\n */\ninterface PatchOptions {\n  document$: Observable<Document>      /* Document observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Check whether the given device is an Apple device\n *\n * @returns Test result\n */\nfunction isAppleDevice(): boolean {\n  return /(iPad|iPhone|iPod)/.test(navigator.userAgent)\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch all elements with `data-md-scrollfix` attributes\n *\n * This is a year-old patch which ensures that overflow scrolling works at the\n * top and bottom of containers on iOS by ensuring a `1px` scroll offset upon\n * the start of a touch event.\n *\n * @see https://bit.ly/2SCtAOO - Original source\n *\n * @param options - Options\n */\nexport function patchScrollfix(\n  { document$ }: PatchOptions\n): void {\n  document$\n    .pipe(\n      switchMap(() => getElements(\"[data-md-scrollfix]\")),\n      tap(el => el.removeAttribute(\"data-md-scrollfix\")),\n      filter(isAppleDevice),\n      mergeMap(el => fromEvent(el, \"touchstart\")\n        .pipe(\n          map(() => el)\n        )\n      )\n    )\n      .subscribe(el => {\n        const top = el.scrollTop\n\n        /* We're at the top of the container */\n        if (top === 0) {\n          el.scrollTop = 1\n\n        /* We're at the bottom of the container */\n        } else if (top + el.offsetHeight === el.scrollHeight) {\n          el.scrollTop = top - 1\n        }\n      })\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  combineLatest,\n  delay,\n  map,\n  of,\n  switchMap,\n  withLatestFrom\n} from \"rxjs\"\n\nimport {\n  Viewport,\n  watchToggle\n} from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch options\n */\ninterface PatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  tablet$: Observable<boolean>         /* Media tablet observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch the document body to lock when search is open\n *\n * For mobile and tablet viewports, the search is rendered full screen, which\n * leads to scroll leaking when at the top or bottom of the search result. This\n * function locks the body when the search is in full screen mode, and restores\n * the scroll position when leaving.\n *\n * @param options - Options\n */\nexport function patchScrolllock(\n  { viewport$, tablet$ }: PatchOptions\n): void {\n  combineLatest([watchToggle(\"search\"), tablet$])\n    .pipe(\n      map(([active, tablet]) => active && !tablet),\n      switchMap(active => of(active)\n        .pipe(\n          delay(active ? 400 : 100)\n        )\n      ),\n      withLatestFrom(viewport$)\n    )\n      .subscribe(([active, { offset: { y }}]) => {\n        if (active) {\n          document.body.setAttribute(\"data-md-scrolllock\", \"\")\n          document.body.style.top = `-${y}px`\n        } else {\n          const value = -1 * parseInt(document.body.style.top, 10)\n          document.body.removeAttribute(\"data-md-scrolllock\")\n          document.body.style.top = \"\"\n          if (value)\n            window.scrollTo(0, value)\n        }\n      })\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Polyfills\n * ------------------------------------------------------------------------- */\n\n/* Polyfill `Object.entries` */\nif (!Object.entries)\n  Object.entries = function (obj: object) {\n    const data: [string, string][] = []\n    for (const key of Object.keys(obj))\n      // @ts-expect-error - ignore property access warning\n      data.push([key, obj[key]])\n\n    /* Return entries */\n    return data\n  }\n\n/* Polyfill `Object.values` */\nif (!Object.values)\n  Object.values = function (obj: object) {\n    const data: string[] = []\n    for (const key of Object.keys(obj))\n      // @ts-expect-error - ignore property access warning\n      data.push(obj[key])\n\n    /* Return values */\n    return data\n  }\n\n/* ------------------------------------------------------------------------- */\n\n/* Polyfills for `Element` */\nif (typeof Element !== \"undefined\") {\n\n  /* Polyfill `Element.scrollTo` */\n  if (!Element.prototype.scrollTo)\n    Element.prototype.scrollTo = function (\n      x?: ScrollToOptions | number, y?: number\n    ): void {\n      if (typeof x === \"object\") {\n        this.scrollLeft = x.left!\n        this.scrollTop = x.top!\n      } else {\n        this.scrollLeft = x!\n        this.scrollTop = y!\n      }\n    }\n\n  /* Polyfill `Element.replaceWith` */\n  if (!Element.prototype.replaceWith)\n    Element.prototype.replaceWith = function (\n      ...nodes: Array<string | Node>\n    ): void {\n      const parent = this.parentNode\n      if (parent) {\n        if (nodes.length === 0)\n          parent.removeChild(this)\n\n        /* Replace children and create text nodes */\n        for (let i = nodes.length - 1; i >= 0; i--) {\n          let node = nodes[i]\n          if (typeof node === \"string\")\n            node = document.createTextNode(node)\n          else if (node.parentNode)\n            node.parentNode.removeChild(node)\n\n          /* Replace child or insert before previous sibling */\n          if (!i)\n            parent.replaceChild(node, this)\n          else\n            parent.insertBefore(this.previousSibling!, node)\n        }\n      }\n    }\n}\n"],
-  "mappings": 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e=="object"?(this.scrollLeft=e.left,this.scrollTop=e.top):(this.scrollLeft=e,this.scrollTop=t)}),Element.prototype.replaceWith||(Element.prototype.replaceWith=function(...e){let t=this.parentNode;if(t){e.length===0&&t.removeChild(this);for(let r=e.length-1;r>=0;r--){let n=e[r];typeof n=="string"?n=document.createTextNode(n):n.parentNode&&n.parentNode.removeChild(n),r?t.insertBefore(this.previousSibling,n):t.replaceChild(n,this)}}}));document.documentElement.classList.remove("no-js");document.documentElement.classList.add("js");var tt=go(),vr=ko(),gt=jo(),on=Ro(),we=qo(),gr=Kr("(min-width: 960px)"),Ki=Kr("(min-width: 1220px)"),Qi=Fo(),Yi=le(),Bi=document.forms.namedItem("search")?(__search==null?void 0:__search.index)||je(new URL("search/search_index.json",Yi.base)):Se,an=new E;wi({alert$:an});Z("navigation.instant")&&Ei({document$:tt,location$:vr,viewport$:we});var qi;((qi=Yi.version)==null?void 0:qi.provider)==="mike"&&Li({document$:tt});A(vr,gt).pipe(ke(125)).subscribe(()=>{Ke("drawer",!1),Ke("search",!1)});on.pipe(x(({mode:e})=>e==="global")).subscribe(e=>{switch(e.type){case"p":case",":let t=pe("[href][rel=prev]");typeof t!="undefined"&&t.click();break;case"n":case".":let r=pe("[href][rel=next]");typeof r!="undefined"&&r.click();break}});Vi({document$:tt,tablet$:gr});zi({document$:tt});Ni({viewport$:we,tablet$:gr});var Qe=bi(_e("header"),{viewport$:we}),br=tt.pipe(m(()=>_e("main")),S(e=>yi(e,{viewport$:we,header$:Qe})),X(1)),Ws=A(...te("consent").map(e=>Yo(e,{target$:gt})),...te("dialog").map(e=>hi(e,{alert$:an})),...te("header").map(e=>vi(e,{viewport$:we,header$:Qe,main$:br})),...te("palette").map(e=>xi(e)),...te("search").map(e=>Hi(e,{index$:Bi,keyboard$:on})),...te("source").map(e=>Fi(e))),Vs=P(()=>A(...te("announce").map(e=>Qo(e)),...te("content").map(e=>di(e,{viewport$:we,target$:gt,print$:Qi})),...te("content").map(e=>Z("search.highlight")?Pi(e,{index$:Bi,location$:vr}):R),...te("header-title").map(e=>gi(e,{viewport$:we,header$:Qe})),...te("sidebar").map(e=>e.getAttribute("data-md-type")==="navigation"?Qr(Ki,()=>nn(e,{viewport$:we,header$:Qe,main$:br})):Qr(gr,()=>nn(e,{viewport$:we,header$:Qe,main$:br}))),...te("tabs").map(e=>Ui(e,{viewport$:we,header$:Qe})),...te("toc").map(e=>Di(e,{viewport$:we,header$:Qe,target$:gt})),...te("top").map(e=>Wi(e,{viewport$:we,header$:Qe,main$:br,target$:gt})))),Gi=tt.pipe(S(()=>Vs),et(Ws),X(1));Gi.subscribe();window.document$=tt;window.location$=vr;window.target$=gt;window.keyboard$=on;window.viewport$=we;window.tablet$=gr;window.screen$=Ki;window.print$=Qi;window.alert$=an;window.component$=Gi;})();
+//# sourceMappingURL=bundle.d6c3db9e.min.js.map
+
diff --git a/assets/javascripts/bundle.d6c3db9e.min.js.map b/assets/javascripts/bundle.d6c3db9e.min.js.map
new file mode 100644
index 0000000000..d7449516ce
--- /dev/null
+++ b/assets/javascripts/bundle.d6c3db9e.min.js.map
@@ -0,0 +1,8 @@
+{
+  "version": 3,
+  "sources": ["node_modules/focus-visible/dist/focus-visible.js", "node_modules/url-polyfill/url-polyfill.js", "node_modules/rxjs/node_modules/tslib/tslib.js", "node_modules/clipboard/dist/clipboard.js", "node_modules/escape-html/index.js", "node_modules/array-flat-polyfill/index.mjs", "src/assets/javascripts/bundle.ts", "node_modules/unfetch/polyfill/index.js", "node_modules/rxjs/node_modules/tslib/modules/index.js", "node_modules/rxjs/src/internal/util/isFunction.ts", "node_modules/rxjs/src/internal/util/createErrorClass.ts", "node_modules/rxjs/src/internal/util/UnsubscriptionError.ts", "node_modules/rxjs/src/internal/util/arrRemove.ts", "node_modules/rxjs/src/internal/Subscription.ts", "node_modules/rxjs/src/internal/config.ts", "node_modules/rxjs/src/internal/scheduler/timeoutProvider.ts", "node_modules/rxjs/src/internal/util/reportUnhandledError.ts", "node_modules/rxjs/src/internal/util/noop.ts", "node_modules/rxjs/src/internal/NotificationFactories.ts", "node_modules/rxjs/src/internal/util/errorContext.ts", "node_modules/rxjs/src/internal/Subscriber.ts", "node_modules/rxjs/src/internal/symbol/observable.ts", "node_modules/rxjs/src/internal/util/identity.ts", "node_modules/rxjs/src/internal/util/pipe.ts", "node_modules/rxjs/src/internal/Observable.ts", "node_modules/rxjs/src/internal/util/lift.ts", "node_modules/rxjs/src/internal/operators/OperatorSubscriber.ts", "node_modules/rxjs/src/internal/scheduler/animationFrameProvider.ts", "node_modules/rxjs/src/internal/util/ObjectUnsubscribedError.ts", "node_modules/rxjs/src/internal/Subject.ts", "node_modules/rxjs/src/internal/scheduler/dateTimestampProvider.ts", "node_modules/rxjs/src/internal/ReplaySubject.ts", "node_modules/rxjs/src/internal/scheduler/Action.ts", "node_modules/rxjs/src/internal/scheduler/intervalProvider.ts", "node_modules/rxjs/src/internal/scheduler/AsyncAction.ts", "node_modules/rxjs/src/internal/Scheduler.ts", "node_modules/rxjs/src/internal/scheduler/AsyncScheduler.ts", "node_modules/rxjs/src/internal/scheduler/async.ts", "node_modules/rxjs/src/internal/scheduler/AnimationFrameAction.ts", "node_modules/rxjs/src/internal/scheduler/AnimationFrameScheduler.ts", "node_modules/rxjs/src/internal/scheduler/animationFrame.ts", "node_modules/rxjs/src/internal/observable/empty.ts", "node_modules/rxjs/src/internal/util/isScheduler.ts", "node_modules/rxjs/src/internal/util/args.ts", "node_modules/rxjs/src/internal/util/isArrayLike.ts", "node_modules/rxjs/src/internal/util/isPromise.ts", "node_modules/rxjs/src/internal/util/isInteropObservable.ts", "node_modules/rxjs/src/internal/util/isAsyncIterable.ts", "node_modules/rxjs/src/internal/util/throwUnobservableError.ts", "node_modules/rxjs/src/internal/symbol/iterator.ts", "node_modules/rxjs/src/internal/util/isIterable.ts", "node_modules/rxjs/src/internal/util/isReadableStreamLike.ts", "node_modules/rxjs/src/internal/observable/innerFrom.ts", "node_modules/rxjs/src/internal/util/executeSchedule.ts", "node_modules/rxjs/src/internal/operators/observeOn.ts", "node_modules/rxjs/src/internal/operators/subscribeOn.ts", "node_modules/rxjs/src/internal/scheduled/scheduleObservable.ts", "node_modules/rxjs/src/internal/scheduled/schedulePromise.ts", "node_modules/rxjs/src/internal/scheduled/scheduleArray.ts", "node_modules/rxjs/src/internal/scheduled/scheduleIterable.ts", "node_modules/rxjs/src/internal/scheduled/scheduleAsyncIterable.ts", "node_modules/rxjs/src/internal/scheduled/scheduleReadableStreamLike.ts", "node_modules/rxjs/src/internal/scheduled/scheduled.ts", "node_modules/rxjs/src/internal/observable/from.ts", "node_modules/rxjs/src/internal/observable/of.ts", "node_modules/rxjs/src/internal/observable/throwError.ts", "node_modules/rxjs/src/internal/util/isDate.ts", "node_modules/rxjs/src/internal/operators/map.ts", "node_modules/rxjs/src/internal/util/mapOneOrManyArgs.ts", "node_modules/rxjs/src/internal/util/argsArgArrayOrObject.ts", "node_modules/rxjs/src/internal/util/createObject.ts", "node_modules/rxjs/src/internal/observable/combineLatest.ts", "node_modules/rxjs/src/internal/operators/mergeInternals.ts", "node_modules/rxjs/src/internal/operators/mergeMap.ts", "node_modules/rxjs/src/internal/operators/mergeAll.ts", "node_modules/rxjs/src/internal/operators/concatAll.ts", "node_modules/rxjs/src/internal/observable/concat.ts", "node_modules/rxjs/src/internal/observable/defer.ts", "node_modules/rxjs/src/internal/observable/fromEvent.ts", "node_modules/rxjs/src/internal/observable/fromEventPattern.ts", "node_modules/rxjs/src/internal/observable/timer.ts", "node_modules/rxjs/src/internal/observable/merge.ts", "node_modules/rxjs/src/internal/observable/never.ts", "node_modules/rxjs/src/internal/util/argsOrArgArray.ts", "node_modules/rxjs/src/internal/operators/filter.ts", "node_modules/rxjs/src/internal/observable/zip.ts", "node_modules/rxjs/src/internal/operators/audit.ts", "node_modules/rxjs/src/internal/operators/auditTime.ts", "node_modules/rxjs/src/internal/operators/bufferCount.ts", "node_modules/rxjs/src/internal/operators/catchError.ts", "node_modules/rxjs/src/internal/operators/scanInternals.ts", "node_modules/rxjs/src/internal/operators/combineLatest.ts", "node_modules/rxjs/src/internal/operators/combineLatestWith.ts", "node_modules/rxjs/src/internal/operators/concatMap.ts", "node_modules/rxjs/src/internal/operators/debounceTime.ts", "node_modules/rxjs/src/internal/operators/defaultIfEmpty.ts", "node_modules/rxjs/src/internal/operators/take.ts", "node_modules/rxjs/src/internal/operators/ignoreElements.ts", "node_modules/rxjs/src/internal/operators/mapTo.ts", "node_modules/rxjs/src/internal/operators/delayWhen.ts", "node_modules/rxjs/src/internal/operators/delay.ts", "node_modules/rxjs/src/internal/operators/distinctUntilChanged.ts", "node_modules/rxjs/src/internal/operators/distinctUntilKeyChanged.ts", "node_modules/rxjs/src/internal/operators/endWith.ts", "node_modules/rxjs/src/internal/operators/finalize.ts", "node_modules/rxjs/src/internal/operators/takeLast.ts", "node_modules/rxjs/src/internal/operators/merge.ts", "node_modules/rxjs/src/internal/operators/mergeWith.ts", "node_modules/rxjs/src/internal/operators/repeat.ts", "node_modules/rxjs/src/internal/operators/sample.ts", "node_modules/rxjs/src/internal/operators/scan.ts", "node_modules/rxjs/src/internal/operators/share.ts", "node_modules/rxjs/src/internal/operators/shareReplay.ts", "node_modules/rxjs/src/internal/operators/skip.ts", "node_modules/rxjs/src/internal/operators/skipUntil.ts", "node_modules/rxjs/src/internal/operators/startWith.ts", "node_modules/rxjs/src/internal/operators/switchMap.ts", "node_modules/rxjs/src/internal/operators/takeUntil.ts", "node_modules/rxjs/src/internal/operators/takeWhile.ts", "node_modules/rxjs/src/internal/operators/tap.ts", "node_modules/rxjs/src/internal/operators/throttle.ts", "node_modules/rxjs/src/internal/operators/throttleTime.ts", "node_modules/rxjs/src/internal/operators/withLatestFrom.ts", "node_modules/rxjs/src/internal/operators/zip.ts", "node_modules/rxjs/src/internal/operators/zipWith.ts", "src/assets/javascripts/browser/document/index.ts", "src/assets/javascripts/browser/element/_/index.ts", "src/assets/javascripts/browser/element/focus/index.ts", "src/assets/javascripts/browser/element/offset/_/index.ts", "src/assets/javascripts/browser/element/offset/content/index.ts", "node_modules/resize-observer-polyfill/dist/ResizeObserver.es.js", "src/assets/javascripts/browser/element/size/_/index.ts", "src/assets/javascripts/browser/element/size/content/index.ts", "src/assets/javascripts/browser/element/visibility/index.ts", "src/assets/javascripts/browser/toggle/index.ts", "src/assets/javascripts/browser/keyboard/index.ts", "src/assets/javascripts/browser/location/_/index.ts", "src/assets/javascripts/utilities/h/index.ts", "src/assets/javascripts/utilities/string/index.ts", "src/assets/javascripts/browser/location/hash/index.ts", "src/assets/javascripts/browser/media/index.ts", "src/assets/javascripts/browser/request/index.ts", "src/assets/javascripts/browser/script/index.ts", "src/assets/javascripts/browser/viewport/offset/index.ts", "src/assets/javascripts/browser/viewport/size/index.ts", "src/assets/javascripts/browser/viewport/_/index.ts", "src/assets/javascripts/browser/viewport/at/index.ts", "src/assets/javascripts/browser/worker/index.ts", "src/assets/javascripts/_/index.ts", "src/assets/javascripts/components/_/index.ts", "src/assets/javascripts/components/announce/index.ts", "src/assets/javascripts/components/consent/index.ts", "src/assets/javascripts/components/content/code/_/index.ts", "src/assets/javascripts/templates/tooltip/index.tsx", "src/assets/javascripts/templates/annotation/index.tsx", "src/assets/javascripts/templates/clipboard/index.tsx", "src/assets/javascripts/templates/search/index.tsx", "src/assets/javascripts/templates/source/index.tsx", "src/assets/javascripts/templates/tabbed/index.tsx", "src/assets/javascripts/templates/table/index.tsx", "src/assets/javascripts/templates/version/index.tsx", "src/assets/javascripts/components/content/annotation/_/index.ts", "src/assets/javascripts/components/content/annotation/list/index.ts", "src/assets/javascripts/components/content/code/mermaid/index.ts", "src/assets/javascripts/components/content/details/index.ts", "src/assets/javascripts/components/content/table/index.ts", "src/assets/javascripts/components/content/tabs/index.ts", "src/assets/javascripts/components/content/_/index.ts", "src/assets/javascripts/components/dialog/index.ts", "src/assets/javascripts/components/header/_/index.ts", "src/assets/javascripts/components/header/title/index.ts", "src/assets/javascripts/components/main/index.ts", "src/assets/javascripts/components/palette/index.ts", "src/assets/javascripts/integrations/clipboard/index.ts", "src/assets/javascripts/integrations/sitemap/index.ts", "src/assets/javascripts/integrations/instant/index.ts", "src/assets/javascripts/integrations/search/document/index.ts", "src/assets/javascripts/integrations/search/highlighter/index.ts", "src/assets/javascripts/integrations/search/query/transform/index.ts", "src/assets/javascripts/integrations/search/worker/message/index.ts", "src/assets/javascripts/integrations/search/worker/_/index.ts", "src/assets/javascripts/integrations/version/index.ts", "src/assets/javascripts/components/search/query/index.ts", "src/assets/javascripts/components/search/result/index.ts", "src/assets/javascripts/components/search/share/index.ts", "src/assets/javascripts/components/search/suggest/index.ts", "src/assets/javascripts/components/search/_/index.ts", "src/assets/javascripts/components/search/highlight/index.ts", "src/assets/javascripts/components/sidebar/index.ts", "src/assets/javascripts/components/source/facts/github/index.ts", "src/assets/javascripts/components/source/facts/gitlab/index.ts", "src/assets/javascripts/components/source/facts/_/index.ts", "src/assets/javascripts/components/source/_/index.ts", "src/assets/javascripts/components/tabs/index.ts", "src/assets/javascripts/components/toc/index.ts", "src/assets/javascripts/components/top/index.ts", "src/assets/javascripts/patches/indeterminate/index.ts", "src/assets/javascripts/patches/scrollfix/index.ts", "src/assets/javascripts/patches/scrolllock/index.ts", "src/assets/javascripts/polyfills/index.ts"],
+  "sourceRoot": "../../../..",
+  "sourcesContent": ["(function (global, factory) {\n  typeof exports === 'object' && typeof module !== 'undefined' ? factory() :\n  typeof define === 'function' && define.amd ? define(factory) :\n  (factory());\n}(this, (function () { 'use strict';\n\n  /**\n   * Applies the :focus-visible polyfill at the given scope.\n   * A scope in this case is either the top-level Document or a Shadow Root.\n   *\n   * @param {(Document|ShadowRoot)} scope\n   * @see https://github.com/WICG/focus-visible\n   */\n  function applyFocusVisiblePolyfill(scope) {\n    var hadKeyboardEvent = true;\n    var hadFocusVisibleRecently = false;\n    var hadFocusVisibleRecentlyTimeout = null;\n\n    var inputTypesAllowlist = {\n      text: true,\n      search: true,\n      url: true,\n      tel: true,\n      email: true,\n      password: true,\n      number: true,\n      date: true,\n      month: true,\n      week: true,\n      time: true,\n      datetime: true,\n      'datetime-local': true\n    };\n\n    /**\n     * Helper function for legacy browsers and iframes which sometimes focus\n     * elements like document, body, and non-interactive SVG.\n     * @param {Element} el\n     */\n    function isValidFocusTarget(el) {\n      if (\n        el &&\n        el !== document &&\n        el.nodeName !== 'HTML' &&\n        el.nodeName !== 'BODY' &&\n        'classList' in el &&\n        'contains' in el.classList\n      ) {\n        return true;\n      }\n      return false;\n    }\n\n    /**\n     * Computes whether the given element should automatically trigger the\n     * `focus-visible` class being added, i.e. whether it should always match\n     * `:focus-visible` when focused.\n     * @param {Element} el\n     * @return {boolean}\n     */\n    function focusTriggersKeyboardModality(el) {\n      var type = el.type;\n      var tagName = el.tagName;\n\n      if (tagName === 'INPUT' && inputTypesAllowlist[type] && !el.readOnly) {\n        return true;\n      }\n\n      if (tagName === 'TEXTAREA' && !el.readOnly) {\n        return true;\n      }\n\n      if (el.isContentEditable) {\n        return true;\n      }\n\n      return false;\n    }\n\n    /**\n     * Add the `focus-visible` class to the given element if it was not added by\n     * the author.\n     * @param {Element} el\n     */\n    function addFocusVisibleClass(el) {\n      if (el.classList.contains('focus-visible')) {\n        return;\n      }\n      el.classList.add('focus-visible');\n      el.setAttribute('data-focus-visible-added', '');\n    }\n\n    /**\n     * Remove the `focus-visible` class from the given element if it was not\n     * originally added by the author.\n     * @param {Element} el\n     */\n    function removeFocusVisibleClass(el) {\n      if (!el.hasAttribute('data-focus-visible-added')) {\n        return;\n      }\n      el.classList.remove('focus-visible');\n      el.removeAttribute('data-focus-visible-added');\n    }\n\n    /**\n     * If the most recent user interaction was via the keyboard;\n     * and the key press did not include a meta, alt/option, or control key;\n     * then the modality is keyboard. Otherwise, the modality is not keyboard.\n     * Apply `focus-visible` to any current active element and keep track\n     * of our keyboard modality state with `hadKeyboardEvent`.\n     * @param {KeyboardEvent} e\n     */\n    function onKeyDown(e) {\n      if (e.metaKey || e.altKey || e.ctrlKey) {\n        return;\n      }\n\n      if (isValidFocusTarget(scope.activeElement)) {\n        addFocusVisibleClass(scope.activeElement);\n      }\n\n      hadKeyboardEvent = true;\n    }\n\n    /**\n     * If at any point a user clicks with a pointing device, ensure that we change\n     * the modality away from keyboard.\n     * This avoids the situation where a user presses a key on an already focused\n     * element, and then clicks on a different element, focusing it with a\n     * pointing device, while we still think we're in keyboard modality.\n     * @param {Event} e\n     */\n    function onPointerDown(e) {\n      hadKeyboardEvent = false;\n    }\n\n    /**\n     * On `focus`, add the `focus-visible` class to the target if:\n     * - the target received focus as a result of keyboard navigation, or\n     * - the event target is an element that will likely require interaction\n     *   via the keyboard (e.g. a text box)\n     * @param {Event} e\n     */\n    function onFocus(e) {\n      // Prevent IE from focusing the document or HTML element.\n      if (!isValidFocusTarget(e.target)) {\n        return;\n      }\n\n      if (hadKeyboardEvent || focusTriggersKeyboardModality(e.target)) {\n        addFocusVisibleClass(e.target);\n      }\n    }\n\n    /**\n     * On `blur`, remove the `focus-visible` class from the target.\n     * @param {Event} e\n     */\n    function onBlur(e) {\n      if (!isValidFocusTarget(e.target)) {\n        return;\n      }\n\n      if (\n        e.target.classList.contains('focus-visible') ||\n        e.target.hasAttribute('data-focus-visible-added')\n      ) {\n        // To detect a tab/window switch, we look for a blur event followed\n        // rapidly by a visibility change.\n        // If we don't see a visibility change within 100ms, it's probably a\n        // regular focus change.\n        hadFocusVisibleRecently = true;\n        window.clearTimeout(hadFocusVisibleRecentlyTimeout);\n        hadFocusVisibleRecentlyTimeout = window.setTimeout(function() {\n          hadFocusVisibleRecently = false;\n        }, 100);\n        removeFocusVisibleClass(e.target);\n      }\n    }\n\n    /**\n     * If the user changes tabs, keep track of whether or not the previously\n     * focused element had .focus-visible.\n     * @param {Event} e\n     */\n    function onVisibilityChange(e) {\n      if (document.visibilityState === 'hidden') {\n        // If the tab becomes active again, the browser will handle calling focus\n        // on the element (Safari actually calls it twice).\n        // If this tab change caused a blur on an element with focus-visible,\n        // re-apply the class when the user switches back to the tab.\n        if (hadFocusVisibleRecently) {\n          hadKeyboardEvent = true;\n        }\n        addInitialPointerMoveListeners();\n      }\n    }\n\n    /**\n     * Add a group of listeners to detect usage of any pointing devices.\n     * These listeners will be added when the polyfill first loads, and anytime\n     * the window is blurred, so that they are active when the window regains\n     * focus.\n     */\n    function addInitialPointerMoveListeners() {\n      document.addEventListener('mousemove', onInitialPointerMove);\n      document.addEventListener('mousedown', onInitialPointerMove);\n      document.addEventListener('mouseup', onInitialPointerMove);\n      document.addEventListener('pointermove', onInitialPointerMove);\n      document.addEventListener('pointerdown', onInitialPointerMove);\n      document.addEventListener('pointerup', onInitialPointerMove);\n      document.addEventListener('touchmove', onInitialPointerMove);\n      document.addEventListener('touchstart', onInitialPointerMove);\n      document.addEventListener('touchend', onInitialPointerMove);\n    }\n\n    function removeInitialPointerMoveListeners() {\n      document.removeEventListener('mousemove', onInitialPointerMove);\n      document.removeEventListener('mousedown', onInitialPointerMove);\n      document.removeEventListener('mouseup', onInitialPointerMove);\n      document.removeEventListener('pointermove', onInitialPointerMove);\n      document.removeEventListener('pointerdown', onInitialPointerMove);\n      document.removeEventListener('pointerup', onInitialPointerMove);\n      document.removeEventListener('touchmove', onInitialPointerMove);\n      document.removeEventListener('touchstart', onInitialPointerMove);\n      document.removeEventListener('touchend', onInitialPointerMove);\n    }\n\n    /**\n     * When the polfyill first loads, assume the user is in keyboard modality.\n     * If any event is received from a pointing device (e.g. mouse, pointer,\n     * touch), turn off keyboard modality.\n     * This accounts for situations where focus enters the page from the URL bar.\n     * @param {Event} e\n     */\n    function onInitialPointerMove(e) {\n      // Work around a Safari quirk that fires a mousemove on <html> whenever the\n      // window blurs, even if you're tabbing out of the page. \u00AF\\_(\u30C4)_/\u00AF\n      if (e.target.nodeName && e.target.nodeName.toLowerCase() === 'html') {\n        return;\n      }\n\n      hadKeyboardEvent = false;\n      removeInitialPointerMoveListeners();\n    }\n\n    // For some kinds of state, we are interested in changes at the global scope\n    // only. For example, global pointer input, global key presses and global\n    // visibility change should affect the state at every scope:\n    document.addEventListener('keydown', onKeyDown, true);\n    document.addEventListener('mousedown', onPointerDown, true);\n    document.addEventListener('pointerdown', onPointerDown, true);\n    document.addEventListener('touchstart', onPointerDown, true);\n    document.addEventListener('visibilitychange', onVisibilityChange, true);\n\n    addInitialPointerMoveListeners();\n\n    // For focus and blur, we specifically care about state changes in the local\n    // scope. This is because focus / blur events that originate from within a\n    // shadow root are not re-dispatched from the host element if it was already\n    // the active element in its own scope:\n    scope.addEventListener('focus', onFocus, true);\n    scope.addEventListener('blur', onBlur, true);\n\n    // We detect that a node is a ShadowRoot by ensuring that it is a\n    // DocumentFragment and also has a host property. This check covers native\n    // implementation and polyfill implementation transparently. If we only cared\n    // about the native implementation, we could just check if the scope was\n    // an instance of a ShadowRoot.\n    if (scope.nodeType === Node.DOCUMENT_FRAGMENT_NODE && scope.host) {\n      // Since a ShadowRoot is a special kind of DocumentFragment, it does not\n      // have a root element to add a class to. So, we add this attribute to the\n      // host element instead:\n      scope.host.setAttribute('data-js-focus-visible', '');\n    } else if (scope.nodeType === Node.DOCUMENT_NODE) {\n      document.documentElement.classList.add('js-focus-visible');\n      document.documentElement.setAttribute('data-js-focus-visible', '');\n    }\n  }\n\n  // It is important to wrap all references to global window and document in\n  // these checks to support server-side rendering use cases\n  // @see https://github.com/WICG/focus-visible/issues/199\n  if (typeof window !== 'undefined' && typeof document !== 'undefined') {\n    // Make the polyfill helper globally available. This can be used as a signal\n    // to interested libraries that wish to coordinate with the polyfill for e.g.,\n    // applying the polyfill to a shadow root:\n    window.applyFocusVisiblePolyfill = applyFocusVisiblePolyfill;\n\n    // Notify interested libraries of the polyfill's presence, in case the\n    // polyfill was loaded lazily:\n    var event;\n\n    try {\n      event = new CustomEvent('focus-visible-polyfill-ready');\n    } catch (error) {\n      // IE11 does not support using CustomEvent as a constructor directly:\n      event = document.createEvent('CustomEvent');\n      event.initCustomEvent('focus-visible-polyfill-ready', false, false, {});\n    }\n\n    window.dispatchEvent(event);\n  }\n\n  if (typeof document !== 'undefined') {\n    // Apply the polyfill to the global document, so that no JavaScript\n    // coordination is required to use the polyfill in the top-level document:\n    applyFocusVisiblePolyfill(document);\n  }\n\n})));\n", "(function(global) {\r\n  /**\r\n   * Polyfill URLSearchParams\r\n   *\r\n   * Inspired from : https://github.com/WebReflection/url-search-params/blob/master/src/url-search-params.js\r\n   */\r\n\r\n  var checkIfIteratorIsSupported = function() {\r\n    try {\r\n      return !!Symbol.iterator;\r\n    } catch (error) {\r\n      return false;\r\n    }\r\n  };\r\n\r\n\r\n  var iteratorSupported = checkIfIteratorIsSupported();\r\n\r\n  var createIterator = function(items) {\r\n    var iterator = {\r\n      next: function() {\r\n        var value = items.shift();\r\n        return { done: value === void 0, value: value };\r\n      }\r\n    };\r\n\r\n    if (iteratorSupported) {\r\n      iterator[Symbol.iterator] = function() {\r\n        return iterator;\r\n      };\r\n    }\r\n\r\n    return iterator;\r\n  };\r\n\r\n  /**\r\n   * Search param name and values should be encoded according to https://url.spec.whatwg.org/#urlencoded-serializing\r\n   * encodeURIComponent() produces the same result except encoding spaces as `%20` instead of `+`.\r\n   */\r\n  var serializeParam = function(value) {\r\n    return encodeURIComponent(value).replace(/%20/g, '+');\r\n  };\r\n\r\n  var deserializeParam = function(value) {\r\n    return decodeURIComponent(String(value).replace(/\\+/g, ' '));\r\n  };\r\n\r\n  var polyfillURLSearchParams = function() {\r\n\r\n    var URLSearchParams = function(searchString) {\r\n      Object.defineProperty(this, '_entries', { writable: true, value: {} });\r\n      var typeofSearchString = typeof searchString;\r\n\r\n      if (typeofSearchString === 'undefined') {\r\n        // do nothing\r\n      } else if (typeofSearchString === 'string') {\r\n        if (searchString !== '') {\r\n          this._fromString(searchString);\r\n        }\r\n      } else if (searchString instanceof URLSearchParams) {\r\n        var _this = this;\r\n        searchString.forEach(function(value, name) {\r\n          _this.append(name, value);\r\n        });\r\n      } else if ((searchString !== null) && (typeofSearchString === 'object')) {\r\n        if (Object.prototype.toString.call(searchString) === '[object Array]') {\r\n          for (var i = 0; i < searchString.length; i++) {\r\n            var entry = searchString[i];\r\n            if ((Object.prototype.toString.call(entry) === '[object Array]') || (entry.length !== 2)) {\r\n              this.append(entry[0], entry[1]);\r\n            } else {\r\n              throw new TypeError('Expected [string, any] as entry at index ' + i + ' of URLSearchParams\\'s input');\r\n            }\r\n          }\r\n        } else {\r\n          for (var key in searchString) {\r\n            if (searchString.hasOwnProperty(key)) {\r\n              this.append(key, searchString[key]);\r\n            }\r\n          }\r\n        }\r\n      } else {\r\n        throw new TypeError('Unsupported input\\'s type for URLSearchParams');\r\n      }\r\n    };\r\n\r\n    var proto = URLSearchParams.prototype;\r\n\r\n    proto.append = function(name, value) {\r\n      if (name in this._entries) {\r\n        this._entries[name].push(String(value));\r\n      } else {\r\n        this._entries[name] = [String(value)];\r\n      }\r\n    };\r\n\r\n    proto.delete = function(name) {\r\n      delete this._entries[name];\r\n    };\r\n\r\n    proto.get = function(name) {\r\n      return (name in this._entries) ? this._entries[name][0] : null;\r\n    };\r\n\r\n    proto.getAll = function(name) {\r\n      return (name in this._entries) ? this._entries[name].slice(0) : [];\r\n    };\r\n\r\n    proto.has = function(name) {\r\n      return (name in this._entries);\r\n    };\r\n\r\n    proto.set = function(name, value) {\r\n      this._entries[name] = [String(value)];\r\n    };\r\n\r\n    proto.forEach = function(callback, thisArg) {\r\n      var entries;\r\n      for (var name in this._entries) {\r\n        if (this._entries.hasOwnProperty(name)) {\r\n          entries = this._entries[name];\r\n          for (var i = 0; i < entries.length; i++) {\r\n            callback.call(thisArg, entries[i], name, this);\r\n          }\r\n        }\r\n      }\r\n    };\r\n\r\n    proto.keys = function() {\r\n      var items = [];\r\n      this.forEach(function(value, name) {\r\n        items.push(name);\r\n      });\r\n      return createIterator(items);\r\n    };\r\n\r\n    proto.values = function() {\r\n      var items = [];\r\n      this.forEach(function(value) {\r\n        items.push(value);\r\n      });\r\n      return createIterator(items);\r\n    };\r\n\r\n    proto.entries = function() {\r\n      var items = [];\r\n      this.forEach(function(value, name) {\r\n        items.push([name, value]);\r\n      });\r\n      return createIterator(items);\r\n    };\r\n\r\n    if (iteratorSupported) {\r\n      proto[Symbol.iterator] = proto.entries;\r\n    }\r\n\r\n    proto.toString = function() {\r\n      var searchArray = [];\r\n      this.forEach(function(value, name) {\r\n        searchArray.push(serializeParam(name) + '=' + serializeParam(value));\r\n      });\r\n      return searchArray.join('&');\r\n    };\r\n\r\n\r\n    global.URLSearchParams = URLSearchParams;\r\n  };\r\n\r\n  var checkIfURLSearchParamsSupported = function() {\r\n    try {\r\n      var URLSearchParams = global.URLSearchParams;\r\n\r\n      return (\r\n        (new URLSearchParams('?a=1').toString() === 'a=1') &&\r\n        (typeof URLSearchParams.prototype.set === 'function') &&\r\n        (typeof URLSearchParams.prototype.entries === 'function')\r\n      );\r\n    } catch (e) {\r\n      return false;\r\n    }\r\n  };\r\n\r\n  if (!checkIfURLSearchParamsSupported()) {\r\n    polyfillURLSearchParams();\r\n  }\r\n\r\n  var proto = global.URLSearchParams.prototype;\r\n\r\n  if (typeof proto.sort !== 'function') {\r\n    proto.sort = function() {\r\n      var _this = this;\r\n      var items = [];\r\n      this.forEach(function(value, name) {\r\n        items.push([name, value]);\r\n        if (!_this._entries) {\r\n          _this.delete(name);\r\n        }\r\n      });\r\n      items.sort(function(a, b) {\r\n        if (a[0] < b[0]) {\r\n          return -1;\r\n        } else if (a[0] > b[0]) {\r\n          return +1;\r\n        } else {\r\n          return 0;\r\n        }\r\n      });\r\n      if (_this._entries) { // force reset because IE keeps keys index\r\n        _this._entries = {};\r\n      }\r\n      for (var i = 0; i < items.length; i++) {\r\n        this.append(items[i][0], items[i][1]);\r\n      }\r\n    };\r\n  }\r\n\r\n  if (typeof proto._fromString !== 'function') {\r\n    Object.defineProperty(proto, '_fromString', {\r\n      enumerable: false,\r\n      configurable: false,\r\n      writable: false,\r\n      value: function(searchString) {\r\n        if (this._entries) {\r\n          this._entries = {};\r\n        } else {\r\n          var keys = [];\r\n          this.forEach(function(value, name) {\r\n            keys.push(name);\r\n          });\r\n          for (var i = 0; i < keys.length; i++) {\r\n            this.delete(keys[i]);\r\n          }\r\n        }\r\n\r\n        searchString = searchString.replace(/^\\?/, '');\r\n        var attributes = searchString.split('&');\r\n        var attribute;\r\n        for (var i = 0; i < attributes.length; i++) {\r\n          attribute = attributes[i].split('=');\r\n          this.append(\r\n            deserializeParam(attribute[0]),\r\n            (attribute.length > 1) ? deserializeParam(attribute[1]) : ''\r\n          );\r\n        }\r\n      }\r\n    });\r\n  }\r\n\r\n  // HTMLAnchorElement\r\n\r\n})(\r\n  (typeof global !== 'undefined') ? global\r\n    : ((typeof window !== 'undefined') ? window\r\n    : ((typeof self !== 'undefined') ? self : this))\r\n);\r\n\r\n(function(global) {\r\n  /**\r\n   * Polyfill URL\r\n   *\r\n   * Inspired from : https://github.com/arv/DOM-URL-Polyfill/blob/master/src/url.js\r\n   */\r\n\r\n  var checkIfURLIsSupported = function() {\r\n    try {\r\n      var u = new global.URL('b', 'http://a');\r\n      u.pathname = 'c d';\r\n      return (u.href === 'http://a/c%20d') && u.searchParams;\r\n    } catch (e) {\r\n      return false;\r\n    }\r\n  };\r\n\r\n\r\n  var polyfillURL = function() {\r\n    var _URL = global.URL;\r\n\r\n    var URL = function(url, base) {\r\n      if (typeof url !== 'string') url = String(url);\r\n      if (base && typeof base !== 'string') base = String(base);\r\n\r\n      // Only create another document if the base is different from current location.\r\n      var doc = document, baseElement;\r\n      if (base && (global.location === void 0 || base !== global.location.href)) {\r\n        base = base.toLowerCase();\r\n        doc = document.implementation.createHTMLDocument('');\r\n        baseElement = doc.createElement('base');\r\n        baseElement.href = base;\r\n        doc.head.appendChild(baseElement);\r\n        try {\r\n          if (baseElement.href.indexOf(base) !== 0) throw new Error(baseElement.href);\r\n        } catch (err) {\r\n          throw new Error('URL unable to set base ' + base + ' due to ' + err);\r\n        }\r\n      }\r\n\r\n      var anchorElement = doc.createElement('a');\r\n      anchorElement.href = url;\r\n      if (baseElement) {\r\n        doc.body.appendChild(anchorElement);\r\n        anchorElement.href = anchorElement.href; // force href to refresh\r\n      }\r\n\r\n      var inputElement = doc.createElement('input');\r\n      inputElement.type = 'url';\r\n      inputElement.value = url;\r\n\r\n      if (anchorElement.protocol === ':' || !/:/.test(anchorElement.href) || (!inputElement.checkValidity() && !base)) {\r\n        throw new TypeError('Invalid URL');\r\n      }\r\n\r\n      Object.defineProperty(this, '_anchorElement', {\r\n        value: anchorElement\r\n      });\r\n\r\n\r\n      // create a linked searchParams which reflect its changes on URL\r\n      var searchParams = new global.URLSearchParams(this.search);\r\n      var enableSearchUpdate = true;\r\n      var enableSearchParamsUpdate = true;\r\n      var _this = this;\r\n      ['append', 'delete', 'set'].forEach(function(methodName) {\r\n        var method = searchParams[methodName];\r\n        searchParams[methodName] = function() {\r\n          method.apply(searchParams, arguments);\r\n          if (enableSearchUpdate) {\r\n            enableSearchParamsUpdate = false;\r\n            _this.search = searchParams.toString();\r\n            enableSearchParamsUpdate = true;\r\n          }\r\n        };\r\n      });\r\n\r\n      Object.defineProperty(this, 'searchParams', {\r\n        value: searchParams,\r\n        enumerable: true\r\n      });\r\n\r\n      var search = void 0;\r\n      Object.defineProperty(this, '_updateSearchParams', {\r\n        enumerable: false,\r\n        configurable: false,\r\n        writable: false,\r\n        value: function() {\r\n          if (this.search !== search) {\r\n            search = this.search;\r\n            if (enableSearchParamsUpdate) {\r\n              enableSearchUpdate = false;\r\n              this.searchParams._fromString(this.search);\r\n              enableSearchUpdate = true;\r\n            }\r\n          }\r\n        }\r\n      });\r\n    };\r\n\r\n    var proto = URL.prototype;\r\n\r\n    var linkURLWithAnchorAttribute = function(attributeName) {\r\n      Object.defineProperty(proto, attributeName, {\r\n        get: function() {\r\n          return this._anchorElement[attributeName];\r\n        },\r\n        set: function(value) {\r\n          this._anchorElement[attributeName] = value;\r\n        },\r\n        enumerable: true\r\n      });\r\n    };\r\n\r\n    ['hash', 'host', 'hostname', 'port', 'protocol']\r\n      .forEach(function(attributeName) {\r\n        linkURLWithAnchorAttribute(attributeName);\r\n      });\r\n\r\n    Object.defineProperty(proto, 'search', {\r\n      get: function() {\r\n        return this._anchorElement['search'];\r\n      },\r\n      set: function(value) {\r\n        this._anchorElement['search'] = value;\r\n        this._updateSearchParams();\r\n      },\r\n      enumerable: true\r\n    });\r\n\r\n    Object.defineProperties(proto, {\r\n\r\n      'toString': {\r\n        get: function() {\r\n          var _this = this;\r\n          return function() {\r\n            return _this.href;\r\n          };\r\n        }\r\n      },\r\n\r\n      'href': {\r\n        get: function() {\r\n          return this._anchorElement.href.replace(/\\?$/, '');\r\n        },\r\n        set: function(value) {\r\n          this._anchorElement.href = value;\r\n          this._updateSearchParams();\r\n        },\r\n        enumerable: true\r\n      },\r\n\r\n      'pathname': {\r\n        get: function() {\r\n          return this._anchorElement.pathname.replace(/(^\\/?)/, '/');\r\n        },\r\n        set: function(value) {\r\n          this._anchorElement.pathname = value;\r\n        },\r\n        enumerable: true\r\n      },\r\n\r\n      'origin': {\r\n        get: function() {\r\n          // get expected port from protocol\r\n          var expectedPort = { 'http:': 80, 'https:': 443, 'ftp:': 21 }[this._anchorElement.protocol];\r\n          // add port to origin if, expected port is different than actual port\r\n          // and it is not empty f.e http://foo:8080\r\n          // 8080 != 80 && 8080 != ''\r\n          var addPortToOrigin = this._anchorElement.port != expectedPort &&\r\n            this._anchorElement.port !== '';\r\n\r\n          return this._anchorElement.protocol +\r\n            '//' +\r\n            this._anchorElement.hostname +\r\n            (addPortToOrigin ? (':' + this._anchorElement.port) : '');\r\n        },\r\n        enumerable: true\r\n      },\r\n\r\n      'password': { // TODO\r\n        get: function() {\r\n          return '';\r\n        },\r\n        set: function(value) {\r\n        },\r\n        enumerable: true\r\n      },\r\n\r\n      'username': { // TODO\r\n        get: function() {\r\n          return '';\r\n        },\r\n        set: function(value) {\r\n        },\r\n        enumerable: true\r\n      },\r\n    });\r\n\r\n    URL.createObjectURL = function(blob) {\r\n      return _URL.createObjectURL.apply(_URL, arguments);\r\n    };\r\n\r\n    URL.revokeObjectURL = function(url) {\r\n      return _URL.revokeObjectURL.apply(_URL, arguments);\r\n    };\r\n\r\n    global.URL = URL;\r\n\r\n  };\r\n\r\n  if (!checkIfURLIsSupported()) {\r\n    polyfillURL();\r\n  }\r\n\r\n  if ((global.location !== void 0) && !('origin' in global.location)) {\r\n    var getOrigin = function() {\r\n      return global.location.protocol + '//' + global.location.hostname + (global.location.port ? 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Simulate it. https://developer.mozilla.org/en-US/docs/Web/API/HTMLInputElement/setSelectionRange\n    selectedText = fakeCopyAction(target.value, options);\n  } else {\n    selectedText = select_default()(target);\n    command('copy');\n  }\n\n  return selectedText;\n};\n\n/* harmony default export */ var actions_copy = (ClipboardActionCopy);\n;// CONCATENATED MODULE: ./src/actions/default.js\nfunction _typeof(obj) { \"@babel/helpers - typeof\"; if (typeof Symbol === \"function\" && typeof Symbol.iterator === \"symbol\") { _typeof = function _typeof(obj) { return typeof obj; }; } else { _typeof = function _typeof(obj) { return obj && typeof Symbol === \"function\" && obj.constructor === Symbol && obj !== Symbol.prototype ? \"symbol\" : typeof obj; }; } return _typeof(obj); }\n\n\n\n/**\n * Inner function which performs selection from either `text` or `target`\n * properties and then executes copy or cut operations.\n * @param {Object} options\n */\n\nvar ClipboardActionDefault = function ClipboardActionDefault() {\n  var options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};\n  // Defines base properties passed from constructor.\n  var _options$action = options.action,\n      action = _options$action === void 0 ? 'copy' : _options$action,\n      container = options.container,\n      target = options.target,\n      text = options.text; // Sets the `action` to be performed which can be either 'copy' or 'cut'.\n\n  if (action !== 'copy' && action !== 'cut') {\n    throw new Error('Invalid \"action\" value, use either \"copy\" or \"cut\"');\n  } // Sets the `target` property using an element that will be have its content copied.\n\n\n  if (target !== undefined) {\n    if (target && _typeof(target) === 'object' && target.nodeType === 1) {\n      if (action === 'copy' && target.hasAttribute('disabled')) {\n        throw new Error('Invalid \"target\" attribute. Please use \"readonly\" instead of \"disabled\" attribute');\n      }\n\n      if (action === 'cut' && (target.hasAttribute('readonly') || target.hasAttribute('disabled'))) {\n        throw new Error('Invalid \"target\" attribute. You can\\'t cut text from elements with \"readonly\" or \"disabled\" attributes');\n      }\n    } else {\n      throw new Error('Invalid \"target\" value, use a valid Element');\n    }\n  } // Define selection strategy based on `text` property.\n\n\n  if (text) {\n    return actions_copy(text, {\n      container: container\n    });\n  } // Defines which selection strategy based on `target` property.\n\n\n  if (target) {\n    return action === 'cut' ? actions_cut(target) : actions_copy(target, {\n      container: container\n    });\n  }\n};\n\n/* harmony default export */ var actions_default = (ClipboardActionDefault);\n;// CONCATENATED MODULE: ./src/clipboard.js\nfunction clipboard_typeof(obj) { \"@babel/helpers - typeof\"; if (typeof Symbol === \"function\" && typeof Symbol.iterator === \"symbol\") { clipboard_typeof = function _typeof(obj) { return typeof obj; }; } else { clipboard_typeof = function _typeof(obj) { return obj && typeof Symbol === \"function\" && obj.constructor === Symbol && obj !== Symbol.prototype ? \"symbol\" : typeof obj; }; } return clipboard_typeof(obj); }\n\nfunction _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError(\"Cannot call a class as a function\"); } }\n\nfunction _defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if (\"value\" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } }\n\nfunction _createClass(Constructor, protoProps, staticProps) { if (protoProps) _defineProperties(Constructor.prototype, protoProps); if (staticProps) _defineProperties(Constructor, staticProps); return Constructor; }\n\nfunction _inherits(subClass, superClass) { if (typeof superClass !== \"function\" && superClass !== null) { throw new TypeError(\"Super expression must either be null or a function\"); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, writable: true, configurable: true } }); if (superClass) _setPrototypeOf(subClass, superClass); }\n\nfunction _setPrototypeOf(o, p) { _setPrototypeOf = Object.setPrototypeOf || function _setPrototypeOf(o, p) { o.__proto__ = p; return o; }; return _setPrototypeOf(o, p); }\n\nfunction _createSuper(Derived) { var hasNativeReflectConstruct = _isNativeReflectConstruct(); return function _createSuperInternal() { var Super = _getPrototypeOf(Derived), result; if (hasNativeReflectConstruct) { var NewTarget = _getPrototypeOf(this).constructor; result = Reflect.construct(Super, arguments, NewTarget); } else { result = Super.apply(this, arguments); } return _possibleConstructorReturn(this, result); }; }\n\nfunction _possibleConstructorReturn(self, call) { if (call && (clipboard_typeof(call) === \"object\" || typeof call === \"function\")) { return call; } return _assertThisInitialized(self); }\n\nfunction _assertThisInitialized(self) { if (self === void 0) { throw new ReferenceError(\"this hasn't been initialised - super() hasn't been called\"); } return self; }\n\nfunction _isNativeReflectConstruct() { if (typeof Reflect === \"undefined\" || !Reflect.construct) return false; if (Reflect.construct.sham) return false; if (typeof Proxy === \"function\") return true; try { Date.prototype.toString.call(Reflect.construct(Date, [], function () {})); return true; } catch (e) { return false; } }\n\nfunction _getPrototypeOf(o) { _getPrototypeOf = Object.setPrototypeOf ? Object.getPrototypeOf : function _getPrototypeOf(o) { return o.__proto__ || Object.getPrototypeOf(o); }; return _getPrototypeOf(o); }\n\n\n\n\n\n\n/**\n * Helper function to retrieve attribute value.\n * @param {String} suffix\n * @param {Element} element\n */\n\nfunction getAttributeValue(suffix, element) {\n  var attribute = \"data-clipboard-\".concat(suffix);\n\n  if (!element.hasAttribute(attribute)) {\n    return;\n  }\n\n  return element.getAttribute(attribute);\n}\n/**\n * Base class which takes one or more elements, adds event listeners to them,\n * and instantiates a new `ClipboardAction` on each click.\n */\n\n\nvar Clipboard = /*#__PURE__*/function (_Emitter) {\n  _inherits(Clipboard, _Emitter);\n\n  var _super = _createSuper(Clipboard);\n\n  /**\n   * @param {String|HTMLElement|HTMLCollection|NodeList} trigger\n   * @param {Object} options\n   */\n  function Clipboard(trigger, options) {\n    var _this;\n\n    _classCallCheck(this, Clipboard);\n\n    _this = _super.call(this);\n\n    _this.resolveOptions(options);\n\n    _this.listenClick(trigger);\n\n    return _this;\n  }\n  /**\n   * Defines if attributes would be resolved using internal setter functions\n   * or custom functions that were passed in the constructor.\n   * @param {Object} options\n   */\n\n\n  _createClass(Clipboard, [{\n    key: \"resolveOptions\",\n    value: function resolveOptions() {\n      var options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};\n      this.action = typeof options.action === 'function' ? options.action : this.defaultAction;\n      this.target = typeof options.target === 'function' ? options.target : this.defaultTarget;\n      this.text = typeof options.text === 'function' ? options.text : this.defaultText;\n      this.container = clipboard_typeof(options.container) === 'object' ? options.container : document.body;\n    }\n    /**\n     * Adds a click event listener to the passed trigger.\n     * @param {String|HTMLElement|HTMLCollection|NodeList} trigger\n     */\n\n  }, {\n    key: \"listenClick\",\n    value: function listenClick(trigger) {\n      var _this2 = this;\n\n      this.listener = listen_default()(trigger, 'click', function (e) {\n        return _this2.onClick(e);\n      });\n    }\n    /**\n     * Defines a new `ClipboardAction` on each click event.\n     * @param {Event} e\n     */\n\n  }, {\n    key: \"onClick\",\n    value: function onClick(e) {\n      var trigger = e.delegateTarget || e.currentTarget;\n      var action = this.action(trigger) || 'copy';\n      var text = actions_default({\n        action: action,\n        container: this.container,\n        target: this.target(trigger),\n        text: this.text(trigger)\n      }); // Fires an event based on the copy operation result.\n\n      this.emit(text ? 'success' : 'error', {\n        action: action,\n        text: text,\n        trigger: trigger,\n        clearSelection: function clearSelection() {\n          if (trigger) {\n            trigger.focus();\n          }\n\n          window.getSelection().removeAllRanges();\n        }\n      });\n    }\n    /**\n     * Default `action` lookup function.\n     * @param {Element} trigger\n     */\n\n  }, {\n    key: \"defaultAction\",\n    value: function defaultAction(trigger) {\n      return getAttributeValue('action', trigger);\n    }\n    /**\n     * Default `target` lookup function.\n     * @param {Element} trigger\n     */\n\n  }, {\n    key: \"defaultTarget\",\n    value: function defaultTarget(trigger) {\n      var selector = getAttributeValue('target', trigger);\n\n      if (selector) {\n        return document.querySelector(selector);\n      }\n    }\n    /**\n     * Allow fire programmatically a copy action\n     * @param {String|HTMLElement} target\n     * @param {Object} options\n     * @returns Text copied.\n     */\n\n  }, {\n    key: \"defaultText\",\n\n    /**\n     * Default `text` lookup function.\n     * @param {Element} trigger\n     */\n    value: function defaultText(trigger) {\n      return getAttributeValue('text', trigger);\n    }\n    /**\n     * Destroy lifecycle.\n     */\n\n  }, {\n    key: \"destroy\",\n    value: function destroy() {\n      this.listener.destroy();\n    }\n  }], [{\n    key: \"copy\",\n    value: function copy(target) {\n      var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {\n        container: document.body\n      };\n      return actions_copy(target, options);\n    }\n    /**\n     * Allow fire programmatically a cut action\n     * @param {String|HTMLElement} target\n     * @returns Text cutted.\n     */\n\n  }, {\n    key: \"cut\",\n    value: function cut(target) {\n      return actions_cut(target);\n    }\n    /**\n     * Returns the support of the given action, or all actions if no action is\n     * given.\n     * @param {String} [action]\n     */\n\n  }, {\n    key: \"isSupported\",\n    value: function isSupported() {\n      var action = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : ['copy', 'cut'];\n      var actions = typeof action === 'string' ? [action] : action;\n      var support = !!document.queryCommandSupported;\n      actions.forEach(function (action) {\n        support = support && !!document.queryCommandSupported(action);\n      });\n      return support;\n    }\n  }]);\n\n  return Clipboard;\n}((tiny_emitter_default()));\n\n/* harmony default export */ var clipboard = (Clipboard);\n\n/***/ }),\n\n/***/ 828:\n/***/ (function(module) {\n\nvar DOCUMENT_NODE_TYPE = 9;\n\n/**\n * A polyfill for Element.matches()\n */\nif (typeof Element !== 'undefined' && !Element.prototype.matches) {\n    var proto = Element.prototype;\n\n    proto.matches = proto.matchesSelector ||\n                    proto.mozMatchesSelector ||\n                    proto.msMatchesSelector ||\n                    proto.oMatchesSelector ||\n                    proto.webkitMatchesSelector;\n}\n\n/**\n * Finds the closest parent that matches a selector.\n *\n * @param {Element} element\n * @param {String} selector\n * @return {Function}\n */\nfunction closest (element, selector) {\n    while (element && element.nodeType !== DOCUMENT_NODE_TYPE) {\n        if (typeof element.matches === 'function' &&\n            element.matches(selector)) {\n          return element;\n        }\n        element = element.parentNode;\n    }\n}\n\nmodule.exports = closest;\n\n\n/***/ }),\n\n/***/ 438:\n/***/ (function(module, __unused_webpack_exports, __webpack_require__) {\n\nvar closest = __webpack_require__(828);\n\n/**\n * Delegates event to a selector.\n *\n * @param {Element} element\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @param {Boolean} useCapture\n * @return {Object}\n */\nfunction _delegate(element, selector, type, callback, useCapture) {\n    var listenerFn = listener.apply(this, arguments);\n\n    element.addEventListener(type, listenerFn, useCapture);\n\n    return {\n        destroy: function() {\n            element.removeEventListener(type, listenerFn, useCapture);\n        }\n    }\n}\n\n/**\n * Delegates event to a selector.\n *\n * @param {Element|String|Array} [elements]\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @param {Boolean} useCapture\n * @return {Object}\n */\nfunction delegate(elements, selector, type, callback, useCapture) {\n    // Handle the regular Element usage\n    if (typeof elements.addEventListener === 'function') {\n        return _delegate.apply(null, arguments);\n    }\n\n    // Handle Element-less usage, it defaults to global delegation\n    if (typeof type === 'function') {\n        // Use `document` as the first parameter, then apply arguments\n        // This is a short way to .unshift `arguments` without running into deoptimizations\n        return _delegate.bind(null, document).apply(null, arguments);\n    }\n\n    // Handle Selector-based usage\n    if (typeof elements === 'string') {\n        elements = document.querySelectorAll(elements);\n    }\n\n    // Handle Array-like based usage\n    return Array.prototype.map.call(elements, function (element) {\n        return _delegate(element, selector, type, callback, useCapture);\n    });\n}\n\n/**\n * Finds closest match and invokes callback.\n *\n * @param {Element} element\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @return {Function}\n */\nfunction listener(element, selector, type, callback) {\n    return function(e) {\n        e.delegateTarget = closest(e.target, selector);\n\n        if (e.delegateTarget) {\n            callback.call(element, e);\n        }\n    }\n}\n\nmodule.exports = delegate;\n\n\n/***/ }),\n\n/***/ 879:\n/***/ (function(__unused_webpack_module, exports) {\n\n/**\n * Check if argument is a HTML element.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.node = function(value) {\n    return value !== undefined\n        && value instanceof HTMLElement\n        && value.nodeType === 1;\n};\n\n/**\n * Check if argument is a list of HTML elements.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.nodeList = function(value) {\n    var type = Object.prototype.toString.call(value);\n\n    return value !== undefined\n        && (type === '[object NodeList]' || type === '[object HTMLCollection]')\n        && ('length' in value)\n        && (value.length === 0 || exports.node(value[0]));\n};\n\n/**\n * Check if argument is a string.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.string = function(value) {\n    return typeof value === 'string'\n        || value instanceof String;\n};\n\n/**\n * Check if argument is a function.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.fn = function(value) {\n    var type = Object.prototype.toString.call(value);\n\n    return type === '[object Function]';\n};\n\n\n/***/ }),\n\n/***/ 370:\n/***/ (function(module, __unused_webpack_exports, __webpack_require__) {\n\nvar is = __webpack_require__(879);\nvar delegate = __webpack_require__(438);\n\n/**\n * Validates all params and calls the right\n * listener function based on its target type.\n *\n * @param {String|HTMLElement|HTMLCollection|NodeList} target\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listen(target, type, callback) {\n    if (!target && !type && !callback) {\n        throw new Error('Missing required arguments');\n    }\n\n    if (!is.string(type)) {\n        throw new TypeError('Second argument must be a String');\n    }\n\n    if (!is.fn(callback)) {\n        throw new TypeError('Third argument must be a Function');\n    }\n\n    if (is.node(target)) {\n        return listenNode(target, type, callback);\n    }\n    else if (is.nodeList(target)) {\n        return listenNodeList(target, type, callback);\n    }\n    else if (is.string(target)) {\n        return listenSelector(target, type, callback);\n    }\n    else {\n        throw new TypeError('First argument must be a String, HTMLElement, HTMLCollection, or NodeList');\n    }\n}\n\n/**\n * Adds an event listener to a HTML element\n * and returns a remove listener function.\n *\n * @param {HTMLElement} node\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenNode(node, type, callback) {\n    node.addEventListener(type, callback);\n\n    return {\n        destroy: function() {\n            node.removeEventListener(type, callback);\n        }\n    }\n}\n\n/**\n * Add an event listener to a list of HTML elements\n * and returns a remove listener function.\n *\n * @param {NodeList|HTMLCollection} nodeList\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenNodeList(nodeList, type, callback) {\n    Array.prototype.forEach.call(nodeList, function(node) {\n        node.addEventListener(type, callback);\n    });\n\n    return {\n        destroy: function() {\n            Array.prototype.forEach.call(nodeList, function(node) {\n                node.removeEventListener(type, callback);\n            });\n        }\n    }\n}\n\n/**\n * Add an event listener to a selector\n * and returns a remove listener function.\n *\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenSelector(selector, type, callback) {\n    return delegate(document.body, selector, type, callback);\n}\n\nmodule.exports = listen;\n\n\n/***/ }),\n\n/***/ 817:\n/***/ (function(module) {\n\nfunction select(element) {\n    var selectedText;\n\n    if (element.nodeName === 'SELECT') {\n        element.focus();\n\n        selectedText = element.value;\n    }\n    else if (element.nodeName === 'INPUT' || element.nodeName === 'TEXTAREA') {\n        var isReadOnly = element.hasAttribute('readonly');\n\n        if (!isReadOnly) {\n            element.setAttribute('readonly', '');\n        }\n\n        element.select();\n        element.setSelectionRange(0, element.value.length);\n\n        if (!isReadOnly) {\n            element.removeAttribute('readonly');\n        }\n\n        selectedText = element.value;\n    }\n    else {\n        if (element.hasAttribute('contenteditable')) {\n            element.focus();\n        }\n\n        var selection = window.getSelection();\n        var range = document.createRange();\n\n        range.selectNodeContents(element);\n        selection.removeAllRanges();\n        selection.addRange(range);\n\n        selectedText = selection.toString();\n    }\n\n    return selectedText;\n}\n\nmodule.exports = select;\n\n\n/***/ }),\n\n/***/ 279:\n/***/ (function(module) {\n\nfunction E () {\n  // Keep this empty so it's easier to inherit from\n  // (via https://github.com/lipsmack from https://github.com/scottcorgan/tiny-emitter/issues/3)\n}\n\nE.prototype = {\n  on: function (name, callback, ctx) {\n    var e = this.e || (this.e = {});\n\n    (e[name] || (e[name] = [])).push({\n      fn: callback,\n      ctx: ctx\n    });\n\n    return this;\n  },\n\n  once: function (name, callback, ctx) {\n    var self = this;\n    function listener () {\n      self.off(name, listener);\n      callback.apply(ctx, arguments);\n    };\n\n    listener._ = callback\n    return this.on(name, listener, ctx);\n  },\n\n  emit: function (name) {\n    var data = [].slice.call(arguments, 1);\n    var evtArr = ((this.e || (this.e = {}))[name] || []).slice();\n    var i = 0;\n    var len = evtArr.length;\n\n    for (i; i < len; i++) {\n      evtArr[i].fn.apply(evtArr[i].ctx, data);\n    }\n\n    return this;\n  },\n\n  off: function (name, callback) {\n    var e = this.e || (this.e = {});\n    var evts = e[name];\n    var liveEvents = [];\n\n    if (evts && callback) {\n      for (var i = 0, len = evts.length; i < len; i++) {\n        if (evts[i].fn !== callback && evts[i].fn._ !== callback)\n          liveEvents.push(evts[i]);\n      }\n    }\n\n    // Remove event from queue to prevent memory leak\n    // Suggested by https://github.com/lazd\n    // Ref: https://github.com/scottcorgan/tiny-emitter/commit/c6ebfaa9bc973b33d110a84a307742b7cf94c953#commitcomment-5024910\n\n    (liveEvents.length)\n      ? e[name] = liveEvents\n      : delete e[name];\n\n    return this;\n  }\n};\n\nmodule.exports = E;\nmodule.exports.TinyEmitter = E;\n\n\n/***/ })\n\n/******/ \t});\n/************************************************************************/\n/******/ \t// The module cache\n/******/ \tvar __webpack_module_cache__ = {};\n/******/ \t\n/******/ \t// The require function\n/******/ \tfunction __webpack_require__(moduleId) {\n/******/ \t\t// Check if module is in cache\n/******/ \t\tif(__webpack_module_cache__[moduleId]) {\n/******/ \t\t\treturn __webpack_module_cache__[moduleId].exports;\n/******/ \t\t}\n/******/ \t\t// Create a new module (and put it into the cache)\n/******/ \t\tvar module = __webpack_module_cache__[moduleId] = {\n/******/ \t\t\t// no module.id needed\n/******/ \t\t\t// no module.loaded needed\n/******/ \t\t\texports: {}\n/******/ \t\t};\n/******/ \t\n/******/ \t\t// Execute the module function\n/******/ \t\t__webpack_modules__[moduleId](module, module.exports, __webpack_require__);\n/******/ \t\n/******/ \t\t// Return the exports of the module\n/******/ \t\treturn module.exports;\n/******/ \t}\n/******/ \t\n/************************************************************************/\n/******/ \t/* webpack/runtime/compat get default export */\n/******/ \t!function() {\n/******/ \t\t// getDefaultExport function for compatibility with non-harmony modules\n/******/ \t\t__webpack_require__.n = function(module) {\n/******/ \t\t\tvar getter = module && module.__esModule ?\n/******/ \t\t\t\tfunction() { return module['default']; } :\n/******/ \t\t\t\tfunction() { return module; };\n/******/ \t\t\t__webpack_require__.d(getter, { a: getter });\n/******/ \t\t\treturn getter;\n/******/ \t\t};\n/******/ \t}();\n/******/ \t\n/******/ \t/* webpack/runtime/define property getters */\n/******/ \t!function() {\n/******/ \t\t// define getter functions for harmony exports\n/******/ \t\t__webpack_require__.d = function(exports, definition) {\n/******/ \t\t\tfor(var key in definition) {\n/******/ \t\t\t\tif(__webpack_require__.o(definition, key) && !__webpack_require__.o(exports, key)) {\n/******/ \t\t\t\t\tObject.defineProperty(exports, key, { enumerable: true, get: definition[key] });\n/******/ \t\t\t\t}\n/******/ \t\t\t}\n/******/ \t\t};\n/******/ \t}();\n/******/ \t\n/******/ \t/* webpack/runtime/hasOwnProperty shorthand */\n/******/ \t!function() {\n/******/ \t\t__webpack_require__.o = function(obj, prop) { return Object.prototype.hasOwnProperty.call(obj, prop); }\n/******/ \t}();\n/******/ \t\n/************************************************************************/\n/******/ \t// module exports must be returned from runtime so entry inlining is disabled\n/******/ \t// startup\n/******/ \t// Load entry module and return exports\n/******/ \treturn __webpack_require__(686);\n/******/ })()\n.default;\n});", "/*!\n * escape-html\n * Copyright(c) 2012-2013 TJ Holowaychuk\n * Copyright(c) 2015 Andreas Lubbe\n * Copyright(c) 2015 Tiancheng \"Timothy\" Gu\n * MIT Licensed\n */\n\n'use strict';\n\n/**\n * Module variables.\n * @private\n */\n\nvar matchHtmlRegExp = /[\"'&<>]/;\n\n/**\n * Module exports.\n * @public\n */\n\nmodule.exports = escapeHtml;\n\n/**\n * Escape special characters in the given string of html.\n *\n * @param  {string} string The string to escape for inserting into HTML\n * @return {string}\n * @public\n */\n\nfunction escapeHtml(string) {\n  var str = '' + string;\n  var match = matchHtmlRegExp.exec(str);\n\n  if (!match) {\n    return str;\n  }\n\n  var escape;\n  var html = '';\n  var index = 0;\n  var lastIndex = 0;\n\n  for (index = match.index; index < str.length; index++) {\n    switch (str.charCodeAt(index)) {\n      case 34: // \"\n        escape = '&quot;';\n        break;\n      case 38: // &\n        escape = '&amp;';\n        break;\n      case 39: // '\n        escape = '&#39;';\n        break;\n      case 60: // <\n        escape = '&lt;';\n        break;\n      case 62: // >\n        escape = '&gt;';\n        break;\n      default:\n        continue;\n    }\n\n    if (lastIndex !== index) {\n      html += str.substring(lastIndex, index);\n    }\n\n    lastIndex = index + 1;\n    html += escape;\n  }\n\n  return lastIndex !== index\n    ? html + str.substring(lastIndex, index)\n    : html;\n}\n", "Array.prototype.flat||Object.defineProperty(Array.prototype,\"flat\",{configurable:!0,value:function r(){var t=isNaN(arguments[0])?1:Number(arguments[0]);return t?Array.prototype.reduce.call(this,function(a,e){return Array.isArray(e)?a.push.apply(a,r.call(e,t-1)):a.push(e),a},[]):Array.prototype.slice.call(this)},writable:!0}),Array.prototype.flatMap||Object.defineProperty(Array.prototype,\"flatMap\",{configurable:!0,value:function(r){return Array.prototype.map.apply(this,arguments).flat()},writable:!0})\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport \"array-flat-polyfill\"\nimport \"focus-visible\"\nimport \"unfetch/polyfill\"\nimport \"url-polyfill\"\n\nimport {\n  EMPTY,\n  NEVER,\n  Subject,\n  defer,\n  delay,\n  filter,\n  map,\n  merge,\n  mergeWith,\n  shareReplay,\n  switchMap\n} from \"rxjs\"\n\nimport { configuration, feature } from \"./_\"\nimport {\n  at,\n  getOptionalElement,\n  requestJSON,\n  setToggle,\n  watchDocument,\n  watchKeyboard,\n  watchLocation,\n  watchLocationTarget,\n  watchMedia,\n  watchPrint,\n  watchViewport\n} from \"./browser\"\nimport {\n  getComponentElement,\n  getComponentElements,\n  mountAnnounce,\n  mountBackToTop,\n  mountConsent,\n  mountContent,\n  mountDialog,\n  mountHeader,\n  mountHeaderTitle,\n  mountPalette,\n  mountSearch,\n  mountSearchHiglight,\n  mountSidebar,\n  mountSource,\n  mountTableOfContents,\n  mountTabs,\n  watchHeader,\n  watchMain\n} from \"./components\"\nimport {\n  SearchIndex,\n  setupClipboardJS,\n  setupInstantLoading,\n  setupVersionSelector\n} from \"./integrations\"\nimport {\n  patchIndeterminate,\n  patchScrollfix,\n  patchScrolllock\n} from \"./patches\"\nimport \"./polyfills\"\n\n/* ----------------------------------------------------------------------------\n * Application\n * ------------------------------------------------------------------------- */\n\n/* Yay, JavaScript is available */\ndocument.documentElement.classList.remove(\"no-js\")\ndocument.documentElement.classList.add(\"js\")\n\n/* Set up navigation observables and subjects */\nconst document$ = watchDocument()\nconst location$ = watchLocation()\nconst target$   = watchLocationTarget()\nconst keyboard$ = watchKeyboard()\n\n/* Set up media observables */\nconst viewport$ = watchViewport()\nconst tablet$   = watchMedia(\"(min-width: 960px)\")\nconst screen$   = watchMedia(\"(min-width: 1220px)\")\nconst print$    = watchPrint()\n\n/* Retrieve search index, if search is enabled */\nconst config = configuration()\nconst index$ = document.forms.namedItem(\"search\")\n  ? __search?.index || requestJSON<SearchIndex>(\n    new URL(\"search/search_index.json\", config.base)\n  )\n  : NEVER\n\n/* Set up Clipboard.js integration */\nconst alert$ = new Subject<string>()\nsetupClipboardJS({ alert$ })\n\n/* Set up instant loading, if enabled */\nif (feature(\"navigation.instant\"))\n  setupInstantLoading({ document$, location$, viewport$ })\n\n/* Set up version selector */\nif (config.version?.provider === \"mike\")\n  setupVersionSelector({ document$ })\n\n/* Always close drawer and search on navigation */\nmerge(location$, target$)\n  .pipe(\n    delay(125)\n  )\n    .subscribe(() => {\n      setToggle(\"drawer\", false)\n      setToggle(\"search\", false)\n    })\n\n/* Set up global keyboard handlers */\nkeyboard$\n  .pipe(\n    filter(({ mode }) => mode === \"global\")\n  )\n    .subscribe(key => {\n      switch (key.type) {\n\n        /* Go to previous page */\n        case \"p\":\n        case \",\":\n          const prev = getOptionalElement(\"[href][rel=prev]\")\n          if (typeof prev !== \"undefined\")\n            prev.click()\n          break\n\n        /* Go to next page */\n        case \"n\":\n        case \".\":\n          const next = getOptionalElement(\"[href][rel=next]\")\n          if (typeof next !== \"undefined\")\n            next.click()\n          break\n      }\n    })\n\n/* Set up patches */\npatchIndeterminate({ document$, tablet$ })\npatchScrollfix({ document$ })\npatchScrolllock({ viewport$, tablet$ })\n\n/* Set up header and main area observable */\nconst header$ = watchHeader(getComponentElement(\"header\"), { viewport$ })\nconst main$ = document$\n  .pipe(\n    map(() => getComponentElement(\"main\")),\n    switchMap(el => watchMain(el, { viewport$, header$ })),\n    shareReplay(1)\n  )\n\n/* Set up control component observables */\nconst control$ = merge(\n\n  /* Consent */\n  ...getComponentElements(\"consent\")\n    .map(el => mountConsent(el, { target$ })),\n\n  /* Dialog */\n  ...getComponentElements(\"dialog\")\n    .map(el => mountDialog(el, { alert$ })),\n\n  /* Header */\n  ...getComponentElements(\"header\")\n    .map(el => mountHeader(el, { viewport$, header$, main$ })),\n\n  /* Color palette */\n  ...getComponentElements(\"palette\")\n    .map(el => mountPalette(el)),\n\n  /* Search */\n  ...getComponentElements(\"search\")\n    .map(el => mountSearch(el, { index$, keyboard$ })),\n\n  /* Repository information */\n  ...getComponentElements(\"source\")\n    .map(el => mountSource(el))\n)\n\n/* Set up content component observables */\nconst content$ = defer(() => merge(\n\n  /* Announcement bar */\n  ...getComponentElements(\"announce\")\n    .map(el => mountAnnounce(el)),\n\n  /* Content */\n  ...getComponentElements(\"content\")\n    .map(el => mountContent(el, { viewport$, target$, print$ })),\n\n  /* Search highlighting */\n  ...getComponentElements(\"content\")\n    .map(el => feature(\"search.highlight\")\n      ? mountSearchHiglight(el, { index$, location$ })\n      : EMPTY\n    ),\n\n  /* Header title */\n  ...getComponentElements(\"header-title\")\n    .map(el => mountHeaderTitle(el, { viewport$, header$ })),\n\n  /* Sidebar */\n  ...getComponentElements(\"sidebar\")\n    .map(el => el.getAttribute(\"data-md-type\") === \"navigation\"\n      ? at(screen$, () => mountSidebar(el, { viewport$, header$, main$ }))\n      : at(tablet$, () => mountSidebar(el, { viewport$, header$, main$ }))\n    ),\n\n  /* Navigation tabs */\n  ...getComponentElements(\"tabs\")\n    .map(el => mountTabs(el, { viewport$, header$ })),\n\n  /* Table of contents */\n  ...getComponentElements(\"toc\")\n    .map(el => mountTableOfContents(el, { viewport$, header$, target$ })),\n\n  /* Back-to-top button */\n  ...getComponentElements(\"top\")\n    .map(el => mountBackToTop(el, { viewport$, header$, main$, target$ }))\n))\n\n/* Set up component observables */\nconst component$ = document$\n  .pipe(\n    switchMap(() => content$),\n    mergeWith(control$),\n    shareReplay(1)\n  )\n\n/* Subscribe to all components */\ncomponent$.subscribe()\n\n/* ----------------------------------------------------------------------------\n * Exports\n * ------------------------------------------------------------------------- */\n\nwindow.document$  = document$          /* Document observable */\nwindow.location$  = location$          /* Location subject */\nwindow.target$    = target$            /* Location target observable */\nwindow.keyboard$  = keyboard$          /* Keyboard observable */\nwindow.viewport$  = viewport$          /* Viewport observable */\nwindow.tablet$    = tablet$            /* Media tablet observable */\nwindow.screen$    = screen$            /* Media screen observable */\nwindow.print$     = print$             /* Media print observable */\nwindow.alert$     = alert$             /* Alert subject */\nwindow.component$ = component$         /* Component observable */\n", "self.fetch||(self.fetch=function(e,n){return n=n||{},new Promise(function(t,s){var r=new XMLHttpRequest,o=[],u=[],i={},a=function(){return{ok:2==(r.status/100|0),statusText:r.statusText,status:r.status,url:r.responseURL,text:function(){return Promise.resolve(r.responseText)},json:function(){return Promise.resolve(r.responseText).then(JSON.parse)},blob:function(){return Promise.resolve(new Blob([r.response]))},clone:a,headers:{keys:function(){return o},entries:function(){return u},get:function(e){return i[e.toLowerCase()]},has:function(e){return e.toLowerCase()in i}}}};for(var c in r.open(n.method||\"get\",e,!0),r.onload=function(){r.getAllResponseHeaders().replace(/^(.*?):[^\\S\\n]*([\\s\\S]*?)$/gm,function(e,n,t){o.push(n=n.toLowerCase()),u.push([n,t]),i[n]=i[n]?i[n]+\",\"+t:t}),t(a())},r.onerror=s,r.withCredentials=\"include\"==n.credentials,n.headers)r.setRequestHeader(c,n.headers[c]);r.send(n.body||null)})});\n", "import tslib from '../tslib.js';\r\nconst {\r\n    __extends,\r\n    __assign,\r\n    __rest,\r\n    __decorate,\r\n    __param,\r\n    __metadata,\r\n    __awaiter,\r\n    __generator,\r\n    __exportStar,\r\n    __createBinding,\r\n    __values,\r\n    __read,\r\n    __spread,\r\n    __spreadArrays,\r\n    __spreadArray,\r\n    __await,\r\n    __asyncGenerator,\r\n    __asyncDelegator,\r\n    __asyncValues,\r\n    __makeTemplateObject,\r\n    __importStar,\r\n    __importDefault,\r\n    __classPrivateFieldGet,\r\n    __classPrivateFieldSet,\r\n} = tslib;\r\nexport {\r\n    __extends,\r\n    __assign,\r\n    __rest,\r\n    __decorate,\r\n    __param,\r\n    __metadata,\r\n    __awaiter,\r\n    __generator,\r\n    __exportStar,\r\n    __createBinding,\r\n    __values,\r\n    __read,\r\n    __spread,\r\n    __spreadArrays,\r\n    __spreadArray,\r\n    __await,\r\n    __asyncGenerator,\r\n    __asyncDelegator,\r\n    __asyncValues,\r\n    __makeTemplateObject,\r\n    __importStar,\r\n    __importDefault,\r\n    __classPrivateFieldGet,\r\n    __classPrivateFieldSet,\r\n};\r\n", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  ReplaySubject,\n  Subject,\n  fromEvent\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch document\n *\n * Documents are implemented as subjects, so all downstream observables are\n * automatically updated when a new document is emitted.\n *\n * @returns Document subject\n */\nexport function watchDocument(): Subject<Document> {\n  const document$ = new ReplaySubject<Document>(1)\n  fromEvent(document, \"DOMContentLoaded\", { once: true })\n    .subscribe(() => document$.next(document))\n\n  /* Return document */\n  return document$\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve all elements matching the query selector\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Elements\n */\nexport function getElements<T extends keyof HTMLElementTagNameMap>(\n  selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T][]\n\nexport function getElements<T extends HTMLElement>(\n  selector: string, node?: ParentNode\n): T[]\n\nexport function getElements<T extends HTMLElement>(\n  selector: string, node: ParentNode = document\n): T[] {\n  return Array.from(node.querySelectorAll<T>(selector))\n}\n\n/**\n * Retrieve an element matching a query selector or throw a reference error\n *\n * Note that this function assumes that the element is present. If unsure if an\n * element is existent, use the `getOptionalElement` function instead.\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Element\n */\nexport function getElement<T extends keyof HTMLElementTagNameMap>(\n  selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T]\n\nexport function getElement<T extends HTMLElement>(\n  selector: string, node?: ParentNode\n): T\n\nexport function getElement<T extends HTMLElement>(\n  selector: string, node: ParentNode = document\n): T {\n  const el = getOptionalElement<T>(selector, node)\n  if (typeof el === \"undefined\")\n    throw new ReferenceError(\n      `Missing element: expected \"${selector}\" to be present`\n    )\n\n  /* Return element */\n  return el\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Retrieve an optional element matching the query selector\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Element or nothing\n */\nexport function getOptionalElement<T extends keyof HTMLElementTagNameMap>(\n  selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T] | undefined\n\nexport function getOptionalElement<T extends HTMLElement>(\n  selector: string, node?: ParentNode\n): T | undefined\n\nexport function getOptionalElement<T extends HTMLElement>(\n  selector: string, node: ParentNode = document\n): T | undefined {\n  return node.querySelector<T>(selector) || undefined\n}\n\n/**\n * Retrieve the currently active element\n *\n * @returns Element or nothing\n */\nexport function getActiveElement(): HTMLElement | undefined {\n  return document.activeElement instanceof HTMLElement\n    ? document.activeElement || undefined\n    : undefined\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  debounceTime,\n  distinctUntilChanged,\n  fromEvent,\n  map,\n  merge,\n  startWith\n} from \"rxjs\"\n\nimport { getActiveElement } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch element focus\n *\n * Previously, this function used `focus` and `blur` events to determine whether\n * an element is focused, but this doesn't work if there are focusable elements\n * within the elements itself. A better solutions are `focusin` and `focusout`\n * events, which bubble up the tree and allow for more fine-grained control.\n *\n * `debounceTime` is necessary, because when a focus change happens inside an\n * element, the observable would first emit `false` and then `true` again.\n *\n * @param el - Element\n *\n * @returns Element focus observable\n */\nexport function watchElementFocus(\n  el: HTMLElement\n): Observable<boolean> {\n  return merge(\n    fromEvent(document.body, \"focusin\"),\n    fromEvent(document.body, \"focusout\")\n  )\n    .pipe(\n      debounceTime(1),\n      map(() => {\n        const active = getActiveElement()\n        return typeof active !== \"undefined\"\n          ? el.contains(active)\n          : false\n      }),\n      startWith(el === getActiveElement()),\n      distinctUntilChanged()\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  animationFrameScheduler,\n  auditTime,\n  fromEvent,\n  map,\n  merge,\n  startWith\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Element offset\n */\nexport interface ElementOffset {\n  x: number                            /* Horizontal offset */\n  y: number                            /* Vertical offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element offset\n *\n * @param el - Element\n *\n * @returns Element offset\n */\nexport function getElementOffset(\n  el: HTMLElement\n): ElementOffset {\n  return {\n    x: el.offsetLeft,\n    y: el.offsetTop\n  }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch element offset\n *\n * @param el - Element\n *\n * @returns Element offset observable\n */\nexport function watchElementOffset(\n  el: HTMLElement\n): Observable<ElementOffset> {\n  return merge(\n    fromEvent(window, \"load\"),\n    fromEvent(window, \"resize\")\n  )\n    .pipe(\n      auditTime(0, animationFrameScheduler),\n      map(() => getElementOffset(el)),\n      startWith(getElementOffset(el))\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  animationFrameScheduler,\n  auditTime,\n  fromEvent,\n  map,\n  merge,\n  startWith\n} from \"rxjs\"\n\nimport { ElementOffset } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element content offset (= scroll offset)\n *\n * @param el - Element\n *\n * @returns Element content offset\n */\nexport function getElementContentOffset(\n  el: HTMLElement\n): ElementOffset {\n  return {\n    x: el.scrollLeft,\n    y: el.scrollTop\n  }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch element content offset\n *\n * @param el - Element\n *\n * @returns Element content offset observable\n */\nexport function watchElementContentOffset(\n  el: HTMLElement\n): Observable<ElementOffset> {\n  return merge(\n    fromEvent(el, \"scroll\"),\n    fromEvent(window, \"resize\")\n  )\n    .pipe(\n      auditTime(0, animationFrameScheduler),\n      map(() => getElementContentOffset(el)),\n      startWith(getElementContentOffset(el))\n    )\n}\n", "/**\r\n * A collection of shims that provide minimal functionality of the ES6 collections.\r\n *\r\n * These implementations are not meant to be used outside of the ResizeObserver\r\n * modules as they cover only a limited range of use cases.\r\n */\r\n/* eslint-disable require-jsdoc, valid-jsdoc */\r\nvar MapShim = (function () {\r\n    if (typeof Map !== 'undefined') {\r\n        return Map;\r\n    }\r\n    /**\r\n     * Returns index in provided array that matches the specified key.\r\n     *\r\n     * @param {Array<Array>} arr\r\n     * @param {*} key\r\n     * @returns {number}\r\n     */\r\n    function getIndex(arr, key) {\r\n        var result = -1;\r\n        arr.some(function (entry, index) {\r\n            if (entry[0] === key) {\r\n                result = index;\r\n                return true;\r\n            }\r\n            return false;\r\n        });\r\n        return result;\r\n    }\r\n    return /** @class */ (function () {\r\n        function class_1() {\r\n            this.__entries__ = [];\r\n        }\r\n        Object.defineProperty(class_1.prototype, \"size\", {\r\n            /**\r\n             * @returns {boolean}\r\n             */\r\n            get: function () {\r\n                return this.__entries__.length;\r\n            },\r\n            enumerable: true,\r\n            configurable: true\r\n        });\r\n        /**\r\n         * @param {*} key\r\n         * @returns {*}\r\n         */\r\n        class_1.prototype.get = function (key) {\r\n            var index = getIndex(this.__entries__, key);\r\n            var entry = this.__entries__[index];\r\n            return entry && entry[1];\r\n        };\r\n        /**\r\n         * @param {*} key\r\n         * @param {*} value\r\n         * @returns {void}\r\n         */\r\n        class_1.prototype.set = function (key, value) {\r\n            var index = getIndex(this.__entries__, key);\r\n            if (~index) {\r\n                this.__entries__[index][1] = value;\r\n            }\r\n            else {\r\n                this.__entries__.push([key, value]);\r\n            }\r\n        };\r\n        /**\r\n         * @param {*} key\r\n         * @returns {void}\r\n         */\r\n        class_1.prototype.delete = function (key) {\r\n            var entries = this.__entries__;\r\n            var index = getIndex(entries, key);\r\n            if (~index) {\r\n                entries.splice(index, 1);\r\n            }\r\n        };\r\n        /**\r\n         * @param {*} key\r\n         * @returns {void}\r\n         */\r\n        class_1.prototype.has = function (key) {\r\n            return !!~getIndex(this.__entries__, key);\r\n        };\r\n        /**\r\n         * @returns {void}\r\n         */\r\n        class_1.prototype.clear = function () {\r\n            this.__entries__.splice(0);\r\n        };\r\n        /**\r\n         * @param {Function} callback\r\n         * @param {*} [ctx=null]\r\n         * @returns {void}\r\n         */\r\n        class_1.prototype.forEach = function (callback, ctx) {\r\n            if (ctx === void 0) { ctx = null; }\r\n            for (var _i = 0, _a = this.__entries__; _i < _a.length; _i++) {\r\n                var entry = _a[_i];\r\n                callback.call(ctx, entry[1], entry[0]);\r\n            }\r\n        };\r\n        return class_1;\r\n    }());\r\n})();\n\n/**\r\n * Detects whether window and document objects are available in current environment.\r\n */\r\nvar isBrowser = typeof window !== 'undefined' && typeof document !== 'undefined' && window.document === document;\n\n// Returns global object of a current environment.\r\nvar global$1 = (function () {\r\n    if (typeof global !== 'undefined' && global.Math === Math) {\r\n        return global;\r\n    }\r\n    if (typeof self !== 'undefined' && self.Math === Math) {\r\n        return self;\r\n    }\r\n    if (typeof window !== 'undefined' && window.Math === Math) {\r\n        return window;\r\n    }\r\n    // eslint-disable-next-line no-new-func\r\n    return Function('return this')();\r\n})();\n\n/**\r\n * A shim for the requestAnimationFrame which falls back to the setTimeout if\r\n * first one is not supported.\r\n *\r\n * @returns {number} Requests' identifier.\r\n */\r\nvar requestAnimationFrame$1 = (function () {\r\n    if (typeof requestAnimationFrame === 'function') {\r\n        // It's required to use a bounded function because IE sometimes throws\r\n        // an \"Invalid calling object\" error if rAF is invoked without the global\r\n        // object on the left hand side.\r\n        return requestAnimationFrame.bind(global$1);\r\n    }\r\n    return function (callback) { return setTimeout(function () { return callback(Date.now()); }, 1000 / 60); };\r\n})();\n\n// Defines minimum timeout before adding a trailing call.\r\nvar trailingTimeout = 2;\r\n/**\r\n * Creates a wrapper function which ensures that provided callback will be\r\n * invoked only once during the specified delay period.\r\n *\r\n * @param {Function} callback - Function to be invoked after the delay period.\r\n * @param {number} delay - Delay after which to invoke callback.\r\n * @returns {Function}\r\n */\r\nfunction throttle (callback, delay) {\r\n    var leadingCall = false, trailingCall = false, lastCallTime = 0;\r\n    /**\r\n     * Invokes the original callback function and schedules new invocation if\r\n     * the \"proxy\" was called during current request.\r\n     *\r\n     * @returns {void}\r\n     */\r\n    function resolvePending() {\r\n        if (leadingCall) {\r\n            leadingCall = false;\r\n            callback();\r\n        }\r\n        if (trailingCall) {\r\n            proxy();\r\n        }\r\n    }\r\n    /**\r\n     * Callback invoked after the specified delay. It will further postpone\r\n     * invocation of the original function delegating it to the\r\n     * requestAnimationFrame.\r\n     *\r\n     * @returns {void}\r\n     */\r\n    function timeoutCallback() {\r\n        requestAnimationFrame$1(resolvePending);\r\n    }\r\n    /**\r\n     * Schedules invocation of the original function.\r\n     *\r\n     * @returns {void}\r\n     */\r\n    function proxy() {\r\n        var timeStamp = Date.now();\r\n        if (leadingCall) {\r\n            // Reject immediately following calls.\r\n            if (timeStamp - lastCallTime < trailingTimeout) {\r\n                return;\r\n            }\r\n            // Schedule new call to be in invoked when the pending one is resolved.\r\n            // This is important for \"transitions\" which never actually start\r\n            // immediately so there is a chance that we might miss one if change\r\n            // happens amids the pending invocation.\r\n            trailingCall = true;\r\n        }\r\n        else {\r\n            leadingCall = true;\r\n            trailingCall = false;\r\n            setTimeout(timeoutCallback, delay);\r\n        }\r\n        lastCallTime = timeStamp;\r\n    }\r\n    return proxy;\r\n}\n\n// Minimum delay before invoking the update of observers.\r\nvar REFRESH_DELAY = 20;\r\n// A list of substrings of CSS properties used to find transition events that\r\n// might affect dimensions of observed elements.\r\nvar transitionKeys = ['top', 'right', 'bottom', 'left', 'width', 'height', 'size', 'weight'];\r\n// Check if MutationObserver is available.\r\nvar mutationObserverSupported = typeof MutationObserver !== 'undefined';\r\n/**\r\n * Singleton controller class which handles updates of ResizeObserver instances.\r\n */\r\nvar ResizeObserverController = /** @class */ (function () {\r\n    /**\r\n     * Creates a new instance of ResizeObserverController.\r\n     *\r\n     * @private\r\n     */\r\n    function ResizeObserverController() {\r\n        /**\r\n         * Indicates whether DOM listeners have been added.\r\n         *\r\n         * @private {boolean}\r\n         */\r\n        this.connected_ = false;\r\n        /**\r\n         * Tells that controller has subscribed for Mutation Events.\r\n         *\r\n         * @private {boolean}\r\n         */\r\n        this.mutationEventsAdded_ = false;\r\n        /**\r\n         * Keeps reference to the instance of MutationObserver.\r\n         *\r\n         * @private {MutationObserver}\r\n         */\r\n        this.mutationsObserver_ = null;\r\n        /**\r\n         * A list of connected observers.\r\n         *\r\n         * @private {Array<ResizeObserverSPI>}\r\n         */\r\n        this.observers_ = [];\r\n        this.onTransitionEnd_ = this.onTransitionEnd_.bind(this);\r\n        this.refresh = throttle(this.refresh.bind(this), REFRESH_DELAY);\r\n    }\r\n    /**\r\n     * Adds observer to observers list.\r\n     *\r\n     * @param {ResizeObserverSPI} observer - Observer to be added.\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverController.prototype.addObserver = function (observer) {\r\n        if (!~this.observers_.indexOf(observer)) {\r\n            this.observers_.push(observer);\r\n        }\r\n        // Add listeners if they haven't been added yet.\r\n        if (!this.connected_) {\r\n            this.connect_();\r\n        }\r\n    };\r\n    /**\r\n     * Removes observer from observers list.\r\n     *\r\n     * @param {ResizeObserverSPI} observer - Observer to be removed.\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverController.prototype.removeObserver = function (observer) {\r\n        var observers = this.observers_;\r\n        var index = observers.indexOf(observer);\r\n        // Remove observer if it's present in registry.\r\n        if (~index) {\r\n            observers.splice(index, 1);\r\n        }\r\n        // Remove listeners if controller has no connected observers.\r\n        if (!observers.length && this.connected_) {\r\n            this.disconnect_();\r\n        }\r\n    };\r\n    /**\r\n     * Invokes the update of observers. It will continue running updates insofar\r\n     * it detects changes.\r\n     *\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverController.prototype.refresh = function () {\r\n        var changesDetected = this.updateObservers_();\r\n        // Continue running updates if changes have been detected as there might\r\n        // be future ones caused by CSS transitions.\r\n        if (changesDetected) {\r\n            this.refresh();\r\n        }\r\n    };\r\n    /**\r\n     * Updates every observer from observers list and notifies them of queued\r\n     * entries.\r\n     *\r\n     * @private\r\n     * @returns {boolean} Returns \"true\" if any observer has detected changes in\r\n     *      dimensions of it's elements.\r\n     */\r\n    ResizeObserverController.prototype.updateObservers_ = function () {\r\n        // Collect observers that have active observations.\r\n        var activeObservers = this.observers_.filter(function (observer) {\r\n            return observer.gatherActive(), observer.hasActive();\r\n        });\r\n        // Deliver notifications in a separate cycle in order to avoid any\r\n        // collisions between observers, e.g. when multiple instances of\r\n        // ResizeObserver are tracking the same element and the callback of one\r\n        // of them changes content dimensions of the observed target. Sometimes\r\n        // this may result in notifications being blocked for the rest of observers.\r\n        activeObservers.forEach(function (observer) { return observer.broadcastActive(); });\r\n        return activeObservers.length > 0;\r\n    };\r\n    /**\r\n     * Initializes DOM listeners.\r\n     *\r\n     * @private\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverController.prototype.connect_ = function () {\r\n        // Do nothing if running in a non-browser environment or if listeners\r\n        // have been already added.\r\n        if (!isBrowser || this.connected_) {\r\n            return;\r\n        }\r\n        // Subscription to the \"Transitionend\" event is used as a workaround for\r\n        // delayed transitions. This way it's possible to capture at least the\r\n        // final state of an element.\r\n        document.addEventListener('transitionend', this.onTransitionEnd_);\r\n        window.addEventListener('resize', this.refresh);\r\n        if (mutationObserverSupported) {\r\n            this.mutationsObserver_ = new MutationObserver(this.refresh);\r\n            this.mutationsObserver_.observe(document, {\r\n                attributes: true,\r\n                childList: true,\r\n                characterData: true,\r\n                subtree: true\r\n            });\r\n        }\r\n        else {\r\n            document.addEventListener('DOMSubtreeModified', this.refresh);\r\n            this.mutationEventsAdded_ = true;\r\n        }\r\n        this.connected_ = true;\r\n    };\r\n    /**\r\n     * Removes DOM listeners.\r\n     *\r\n     * @private\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverController.prototype.disconnect_ = function () {\r\n        // Do nothing if running in a non-browser environment or if listeners\r\n        // have been already removed.\r\n        if (!isBrowser || !this.connected_) {\r\n            return;\r\n        }\r\n        document.removeEventListener('transitionend', this.onTransitionEnd_);\r\n        window.removeEventListener('resize', this.refresh);\r\n        if (this.mutationsObserver_) {\r\n            this.mutationsObserver_.disconnect();\r\n        }\r\n        if (this.mutationEventsAdded_) {\r\n            document.removeEventListener('DOMSubtreeModified', this.refresh);\r\n        }\r\n        this.mutationsObserver_ = null;\r\n        this.mutationEventsAdded_ = false;\r\n        this.connected_ = false;\r\n    };\r\n    /**\r\n     * \"Transitionend\" event handler.\r\n     *\r\n     * @private\r\n     * @param {TransitionEvent} event\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverController.prototype.onTransitionEnd_ = function (_a) {\r\n        var _b = _a.propertyName, propertyName = _b === void 0 ? '' : _b;\r\n        // Detect whether transition may affect dimensions of an element.\r\n        var isReflowProperty = transitionKeys.some(function (key) {\r\n            return !!~propertyName.indexOf(key);\r\n        });\r\n        if (isReflowProperty) {\r\n            this.refresh();\r\n        }\r\n    };\r\n    /**\r\n     * Returns instance of the ResizeObserverController.\r\n     *\r\n     * @returns {ResizeObserverController}\r\n     */\r\n    ResizeObserverController.getInstance = function () {\r\n        if (!this.instance_) {\r\n            this.instance_ = new ResizeObserverController();\r\n        }\r\n        return this.instance_;\r\n    };\r\n    /**\r\n     * Holds reference to the controller's instance.\r\n     *\r\n     * @private {ResizeObserverController}\r\n     */\r\n    ResizeObserverController.instance_ = null;\r\n    return ResizeObserverController;\r\n}());\n\n/**\r\n * Defines non-writable/enumerable properties of the provided target object.\r\n *\r\n * @param {Object} target - Object for which to define properties.\r\n * @param {Object} props - Properties to be defined.\r\n * @returns {Object} Target object.\r\n */\r\nvar defineConfigurable = (function (target, props) {\r\n    for (var _i = 0, _a = Object.keys(props); _i < _a.length; _i++) {\r\n        var key = _a[_i];\r\n        Object.defineProperty(target, key, {\r\n            value: props[key],\r\n            enumerable: false,\r\n            writable: false,\r\n            configurable: true\r\n        });\r\n    }\r\n    return target;\r\n});\n\n/**\r\n * Returns the global object associated with provided element.\r\n *\r\n * @param {Object} target\r\n * @returns {Object}\r\n */\r\nvar getWindowOf = (function (target) {\r\n    // Assume that the element is an instance of Node, which means that it\r\n    // has the \"ownerDocument\" property from which we can retrieve a\r\n    // corresponding global object.\r\n    var ownerGlobal = target && target.ownerDocument && target.ownerDocument.defaultView;\r\n    // Return the local global object if it's not possible extract one from\r\n    // provided element.\r\n    return ownerGlobal || global$1;\r\n});\n\n// Placeholder of an empty content rectangle.\r\nvar emptyRect = createRectInit(0, 0, 0, 0);\r\n/**\r\n * Converts provided string to a number.\r\n *\r\n * @param {number|string} value\r\n * @returns {number}\r\n */\r\nfunction toFloat(value) {\r\n    return parseFloat(value) || 0;\r\n}\r\n/**\r\n * Extracts borders size from provided styles.\r\n *\r\n * @param {CSSStyleDeclaration} styles\r\n * @param {...string} positions - Borders positions (top, right, ...)\r\n * @returns {number}\r\n */\r\nfunction getBordersSize(styles) {\r\n    var positions = [];\r\n    for (var _i = 1; _i < arguments.length; _i++) {\r\n        positions[_i - 1] = arguments[_i];\r\n    }\r\n    return positions.reduce(function (size, position) {\r\n        var value = styles['border-' + position + '-width'];\r\n        return size + toFloat(value);\r\n    }, 0);\r\n}\r\n/**\r\n * Extracts paddings sizes from provided styles.\r\n *\r\n * @param {CSSStyleDeclaration} styles\r\n * @returns {Object} Paddings box.\r\n */\r\nfunction getPaddings(styles) {\r\n    var positions = ['top', 'right', 'bottom', 'left'];\r\n    var paddings = {};\r\n    for (var _i = 0, positions_1 = positions; _i < positions_1.length; _i++) {\r\n        var position = positions_1[_i];\r\n        var value = styles['padding-' + position];\r\n        paddings[position] = toFloat(value);\r\n    }\r\n    return paddings;\r\n}\r\n/**\r\n * Calculates content rectangle of provided SVG element.\r\n *\r\n * @param {SVGGraphicsElement} target - Element content rectangle of which needs\r\n *      to be calculated.\r\n * @returns {DOMRectInit}\r\n */\r\nfunction getSVGContentRect(target) {\r\n    var bbox = target.getBBox();\r\n    return createRectInit(0, 0, bbox.width, bbox.height);\r\n}\r\n/**\r\n * Calculates content rectangle of provided HTMLElement.\r\n *\r\n * @param {HTMLElement} target - Element for which to calculate the content rectangle.\r\n * @returns {DOMRectInit}\r\n */\r\nfunction getHTMLElementContentRect(target) {\r\n    // Client width & height properties can't be\r\n    // used exclusively as they provide rounded values.\r\n    var clientWidth = target.clientWidth, clientHeight = target.clientHeight;\r\n    // By this condition we can catch all non-replaced inline, hidden and\r\n    // detached elements. Though elements with width & height properties less\r\n    // than 0.5 will be discarded as well.\r\n    //\r\n    // Without it we would need to implement separate methods for each of\r\n    // those cases and it's not possible to perform a precise and performance\r\n    // effective test for hidden elements. E.g. even jQuery's ':visible' filter\r\n    // gives wrong results for elements with width & height less than 0.5.\r\n    if (!clientWidth && !clientHeight) {\r\n        return emptyRect;\r\n    }\r\n    var styles = getWindowOf(target).getComputedStyle(target);\r\n    var paddings = getPaddings(styles);\r\n    var horizPad = paddings.left + paddings.right;\r\n    var vertPad = paddings.top + paddings.bottom;\r\n    // Computed styles of width & height are being used because they are the\r\n    // only dimensions available to JS that contain non-rounded values. It could\r\n    // be possible to utilize the getBoundingClientRect if only it's data wasn't\r\n    // affected by CSS transformations let alone paddings, borders and scroll bars.\r\n    var width = toFloat(styles.width), height = toFloat(styles.height);\r\n    // Width & height include paddings and borders when the 'border-box' box\r\n    // model is applied (except for IE).\r\n    if (styles.boxSizing === 'border-box') {\r\n        // Following conditions are required to handle Internet Explorer which\r\n        // doesn't include paddings and borders to computed CSS dimensions.\r\n        //\r\n        // We can say that if CSS dimensions + paddings are equal to the \"client\"\r\n        // properties then it's either IE, and thus we don't need to subtract\r\n        // anything, or an element merely doesn't have paddings/borders styles.\r\n        if (Math.round(width + horizPad) !== clientWidth) {\r\n            width -= getBordersSize(styles, 'left', 'right') + horizPad;\r\n        }\r\n        if (Math.round(height + vertPad) !== clientHeight) {\r\n            height -= getBordersSize(styles, 'top', 'bottom') + vertPad;\r\n        }\r\n    }\r\n    // Following steps can't be applied to the document's root element as its\r\n    // client[Width/Height] properties represent viewport area of the window.\r\n    // Besides, it's as well not necessary as the <html> itself neither has\r\n    // rendered scroll bars nor it can be clipped.\r\n    if (!isDocumentElement(target)) {\r\n        // In some browsers (only in Firefox, actually) CSS width & height\r\n        // include scroll bars size which can be removed at this step as scroll\r\n        // bars are the only difference between rounded dimensions + paddings\r\n        // and \"client\" properties, though that is not always true in Chrome.\r\n        var vertScrollbar = Math.round(width + horizPad) - clientWidth;\r\n        var horizScrollbar = Math.round(height + vertPad) - clientHeight;\r\n        // Chrome has a rather weird rounding of \"client\" properties.\r\n        // E.g. for an element with content width of 314.2px it sometimes gives\r\n        // the client width of 315px and for the width of 314.7px it may give\r\n        // 314px. And it doesn't happen all the time. So just ignore this delta\r\n        // as a non-relevant.\r\n        if (Math.abs(vertScrollbar) !== 1) {\r\n            width -= vertScrollbar;\r\n        }\r\n        if (Math.abs(horizScrollbar) !== 1) {\r\n            height -= horizScrollbar;\r\n        }\r\n    }\r\n    return createRectInit(paddings.left, paddings.top, width, height);\r\n}\r\n/**\r\n * Checks whether provided element is an instance of the SVGGraphicsElement.\r\n *\r\n * @param {Element} target - Element to be checked.\r\n * @returns {boolean}\r\n */\r\nvar isSVGGraphicsElement = (function () {\r\n    // Some browsers, namely IE and Edge, don't have the SVGGraphicsElement\r\n    // interface.\r\n    if (typeof SVGGraphicsElement !== 'undefined') {\r\n        return function (target) { return target instanceof getWindowOf(target).SVGGraphicsElement; };\r\n    }\r\n    // If it's so, then check that element is at least an instance of the\r\n    // SVGElement and that it has the \"getBBox\" method.\r\n    // eslint-disable-next-line no-extra-parens\r\n    return function (target) { return (target instanceof getWindowOf(target).SVGElement &&\r\n        typeof target.getBBox === 'function'); };\r\n})();\r\n/**\r\n * Checks whether provided element is a document element (<html>).\r\n *\r\n * @param {Element} target - Element to be checked.\r\n * @returns {boolean}\r\n */\r\nfunction isDocumentElement(target) {\r\n    return target === getWindowOf(target).document.documentElement;\r\n}\r\n/**\r\n * Calculates an appropriate content rectangle for provided html or svg element.\r\n *\r\n * @param {Element} target - Element content rectangle of which needs to be calculated.\r\n * @returns {DOMRectInit}\r\n */\r\nfunction getContentRect(target) {\r\n    if (!isBrowser) {\r\n        return emptyRect;\r\n    }\r\n    if (isSVGGraphicsElement(target)) {\r\n        return getSVGContentRect(target);\r\n    }\r\n    return getHTMLElementContentRect(target);\r\n}\r\n/**\r\n * Creates rectangle with an interface of the DOMRectReadOnly.\r\n * Spec: https://drafts.fxtf.org/geometry/#domrectreadonly\r\n *\r\n * @param {DOMRectInit} rectInit - Object with rectangle's x/y coordinates and dimensions.\r\n * @returns {DOMRectReadOnly}\r\n */\r\nfunction createReadOnlyRect(_a) {\r\n    var x = _a.x, y = _a.y, width = _a.width, height = _a.height;\r\n    // If DOMRectReadOnly is available use it as a prototype for the rectangle.\r\n    var Constr = typeof DOMRectReadOnly !== 'undefined' ? DOMRectReadOnly : Object;\r\n    var rect = Object.create(Constr.prototype);\r\n    // Rectangle's properties are not writable and non-enumerable.\r\n    defineConfigurable(rect, {\r\n        x: x, y: y, width: width, height: height,\r\n        top: y,\r\n        right: x + width,\r\n        bottom: height + y,\r\n        left: x\r\n    });\r\n    return rect;\r\n}\r\n/**\r\n * Creates DOMRectInit object based on the provided dimensions and the x/y coordinates.\r\n * Spec: https://drafts.fxtf.org/geometry/#dictdef-domrectinit\r\n *\r\n * @param {number} x - X coordinate.\r\n * @param {number} y - Y coordinate.\r\n * @param {number} width - Rectangle's width.\r\n * @param {number} height - Rectangle's height.\r\n * @returns {DOMRectInit}\r\n */\r\nfunction createRectInit(x, y, width, height) {\r\n    return { x: x, y: y, width: width, height: height };\r\n}\n\n/**\r\n * Class that is responsible for computations of the content rectangle of\r\n * provided DOM element and for keeping track of it's changes.\r\n */\r\nvar ResizeObservation = /** @class */ (function () {\r\n    /**\r\n     * Creates an instance of ResizeObservation.\r\n     *\r\n     * @param {Element} target - Element to be observed.\r\n     */\r\n    function ResizeObservation(target) {\r\n        /**\r\n         * Broadcasted width of content rectangle.\r\n         *\r\n         * @type {number}\r\n         */\r\n        this.broadcastWidth = 0;\r\n        /**\r\n         * Broadcasted height of content rectangle.\r\n         *\r\n         * @type {number}\r\n         */\r\n        this.broadcastHeight = 0;\r\n        /**\r\n         * Reference to the last observed content rectangle.\r\n         *\r\n         * @private {DOMRectInit}\r\n         */\r\n        this.contentRect_ = createRectInit(0, 0, 0, 0);\r\n        this.target = target;\r\n    }\r\n    /**\r\n     * Updates content rectangle and tells whether it's width or height properties\r\n     * have changed since the last broadcast.\r\n     *\r\n     * @returns {boolean}\r\n     */\r\n    ResizeObservation.prototype.isActive = function () {\r\n        var rect = getContentRect(this.target);\r\n        this.contentRect_ = rect;\r\n        return (rect.width !== this.broadcastWidth ||\r\n            rect.height !== this.broadcastHeight);\r\n    };\r\n    /**\r\n     * Updates 'broadcastWidth' and 'broadcastHeight' properties with a data\r\n     * from the corresponding properties of the last observed content rectangle.\r\n     *\r\n     * @returns {DOMRectInit} Last observed content rectangle.\r\n     */\r\n    ResizeObservation.prototype.broadcastRect = function () {\r\n        var rect = this.contentRect_;\r\n        this.broadcastWidth = rect.width;\r\n        this.broadcastHeight = rect.height;\r\n        return rect;\r\n    };\r\n    return ResizeObservation;\r\n}());\n\nvar ResizeObserverEntry = /** @class */ (function () {\r\n    /**\r\n     * Creates an instance of ResizeObserverEntry.\r\n     *\r\n     * @param {Element} target - Element that is being observed.\r\n     * @param {DOMRectInit} rectInit - Data of the element's content rectangle.\r\n     */\r\n    function ResizeObserverEntry(target, rectInit) {\r\n        var contentRect = createReadOnlyRect(rectInit);\r\n        // According to the specification following properties are not writable\r\n        // and are also not enumerable in the native implementation.\r\n        //\r\n        // Property accessors are not being used as they'd require to define a\r\n        // private WeakMap storage which may cause memory leaks in browsers that\r\n        // don't support this type of collections.\r\n        defineConfigurable(this, { target: target, contentRect: contentRect });\r\n    }\r\n    return ResizeObserverEntry;\r\n}());\n\nvar ResizeObserverSPI = /** @class */ (function () {\r\n    /**\r\n     * Creates a new instance of ResizeObserver.\r\n     *\r\n     * @param {ResizeObserverCallback} callback - Callback function that is invoked\r\n     *      when one of the observed elements changes it's content dimensions.\r\n     * @param {ResizeObserverController} controller - Controller instance which\r\n     *      is responsible for the updates of observer.\r\n     * @param {ResizeObserver} callbackCtx - Reference to the public\r\n     *      ResizeObserver instance which will be passed to callback function.\r\n     */\r\n    function ResizeObserverSPI(callback, controller, callbackCtx) {\r\n        /**\r\n         * Collection of resize observations that have detected changes in dimensions\r\n         * of elements.\r\n         *\r\n         * @private {Array<ResizeObservation>}\r\n         */\r\n        this.activeObservations_ = [];\r\n        /**\r\n         * Registry of the ResizeObservation instances.\r\n         *\r\n         * @private {Map<Element, ResizeObservation>}\r\n         */\r\n        this.observations_ = new MapShim();\r\n        if (typeof callback !== 'function') {\r\n            throw new TypeError('The callback provided as parameter 1 is not a function.');\r\n        }\r\n        this.callback_ = callback;\r\n        this.controller_ = controller;\r\n        this.callbackCtx_ = callbackCtx;\r\n    }\r\n    /**\r\n     * Starts observing provided element.\r\n     *\r\n     * @param {Element} target - Element to be observed.\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverSPI.prototype.observe = function (target) {\r\n        if (!arguments.length) {\r\n            throw new TypeError('1 argument required, but only 0 present.');\r\n        }\r\n        // Do nothing if current environment doesn't have the Element interface.\r\n        if (typeof Element === 'undefined' || !(Element instanceof Object)) {\r\n            return;\r\n        }\r\n        if (!(target instanceof getWindowOf(target).Element)) {\r\n            throw new TypeError('parameter 1 is not of type \"Element\".');\r\n        }\r\n        var observations = this.observations_;\r\n        // Do nothing if element is already being observed.\r\n        if (observations.has(target)) {\r\n            return;\r\n        }\r\n        observations.set(target, new ResizeObservation(target));\r\n        this.controller_.addObserver(this);\r\n        // Force the update of observations.\r\n        this.controller_.refresh();\r\n    };\r\n    /**\r\n     * Stops observing provided element.\r\n     *\r\n     * @param {Element} target - Element to stop observing.\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverSPI.prototype.unobserve = function (target) {\r\n        if (!arguments.length) {\r\n            throw new TypeError('1 argument required, but only 0 present.');\r\n        }\r\n        // Do nothing if current environment doesn't have the Element interface.\r\n        if (typeof Element === 'undefined' || !(Element instanceof Object)) {\r\n            return;\r\n        }\r\n        if (!(target instanceof getWindowOf(target).Element)) {\r\n            throw new TypeError('parameter 1 is not of type \"Element\".');\r\n        }\r\n        var observations = this.observations_;\r\n        // Do nothing if element is not being observed.\r\n        if (!observations.has(target)) {\r\n            return;\r\n        }\r\n        observations.delete(target);\r\n        if (!observations.size) {\r\n            this.controller_.removeObserver(this);\r\n        }\r\n    };\r\n    /**\r\n     * Stops observing all elements.\r\n     *\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverSPI.prototype.disconnect = function () {\r\n        this.clearActive();\r\n        this.observations_.clear();\r\n        this.controller_.removeObserver(this);\r\n    };\r\n    /**\r\n     * Collects observation instances the associated element of which has changed\r\n     * it's content rectangle.\r\n     *\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverSPI.prototype.gatherActive = function () {\r\n        var _this = this;\r\n        this.clearActive();\r\n        this.observations_.forEach(function (observation) {\r\n            if (observation.isActive()) {\r\n                _this.activeObservations_.push(observation);\r\n            }\r\n        });\r\n    };\r\n    /**\r\n     * Invokes initial callback function with a list of ResizeObserverEntry\r\n     * instances collected from active resize observations.\r\n     *\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverSPI.prototype.broadcastActive = function () {\r\n        // Do nothing if observer doesn't have active observations.\r\n        if (!this.hasActive()) {\r\n            return;\r\n        }\r\n        var ctx = this.callbackCtx_;\r\n        // Create ResizeObserverEntry instance for every active observation.\r\n        var entries = this.activeObservations_.map(function (observation) {\r\n            return new ResizeObserverEntry(observation.target, observation.broadcastRect());\r\n        });\r\n        this.callback_.call(ctx, entries, ctx);\r\n        this.clearActive();\r\n    };\r\n    /**\r\n     * Clears the collection of active observations.\r\n     *\r\n     * @returns {void}\r\n     */\r\n    ResizeObserverSPI.prototype.clearActive = function () {\r\n        this.activeObservations_.splice(0);\r\n    };\r\n    /**\r\n     * Tells whether observer has active observations.\r\n     *\r\n     * @returns {boolean}\r\n     */\r\n    ResizeObserverSPI.prototype.hasActive = function () {\r\n        return this.activeObservations_.length > 0;\r\n    };\r\n    return ResizeObserverSPI;\r\n}());\n\n// Registry of internal observers. If WeakMap is not available use current shim\r\n// for the Map collection as it has all required methods and because WeakMap\r\n// can't be fully polyfilled anyway.\r\nvar observers = typeof WeakMap !== 'undefined' ? new WeakMap() : new MapShim();\r\n/**\r\n * ResizeObserver API. Encapsulates the ResizeObserver SPI implementation\r\n * exposing only those methods and properties that are defined in the spec.\r\n */\r\nvar ResizeObserver = /** @class */ (function () {\r\n    /**\r\n     * Creates a new instance of ResizeObserver.\r\n     *\r\n     * @param {ResizeObserverCallback} callback - Callback that is invoked when\r\n     *      dimensions of the observed elements change.\r\n     */\r\n    function ResizeObserver(callback) {\r\n        if (!(this instanceof ResizeObserver)) {\r\n            throw new TypeError('Cannot call a class as a function.');\r\n        }\r\n        if (!arguments.length) {\r\n            throw new TypeError('1 argument required, but only 0 present.');\r\n        }\r\n        var controller = ResizeObserverController.getInstance();\r\n        var observer = new ResizeObserverSPI(callback, controller, this);\r\n        observers.set(this, observer);\r\n    }\r\n    return ResizeObserver;\r\n}());\r\n// Expose public methods of ResizeObserver.\r\n[\r\n    'observe',\r\n    'unobserve',\r\n    'disconnect'\r\n].forEach(function (method) {\r\n    ResizeObserver.prototype[method] = function () {\r\n        var _a;\r\n        return (_a = observers.get(this))[method].apply(_a, arguments);\r\n    };\r\n});\n\nvar index = (function () {\r\n    // Export existing implementation if available.\r\n    if (typeof global$1.ResizeObserver !== 'undefined') {\r\n        return global$1.ResizeObserver;\r\n    }\r\n    return ResizeObserver;\r\n})();\n\nexport default index;\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport ResizeObserver from \"resize-observer-polyfill\"\nimport {\n  NEVER,\n  Observable,\n  Subject,\n  defer,\n  filter,\n  finalize,\n  map,\n  merge,\n  of,\n  shareReplay,\n  startWith,\n  switchMap,\n  tap\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Element offset\n */\nexport interface ElementSize {\n  width: number                        /* Element width */\n  height: number                       /* Element height */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Resize observer entry subject\n */\nconst entry$ = new Subject<ResizeObserverEntry>()\n\n/**\n * Resize observer observable\n *\n * This observable will create a `ResizeObserver` on the first subscription\n * and will automatically terminate it when there are no more subscribers.\n * It's quite important to centralize observation in a single `ResizeObserver`,\n * as the performance difference can be quite dramatic, as the link shows.\n *\n * @see https://bit.ly/3iIYfEm - Google Groups on performance\n */\nconst observer$ = defer(() => of(\n  new ResizeObserver(entries => {\n    for (const entry of entries)\n      entry$.next(entry)\n  })\n))\n  .pipe(\n    switchMap(observer => merge(NEVER, of(observer))\n      .pipe(\n        finalize(() => observer.disconnect())\n      )\n    ),\n    shareReplay(1)\n  )\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element size\n *\n * @param el - Element\n *\n * @returns Element size\n */\nexport function getElementSize(\n  el: HTMLElement\n): ElementSize {\n  return {\n    width:  el.offsetWidth,\n    height: el.offsetHeight\n  }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch element size\n *\n * This function returns an observable that subscribes to a single internal\n * instance of `ResizeObserver` upon subscription, and emit resize events until\n * termination. Note that this function should not be called with the same\n * element twice, as the first unsubscription will terminate observation.\n *\n * Sadly, we can't use the `DOMRect` objects returned by the observer, because\n * we need the emitted values to be consistent with `getElementSize`, which will\n * return the used values (rounded) and not actual values (unrounded). Thus, we\n * use the `offset*` properties. See the linked GitHub issue.\n *\n * @see https://bit.ly/3m0k3he - GitHub issue\n *\n * @param el - Element\n *\n * @returns Element size observable\n */\nexport function watchElementSize(\n  el: HTMLElement\n): Observable<ElementSize> {\n  return observer$\n    .pipe(\n      tap(observer => observer.observe(el)),\n      switchMap(observer => entry$\n        .pipe(\n          filter(({ target }) => target === el),\n          finalize(() => observer.unobserve(el)),\n          map(() => getElementSize(el))\n        )\n      ),\n      startWith(getElementSize(el))\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { ElementSize } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element content size (= scroll width and height)\n *\n * @param el - Element\n *\n * @returns Element content size\n */\nexport function getElementContentSize(\n  el: HTMLElement\n): ElementSize {\n  return {\n    width:  el.scrollWidth,\n    height: el.scrollHeight\n  }\n}\n\n/**\n * Retrieve the overflowing container of an element, if any\n *\n * @param el - Element\n *\n * @returns Overflowing container or nothing\n */\nexport function getElementContainer(\n  el: HTMLElement\n): HTMLElement | undefined {\n  let parent = el.parentElement\n  while (parent)\n    if (\n      el.scrollWidth <= parent.scrollWidth &&\n      el.scrollHeight <= parent.scrollHeight\n    )\n      parent = (el = parent).parentElement\n    else\n      break\n\n  /* Return overflowing container */\n  return parent ? el : undefined\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  NEVER,\n  Observable,\n  Subject,\n  defer,\n  distinctUntilChanged,\n  filter,\n  finalize,\n  map,\n  merge,\n  of,\n  shareReplay,\n  switchMap,\n  tap\n} from \"rxjs\"\n\nimport {\n  getElementContentSize,\n  getElementSize,\n  watchElementContentOffset\n} from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Intersection observer entry subject\n */\nconst entry$ = new Subject<IntersectionObserverEntry>()\n\n/**\n * Intersection observer observable\n *\n * This observable will create an `IntersectionObserver` on first subscription\n * and will automatically terminate it when there are no more subscribers.\n *\n * @see https://bit.ly/3iIYfEm - Google Groups on performance\n */\nconst observer$ = defer(() => of(\n  new IntersectionObserver(entries => {\n    for (const entry of entries)\n      entry$.next(entry)\n  }, {\n    threshold: 0\n  })\n))\n  .pipe(\n    switchMap(observer => merge(NEVER, of(observer))\n      .pipe(\n        finalize(() => observer.disconnect())\n      )\n    ),\n    shareReplay(1)\n  )\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch element visibility\n *\n * @param el - Element\n *\n * @returns Element visibility observable\n */\nexport function watchElementVisibility(\n  el: HTMLElement\n): Observable<boolean> {\n  return observer$\n    .pipe(\n      tap(observer => observer.observe(el)),\n      switchMap(observer => entry$\n        .pipe(\n          filter(({ target }) => target === el),\n          finalize(() => observer.unobserve(el)),\n          map(({ isIntersecting }) => isIntersecting)\n        )\n      )\n    )\n}\n\n/**\n * Watch element boundary\n *\n * This function returns an observable which emits whether the bottom content\n * boundary (= scroll offset) of an element is within a certain threshold.\n *\n * @param el - Element\n * @param threshold - Threshold\n *\n * @returns Element boundary observable\n */\nexport function watchElementBoundary(\n  el: HTMLElement, threshold = 16\n): Observable<boolean> {\n  return watchElementContentOffset(el)\n    .pipe(\n      map(({ y }) => {\n        const visible = getElementSize(el)\n        const content = getElementContentSize(el)\n        return y >= (\n          content.height - visible.height - threshold\n        )\n      }),\n      distinctUntilChanged()\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  fromEvent,\n  map,\n  startWith\n} from \"rxjs\"\n\nimport { getElement } from \"../element\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Toggle\n */\nexport type Toggle =\n  | \"drawer\"                           /* Toggle for drawer */\n  | \"search\"                           /* Toggle for search */\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Toggle map\n */\nconst toggles: Record<Toggle, HTMLInputElement> = {\n  drawer: getElement(\"[data-md-toggle=drawer]\"),\n  search: getElement(\"[data-md-toggle=search]\")\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve the value of a toggle\n *\n * @param name - Toggle\n *\n * @returns Toggle value\n */\nexport function getToggle(name: Toggle): boolean {\n  return toggles[name].checked\n}\n\n/**\n * Set toggle\n *\n * Simulating a click event seems to be the most cross-browser compatible way\n * of changing the value while also emitting a `change` event. Before, Material\n * used `CustomEvent` to programmatically change the value of a toggle, but this\n * is a much simpler and cleaner solution which doesn't require a polyfill.\n *\n * @param name - Toggle\n * @param value - Toggle value\n */\nexport function setToggle(name: Toggle, value: boolean): void {\n  if (toggles[name].checked !== value)\n    toggles[name].click()\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch toggle\n *\n * @param name - Toggle\n *\n * @returns Toggle value observable\n */\nexport function watchToggle(name: Toggle): Observable<boolean> {\n  const el = toggles[name]\n  return fromEvent(el, \"change\")\n    .pipe(\n      map(() => el.checked),\n      startWith(el.checked)\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  filter,\n  fromEvent,\n  map,\n  share\n} from \"rxjs\"\n\nimport { getActiveElement } from \"../element\"\nimport { getToggle } from \"../toggle\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Keyboard mode\n */\nexport type KeyboardMode =\n  | \"global\"                           /* Global */\n  | \"search\"                           /* Search is open */\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Keyboard\n */\nexport interface Keyboard {\n  mode: KeyboardMode                   /* Keyboard mode */\n  type: string                         /* Key type */\n  claim(): void                        /* Key claim */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Check whether an element may receive keyboard input\n *\n * @param el - Element\n * @param type - Key type\n *\n * @returns Test result\n */\nfunction isSusceptibleToKeyboard(\n  el: HTMLElement, type: string\n): boolean {\n  switch (el.constructor) {\n\n    /* Input elements */\n    case HTMLInputElement:\n      /* @ts-expect-error - omit unnecessary type cast */\n      if (el.type === \"radio\")\n        return /^Arrow/.test(type)\n      else\n        return true\n\n    /* Select element and textarea */\n    case HTMLSelectElement:\n    case HTMLTextAreaElement:\n      return true\n\n    /* Everything else */\n    default:\n      return el.isContentEditable\n  }\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch keyboard\n *\n * @returns Keyboard observable\n */\nexport function watchKeyboard(): Observable<Keyboard> {\n  return fromEvent<KeyboardEvent>(window, \"keydown\")\n    .pipe(\n      filter(ev => !(ev.metaKey || ev.ctrlKey)),\n      map(ev => ({\n        mode: getToggle(\"search\") ? \"search\" : \"global\",\n        type: ev.key,\n        claim() {\n          ev.preventDefault()\n          ev.stopPropagation()\n        }\n      } as Keyboard)),\n      filter(({ mode, type }) => {\n        if (mode === \"global\") {\n          const active = getActiveElement()\n          if (typeof active !== \"undefined\")\n            return !isSusceptibleToKeyboard(active, type)\n        }\n        return true\n      }),\n      share()\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Subject } from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve location\n *\n * This function returns a `URL` object (and not `Location`) to normalize the\n * typings across the application. Furthermore, locations need to be tracked\n * without setting them and `Location` is a singleton which represents the\n * current location.\n *\n * @returns URL\n */\nexport function getLocation(): URL {\n  return new URL(location.href)\n}\n\n/**\n * Set location\n *\n * @param url - URL to change to\n */\nexport function setLocation(url: URL): void {\n  location.href = url.href\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch location\n *\n * @returns Location subject\n */\nexport function watchLocation(): Subject<URL> {\n  return new Subject<URL>()\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { JSX as JSXInternal } from \"preact\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * HTML attributes\n */\ntype Attributes =\n  & JSXInternal.HTMLAttributes\n  & JSXInternal.SVGAttributes\n  & Record<string, any>\n\n/**\n * Child element\n */\ntype Child =\n  | HTMLElement\n  | Text\n  | string\n  | number\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Append a child node to an element\n *\n * @param el - Element\n * @param child - Child node(s)\n */\nfunction appendChild(el: HTMLElement, child: Child | Child[]): void {\n\n  /* Handle primitive types (including raw HTML) */\n  if (typeof child === \"string\" || typeof child === \"number\") {\n    el.innerHTML += child.toString()\n\n  /* Handle nodes */\n  } else if (child instanceof Node) {\n    el.appendChild(child)\n\n  /* Handle nested children */\n  } else if (Array.isArray(child)) {\n    for (const node of child)\n      appendChild(el, node)\n  }\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * JSX factory\n *\n * @template T - Element type\n *\n * @param tag - HTML tag\n * @param attributes - HTML attributes\n * @param children - Child elements\n *\n * @returns Element\n */\nexport function h<T extends keyof HTMLElementTagNameMap>(\n  tag: T, attributes?: Attributes | null, ...children: Child[]\n): HTMLElementTagNameMap[T]\n\nexport function h<T extends h.JSX.Element>(\n  tag: string, attributes?: Attributes | null, ...children: Child[]\n): T\n\nexport function h<T extends h.JSX.Element>(\n  tag: string, attributes?: Attributes | null, ...children: Child[]\n): T {\n  const el = document.createElement(tag)\n\n  /* Set attributes, if any */\n  if (attributes)\n    for (const attr of Object.keys(attributes)) {\n      if (typeof attributes[attr] === \"undefined\")\n        continue\n\n      /* Set default attribute or boolean */\n      if (typeof attributes[attr] !== \"boolean\")\n        el.setAttribute(attr, attributes[attr])\n      else\n        el.setAttribute(attr, \"\")\n    }\n\n  /* Append child nodes */\n  for (const child of children)\n    appendChild(el, child)\n\n  /* Return element */\n  return el as T\n}\n\n/* ----------------------------------------------------------------------------\n * Namespace\n * ------------------------------------------------------------------------- */\n\nexport declare namespace h {\n  namespace JSX {\n    type Element = HTMLElement\n    type IntrinsicElements = JSXInternal.IntrinsicElements\n  }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Truncate a string after the given number of characters\n *\n * This is not a very reasonable approach, since the summaries kind of suck.\n * It would be better to create something more intelligent, highlighting the\n * search occurrences and making a better summary out of it, but this note was\n * written three years ago, so who knows if we'll ever fix it.\n *\n * @param value - Value to be truncated\n * @param n - Number of characters\n *\n * @returns Truncated value\n */\nexport function truncate(value: string, n: number): string {\n  let i = n\n  if (value.length > i) {\n    while (value[i] !== \" \" && --i > 0) { /* keep eating */ }\n    return `${value.substring(0, i)}...`\n  }\n  return value\n}\n\n/**\n * Round a number for display with repository facts\n *\n * This is a reverse-engineered version of GitHub's weird rounding algorithm\n * for stars, forks and all other numbers. While all numbers below `1,000` are\n * returned as-is, bigger numbers are converted to fixed numbers:\n *\n * - `1,049` => `1k`\n * - `1,050` => `1.1k`\n * - `1,949` => `1.9k`\n * - `1,950` => `2k`\n *\n * @param value - Original value\n *\n * @returns Rounded value\n */\nexport function round(value: number): string {\n  if (value > 999) {\n    const digits = +((value - 950) % 1000 > 99)\n    return `${((value + 0.000001) / 1000).toFixed(digits)}k`\n  } else {\n    return value.toString()\n  }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  filter,\n  fromEvent,\n  map,\n  shareReplay,\n  startWith\n} from \"rxjs\"\n\nimport { getOptionalElement } from \"~/browser\"\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve location hash\n *\n * @returns Location hash\n */\nexport function getLocationHash(): string {\n  return location.hash.substring(1)\n}\n\n/**\n * Set location hash\n *\n * Setting a new fragment identifier via `location.hash` will have no effect\n * if the value doesn't change. When a new fragment identifier is set, we want\n * the browser to target the respective element at all times, which is why we\n * use this dirty little trick.\n *\n * @param hash - Location hash\n */\nexport function setLocationHash(hash: string): void {\n  const el = h(\"a\", { href: hash })\n  el.addEventListener(\"click\", ev => ev.stopPropagation())\n  el.click()\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch location hash\n *\n * @returns Location hash observable\n */\nexport function watchLocationHash(): Observable<string> {\n  return fromEvent<HashChangeEvent>(window, \"hashchange\")\n    .pipe(\n      map(getLocationHash),\n      startWith(getLocationHash()),\n      filter(hash => hash.length > 0),\n      shareReplay(1)\n    )\n}\n\n/**\n * Watch location target\n *\n * @returns Location target observable\n */\nexport function watchLocationTarget(): Observable<HTMLElement> {\n  return watchLocationHash()\n    .pipe(\n      map(id => getOptionalElement(`[id=\"${id}\"]`)!),\n      filter(el => typeof el !== \"undefined\")\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  fromEvent,\n  fromEventPattern,\n  map,\n  merge,\n  startWith,\n  switchMap\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch media query\n *\n * Note that although `MediaQueryList.addListener` is deprecated we have to\n * use it, because it's the only way to ensure proper downward compatibility.\n *\n * @see https://bit.ly/3dUBH2m - GitHub issue\n *\n * @param query - Media query\n *\n * @returns Media observable\n */\nexport function watchMedia(query: string): Observable<boolean> {\n  const media = matchMedia(query)\n  return fromEventPattern<boolean>(next => (\n    media.addListener(() => next(media.matches))\n  ))\n    .pipe(\n      startWith(media.matches)\n    )\n}\n\n/**\n * Watch print mode\n *\n * @returns Print observable\n */\nexport function watchPrint(): Observable<boolean> {\n  const media = matchMedia(\"print\")\n  return merge(\n    fromEvent(window, \"beforeprint\").pipe(map(() => true)),\n    fromEvent(window, \"afterprint\").pipe(map(() => false))\n  )\n    .pipe(\n      startWith(media.matches)\n    )\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Toggle an observable with a media observable\n *\n * @template T - Data type\n *\n * @param query$ - Media observable\n * @param factory - Observable factory\n *\n * @returns Toggled observable\n */\nexport function at<T>(\n  query$: Observable<boolean>, factory: () => Observable<T>\n): Observable<T> {\n  return query$\n    .pipe(\n      switchMap(active => active ? factory() : EMPTY)\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  catchError,\n  from,\n  map,\n  of,\n  shareReplay,\n  switchMap,\n  throwError\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch the given URL\n *\n * If the request fails (e.g. when dispatched from `file://` locations), the\n * observable will complete without emitting a value.\n *\n * @param url - Request URL\n * @param options - Options\n *\n * @returns Response observable\n */\nexport function request(\n  url: URL | string, options: RequestInit = { credentials: \"same-origin\" }\n): Observable<Response> {\n  return from(fetch(`${url}`, options))\n    .pipe(\n      catchError(() => EMPTY),\n      switchMap(res => res.status !== 200\n        ? throwError(() => new Error(res.statusText))\n        : of(res)\n      )\n    )\n}\n\n/**\n * Fetch JSON from the given URL\n *\n * @template T - Data type\n *\n * @param url - Request URL\n * @param options - Options\n *\n * @returns Data observable\n */\nexport function requestJSON<T>(\n  url: URL | string, options?: RequestInit\n): Observable<T> {\n  return request(url, options)\n    .pipe(\n      switchMap(res => res.json()),\n      shareReplay(1)\n    )\n}\n\n/**\n * Fetch XML from the given URL\n *\n * @param url - Request URL\n * @param options - Options\n *\n * @returns Data observable\n */\nexport function requestXML(\n  url: URL | string, options?: RequestInit\n): Observable<Document> {\n  const dom = new DOMParser()\n  return request(url, options)\n    .pipe(\n      switchMap(res => res.text()),\n      map(res => dom.parseFromString(res, \"text/xml\")),\n      shareReplay(1)\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  defer,\n  finalize,\n  fromEvent,\n  map,\n  merge,\n  switchMap,\n  take,\n  throwError\n} from \"rxjs\"\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create and load a `script` element\n *\n * This function returns an observable that will emit when the script was\n * successfully loaded, or throw an error if it didn't.\n *\n * @param src - Script URL\n *\n * @returns Script observable\n */\nexport function watchScript(src: string): Observable<void> {\n  const script = h(\"script\", { src })\n  return defer(() => {\n    document.head.appendChild(script)\n    return merge(\n      fromEvent(script, \"load\"),\n      fromEvent(script, \"error\")\n        .pipe(\n          switchMap(() => (\n            throwError(() => new ReferenceError(`Invalid script: ${src}`))\n          ))\n        )\n    )\n      .pipe(\n        map(() => undefined),\n        finalize(() => document.head.removeChild(script)),\n        take(1)\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  fromEvent,\n  map,\n  merge,\n  startWith\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Viewport offset\n */\nexport interface ViewportOffset {\n  x: number                            /* Horizontal offset */\n  y: number                            /* Vertical offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve viewport offset\n *\n * On iOS Safari, viewport offset can be negative due to overflow scrolling.\n * As this may induce strange behaviors downstream, we'll just limit it to 0.\n *\n * @returns Viewport offset\n */\nexport function getViewportOffset(): ViewportOffset {\n  return {\n    x: Math.max(0, scrollX),\n    y: Math.max(0, scrollY)\n  }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport offset\n *\n * @returns Viewport offset observable\n */\nexport function watchViewportOffset(): Observable<ViewportOffset> {\n  return merge(\n    fromEvent(window, \"scroll\", { passive: true }),\n    fromEvent(window, \"resize\", { passive: true })\n  )\n    .pipe(\n      map(getViewportOffset),\n      startWith(getViewportOffset())\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  fromEvent,\n  map,\n  startWith\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Viewport size\n */\nexport interface ViewportSize {\n  width: number                        /* Viewport width */\n  height: number                       /* Viewport height */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve viewport size\n *\n * @returns Viewport size\n */\nexport function getViewportSize(): ViewportSize {\n  return {\n    width:  innerWidth,\n    height: innerHeight\n  }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport size\n *\n * @returns Viewport size observable\n */\nexport function watchViewportSize(): Observable<ViewportSize> {\n  return fromEvent(window, \"resize\", { passive: true })\n    .pipe(\n      map(getViewportSize),\n      startWith(getViewportSize())\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  combineLatest,\n  map,\n  shareReplay\n} from \"rxjs\"\n\nimport {\n  ViewportOffset,\n  watchViewportOffset\n} from \"../offset\"\nimport {\n  ViewportSize,\n  watchViewportSize\n} from \"../size\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Viewport\n */\nexport interface Viewport {\n  offset: ViewportOffset               /* Viewport offset */\n  size: ViewportSize                   /* Viewport size */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport\n *\n * @returns Viewport observable\n */\nexport function watchViewport(): Observable<Viewport> {\n  return combineLatest([\n    watchViewportOffset(),\n    watchViewportSize()\n  ])\n    .pipe(\n      map(([offset, size]) => ({ offset, size })),\n      shareReplay(1)\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  combineLatest,\n  distinctUntilKeyChanged,\n  map\n} from \"rxjs\"\n\nimport { Header } from \"~/components\"\n\nimport { getElementOffset } from \"../../element\"\nimport { Viewport } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport relative to element\n *\n * @param el - Element\n * @param options - Options\n *\n * @returns Viewport observable\n */\nexport function watchViewportAt(\n  el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable<Viewport> {\n  const size$ = viewport$\n    .pipe(\n      distinctUntilKeyChanged(\"size\")\n    )\n\n  /* Compute element offset */\n  const offset$ = combineLatest([size$, header$])\n    .pipe(\n      map(() => getElementOffset(el))\n    )\n\n  /* Compute relative viewport, return hot observable */\n  return combineLatest([header$, viewport$, offset$])\n    .pipe(\n      map(([{ height }, { offset, size }, { x, y }]) => ({\n        offset: {\n          x: offset.x - x,\n          y: offset.y - y + height\n        },\n        size\n      }))\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  fromEvent,\n  map,\n  share,\n  switchMap,\n  tap,\n  throttle\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Worker message\n */\nexport interface WorkerMessage {\n  type: unknown                        /* Message type */\n  data?: unknown                       /* Message data */\n}\n\n/**\n * Worker handler\n *\n * @template T - Message type\n */\nexport interface WorkerHandler<\n  T extends WorkerMessage\n> {\n  tx$: Subject<T>                      /* Message transmission subject */\n  rx$: Observable<T>                   /* Message receive observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n *\n * @template T - Worker message type\n */\ninterface WatchOptions<T extends WorkerMessage> {\n  tx$: Observable<T>                   /* Message transmission observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch a web worker\n *\n * This function returns an observable that sends all values emitted by the\n * message observable to the web worker. Web worker communication is expected\n * to be bidirectional (request-response) and synchronous. Messages that are\n * emitted during a pending request are throttled, the last one is emitted.\n *\n * @param worker - Web worker\n * @param options - Options\n *\n * @returns Worker message observable\n */\nexport function watchWorker<T extends WorkerMessage>(\n  worker: Worker, { tx$ }: WatchOptions<T>\n): Observable<T> {\n\n  /* Intercept messages from worker-like objects */\n  const rx$ = fromEvent<MessageEvent>(worker, \"message\")\n    .pipe(\n      map(({ data }) => data as T)\n    )\n\n  /* Send and receive messages, return hot observable */\n  return tx$\n    .pipe(\n      throttle(() => rx$, { leading: true, trailing: true }),\n      tap(message => worker.postMessage(message)),\n      switchMap(() => rx$),\n      share()\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { getElement, getLocation } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Feature flag\n */\nexport type Flag =\n  | \"announce.dismiss\"                 /* Dismissable announcement bar */\n  | \"content.code.annotate\"            /* Code annotations */\n  | \"content.lazy\"                     /* Lazy content elements */\n  | \"content.tabs.link\"                /* Link content tabs */\n  | \"header.autohide\"                  /* Hide header */\n  | \"navigation.expand\"                /* Automatic expansion */\n  | \"navigation.indexes\"               /* Section pages */\n  | \"navigation.instant\"               /* Instant loading */\n  | \"navigation.sections\"              /* Section navigation */\n  | \"navigation.tabs\"                  /* Tabs navigation */\n  | \"navigation.tabs.sticky\"           /* Tabs navigation (sticky) */\n  | \"navigation.top\"                   /* Back-to-top button */\n  | \"navigation.tracking\"              /* Anchor tracking */\n  | \"search.highlight\"                 /* Search highlighting */\n  | \"search.share\"                     /* Search sharing */\n  | \"search.suggest\"                   /* Search suggestions */\n  | \"toc.follow\"                       /* Following table of contents */\n  | \"toc.integrate\"                    /* Integrated table of contents */\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Translation\n */\nexport type Translation =\n  | \"clipboard.copy\"                   /* Copy to clipboard */\n  | \"clipboard.copied\"                 /* Copied to clipboard */\n  | \"search.config.lang\"               /* Search language */\n  | \"search.config.pipeline\"           /* Search pipeline */\n  | \"search.config.separator\"          /* Search separator */\n  | \"search.placeholder\"               /* Search */\n  | \"search.result.placeholder\"        /* Type to start searching */\n  | \"search.result.none\"               /* No matching documents */\n  | \"search.result.one\"                /* 1 matching document */\n  | \"search.result.other\"              /* # matching documents */\n  | \"search.result.more.one\"           /* 1 more on this page */\n  | \"search.result.more.other\"         /* # more on this page */\n  | \"search.result.term.missing\"       /* Missing */\n  | \"select.version.title\"             /* Version selector */\n\n/**\n * Translations\n */\nexport type Translations = Record<Translation, string>\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Versioning\n */\nexport interface Versioning {\n  provider: \"mike\"                     /* Version provider */\n  default?: string                     /* Default version */\n}\n\n/**\n * Configuration\n */\nexport interface Config {\n  base: string                         /* Base URL */\n  features: Flag[]                     /* Feature flags */\n  translations: Translations           /* Translations */\n  search: string                       /* Search worker URL */\n  tags?: Record<string, string>        /* Tags mapping */\n  version?: Versioning                 /* Versioning */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve global configuration and make base URL absolute\n */\nconst script = getElement(\"#__config\")\nconst config: Config = JSON.parse(script.textContent!)\nconfig.base = `${new URL(config.base, getLocation())}`\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve global configuration\n *\n * @returns Global configuration\n */\nexport function configuration(): Config {\n  return config\n}\n\n/**\n * Check whether a feature flag is enabled\n *\n * @param flag - Feature flag\n *\n * @returns Test result\n */\nexport function feature(flag: Flag): boolean {\n  return config.features.includes(flag)\n}\n\n/**\n * Retrieve the translation for the given key\n *\n * @param key - Key to be translated\n * @param value - Positional value, if any\n *\n * @returns Translation\n */\nexport function translation(\n  key: Translation, value?: string | number\n): string {\n  return typeof value !== \"undefined\"\n    ? config.translations[key].replace(\"#\", value.toString())\n    : config.translations[key]\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { getElement, getElements } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Component type\n */\nexport type ComponentType =\n  | \"announce\"                         /* Announcement bar */\n  | \"container\"                        /* Container */\n  | \"consent\"                          /* Consent */\n  | \"content\"                          /* Content */\n  | \"dialog\"                           /* Dialog */\n  | \"header\"                           /* Header */\n  | \"header-title\"                     /* Header title */\n  | \"header-topic\"                     /* Header topic */\n  | \"main\"                             /* Main area */\n  | \"outdated\"                         /* Version warning */\n  | \"palette\"                          /* Color palette */\n  | \"search\"                           /* Search */\n  | \"search-query\"                     /* Search input */\n  | \"search-result\"                    /* Search results */\n  | \"search-share\"                     /* Search sharing */\n  | \"search-suggest\"                   /* Search suggestions */\n  | \"sidebar\"                          /* Sidebar */\n  | \"skip\"                             /* Skip link */\n  | \"source\"                           /* Repository information */\n  | \"tabs\"                             /* Navigation tabs */\n  | \"toc\"                              /* Table of contents */\n  | \"top\"                              /* Back-to-top button */\n\n/**\n * Component\n *\n * @template T - Component type\n * @template U - Reference type\n */\nexport type Component<\n  T extends {} = {},\n  U extends HTMLElement = HTMLElement\n> =\n  T & {\n    ref: U                             /* Component reference */\n  }\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Component type map\n */\ninterface ComponentTypeMap {\n  \"announce\": HTMLElement              /* Announcement bar */\n  \"container\": HTMLElement             /* Container */\n  \"consent\": HTMLElement               /* Consent */\n  \"content\": HTMLElement               /* Content */\n  \"dialog\": HTMLElement                /* Dialog */\n  \"header\": HTMLElement                /* Header */\n  \"header-title\": HTMLElement          /* Header title */\n  \"header-topic\": HTMLElement          /* Header topic */\n  \"main\": HTMLElement                  /* Main area */\n  \"outdated\": HTMLElement              /* Version warning */\n  \"palette\": HTMLElement               /* Color palette */\n  \"search\": HTMLElement                /* Search */\n  \"search-query\": HTMLInputElement     /* Search input */\n  \"search-result\": HTMLElement         /* Search results */\n  \"search-share\": HTMLAnchorElement    /* Search sharing */\n  \"search-suggest\": HTMLElement        /* Search suggestions */\n  \"sidebar\": HTMLElement               /* Sidebar */\n  \"skip\": HTMLAnchorElement            /* Skip link */\n  \"source\": HTMLAnchorElement          /* Repository information */\n  \"tabs\": HTMLElement                  /* Navigation tabs */\n  \"toc\": HTMLElement                   /* Table of contents */\n  \"top\": HTMLAnchorElement             /* Back-to-top button */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve the element for a given component or throw a reference error\n *\n * @template T - Component type\n *\n * @param type - Component type\n * @param node - Node of reference\n *\n * @returns Element\n */\nexport function getComponentElement<T extends ComponentType>(\n  type: T, node: ParentNode = document\n): ComponentTypeMap[T] {\n  return getElement(`[data-md-component=${type}]`, node)\n}\n\n/**\n * Retrieve all elements for a given component\n *\n * @template T - Component type\n *\n * @param type - Component type\n * @param node - Node of reference\n *\n * @returns Elements\n */\nexport function getComponentElements<T extends ComponentType>(\n  type: T, node: ParentNode = document\n): ComponentTypeMap[T][] {\n  return getElements(`[data-md-component=${type}]`, node)\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  Subject,\n  defer,\n  finalize,\n  fromEvent,\n  map,\n  startWith,\n  tap\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport { getElement } from \"~/browser\"\n\nimport { Component } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Announcement bar\n */\nexport interface Announce {\n  hash: number                        /* Content hash */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch announcement bar\n *\n * @param el - Announcement bar element\n *\n * @returns Announcement bar observable\n */\nexport function watchAnnounce(\n  el: HTMLElement\n): Observable<Announce> {\n  const button = getElement(\".md-typeset > :first-child\", el)\n  return fromEvent(button, \"click\", { once: true })\n    .pipe(\n      map(() => getElement(\".md-typeset\", el)),\n      map(content => ({ hash: __md_hash(content.innerHTML) }))\n    )\n}\n\n/**\n * Mount announcement bar\n *\n * @param el - Announcement bar element\n *\n * @returns Announcement bar component observable\n */\nexport function mountAnnounce(\n  el: HTMLElement\n): Observable<Component<Announce>> {\n  if (!feature(\"announce.dismiss\") || !el.childElementCount)\n    return EMPTY\n\n  /* Mount component on subscription */\n  return defer(() => {\n    const push$ = new Subject<Announce>()\n    push$\n      .pipe(\n        startWith({ hash: __md_get<number>(\"__announce\") })\n      )\n        .subscribe(({ hash }) => {\n          if (hash && hash === (__md_get<number>(\"__announce\") ?? hash)) {\n            el.hidden = true\n\n            /* Persist preference in local storage */\n            __md_set<number>(\"__announce\", hash)\n          }\n        })\n\n    /* Create and return component */\n    return watchAnnounce(el)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  finalize,\n  map,\n  tap\n} from \"rxjs\"\n\nimport { Component } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Consent\n */\nexport interface Consent {\n  hidden: boolean                      /* Consent is hidden */\n}\n\n/**\n * Consent defaults\n */\nexport interface ConsentDefaults {\n  analytics?: boolean                  /* Consent for Analytics */\n  github?: boolean                     /* Consent for GitHub */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  target$: Observable<HTMLElement>     /* Target observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  target$: Observable<HTMLElement>     /* Target observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch consent\n *\n * @param el - Consent element\n * @param options - Options\n *\n * @returns Consent observable\n */\nexport function watchConsent(\n  el: HTMLElement, { target$ }: WatchOptions\n): Observable<Consent> {\n  return target$\n    .pipe(\n      map(target => ({ hidden: target !== el }))\n    )\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Mount consent\n *\n * @param el - Consent element\n * @param options - Options\n *\n * @returns Consent component observable\n */\nexport function mountConsent(\n  el: HTMLElement, options: MountOptions\n): Observable<Component<Consent>> {\n  const internal$ = new Subject<Consent>()\n  internal$.subscribe(({ hidden }) => {\n    el.hidden = hidden\n  })\n\n  /* Create and return component */\n  return watchConsent(el, options)\n    .pipe(\n      tap(state => internal$.next(state)),\n      finalize(() => internal$.complete()),\n      map(state => ({ ref: el, ...state }))\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport ClipboardJS from \"clipboard\"\nimport {\n  EMPTY,\n  Observable,\n  Subject,\n  defer,\n  distinctUntilChanged,\n  distinctUntilKeyChanged,\n  filter,\n  finalize,\n  map,\n  mergeWith,\n  switchMap,\n  take,\n  tap\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n  getElementContentSize,\n  watchElementSize,\n  watchElementVisibility\n} from \"~/browser\"\nimport { renderClipboardButton } from \"~/templates\"\n\nimport { Component } from \"../../../_\"\nimport {\n  Annotation,\n  mountAnnotationList\n} from \"../../annotation\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Code block\n */\nexport interface CodeBlock {\n  scrollable: boolean                  /* Code block overflows */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  target$: Observable<HTMLElement>     /* Location target observable */\n  print$: Observable<boolean>          /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Global sequence number for code blocks\n */\nlet sequence = 0\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Find candidate list element directly following a code block\n *\n * @param el - Code block element\n *\n * @returns List element or nothing\n */\nfunction findCandidateList(el: HTMLElement): HTMLElement | undefined {\n  if (el.nextElementSibling) {\n    const sibling = el.nextElementSibling as HTMLElement\n    if (sibling.tagName === \"OL\")\n      return sibling\n\n    /* Skip empty paragraphs - see https://bit.ly/3r4ZJ2O */\n    else if (sibling.tagName === \"P\" && !sibling.children.length)\n      return findCandidateList(sibling)\n  }\n\n  /* Everything else */\n  return undefined\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch code block\n *\n * This function monitors size changes of the viewport, as well as switches of\n * content tabs with embedded code blocks, as both may trigger overflow.\n *\n * @param el - Code block element\n *\n * @returns Code block observable\n */\nexport function watchCodeBlock(\n  el: HTMLElement\n): Observable<CodeBlock> {\n  return watchElementSize(el)\n    .pipe(\n      map(({ width }) => {\n        const content = getElementContentSize(el)\n        return {\n          scrollable: content.width > width\n        }\n      }),\n      distinctUntilKeyChanged(\"scrollable\")\n    )\n}\n\n/**\n * Mount code block\n *\n * This function ensures that an overflowing code block is focusable through\n * keyboard, so it can be scrolled without a mouse to improve on accessibility.\n * Furthermore, if code annotations are enabled, they are mounted if and only\n * if the code block is currently visible, e.g., not in a hidden content tab.\n *\n * Note that code blocks may be mounted eagerly or lazily. If they're mounted\n * lazily (on first visibility), code annotation anchor links will not work,\n * as they are evaluated on initial page load, and code annotations in general\n * might feel a little bumpier.\n *\n * @param el - Code block element\n * @param options - Options\n *\n * @returns Code block and annotation component observable\n */\nexport function mountCodeBlock(\n  el: HTMLElement, options: MountOptions\n): Observable<Component<CodeBlock | Annotation>> {\n  const { matches: hover } = matchMedia(\"(hover)\")\n\n  /* Defer mounting of code block - see https://bit.ly/3vHVoVD */\n  const factory$ = defer(() => {\n    const push$ = new Subject<CodeBlock>()\n    push$.subscribe(({ scrollable }) => {\n      if (scrollable && hover)\n        el.setAttribute(\"tabindex\", \"0\")\n      else\n        el.removeAttribute(\"tabindex\")\n    })\n\n    /* Render button for Clipboard.js integration */\n    if (ClipboardJS.isSupported()) {\n      const parent = el.closest(\"pre\")!\n      parent.id = `__code_${++sequence}`\n      parent.insertBefore(\n        renderClipboardButton(parent.id),\n        el\n      )\n    }\n\n    /* Handle code annotations */\n    const container = el.closest(\".highlight\")\n    if (container instanceof HTMLElement) {\n      const list = findCandidateList(container)\n\n      /* Mount code annotations, if enabled */\n      if (typeof list !== \"undefined\" && (\n        container.classList.contains(\"annotate\") ||\n        feature(\"content.code.annotate\")\n      )) {\n        const annotations$ = mountAnnotationList(list, el, options)\n\n        /* Create and return component */\n        return watchCodeBlock(el)\n          .pipe(\n            tap(state => push$.next(state)),\n            finalize(() => push$.complete()),\n            map(state => ({ ref: el, ...state })),\n            mergeWith(\n              watchElementSize(container)\n                .pipe(\n                  map(({ width, height }) => width && height),\n                  distinctUntilChanged(),\n                  switchMap(active => active ? annotations$ : EMPTY)\n                )\n            )\n          )\n      }\n    }\n\n    /* Create and return component */\n    return watchCodeBlock(el)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n\n  /* Mount code block lazily */\n  if (feature(\"content.lazy\"))\n    return watchElementVisibility(el)\n      .pipe(\n        filter(visible => visible),\n        take(1),\n        switchMap(() => factory$)\n      )\n\n  /* Mount code block */\n  return factory$\n}\n", "/*\n * Copyright (c) 2016-2021 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a tooltip\n *\n * @param id - Tooltip identifier\n *\n * @returns Element\n */\nexport function renderTooltip(id?: string): HTMLElement {\n  return (\n    <div class=\"md-tooltip\" id={id}>\n      <div class=\"md-tooltip__inner md-typeset\"></div>\n    </div>\n  )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { h } from \"~/utilities\"\n\nimport { renderTooltip } from \"../tooltip\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render an annotation\n *\n * @param id - Annotation identifier\n * @param prefix - Tooltip identifier prefix\n *\n * @returns Element\n */\nexport function renderAnnotation(\n  id: string | number, prefix?: string\n): HTMLElement {\n  prefix = prefix ? `${prefix}_annotation_${id}` : undefined\n\n  /* Render tooltip with anchor, if given */\n  if (prefix) {\n    const anchor = prefix ? `#${prefix}` : undefined\n    return (\n      <aside class=\"md-annotation\" tabIndex={0}>\n        {renderTooltip(prefix)}\n        <a href={anchor} class=\"md-annotation__index\" tabIndex={-1}>\n          <span data-md-annotation-id={id}></span>\n        </a>\n      </aside>\n    )\n  } else {\n    return (\n      <aside class=\"md-annotation\" tabIndex={0}>\n        {renderTooltip(prefix)}\n        <span class=\"md-annotation__index\" tabIndex={-1}>\n          <span data-md-annotation-id={id}></span>\n        </span>\n      </aside>\n    )\n  }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { translation } from \"~/_\"\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a 'copy-to-clipboard' button\n *\n * @param id - Unique identifier\n *\n * @returns Element\n */\nexport function renderClipboardButton(id: string): HTMLElement {\n  return (\n    <button\n      class=\"md-clipboard md-icon\"\n      title={translation(\"clipboard.copy\")}\n      data-clipboard-target={`#${id} > code`}\n    ></button>\n  )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { ComponentChild } from \"preact\"\n\nimport { configuration, feature, translation } from \"~/_\"\nimport {\n  SearchDocument,\n  SearchMetadata,\n  SearchResultItem\n} from \"~/integrations/search\"\nimport { h, truncate } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Render flag\n */\nconst enum Flag {\n  TEASER = 1,                          /* Render teaser */\n  PARENT = 2                           /* Render as parent */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper function\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a search document\n *\n * @param document - Search document\n * @param flag - Render flags\n *\n * @returns Element\n */\nfunction renderSearchDocument(\n  document: SearchDocument & SearchMetadata, flag: Flag\n): HTMLElement {\n  const parent = flag & Flag.PARENT\n  const teaser = flag & Flag.TEASER\n\n  /* Render missing query terms */\n  const missing = Object.keys(document.terms)\n    .filter(key => !document.terms[key])\n    .reduce<ComponentChild[]>((list, key) => [\n      ...list, <del>{key}</del>, \" \"\n    ], [])\n    .slice(0, -1)\n\n  /* Assemble query string for highlighting */\n  const url = new URL(document.location)\n  if (feature(\"search.highlight\"))\n    url.searchParams.set(\"h\", Object.entries(document.terms)\n      .filter(([, match]) => match)\n      .reduce((highlight, [value]) => `${highlight} ${value}`.trim(), \"\")\n    )\n\n  /* Render article or section, depending on flags */\n  const { tags } = configuration()\n  return (\n    <a href={`${url}`} class=\"md-search-result__link\" tabIndex={-1}>\n      <article\n        class={[\"md-search-result__article\", ...parent\n          ? [\"md-search-result__article--document\"]\n          : []\n        ].join(\" \")}\n        data-md-score={document.score.toFixed(2)}\n      >\n        {parent > 0 && <div class=\"md-search-result__icon md-icon\"></div>}\n        <h1 class=\"md-search-result__title\">{document.title}</h1>\n        {teaser > 0 && document.text.length > 0 &&\n          <p class=\"md-search-result__teaser\">\n            {truncate(document.text, 320)}\n          </p>\n        }\n        {document.tags && (\n          <div class=\"md-typeset\">\n            {document.tags.map(tag => {\n              const id = tag.replace(/<[^>]+>/g, \"\")\n              const type = tags\n                ? id in tags\n                  ? `md-tag-icon md-tag-icon--${tags[id]}`\n                  : \"md-tag-icon\"\n                : \"\"\n              return (\n                <span class={`md-tag ${type}`}>{tag}</span>\n              )\n            })}\n          </div>\n        )}\n        {teaser > 0 && missing.length > 0 &&\n          <p class=\"md-search-result__terms\">\n            {translation(\"search.result.term.missing\")}: {...missing}\n          </p>\n        }\n      </article>\n    </a>\n  )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a search result\n *\n * @param result - Search result\n *\n * @returns Element\n */\nexport function renderSearchResultItem(\n  result: SearchResultItem\n): HTMLElement {\n  const threshold = result[0].score\n  const docs = [...result]\n\n  /* Find and extract parent article */\n  const parent = docs.findIndex(doc => !doc.location.includes(\"#\"))\n  const [article] = docs.splice(parent, 1)\n\n  /* Determine last index above threshold */\n  let index = docs.findIndex(doc => doc.score < threshold)\n  if (index === -1)\n    index = docs.length\n\n  /* Partition sections */\n  const best = docs.slice(0, index)\n  const more = docs.slice(index)\n\n  /* Render children */\n  const children = [\n    renderSearchDocument(article, Flag.PARENT | +(!parent && index === 0)),\n    ...best.map(section => renderSearchDocument(section, Flag.TEASER)),\n    ...more.length ? [\n      <details class=\"md-search-result__more\">\n        <summary tabIndex={-1}>\n          {more.length > 0 && more.length === 1\n            ? translation(\"search.result.more.one\")\n            : translation(\"search.result.more.other\", more.length)\n          }\n        </summary>\n        {...more.map(section => renderSearchDocument(section, Flag.TEASER))}\n      </details>\n    ] : []\n  ]\n\n  /* Render search result */\n  return (\n    <li class=\"md-search-result__item\">\n      {children}\n    </li>\n  )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { SourceFacts } from \"~/components\"\nimport { h, round } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render repository facts\n *\n * @param facts - Repository facts\n *\n * @returns Element\n */\nexport function renderSourceFacts(facts: SourceFacts): HTMLElement {\n  return (\n    <ul class=\"md-source__facts\">\n      {Object.entries(facts).map(([key, value]) => (\n        <li class={`md-source__fact md-source__fact--${key}`}>\n          {typeof value === \"number\" ? round(value) : value}\n        </li>\n      ))}\n    </ul>\n  )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Tabbed control type\n */\ntype TabbedControlType =\n  | \"prev\"\n  | \"next\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render control for content tabs\n *\n * @param type - Control type\n *\n * @returns Element\n */\nexport function renderTabbedControl(\n  type: TabbedControlType\n): HTMLElement {\n  const classes = `tabbed-control tabbed-control--${type}`\n  return (\n    <div class={classes} hidden>\n      <button class=\"tabbed-button\" tabIndex={-1}></button>\n    </div>\n  )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a table inside a wrapper to improve scrolling on mobile\n *\n * @param table - Table element\n *\n * @returns Element\n */\nexport function renderTable(table: HTMLElement): HTMLElement {\n  return (\n    <div class=\"md-typeset__scrollwrap\">\n      <div class=\"md-typeset__table\">\n        {table}\n      </div>\n    </div>\n  )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { configuration, translation } from \"~/_\"\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Version\n */\nexport interface Version {\n  version: string                      /* Version identifier */\n  title: string                        /* Version title */\n  aliases: string[]                    /* Version aliases */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a version\n *\n * @param version - Version\n *\n * @returns Element\n */\nfunction renderVersion(version: Version): HTMLElement {\n  const config = configuration()\n\n  /* Ensure trailing slash - see https://bit.ly/3rL5u3f */\n  const url = new URL(`../${version.version}/`, config.base)\n  return (\n    <li class=\"md-version__item\">\n      <a href={`${url}`} class=\"md-version__link\">\n        {version.title}\n      </a>\n    </li>\n  )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a version selector\n *\n * @param versions - Versions\n * @param active - Active version\n *\n * @returns Element\n */\nexport function renderVersionSelector(\n  versions: Version[], active: Version\n): HTMLElement {\n  return (\n    <div class=\"md-version\">\n      <button\n        class=\"md-version__current\"\n        aria-label={translation(\"select.version.title\")}\n      >\n        {active.title}\n      </button>\n      <ul class=\"md-version__list\">\n        {versions.map(renderVersion)}\n      </ul>\n    </div>\n  )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  animationFrameScheduler,\n  auditTime,\n  combineLatest,\n  debounceTime,\n  defer,\n  delay,\n  filter,\n  finalize,\n  fromEvent,\n  map,\n  merge,\n  switchMap,\n  take,\n  takeLast,\n  takeUntil,\n  tap,\n  throttleTime,\n  withLatestFrom\n} from \"rxjs\"\n\nimport {\n  ElementOffset,\n  getActiveElement,\n  getElementSize,\n  watchElementContentOffset,\n  watchElementFocus,\n  watchElementOffset,\n  watchElementVisibility\n} from \"~/browser\"\n\nimport { Component } from \"../../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Annotation\n */\nexport interface Annotation {\n  active: boolean                      /* Annotation is active */\n  offset: ElementOffset                /* Annotation offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  target$: Observable<HTMLElement>     /* Location target observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch annotation\n *\n * @param el - Annotation element\n * @param container - Containing element\n *\n * @returns Annotation observable\n */\nexport function watchAnnotation(\n  el: HTMLElement, container: HTMLElement\n): Observable<Annotation> {\n  const offset$ = defer(() => combineLatest([\n    watchElementOffset(el),\n    watchElementContentOffset(container)\n  ]))\n    .pipe(\n      map(([{ x, y }, scroll]): ElementOffset => {\n        const { width, height } = getElementSize(el)\n        return ({\n          x: x - scroll.x + width / 2,\n          y: y - scroll.y + height / 2\n        })\n      })\n    )\n\n  /* Actively watch annotation on focus */\n  return watchElementFocus(el)\n    .pipe(\n      switchMap(active => offset$\n        .pipe(\n          map(offset => ({ active, offset })),\n          take(+!active || Infinity)\n        )\n      )\n    )\n}\n\n/**\n * Mount annotation\n *\n * @param el - Annotation element\n * @param container - Containing element\n * @param options - Options\n *\n * @returns Annotation component observable\n */\nexport function mountAnnotation(\n  el: HTMLElement, container: HTMLElement, { target$ }: MountOptions\n): Observable<Component<Annotation>> {\n  const [tooltip, index] = Array.from(el.children)\n\n  /* Mount component on subscription */\n  return defer(() => {\n    const push$ = new Subject<Annotation>()\n    const done$ = push$.pipe(takeLast(1))\n    push$.subscribe({\n\n      /* Handle emission */\n      next({ offset }) {\n        el.style.setProperty(\"--md-tooltip-x\", `${offset.x}px`)\n        el.style.setProperty(\"--md-tooltip-y\", `${offset.y}px`)\n      },\n\n      /* Handle complete */\n      complete() {\n        el.style.removeProperty(\"--md-tooltip-x\")\n        el.style.removeProperty(\"--md-tooltip-y\")\n      }\n    })\n\n    /* Start animation only when annotation is visible */\n    watchElementVisibility(el)\n      .pipe(\n        takeUntil(done$)\n      )\n        .subscribe(visible => {\n          el.toggleAttribute(\"data-md-visible\", visible)\n        })\n\n    /* Toggle tooltip presence to mitigate empty lines when copying */\n    merge(\n      push$.pipe(filter(({ active }) => active)),\n      push$.pipe(debounceTime(250), filter(({ active }) => !active))\n    )\n      .subscribe({\n\n        /* Handle emission */\n        next({ active }) {\n          if (active)\n            el.prepend(tooltip)\n          else\n            tooltip.remove()\n        },\n\n        /* Handle complete */\n        complete() {\n          el.prepend(tooltip)\n        }\n      })\n\n    /* Toggle tooltip visibility */\n    push$\n      .pipe(\n        auditTime(16, animationFrameScheduler)\n      )\n        .subscribe(({ active }) => {\n          tooltip.classList.toggle(\"md-tooltip--active\", active)\n        })\n\n    /* Track relative origin of tooltip */\n    push$\n      .pipe(\n        throttleTime(125, animationFrameScheduler),\n        filter(() => !!el.offsetParent),\n        map(() => el.offsetParent!.getBoundingClientRect()),\n        map(({ x }) => x)\n      )\n        .subscribe({\n\n          /* Handle emission */\n          next(origin) {\n            if (origin)\n              el.style.setProperty(\"--md-tooltip-0\", `${-origin}px`)\n            else\n              el.style.removeProperty(\"--md-tooltip-0\")\n          },\n\n          /* Handle complete */\n          complete() {\n            el.style.removeProperty(\"--md-tooltip-0\")\n          }\n        })\n\n    /* Allow to copy link without scrolling to anchor */\n    fromEvent<MouseEvent>(index, \"click\")\n      .pipe(\n        takeUntil(done$),\n        filter(ev => !(ev.metaKey || ev.ctrlKey))\n      )\n        .subscribe(ev => ev.preventDefault())\n\n    /* Allow to open link in new tab or blur on close */\n    fromEvent<MouseEvent>(index, \"mousedown\")\n      .pipe(\n        takeUntil(done$),\n        withLatestFrom(push$)\n      )\n        .subscribe(([ev, { active }]) => {\n\n          /* Open in new tab */\n          if (ev.button !== 0 || ev.metaKey || ev.ctrlKey) {\n            ev.preventDefault()\n\n          /* Close annotation */\n          } else if (active) {\n            ev.preventDefault()\n\n            /* Focus parent annotation, if any */\n            const parent = el.parentElement!.closest(\".md-annotation\")\n            if (parent instanceof HTMLElement)\n              parent.focus()\n            else\n              getActiveElement()?.blur()\n          }\n        })\n\n    /* Open and focus annotation on location target */\n    target$\n      .pipe(\n        takeUntil(done$),\n        filter(target => target === tooltip),\n        delay(125)\n      )\n        .subscribe(() => el.focus())\n\n    /* Create and return component */\n    return watchAnnotation(el, container)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  Subject,\n  defer,\n  finalize,\n  merge,\n  share,\n  takeLast,\n  takeUntil\n} from \"rxjs\"\n\nimport {\n  getElement,\n  getElements,\n  getOptionalElement\n} from \"~/browser\"\nimport { renderAnnotation } from \"~/templates\"\n\nimport { Component } from \"../../../_\"\nimport {\n  Annotation,\n  mountAnnotation\n} from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  target$: Observable<HTMLElement>     /* Location target observable */\n  print$: Observable<boolean>          /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Find all annotation markers in the given code block\n *\n * @param container - Containing element\n *\n * @returns Annotation markers\n */\nfunction findAnnotationMarkers(container: HTMLElement): Text[] {\n  const markers: Text[] = []\n  for (const el of getElements(\".c, .c1, .cm\", container)) {\n    const nodes: Text[] = []\n\n    /* Find all text nodes in current element */\n    const it = document.createNodeIterator(el, NodeFilter.SHOW_TEXT)\n    for (let node = it.nextNode(); node; node = it.nextNode())\n      nodes.push(node as Text)\n\n    /* Find all markers in each text node */\n    for (let text of nodes) {\n      let match: RegExpExecArray | null\n\n      /* Split text at marker and add to list */\n      while ((match = /(\\(\\d+\\))(!)?/.exec(text.textContent!))) {\n        const [, id, force] = match\n        if (typeof force === \"undefined\") {\n          const marker = text.splitText(match.index)\n          text = marker.splitText(id.length)\n          markers.push(marker)\n\n        /* Replace entire text with marker */\n        } else {\n          text.textContent = id\n          markers.push(text)\n          break\n        }\n      }\n    }\n  }\n  return markers\n}\n\n/**\n * Swap the child nodes of two elements\n *\n * @param source - Source element\n * @param target - Target element\n */\nfunction swap(source: HTMLElement, target: HTMLElement): void {\n  target.append(...Array.from(source.childNodes))\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount annotation list\n *\n * This function analyzes the containing code block and checks for markers\n * referring to elements in the given annotation list. If no markers are found,\n * the list is left untouched. Otherwise, list elements are rendered as\n * annotations inside the code block.\n *\n * @param el - Annotation list element\n * @param container - Containing element\n * @param options - Options\n *\n * @returns Annotation component observable\n */\nexport function mountAnnotationList(\n  el: HTMLElement, container: HTMLElement, { target$, print$ }: MountOptions\n): Observable<Component<Annotation>> {\n\n  /* Compute prefix for tooltip anchors */\n  const parent = container.closest(\"[id]\")\n  const prefix = parent?.id\n\n  /* Find and replace all markers with empty annotations */\n  const annotations = new Map<string, HTMLElement>()\n  for (const marker of findAnnotationMarkers(container)) {\n    const [, id] = marker.textContent!.match(/\\((\\d+)\\)/)!\n    if (getOptionalElement(`li:nth-child(${id})`, el)) {\n      annotations.set(id, renderAnnotation(id, prefix))\n      marker.replaceWith(annotations.get(id)!)\n    }\n  }\n\n  /* Keep list if there are no annotations to render */\n  if (annotations.size === 0)\n    return EMPTY\n\n  /* Mount component on subscription */\n  return defer(() => {\n    const done$ = new Subject()\n\n    /* Retrieve container pairs for swapping */\n    const pairs: [HTMLElement, HTMLElement][] = []\n    for (const [id, annotation] of annotations)\n      pairs.push([\n        getElement(\".md-typeset\", annotation),\n        getElement(`li:nth-child(${id})`, el)\n      ])\n\n    /* Handle print mode - see https://bit.ly/3rgPdpt */\n    print$\n      .pipe(\n        takeUntil(done$.pipe(takeLast(1)))\n      )\n        .subscribe(active => {\n          el.hidden = !active\n\n          /* Show annotations in code block or list (print) */\n          for (const [inner, child] of pairs)\n            if (!active)\n              swap(child, inner)\n            else\n              swap(inner, child)\n        })\n\n    /* Create and return component */\n    return merge(...[...annotations]\n      .map(([, annotation]) => (\n        mountAnnotation(annotation, container, { target$ })\n      ))\n    )\n      .pipe(\n        finalize(() => done$.complete()),\n        share()\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  map,\n  of,\n  shareReplay,\n  tap\n} from \"rxjs\"\n\nimport { watchScript } from \"~/browser\"\nimport { h } from \"~/utilities\"\n\nimport { Component } from \"../../../_\"\n\nimport themeCSS from \"./index.css\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mermaid diagram\n */\nexport interface Mermaid {}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Mermaid instance observable\n */\nlet mermaid$: Observable<void>\n\n/**\n * Global sequence number for diagrams\n */\nlet sequence = 0\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch Mermaid script\n *\n * @returns Mermaid scripts observable\n */\nfunction fetchScripts(): Observable<void> {\n  return typeof mermaid === \"undefined\" || mermaid instanceof Element\n    ? watchScript(\"https://unpkg.com/mermaid@9.1.7/dist/mermaid.min.js\")\n    : of(undefined)\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount Mermaid diagram\n *\n * @param el - Code block element\n *\n * @returns Mermaid diagram component observable\n */\nexport function mountMermaid(\n  el: HTMLElement\n): Observable<Component<Mermaid>> {\n  el.classList.remove(\"mermaid\") // Hack: mitigate https://bit.ly/3CiN6Du\n  mermaid$ ||= fetchScripts()\n    .pipe(\n      tap(() => mermaid.initialize({\n        startOnLoad: false,\n        themeCSS,\n        sequence: {\n          actorFontSize: \"16px\", // Hack: mitigate https://bit.ly/3y0NEi3\n          messageFontSize: \"16px\",\n          noteFontSize: \"16px\"\n        }\n      })),\n      map(() => undefined),\n      shareReplay(1)\n    )\n\n  /* Render diagram */\n  mermaid$.subscribe(() => {\n    el.classList.add(\"mermaid\") // Hack: mitigate https://bit.ly/3CiN6Du\n    const id = `__mermaid_${sequence++}`\n    const host = h(\"div\", { class: \"mermaid\" })\n    mermaid.mermaidAPI.render(id, el.textContent, (svg: string) => {\n\n      /* Create a shadow root and inject diagram */\n      const shadow = host.attachShadow({ mode: \"closed\" })\n      shadow.innerHTML = svg\n\n      /* Replace code block with diagram */\n      el.replaceWith(host)\n    })\n  })\n\n  /* Create and return component */\n  return mermaid$\n    .pipe(\n      map(() => ({ ref: el }))\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  defer,\n  filter,\n  finalize,\n  map,\n  merge,\n  tap\n} from \"rxjs\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Details\n */\nexport interface Details {\n  action: \"open\" | \"close\"             /* Details state */\n  reveal?: boolean                     /* Details is revealed */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  target$: Observable<HTMLElement>     /* Location target observable */\n  print$: Observable<boolean>          /* Media print observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  target$: Observable<HTMLElement>     /* Location target observable */\n  print$: Observable<boolean>          /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch details\n *\n * @param el - Details element\n * @param options - Options\n *\n * @returns Details observable\n */\nexport function watchDetails(\n  el: HTMLDetailsElement, { target$, print$ }: WatchOptions\n): Observable<Details> {\n  let open = true\n  return merge(\n\n    /* Open and focus details on location target */\n    target$\n      .pipe(\n        map(target => target.closest(\"details:not([open])\")!),\n        filter(details => el === details),\n        map(() => ({\n          action: \"open\", reveal: true\n        }) as Details)\n      ),\n\n    /* Open details on print and close afterwards */\n    print$\n      .pipe(\n        filter(active => active || !open),\n        tap(() => open = el.open),\n        map(active => ({\n          action: active ? \"open\" : \"close\"\n        }) as Details)\n      )\n  )\n}\n\n/**\n * Mount details\n *\n * This function ensures that `details` tags are opened on anchor jumps and\n * prior to printing, so the whole content of the page is visible.\n *\n * @param el - Details element\n * @param options - Options\n *\n * @returns Details component observable\n */\nexport function mountDetails(\n  el: HTMLDetailsElement, options: MountOptions\n): Observable<Component<Details>> {\n  return defer(() => {\n    const push$ = new Subject<Details>()\n    push$.subscribe(({ action, reveal }) => {\n      el.toggleAttribute(\"open\", action === \"open\")\n      if (reveal)\n        el.scrollIntoView()\n    })\n\n    /* Create and return component */\n    return watchDetails(el, options)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Observable, of } from \"rxjs\"\n\nimport { renderTable } from \"~/templates\"\nimport { h } from \"~/utilities\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Data table\n */\nexport interface DataTable {}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Sentinel for replacement\n */\nconst sentinel = h(\"table\")\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount data table\n *\n * This function wraps a data table in another scrollable container, so it can\n * be smoothly scrolled on smaller screen sizes and won't break the layout.\n *\n * @param el - Data table element\n *\n * @returns Data table component observable\n */\nexport function mountDataTable(\n  el: HTMLElement\n): Observable<Component<DataTable>> {\n  el.replaceWith(sentinel)\n  sentinel.replaceWith(renderTable(el))\n\n  /* Create and return component */\n  return of({ ref: el })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  animationFrameScheduler,\n  asyncScheduler,\n  auditTime,\n  combineLatest,\n  defer,\n  finalize,\n  fromEvent,\n  map,\n  merge,\n  skip,\n  startWith,\n  subscribeOn,\n  takeLast,\n  takeUntil,\n  tap,\n  withLatestFrom\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n  Viewport,\n  getElement,\n  getElementContentOffset,\n  getElementContentSize,\n  getElementOffset,\n  getElementSize,\n  getElements,\n  watchElementContentOffset,\n  watchElementSize\n} from \"~/browser\"\nimport { renderTabbedControl } from \"~/templates\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Content tabs\n */\nexport interface ContentTabs {\n  active: HTMLLabelElement             /* Active tab label */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch content tabs\n *\n * @param el - Content tabs element\n *\n * @returns Content tabs observable\n */\nexport function watchContentTabs(\n  el: HTMLElement\n): Observable<ContentTabs> {\n  const inputs = getElements<HTMLInputElement>(\":scope > input\", el)\n  const initial = inputs.find(input => input.checked) || inputs[0]\n  return merge(...inputs.map(input => fromEvent(input, \"change\")\n    .pipe(\n      map(() => getElement<HTMLLabelElement>(`label[for=\"${input.id}\"]`))\n    )\n  ))\n    .pipe(\n      startWith(getElement<HTMLLabelElement>(`label[for=\"${initial.id}\"]`)),\n      map(active => ({ active }))\n    )\n}\n\n/**\n * Mount content tabs\n *\n * This function scrolls the active tab into view. While this functionality is\n * provided by browsers as part of `scrollInfoView`, browsers will always also\n * scroll the vertical axis, which we do not want. Thus, we decided to provide\n * this functionality ourselves.\n *\n * @param el - Content tabs element\n * @param options - Options\n *\n * @returns Content tabs component observable\n */\nexport function mountContentTabs(\n  el: HTMLElement, { viewport$ }: MountOptions\n): Observable<Component<ContentTabs>> {\n\n  /* Render content tab previous button for pagination */\n  const prev = renderTabbedControl(\"prev\")\n  el.append(prev)\n\n  /* Render content tab next button for pagination */\n  const next = renderTabbedControl(\"next\")\n  el.append(next)\n\n  /* Mount component on subscription */\n  const container = getElement(\".tabbed-labels\", el)\n  return defer(() => {\n    const push$ = new Subject<ContentTabs>()\n    const done$ = push$.pipe(takeLast(1))\n    combineLatest([push$, watchElementSize(el)])\n      .pipe(\n        auditTime(1, animationFrameScheduler),\n        takeUntil(done$)\n      )\n        .subscribe({\n\n          /* Handle emission */\n          next([{ active }, size]) {\n            const offset = getElementOffset(active)\n            const { width } = getElementSize(active)\n\n            /* Set tab indicator offset and width */\n            el.style.setProperty(\"--md-indicator-x\", `${offset.x}px`)\n            el.style.setProperty(\"--md-indicator-width\", `${width}px`)\n\n            /* Scroll container to active content tab */\n            const content = getElementContentOffset(container)\n            if (\n              offset.x         < content.x              ||\n              offset.x + width > content.x + size.width\n            )\n              container.scrollTo({\n                left: Math.max(0, offset.x - 16),\n                behavior: \"smooth\"\n              })\n          },\n\n          /* Handle complete */\n          complete() {\n            el.style.removeProperty(\"--md-indicator-x\")\n            el.style.removeProperty(\"--md-indicator-width\")\n          }\n        })\n\n    /* Hide content tab buttons on borders */\n    combineLatest([\n      watchElementContentOffset(container),\n      watchElementSize(container)\n    ])\n      .pipe(\n        takeUntil(done$)\n      )\n        .subscribe(([offset, size]) => {\n          const content = getElementContentSize(container)\n          prev.hidden = offset.x < 16\n          next.hidden = offset.x > content.width - size.width - 16\n        })\n\n    /* Paginate content tab container on click */\n    merge(\n      fromEvent(prev, \"click\").pipe(map(() => -1)),\n      fromEvent(next, \"click\").pipe(map(() => +1))\n    )\n      .pipe(\n        takeUntil(done$)\n      )\n        .subscribe(direction => {\n          const { width } = getElementSize(container)\n          container.scrollBy({\n            left: width * direction,\n            behavior: \"smooth\"\n          })\n        })\n\n    /* Set up linking of content tabs, if enabled */\n    if (feature(\"content.tabs.link\"))\n      push$.pipe(\n        skip(1),\n        withLatestFrom(viewport$)\n      )\n        .subscribe(([{ active }, { offset }]) => {\n          const tab = active.innerText.trim()\n          if (active.hasAttribute(\"data-md-switching\")) {\n            active.removeAttribute(\"data-md-switching\")\n\n          /* Determine viewport offset of active tab */\n          } else {\n            const y = el.offsetTop - offset.y\n\n            /* Passively activate other tabs */\n            for (const set of getElements(\"[data-tabs]\"))\n              for (const input of getElements<HTMLInputElement>(\n                \":scope > input\", set\n              )) {\n                const label = getElement(`label[for=\"${input.id}\"]`)\n                if (\n                  label !== active &&\n                  label.innerText.trim() === tab\n                ) {\n                  label.setAttribute(\"data-md-switching\", \"\")\n                  input.click()\n                  break\n                }\n              }\n\n            /* Bring active tab into view */\n            window.scrollTo({\n              top: el.offsetTop - y\n            })\n\n            /* Persist active tabs in local storage */\n            const tabs = __md_get<string[]>(\"__tabs\") || []\n            __md_set(\"__tabs\", [...new Set([tab, ...tabs])])\n          }\n        })\n\n    /* Create and return component */\n    return watchContentTabs(el)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n    .pipe(\n      subscribeOn(asyncScheduler)\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Observable, merge } from \"rxjs\"\n\nimport { Viewport, getElements } from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { Annotation } from \"../annotation\"\nimport {\n  CodeBlock,\n  Mermaid,\n  mountCodeBlock,\n  mountMermaid\n} from \"../code\"\nimport {\n  Details,\n  mountDetails\n} from \"../details\"\nimport {\n  DataTable,\n  mountDataTable\n} from \"../table\"\nimport {\n  ContentTabs,\n  mountContentTabs\n} from \"../tabs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Content\n */\nexport type Content =\n  | Annotation\n  | ContentTabs\n  | CodeBlock\n  | Mermaid\n  | DataTable\n  | Details\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  target$: Observable<HTMLElement>     /* Location target observable */\n  print$: Observable<boolean>          /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount content\n *\n * This function mounts all components that are found in the content of the\n * actual article, including code blocks, data tables and details.\n *\n * @param el - Content element\n * @param options - Options\n *\n * @returns Content component observable\n */\nexport function mountContent(\n  el: HTMLElement, { viewport$, target$, print$ }: MountOptions\n): Observable<Component<Content>> {\n  return merge(\n\n    /* Code blocks */\n    ...getElements(\"pre:not(.mermaid) > code\", el)\n      .map(child => mountCodeBlock(child, { target$, print$ })),\n\n    /* Mermaid diagrams */\n    ...getElements(\"pre.mermaid\", el)\n      .map(child => mountMermaid(child)),\n\n    /* Data tables */\n    ...getElements(\"table:not([class])\", el)\n      .map(child => mountDataTable(child)),\n\n    /* Details */\n    ...getElements(\"details\", el)\n      .map(child => mountDetails(child, { target$, print$ })),\n\n    /* Content tabs */\n    ...getElements(\"[data-tabs]\", el)\n      .map(child => mountContentTabs(child, { viewport$ }))\n  )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  defer,\n  delay,\n  finalize,\n  map,\n  merge,\n  of,\n  switchMap,\n  tap\n} from \"rxjs\"\n\nimport { getElement } from \"~/browser\"\n\nimport { Component } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Dialog\n */\nexport interface Dialog {\n  message: string                      /* Dialog message */\n  active: boolean                      /* Dialog is active */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  alert$: Subject<string>              /* Alert subject */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  alert$: Subject<string>              /* Alert subject */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch dialog\n *\n * @param _el - Dialog element\n * @param options - Options\n *\n * @returns Dialog observable\n */\nexport function watchDialog(\n  _el: HTMLElement, { alert$ }: WatchOptions\n): Observable<Dialog> {\n  return alert$\n    .pipe(\n      switchMap(message => merge(\n        of(true),\n        of(false).pipe(delay(2000))\n      )\n        .pipe(\n          map(active => ({ message, active }))\n        )\n      )\n    )\n}\n\n/**\n * Mount dialog\n *\n * This function reveals the dialog in the right corner when a new alert is\n * emitted through the subject that is passed as part of the options.\n *\n * @param el - Dialog element\n * @param options - Options\n *\n * @returns Dialog component observable\n */\nexport function mountDialog(\n  el: HTMLElement, options: MountOptions\n): Observable<Component<Dialog>> {\n  const inner = getElement(\".md-typeset\", el)\n  return defer(() => {\n    const push$ = new Subject<Dialog>()\n    push$.subscribe(({ message, active }) => {\n      el.classList.toggle(\"md-dialog--active\", active)\n      inner.textContent = message\n    })\n\n    /* Create and return component */\n    return watchDialog(el, options)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  bufferCount,\n  combineLatest,\n  combineLatestWith,\n  defer,\n  distinctUntilChanged,\n  distinctUntilKeyChanged,\n  filter,\n  map,\n  of,\n  shareReplay,\n  startWith,\n  switchMap,\n  takeLast,\n  takeUntil\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n  Viewport,\n  watchElementSize,\n  watchToggle\n} from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { Main } from \"../../main\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Header\n */\nexport interface Header {\n  height: number                       /* Header visible height */\n  hidden: boolean                      /* Header is hidden */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n  main$: Observable<Main>              /* Main area observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Compute whether the header is hidden\n *\n * If the user scrolls past a certain threshold, the header can be hidden when\n * scrolling down, and shown when scrolling up.\n *\n * @param options - Options\n *\n * @returns Toggle observable\n */\nfunction isHidden({ viewport$ }: WatchOptions): Observable<boolean> {\n  if (!feature(\"header.autohide\"))\n    return of(false)\n\n  /* Compute direction and turning point */\n  const direction$ = viewport$\n    .pipe(\n      map(({ offset: { y } }) => y),\n      bufferCount(2, 1),\n      map(([a, b]) => [a < b, b] as const),\n      distinctUntilKeyChanged(0)\n    )\n\n  /* Compute whether header should be hidden */\n  const hidden$ = combineLatest([viewport$, direction$])\n    .pipe(\n      filter(([{ offset }, [, y]]) => Math.abs(y - offset.y) > 100),\n      map(([, [direction]]) => direction),\n      distinctUntilChanged()\n    )\n\n  /* Compute threshold for hiding */\n  const search$ = watchToggle(\"search\")\n  return combineLatest([viewport$, search$])\n    .pipe(\n      map(([{ offset }, search]) => offset.y > 400 && !search),\n      distinctUntilChanged(),\n      switchMap(active => active ? hidden$ : of(false)),\n      startWith(false)\n    )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch header\n *\n * @param el - Header element\n * @param options - Options\n *\n * @returns Header observable\n */\nexport function watchHeader(\n  el: HTMLElement, options: WatchOptions\n): Observable<Header> {\n  return defer(() => combineLatest([\n    watchElementSize(el),\n    isHidden(options)\n  ]))\n    .pipe(\n      map(([{ height }, hidden]) => ({\n        height,\n        hidden\n      })),\n      distinctUntilChanged((a, b) => (\n        a.height === b.height &&\n        a.hidden === b.hidden\n      )),\n      shareReplay(1)\n    )\n}\n\n/**\n * Mount header\n *\n * This function manages the different states of the header, i.e. whether it's\n * hidden or rendered with a shadow. This depends heavily on the main area.\n *\n * @param el - Header element\n * @param options - Options\n *\n * @returns Header component observable\n */\nexport function mountHeader(\n  el: HTMLElement, { header$, main$ }: MountOptions\n): Observable<Component<Header>> {\n  return defer(() => {\n    const push$ = new Subject<Main>()\n    const done$ = push$.pipe(takeLast(1))\n    push$\n      .pipe(\n        distinctUntilKeyChanged(\"active\"),\n        combineLatestWith(header$)\n      )\n        .subscribe(([{ active }, { hidden }]) => {\n          el.classList.toggle(\"md-header--shadow\", active && !hidden)\n          el.hidden = hidden\n        })\n\n    /* Link to main area */\n    main$.subscribe(push$)\n\n    /* Create and return component */\n    return header$\n      .pipe(\n        takeUntil(done$),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  Subject,\n  defer,\n  distinctUntilKeyChanged,\n  finalize,\n  map,\n  tap\n} from \"rxjs\"\n\nimport {\n  Viewport,\n  getElementSize,\n  getOptionalElement,\n  watchViewportAt\n} from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { Header } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Header\n */\nexport interface HeaderTitle {\n  active: boolean                      /* Header title is active */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch header title\n *\n * @param el - Heading element\n * @param options - Options\n *\n * @returns Header title observable\n */\nexport function watchHeaderTitle(\n  el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable<HeaderTitle> {\n  return watchViewportAt(el, { viewport$, header$ })\n    .pipe(\n      map(({ offset: { y } }) => {\n        const { height } = getElementSize(el)\n        return {\n          active: y >= height\n        }\n      }),\n      distinctUntilKeyChanged(\"active\")\n    )\n}\n\n/**\n * Mount header title\n *\n * This function swaps the header title from the site title to the title of the\n * current page when the user scrolls past the first headline.\n *\n * @param el - Header title element\n * @param options - Options\n *\n * @returns Header title component observable\n */\nexport function mountHeaderTitle(\n  el: HTMLElement, options: MountOptions\n): Observable<Component<HeaderTitle>> {\n  return defer(() => {\n    const push$ = new Subject<HeaderTitle>()\n    push$.subscribe(({ active }) => {\n      el.classList.toggle(\"md-header__title--active\", active)\n    })\n\n    /* Obtain headline, if any */\n    const heading = getOptionalElement(\"article h1\")\n    if (typeof heading === \"undefined\")\n      return EMPTY\n\n    /* Create and return component */\n    return watchHeaderTitle(heading, options)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  combineLatest,\n  distinctUntilChanged,\n  distinctUntilKeyChanged,\n  map,\n  switchMap\n} from \"rxjs\"\n\nimport {\n  Viewport,\n  watchElementSize\n} from \"~/browser\"\n\nimport { Header } from \"../header\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Main area\n */\nexport interface Main {\n  offset: number                       /* Main area top offset */\n  height: number                       /* Main area visible height */\n  active: boolean                      /* Main area is active */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch main area\n *\n * This function returns an observable that computes the visual parameters of\n * the main area which depends on the viewport vertical offset and height, as\n * well as the height of the header element, if the header is fixed.\n *\n * @param el - Main area element\n * @param options - Options\n *\n * @returns Main area observable\n */\nexport function watchMain(\n  el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable<Main> {\n\n  /* Compute necessary adjustment for header */\n  const adjust$ = header$\n    .pipe(\n      map(({ height }) => height),\n      distinctUntilChanged()\n    )\n\n  /* Compute the main area's top and bottom borders */\n  const border$ = adjust$\n    .pipe(\n      switchMap(() => watchElementSize(el)\n        .pipe(\n          map(({ height }) => ({\n            top:    el.offsetTop,\n            bottom: el.offsetTop + height\n          })),\n          distinctUntilKeyChanged(\"bottom\")\n        )\n      )\n    )\n\n  /* Compute the main area's offset, visible height and if we scrolled past */\n  return combineLatest([adjust$, border$, viewport$])\n    .pipe(\n      map(([header, { top, bottom }, { offset: { y }, size: { height } }]) => {\n        height = Math.max(0, height\n          - Math.max(0, top    - y,  header)\n          - Math.max(0, height + y - bottom)\n        )\n        return {\n          offset: top - header,\n          height,\n          active: top - header <= y\n        }\n      }),\n      distinctUntilChanged((a, b) => (\n        a.offset === b.offset &&\n        a.height === b.height &&\n        a.active === b.active\n      ))\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  asyncScheduler,\n  defer,\n  finalize,\n  fromEvent,\n  map,\n  mergeMap,\n  observeOn,\n  of,\n  shareReplay,\n  startWith,\n  tap\n} from \"rxjs\"\n\nimport { getElements } from \"~/browser\"\n\nimport { Component } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Palette colors\n */\nexport interface PaletteColor {\n  scheme?: string                      /* Color scheme */\n  primary?: string                     /* Primary color */\n  accent?: string                      /* Accent color */\n}\n\n/**\n * Palette\n */\nexport interface Palette {\n  index: number                        /* Palette index */\n  color: PaletteColor                  /* Palette colors */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch color palette\n *\n * @param inputs - Color palette element\n *\n * @returns Color palette observable\n */\nexport function watchPalette(\n  inputs: HTMLInputElement[]\n): Observable<Palette> {\n  const current = __md_get<Palette>(\"__palette\") || {\n    index: inputs.findIndex(input => matchMedia(\n      input.getAttribute(\"data-md-color-media\")!\n    ).matches)\n  }\n\n  /* Emit changes in color palette */\n  return of(...inputs)\n    .pipe(\n      mergeMap(input => fromEvent(input, \"change\")\n        .pipe(\n          map(() => input)\n        )\n      ),\n      startWith(inputs[Math.max(0, current.index)]),\n      map(input => ({\n        index: inputs.indexOf(input),\n        color: {\n          scheme:  input.getAttribute(\"data-md-color-scheme\"),\n          primary: input.getAttribute(\"data-md-color-primary\"),\n          accent:  input.getAttribute(\"data-md-color-accent\")\n        }\n      } as Palette)),\n      shareReplay(1)\n    )\n}\n\n/**\n * Mount color palette\n *\n * @param el - Color palette element\n *\n * @returns Color palette component observable\n */\nexport function mountPalette(\n  el: HTMLElement\n): Observable<Component<Palette>> {\n  return defer(() => {\n    const push$ = new Subject<Palette>()\n    push$.subscribe(palette => {\n      document.body.setAttribute(\"data-md-color-switching\", \"\")\n\n      /* Set color palette */\n      for (const [key, value] of Object.entries(palette.color))\n        document.body.setAttribute(`data-md-color-${key}`, value)\n\n      /* Toggle visibility */\n      for (let index = 0; index < inputs.length; index++) {\n        const label = inputs[index].nextElementSibling\n        if (label instanceof HTMLElement)\n          label.hidden = palette.index !== index\n      }\n\n      /* Persist preference in local storage */\n      __md_set(\"__palette\", palette)\n    })\n\n    /* Revert transition durations after color switch */\n    push$.pipe(observeOn(asyncScheduler))\n      .subscribe(() => {\n        document.body.removeAttribute(\"data-md-color-switching\")\n      })\n\n    /* Create and return component */\n    const inputs = getElements<HTMLInputElement>(\"input\", el)\n    return watchPalette(inputs)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport ClipboardJS from \"clipboard\"\nimport {\n  Observable,\n  Subject,\n  map,\n  tap\n} from \"rxjs\"\n\nimport { translation } from \"~/_\"\nimport { getElement } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Setup options\n */\ninterface SetupOptions {\n  alert$: Subject<string>              /* Alert subject */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Extract text to copy\n *\n * @param el - HTML element\n *\n * @returns Extracted text\n */\nfunction extract(el: HTMLElement): string {\n  el.setAttribute(\"data-md-copying\", \"\")\n  const text = el.innerText\n  el.removeAttribute(\"data-md-copying\")\n  return text\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up Clipboard.js integration\n *\n * @param options - Options\n */\nexport function setupClipboardJS(\n  { alert$ }: SetupOptions\n): void {\n  if (ClipboardJS.isSupported()) {\n    new Observable<ClipboardJS.Event>(subscriber => {\n      new ClipboardJS(\"[data-clipboard-target], [data-clipboard-text]\", {\n        text: el => (\n          el.getAttribute(\"data-clipboard-text\")! ||\n          extract(getElement(\n            el.getAttribute(\"data-clipboard-target\")!\n          ))\n        )\n      })\n        .on(\"success\", ev => subscriber.next(ev))\n    })\n      .pipe(\n        tap(ev => {\n          const trigger = ev.trigger as HTMLElement\n          trigger.focus()\n        }),\n        map(() => translation(\"clipboard.copied\"))\n      )\n        .subscribe(alert$)\n  }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  catchError,\n  defaultIfEmpty,\n  map,\n  of,\n  tap\n} from \"rxjs\"\n\nimport { configuration } from \"~/_\"\nimport { getElements, requestXML } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Sitemap, i.e. a list of URLs\n */\nexport type Sitemap = string[]\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Preprocess a list of URLs\n *\n * This function replaces the `site_url` in the sitemap with the actual base\n * URL, to allow instant loading to work in occasions like Netlify previews.\n *\n * @param urls - URLs\n *\n * @returns URL path parts\n */\nfunction preprocess(urls: Sitemap): Sitemap {\n  if (urls.length < 2)\n    return [\"\"]\n\n  /* Take the first two URLs and remove everything after the last slash */\n  const [root, next] = [...urls]\n    .sort((a, b) => a.length - b.length)\n    .map(url => url.replace(/[^/]+$/, \"\"))\n\n  /* Compute common prefix */\n  let index = 0\n  if (root === next)\n    index = root.length\n  else\n    while (root.charCodeAt(index) === next.charCodeAt(index))\n      index++\n\n  /* Remove common prefix and return in original order */\n  return urls.map(url => url.replace(root.slice(0, index), \"\"))\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch the sitemap for the given base URL\n *\n * @param base - Base URL\n *\n * @returns Sitemap observable\n */\nexport function fetchSitemap(base?: URL): Observable<Sitemap> {\n  const cached = __md_get<Sitemap>(\"__sitemap\", sessionStorage, base)\n  if (cached) {\n    return of(cached)\n  } else {\n    const config = configuration()\n    return requestXML(new URL(\"sitemap.xml\", base || config.base))\n      .pipe(\n        map(sitemap => preprocess(getElements(\"loc\", sitemap)\n          .map(node => node.textContent!)\n        )),\n        catchError(() => EMPTY), // @todo refactor instant loading\n        defaultIfEmpty([]),\n        tap(sitemap => __md_set(\"__sitemap\", sitemap, sessionStorage, base))\n      )\n  }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  NEVER,\n  Observable,\n  Subject,\n  bufferCount,\n  catchError,\n  concatMap,\n  debounceTime,\n  distinctUntilChanged,\n  distinctUntilKeyChanged,\n  filter,\n  fromEvent,\n  map,\n  merge,\n  of,\n  sample,\n  share,\n  skip,\n  skipUntil,\n  switchMap\n} from \"rxjs\"\n\nimport { configuration, feature } from \"~/_\"\nimport {\n  Viewport,\n  ViewportOffset,\n  getElements,\n  getOptionalElement,\n  request,\n  setLocation,\n  setLocationHash\n} from \"~/browser\"\nimport { getComponentElement } from \"~/components\"\nimport { h } from \"~/utilities\"\n\nimport { fetchSitemap } from \"../sitemap\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * History state\n */\nexport interface HistoryState {\n  url: URL                             /* State URL */\n  offset?: ViewportOffset              /* State viewport offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Setup options\n */\ninterface SetupOptions {\n  document$: Subject<Document>         /* Document subject */\n  location$: Subject<URL>              /* Location subject */\n  viewport$: Observable<Viewport>      /* Viewport observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up instant loading\n *\n * When fetching, theoretically, we could use `responseType: \"document\"`, but\n * since all MkDocs links are relative, we need to make sure that the current\n * location matches the document we just loaded. Otherwise any relative links\n * in the document could use the old location.\n *\n * This is the reason why we need to synchronize history events and the process\n * of fetching the document for navigation changes (except `popstate` events):\n *\n * 1. Fetch document via `XMLHTTPRequest`\n * 2. Set new location via `history.pushState`\n * 3. Parse and emit fetched document\n *\n * For `popstate` events, we must not use `history.pushState`, or the forward\n * history will be irreversibly overwritten. In case the request fails, the\n * location change is dispatched regularly.\n *\n * @param options - Options\n */\nexport function setupInstantLoading(\n  { document$, location$, viewport$ }: SetupOptions\n): void {\n  const config = configuration()\n  if (location.protocol === \"file:\")\n    return\n\n  /* Disable automatic scroll restoration */\n  if (\"scrollRestoration\" in history) {\n    history.scrollRestoration = \"manual\"\n\n    /* Hack: ensure that reloads restore viewport offset */\n    fromEvent(window, \"beforeunload\")\n      .subscribe(() => {\n        history.scrollRestoration = \"auto\"\n      })\n  }\n\n  /* Hack: ensure absolute favicon link to omit 404s when switching */\n  const favicon = getOptionalElement<HTMLLinkElement>(\"link[rel=icon]\")\n  if (typeof favicon !== \"undefined\")\n    favicon.href = favicon.href\n\n  /* Intercept internal navigation */\n  const push$ = fetchSitemap()\n    .pipe(\n      map(paths => paths.map(path => `${new URL(path, config.base)}`)),\n      switchMap(urls => fromEvent<MouseEvent>(document.body, \"click\")\n        .pipe(\n          filter(ev => !ev.metaKey && !ev.ctrlKey),\n          switchMap(ev => {\n            if (ev.target instanceof Element) {\n              const el = ev.target.closest(\"a\")\n              if (el && !el.target) {\n                const url = new URL(el.href)\n\n                /* Canonicalize URL */\n                url.search = \"\"\n                url.hash = \"\"\n\n                /* Check if URL should be intercepted */\n                if (\n                  url.pathname !== location.pathname &&\n                  urls.includes(url.toString())\n                ) {\n                  ev.preventDefault()\n                  return of({\n                    url: new URL(el.href)\n                  })\n                }\n              }\n            }\n            return NEVER\n          })\n        )\n      ),\n      share<HistoryState>()\n    )\n\n  /* Intercept history back and forward */\n  const pop$ = fromEvent<PopStateEvent>(window, \"popstate\")\n    .pipe(\n      filter(ev => ev.state !== null),\n      map(ev => ({\n        url: new URL(location.href),\n        offset: ev.state\n      })),\n      share<HistoryState>()\n    )\n\n  /* Emit location change */\n  merge(push$, pop$)\n    .pipe(\n      distinctUntilChanged((a, b) => a.url.href === b.url.href),\n      map(({ url }) => url)\n    )\n      .subscribe(location$)\n\n  /* Fetch document via `XMLHTTPRequest` */\n  const response$ = location$\n    .pipe(\n      distinctUntilKeyChanged(\"pathname\"),\n      switchMap(url => request(url.href)\n        .pipe(\n          catchError(() => {\n            setLocation(url)\n            return NEVER\n          })\n        )\n      ),\n      share()\n    )\n\n  /* Set new location via `history.pushState` */\n  push$\n    .pipe(\n      sample(response$)\n    )\n      .subscribe(({ url }) => {\n        history.pushState({}, \"\", `${url}`)\n      })\n\n  /* Parse and emit fetched document */\n  const dom = new DOMParser()\n  response$\n    .pipe(\n      switchMap(res => res.text()),\n      map(res => dom.parseFromString(res, \"text/html\"))\n    )\n      .subscribe(document$)\n\n  /* Replace meta tags and components */\n  document$\n    .pipe(\n      skip(1)\n    )\n      .subscribe(replacement => {\n        for (const selector of [\n\n          /* Meta tags */\n          \"title\",\n          \"link[rel=canonical]\",\n          \"meta[name=author]\",\n          \"meta[name=description]\",\n\n          /* Components */\n          \"[data-md-component=announce]\",\n          \"[data-md-component=container]\",\n          \"[data-md-component=header-topic]\",\n          \"[data-md-component=outdated]\",\n          \"[data-md-component=logo]\",\n          \"[data-md-component=skip]\",\n          ...feature(\"navigation.tabs.sticky\")\n            ? [\"[data-md-component=tabs]\"]\n            : []\n        ]) {\n          const source = getOptionalElement(selector)\n          const target = getOptionalElement(selector, replacement)\n          if (\n            typeof source !== \"undefined\" &&\n            typeof target !== \"undefined\"\n          ) {\n            source.replaceWith(target)\n          }\n        }\n      })\n\n  /* Re-evaluate scripts */\n  document$\n    .pipe(\n      skip(1),\n      map(() => getComponentElement(\"container\")),\n      switchMap(el => getElements(\"script\", el)),\n      concatMap(el => {\n        const script = h(\"script\")\n        if (el.src) {\n          for (const name of el.getAttributeNames())\n            script.setAttribute(name, el.getAttribute(name)!)\n          el.replaceWith(script)\n\n          /* Complete when script is loaded */\n          return new Observable(observer => {\n            script.onload = () => observer.complete()\n          })\n\n        /* Complete immediately */\n        } else {\n          script.textContent = el.textContent\n          el.replaceWith(script)\n          return EMPTY\n        }\n      })\n    )\n      .subscribe()\n\n  /* Emit history state change */\n  merge(push$, pop$)\n    .pipe(\n      sample(document$)\n    )\n      .subscribe(({ url, offset }) => {\n        if (url.hash && !offset) {\n          setLocationHash(url.hash)\n        } else {\n          window.scrollTo(0, offset?.y || 0)\n        }\n      })\n\n  /* Debounce update of viewport offset */\n  viewport$\n    .pipe(\n      skipUntil(push$),\n      debounceTime(250),\n      distinctUntilKeyChanged(\"offset\")\n    )\n      .subscribe(({ offset }) => {\n        history.replaceState(offset, \"\")\n      })\n\n  /* Set viewport offset from history */\n  merge(push$, pop$)\n    .pipe(\n      bufferCount(2, 1),\n      filter(([a, b]) => a.url.pathname === b.url.pathname),\n      map(([, state]) => state)\n    )\n      .subscribe(({ offset }) => {\n        window.scrollTo(0, offset?.y || 0)\n      })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport escapeHTML from \"escape-html\"\n\nimport { SearchIndexDocument } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search document\n */\nexport interface SearchDocument extends SearchIndexDocument {\n  parent?: SearchIndexDocument         /* Parent article */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search document mapping\n */\nexport type SearchDocumentMap = Map<string, SearchDocument>\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create a search document mapping\n *\n * @param docs - Search index documents\n *\n * @returns Search document map\n */\nexport function setupSearchDocumentMap(\n  docs: SearchIndexDocument[]\n): SearchDocumentMap {\n  const documents = new Map<string, SearchDocument>()\n  const parents   = new Set<SearchDocument>()\n  for (const doc of docs) {\n    const [path, hash] = doc.location.split(\"#\")\n\n    /* Extract location, title and tags */\n    const location = doc.location\n    const title    = doc.title\n    const tags     = doc.tags\n\n    /* Escape and cleanup text */\n    const text = escapeHTML(doc.text)\n      .replace(/\\s+(?=[,.:;!?])/g, \"\")\n      .replace(/\\s+/g, \" \")\n\n    /* Handle section */\n    if (hash) {\n      const parent = documents.get(path)!\n\n      /* Ignore first section, override article */\n      if (!parents.has(parent)) {\n        parent.title = doc.title\n        parent.text  = text\n\n        /* Remember that we processed the article */\n        parents.add(parent)\n\n      /* Add subsequent section */\n      } else {\n        documents.set(location, {\n          location,\n          title,\n          text,\n          parent\n        })\n      }\n\n    /* Add article */\n    } else {\n      documents.set(location, {\n        location,\n        title,\n        text,\n        ...tags && { tags }\n      })\n    }\n  }\n  return documents\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport escapeHTML from \"escape-html\"\n\nimport { SearchIndexConfig } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search highlight function\n *\n * @param value - Value\n *\n * @returns Highlighted value\n */\nexport type SearchHighlightFn = (value: string) => string\n\n/**\n * Search highlight factory function\n *\n * @param query - Query value\n *\n * @returns Search highlight function\n */\nexport type SearchHighlightFactoryFn = (query: string) => SearchHighlightFn\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create a search highlighter\n *\n * @param config - Search index configuration\n * @param escape - Whether to escape HTML\n *\n * @returns Search highlight factory function\n */\nexport function setupSearchHighlighter(\n  config: SearchIndexConfig, escape: boolean\n): SearchHighlightFactoryFn {\n  const separator = new RegExp(config.separator, \"img\")\n  const highlight = (_: unknown, data: string, term: string) => {\n    return `${data}<mark data-md-highlight>${term}</mark>`\n  }\n\n  /* Return factory function */\n  return (query: string) => {\n    query = query\n      .replace(/[\\s*+\\-:~^]+/g, \" \")\n      .trim()\n\n    /* Create search term match expression */\n    const match = new RegExp(`(^|${config.separator})(${\n      query\n        .replace(/[|\\\\{}()[\\]^$+*?.-]/g, \"\\\\$&\")\n        .replace(separator, \"|\")\n    })`, \"img\")\n\n    /* Highlight string value */\n    return value => (\n      escape\n        ? escapeHTML(value)\n        : value\n      )\n        .replace(match, highlight)\n        .replace(/<\\/mark>(\\s+)<mark[^>]*>/img, \"$1\")\n  }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search transformation function\n *\n * @param value - Query value\n *\n * @returns Transformed query value\n */\nexport type SearchTransformFn = (value: string) => string\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Default transformation function\n *\n * 1. Search for terms in quotation marks and prepend a `+` modifier to denote\n *    that the resulting document must contain all terms, converting the query\n *    to an `AND` query (as opposed to the default `OR` behavior). While users\n *    may expect terms enclosed in quotation marks to map to span queries, i.e.\n *    for which order is important, Lunr.js doesn't support them, so the best\n *    we can do is to convert the terms to an `AND` query.\n *\n * 2. Replace control characters which are not located at the beginning of the\n *    query or preceded by white space, or are not followed by a non-whitespace\n *    character or are at the end of the query string. Furthermore, filter\n *    unmatched quotation marks.\n *\n * 3. Trim excess whitespace from left and right.\n *\n * @param query - Query value\n *\n * @returns Transformed query value\n */\nexport function defaultTransform(query: string): string {\n  return query\n    .split(/\"([^\"]+)\"/g)                            /* => 1 */\n      .map((terms, index) => index & 1\n        ? terms.replace(/^\\b|^(?![^\\x00-\\x7F]|$)|\\s+/g, \" +\")\n        : terms\n      )\n      .join(\"\")\n    .replace(/\"|(?:^|\\s+)[*+\\-:^~]+(?=\\s+|$)/g, \"\") /* => 2 */\n    .trim()                                         /* => 3 */\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A RTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { SearchIndex, SearchResult } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search message type\n */\nexport const enum SearchMessageType {\n  SETUP,                               /* Search index setup */\n  READY,                               /* Search index ready */\n  QUERY,                               /* Search query */\n  RESULT                               /* Search results */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Message containing the data necessary to setup the search index\n */\nexport interface SearchSetupMessage {\n  type: SearchMessageType.SETUP        /* Message type */\n  data: SearchIndex                    /* Message data */\n}\n\n/**\n * Message indicating the search index is ready\n */\nexport interface SearchReadyMessage {\n  type: SearchMessageType.READY        /* Message type */\n}\n\n/**\n * Message containing a search query\n */\nexport interface SearchQueryMessage {\n  type: SearchMessageType.QUERY        /* Message type */\n  data: string                         /* Message data */\n}\n\n/**\n * Message containing results for a search query\n */\nexport interface SearchResultMessage {\n  type: SearchMessageType.RESULT       /* Message type */\n  data: SearchResult                   /* Message data */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Message exchanged with the search worker\n */\nexport type SearchMessage =\n  | SearchSetupMessage\n  | SearchReadyMessage\n  | SearchQueryMessage\n  | SearchResultMessage\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Type guard for search setup messages\n *\n * @param message - Search worker message\n *\n * @returns Test result\n */\nexport function isSearchSetupMessage(\n  message: SearchMessage\n): message is SearchSetupMessage {\n  return message.type === SearchMessageType.SETUP\n}\n\n/**\n * Type guard for search ready messages\n *\n * @param message - Search worker message\n *\n * @returns Test result\n */\nexport function isSearchReadyMessage(\n  message: SearchMessage\n): message is SearchReadyMessage {\n  return message.type === SearchMessageType.READY\n}\n\n/**\n * Type guard for search query messages\n *\n * @param message - Search worker message\n *\n * @returns Test result\n */\nexport function isSearchQueryMessage(\n  message: SearchMessage\n): message is SearchQueryMessage {\n  return message.type === SearchMessageType.QUERY\n}\n\n/**\n * Type guard for search result messages\n *\n * @param message - Search worker message\n *\n * @returns Test result\n */\nexport function isSearchResultMessage(\n  message: SearchMessage\n): message is SearchResultMessage {\n  return message.type === SearchMessageType.RESULT\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A RTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  ObservableInput,\n  Subject,\n  from,\n  map,\n  share\n} from \"rxjs\"\n\nimport { configuration, feature, translation } from \"~/_\"\nimport { WorkerHandler, watchWorker } from \"~/browser\"\n\nimport { SearchIndex } from \"../../_\"\nimport {\n  SearchOptions,\n  SearchPipeline\n} from \"../../options\"\nimport {\n  SearchMessage,\n  SearchMessageType,\n  SearchSetupMessage,\n  isSearchResultMessage\n} from \"../message\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search worker\n */\nexport type SearchWorker = WorkerHandler<SearchMessage>\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up search index\n *\n * @param data - Search index\n *\n * @returns Search index\n */\nfunction setupSearchIndex({ config, docs }: SearchIndex): SearchIndex {\n\n  /* Override default language with value from translation */\n  if (config.lang.length === 1 && config.lang[0] === \"en\")\n    config.lang = [\n      translation(\"search.config.lang\")\n    ]\n\n  /* Override default separator with value from translation */\n  if (config.separator === \"[\\\\s\\\\-]+\")\n    config.separator = translation(\"search.config.separator\")\n\n  /* Set pipeline from translation */\n  const pipeline = translation(\"search.config.pipeline\")\n    .split(/\\s*,\\s*/)\n    .filter(Boolean) as SearchPipeline\n\n  /* Determine search options */\n  const options: SearchOptions = {\n    pipeline,\n    suggestions: feature(\"search.suggest\")\n  }\n\n  /* Return search index after defaulting */\n  return { config, docs, options }\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up search worker\n *\n * This function creates a web worker to set up and query the search index,\n * which is done using Lunr.js. The index must be passed as an observable to\n * enable hacks like _localsearch_ via search index embedding as JSON.\n *\n * @param url - Worker URL\n * @param index - Search index observable input\n *\n * @returns Search worker\n */\nexport function setupSearchWorker(\n  url: string, index: ObservableInput<SearchIndex>\n): SearchWorker {\n  const config = configuration()\n  const worker = new Worker(url)\n\n  /* Create communication channels and resolve relative links */\n  const tx$ = new Subject<SearchMessage>()\n  const rx$ = watchWorker(worker, { tx$ })\n    .pipe(\n      map(message => {\n        if (isSearchResultMessage(message)) {\n          for (const result of message.data.items)\n            for (const document of result)\n              document.location = `${new URL(document.location, config.base)}`\n        }\n        return message\n      }),\n      share()\n    )\n\n  /* Set up search index */\n  from(index)\n    .pipe(\n      map(data => ({\n        type: SearchMessageType.SETUP,\n        data: setupSearchIndex(data)\n      } as SearchSetupMessage))\n    )\n      .subscribe(tx$.next.bind(tx$))\n\n  /* Return search worker */\n  return { tx$, rx$ }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Subject,\n  catchError,\n  combineLatest,\n  filter,\n  fromEvent,\n  map,\n  of,\n  switchMap,\n  withLatestFrom\n} from \"rxjs\"\n\nimport { configuration } from \"~/_\"\nimport {\n  getElement,\n  getLocation,\n  requestJSON,\n  setLocation\n} from \"~/browser\"\nimport { getComponentElements } from \"~/components\"\nimport {\n  Version,\n  renderVersionSelector\n} from \"~/templates\"\n\nimport { fetchSitemap } from \"../sitemap\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Setup options\n */\ninterface SetupOptions {\n  document$: Subject<Document>         /* Document subject */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up version selector\n *\n * @param options - Options\n */\nexport function setupVersionSelector(\n  { document$ }: SetupOptions\n): void {\n  const config = configuration()\n  const versions$ = requestJSON<Version[]>(\n    new URL(\"../versions.json\", config.base)\n  )\n    .pipe(\n      catchError(() => EMPTY) // @todo refactor instant loading\n    )\n\n  /* Determine current version */\n  const current$ = versions$\n    .pipe(\n      map(versions => {\n        const [, current] = config.base.match(/([^/]+)\\/?$/)!\n        return versions.find(({ version, aliases }) => (\n          version === current || aliases.includes(current)\n        )) || versions[0]\n      })\n    )\n\n  /* Intercept inter-version navigation */\n  versions$\n    .pipe(\n      map(versions => new Map(versions.map(version => [\n        `${new URL(`../${version.version}/`, config.base)}`,\n        version\n      ]))),\n      switchMap(urls => fromEvent<MouseEvent>(document.body, \"click\")\n        .pipe(\n          filter(ev => !ev.metaKey && !ev.ctrlKey),\n          withLatestFrom(current$),\n          switchMap(([ev, current]) => {\n            if (ev.target instanceof Element) {\n              const el = ev.target.closest(\"a\")\n              if (el && !el.target && urls.has(el.href)) {\n                const url = el.href\n                // This is a temporary hack to detect if a version inside the\n                // version selector or on another part of the site was clicked.\n                // If we're inside the version selector, we definitely want to\n                // find the same page, as we might have different deployments\n                // due to aliases. However, if we're outside the version\n                // selector, we must abort here, because we might otherwise\n                // interfere with instant loading. We need to refactor this\n                // at some point together with instant loading.\n                //\n                // See https://github.com/squidfunk/mkdocs-material/issues/4012\n                if (!ev.target.closest(\".md-version\")) {\n                  const version = urls.get(url)!\n                  if (version === current)\n                    return EMPTY\n                }\n                ev.preventDefault()\n                return of(url)\n              }\n            }\n            return EMPTY\n          }),\n          switchMap(url => {\n            const { version } = urls.get(url)!\n            return fetchSitemap(new URL(url))\n              .pipe(\n                map(sitemap => {\n                  const location = getLocation()\n                  const path = location.href.replace(config.base, \"\")\n                  return sitemap.includes(path.split(\"#\")[0])\n                    ? new URL(`../${version}/${path}`, config.base)\n                    : new URL(url)\n                })\n              )\n          })\n        )\n      )\n    )\n      .subscribe(url => setLocation(url))\n\n  /* Render version selector and warning */\n  combineLatest([versions$, current$])\n    .subscribe(([versions, current]) => {\n      const topic = getElement(\".md-header__topic\")\n      topic.appendChild(renderVersionSelector(versions, current))\n    })\n\n  /* Integrate outdated version banner with instant loading */\n  document$.pipe(switchMap(() => current$))\n    .subscribe(current => {\n\n      /* Check if version state was already determined */\n      let outdated = __md_get(\"__outdated\", sessionStorage)\n      if (outdated === null) {\n        const latest = config.version?.default || \"latest\"\n        outdated = !current.aliases.includes(latest)\n\n        /* Persist version state in session storage */\n        __md_set(\"__outdated\", outdated, sessionStorage)\n      }\n\n      /* Unhide outdated version banner */\n      if (outdated)\n        for (const warning of getComponentElements(\"outdated\"))\n          warning.hidden = false\n    })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  combineLatest,\n  delay,\n  distinctUntilChanged,\n  distinctUntilKeyChanged,\n  filter,\n  finalize,\n  fromEvent,\n  map,\n  merge,\n  share,\n  shareReplay,\n  startWith,\n  take,\n  takeLast,\n  takeUntil,\n  tap\n} from \"rxjs\"\n\nimport { translation } from \"~/_\"\nimport {\n  getLocation,\n  setToggle,\n  watchElementFocus,\n  watchToggle\n} from \"~/browser\"\nimport {\n  SearchMessageType,\n  SearchQueryMessage,\n  SearchWorker,\n  defaultTransform,\n  isSearchReadyMessage\n} from \"~/integrations\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search query\n */\nexport interface SearchQuery {\n  value: string                        /* Query value */\n  focus: boolean                       /* Query focus */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch search query\n *\n * Note that the focus event which triggers re-reading the current query value\n * is delayed by `1ms` so the input's empty state is allowed to propagate.\n *\n * @param el - Search query element\n * @param worker - Search worker\n *\n * @returns Search query observable\n */\nexport function watchSearchQuery(\n  el: HTMLInputElement, { rx$ }: SearchWorker\n): Observable<SearchQuery> {\n  const fn = __search?.transform || defaultTransform\n\n  /* Immediately show search dialog */\n  const { searchParams } = getLocation()\n  if (searchParams.has(\"q\"))\n    setToggle(\"search\", true)\n\n  /* Intercept query parameter (deep link) */\n  const param$ = rx$\n    .pipe(\n      filter(isSearchReadyMessage),\n      take(1),\n      map(() => searchParams.get(\"q\") || \"\")\n    )\n\n  /* Remove query parameter when search is closed */\n  watchToggle(\"search\")\n    .pipe(\n      filter(active => !active),\n      take(1)\n    )\n      .subscribe(() => {\n        const url = new URL(location.href)\n        url.searchParams.delete(\"q\")\n        history.replaceState({}, \"\", `${url}`)\n      })\n\n  /* Set query from parameter */\n  param$.subscribe(value => { // TODO: not ideal - find a better way\n    if (value) {\n      el.value = value\n      el.focus()\n    }\n  })\n\n  /* Intercept focus and input events */\n  const focus$ = watchElementFocus(el)\n  const value$ = merge(\n    fromEvent(el, \"keyup\"),\n    fromEvent(el, \"focus\").pipe(delay(1)),\n    param$\n  )\n    .pipe(\n      map(() => fn(el.value)),\n      startWith(\"\"),\n      distinctUntilChanged(),\n    )\n\n  /* Combine into single observable */\n  return combineLatest([value$, focus$])\n    .pipe(\n      map(([value, focus]) => ({ value, focus })),\n      shareReplay(1)\n    )\n}\n\n/**\n * Mount search query\n *\n * @param el - Search query element\n * @param worker - Search worker\n *\n * @returns Search query component observable\n */\nexport function mountSearchQuery(\n  el: HTMLInputElement, { tx$, rx$ }: SearchWorker\n): Observable<Component<SearchQuery, HTMLInputElement>> {\n  const push$ = new Subject<SearchQuery>()\n  const done$ = push$.pipe(takeLast(1))\n\n  /* Handle value changes */\n  push$\n    .pipe(\n      distinctUntilKeyChanged(\"value\"),\n      map(({ value }): SearchQueryMessage => ({\n        type: SearchMessageType.QUERY,\n        data: value\n      }))\n    )\n      .subscribe(tx$.next.bind(tx$))\n\n  /* Handle focus changes */\n  push$\n    .pipe(\n      distinctUntilKeyChanged(\"focus\")\n    )\n      .subscribe(({ focus }) => {\n        if (focus) {\n          setToggle(\"search\", focus)\n          el.placeholder = \"\"\n        } else {\n          el.placeholder = translation(\"search.placeholder\")\n        }\n      })\n\n  /* Handle reset */\n  fromEvent(el.form!, \"reset\")\n    .pipe(\n      takeUntil(done$)\n    )\n      .subscribe(() => el.focus())\n\n  /* Create and return component */\n  return watchSearchQuery(el, { tx$, rx$ })\n    .pipe(\n      tap(state => push$.next(state)),\n      finalize(() => push$.complete()),\n      map(state => ({ ref: el, ...state })),\n      share()\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  bufferCount,\n  filter,\n  finalize,\n  map,\n  merge,\n  of,\n  skipUntil,\n  switchMap,\n  take,\n  tap,\n  withLatestFrom,\n  zipWith\n} from \"rxjs\"\n\nimport { translation } from \"~/_\"\nimport {\n  getElement,\n  watchElementBoundary\n} from \"~/browser\"\nimport {\n  SearchResult,\n  SearchWorker,\n  isSearchReadyMessage,\n  isSearchResultMessage\n} from \"~/integrations\"\nimport { renderSearchResultItem } from \"~/templates\"\nimport { round } from \"~/utilities\"\n\nimport { Component } from \"../../_\"\nimport { SearchQuery } from \"../query\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  query$: Observable<SearchQuery>      /* Search query observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search result list\n *\n * This function performs a lazy rendering of the search results, depending on\n * the vertical offset of the search result container.\n *\n * @param el - Search result list element\n * @param worker - Search worker\n * @param options - Options\n *\n * @returns Search result list component observable\n */\nexport function mountSearchResult(\n  el: HTMLElement, { rx$ }: SearchWorker, { query$ }: MountOptions\n): Observable<Component<SearchResult>> {\n  const push$ = new Subject<SearchResult>()\n  const boundary$ = watchElementBoundary(el.parentElement!)\n    .pipe(\n      filter(Boolean)\n    )\n\n  /* Retrieve nested components */\n  const meta = getElement(\":scope > :first-child\", el)\n  const list = getElement(\":scope > :last-child\", el)\n\n  /* Wait until search is ready */\n  const ready$ = rx$\n    .pipe(\n      filter(isSearchReadyMessage),\n      take(1)\n    )\n\n  /* Update search result metadata */\n  push$\n    .pipe(\n      withLatestFrom(query$),\n      skipUntil(ready$)\n    )\n      .subscribe(([{ items }, { value }]) => {\n        if (value) {\n          switch (items.length) {\n\n            /* No results */\n            case 0:\n              meta.textContent = translation(\"search.result.none\")\n              break\n\n            /* One result */\n            case 1:\n              meta.textContent = translation(\"search.result.one\")\n              break\n\n            /* Multiple result */\n            default:\n              meta.textContent = translation(\n                \"search.result.other\",\n                round(items.length)\n              )\n          }\n        } else {\n          meta.textContent = translation(\"search.result.placeholder\")\n        }\n      })\n\n  /* Update search result list */\n  push$\n    .pipe(\n      tap(() => list.innerHTML = \"\"),\n      switchMap(({ items }) => merge(\n        of(...items.slice(0, 10)),\n        of(...items.slice(10))\n          .pipe(\n            bufferCount(4),\n            zipWith(boundary$),\n            switchMap(([chunk]) => chunk)\n          )\n      ))\n    )\n      .subscribe(result => list.appendChild(\n        renderSearchResultItem(result)\n      ))\n\n  /* Filter search result message */\n  const result$ = rx$\n    .pipe(\n      filter(isSearchResultMessage),\n      map(({ data }) => data)\n    )\n\n  /* Create and return component */\n  return result$\n    .pipe(\n      tap(state => push$.next(state)),\n      finalize(() => push$.complete()),\n      map(state => ({ ref: el, ...state }))\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  finalize,\n  fromEvent,\n  map,\n  tap\n} from \"rxjs\"\n\nimport { getLocation } from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { SearchQuery } from \"../query\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search sharing\n */\nexport interface SearchShare {\n  url: URL                             /* Deep link for sharing */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  query$: Observable<SearchQuery>      /* Search query observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  query$: Observable<SearchQuery>      /* Search query observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search sharing\n *\n * @param _el - Search sharing element\n * @param options - Options\n *\n * @returns Search sharing observable\n */\nexport function watchSearchShare(\n  _el: HTMLElement, { query$ }: WatchOptions\n): Observable<SearchShare> {\n  return query$\n    .pipe(\n      map(({ value }) => {\n        const url = getLocation()\n        url.hash = \"\"\n        url.searchParams.delete(\"h\")\n        url.searchParams.set(\"q\", value)\n        return { url }\n      })\n    )\n}\n\n/**\n * Mount search sharing\n *\n * @param el - Search sharing element\n * @param options - Options\n *\n * @returns Search sharing component observable\n */\nexport function mountSearchShare(\n  el: HTMLAnchorElement, options: MountOptions\n): Observable<Component<SearchShare>> {\n  const push$ = new Subject<SearchShare>()\n  push$.subscribe(({ url }) => {\n    el.setAttribute(\"data-clipboard-text\", el.href)\n    el.href = `${url}`\n  })\n\n  /* Prevent following of link */\n  fromEvent(el, \"click\")\n    .subscribe(ev => ev.preventDefault())\n\n  /* Create and return component */\n  return watchSearchShare(el, options)\n    .pipe(\n      tap(state => push$.next(state)),\n      finalize(() => push$.complete()),\n      map(state => ({ ref: el, ...state }))\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  asyncScheduler,\n  combineLatestWith,\n  distinctUntilChanged,\n  filter,\n  finalize,\n  fromEvent,\n  map,\n  merge,\n  observeOn,\n  tap\n} from \"rxjs\"\n\nimport { Keyboard } from \"~/browser\"\nimport {\n  SearchResult,\n  SearchWorker,\n  isSearchResultMessage\n} from \"~/integrations\"\n\nimport { Component, getComponentElement } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search suggestions\n */\nexport interface SearchSuggest {}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  keyboard$: Observable<Keyboard>      /* Keyboard observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search suggestions\n *\n * This function will perform a lazy rendering of the search results, depending\n * on the vertical offset of the search result container.\n *\n * @param el - Search result list element\n * @param worker - Search worker\n * @param options - Options\n *\n * @returns Search result list component observable\n */\nexport function mountSearchSuggest(\n  el: HTMLElement, { rx$ }: SearchWorker, { keyboard$ }: MountOptions\n): Observable<Component<SearchSuggest>> {\n  const push$ = new Subject<SearchResult>()\n\n  /* Retrieve query component and track all changes */\n  const query  = getComponentElement(\"search-query\")\n  const query$ = merge(\n    fromEvent(query, \"keydown\"),\n    fromEvent(query, \"focus\")\n  )\n    .pipe(\n      observeOn(asyncScheduler),\n      map(() => query.value),\n      distinctUntilChanged(),\n    )\n\n  /* Update search suggestions */\n  push$\n    .pipe(\n      combineLatestWith(query$),\n      map(([{ suggestions }, value]) => {\n        const words = value.split(/([\\s-]+)/)\n        if (suggestions?.length && words[words.length - 1]) {\n          const last = suggestions[suggestions.length - 1]\n          if (last.startsWith(words[words.length - 1]))\n            words[words.length - 1] = last\n        } else {\n          words.length = 0\n        }\n        return words\n      })\n    )\n      .subscribe(words => el.innerHTML = words\n        .join(\"\")\n        .replace(/\\s/g, \"&nbsp;\")\n      )\n\n  /* Set up search keyboard handlers */\n  keyboard$\n    .pipe(\n      filter(({ mode }) => mode === \"search\")\n    )\n      .subscribe(key => {\n        switch (key.type) {\n\n          /* Right arrow: accept current suggestion */\n          case \"ArrowRight\":\n            if (\n              el.innerText.length &&\n              query.selectionStart === query.value.length\n            )\n              query.value = el.innerText\n            break\n        }\n      })\n\n  /* Filter search result message */\n  const result$ = rx$\n    .pipe(\n      filter(isSearchResultMessage),\n      map(({ data }) => data)\n    )\n\n  /* Create and return component */\n  return result$\n    .pipe(\n      tap(state => push$.next(state)),\n      finalize(() => push$.complete()),\n      map(() => ({ ref: el }))\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  NEVER,\n  Observable,\n  ObservableInput,\n  filter,\n  merge,\n  mergeWith,\n  sample,\n  take\n} from \"rxjs\"\n\nimport { configuration } from \"~/_\"\nimport {\n  Keyboard,\n  getActiveElement,\n  getElements,\n  setToggle\n} from \"~/browser\"\nimport {\n  SearchIndex,\n  SearchResult,\n  isSearchQueryMessage,\n  isSearchReadyMessage,\n  setupSearchWorker\n} from \"~/integrations\"\n\nimport {\n  Component,\n  getComponentElement,\n  getComponentElements\n} from \"../../_\"\nimport {\n  SearchQuery,\n  mountSearchQuery\n} from \"../query\"\nimport { mountSearchResult } from \"../result\"\nimport {\n  SearchShare,\n  mountSearchShare\n} from \"../share\"\nimport {\n  SearchSuggest,\n  mountSearchSuggest\n} from \"../suggest\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search\n */\nexport type Search =\n  | SearchQuery\n  | SearchResult\n  | SearchShare\n  | SearchSuggest\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  index$: ObservableInput<SearchIndex> /* Search index observable */\n  keyboard$: Observable<Keyboard>      /* Keyboard observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search\n *\n * This function sets up the search functionality, including the underlying\n * web worker and all keyboard bindings.\n *\n * @param el - Search element\n * @param options - Options\n *\n * @returns Search component observable\n */\nexport function mountSearch(\n  el: HTMLElement, { index$, keyboard$ }: MountOptions\n): Observable<Component<Search>> {\n  const config = configuration()\n  try {\n    const url = __search?.worker || config.search\n    const worker = setupSearchWorker(url, index$)\n\n    /* Retrieve query and result components */\n    const query  = getComponentElement(\"search-query\", el)\n    const result = getComponentElement(\"search-result\", el)\n\n    /* Re-emit query when search is ready */\n    const { tx$, rx$ } = worker\n    tx$\n      .pipe(\n        filter(isSearchQueryMessage),\n        sample(rx$.pipe(filter(isSearchReadyMessage))),\n        take(1)\n      )\n        .subscribe(tx$.next.bind(tx$))\n\n    /* Set up search keyboard handlers */\n    keyboard$\n      .pipe(\n        filter(({ mode }) => mode === \"search\")\n      )\n        .subscribe(key => {\n          const active = getActiveElement()\n          switch (key.type) {\n\n            /* Enter: go to first (best) result */\n            case \"Enter\":\n              if (active === query) {\n                const anchors = new Map<HTMLAnchorElement, number>()\n                for (const anchor of getElements<HTMLAnchorElement>(\n                  \":first-child [href]\", result\n                )) {\n                  const article = anchor.firstElementChild!\n                  anchors.set(anchor, parseFloat(\n                    article.getAttribute(\"data-md-score\")!\n                  ))\n                }\n\n                /* Go to result with highest score, if any */\n                if (anchors.size) {\n                  const [[best]] = [...anchors].sort(([, a], [, b]) => b - a)\n                  best.click()\n                }\n\n                /* Otherwise omit form submission */\n                key.claim()\n              }\n              break\n\n            /* Escape or Tab: close search */\n            case \"Escape\":\n            case \"Tab\":\n              setToggle(\"search\", false)\n              query.blur()\n              break\n\n            /* Vertical arrows: select previous or next search result */\n            case \"ArrowUp\":\n            case \"ArrowDown\":\n              if (typeof active === \"undefined\") {\n                query.focus()\n              } else {\n                const els = [query, ...getElements(\n                  \":not(details) > [href], summary, details[open] [href]\",\n                  result\n                )]\n                const i = Math.max(0, (\n                  Math.max(0, els.indexOf(active)) + els.length + (\n                    key.type === \"ArrowUp\" ? -1 : +1\n                  )\n                ) % els.length)\n                els[i].focus()\n              }\n\n              /* Prevent scrolling of page */\n              key.claim()\n              break\n\n            /* All other keys: hand to search query */\n            default:\n              if (query !== getActiveElement())\n                query.focus()\n          }\n        })\n\n    /* Set up global keyboard handlers */\n    keyboard$\n      .pipe(\n        filter(({ mode }) => mode === \"global\"),\n      )\n        .subscribe(key => {\n          switch (key.type) {\n\n            /* Open search and select query */\n            case \"f\":\n            case \"s\":\n            case \"/\":\n              query.focus()\n              query.select()\n\n              /* Prevent scrolling of page */\n              key.claim()\n              break\n          }\n        })\n\n    /* Create and return component */\n    const query$  = mountSearchQuery(query, worker)\n    const result$ = mountSearchResult(result, worker, { query$ })\n    return merge(query$, result$)\n      .pipe(\n        mergeWith(\n\n          /* Search sharing */\n          ...getComponentElements(\"search-share\", el)\n            .map(child => mountSearchShare(child, { query$ })),\n\n          /* Search suggestions */\n          ...getComponentElements(\"search-suggest\", el)\n            .map(child => mountSearchSuggest(child, worker, { keyboard$ }))\n        )\n      )\n\n  /* Gracefully handle broken search */\n  } catch (err) {\n    el.hidden = true\n    return NEVER\n  }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  ObservableInput,\n  combineLatest,\n  filter,\n  map,\n  startWith\n} from \"rxjs\"\n\nimport { getLocation } from \"~/browser\"\nimport {\n  SearchIndex,\n  setupSearchHighlighter\n} from \"~/integrations\"\nimport { h } from \"~/utilities\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search highlighting\n */\nexport interface SearchHighlight {\n  nodes: Map<ChildNode, string>        /* Map of replacements */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  index$: ObservableInput<SearchIndex> /* Search index observable */\n  location$: Observable<URL>           /* Location observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search highlighting\n *\n * @param el - Content element\n * @param options - Options\n *\n * @returns Search highlighting component observable\n */\nexport function mountSearchHiglight(\n  el: HTMLElement, { index$, location$ }: MountOptions\n): Observable<Component<SearchHighlight>> {\n  return combineLatest([\n    index$,\n    location$\n      .pipe(\n        startWith(getLocation()),\n        filter(url => !!url.searchParams.get(\"h\"))\n      )\n  ])\n    .pipe(\n      map(([index, url]) => setupSearchHighlighter(index.config, true)(\n        url.searchParams.get(\"h\")!\n      )),\n      map(fn => {\n        const nodes = new Map<ChildNode, string>()\n\n        /* Traverse text nodes and collect matches */\n        const it = document.createNodeIterator(el, NodeFilter.SHOW_TEXT)\n        for (let node = it.nextNode(); node; node = it.nextNode()) {\n          if (node.parentElement?.offsetHeight) {\n            const original = node.textContent!\n            const replaced = fn(original)\n            if (replaced.length > original.length)\n              nodes.set(node as ChildNode, replaced)\n          }\n        }\n\n        /* Replace original nodes with matches */\n        for (const [node, text] of nodes) {\n          const { childNodes } = h(\"span\", null, text)\n          node.replaceWith(...Array.from(childNodes))\n        }\n\n        /* Return component */\n        return { ref: el, nodes }\n      })\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  animationFrameScheduler,\n  auditTime,\n  combineLatest,\n  defer,\n  distinctUntilChanged,\n  finalize,\n  map,\n  observeOn,\n  take,\n  tap,\n  withLatestFrom\n} from \"rxjs\"\n\nimport {\n  Viewport,\n  getElement,\n  getElementContainer,\n  getElementOffset,\n  getElementSize,\n  getElements\n} from \"~/browser\"\n\nimport { Component } from \"../_\"\nimport { Header } from \"../header\"\nimport { Main } from \"../main\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Sidebar\n */\nexport interface Sidebar {\n  height: number                       /* Sidebar height */\n  locked: boolean                      /* Sidebar is locked */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  main$: Observable<Main>              /* Main area observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n  main$: Observable<Main>              /* Main area observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch sidebar\n *\n * This function returns an observable that computes the visual parameters of\n * the sidebar which depends on the vertical viewport offset, as well as the\n * height of the main area. When the page is scrolled beyond the header, the\n * sidebar is locked and fills the remaining space.\n *\n * @param el - Sidebar element\n * @param options - Options\n *\n * @returns Sidebar observable\n */\nexport function watchSidebar(\n  el: HTMLElement, { viewport$, main$ }: WatchOptions\n): Observable<Sidebar> {\n  const parent = el.parentElement!\n  const adjust =\n    parent.offsetTop -\n    parent.parentElement!.offsetTop\n\n  /* Compute the sidebar's available height and if it should be locked */\n  return combineLatest([main$, viewport$])\n    .pipe(\n      map(([{ offset, height }, { offset: { y } }]) => {\n        height = height\n          + Math.min(adjust, Math.max(0, y - offset))\n          - adjust\n        return {\n          height,\n          locked: y >= offset + adjust\n        }\n      }),\n      distinctUntilChanged((a, b) => (\n        a.height === b.height &&\n        a.locked === b.locked\n      ))\n    )\n}\n\n/**\n * Mount sidebar\n *\n * This function doesn't set the height of the actual sidebar, but of its first\n * child \u2013 the `.md-sidebar__scrollwrap` element in order to mitigiate jittery\n * sidebars when the footer is scrolled into view. At some point we switched\n * from `absolute` / `fixed` positioning to `sticky` positioning, significantly\n * reducing jitter in some browsers (respectively Firefox and Safari) when\n * scrolling from the top. However, top-aligned sticky positioning means that\n * the sidebar snaps to the bottom when the end of the container is reached.\n * This is what leads to the mentioned jitter, as the sidebar's height may be\n * updated too slowly.\n *\n * This behaviour can be mitigiated by setting the height of the sidebar to `0`\n * while preserving the padding, and the height on its first element.\n *\n * @param el - Sidebar element\n * @param options - Options\n *\n * @returns Sidebar component observable\n */\nexport function mountSidebar(\n  el: HTMLElement, { header$, ...options }: MountOptions\n): Observable<Component<Sidebar>> {\n  const inner = getElement(\".md-sidebar__scrollwrap\", el)\n  const { y } = getElementOffset(inner)\n  return defer(() => {\n    const push$ = new Subject<Sidebar>()\n    push$\n      .pipe(\n        auditTime(0, animationFrameScheduler),\n        withLatestFrom(header$)\n      )\n        .subscribe({\n\n          /* Handle emission */\n          next([{ height }, { height: offset }]) {\n            inner.style.height = `${height - 2 * y}px`\n            el.style.top       = `${offset}px`\n          },\n\n          /* Handle complete */\n          complete() {\n            inner.style.height = \"\"\n            el.style.top       = \"\"\n          }\n        })\n\n    /* Bring active item into view on initial load */\n    push$\n      .pipe(\n        observeOn(animationFrameScheduler),\n        take(1)\n      )\n        .subscribe(() => {\n          for (const item of getElements(\".md-nav__link--active[href]\", el)) {\n            const container = getElementContainer(item)\n            if (typeof container !== \"undefined\") {\n              const offset = item.offsetTop - container.offsetTop\n              const { height } = getElementSize(container)\n              container.scrollTo({\n                top: offset - height / 2\n              })\n            }\n          }\n        })\n\n    /* Create and return component */\n    return watchSidebar(el, options)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Repo, User } from \"github-types\"\nimport {\n  EMPTY,\n  Observable,\n  catchError,\n  defaultIfEmpty,\n  map,\n  zip\n} from \"rxjs\"\n\nimport { requestJSON } from \"~/browser\"\n\nimport { SourceFacts } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * GitHub release (partial)\n */\ninterface Release {\n  tag_name: string                     /* Tag name */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch GitHub repository facts\n *\n * @param user - GitHub user or organization\n * @param repo - GitHub repository\n *\n * @returns Repository facts observable\n */\nexport function fetchSourceFactsFromGitHub(\n  user: string, repo?: string\n): Observable<SourceFacts> {\n  if (typeof repo !== \"undefined\") {\n    const url = `https://api.github.com/repos/${user}/${repo}`\n    return zip(\n\n      /* Fetch version */\n      requestJSON<Release>(`${url}/releases/latest`)\n        .pipe(\n          catchError(() => EMPTY), // @todo refactor instant loading\n          map(release => ({\n            version: release.tag_name\n          })),\n          defaultIfEmpty({})\n        ),\n\n      /* Fetch stars and forks */\n      requestJSON<Repo>(url)\n        .pipe(\n          catchError(() => EMPTY), // @todo refactor instant loading\n          map(info => ({\n            stars: info.stargazers_count,\n            forks: info.forks_count\n          })),\n          defaultIfEmpty({})\n        )\n    )\n      .pipe(\n        map(([release, info]) => ({ ...release, ...info }))\n      )\n\n  /* User or organization */\n  } else {\n    const url = `https://api.github.com/users/${user}`\n    return requestJSON<User>(url)\n      .pipe(\n        map(info => ({\n          repositories: info.public_repos\n        })),\n        defaultIfEmpty({})\n      )\n  }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { ProjectSchema } from \"gitlab\"\nimport {\n  EMPTY,\n  Observable,\n  catchError,\n  defaultIfEmpty,\n  map\n} from \"rxjs\"\n\nimport { requestJSON } from \"~/browser\"\n\nimport { SourceFacts } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch GitLab repository facts\n *\n * @param base - GitLab base\n * @param project - GitLab project\n *\n * @returns Repository facts observable\n */\nexport function fetchSourceFactsFromGitLab(\n  base: string, project: string\n): Observable<SourceFacts> {\n  const url = `https://${base}/api/v4/projects/${encodeURIComponent(project)}`\n  return requestJSON<ProjectSchema>(url)\n    .pipe(\n      catchError(() => EMPTY), // @todo refactor instant loading\n      map(({ star_count, forks_count }) => ({\n        stars: star_count,\n        forks: forks_count\n      })),\n      defaultIfEmpty({})\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { EMPTY, Observable } from \"rxjs\"\n\nimport { fetchSourceFactsFromGitHub } from \"../github\"\nimport { fetchSourceFactsFromGitLab } from \"../gitlab\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Repository facts for repositories\n */\nexport interface RepositoryFacts {\n  stars?: number                       /* Number of stars */\n  forks?: number                       /* Number of forks */\n  version?: string                     /* Latest version */\n}\n\n/**\n * Repository facts for organizations\n */\nexport interface OrganizationFacts {\n  repositories?: number                /* Number of repositories */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Repository facts\n */\nexport type SourceFacts =\n  | RepositoryFacts\n  | OrganizationFacts\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch repository facts\n *\n * @param url - Repository URL\n *\n * @returns Repository facts observable\n */\nexport function fetchSourceFacts(\n  url: string\n): Observable<SourceFacts> {\n\n  /* Try to match GitHub repository */\n  let match = url.match(/^.+github\\.com\\/([^/]+)\\/?([^/]+)?/i)\n  if (match) {\n    const [, user, repo] = match\n    return fetchSourceFactsFromGitHub(user, repo)\n  }\n\n  /* Try to match GitLab repository */\n  match = url.match(/^.+?([^/]*gitlab[^/]+)\\/(.+?)\\/?$/i)\n  if (match) {\n    const [, base, slug] = match\n    return fetchSourceFactsFromGitLab(base, slug)\n  }\n\n  /* Fallback */\n  return EMPTY\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  EMPTY,\n  Observable,\n  Subject,\n  catchError,\n  defer,\n  filter,\n  finalize,\n  map,\n  of,\n  shareReplay,\n  tap\n} from \"rxjs\"\n\nimport { getElement } from \"~/browser\"\nimport { ConsentDefaults } from \"~/components/consent\"\nimport { renderSourceFacts } from \"~/templates\"\n\nimport {\n  Component,\n  getComponentElements\n} from \"../../_\"\nimport {\n  SourceFacts,\n  fetchSourceFacts\n} from \"../facts\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Repository information\n */\nexport interface Source {\n  facts: SourceFacts                   /* Repository facts */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Repository information observable\n */\nlet fetch$: Observable<Source>\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch repository information\n *\n * This function tries to read the repository facts from session storage, and\n * if unsuccessful, fetches them from the underlying provider.\n *\n * @param el - Repository information element\n *\n * @returns Repository information observable\n */\nexport function watchSource(\n  el: HTMLAnchorElement\n): Observable<Source> {\n  return fetch$ ||= defer(() => {\n    const cached = __md_get<SourceFacts>(\"__source\", sessionStorage)\n    if (cached) {\n      return of(cached)\n    } else {\n\n      /* Check if consent is configured and was given */\n      const els = getComponentElements(\"consent\")\n      if (els.length) {\n        const consent = __md_get<ConsentDefaults>(\"__consent\")\n        if (!(consent && consent.github))\n          return EMPTY\n      }\n\n      /* Fetch repository facts */\n      return fetchSourceFacts(el.href)\n        .pipe(\n          tap(facts => __md_set(\"__source\", facts, sessionStorage))\n        )\n    }\n  })\n    .pipe(\n      catchError(() => EMPTY),\n      filter(facts => Object.keys(facts).length > 0),\n      map(facts => ({ facts })),\n      shareReplay(1)\n    )\n}\n\n/**\n * Mount repository information\n *\n * @param el - Repository information element\n *\n * @returns Repository information component observable\n */\nexport function mountSource(\n  el: HTMLAnchorElement\n): Observable<Component<Source>> {\n  const inner = getElement(\":scope > :last-child\", el)\n  return defer(() => {\n    const push$ = new Subject<Source>()\n    push$.subscribe(({ facts }) => {\n      inner.appendChild(renderSourceFacts(facts))\n      inner.classList.add(\"md-source__repository--active\")\n    })\n\n    /* Create and return component */\n    return watchSource(el)\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  defer,\n  distinctUntilKeyChanged,\n  finalize,\n  map,\n  of,\n  switchMap,\n  tap\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n  Viewport,\n  watchElementSize,\n  watchViewportAt\n} from \"~/browser\"\n\nimport { Component } from \"../_\"\nimport { Header } from \"../header\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Navigation tabs\n */\nexport interface Tabs {\n  hidden: boolean                      /* Navigation tabs are hidden */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch navigation tabs\n *\n * @param el - Navigation tabs element\n * @param options - Options\n *\n * @returns Navigation tabs observable\n */\nexport function watchTabs(\n  el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable<Tabs> {\n  return watchElementSize(document.body)\n    .pipe(\n      switchMap(() => watchViewportAt(el, { header$, viewport$ })),\n      map(({ offset: { y } }) => {\n        return {\n          hidden: y >= 10\n        }\n      }),\n      distinctUntilKeyChanged(\"hidden\")\n    )\n}\n\n/**\n * Mount navigation tabs\n *\n * This function hides the navigation tabs when scrolling past the threshold\n * and makes them reappear in a nice CSS animation when scrolling back up.\n *\n * @param el - Navigation tabs element\n * @param options - Options\n *\n * @returns Navigation tabs component observable\n */\nexport function mountTabs(\n  el: HTMLElement, options: MountOptions\n): Observable<Component<Tabs>> {\n  return defer(() => {\n    const push$ = new Subject<Tabs>()\n    push$.subscribe({\n\n      /* Handle emission */\n      next({ hidden }) {\n        el.hidden = hidden\n      },\n\n      /* Handle complete */\n      complete() {\n        el.hidden = false\n      }\n    })\n\n    /* Create and return component */\n    return (\n      feature(\"navigation.tabs.sticky\")\n        ? of({ hidden: false })\n        : watchTabs(el, options)\n    )\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  bufferCount,\n  combineLatestWith,\n  debounceTime,\n  defer,\n  distinctUntilChanged,\n  distinctUntilKeyChanged,\n  filter,\n  finalize,\n  map,\n  merge,\n  of,\n  repeat,\n  scan,\n  share,\n  skip,\n  startWith,\n  switchMap,\n  takeLast,\n  takeUntil,\n  tap,\n  withLatestFrom\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n  Viewport,\n  getElement,\n  getElementContainer,\n  getElementSize,\n  getElements,\n  getLocation,\n  getOptionalElement,\n  watchElementSize\n} from \"~/browser\"\n\nimport {\n  Component,\n  getComponentElement\n} from \"../_\"\nimport { Header } from \"../header\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Table of contents\n */\nexport interface TableOfContents {\n  prev: HTMLAnchorElement[][]          /* Anchors (previous) */\n  next: HTMLAnchorElement[][]          /* Anchors (next) */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n  target$: Observable<HTMLElement>     /* Location target observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch table of contents\n *\n * This is effectively a scroll spy implementation which will account for the\n * fixed header and automatically re-calculate anchor offsets when the viewport\n * is resized. The returned observable will only emit if the table of contents\n * needs to be repainted.\n *\n * This implementation tracks an anchor element's entire path starting from its\n * level up to the top-most anchor element, e.g. `[h3, h2, h1]`. Although the\n * Material theme currently doesn't make use of this information, it enables\n * the styling of the entire hierarchy through customization.\n *\n * Note that the current anchor is the last item of the `prev` anchor list.\n *\n * @param el - Table of contents element\n * @param options - Options\n *\n * @returns Table of contents observable\n */\nexport function watchTableOfContents(\n  el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable<TableOfContents> {\n  const table = new Map<HTMLAnchorElement, HTMLElement>()\n\n  /* Compute anchor-to-target mapping */\n  const anchors = getElements<HTMLAnchorElement>(\"[href^=\\\\#]\", el)\n  for (const anchor of anchors) {\n    const id = decodeURIComponent(anchor.hash.substring(1))\n    const target = getOptionalElement(`[id=\"${id}\"]`)\n    if (typeof target !== \"undefined\")\n      table.set(anchor, target)\n  }\n\n  /* Compute necessary adjustment for header */\n  const adjust$ = header$\n    .pipe(\n      distinctUntilKeyChanged(\"height\"),\n      map(({ height }) => {\n        const main = getComponentElement(\"main\")\n        const grid = getElement(\":scope > :first-child\", main)\n        return height + 0.8 * (\n          grid.offsetTop -\n          main.offsetTop\n        )\n      }),\n      share()\n    )\n\n  /* Compute partition of previous and next anchors */\n  const partition$ = watchElementSize(document.body)\n    .pipe(\n      distinctUntilKeyChanged(\"height\"),\n\n      /* Build index to map anchor paths to vertical offsets */\n      switchMap(body => defer(() => {\n        let path: HTMLAnchorElement[] = []\n        return of([...table].reduce((index, [anchor, target]) => {\n          while (path.length) {\n            const last = table.get(path[path.length - 1])!\n            if (last.tagName >= target.tagName) {\n              path.pop()\n            } else {\n              break\n            }\n          }\n\n          /* If the current anchor is hidden, continue with its parent */\n          let offset = target.offsetTop\n          while (!offset && target.parentElement) {\n            target = target.parentElement\n            offset = target.offsetTop\n          }\n\n          /* Map reversed anchor path to vertical offset */\n          return index.set(\n            [...path = [...path, anchor]].reverse(),\n            offset\n          )\n        }, new Map<HTMLAnchorElement[], number>()))\n      })\n        .pipe(\n\n          /* Sort index by vertical offset (see https://bit.ly/30z6QSO) */\n          map(index => new Map([...index].sort(([, a], [, b]) => a - b))),\n          combineLatestWith(adjust$),\n\n          /* Re-compute partition when viewport offset changes */\n          switchMap(([index, adjust]) => viewport$\n            .pipe(\n              scan(([prev, next], { offset: { y }, size }) => {\n                const last = y + size.height >= Math.floor(body.height)\n\n                /* Look forward */\n                while (next.length) {\n                  const [, offset] = next[0]\n                  if (offset - adjust < y || last) {\n                    prev = [...prev, next.shift()!]\n                  } else {\n                    break\n                  }\n                }\n\n                /* Look backward */\n                while (prev.length) {\n                  const [, offset] = prev[prev.length - 1]\n                  if (offset - adjust >= y && !last) {\n                    next = [prev.pop()!, ...next]\n                  } else {\n                    break\n                  }\n                }\n\n                /* Return partition */\n                return [prev, next]\n              }, [[], [...index]]),\n              distinctUntilChanged((a, b) => (\n                a[0] === b[0] &&\n                a[1] === b[1]\n              ))\n            )\n          )\n        )\n      )\n    )\n\n  /* Compute and return anchor list migrations */\n  return partition$\n    .pipe(\n      map(([prev, next]) => ({\n        prev: prev.map(([path]) => path),\n        next: next.map(([path]) => path)\n      })),\n\n      /* Extract anchor list migrations */\n      startWith({ prev: [], next: [] }),\n      bufferCount(2, 1),\n      map(([a, b]) => {\n\n        /* Moving down */\n        if (a.prev.length < b.prev.length) {\n          return {\n            prev: b.prev.slice(Math.max(0, a.prev.length - 1), b.prev.length),\n            next: []\n          }\n\n        /* Moving up */\n        } else {\n          return {\n            prev: b.prev.slice(-1),\n            next: b.next.slice(0, b.next.length - a.next.length)\n          }\n        }\n      })\n    )\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Mount table of contents\n *\n * @param el - Table of contents element\n * @param options - Options\n *\n * @returns Table of contents component observable\n */\nexport function mountTableOfContents(\n  el: HTMLElement, { viewport$, header$, target$ }: MountOptions\n): Observable<Component<TableOfContents>> {\n  return defer(() => {\n    const push$ = new Subject<TableOfContents>()\n    const done$ = push$.pipe(takeLast(1))\n    push$.subscribe(({ prev, next }) => {\n\n      /* Look forward */\n      for (const [anchor] of next) {\n        anchor.classList.remove(\"md-nav__link--passed\")\n        anchor.classList.remove(\"md-nav__link--active\")\n      }\n\n      /* Look backward */\n      for (const [index, [anchor]] of prev.entries()) {\n        anchor.classList.add(\"md-nav__link--passed\")\n        anchor.classList.toggle(\n          \"md-nav__link--active\",\n          index === prev.length - 1\n        )\n      }\n    })\n\n    /* Set up following, if enabled */\n    if (feature(\"toc.follow\")) {\n\n      /* Toggle smooth scrolling only for anchor clicks */\n      const smooth$ = merge(\n        viewport$.pipe(debounceTime(1), map(() => undefined)),\n        viewport$.pipe(debounceTime(250), map(() => \"smooth\" as const))\n      )\n\n      /* Bring active anchor into view */\n      push$\n        .pipe(\n          filter(({ prev }) => prev.length > 0),\n          withLatestFrom(smooth$)\n        )\n          .subscribe(([{ prev }, behavior]) => {\n            const [anchor] = prev[prev.length - 1]\n            if (anchor.offsetHeight) {\n\n              /* Retrieve overflowing container and scroll */\n              const container = getElementContainer(anchor)\n              if (typeof container !== \"undefined\") {\n                const offset = anchor.offsetTop - container.offsetTop\n                const { height } = getElementSize(container)\n                container.scrollTo({\n                  top: offset - height / 2,\n                  behavior\n                })\n              }\n            }\n          })\n    }\n\n    /* Set up anchor tracking, if enabled */\n    if (feature(\"navigation.tracking\"))\n      viewport$\n        .pipe(\n          takeUntil(done$),\n          distinctUntilKeyChanged(\"offset\"),\n          debounceTime(250),\n          skip(1),\n          takeUntil(target$.pipe(skip(1))),\n          repeat({ delay: 250 }),\n          withLatestFrom(push$)\n        )\n          .subscribe(([, { prev }]) => {\n            const url = getLocation()\n\n            /* Set hash fragment to active anchor */\n            const anchor = prev[prev.length - 1]\n            if (anchor && anchor.length) {\n              const [active] = anchor\n              const { hash } = new URL(active.href)\n              if (url.hash !== hash) {\n                url.hash = hash\n                history.replaceState({}, \"\", `${url}`)\n              }\n\n            /* Reset anchor when at the top */\n            } else {\n              url.hash = \"\"\n              history.replaceState({}, \"\", `${url}`)\n            }\n          })\n\n    /* Create and return component */\n    return watchTableOfContents(el, { viewport$, header$ })\n      .pipe(\n        tap(state => push$.next(state)),\n        finalize(() => push$.complete()),\n        map(state => ({ ref: el, ...state }))\n      )\n  })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  Subject,\n  bufferCount,\n  combineLatest,\n  distinctUntilChanged,\n  distinctUntilKeyChanged,\n  endWith,\n  finalize,\n  map,\n  repeat,\n  skip,\n  takeLast,\n  takeUntil,\n  tap\n} from \"rxjs\"\n\nimport { Viewport } from \"~/browser\"\n\nimport { Component } from \"../_\"\nimport { Header } from \"../header\"\nimport { Main } from \"../main\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Back-to-top button\n */\nexport interface BackToTop {\n  hidden: boolean                      /* Back-to-top button is hidden */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  main$: Observable<Main>              /* Main area observable */\n  target$: Observable<HTMLElement>     /* Location target observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  header$: Observable<Header>          /* Header observable */\n  main$: Observable<Main>              /* Main area observable */\n  target$: Observable<HTMLElement>     /* Location target observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch back-to-top\n *\n * @param _el - Back-to-top element\n * @param options - Options\n *\n * @returns Back-to-top observable\n */\nexport function watchBackToTop(\n  _el: HTMLElement, { viewport$, main$, target$ }: WatchOptions\n): Observable<BackToTop> {\n\n  /* Compute direction */\n  const direction$ = viewport$\n    .pipe(\n      map(({ offset: { y } }) => y),\n      bufferCount(2, 1),\n      map(([a, b]) => a > b && b > 0),\n      distinctUntilChanged()\n    )\n\n  /* Compute whether main area is active */\n  const active$ = main$\n    .pipe(\n      map(({ active }) => active)\n    )\n\n  /* Compute threshold for hiding */\n  return combineLatest([active$, direction$])\n    .pipe(\n      map(([active, direction]) => !(active && direction)),\n      distinctUntilChanged(),\n      takeUntil(target$.pipe(skip(1))),\n      endWith(true),\n      repeat({ delay: 250 }),\n      map(hidden => ({ hidden }))\n    )\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Mount back-to-top\n *\n * @param el - Back-to-top element\n * @param options - Options\n *\n * @returns Back-to-top component observable\n */\nexport function mountBackToTop(\n  el: HTMLElement, { viewport$, header$, main$, target$ }: MountOptions\n): Observable<Component<BackToTop>> {\n  const push$ = new Subject<BackToTop>()\n  const done$ = push$.pipe(takeLast(1))\n  push$.subscribe({\n\n    /* Handle emission */\n    next({ hidden }) {\n      el.hidden = hidden\n      if (hidden) {\n        el.setAttribute(\"tabindex\", \"-1\")\n        el.blur()\n      } else {\n        el.removeAttribute(\"tabindex\")\n      }\n    },\n\n    /* Handle complete */\n    complete() {\n      el.style.top = \"\"\n      el.hidden = true\n      el.removeAttribute(\"tabindex\")\n    }\n  })\n\n  /* Watch header height */\n  header$\n    .pipe(\n      takeUntil(done$),\n      distinctUntilKeyChanged(\"height\")\n    )\n      .subscribe(({ height }) => {\n        el.style.top = `${height + 16}px`\n      })\n\n  /* Create and return component */\n  return watchBackToTop(el, { viewport$, main$, target$ })\n    .pipe(\n      tap(state => push$.next(state)),\n      finalize(() => push$.complete()),\n      map(state => ({ ref: el, ...state }))\n    )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  fromEvent,\n  map,\n  mergeMap,\n  switchMap,\n  takeWhile,\n  tap,\n  withLatestFrom\n} from \"rxjs\"\n\nimport { getElements } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch options\n */\ninterface PatchOptions {\n  document$: Observable<Document>      /* Document observable */\n  tablet$: Observable<boolean>         /* Media tablet observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch indeterminate checkboxes\n *\n * This function replaces the indeterminate \"pseudo state\" with the actual\n * indeterminate state, which is used to keep navigation always expanded.\n *\n * @param options - Options\n */\nexport function patchIndeterminate(\n  { document$, tablet$ }: PatchOptions\n): void {\n  document$\n    .pipe(\n      switchMap(() => getElements<HTMLInputElement>(\n        // @todo `data-md-state` is deprecated and removed in v9\n        \".md-toggle--indeterminate, [data-md-state=indeterminate]\"\n      )),\n      tap(el => {\n        el.indeterminate = true\n        el.checked = false\n      }),\n      mergeMap(el => fromEvent(el, \"change\")\n        .pipe(\n          takeWhile(() => el.classList.contains(\"md-toggle--indeterminate\")),\n          map(() => el)\n        )\n      ),\n      withLatestFrom(tablet$)\n    )\n      .subscribe(([el, tablet]) => {\n        el.classList.remove(\"md-toggle--indeterminate\")\n        if (tablet)\n          el.checked = false\n      })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  filter,\n  fromEvent,\n  map,\n  mergeMap,\n  switchMap,\n  tap\n} from \"rxjs\"\n\nimport { getElements } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch options\n */\ninterface PatchOptions {\n  document$: Observable<Document>      /* Document observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Check whether the given device is an Apple device\n *\n * @returns Test result\n */\nfunction isAppleDevice(): boolean {\n  return /(iPad|iPhone|iPod)/.test(navigator.userAgent)\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch all elements with `data-md-scrollfix` attributes\n *\n * This is a year-old patch which ensures that overflow scrolling works at the\n * top and bottom of containers on iOS by ensuring a `1px` scroll offset upon\n * the start of a touch event.\n *\n * @see https://bit.ly/2SCtAOO - Original source\n *\n * @param options - Options\n */\nexport function patchScrollfix(\n  { document$ }: PatchOptions\n): void {\n  document$\n    .pipe(\n      switchMap(() => getElements(\"[data-md-scrollfix]\")),\n      tap(el => el.removeAttribute(\"data-md-scrollfix\")),\n      filter(isAppleDevice),\n      mergeMap(el => fromEvent(el, \"touchstart\")\n        .pipe(\n          map(() => el)\n        )\n      )\n    )\n      .subscribe(el => {\n        const top = el.scrollTop\n\n        /* We're at the top of the container */\n        if (top === 0) {\n          el.scrollTop = 1\n\n        /* We're at the bottom of the container */\n        } else if (top + el.offsetHeight === el.scrollHeight) {\n          el.scrollTop = top - 1\n        }\n      })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  Observable,\n  combineLatest,\n  delay,\n  map,\n  of,\n  switchMap,\n  withLatestFrom\n} from \"rxjs\"\n\nimport {\n  Viewport,\n  watchToggle\n} from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch options\n */\ninterface PatchOptions {\n  viewport$: Observable<Viewport>      /* Viewport observable */\n  tablet$: Observable<boolean>         /* Media tablet observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch the document body to lock when search is open\n *\n * For mobile and tablet viewports, the search is rendered full screen, which\n * leads to scroll leaking when at the top or bottom of the search result. This\n * function locks the body when the search is in full screen mode, and restores\n * the scroll position when leaving.\n *\n * @param options - Options\n */\nexport function patchScrolllock(\n  { viewport$, tablet$ }: PatchOptions\n): void {\n  combineLatest([watchToggle(\"search\"), tablet$])\n    .pipe(\n      map(([active, tablet]) => active && !tablet),\n      switchMap(active => of(active)\n        .pipe(\n          delay(active ? 400 : 100)\n        )\n      ),\n      withLatestFrom(viewport$)\n    )\n      .subscribe(([active, { offset: { y }}]) => {\n        if (active) {\n          document.body.setAttribute(\"data-md-scrolllock\", \"\")\n          document.body.style.top = `-${y}px`\n        } else {\n          const value = -1 * parseInt(document.body.style.top, 10)\n          document.body.removeAttribute(\"data-md-scrolllock\")\n          document.body.style.top = \"\"\n          if (value)\n            window.scrollTo(0, value)\n        }\n      })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Polyfills\n * ------------------------------------------------------------------------- */\n\n/* Polyfill `Object.entries` */\nif (!Object.entries)\n  Object.entries = function (obj: object) {\n    const data: [string, string][] = []\n    for (const key of Object.keys(obj))\n      // @ts-expect-error - ignore property access warning\n      data.push([key, obj[key]])\n\n    /* Return entries */\n    return data\n  }\n\n/* Polyfill `Object.values` */\nif (!Object.values)\n  Object.values = function (obj: object) {\n    const data: string[] = []\n    for (const key of Object.keys(obj))\n      // @ts-expect-error - ignore property access warning\n      data.push(obj[key])\n\n    /* Return values */\n    return data\n  }\n\n/* ------------------------------------------------------------------------- */\n\n/* Polyfills for `Element` */\nif (typeof Element !== \"undefined\") {\n\n  /* Polyfill `Element.scrollTo` */\n  if (!Element.prototype.scrollTo)\n    Element.prototype.scrollTo = function (\n      x?: ScrollToOptions | number, y?: number\n    ): void {\n      if (typeof x === \"object\") {\n        this.scrollLeft = x.left!\n        this.scrollTop = x.top!\n      } else {\n        this.scrollLeft = x!\n        this.scrollTop = y!\n      }\n    }\n\n  /* Polyfill `Element.replaceWith` */\n  if (!Element.prototype.replaceWith)\n    Element.prototype.replaceWith = function (\n      ...nodes: Array<string | Node>\n    ): void {\n      const parent = this.parentNode\n      if (parent) {\n        if (nodes.length === 0)\n          parent.removeChild(this)\n\n        /* Replace children and create text nodes */\n        for (let i = nodes.length - 1; i >= 0; i--) {\n          let node = nodes[i]\n          if (typeof node === \"string\")\n            node = document.createTextNode(node)\n          else if (node.parentNode)\n            node.parentNode.removeChild(node)\n\n          /* Replace child or insert before previous sibling */\n          if (!i)\n            parent.replaceChild(node, this)\n          else\n            parent.insertBefore(this.previousSibling!, node)\n        }\n      }\n    }\n}\n"],
+  "mappings": 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"distinctUntilChanged", "switchMap", "active", "EMPTY", "watchElementVisibility", "filter", "visible", "take", "mermaid$", "sequence", "fetchScripts", "watchScript", "of", "mountMermaid", "el", "tap", "mermaid_default", "map", "shareReplay", "id", "host", "h", "svg", "shadow", "watchDetails", "el", "target$", "print$", "open", "merge", "map", "target", "filter", "details", "active", "tap", "mountDetails", "options", "defer", "push$", "Subject", "action", "reveal", "state", "finalize", "__spreadValues", "sentinel", "h", "mountDataTable", "el", "renderTable", "of", "watchContentTabs", "el", "inputs", "getElements", "initial", "input", "merge", "fromEvent", "map", "getElement", "startWith", "active", "mountContentTabs", "viewport$", "prev", "renderTabbedControl", "next", "container", "defer", "push$", "Subject", "done$", "takeLast", "combineLatest", "watchElementSize", "auditTime", "animationFrameScheduler", "takeUntil", "size", "offset", "getElementOffset", "width", "getElementSize", "content", "getElementContentOffset", "watchElementContentOffset", "getElementContentSize", "direction", "feature", "skip", "withLatestFrom", "tab", "y", "set", "label", "tabs", "tap", "state", "finalize", "__spreadValues", "subscribeOn", "asyncScheduler", "mountContent", "el", "viewport$", "target$", "print$", "merge", "getElements", "child", "mountCodeBlock", "mountMermaid", "mountDataTable", "mountDetails", "mountContentTabs", "watchDialog", "_el", "alert$", "switchMap", "message", "merge", "of", "delay", "map", "active", "mountDialog", "el", "options", "inner", "getElement", "defer", "push$", "Subject", "tap", "state", "finalize", "__spreadValues", "isHidden", "viewport$", "feature", "of", "direction$", "map", "y", "bufferCount", "a", "b", "distinctUntilKeyChanged", "hidden$", "combineLatest", "filter", "offset", "direction", "distinctUntilChanged", "search$", "watchToggle", "search", "switchMap", "active", "startWith", "watchHeader", "el", "options", "defer", "watchElementSize", "height", "hidden", "shareReplay", "mountHeader", "header$", "main$", "push$", "Subject", "done$", "takeLast", "combineLatestWith", "takeUntil", "state", "__spreadValues", "watchHeaderTitle", "el", "viewport$", "header$", "watchViewportAt", "map", "y", "height", "getElementSize", "distinctUntilKeyChanged", "mountHeaderTitle", "options", "defer", "push$", "Subject", "active", "heading", "getOptionalElement", "EMPTY", "tap", "state", "finalize", "__spreadValues", "watchMain", "el", "viewport$", "header$", "adjust$", "map", "height", "distinctUntilChanged", "border$", "switchMap", "watchElementSize", "distinctUntilKeyChanged", "combineLatest", "header", "top", "bottom", "y", "a", "b", "watchPalette", "inputs", "current", "input", "of", "mergeMap", "fromEvent", "map", "startWith", "shareReplay", "mountPalette", "el", "defer", "push$", "Subject", "palette", "key", "value", "index", "label", "observeOn", "asyncScheduler", "getElements", "tap", "state", "finalize", "__spreadValues", "import_clipboard", "extract", "el", "text", "setupClipboardJS", "alert$", "ClipboardJS", "Observable", "subscriber", "getElement", "ev", "tap", "map", "translation", "preprocess", "urls", "root", "next", "a", "b", "url", "index", "fetchSitemap", "base", "cached", "of", "config", "configuration", "requestXML", "map", "sitemap", "getElements", "node", "catchError", "EMPTY", "defaultIfEmpty", "tap", "setupInstantLoading", "document$", "location$", "viewport$", "config", "configuration", "fromEvent", "favicon", "getOptionalElement", "push$", "fetchSitemap", "map", "paths", "path", "switchMap", "urls", "filter", "ev", "el", "url", "of", "NEVER", "share", "pop$", "merge", "distinctUntilChanged", "a", "b", "response$", "distinctUntilKeyChanged", "request", "catchError", "setLocation", "sample", "dom", "res", "skip", "replacement", "selector", "feature", "source", "target", "getComponentElement", "getElements", "concatMap", "script", "h", "name", "Observable", "observer", "EMPTY", "offset", 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"getElement", "renderVersionSelector", "_a", "outdated", "latest", "warning", "getComponentElements", "watchSearchQuery", "el", "rx$", "fn", "defaultTransform", "searchParams", "getLocation", "setToggle", "param$", "filter", "isSearchReadyMessage", "take", "map", "watchToggle", "active", "url", "value", "focus$", "watchElementFocus", "value$", "merge", "fromEvent", "delay", "startWith", "distinctUntilChanged", "combineLatest", "focus", "shareReplay", "mountSearchQuery", "tx$", "push$", "Subject", "done$", "takeLast", "distinctUntilKeyChanged", "translation", "takeUntil", "tap", "state", "finalize", "__spreadValues", "share", "mountSearchResult", "el", "rx$", "query$", "push$", "Subject", "boundary$", "watchElementBoundary", "filter", "meta", "getElement", "list", "ready$", "isSearchReadyMessage", "take", "withLatestFrom", "skipUntil", "items", "value", "translation", "round", "tap", "switchMap", "merge", "of", "bufferCount", "zipWith", "chunk", "result", "renderSearchResultItem", "isSearchResultMessage", "map", "data", "state", "finalize", "__spreadValues", "watchSearchShare", "_el", "query$", "map", "value", "url", "getLocation", "mountSearchShare", "el", "options", "push$", "Subject", "fromEvent", "ev", "tap", "state", "finalize", "__spreadValues", "mountSearchSuggest", "el", "rx$", "keyboard$", "push$", "Subject", "query", "getComponentElement", "query$", "merge", "fromEvent", "observeOn", "asyncScheduler", "map", "distinctUntilChanged", "combineLatestWith", "suggestions", "value", "words", "last", "filter", "mode", "key", "isSearchResultMessage", "data", "tap", "state", "finalize", "mountSearch", "el", "index$", "keyboard$", "config", "configuration", "url", "worker", "setupSearchWorker", "query", "getComponentElement", "result", "tx$", "rx$", "filter", "isSearchQueryMessage", "sample", "isSearchReadyMessage", "take", "mode", "key", "active", "getActiveElement", "anchors", "anchor", "getElements", "article", "best", "a", "b", "setToggle", "els", "i", "query$", "mountSearchQuery", "result$", "mountSearchResult", "merge", "mergeWith", "getComponentElements", "child", "mountSearchShare", "mountSearchSuggest", "err", "NEVER", "mountSearchHiglight", "el", "index$", "location$", "combineLatest", "startWith", "getLocation", "filter", "url", "map", "index", "setupSearchHighlighter", "fn", "_a", "nodes", "it", "node", "original", "replaced", "text", "childNodes", "h", "watchSidebar", "el", "viewport$", "main$", "parent", "adjust", "combineLatest", "map", "offset", "height", "y", "distinctUntilChanged", "a", "b", "mountSidebar", "_a", "_b", "header$", "options", "__objRest", "inner", "getElement", "getElementOffset", "defer", "push$", "Subject", "auditTime", "animationFrameScheduler", "withLatestFrom", "observeOn", "take", "item", "getElements", "container", "getElementContainer", "getElementSize", "tap", "state", "finalize", "__spreadValues", "fetchSourceFactsFromGitHub", "user", "repo", "url", "zip", "requestJSON", "catchError", "EMPTY", "map", "release", "defaultIfEmpty", "info", "__spreadValues", "fetchSourceFactsFromGitLab", "base", "project", "url", "requestJSON", "catchError", "EMPTY", "map", "star_count", "forks_count", "defaultIfEmpty", "fetchSourceFacts", "url", "match", "user", "repo", "fetchSourceFactsFromGitHub", "base", "slug", "fetchSourceFactsFromGitLab", "EMPTY", "fetch$", "watchSource", "el", "defer", "cached", "of", "getComponentElements", "consent", "EMPTY", "fetchSourceFacts", "tap", "facts", "catchError", "filter", "map", "shareReplay", "mountSource", "inner", "getElement", "push$", "Subject", "renderSourceFacts", "state", "finalize", "__spreadValues", "watchTabs", "el", "viewport$", "header$", "watchElementSize", "switchMap", "watchViewportAt", "map", "y", "distinctUntilKeyChanged", "mountTabs", "options", "defer", "push$", "Subject", "hidden", "feature", "of", "tap", "state", "finalize", "__spreadValues", "watchTableOfContents", "el", "viewport$", "header$", "table", "anchors", "getElements", "anchor", "id", "target", "getOptionalElement", "adjust$", "distinctUntilKeyChanged", "map", "height", "main", "getComponentElement", "grid", "getElement", "share", "watchElementSize", "switchMap", "body", "defer", "path", "of", "index", "offset", "a", "b", "combineLatestWith", "adjust", "scan", "prev", "next", "y", "size", "last", "distinctUntilChanged", "startWith", "bufferCount", "mountTableOfContents", "target$", "push$", "Subject", "done$", "takeLast", "feature", "smooth$", "merge", "debounceTime", "filter", "withLatestFrom", "behavior", "container", "getElementContainer", "getElementSize", "takeUntil", "skip", "repeat", "url", "getLocation", "active", "hash", "tap", "state", "finalize", "__spreadValues", "watchBackToTop", "_el", "viewport$", "main$", "target$", "direction$", "map", "y", "bufferCount", "a", "b", "distinctUntilChanged", "active$", "active", "combineLatest", "direction", "takeUntil", "skip", "endWith", "repeat", "hidden", "mountBackToTop", "el", "header$", "push$", "Subject", "done$", "takeLast", "distinctUntilKeyChanged", "height", "tap", "state", "finalize", "__spreadValues", "patchIndeterminate", "document$", "tablet$", "switchMap", "getElements", "tap", "el", "mergeMap", "fromEvent", "takeWhile", "map", "withLatestFrom", "tablet", "isAppleDevice", "patchScrollfix", "document$", "switchMap", "getElements", "tap", "el", "filter", "mergeMap", "fromEvent", "map", "top", "patchScrolllock", "viewport$", "tablet$", "combineLatest", "watchToggle", "map", "active", "tablet", "switchMap", "of", "delay", "withLatestFrom", "y", "value", "obj", "data", "key", "x", "y", "nodes", "parent", "i", "node", "document$", "watchDocument", "location$", "watchLocation", "target$", "watchLocationTarget", "keyboard$", "watchKeyboard", "viewport$", "watchViewport", "tablet$", "watchMedia", "screen$", "print$", "watchPrint", "config", "configuration", "index$", "requestJSON", "NEVER", "alert$", "Subject", "setupClipboardJS", "feature", "setupInstantLoading", "_a", "setupVersionSelector", "merge", "delay", "setToggle", "filter", "mode", "key", "prev", "getOptionalElement", "next", "patchIndeterminate", "patchScrollfix", "patchScrolllock", "header$", "watchHeader", "getComponentElement", "main$", "map", "switchMap", "el", "watchMain", "shareReplay", "control$", "getComponentElements", "mountConsent", "mountDialog", "mountHeader", "mountPalette", "mountSearch", "mountSource", "content$", "defer", "mountAnnounce", "mountContent", "mountSearchHiglight", "EMPTY", "mountHeaderTitle", "at", "mountSidebar", "mountTabs", "mountTableOfContents", "mountBackToTop", "component$", "mergeWith"]
+}
diff --git a/assets/javascripts/lunr/min/lunr.hy.min.js b/assets/javascripts/lunr/min/lunr.hy.min.js
deleted file mode 100644
index b37f792985..0000000000
--- a/assets/javascripts/lunr/min/lunr.hy.min.js
+++ /dev/null
@@ -1 +0,0 @@
-!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.hy=function(){this.pipeline.reset(),this.pipeline.add(e.hy.trimmer,e.hy.stopWordFilter)},e.hy.wordCharacters="[A-Za-z԰-֏ff-ﭏ]",e.hy.trimmer=e.trimmerSupport.generateTrimmer(e.hy.wordCharacters),e.Pipeline.registerFunction(e.hy.trimmer,"trimmer-hy"),e.hy.stopWordFilter=e.generateStopWordFilter("դու և եք էիր էիք հետո նաև նրանք որը վրա է որ պիտի են այս մեջ ն իր ու ի այդ որոնք այն կամ էր մի ես համար այլ իսկ էին ենք հետ ին թ էինք մենք նրա նա դուք եմ էի ըստ որպես ում".split(" ")),e.Pipeline.registerFunction(e.hy.stopWordFilter,"stopWordFilter-hy"),e.hy.stemmer=function(){return function(e){return"function"==typeof e.update?e.update(function(e){return e}):e}}(),e.Pipeline.registerFunction(e.hy.stemmer,"stemmer-hy")}});
\ No newline at end of file
diff --git a/assets/javascripts/lunr/min/lunr.kn.min.js b/assets/javascripts/lunr/min/lunr.kn.min.js
deleted file mode 100644
index 1cef9befda..0000000000
--- a/assets/javascripts/lunr/min/lunr.kn.min.js
+++ /dev/null
@@ -1 +0,0 @@
-!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.kn=function(){this.pipeline.reset(),this.pipeline.add(e.kn.trimmer,e.kn.stopWordFilter,e.kn.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.kn.stemmer))},e.kn.wordCharacters="ಀ-಄ಅ-ಔಕ-ಹಾ-ೌ಼-ಽೕ-ೖೝ-ೞೠ-ೡೢ-ೣ೤೥೦-೯ೱ-ೳ",e.kn.trimmer=e.trimmerSupport.generateTrimmer(e.kn.wordCharacters),e.Pipeline.registerFunction(e.kn.trimmer,"trimmer-kn"),e.kn.stopWordFilter=e.generateStopWordFilter("ಮತ್ತು ಈ ಒಂದು ರಲ್ಲಿ ಹಾಗೂ ಎಂದು ಅಥವಾ ಇದು ರ ಅವರು ಎಂಬ ಮೇಲೆ ಅವರ ತನ್ನ ಆದರೆ ತಮ್ಮ ನಂತರ ಮೂಲಕ ಹೆಚ್ಚು ನ ಆ ಕೆಲವು ಅನೇಕ ಎರಡು ಹಾಗು ಪ್ರಮುಖ ಇದನ್ನು ಇದರ ಸುಮಾರು ಅದರ ಅದು ಮೊದಲ ಬಗ್ಗೆ ನಲ್ಲಿ ರಂದು ಇತರ ಅತ್ಯಂತ ಹೆಚ್ಚಿನ ಸಹ ಸಾಮಾನ್ಯವಾಗಿ ನೇ ಹಲವಾರು ಹೊಸ ದಿ ಕಡಿಮೆ ಯಾವುದೇ ಹೊಂದಿದೆ ದೊಡ್ಡ ಅನ್ನು ಇವರು ಪ್ರಕಾರ ಇದೆ ಮಾತ್ರ ಕೂಡ ಇಲ್ಲಿ ಎಲ್ಲಾ ವಿವಿಧ ಅದನ್ನು ಹಲವು ರಿಂದ ಕೇವಲ ದ ದಕ್ಷಿಣ ಗೆ ಅವನ ಅತಿ ನೆಯ ಬಹಳ ಕೆಲಸ ಎಲ್ಲ ಪ್ರತಿ ಇತ್ಯಾದಿ ಇವು ಬೇರೆ ಹೀಗೆ ನಡುವೆ ಇದಕ್ಕೆ ಎಸ್ ಇವರ ಮೊದಲು ಶ್ರೀ ಮಾಡುವ ಇದರಲ್ಲಿ ರೀತಿಯ ಮಾಡಿದ ಕಾಲ ಅಲ್ಲಿ ಮಾಡಲು ಅದೇ ಈಗ ಅವು ಗಳು ಎ ಎಂಬುದು ಅವನು ಅಂದರೆ ಅವರಿಗೆ ಇರುವ ವಿಶೇಷ ಮುಂದೆ ಅವುಗಳ ಮುಂತಾದ ಮೂಲ ಬಿ ಮೀ ಒಂದೇ ಇನ್ನೂ ಹೆಚ್ಚಾಗಿ ಮಾಡಿ ಅವರನ್ನು ಇದೇ ಯ ರೀತಿಯಲ್ಲಿ ಜೊತೆ ಅದರಲ್ಲಿ ಮಾಡಿದರು ನಡೆದ ಆಗ ಮತ್ತೆ ಪೂರ್ವ ಆತ ಬಂದ ಯಾವ ಒಟ್ಟು ಇತರೆ ಹಿಂದೆ ಪ್ರಮಾಣದ ಗಳನ್ನು ಕುರಿತು ಯು ಆದ್ದರಿಂದ ಅಲ್ಲದೆ ನಗರದ ಮೇಲಿನ ಏಕೆಂದರೆ ರಷ್ಟು ಎಂಬುದನ್ನು ಬಾರಿ ಎಂದರೆ ಹಿಂದಿನ ಆದರೂ ಆದ ಸಂಬಂಧಿಸಿದ ಮತ್ತೊಂದು ಸಿ ಆತನ ".split(" ")),e.kn.stemmer=function(){return function(e){return"function"==typeof e.update?e.update(function(e){return e}):e}}();var r=e.wordcut;r.init(),e.kn.tokenizer=function(t){if(!arguments.length||null==t||void 0==t)return[];if(Array.isArray(t))return t.map(function(r){return isLunr2?new e.Token(r.toLowerCase()):r.toLowerCase()});var n=t.toString().toLowerCase().replace(/^\s+/,"");return r.cut(n).split("|")},e.Pipeline.registerFunction(e.kn.stemmer,"stemmer-kn"),e.Pipeline.registerFunction(e.kn.stopWordFilter,"stopWordFilter-kn")}});
\ No newline at end of file
diff --git a/assets/javascripts/lunr/min/lunr.ko.min.js b/assets/javascripts/lunr/min/lunr.ko.min.js
index eaf9dabf71..749abfe02b 100644
--- a/assets/javascripts/lunr/min/lunr.ko.min.js
+++ b/assets/javascripts/lunr/min/lunr.ko.min.js
@@ -1 +1 @@
-!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.ko=function(){this.pipeline.reset(),this.pipeline.add(e.ko.trimmer,e.ko.stopWordFilter)},e.ko.wordCharacters="[A-Za-z가-힣]",e.ko.trimmer=e.trimmerSupport.generateTrimmer(e.ko.wordCharacters),e.Pipeline.registerFunction(e.ko.trimmer,"trimmer-ko"),e.ko.stopWordFilter=e.generateStopWordFilter("아 휴 아이구 아이쿠 아이고 어 나 우리 저희 따라 의해 을 를 에 의 가 으로 로 에게 뿐이다 의거하여 근거하여 입각하여 기준으로 예하면 예를 들면 예를 들자면 저 소인 소생 저희 지말고 하지마 하지마라 다른 물론 또한 그리고 비길수 없다 해서는 안된다 뿐만 아니라 만이 아니다 만은 아니다 막론하고 관계없이 그치지 않다 그러나 그런데 하지만 든간에 논하지 않다 따지지 않다 설사 비록 더라도 아니면 만 못하다 하는 편이 낫다 불문하고 향하여 향해서 향하다 쪽으로 틈타 이용하여 타다 오르다 제외하고 이 외에 이 밖에 하여야 비로소 한다면 몰라도 외에도 이곳 여기 부터 기점으로 따라서 할 생각이다 하려고하다 이리하여 그리하여 그렇게 함으로써 하지만 일때 할때 앞에서 중에서 보는데서 으로써 로써 까지 해야한다 일것이다 반드시 할줄알다 할수있다 할수있어 임에 틀림없다 한다면 등 등등 제 겨우 단지 다만 할뿐 딩동 댕그 대해서 대하여 대하면 훨씬 얼마나 얼마만큼 얼마큼 남짓 여 얼마간 약간 다소 좀 조금 다수 몇 얼마 지만 하물며 또한 그러나 그렇지만 하지만 이외에도 대해 말하자면 뿐이다 다음에 반대로 반대로 말하자면 이와 반대로 바꾸어서 말하면 바꾸어서 한다면 만약 그렇지않으면 까악 툭 딱 삐걱거리다 보드득 비걱거리다 꽈당 응당 해야한다 에 가서 각 각각 여러분 각종 각자 제각기 하도록하다 와 과 그러므로 그래서 고로 한 까닭에 하기 때문에 거니와 이지만 대하여 관하여 관한 과연 실로 아니나다를가 생각한대로 진짜로 한적이있다 하곤하였다 하 하하 허허 아하 거바 와 오 왜 어째서 무엇때문에 어찌 하겠는가 무슨 어디 어느곳 더군다나 하물며 더욱이는 어느때 언제 야 이봐 어이 여보시오 흐흐 흥 휴 헉헉 헐떡헐떡 영차 여차 어기여차 끙끙 아야 앗 아야 콸콸 졸졸 좍좍 뚝뚝 주룩주룩 솨 우르르 그래도 또 그리고 바꾸어말하면 바꾸어말하자면 혹은 혹시 답다 및 그에 따르는 때가 되어 즉 지든지 설령 가령 하더라도 할지라도 일지라도 지든지 몇 거의 하마터면 인젠 이젠 된바에야 된이상 만큼\t어찌됏든 그위에 게다가 점에서 보아 비추어 보아 고려하면 하게될것이다 일것이다 비교적 좀 보다더 비하면 시키다 하게하다 할만하다 의해서 연이서 이어서 잇따라 뒤따라 뒤이어 결국 의지하여 기대여 통하여 자마자 더욱더 불구하고 얼마든지 마음대로 주저하지 않고 곧 즉시 바로 당장 하자마자 밖에 안된다 하면된다 그래 그렇지 요컨대 다시 말하자면 바꿔 말하면 즉 구체적으로 말하자면 시작하여 시초에 이상 허 헉 허걱 바와같이 해도좋다 해도된다 게다가 더구나 하물며 와르르 팍 퍽 펄렁 동안 이래 하고있었다 이었다 에서 로부터 까지 예하면 했어요 해요 함께 같이 더불어 마저 마저도 양자 모두 습니다 가까스로 하려고하다 즈음하여 다른 다른 방면으로 해봐요 습니까 했어요 말할것도 없고 무릎쓰고 개의치않고 하는것만 못하다 하는것이 낫다 매 매번 들 모 어느것 어느 로써 갖고말하자면 어디 어느쪽 어느것 어느해 어느 년도 라 해도 언젠가 어떤것 어느것 저기 저쪽 저것 그때 그럼 그러면 요만한걸 그래 그때 저것만큼 그저 이르기까지 할 줄 안다 할 힘이 있다 너 너희 당신 어찌 설마 차라리 할지언정 할지라도 할망정 할지언정 구토하다 게우다 토하다 메쓰겁다 옆사람 퉤 쳇 의거하여 근거하여 의해 따라 힘입어 그 다음 버금 두번째로 기타 첫번째로 나머지는 그중에서 견지에서 형식으로 쓰여 입장에서 위해서 단지 의해되다 하도록시키다 뿐만아니라 반대로 전후 전자 앞의것 잠시 잠깐 하면서 그렇지만 다음에 그러한즉 그런즉 남들 아무거나 어찌하든지 같다 비슷하다 예컨대 이럴정도로 어떻게 만약 만일 위에서 서술한바와같이 인 듯하다 하지 않는다면 만약에 무엇 무슨 어느 어떤 아래윗 조차 한데 그럼에도 불구하고 여전히 심지어 까지도 조차도 하지 않도록 않기 위하여 때 시각 무렵 시간 동안 어때 어떠한 하여금 네 예 우선 누구 누가 알겠는가 아무도 줄은모른다 줄은 몰랏다 하는 김에 겸사겸사 하는바 그런 까닭에 한 이유는 그러니 그러니까 때문에 그 너희 그들 너희들 타인 것 것들 너 위하여 공동으로 동시에 하기 위하여 어찌하여 무엇때문에 붕붕 윙윙 나 우리 엉엉 휘익 윙윙 오호 아하 어쨋든 만 못하다\t하기보다는 차라리 하는 편이 낫다 흐흐 놀라다 상대적으로 말하자면 마치 아니라면 쉿 그렇지 않으면 그렇지 않다면 안 그러면 아니었다면 하든지 아니면 이라면 좋아 알았어 하는것도 그만이다 어쩔수 없다 하나 일 일반적으로 일단 한켠으로는 오자마자 이렇게되면 이와같다면 전부 한마디 한항목 근거로 하기에 아울러 하지 않도록 않기 위해서 이르기까지 이 되다 로 인하여 까닭으로 이유만으로 이로 인하여 그래서 이 때문에 그러므로 그런 까닭에 알 수 있다 결론을 낼 수 있다 으로 인하여 있다 어떤것 관계가 있다 관련이 있다 연관되다 어떤것들 에 대해 이리하여 그리하여 여부 하기보다는 하느니 하면 할수록 운운 이러이러하다 하구나 하도다 다시말하면 다음으로 에 있다 에 달려 있다 우리 우리들 오히려 하기는한데 어떻게 어떻해 어찌됏어 어때 어째서 본대로 자 이 이쪽 여기 이것 이번 이렇게말하자면 이런 이러한 이와 같은 요만큼 요만한 것 얼마 안 되는 것 이만큼 이 정도의 이렇게 많은 것 이와 같다 이때 이렇구나 것과 같이 끼익 삐걱 따위 와 같은 사람들 부류의 사람들 왜냐하면 중의하나 오직 오로지 에 한하다 하기만 하면 도착하다 까지 미치다 도달하다 정도에 이르다 할 지경이다 결과에 이르다 관해서는 여러분 하고 있다 한 후 혼자 자기 자기집 자신 우에 종합한것과같이 총적으로 보면 총적으로 말하면 총적으로 대로 하다 으로서 참 그만이다 할 따름이다 쿵 탕탕 쾅쾅 둥둥 봐 봐라 아이야 아니 와아 응 아이 참나 년 월 일 령 영 일 이 삼 사 오 육 륙 칠 팔 구 이천육 이천칠 이천팔 이천구 하나 둘 셋 넷 다섯 여섯 일곱 여덟 아홉 령 영".split(" ")),e.Pipeline.registerFunction(e.ko.stopWordFilter,"stopWordFilter-ko"),e.ko.stemmer=function(){return function(e){return"function"==typeof e.update?e.update(function(e){return e}):e}}(),e.Pipeline.registerFunction(e.ko.stemmer,"stemmer-ko")}});
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+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.ko=function(){this.pipeline.reset(),this.pipeline.add(e.ko.trimmer,e.ko.stopWordFilter)},e.ko.wordCharacters="[A-Za-z가-힯a]",e.ko.trimmer=e.trimmerSupport.generateTrimmer(e.ko.wordCharacters),e.Pipeline.registerFunction(e.ko.trimmer,"trimmer-ko"),e.ko.stopWordFilter=e.generateStopWordFilter("아 휴 아이구 아이쿠 아이고 어 나 우리 저희 따라 의해 을 를 에 의 가 으로 로 에게 뿐이다 의거하여 근거하여 입각하여 기준으로 예하면 예를 들면 예를 들자면 저 소인 소생 저희 지말고 하지마 하지마라 다른 물론 또한 그리고 비길수 없다 해서는 안된다 뿐만 아니라 만이 아니다 만은 아니다 막론하고 관계없이 그치지 않다 그러나 그런데 하지만 든간에 논하지 않다 따지지 않다 설사 비록 더라도 아니면 만 못하다 하는 편이 낫다 불문하고 향하여 향해서 향하다 쪽으로 틈타 이용하여 타다 오르다 제외하고 이 외에 이 밖에 하여야 비로소 한다면 몰라도 외에도 이곳 여기 부터 기점으로 따라서 할 생각이다 하려고하다 이리하여 그리하여 그렇게 함으로써 하지만 일때 할때 앞에서 중에서 보는데서 으로써 로써 까지 해야한다 일것이다 반드시 할줄알다 할수있다 할수있어 임에 틀림없다 한다면 등 등등 제 겨우 단지 다만 할뿐 딩동 댕그 대해서 대하여 대하면 훨씬 얼마나 얼마만큼 얼마큼 남짓 여 얼마간 약간 다소 좀 조금 다수 몇 얼마 지만 하물며 또한 그러나 그렇지만 하지만 이외에도 대해 말하자면 뿐이다 다음에 반대로 반대로 말하자면 이와 반대로 바꾸어서 말하면 바꾸어서 한다면 만약 그렇지않으면 까악 툭 딱 삐걱거리다 보드득 비걱거리다 꽈당 응당 해야한다 에 가서 각 각각 여러분 각종 각자 제각기 하도록하다 와 과 그러므로 그래서 고로 한 까닭에 하기 때문에 거니와 이지만 대하여 관하여 관한 과연 실로 아니나다를가 생각한대로 진짜로 한적이있다 하곤하였다 하 하하 허허 아하 거바 와 오 왜 어째서 무엇때문에 어찌 하겠는가 무슨 어디 어느곳 더군다나 하물며 더욱이는 어느때 언제 야 이봐 어이 여보시오 흐흐 흥 휴 헉헉 헐떡헐떡 영차 여차 어기여차 끙끙 아야 앗 아야 콸콸 졸졸 좍좍 뚝뚝 주룩주룩 솨 우르르 그래도 또 그리고 바꾸어말하면 바꾸어말하자면 혹은 혹시 답다 및 그에 따르는 때가 되어 즉 지든지 설령 가령 하더라도 할지라도 일지라도 지든지 몇 거의 하마터면 인젠 이젠 된바에야 된이상 만큼\t어찌됏든 그위에 게다가 점에서 보아 비추어 보아 고려하면 하게될것이다 일것이다 비교적 좀 보다더 비하면 시키다 하게하다 할만하다 의해서 연이서 이어서 잇따라 뒤따라 뒤이어 결국 의지하여 기대여 통하여 자마자 더욱더 불구하고 얼마든지 마음대로 주저하지 않고 곧 즉시 바로 당장 하자마자 밖에 안된다 하면된다 그래 그렇지 요컨대 다시 말하자면 바꿔 말하면 즉 구체적으로 말하자면 시작하여 시초에 이상 허 헉 허걱 바와같이 해도좋다 해도된다 게다가 더구나 하물며 와르르 팍 퍽 펄렁 동안 이래 하고있었다 이었다 에서 로부터 까지 예하면 했어요 해요 함께 같이 더불어 마저 마저도 양자 모두 습니다 가까스로 하려고하다 즈음하여 다른 다른 방면으로 해봐요 습니까 했어요 말할것도 없고 무릎쓰고 개의치않고 하는것만 못하다 하는것이 낫다 매 매번 들 모 어느것 어느 로써 갖고말하자면 어디 어느쪽 어느것 어느해 어느 년도 라 해도 언젠가 어떤것 어느것 저기 저쪽 저것 그때 그럼 그러면 요만한걸 그래 그때 저것만큼 그저 이르기까지 할 줄 안다 할 힘이 있다 너 너희 당신 어찌 설마 차라리 할지언정 할지라도 할망정 할지언정 구토하다 게우다 토하다 메쓰겁다 옆사람 퉤 쳇 의거하여 근거하여 의해 따라 힘입어 그 다음 버금 두번째로 기타 첫번째로 나머지는 그중에서 견지에서 형식으로 쓰여 입장에서 위해서 단지 의해되다 하도록시키다 뿐만아니라 반대로 전후 전자 앞의것 잠시 잠깐 하면서 그렇지만 다음에 그러한즉 그런즉 남들 아무거나 어찌하든지 같다 비슷하다 예컨대 이럴정도로 어떻게 만약 만일 위에서 서술한바와같이 인 듯하다 하지 않는다면 만약에 무엇 무슨 어느 어떤 아래윗 조차 한데 그럼에도 불구하고 여전히 심지어 까지도 조차도 하지 않도록 않기 위하여 때 시각 무렵 시간 동안 어때 어떠한 하여금 네 예 우선 누구 누가 알겠는가 아무도 줄은모른다 줄은 몰랏다 하는 김에 겸사겸사 하는바 그런 까닭에 한 이유는 그러니 그러니까 때문에 그 너희 그들 너희들 타인 것 것들 너 위하여 공동으로 동시에 하기 위하여 어찌하여 무엇때문에 붕붕 윙윙 나 우리 엉엉 휘익 윙윙 오호 아하 어쨋든 만 못하다\t하기보다는 차라리 하는 편이 낫다 흐흐 놀라다 상대적으로 말하자면 마치 아니라면 쉿 그렇지 않으면 그렇지 않다면 안 그러면 아니었다면 하든지 아니면 이라면 좋아 알았어 하는것도 그만이다 어쩔수 없다 하나 일 일반적으로 일단 한켠으로는 오자마자 이렇게되면 이와같다면 전부 한마디 한항목 근거로 하기에 아울러 하지 않도록 않기 위해서 이르기까지 이 되다 로 인하여 까닭으로 이유만으로 이로 인하여 그래서 이 때문에 그러므로 그런 까닭에 알 수 있다 결론을 낼 수 있다 으로 인하여 있다 어떤것 관계가 있다 관련이 있다 연관되다 어떤것들 에 대해 이리하여 그리하여 여부 하기보다는 하느니 하면 할수록 운운 이러이러하다 하구나 하도다 다시말하면 다음으로 에 있다 에 달려 있다 우리 우리들 오히려 하기는한데 어떻게 어떻해 어찌됏어 어때 어째서 본대로 자 이 이쪽 여기 이것 이번 이렇게말하자면 이런 이러한 이와 같은 요만큼 요만한 것 얼마 안 되는 것 이만큼 이 정도의 이렇게 많은 것 이와 같다 이때 이렇구나 것과 같이 끼익 삐걱 따위 와 같은 사람들 부류의 사람들 왜냐하면 중의하나 오직 오로지 에 한하다 하기만 하면 도착하다 까지 미치다 도달하다 정도에 이르다 할 지경이다 결과에 이르다 관해서는 여러분 하고 있다 한 후 혼자 자기 자기집 자신 우에 종합한것과같이 총적으로 보면 총적으로 말하면 총적으로 대로 하다 으로서 참 그만이다 할 따름이다 쿵 탕탕 쾅쾅 둥둥 봐 봐라 아이야 아니 와아 응 아이 참나 년 월 일 령 영 일 이 삼 사 오 육 륙 칠 팔 구 이천육 이천칠 이천팔 이천구 하나 둘 셋 넷 다섯 여섯 일곱 여덟 아홉 령 영".split(" ")),e.Pipeline.registerFunction(e.ko.stopWordFilter,"stopWordFilter-ko"),e.ko.stemmer=function(){return function(e){return"function"==typeof e.update?e.update(function(e){return e}):e}}(),e.Pipeline.registerFunction(e.ko.stemmer,"stemmer-ko")}});
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diff --git a/assets/javascripts/lunr/min/lunr.sa.min.js b/assets/javascripts/lunr/min/lunr.sa.min.js
deleted file mode 100644
index 50ee564205..0000000000
--- a/assets/javascripts/lunr/min/lunr.sa.min.js
+++ /dev/null
@@ -1 +0,0 @@
-!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.sa=function(){this.pipeline.reset(),this.pipeline.add(e.sa.trimmer,e.sa.stopWordFilter,e.sa.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.sa.stemmer))},e.sa.wordCharacters="ऀ-ःऄ-एऐ-टठ-यर-िी-ॏॐ-य़ॠ-९॰-ॿ꣠-꣱ꣲ-ꣷ꣸-ꣻ꣼-ꣽꣾ-ꣿᆰ0-ᆰ9",e.sa.trimmer=e.trimmerSupport.generateTrimmer(e.sa.wordCharacters),e.Pipeline.registerFunction(e.sa.trimmer,"trimmer-sa"),e.sa.stopWordFilter=e.generateStopWordFilter('तथा अयम्‌ एकम्‌ इत्यस्मिन्‌ तथा तत्‌ वा अयम्‌ इत्यस्य ते आहूत उपरि तेषाम्‌  किन्तु तेषाम्‌ तदा इत्यनेन अधिकः इत्यस्य तत्‌ केचन बहवः द्वि तथा महत्वपूर्णः अयम्‌ अस्य  विषये अयं अस्ति तत्‌ प्रथमः विषये इत्युपरि इत्युपरि इतर अधिकतमः अधिकः अपि सामान्यतया ठ इतरेतर नूतनम्‌ द  न्यूनम्‌ कश्चित्‌ वा विशालः द  सः अस्ति तदनुसारम् तत्र अस्ति केवलम्‌ अपि अत्र सर्वे विविधाः तत्‌ बहवः यतः इदानीम्‌ द  दक्षिण इत्यस्मै तस्य उपरि नथ अतीव कार्यम्‌ सर्वे एकैकम्‌ इत्यादि। एते सन्ति  उत इत्थम्‌ मध्ये एतदर्थं . स कस्य प्रथमः श्री. करोति अस्मिन् प्रकारः निर्मिता कालः तत्र कर्तुं  समान अधुना ते सन्ति स एकः अस्ति सः अर्थात् तेषां कृते . स्थितम्  विशेषः अग्रिम तेषाम्‌ समान स्रोतः ख म समान इदानीमपि अधिकतया करोतु ते समान इत्यस्य वीथी सह यस्मिन्  कृतवान्‌ धृतः तदा पुनः पूर्वं सः आगतः किम्‌ कुल इतर पुरा  मात्रा स विषये उ अतएव अपि नगरस्य  उपरि यतः प्रतिशतं  कतरः कालः साधनानि भूत तथापि जात सम्बन्धि अन्यत्‌ ग अतः अस्माकं स्वकीयाः अस्माकं इदानीं अन्तः इत्यादयः भवन्तः इत्यादयः एते एताः तस्य अस्य इदम् एते तेषां तेषां तेषां तान् तेषां तेषां तेषां समानः सः एकः च तादृशाः बहवः अन्ये च वदन्ति यत् कियत् कस्मै  कस्मै  यस्मै  यस्मै  यस्मै  यस्मै न अतिनीचः किन्तु प्रथमं सम्पूर्णतया  ततः चिरकालानन्तरं पुस्तकं सम्पूर्णतया अन्तः  किन्तु अत्र वा इह इव श्रद्धाय अवशिष्यते  परन्तु अन्ये वर्गाः सन्ति ते सन्ति शक्नुवन्ति सर्वे मिलित्वा सर्वे एकत्र"'.split(" ")),e.sa.stemmer=function(){return function(e){return"function"==typeof e.update?e.update(function(e){return e}):e}}();var r=e.wordcut;r.init(),e.sa.tokenizer=function(t){if(!arguments.length||null==t||void 0==t)return[];if(Array.isArray(t))return t.map(function(r){return isLunr2?new e.Token(r.toLowerCase()):r.toLowerCase()});var i=t.toString().toLowerCase().replace(/^\s+/,"");return r.cut(i).split("|")},e.Pipeline.registerFunction(e.sa.stemmer,"stemmer-sa"),e.Pipeline.registerFunction(e.sa.stopWordFilter,"stopWordFilter-sa")}});
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diff --git a/assets/javascripts/lunr/min/lunr.te.min.js b/assets/javascripts/lunr/min/lunr.te.min.js
deleted file mode 100644
index 9fa7a93b99..0000000000
--- a/assets/javascripts/lunr/min/lunr.te.min.js
+++ /dev/null
@@ -1 +0,0 @@
-!function(e,t){"function"==typeof define&&define.amd?define(t):"object"==typeof exports?module.exports=t():t()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.te=function(){this.pipeline.reset(),this.pipeline.add(e.te.trimmer,e.te.stopWordFilter,e.te.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.te.stemmer))},e.te.wordCharacters="ఀ-ఄఅ-ఔక-హా-ౌౕ-ౖౘ-ౚౠ-ౡౢ-ౣ౦-౯౸-౿఼ఽ్ౝ౷౤౥",e.te.trimmer=e.trimmerSupport.generateTrimmer(e.te.wordCharacters),e.Pipeline.registerFunction(e.te.trimmer,"trimmer-te"),e.te.stopWordFilter=e.generateStopWordFilter("అందరూ అందుబాటులో అడగండి అడగడం అడ్డంగా అనుగుణంగా అనుమతించు అనుమతిస్తుంది అయితే ఇప్పటికే ఉన్నారు ఎక్కడైనా ఎప్పుడు ఎవరైనా ఎవరో ఏ ఏదైనా ఏమైనప్పటికి ఒక ఒకరు కనిపిస్తాయి కాదు కూడా గా గురించి చుట్టూ చేయగలిగింది తగిన తర్వాత దాదాపు దూరంగా నిజంగా పై ప్రకారం ప్రక్కన మధ్య మరియు మరొక మళ్ళీ మాత్రమే మెచ్చుకో వద్ద వెంట వేరుగా వ్యతిరేకంగా సంబంధం".split(" ")),e.te.stemmer=function(){return function(e){return"function"==typeof e.update?e.update(function(e){return e}):e}}();var t=e.wordcut;t.init(),e.te.tokenizer=function(r){if(!arguments.length||null==r||void 0==r)return[];if(Array.isArray(r))return r.map(function(t){return isLunr2?new e.Token(t.toLowerCase()):t.toLowerCase()});var i=r.toString().toLowerCase().replace(/^\s+/,"");return t.cut(i).split("|")},e.Pipeline.registerFunction(e.te.stemmer,"stemmer-te"),e.Pipeline.registerFunction(e.te.stopWordFilter,"stopWordFilter-te")}});
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diff --git a/assets/javascripts/lunr/min/lunr.zh.min.js b/assets/javascripts/lunr/min/lunr.zh.min.js
index fda66e9c57..9838ef96da 100644
--- a/assets/javascripts/lunr/min/lunr.zh.min.js
+++ b/assets/javascripts/lunr/min/lunr.zh.min.js
@@ -1 +1 @@
-!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r(require("@node-rs/jieba")):r()(e.lunr)}(this,function(e){return function(r,t){if(void 0===r)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===r.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");var i="2"==r.version[0];r.zh=function(){this.pipeline.reset(),this.pipeline.add(r.zh.trimmer,r.zh.stopWordFilter,r.zh.stemmer),i?this.tokenizer=r.zh.tokenizer:(r.tokenizer&&(r.tokenizer=r.zh.tokenizer),this.tokenizerFn&&(this.tokenizerFn=r.zh.tokenizer))},r.zh.tokenizer=function(n){if(!arguments.length||null==n||void 0==n)return[];if(Array.isArray(n))return n.map(function(e){return i?new r.Token(e.toLowerCase()):e.toLowerCase()});t&&e.load(t);var o=n.toString().trim().toLowerCase(),s=[];e.cut(o,!0).forEach(function(e){s=s.concat(e.split(" "))}),s=s.filter(function(e){return!!e});var u=0;return s.map(function(e,t){if(i){var n=o.indexOf(e,u),s={};return s.position=[n,e.length],s.index=t,u=n,new r.Token(e,s)}return e})},r.zh.wordCharacters="\\w一-龥",r.zh.trimmer=r.trimmerSupport.generateTrimmer(r.zh.wordCharacters),r.Pipeline.registerFunction(r.zh.trimmer,"trimmer-zh"),r.zh.stemmer=function(){return function(e){return e}}(),r.Pipeline.registerFunction(r.zh.stemmer,"stemmer-zh"),r.zh.stopWordFilter=r.generateStopWordFilter("的 一 不 在 人 有 是 为 為 以 于 於 上 他 而 后 後 之 来 來 及 了 因 下 可 到 由 这 這 与 與 也 此 但 并 並 个 個 其 已 无 無 小 我 们 們 起 最 再 今 去 好 只 又 或 很 亦 某 把 那 你 乃 它 吧 被 比 别 趁 当 當 从 從 得 打 凡 儿 兒 尔 爾 该 該 各 给 給 跟 和 何 还 還 即 几 幾 既 看 据 據 距 靠 啦 另 么 麽 每 嘛 拿 哪 您 凭 憑 且 却 卻 让 讓 仍 啥 如 若 使 谁 誰 虽 雖 随 隨 同 所 她 哇 嗡 往 些 向 沿 哟 喲 用 咱 则 則 怎 曾 至 致 着 著 诸 諸 自".split(" ")),r.Pipeline.registerFunction(r.zh.stopWordFilter,"stopWordFilter-zh")}});
\ No newline at end of file
+!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r(require("@node-rs/jieba")):r()(e.lunr)}(this,function(e){return function(r,t){if(void 0===r)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===r.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");var i="2"==r.version[0];r.zh=function(){this.pipeline.reset(),this.pipeline.add(r.zh.trimmer,r.zh.stopWordFilter,r.zh.stemmer),i?this.tokenizer=r.zh.tokenizer:(r.tokenizer&&(r.tokenizer=r.zh.tokenizer),this.tokenizerFn&&(this.tokenizerFn=r.zh.tokenizer))},r.zh.tokenizer=function(n){if(!arguments.length||null==n||void 0==n)return[];if(Array.isArray(n))return n.map(function(e){return i?new r.Token(e.toLowerCase()):e.toLowerCase()});t&&e.load(t);var o=n.toString().trim().toLowerCase(),s=[];e.cut(o,!0).forEach(function(e){s=s.concat(e.split(" "))}),s=s.filter(function(e){return!!e});var u=0;return s.map(function(e,t){if(i){var n=o.indexOf(e,u),s={};return s.position=[n,e.length],s.index=t,u=n,new r.Token(e,s)}return e})},r.zh.wordCharacters="\\w一-龥",r.zh.trimmer=r.trimmerSupport.generateTrimmer(r.zh.wordCharacters),r.Pipeline.registerFunction(r.zh.trimmer,"trimmer-zh"),r.zh.stemmer=function(){return function(e){return e}}(),r.Pipeline.registerFunction(r.zh.stemmer,"stemmer-zh"),r.zh.stopWordFilter=r.generateStopWordFilter("的 一 不 在 人 有 是 为 以 于 上 他 而 后 之 来 及 了 因 下 可 到 由 这 与 也 此 但 并 个 其 已 无 小 我 们 起 最 再 今 去 好 只 又 或 很 亦 某 把 那 你 乃 它 吧 被 比 别 趁 当 从 到 得 打 凡 儿 尔 该 各 给 跟 和 何 还 即 几 既 看 据 距 靠 啦 了 另 么 每 们 嘛 拿 哪 那 您 凭 且 却 让 仍 啥 如 若 使 谁 虽 随 同 所 她 哇 嗡 往 哪 些 向 沿 哟 用 于 咱 则 怎 曾 至 致 着 诸 自".split(" ")),r.Pipeline.registerFunction(r.zh.stopWordFilter,"stopWordFilter-zh")}});
\ No newline at end of file
diff --git a/assets/javascripts/lunr/wordcut.js b/assets/javascripts/lunr/wordcut.js
index 0d898c9ed1..146f4b44bc 100644
--- a/assets/javascripts/lunr/wordcut.js
+++ b/assets/javascripts/lunr/wordcut.js
@@ -68,7 +68,7 @@ var WordcutDict = {
 
   init: function (dictPathFile, withDefault, words) {
     withDefault = withDefault || false
-    var defaultDict = path.normalize(__dirname + "/..") + "/data/tdict-*.txt";
+    defaultDict = path.normalize(__dirname + "/..") + "/data/tdict-*.txt";
     this.dict=[]
     var dictPathIsDefined = dictPathFile !== undefined
     var dictPath = (withDefault || !dictPathIsDefined) ? [defaultDict]: [];
@@ -106,7 +106,7 @@ var WordcutDict = {
     finalize = finalize===undefined || finalize;
     
     for (var i = 0; i < 1; i++) {
-      var words = "ก.ก.\nก.ก.น.\nก.ข.ค.\nก.ค.\nก.จ.\nก.ช.น.\nก.ฌ.\nก.ต.\nก.ต.ง.\nก.ต.ช.\nก.ตร.\nก.ท.\nก.น.ช.\nก.บช.\nก.บถ.\nก.ป.ส.\nก.พ.\nก.ม.\nก.ย.\nก.ร.\nก.ล.ต.\nก.ว.\nก.ศ.ว.\nก.ส.ท.\nก.ส.ธ.\nก.ส.อ.\nก.อ.\nกก.ตชด.\nกก.ตร.น.\nกก.ภ.จว.\nกก.รสช.\nกกบ.ขส.ทบ.\nกกล.รพน.\nกง.กห.\nกง.ทบ.\nกง.ทร.\nกซข.ป.\nกซม.ป.\nกทม.กรุงเทพมหานคร\nกบ.ทบ.\nกป.สป.\nกพ.ทบ.\nกพ.ทร.\nกพ.ทหาร\nกร.ทบ.\nกรป.กลาง\nกรอ.พอ.\nกศ.ด.\nกศ.บ.\nกศ.บป.\nกศ.ม.\nกษ.ด.\nกษ.บ.\nกษ.ม.\nกส.ด.\nกส.ทบ.\nกส.บ.\nกส.ม.\nกอ.ปค.\nกอ.รพน.\nกอ.รมน.\nกอ.รสต.\nข.ต.ว.\nขว.ทบ.\nขว.ทร.\nขว.ทหาร\nขส.ทบ.\nขส.ทร.\nขส.ทอ.\nค.ด.\nค.บ.\nค.พ.ศ.\nค.ม.\nค.ร.น.\nค.ร.ฟ.\nค.ร.ม.\nค.ศ.\nค.อ.ด.\nค.อ.บ.\nค.อ.ม.\nคศ.ด.\nคศ.บ.\nคศ.ม.\nง.ด.\nจ.จ.\nจ.จ.จ.\nจ.ช.\nจ.ต.\nจ.ท.\nจ.ป.ร.\nจ.ม.\nจ.ศ.\nจ.ส.ต.\nจ.ส.ท.\nจ.ส.อ.\nจ.อ.\nจ.อ.ร.\nจ.๑๘\nจก.ธน.\nจก.สน.\nช.ค.\nช.ค.บ.\nช.พ.ค.\nช.ส.\nช.ส.ค.\nฌ.ป.ค.\nฌ.ศ.ร.\nฌ.ส.อ.\nฐท.สห.\nด.ช.\nด.ญ.\nด.ต.\nด.ศ.ค.\nด.ศ.ร.\nดย.ทร.\nต.ก.\nต.ค.\nต.จ.\nต.จ.ว.\nต.ช.\nต.ต.\nต.บ.\nต.ม.\nต.ร.\nต.ศ.ร.\nต.ห.\nต.อ.\nต.อ.จ.\nตร.กม.\nตร.ซม.\nตร.ต.\nตร.ทล.\nตร.น.\nตร.ปม.\nตร.ภ.\nตร.ม.\nตร.รฟ.\nตร.ว.\nตร.ส.\nตร.สข.\nท.จ.\nท.จ.ว.\nท.ช.\nท.ญ.\nท.ด.\nท.ท.ท.\nท.ทบ.\nท.บ.\nท.พ.\nท.ม.\nท.ศ.\nทก.ด.\nทก.บ.\nทก.ม.\nทส.ปช.\nทส.รมว.กห.\nทุ.ส.นิ.ม.\nธ.ก.ส.\nธ.ค.\nธ.ญ\nธ.บ.\nน.ช.\nน.ญ.\nน.ด.\nน.ต.\nน.ท.\nน.น.\nน.บ.\nน.บ.ท.\nน.ป.ท.\nน.พ.\nน.ม.\nน.ร.\nน.ว.\nน.ศ.\nน.ส.\nน.ส.พ.\nน.ส.๓\nน.สพ.\nน.อ.\nนปพ.ภ.\nนศ.ด.\nนศ.บ.\nนศ.ม.\nบ.ก.\nบ.ข.ส.\nบ.ช.\nบ.ด.ท.\nบ.ตร.\nบ.ภ.\nบ.ม.\nบก.จร.\nบก.ตชด.\nบก.ตม.\nบก.ทล.\nบก.น.\nบก.ป.\nบก.ปค.\nบก.ปม.\nบก.ภ.เขต\nบก.รน.\nบก.รฟ.\nบก.ร้อย.ตชด.\nบก.ส.\nบกข.ป.\nบจพ.ป.\nบช.ก.\nบช.ด.\nบช.ตชด.\nบช.น.\nบช.บ.\nบช.ปส.\nบช.ภ.\nบช.ม.\nบชท.ป.\nบชน.ป.\nบชส.ป.\nบธ.ด.\nบธ.บ.\nบธ.ม.\nบนท.ป.\nบนอ.ป.\nบปช.ป.\nป.กท.\nป.กศ.\nป.กศ.สูง\nป.จ.\nป.จ.ว.\nป.ช.\nป.ธ.\nป.ป.\nป.ป.ก.\nป.ป.ช.\nป.ป.ป.\nป.ป.ร.\nป.ป.ส.\nป.พ.\nป.พ.พ.\nป.พย.\nป.ม.\nป.ม.ก.\nป.ม.ช.\nป.ม.ธ.\nป.ม.ศ.\nป.ม.อ.\nป.ร.ร.๔\nป.ร.ร.๕\nป.ร.ร.๖\nป.ล.\nป.ว.พ.\nป.วิ.อ.\nป.ส.ส.\nป.อ.\nป.อ.ร.ส.\nป.๑\nปม.วส.\nปอ.พ.\nผกก.ภ.\nผช.ผอ.\nผต.มท.\nผบ.ตร.\nผบ.ทบ.\nผบ.ทร.\nผบ.ทสส.\nผบ.ทอ.\nผบก.น.\nผบก.ป.\nผบก.ปค.\nผบก.ปม.\nผบก.ภ.\nผบช.ก.\nผบช.ตชด.\nผบช.น.\nผบช.ภ.\nผว.กทม.\nผอ.ปจ.\nพ.ก.ง.\nพ.กศ.\nพ.ข.ต.\nพ.ค.\nพ.ค.ช.\nพ.ค.ว.\nพ.ค.ศ.\nพ.จ.ต.\nพ.จ.ท.\nพ.จ.อ.\nพ.ช.\nพ.ช.ค.\nพ.ด.\nพ.ต.\nพ.ต.ต.\nพ.ต.ท.\nพ.ต.อ.\nพ.ต.อ.พิเศษ\nพ.ท.\nพ.บ.\nพ.ป.\nพ.ภ.ม.\nพ.ม.\nพ.ม.ช.\nพ.ย.\nพ.ร.ก.\nพ.ร.ฎ.\nพ.ร.ต.\nพ.ร.ธ.\nพ.ร.บ.\nพ.ศ.\nพ.ศ.บ.\nพ.ส.ร.\nพ.ส.ล.\nพ.อ.\nพ.อ.ต.\nพ.อ.ท.\nพ.อ.พิเศษ\nพ.อ.อ.\nพณ.ด.\nพณ.บ.\nพณ.ม.\nพธ.ด.\nพธ.บ.\nพธ.ม.\nพบ.ด.\nพบ.บ.\nพบ.ม.\nพย.ด.\nพย.บ.\nพย.ม.\nพล.จ.\nพล.ต.\nพล.ต.จ.\nพล.ต.ต.\nพล.ต.ท.\nพล.ต.อ.\nพล.ท.\nพล.ปตอ.\nพล.ม.\nพล.ม.๒\nพล.ร.จ.\nพล.ร.ต.\nพล.ร.ท.\nพล.ร.อ.\nพล.อ.\nพล.อ.จ.\nพล.อ.ต.\nพล.อ.ท.\nพล.อ.อ.\nพลา.ทร.\nพศ.ด.\nพศ.บ.\nพศ.ม.\nพอ.สว.\nภ.ง.ด.\nภ.ง.ด.๙\nภ.ด.\nภ.บ.\nภ.บ.ท.๕\nภ.ป.ร.\nภ.พ.\nภ.ม.\nภ.สถ.บ.\nม.ค.\nม.จ.\nม.ป.ท.\nม.ป.ป.\nม.ป.พ.\nม.ร.ว.\nม.ศ.\nม.อ.\nม.อ.ปัตตานี\nมิ.ย.\nมี.ค.\nยศ.ทบ.\nยศ.ทร.\nยศ.ทอ.\nร.ง.\nร.ด.\nร.ต.\nร.ต.ต.\nร.ต.ท.\nร.ต.อ.\nร.ท.\nร.น.\nร.บ.\nร.พ.\nร.ฟ.ล.\nร.ย.ล.\nร.ย.ส.ท.\nร.ล.\nร.ศ.\nร.ส.พ.\nร.อ.\nรป.ม.\nรร.จปร.\nรร.จอ.\nรร.ชท.\nรร.ตท.\nรร.นร.\nรร.นรต.\nรร.นอ.\nล.ญ.\nล.ว.\nลส.ชบ.\nว.ค.\nว.ฉ.\nว.ช.\nว.ด.ป.\nว.ป.ถ.\nวท.บ.\nศ.บ.\nศ.ป.ก.\nศ.ศ.ป.\nศฝร.ภ.\nศศ.บ.\nศษ.บ.\nศส.บ.\nส.ก.\nส.ก.ศ.ท.\nส.ค.\nส.ค.1\nส.ค.ร.\nส.ค.ส.\nส.ต.\nส.ต.ต.\nส.ต.ท.\nส.ต.อ.\nส.ท.\nส.ทร.\nส.ป.ช.\nส.ป.ส.ท.\nส.ป.อ.\nส.ร.\nส.ล.น.\nส.ว.\nส.ว.ท.\nส.ว.ส.ท.\nส.ส.\nส.ส.ท.\nส.ส.ร.\nส.ห.\nส.อ.\nสถ.บ.\nสนง.สสอ.\nสพ.ญ.\nสพ.บ.\nสว.จร.\nสว.ธร.\nสว.ส.\nสว.สป.\nสว.สส.\nสว.อก.\nสส.บ.\nสุ.จิ.ปุ.ลิ.\nห.ร.ม.\nอ.ก.ค.\nอ.ก.จ.\nอ.จ.\nอ.ช.พ.\nอ.ตร.\nอ.บ.\nอ.ส.ท.\nอ.ส.ม.ท.\nอ.ส.ย.\nอ.อ.ป.\nอส.รด.\nอุ.อา.ก.ส.\nฮ.จ.\nฮ.ท.\nฮ.ฝ.\nฮ.ล.\nฮ.ศ.\nเม.ย.\n\nกรีนิช\nกลันตัน\nกัลกัตตา\nกัวลาลัมเปอร์\nกัศมีร์\nกาฐมาณฑุ\nโกลกาตา\nควิเบก\nคอนเนตทิคัต\nคาบูล\nคุชราต\nคุนหมิง\nเคนตักกี\nเคนทักกี\nเคมบริดจ์\nแคชเมียร์\nแคนซัส\nแคนเบอร์รา\nแคโรไลนา\nแคลิฟอร์เนีย\nโคเปนเฮเกน\nโคลัมโบ\nโคโลราโด\nไครสต์เชิร์ช\nไคโร\nจาการ์ตา\nจำปาศักดิ์\nเจนไน\nเจนีวา\nเจ้อเจียง\nฉงชิ่ง\nเฉิงตู\nชานตง\nชิคาโก\nเชนไน\nเชอร์โนบิล\nซัปโปโร\nซานมารีโน\nซาบาห์\nซาราเยโว\nซาราวัก\nซิดนีย์\nซีอาน\nซีแอตเทิล\nซูริก\nซูริค\nเซเชลส์\nเซนได\nเซี่ยงไฮ้\nโซเฟีย\nโซล\nโซโลมอน\nไซ่ง่อน\nไซบีเรีย\nดัลลัส\nดาโคตา\nดานัง\nดีทรอยต์\nดูไบ\nเดนเวอร์\nเดลาแวร์\nเดียนเบียนฟู\nโดเวอร์\nโดฮา\nไดฟุกุ\nไดฟูกุ\nตรังกานู\nตริโปลี\nตูวาลู\nเตหะราน\nโตเกียว\nโตรอนโต\nทมิฬนาฑู\nทริโปลี\nทิเบต\nเทกซัส\nเท็กซัส\nเทนเนสซี\nเทลอาวีฟ\nแทสเมเนีย\nโทรอนโต\nไทเป\nธากา\nนางาซากิ\nนาริตะ\nนิวเจอร์ซีย์\nนิวเดลี\nนิวยอร์ก\nนิวยอร์ค\nนิวแฮมป์เชียร์\nเนบราสกา\nเนแบรสกา\nเนวาดา\nบรัสเซลส์\nบราซิเลีย\nบอมเบย์\nบอสตัน\nบังกาลอร์\nบังคาลอร์\nบูคาเรสต์\nบูดาเปสต์\nเบงกาซี\nเบนกาซี\nเบรุต\nเบลเกรด\nเบอร์ลิน\nแบกแดด\nปอยเปต\nปะลิส\nปะหัง\nปักกิ่ง\nปัญจาบ\nปัฏนา\nปารีส\nปีนัง\nเประ\nเปียงยาง\nพนมเปญ\nพระตะบอง\nพะโค\nพะสิม\nพาราณสี\nพิหารี\nเพนซิลวาเนีย\nเพนซิลเวเนีย\nฟรานซ์\nฟลอริดา\nฟิลาเดลเฟีย\nฟุกุชิมะ\nฟุกุชิมา\nฟุกุโอกะ\nฟูกูโอกะ\nแฟรงก์เฟิร์ต\nมอสโก\nมะนิลา\nมะละแหม่ง\nมัณฑะเลย์\nมัทราส\nมาดริด\nมิชิแกน\nมินนิโซตา\nมิยางิ\nมิลาน\nมิวนิก\nมิสซูรี\nมุมไบ\nเมน\nเมลเบิร์น\nเมาะตะมะ\nเมาะลำเลิง\nแมนจูเรีย\nแมนเชสเตอร์\nแมนฮัตตัน\nแมริแลนด์\nแมรีแลนด์\nแมสซาชูเซตส์\nยะไข่\nย่างกุ้ง\nยูทาห์\nยูนนาน\nเยรูซาเล็ม\nโยโกฮามา\nริยาด\nรีโอเดจาเนโร\nโรดไอแลนด์\nลอนดอน\nลอสแองเจลิส\nลาปาซ\nลาสเวกัส\nลิสบอน\nลุยเซียนา\nโลซาน\nโลซานน์\nวอชิงตัน\nวอร์ซอ\nวิสคอนซิน\nเวนิส\nเวลส์\nเวอร์จิเนีย\nเวอร์มอนต์\nเวียงจันทน์\nเวียนนา\nแวนคูเวอร์\nไวโอมิง\nสกอตแลนด์\nสก็อตแลนด์\nสตอกโฮล์ม\nสลังงอร์\nเสฉวน\nเสียมราฐ\nเสียมเรียบ\nหงสา\nหงสาวดี\nหนานไห่\nหลวงพระบาง\nหูเป่ย\nหูเป่ย์\nหูหนาน\nเหอเป่ย\nเหอเป่ย์\nเหอหนาน\nอชันตา\nอลาสกา\nอวันตี\nออริกอน\nออสโล\nอะแลสกา\nอัตตะปือ\nอัมมาน\nอัมสเตอร์ดัม\nอัสสัม\nอาบูดาบี\nอาร์คันซอ\nอินเดียนา\nอิบารากิ\nอิลลินอยส์\nอิสตันบูล\nอิสลามาบัด\nอุรุมชี\nอูลานบาตอร์\nเอดินบะระ\nเอเธนส์\nแอตแลนตา\nแอริโซนา\nแอลเจียร์\nโอคลาโฮมา\nโอค็อตสค์\nโอกินาวา\nโอซากา\nโอริสสา\nโอเรกอน\nโอไฮโอ\nไอดาโฮ\nไอโอวา\nฮอนโนลูลู\nฮานอย\nฮาเนดะ\nฮาราเร\nฮาวาย\nฮิโรชิมา\nฮุสตัน\nเฮลซิงกิ\n\nมกรา\nกุมภา\nมีนา\nเมษา\nพฤษภา\nมิถุนา\nกรกฎา\nสิงหา\nกันยา\nตุลา\nพฤศจิกา\nธันวา\nเอ\nบี\nซี\nดี\nอี\nเอฟ\nจี\nเอช\nไอ\nเจ\nเค\nแอล\nเอ็ม\nเอ็น\nโอ\nพี\nคิว\nอาร์\nเอส\nที\nยู\nวี\nดับเบิล\nดับบลิว\nเอ็กซ์\nเอ๊กซ์\nวาย\nแซด\nแอลฟา\nแอลฟ่า\nเบตา\nเบต้า\nแกมมา\nแกมม่า\nเดลตา\nเดลต้า\nโอเมกา\nโอเมก้า\nเมกะ\nกิกะ\nนาโน\nไมโคร\n\nกรรมาชน\nกรอบรูป\nกระดี๊กระด๊า\nกระบับ\nกราวนด์\nกรีน\nกรุ๊ป\nกฤษณ์\nกลาส\nก๊วน\nกษัตริยา\nกษัตริยาธิราช\nก่อนหน้า\nกะบับ\nกับดัก\nกัมมันตะ\nก๊าก\nก๋ากั่น\nกาญจน์\nกาญจนาภิเษก\nกามิกาเซ่\nการันตี\nกาหลิบ\nกิฟท์\nกิมจิ\nกีวี\nกึ๊ก\nกึ๋ย\nกุนซือ\nกุมภาพันธ์\nกู๋\nเกจิ\nเกมส์\nเกย์\nเกรด\nเกรย์\nเกสต์เฮาส์\nเก๊ะ\nเก๋ากี้\nเกิร์ล\nแกงค์\nแกรนด์\nแกสโซฮอล์\nแก๊สโซฮอล์\nโกเต็กซ์\nโกลด์\nโกะ\nโก๊ะ\nไกด์\nขั้นตอน\nเขวี้ยง\nคณาญาติ\nครัวซอง\nครัวซองต์\nคร่ำครวญ\nครีเอทีฟ\nครูเสด\nคลับ\nคลาสสิก\nคลิตอริส\nคลิป\nความหมาย\nควิก\nควีน\nคองเกรส\nคอนซูเมอร์\nคอนเซปต์\nคอนเซ็ปต์\nคอนโด\nคอนโดมิเนียม\nคอนเทนเนอร์\nคอนแทค\nคอนแท็ค\nคอนโทรล\nคอนเฟิร์ม\nคอปเตอร์\nคอมพ์\nคอมเพล็กซ์\nคอมมอนส์\nคอมเมนท์\nคอมเมนต์\nคอร์ป\nคอร์ปอเรชั่น\nคอร์รัปชัน\nคอร์รัปชั่น\nคอรัปชัน\nคอรัปชั่น\nคอร์ส\nคอลเล็กชั่น\nคอลัมน์\nคอลัมนิสต์\nคัตเอาต์\nคันคาก\nคันถธุระ\nคันธาระ\nคันยิ\nคัสตาร์ด\nคาราโอเกะ\nคีตกวี\nคีตปฏิภาณ\nคีตราชัน\nคาปูชิโน\nคามิคาเซ่\nคาเฟ่\nคาร์\nคาร์โก้\nคาราเมล\nคาแรกเตอร์\nคาแร็กเตอร์\nคาแรคเตอร์\nคาแร็คเตอร์\nคาวบอย\nคาสิโน\nคิกขุ\nคิวบิก\nคูลเลอร์\nเคบับ\nเครป\nเคลม\nเคลียร์\nเคลื่อนย้าย\nเคส\nเคอร์ฟิว\nแคชเชียร์\nแคทวอล์ค\nแคนดิเดต\nแคนตาลูป\nแคนยอน\nแคนู\nแคป\nแคมป์\nแคมปัส\nแคมเปญ\nแคร์\nแครกเกอร์\nแคร็กเกอร์\nแครอท\nแคสต์\nแคสติง\nแคสติ้ง\nโค้ก\nโค้ช\nโคโยตี\nโคโยตี้\nโครนา\nโคอะล่า\nโคอาลา\nโคอาล่า\nไคลแมกซ์\nไคลแม็กซ์\nงั้น\nง่าว\nงี้\nเง็ง\nโง่เขลา\nไง\nจตุคาม\nจ๊อกกี้\nจอหงวน\nจังโก้\nจัมโบ้\nจ๊าบ\nจารกรรม\nจารชน\nจิ๊ก\nจิ๊กโก๋\nจิ๊กซอว์\nจิตพิสัย\nจิตเภท\nจีดีพี\nจึ๊ก\nจุ๊ย\nจูน\nจูเนียร์\nเจ๊\nเจได\nเจ็ต\nเจล\nเจ๊าะแจ๊ะ\nเจี๊ยว\nแจ็กเก็ต\nแจ๊กเก็ต\nแจ็กพอต\nแจ็กพ็อต\nแจ๊กพอต\nแจ๊กพ็อต\nแจม\nแจ๊ส\nโจ๋\nฉลุย\nเฉิ่ม\nชนะเลิศ\nช็อค\nช็อต\nช็อป\nช็อปปิ้ง\nช็อปเปอร์\nชะโนด\nชัตเตอร์\nชัวร์\nชาร์จ\nชาร์ต\nชาร์ป\nชินบัญชร\nชิฟฟอน\nชีส\nชีอะห์\nเช็ก\nเช็งเม้ง\nเชฟ\nเชลียร์\nเชอร์รี่\nแชเชือน\nแช่แข็ง\nแชมป์\nแชมปิยอง\nแชมเปญ\nแชมเปี้ยน\nแชมพู\nโชว์รูม\nโชห่วย\nใช้งาน\nไชน่า\nซ้อ\nซอมบี้\nซะ\nซังเต\nซันตาคลอส\nซัพพลาย\nซัพพลายเออร์\nซัมเมอร์\nซากุระ\nซาดิสต์\nซาดิสม์\nซาตาน\nซานตาคลอส\nซาฟารี\nซาบะ\nซามูไร\nซาร์\nซาร์ดีน\nซาเล้ง\nซิง\nซิ่ง\nซิงเกิล\nซิตี\nซิตี้\nซินโดรม\nซิม\nซิ้ม\nซิมโฟนี\nซิมโฟนี่\nซิลเวอร์\nซี้\nซี้ซั้ว\nซีดาน\nซีน\nซีนีเพล็กซ์\nซีเนียร์\nซีร็อกซ์\nซีรีส์\nซีเรียส\nซีอีโอ\nซื่อบื้อ\nซุนหนี่\nซุปเปอร์\nซูชิ\nซูเปอร์\nซูม\nซูโม่\nซูเอี๋ย\nซูฮก\nเซ็กซ์\nเซ็กซี่\nเซ็กส์\nเซนเซอร์\nเซ็นเซอร์\nเซนเตอร์\nเซ็นเตอร์\nเซ็นทรัล\nเซนส์\nเซ่นไหว้\nเซฟตี้\nเซรามิก\nเซลส์\nเซลส์แมน\nเซอร์\nเซอร์ไพรส์\nเซอร์วิส\nเซาท์\nเซี้ยว\nแซ็ก\nแซกโซโฟน\nแซ็กโซโฟน\nแซนด์วิช\nแซมบ้า\nแซลมอน\nแซว\nโซเชียล\nโซน\nโซนี่\nโซลาร์\nโซโล\nโซโล่\nญาณทัสสนะ\nดยุก\nดยุค\nดร็อป\nดรัมเมเยอร์\nดรามา\nดราม่า\nดอกเตอร์\nด็อกเตอร์\nดัมพ์\nดั๊มพ์\nดาวน์\nดิกชันนารี\nดิสเครดิต\nดีกรี\nดีเจ\nดีไซน์\nดีไซน์เนอร์\nดีไซเนอร์\nดีเบต\nดีพาร์ตเมนต์\nดีพาร์ตเมนท์\nดีพาร์ทเมนต์\nดีพาร์ทเมนท์\nดีมานด์\nดีล\nดีลเลอร์\nดีเลย์\nเดชานุภาพ\nเดบิต\nเดโม\nเดย์\nเด้อ\nเดอะ\nเด๊ะ\nเดี้ยง\nเดี๊ยะ\nแดนซ์\nแดนเซอร์\nแดรี่\nโดนัท\nโดมิโน\nโดรายากิ\nไดเอ็ต\nตถตา\nตนเอง\nตรวจทาน\nตรวจสอบ\nตอกย้ำ\nต๊อง\nต่อยอด\nต่อรอง\nตะหงิด\nตังค์\nตันเถียน\nตัวตน\nตัวเอง\nตาปรือ\nต้าอ่วย\nติงต๊อง\nติ๋ม\nติ่มซำ\nติว\nติวเตอร์\nตี๋\nตื้บ\nตุ๊ก\nตุ๊กตุ๊ก\nตุ๊ด\nตุ๋ย\nตู้เซฟ\nเต๊ะ\nเตี๊ยม\nแตงกวา\nแตงโม\nแต๋ว\nโต๋เต๋\nโต๊ะจีน\nไตรมาส\nถ่ายทำ\nถูกต้อง\nทงคัตสึ\nทริป\nทรู\nทอม\nท็อป\nทอร์นาโด\nทอล์ค\nทักซิโด\nทันตกรรม\nทันตแพทยศาสตร์\nทับซ้อน\nทัวร์\nทัวร์นาเมนต์\nทัวร์นาเมนท์\nทัวริสต์\nทาเลนต์\nทาวน์\nทาวน์เฮาส์\nทำงาน\nทิป\nทิพยสมบัติ\nทิวลิป\nทีรามิสุ\nทีวี\nทูน่า\nเท็กซ์\nเทค\nเทคโน\nเทคโนแครต\nเทควันโด\nเทป\nเทรด\nเทรนด์\nเทรนเนอร์\nเทรลเลอร์\nเทรลเล่อร์\nเทเลกราฟ\nเทวบัญชา\nเทวบุตร\nเทวา\nเทวาธิราช\nเทโวโรหนะ\nเทอร์โบ\nเที่ยงคืน\nเที่ยงวัน\nเทียมทาน\nแทกติค\nแทคติค\nแทงกั๊ก\nแทงโก้\nโทมาฮอก\nโทมาฮอว์ก\nโทมาฮอว์ค\nโทร\nโทรโข่ง\nไทม์\nไทยแลนด์\nไทเฮา\nธรรมา\nธรรมาภิบาล\nธัมโม\nธีม\nธุรกรรม\nธุหร่ำ\nเธค\nนพมาศ\nนรีแพทย์\nน็อก\nน็อค\nน้องใหม่\nนอมินี\nนอร์ท\nน่ะ\nนางแบบ\nนาฏยศาลา\nนายแบบ\nนายพราน\nนินจา\nนิรันดร์\nนิว\nนิวส์\nนู้ด\nเนอะ\nเนิร์สเซอรี\nเนิร์สเซอรี่\nเนี้ยบ\nโนติส\nไนท์\nไนน์\nบรรพชน\nบร็อกโคลี\nบร็อคโคลี\nบรา\nบริกร\nบริวเวอรี่ส์\nบลอนด์\nบลูเบอร์รี\nบลูเบอร์รี่\nบ๊วย\nบอกซ์\nบ็อกซ์\nบ๊อกซ์\nบอดี้\nบอนด์\nบ๊อบ\nบอมบ์\nบ๋อย\nบอยคอต\nบอยคอตต์\nบอร์ด\nบังเกอร์\nบัตเตอร์\nบัลลาสต์\nบัส\nบาบูน\nบาร์บีคิว\nบาร์บี้\nบาลานซ์\nบิ๊ก\nบิล\nบึม\nบึ้ม\nบุญคุณ\nบุ๋น\nบุปผา\nบู๊\nบูชิโด\nบูติก\nบูติค\nบูม\nเบเกอรี่\nเบญจมบพิตร\nเบตาดีน\nเบนโตะ\nเบนโล\nเบบี้\nเบลอ\nเบอร์เกอร์\nเบอร์รี\nเบิร์ด\nเบิร์น\nแบ็กโฮ\nแบคโฮ\nแบด\nแบต\nแบนเนอร์\nแบรนด์\nแบล็ก\nแบล็ค\nไบโอ\nโบกี้\nโบตั๋น\nโบ้ย\nโบรกเกอร์\nโบรชัวร์\nโบว์\nโบว์ลิ่ง\nไบเบิล\nปฏิสัมพันธ์\nป๊อก\nปอดแหก\nป๊อป\nป๋อหลอ\nปักขคณนา\nปัจเจกชน\nปัจฉิมนิเทศ\nป๊า\nป๋า\nป่าไม้\nปาร์ตี้\nปาสกาล\nปาสคาล\nปาสเตอร์\nปิกอัพ\nปิ๊ง\nปิโตรเคมี\nปิยมิตร\nปึ้ก\nปูอัด\nเปโซ\nเป็นไง\nเปปเปอร์มินต์\nเปเปอร์\nเปราะบาง\nเป๊ะ\nเป่ายิงฉุบ\nเป่ายิ้งฉุบ\nเปียโน\nแป้ก\nแป๋ว\nแป๊ะเจี๊ยะ\nโปร\nโปรเจกต์\nโปรเจ็กต์\nโปรเจกเตอร์\nโปรเจ็กเตอร์\nโปรเจคท์\nโปรเจ็คท์\nโปรดักชั่น\nโปรดิวเซอร์\nโปรโมชั่น\nโปรโมต\nโปรโมเตอร์\nโปรโมท\nโปลิศ\nโปสเตอร์\nผลไม้\nผลักดัน\nผ้าห่ม\nผิดพลาด\nผู้นำ\nแผดเผา\nเฝอ\nพงษ์\nพริตตี้\nพรีเซนต์\nพรีเซ็นเตอร์\nพรีเมียม\nพรีเมียร์\nพฤหัส\nพล็อต\nพลาซ่า\nพลานุภาพ\nพ่อค้า\nพอเพียง\nพะเรอ\nพันธกิจ\nพันธุวิศวกรรม\nพาร์\nพาร์ตเนอร์\nพาร์ทเนอร์\nพาวเวอร์\nพาสเจอร์ไรส์\nพาสตา\nพาสต้า\nพาสปอร์ต\nพาเหรด\nพิซซ่า\nพีเรียด\nพุดดิ้ง\nพุทธภูมิ\nพุทธศตวรรษ\nพุทโธ\nพูล\nเพทนาการ\nเพนกวิน\nเพนตากอน\nเพรส\nเพรียวบาง\nเพลซ\nเพลท\nเพลย์บอย\nเพียบแปร้\nเพียว\nเพาเวอร์\nแพกเกจ\nแพ็ค\nแพตเทิร์น\nแพทเทิร์น\nแพทยสภา\nแพนงเชิญ\nแพนดา\nแพนด้า\nแพลน\nโพลล์\nโพลารอยด์\nโพสต์\nไพลิน\nฟยอร์ด\nฟรังก์\nฟรุต\nฟลอร์\nฟลุก\nฟลุค\nฟลุต\nฟลุท\nฟอยล์\nฟอร์ม\nฟันด์\nฟาวล์\nฟาสต์ฟู้ด\nฟินิกซ์\nฟิวเจอร์\nฟีด\nฟีเวอร์\nฟุตบาท\nเฟรช\nเฟรชชี่\nเฟรม\nเฟมินิสต์\nเฟส\nเฟอร์นิเจอร์\nเฟอร์รี่\nเฟิร์ม\nเฟี้ยวฟ้าว\nแฟกซ์\nแฟ็กซ์\nแฟนซี\nแฟนตาซี\nแฟ้บ\nแฟร์\nแฟรนไชส์\nแฟรี\nแฟรี่\nแฟลช\nแฟล็ต\nโฟน\nโฟม\nโฟล์ค\nไฟต์\nไฟแนนซ์\nไฟลต์\nไฟลท์\nภควัทคีตา\nภควัมบดี\nภควัมปติ\nภคันทลาพาธ\nภววิสัย\nภารตะ\nภูมิทัศน์\nม้ง\nมวลชน\nมยุราภิรมย์\nมลภาวะ\nมหภาค\nมหาอุปราชา\nมอคคา\nมอคค่า\nมอนสเตอร์\nม็อบ\nมอบตัว\nมอยส์เจอไรเซอร์\nมอลล์\nมะกัน\nมั้ง\nมัฟฟิน\nมั้ย\nม้านั่ง\nมาเฟีย\nมาม่า\nมายองเนส\nมายาคติ\nมาร์ก\nมาร์เก็ต\nมาร์เก็ตติ้ง\nมาร์ค\nมาร์จิน\nมาร์ช\nมาร์ต\nมาร์ท\nมาราธอน\nม้าหินอ่อน\nมินต์\nมินท์\nมินิ\nมิลค์\nมิวสิค\nมิสซัง\nมิสไซล์\nมิสเตอร์\nมือถือ\nมุมมอง\nเมคอัพ\nเมจิก\nเมจิค\nเมทัล\nเมเปิล\nเมาท์\nเมี่ยงคำ\nแมกกาซีน\nแม็กกาซีน\nแมคเคอเรล\nแม่ค้า\nแมชชีน\nแมชีน\nแมนชั่น\nแมมบ้า\nแมมโบ้\nโมจิ\nโมเดล\nโมเดิร์น\nโมเต็ล\nโมโนเรล\nโมหจริต\nไมค์\nไมเกรน\nยนตรกรรม\nยอมรับ\nยะเยือก\nยังไง\nยากูซ่า\nยาวี\nยิม\nยิว\nยุวทูต\nยูโทเปีย\nยูโร\nยูวี\nเยน\nเยลลี่\nเย้ว\nเยอบีรา\nเยอบีร่า\nเยอร์บีรา\nเยอร์บีร่า\nแยมโรล\nโยเกิร์ต\nโยโย่\nรวมมิตร\nร็อค\nร็อคเก็ต\nรองรับ\nรอมฎอน\nรอยัลตี้\nระโงก\nรันเวย์\nรัม\nรากหญ้า\nราชบัณฑิตยสถาน\nราชานุญาต\nราชานุสาวรีย์\nรามเทพ\nรามาธิบดี\nรามายณะ\nราเม็ง\nราเมน\nรายชื่อ\nราสเบอร์รี\nริกเตอร์\nริคเตอร์\nรีไซเคิล\nรีดไถ\nรีทัช\nรีเทิร์น\nรีไทร์\nรีแบรนด์\nรีพอร์ท\nรีโมต\nรีโมท\nรีวิว\nรีสอร์ต\nรีสอร์ท\nรีเสิร์ช\nรุมบ้า\nรุสโซ\nรูบิก\nรูบิค\nเรซิน\nเรซิ่น\nเรดิโอ\nเรต\nเรตติ้ง\nแรงใจ\nแรงดูด\nแรงผลัก\nแรลลี\nแรลลี่\nโรดแมป\nโรเนียว\nโรแมนติก\nโรแมนติค\nโรล\nโรลออน\nไรเฟิล\nล็อกเกอร์\nลอจิสติกส์\nล็อต\nล็อบบี้\nลอร์ด\nล้มเหลว\nละติน\nละอ่อน\nลาซานญ่า\nลาติน\nลาเต้\nลานีญา\nลามะ\nลิมิต\nลิมูซีน\nลิสต์\nลีก\nลีด\nลีดเดอร์\nลีเมอร์\nลีลาวดี\nลุค\nลูกชาย\nลูกสาว\nเลกเชอร์\nเลคเชอร์\nเลดี้\nเลสเบี้ยน\nเลิฟ\nแลนด์\nแล็บ\nโลโก้\nโลชั่น\nไลท์\nไลน์\nไลฟ์\nวนาราม\nวราราม\nวโรกาส\nว้อดก้า\nวอเตอร์\nวอฟเฟิล\nว้อย\nวอร์ม\nวอร์มอัพ\nวอร์รูม\nวอล์ก\nวอล์ค\nวอลซ์\nวอลนัต\nวอลนัท\nวอลล์\nว่ะ\nวันเวย์\nวัสสา\nวาซาบิ\nวาทกรรม\nวาทะ\nวานิลลา\nวานิลา\nวาฟเฟิล\nวาริชศาสตร์\nว้าว\nวัคค์\nวัจนะ\nวาไรตี้\nวิก\nวิดีโอ\nวิทย์\nวิน\nวิป\nวิปปิ้ง\nวิภัชภาค\nวิว\nวิลล์\nวิลเลจ\nวีเจ\nวีซ่า\nวีดิทัศน์\nวีน\nวีไอพี\nวืด\nเวณิกา\nเวเฟอร์\nเวสต์\nเวอร์\nเวิร์ก\nเวิร์กช็อป\nเวิร์ค\nเวิร์ลด์\nเวิลด์\nแวมไพร์\nไวกิ้ง\nไวเบรเตอร์\nไวอะกร้า\nไวอากร้า\nศากยบุตร\nศิรินทร์\nศิลปวัฒนธรรม\nศิลปากร\nศิวิไลซ์\nศึกษาศาสตร์\nสกรัม\nสกาย\nสกู๊ป\nสเกตช์\nสเก็ตช์\nสคริปต์\nสแควร์\nสงบสุข\nสจ๊วต\nสตรอเบอร์รี\nสตรอเบอรี\nสตรอว์เบอร์รี\nสตริง\nสต็อก\nสต๊อก\nสต็อค\nสต๊อค\nสตอรี\nสตาร์\nสตาร์ท\nสติกเกอร์\nสติ๊กเกอร์\nสตีล\nสตูดิโอ\nสเตชัน\nสเตชั่น\nสเตเดียม\nสเตนเลส\nสเต็ป\nสเตย์\nสเตริโอ\nสเตอริโอ\nสแตนดาร์ด\nสแตนเลส\nสโตน\nสโตร์\nสไตรค์\nสไตล์\nสถาปัตย์\nสไนเปอร์\nสปอต\nสป็อต\nสปอนเซอร์\nสปอร์ต\nสปา\nสปาย\nสปิริต\nสเปก\nสเปค\nสไปเดอร์\nสมณพราหมณ์\nสมาพันธ์\nสมิติเวช\nสโรชา\nสลัม\nสแล็ก\nสโลแกน\nสโลว์\nสไลด์\nสวีท\nสหรัฐ\nสหัชญาณ\nสหัสวรรษ\nสะกอม\nสะเด่า\nสะบึม\nสะบึมส์\nสะออน\nสังโฆ\nสะโหลสะเหล\nสันทนาการ\nสัมนา\nสามช่า\nสามแยก\nสารขัณฑ์\nสี่แยก\nสึนามิ\nสุนทรีย์\nสุริยยาตร\nสุริยยาตร์\nสุหนี่\nเสกสรรค์\nเสพติด\nเสือโคร่ง\nหงวน\nหน่อมแน้ม\nหมวย\nหมั่นโถว\nหม่านโถว\nหมายปอง\nหมิง\nหยวน\nหลวงตา\nหลวงปู่\nหลวงพี่\nหล่อฮังก้วย\nหลินจือ\nห่วย\nเห็นด้วย\nเหมย\nเห่ย\nเหี่ยวย่น\nแหม็บ\nแหวว\nโหงว\nโหงวเฮ้ง\nโหลน\nโหลยโท่ย\nไหง\nไหร่\nอพาร์ตเมนต์\nอพาร์ตเมนท์\nอพาร์ทเมนต์\nอพาร์ทเมนท์\nอมาตยาธิปไตย\nอยุติธรรม\nอริยสงฆ์\nอ่วม\nอวอร์ด\nออกแบบ\nออดิชั่น\nออดิทอเรียม\nออเดอร์\nออโต้\nออทิสติก\nอ่อนด้อย\nออฟ\nออยล์\nออร์แกน\nออร์แกนิก\nออร์แกนิค\nออร์เดอร์\nออรัล\nออสซี่\nอะ\nอัตลักษณ์\nอัตวิสัย\nอันเดอร์\nอันตรกิริยา\nอัลตรา\nอัลไซเมอร์\nอัลบัม\nอัลบั้ม\nอัลมอนด์\nอาข่า\nอาโนเนะ\nอาฟเตอร์\nอาร์ติสต์\nอาร์พีจี\nอาว์\nอาสวะ\nอิกัวนา\nอินดอร์\nอินดัสตรีส์\nอินเตอร์\nอิ่มแปร้\nอิมพีเรียล\nอิเล็กทริก\nอิเล็กทริค\nอิเลียด\nอิสรชน\nอิเหนา\nอิออน\nอีแต๋น\nอีโรติก\nอีเวนท์\nอีสต์\nอีสเตอร์\nอึ๊บ\nอึ้ม\nอึ๋ม\nอึมครึม\nอุด้ง\nอุตสาหการ\nอุเทน\nอุปการคุณ\nอุปทาน\nอุปนายก\nอุปนายิกา\nอุปสงค์\nอุปัทวเหตุ\nอุรังคธาตุ\nอูคูเลเล่\nอู้ฟู่\nเอ๋\nเอ็กซ์โป\nเอ็กซ์เพรส\nเอ็กโซเซต์\nเอ็กโซเซ่ต์\nเอเซีย\nเอ็นจีโอ\nเอ็นเตอร์เทน\nเอนทรานซ์\nเอ็นทรานซ์\nเอฟเฟ็กต์\nเอเยนต์\nเอลนีโญ\nเอสเปรสโซ\nเอสเพรสโซ\nเอ๋อ\nเอาต์\nเอาท์\nเอาท์ดอร์\nเอ๊าะ\nแอ็กชั่น\nแอ็คชั่น\nแอคทีฟ\nแอดมิชชั่น\nแอดมิสชัน\nแอนด์\nแอ๊บแบ๊ว\nแอปเปิล\nแอปเปิ้ล\nแอปพริคอท\nแอพพริคอท\nแอพริคอต\nแอร์\nแอโรบิก\nแอโรบิค\nแอลมอนด์\nแอสเตอร์\nโอเค\nโอเปอเรเตอร์\nโอเปร่า\nโอเพ่น\nโอ้ย\nโอยัวะ\nโอรสาธิราช\nโอเลี้ยง\nโอวัลติน\nโอเวอร์\nไอซ์\nไอซียู\nไอดอล\nไอเดีย\nไอติม\nฮวงจุ้ย\nฮ่องเต้\nฮองเฮา\nฮอต\nฮ็อต\nฮอตดอก\nฮ็อตด็อก\nฮันนีมูน\nฮัม\nฮัลโลวีน\nฮัลโหล\nฮากกา\nฮาร์ด\nฮาราคีรี\nฮาลาล\nฮาโลวีน\nฮิ\nฮิต\nฮิบรู\nฮิปโป\nฮิปฮอป\nฮีโร่\nฮูลาฮูป\nฮูล่าฮูป\nเฮฟวี\nเฮฟวี่\nเฮอร์ริเคน\nเฮีย\nแฮนด์\nแฮปปี้\nแฮมเบอร์เกอร์\nโฮป\nโฮม\nโฮลดิงส์\nโฮลวีต\nโฮสเตส\nไฮกุ\nไฮแจ็ค\nไฮโซ\nไฮเทค\nไฮบริด\nไฮเปอร์\nไฮไลต์\nไฮไลท์\nไฮเวย์\nไฮสคูล\nไฮเอนด์\n\nกรีซ\nกัมพูชา\nกัวเตมาลา\nกาตาร์\nกานา\nกาบอง\nกายอานา\nกินี\nเกรนาดีนส์\nเกรเนดา\nเกาหลี\nแกมเบีย\nโกตดิวัวร์\nคองโก\nคอโมโรส\nคอสตาริกา\nคาซัคสถาน\nคิตส์\nคิริบาตี\nคิริบาส\nคิวบา\nคีร์กีซสถาน\nคูเวต\nเคนยา\nเคปเวิร์ด\nเคย์แมน\nแคนาดา\nแคเมอรูน\nโครเอเชีย\nโคลอมเบีย\nจอร์เจีย\nจอร์แดน\nจาเมกา\nจิบูตี\nจีน\nชาด\nชิลี\nเช็ก\nซามัว\nซาอุ\nซิมบับเว\nซีเรีย\nซูดาน\nซูรินาเม\nเซนต์\nเซเนกัล\nเซอร์เบีย\nเซาตูเม\nเซียร์รา\nแซมเบีย\nโซมาเลีย\nโซเวียต\nไซปรัส\nญี่ปุ่น\nดารุสซาลาม\nเดนมาร์ก\nโดมินิกัน\nโดมินิกา\nตรินิแดด\nตองกา\nติมอร์\nตุรกี\nตูนิเซีย\nเติร์กเมนิสถาน\nโตโก\nโตเบโก\nไต้หวัน\nทาจิกิสถาน\nแทนซาเนีย\nนอร์เวย์\nนามิเบีย\nนาอูรู\nนิการากัว\nนิวซีแลนด์\nเนเธอร์แลนด์\nเนปาล\nเนวิส\nไนจีเรีย\nไนเจอร์\nบราซิล\nบริติช\nบริเตน\nบรูไน\nบอตสวานา\nบอสเนีย\nบังกลาเทศ\nบังคลาเทศ\nบัลแกเรีย\nบาร์บูดา\nบาร์เบโดส\nบาห์เรน\nบาฮามาส\nบิสเซา\nบุรุนดี\nบูร์กินาฟาโซ\nเบนิน\nเบลเยียม\nเบลารุส\nเบลีซ\nเบอร์มิวดา\nโบลิเวีย\nปรินซิปี\nปากีสถาน\nปานามา\nปาปัวนิวกินี\nปารากวัย\nปาเลสไตน์\nปาเลา\nเปรู\nเปอร์เซีย\nเปอร์โตริโก\nโปรตุเกส\nโปแลนด์\nฝรั่งเศส\nพม่า\nฟิจิ\nฟินแลนด์\nฟิลิปปินส์\nเฟรนช์\nภูฏาน\nภูฐาน\nมองโกเลีย\nมอนเตเนโกร\nมอนแทนา\nมอริเชียส\nมอริเตเนีย\nมอลโดวา\nมอลตา\nมัลดีฟส์\nมาเก๊า\nมาซิโดเนีย\nมาดากัสการ์\nมาร์แชลล์\nมาลาวี\nมาลี\nมาเลเซีย\nเม็กซิโก\nเมียนมาร์\nโมซัมบิก\nโมนาโก\nโมนาโค\nโมร็อกโก\nไมโครนีเซีย\nยูกันดา\nยูโกสลาเวีย\nยูเครน\nเยเมน\nเยอรมนี\nรวันดา\nรัสเซีย\nโรมาเนีย\nลักเซมเบิร์ก\nลัตเวีย\nลาว\nลิกเตนสไตน์\nลิทัวเนีย\nลิเบีย\nลีโอน\nลูเซีย\nเลโซโท\nเลบานอน\nเลสเต\nไลบีเรีย\nวาติกัน\nวานูอาตู\nวินเซนต์\nเวเนซุเอลา\nเวียดนาม\nศรีลังกา\nสเปน\nสโลวะเกีย\nสโลวัก\nสโลวีเนีย\nสวาซิแลนด์\nสวิตเซอร์แลนด์\nสวีเดน\nสหรัฐ\nสหราชอาณาจักร\nสิกขิม\nสิงคโปร์\nอเมริกา\nออสเตรเลีย\nออสเตรีย\nอันดอร์รา\nอัฟกานิสถาน\nอาเซอร์ไบจาน\nอาร์เจนตินา\nอาร์เมเนีย\nอาระเบีย\nอิเควทอเรียล\nอิตาลี\nอินเดีย\nอินโดนีเซีย\nอิรัก\nอิสราเอล\nอิหร่าน\nอียิปต์\nอุซเบกิสถาน\nอุรุกวัย\nเอกวาดอร์\nเอธิโอเปีย\nเอมิเรตส์\nเอริเทรีย\nเอลซัลวาดอร์\nเอสโตเนีย\nแองโกลา\nแอนติกา\nแอลจีเรีย\nแอลเบเนีย\nโอมาน\nไอซ์แลนด์\nไอร์แลนด์\nฮ่องกง\nฮอนดูรัส\nฮังการี\nเฮติ\nเฮอร์เซโกวีนา\n\nกระบี่\nกรุงเทพ\nกาญจนบุรี\nกาฬสินธุ์\nกำแพงเพชร\nขอนแก่น\nจันทบุรี\nฉะเชิงเทรา\nชลบุรี\nชัยนาท\nชัยภูมิ\nชุมพร\nเชียงราย\nเชียงใหม่\nตรัง\nตราด\nตาก\nนครนายก\nนครปฐม\nนครพนม\nนครราชสีมา\nนครศรีธรรมราช\nนครสวรรค์\nนนทบุรี\nนราธิวาส\nน่าน\nบึงกาฬ\nบุรีรัมย์\nปทุมธานี\nประจวบคีรีขันธ์\nปราจีนบุรี\nปัตตานี\nพะเยา\nพังงา\nพัทลุง\nพิจิตร\nพิษณุโลก\nเพชรบุรี\nเพชรบูรณ์\nแพร่\nภูเก็ต\nมหาสารคาม\nมุกดาหาร\nแม่ฮ่องสอน\nยโสธร\nยะลา\nร้อยเอ็ด\nระนอง\nระยอง\nราชบุรี\nลพบุรี\nลำปาง\nลำพูน\nเลย\nศรีสะเกษ\nสกลนคร\nสงขลา\nสตูล\nสมุทรปราการ\nสมุทรสงคราม\nสมุทรสาคร\nสระแก้ว\nสระบุรี\nสิงห์บุรี\nสุโขทัย\nสุพรรณบุรี\nสุราษฎร์\nสุราษฎร์ธานี\nสุรินทร์\nหนองคาย\nหนองบัวลำภู\nอยุธยา\nอ่างทอง\nอำนาจเจริญ\nอุดรธานี\nอุตรดิตถ์\nอุทัยธานี\nอุบลราชธานี\nกันทรลักษ์\nจตุจักร\nไชยา\nซีคอน\nดอนเมือง\nถลาง\nไทรโยค\nธนบุรี\nธัญบุรี\nบางกอก\nบางปะกง\nบางระจัน\nปะทิว\nปาย\nพญาไท\nพัฒน์พงษ์\nพัทยา\nพารากอน\nภูมิซรอล\nรัตนาธิเบศร์\nรังสิต\nลันตา\nลาดพร้าว\nวโรรส\nวิภาวดี\nสตึก\nสมุย\nสัตหีบ\nสิมิลัน\nสุขุมวิท\nสุไหง\nเสลภูมิ\nอังรีดูนังต์\nอ่างขาง\nอินทนนท์\nเอ็มโพเรียม\n\nคิวชู\nแคริบเบียน\nแคสเปียน\nดานูบ\nตะนาวศรี\nนอร์วีเจียน\nนิโคบาร์\nเนรัญชรา\nไนล์\nบอร์เนียว\nบอลติก\nเบงกอล\nปิง\nแปซิฟิก\nมะละกา\nมินดาเนา\nมิสซิสซิปปี\nเมดิเตอร์เรเนียน\nเมโสโปเตเมีย\nยมุนา\nยุโรป\nยูเรเชีย\nยูเรเซีย\nแยงซี\nแยงซีเกียง\nสแกนดิเนเวีย\nสะโตง\nสาละวิน\nสุมาตรา\nสุเอซ\nอะเมซอน\nอันดามัน\nอัลไต\nอาร์กติก\nอาหรับ\nอินโดจีน\nอิรวดี\nอิระวดี\nอีเจียน\nอุษาคเณย์\nอูราล\nเอเชีย\nเอเดรียติก\nเอเวอเรสต์\nแอตแลนติก\nแอนตาร์กติก\nแอนตาร์กติกา\nแอฟริกา\nโอลิมปัส\nไอโอเนียน\nฮวงโห\nฮอกไกโด\nฮอนชู\n\nกบิลพัสดุ์\nกุสินารา\nโกลิยะ\nโกสัมพี\nโคตรบอง\nโคตรบูรณ์\nตองอู\nทรอย\nทวารวดี\nทวาราวดี\nเทวทหะ\nไทรบุรี\nนาลันทา\nไบแซนไทน์\nปรัสเซีย\nปัลลวะ\nปาฏลีบุตร\nพุทธคยา\nมถุรา\nมายัน\nมิถิลา\nราชคฤห์\nล้านช้าง\nล้านนา\nลุมพินี\nวัชชี\nเวสาลี\nสารนาถ\nสาวัตถี\nหริภุญชัย\nอโยธยา\nออตโตมัน\nอังวะ\nอินทปัตถ์\nอุชเชนี\n\nกราฟิก\nกราฟิกส์\nกราฟิค\nกริด\nกิกะไบต์\nกูเกิล\nกูเกิ้ล\nเกตเวย์\nโกลบอล\nคลัสเตอร์\nคลาส\nคลิก\nคลิปอาร์ต\nคอนโซล\nคอนเทนต์\nคอมพิวติ้ง\nคอมไพล์\nคอมไพเลอร์\nคอมมูนิเคชั่น\nคอร์\nคีย์\nคีย์บอร์ด\nเครือข่าย\nเคอร์เซอร์\nเคอร์เนล\nแคช\nแคมฟรอก\nแคมฟร็อก\nแคร็ก\nโค้ด\nจาวา\nจีพีเอส\nชิป\nชิพ\nเชลล์\nแช็ต\nแชนเนล\nแชนแนล\nซ็อกเก็ต\nซอฟต์แวร์\nซอฟท์แวร์\nซอร์ส\nซัพพอร์ต\nซัพพอร์ท\nซีดี\nซีดีรอม\nซีเนอร์\nเซิร์ฟเวอร์\nโซลูชัน\nโซลูชั่น\nไซต์\nไซเบอร์\nทรานแซกชัน\nทรานแซกชั่น\nทรานแซ็กชัน\nทรานแซ็กชั่น\nทรานแซคชัน\nทรานแซคชั่น\nทรานแซ็คชัน\nทรานแซ็คชั่น\nทวิตเตอร์\nทวีต\nทัชแพด\nเทมเพลต\nเทอร์มินัล\nแท็ก\nแท็บ\nแทบเล็ต\nโทรจัน\nเน็ต\nเน็ตบุ๊ก\nเน็ตบุค\nเน็ตบุ๊ค\nเน็ตเวิร์ก\nเน็ตเวิร์ค\nโน้ตบุ๊ก\nโน้ตบุค\nโน้ตบุ๊ค\nดอส\nดาวน์เกรด\nดาวน์โหลด\nดิจิตอล\nดิจิทัล\nดีบั๊ก\nดีวีดี\nดีไวซ์\nเดเบียน\nเดลไฟ\nเดสก์ท็อป\nโดเมน\nไดรว์\nไดรเวอร์\nไดเรกทอรี\nไดโอด\nเทเลคอม\nบล็อกเกอร์\nบรอดแบนด์\nบราวเซอร์\nบลูทูท\nบลูทูธ\nบลูเรย์\nบั๊ก\nบัฟเฟอร์\nบิต\nบิท\nบูต\nเบราว์เซอร์\nแบนด์วิดท์\nไบต์\nไบนารี\nโปรแกรมเมอร์\nโปรเซส\nโปรเซสเซอร์\nโปรโตคอล\nพร็อกซี\nพอร์ต\nพอร์ท\nพาเนล\nพาร์ทิชัน\nพารามิเตอร์\nพาสเวิร์ด\nพิกเซล\nเพจ\nเพจเจอร์\nแพกเก็ต\nแพตช์\nแพลตฟอร์ม\nโพรเซส\nโพรเซสเซอร์\nโพรโทคอล\nไพธอน\nฟล็อปส์\nฟอนต์\nฟอร์แมต\nฟอร์เวิร์ด\nฟอรัม\nฟีเจอร์\nเฟซบุ๊ก\nเฟิร์มแวร์\nแฟล็ก\nโฟลเดอร์\nไฟร์ฟอกซ์\nไฟร์วอลล์\nไฟล์\nมอดูล\nมอนิเตอร์\nมัลติ\nมัลติทัช\nมัลติเพล็กซ์\nมัลแวร์\nมาสเตอร์\nมีเดีย\nเมนู\nเมมโมรี\nเมล\nเมาส์\nแมค\nโมดูล\nโมเด็ม\nโมบาย\nโมบายล์\nโมไบล์\nไมโครซอฟท์\nยูนิกซ์\nยูนิโคด\nยูนิโค้ด\nริงโทน\nรีเฟรช\nรีเลย์\nเราเตอร์\nเรียลไทม์\nลิงก์\nลินุกซ์\nลีนุกซ์\nลูป\nเลเยอร์\nแล็ปท็อป\nไลเซนส์\nไลบรารี\nวิกิ\nวิกิพีเดีย\nวินโดวส์\nวินโดว์ส\nเว็บ\nเวอร์ชวล\nเวอร์ชัน\nเวอร์ชั่น\nเวิร์กสเตชัน\nเวิร์กสเตชั่น\nเวิร์คสเตชัน\nเวิร์คสเตชั่น\nเวิร์ด\nเวิร์ม\nไวแมกซ์\nสกรีน\nสแกน\nสแกนเนอร์\nสแต็ก\nสนิฟเฟอร์\nสปายแวร์\nสเปซ\nสแปม\nสมาร์ท\nสล็อต\nเสิร์ช\nโหลด\nออนไลน์\nออปติก\nออปติคอล\nออปติคัล\nออฟไลน์\nออราเคิล\nอัพเกรด\nอัพเดต\nอัพโหลด\nอัปเกรด\nอัปเดต\nอัปโหลด\nอัลกอริทึม\nอาร์กิวเมนต์\nอินเตอร์เน็ต\nอินทิเกรเตอร์\nอินเทอร์เน็ต\nอินเทอร์เฟซ\nอินเทล\nอินพุต\nอินพุท\nอีเมล\nอีเมล์\nอูบุนตู\nอูบันตู\nเอนจิน\nเอ็นจิน\nเอาต์พุต\nเอาต์พุท\nเอาท์พุต\nเอาท์พุท\nแอนะล็อก\nแอนิเมชัน\nแอนิเมชั่น\nแอปพลิเคชัน\nแอปพลิเคชั่น\nแอพพลิเคชัน\nแอพพลิเคชั่น\nแอสเซมบลี\nแอสเซมเบลอร์\nโอเพน\nไอคอน\nไอซี\nไอพอด\nไอพ็อด\nไอแพด\nไอโฟน\nฮับ\nฮาร์ดดิสก์\nฮาร์ดแวร์\nแฮกเกอร์\nแฮ็กเกอร์\nแฮนด์เฮลด์\nโฮสต์\n\nกรีก\nกัณณาฑ\nกัศมีรี\nคันจิ\nคาตาคานะ\nคุชราตี\nคุรุมุขี\nซีริลลิก\nซูลู\nเซลติก\nเซิร์บ\nตากาล็อก\nเตลุคู\nเติร์ก\nทราวิฑ\nเบงกาลี\nปัญจาบี\nพินอิน\nมลยาฬัม\nมองโกล\nมาราฐี\nมาเลย์\nเม็กซิกัน\nแมนจู\nแมนดาริน\nไมถิลี\nเยอรมัน\nรัสเซียน\nสวาฮิลี\nสวิส\nสินธี\nอูรดู\nอัสสมี\nอารบิก\nอิตาเลียน\nอุยกูร์\nแอฟริกัน\nโอริยา\nไอริช\nฮันกึล\nฮินดี\nฮิรางานะ\nฮีบรู\n\nกรีนพีซ\nกรือเซะ\nกวนอิม\nกวนอู\nกัดดาฟี\nกัลยาณวัตร\nกัสสปะ\nกาลิเลโอ\nกินเนส\nกุมภกรรณ\nกูเตนเบิร์ก\nเกตส์\nเกษมณี\nโกณฑัญญะ\nขงเบ้ง\nคริสโตเฟอร์\nคองคอร์ด\nคอลเกต\nคานธี\nคาเบรียล\nคาร์ฟูร์\nคาร์สัน\nคาราบาว\nคาสิโอ\nคิริน\nคุนลุ้น\nคูโบต้า\nเครมลิน\nแคทรีนา\nโคตมะ\nโคตมี\nโคลัมบัส\nไคฟง\nไครสเลอร์\nง้อไบ๊\nจตุพร\nจ็อบส์\nจอห์น\nจิ้น\nจิม\nจิ๋ม\nจิว\nจุฬาภรณ์\nจุฬาลงกรณ์\nเจมส์\nแจ็กสัน\nโจเซฟ\nโจว\nชมัยมรุเชฐ\nชมัยมรุเชษฐ์\nชเวดากอง\nชาร์ลส์\nชินราช\nชินวัตร\nชุนชิว\nเช็ง\nเชตวัน\nเชฟรอน\nเชฟโรเลต\nเชลซี\nไชยานุชิต\nซ่ง\nซังฮี้\nซัดดัม\nซันซิล\nซัมซุง\nซัวเจ๋ง\nซินหัว\nซีซาร์\nซีแพค\nซูซาน\nซูซูกิ\nซูบารุ\nเซ็นทารา\nเซเวน\nเซเว่น\nโซฟิเทล\nโซยุซ\nโซยูซ\nณัฐวุฒิ\nดาร์ลี่\nดาวโจนส์\nดิสนีย์\nดีแทค\nดูปองท์\nเดโมแครต\nเดลล์\nเดลินิวส์\nเดวิด\nแดวู\nโดนัลด์\nโดราเอมอน\nโดเรมอน\nต๋อง\nตั๊กม้อ\nตากสิน\nตาเมือน\nตาลีบัน\nตูน\nเตมีย์\nโต๋\nโตชิบา\nโตโยต้า\nถังซัมจั๋ง\nถังซำจั๋ง\nทรพา\nทราเวล\nทรูมูฟ\nทีปังกร\nเทปโก\nเทพรัตน\nเทวทัต\nเทสโก้\nโทมัส\nไททานิก\nไททานิค\nไทยรัฐ\nธีออส\nนครินทรา\nนโปเลียน\nนพดล\nนราดูร\nนเรนทร\nนอสตราดามุส\nนาซา\nนาซ่า\nนาซี\nนาโต\nนาโต้\nนาลแก\nนิคอน\nนิโคลัส\nนิด้า\nนินเทนโด\nนิปปอน\nนิวตัน\nนิสสัน\nเนคเทค\nเนชั่น\nเนชันแนล\nเนชั่นแนล\nเนวิน\nเนสเล่\nเนสาด\nแนท\nแนสแดค\nโนเกีย\nโนเบล\nโนเวลล์\nโนโวเทล\nไนเม็กซ์\nบรอดเวย์\nบรัดเลย์\nบรู๊ซ\nบัลเมอร์\nบารัก\nบารัค\nบู๊ตึ๊ง\nเบญกาย\nเบนซ์\nเบ็นซ์\nเบนจามิน\nโบตัน\nไบโอเทค\nประชาธิปัตย์\nปวีณา\nปอเต็กตึ๊ง\nปอเต๊กตึ๊ง\nป่อเต็กตึ๊ง\nปัตตะโชติ\nปารุสก์\nปีเตอร์\nปูติน\nเป๊ปซี่\nเป้ย\nเปอร์โยต์\nเปาบุ้นจิ้น\nโปเกมอน\nโป๊ยก่าย\nพรหมทัต\nพลาโต\nพอลล่า\nพานาโซนิค\nพานาโซนิก\nพิทยานุกูล\nพิมพิสาร\nเพนแทกซ์\nเพลโต\nไพโอเนียร์\nฟรอยด์\nฟรังซิส\nฟรานซิส\nฟลอเรนซ์\nฟอร์ด\nฟิลิปส์\nฟูจิ\nแฟซ่า\nโฟร์โมสต์\nภูมิพล\nภูริทัต\nมงฟอร์ต\nมณโฑ\nมติชน\nมหิตลาธิเบศร\nมโหสถ\nมัจฉานุ\nมาร์กาเร็ต\nมาร์ติน\nมาสด้า\nมิตซูบิชิ\nมิราเคิล\nมุสโสลินี\nเม้ง\nเมจิ\nเมอร์ซีเดส\nเมอร์เซเดส\nแมกซ์เวลล์\nแมกไซไซ\nแมคอินทอช\nแมชีนเนอรี่\nโมคคัลลานะ\nโมโตโรลา\nโมโตโรล่า\nโมเนีย\nไมเคิล\nไมยราพณ์\nยโสธรา\nยะโฮวา\nยะโฮวาห์\nยามาฮ่า\nยาเวห์\nยาฮู\nยูนิเซฟ\nยูเนสโก\nยูไล\nเยโฮวาห์\nรอยเตอร์\nรอยัล\nรัชดา\nรัสปูติน\nราฟาเอล\nรามาวตาร\nราเมศวร\nราหุล\nริชาร์ด\nรีพับลิกัน\nรูนีย์\nเรนโบว์\nแรมโบ้\nโรตารี\nโรนัลโด\nโรนัลโด้\nโรบินสัน\nโรเบิร์ต\nล็อกซเล่ย์\nลิงคอล์น\nลิจฉวี\nลิไท\nลิไทย\nลินคอล์น\nลิเวอร์พูล\nเลโนโว\nเลียดก๊ก\nโลตัส\nวชิราลงกรณ์\nวลาดิเมียร์\nวอลล์สตรีท\nวาเลนไทน์\nวิกตอเรีย\nวิทยานุสรณ์\nวิทยายน\nวิมเบิลดัน\nวิลเลียม\nวีระ\nวุฒิชัย\nเวียดกง\nไวตามิลค์\nศกุนตลา\nศรีวิชัย\nศิริพงษ์\nศิริราช\nศุภชลาศัย\nสดกก๊อกธม\nสดายุ\nสตาลิน\nสตีฟ\nสแตนฟอร์ด\nสวรินทิรา\nสังกัจจายน์\nสาทิตย์\nสารีบุตร\nสิริกิติ์\nสิรินธร\nสิหิงค์\nสีวลี\nสีหนุ\nสีหมุนี\nสีหโมนี\nสุครีพ\nสุทโธทนะ\nสุเทพ\nสุนทราภรณ์\nสุนีย์\nสุรนารี\nสุรยุทธ์\nสุริยาสน์\nเส้าหลิน\nโสกราตีส\nโสภิต\nหนุมาน\nหลินฮุ่ย\nหลุยส์\nเห้งเจีย\nไหหม่า\nองคต\nองคุลิมาล\nอชาตศัตรู\nอดุลยเดช\nอพอลโล\nอริสโตเติล\nอริสมันต์\nอลิซาเบธ\nอ๋อม\nออร์คิด\nออสการ์\nอะพอลโล\nอับราฮัม\nอั้ม\nอัลกออิดะห์\nอัลคาเทล\nอัลจาซีราห์\nอัลเฟรด\nอัลเลาะห์\nอัสซุส\nอัสสชิ\nอัสสัมชัญ\nอาเซม\nอาเซ็ม\nอาเซียน\nอาฟต้า\nอาร์เซนอล\nอินทรชิต\nอินทราทิตย์\nอีซูซุ\nอีเลฟเวน\nอีเลฟเว่น\nอุณรุท\nอุบลรัตน์\nอุบาลี\nอุ๋ย\nเอกทัศน์\nเอเซอร์\nเอ็ดเวิร์ด\nเอดิสัน\nเอแบค\nเอลิซาเบธ\nเอสพลานาด\nเอสพลานาร์ด\nแอคคอร์\nแอคคอร์ด\nแองเจลิน่า\nแอตแลนติส\nแอน\nแอ๋ม\nแอมบาสซาเดอร์\nแอมบาสเดอร์\nแอมเวย์\nแอ๋ว\nโอดีสซีย์\nโอเดียน\nโอบามา\nโอรสาราม\nโอลิมเปีย\nโออิชิ\nไอน์สไตน์\nฮอนด้า\nฮอปกินส์\nฮอลลีวูด\nฮอลลีวู้ด\nฮานามิ\nฮามาส\nฮิตเลอร์\nฮิตาชิ\nฮุนเซน\nฮุนเซ็น\nฮุนได\nฮุสเซ็น\nเฮนรี\nเฮนรี่\nเฮเลน\nโฮจิมินห์\nโฮปเวลล์\nโฮเมอร์\n\nกลีเซอรีน\nกำทอน\nแกนีมีด\nครอส\nคริสตัล\nคลอโรพลาสต์\nคลอไรด์\nควอนตัม\nคอนดักเตอร์\nคอปเปอร์\nคอลลาเจน\nคอเลสเตอรอล\nคอสมิก\nคูลอมบ์\nเคอราติน\nแคโรทีน\nแคสสินี\nโครมาโทกราฟี\nไคโตซาน\nจีโนม\nจุลชีววิทยา\nชิคุนกุนยา\nซัลฟิวริก\nซัลเฟต\nซัลไฟด์\nซิงค์\nซิริอุส\nซิลิกา\nซิลิเกต\nซิลิโคน\nซีเทน\nซีเวิร์ต\nเซ็กเตอร์\nเซ็กเมนต์\nเซมิ\nโซนาร์\nไซบอร์ก\nดอปเปลอร์\nดอปเพลอร์\nไดนามิก\nไดนามิกส์\nไดนามิค\nไดนามิคส์\nไดออกไซด์\nทรานส์\nทามิฟลู\nเทฟลอน\nเทสโทสเตอโรน\nเทอร์โม\nแทนนิน\nไททัน\nไทฟอยด์\nไทรอยด์\nธาลัสซีเมีย\nเนกาตีฟ\nโนวา\nบอแรกซ์\nโบทอกซ์\nโบท็อกซ์\nไบโอติน\nปฏิยานุพันธ์\nโปรเจสเตอโรน\nพอลิเมอร์\nพันธุศาสตร์\nพัลซาร์\nพาร์กินสัน\nพาราเซตามอล\nพาราโบลา\nเพอร์ออกไซด์\nโพรเจสเทอโรน\nโพลาไรซ์\nโพลิเมอร์\nโพลีเอทิลีน\nไพรเมต\nฟลาโวนอยด์\nฟลูออเรสเซนซ์\nฟลูออไรด์\nฟอสซิล\nฟิชชัน\nฟิวชัน\nฟีโรโมน\nไฟเบอร์\nมอนอกไซด์\nมิราจ\nเมตริกซ์\nเมทริกซ์\nเมลานิน\nเมลามีน\nโมเมนตัม\nไมโตคอนเดรีย\nไมโทคอนเดรีย\nยีสต์\nยูริก\nยูเรีย\nรูมาตอยด์\nวีก้า\nเวกเตอร์\nเวก้า\nเวสิเคิล\nโวลต์\nสเกล\nสเกลาร์\nสเต็ม\nสเตียรอยด์\nสปีชีส์\nสเปิร์ม\nสัมพัทธภาพ\nสุริยจักรวาล\nออกเทน\nออโรรา\nออโรร่า\nอะคริลิก\nอะครีลิก\nอะซีติก\nอะซีโตน\nอะมิโน\nอะลูมินา\nอันโดรเมดา\nอัลคาไลน์\nอัลตราซาวด์\nอัลตราซาวนด์\nอัลลอยด์\nอินทิกรัล\nอินทิเกรต\nอีโบลา\nอีโบล่า\nเอ็กซ์โพเนนเชียล\nเอทานอล\nเอทิลีน\nเอนโทรปี\nเอสเตอร์\nเอสโตรเจน\nเอสโทรเจน\nแอนดรอยด์\nแอนแทร็กซ์\nแอมพลิจูด\nแอมโมเนียม\nแอลกอฮอลิซึม\nแอสพาร์แตม\nโอเซลทามิเวียร์\nฮับเบิล\nฮิวมัส\nไฮดรอกไซด์\nไฮดรอลิก\nไฮโดรลิก\nไฮเพอร์โบลา\n\nกงเต็ก\nกฎุมพี\nกฏ\nกฏหมาย\nกบฎ\nกราไฟต์\nก๊อปปี้\nกะทะ\nกังวาล\nกุฎฐัง\nกุฏุมพี\nฃวด\nคฑา\nคลินิค\nคลีนิก\nคลีนิค\nคาทอลิค\nคึ่นฉ่าย\nแคตตาล็อก\nโควต้า\nฅน\nจุมพฎ\nช็อคโกแลต\nแซ่ด\nดัทช์\nทนง\nทรมาณ\nทราก\nทะแยง\nทิฏฐิ\nบล็อค\nบ๊องแบ๊ว\nบัลเล่ต์\nเบรค\nแบงค์\nปรากฎ\nปัคคหะ\nปาฏิโมกข์\nปิติ\nปิรามิด\nโปรเตสแตนท์\nพนิช\nพยักเพยิด\nพริ้ว\nพลูโตเนียม\nพากษ์\nเฟิร์น\nยากี้\nเยภุยยสิกา\nรุสเซีย\nฤาษี\nล็อค\nล็อคเกอร์\nวันทยาหัตถ์\nวานิช\nวิญญาน\nวิศิษฐ์\nศรีษะ\nสเปกโทรสโคป\nสฤษฎ์\nสลิ่ม\nสัตตสดก\nสาราณียากร\nสุกี้\nสุกี้ยากี้\nสูญญากาศ\nหยอมแหยม\nหยอย\nหล่ะ\nอะดรีนาลีน\nอะหลั่ย\nอัตคัต\nอัฟริกา\nอานิสงค์\nอาฟริกา\nอิริยาบท\nอิเลคโทรนิคส์\nอีรุงตุงนัง\nอุตรายัน\nอุลตรา\nอุลตร้า\nโอกาศ\n\nกกหู\nกงการ\nกงจักร\nกฎเกณฑ์\nกฎบัตร\nกฎหมาย\nกฎหมู่\nกดขี่\nกดดัน\nก้นกบ\nก้นบึ้ง\nก้นปล่อง\nกนิษฐภคินี\nกนิษฐภาดา\nกรงเล็บ\nกรมการ\nกรมท่า\nกรมธรรม์\nกรมนา\nกรมวัง\nกรรมกร\nกรรมการ\nกรรมฐาน\nกรรมบถ\nกรรมพันธุ์\nกรรมวิธี\nกรรมสิทธิ์\nกระจกเงา\nกระจกตา\nกระจกนูน\nกระจกเว้า\nกระจอกชวา\nกระจอกเทศ\nกระจ้อยร่อย\nกระจับบก\nกระจับปิ้ง\nกระจับปี่\nกระโชกโฮกฮาก\nกระดานดำ\nกระดานหก\nกระดาษแก้ว\nกระดาษไข\nกระดาษทราย\nกระดาษสา\nกระดูกงู\nกระทาชาย\nกระเท่เร่\nกระบวนการ\nกระบองเพชร\nกระผีกริ้น\nกระยาทิพย์\nกระยาสารท\nกระยาหาร\nกระสอบทราย\nกระสุนปืน\nกระแสจิต\nกระแสน้ำ\nกระแสลม\nกรับคู่\nกรับพวง\nกรับเสภา\nกราดเกรี้ยว\nกราวรูด\nกริ่งเกรง\nกรีฑาสถาน\nกรีดกราย\nกรี๊ดกร๊าด\nกลไก\nกลบท\nกลเม็ด\nกลยุทธ์\nกลวิธี\nกลศาสตร์\nกลอักษร\nกลบเกลื่อน\nกลมกล่อม\nกลมกลืน\nกลมเกลียว\nกล้วยแขก\nกล้วยไม้\nกล่องเสียง\nกล่อมเกลา\nกล่อมเกลี้ยง\nกลัดกลุ้ม\nกลัดมัน\nกลั่นกรอง\nกลั่นแกล้ง\nกลับกลอก\nกลางคน\nกลางคัน\nกลางค่ำ\nกลางคืน\nกลางแจ้ง\nกลางแปลง\nกลางวัน\nกลาดเกลื่อน\nกล่าวขวัญ\nกล่าวโทษ\nกล่าวหา\nกล้ำกราย\nกล้ำกลืน\nกลิ้งกลอก\nกลิ้งเกลือก\nกลิ่นอาย\nกลียุค\nกวดขัน\nกวัดแกว่ง\nกวัดไกว\nกวีนิพนธ์\nก่อกวน\nก่อการ\nก่อตั้ง\nก่อสร้าง\nก่อหวอด\nก้อร่อก้อติก\nกองกลาง\nกองเกิน\nกองโจร\nกองทัพ\nกองทุน\nกองพล\nกองพัน\nกองฟอน\nกองร้อย\nกองหนุน\nกอบโกย\nกะเกณฑ์\nกะบังลม\nกักกัน\nกักขัง\nกักตัว\nกักตุน\nกัณฑ์เทศน์\nกัดฟัน\nกันชน\nกันท่า\nกันสาด\nกันเอง\nกับแกล้ม\nกับข้าว\nกับระเบิด\nกากเพชร\nกากหมู\nกาชาด\nกาญจนาภิเษก\nก้านคอ\nกาฝาก\nก้ามกราม\nกามกิจ\nกามคุณ\nกามเทพ\nกามโรค\nก้ามปู\nกายกรรม\nกายบริหาร\nกายภาพ\nกายวิภาค\nกายสิทธิ์\nก่ายกอง\nการคลัง\nการเงิน\nการบ้าน\nการเปรียญ\nการเมือง\nการเรือน\nการละเล่น\nกาลกิริยา\nกาลเทศะ\nก้าวก่าย\nก้าวร้าว\nก้าวหน้า\nกาสาวพัสตร์\nกาฬพฤกษ์\nกาฬโรค\nกำปั้น\nกำมือ\nกำแพงขาว\nกำลังใจ\nกำลังม้า\nกี่งอำเภอ\nกิจกรรม\nกิจการ\nกิจวัตร\nกิจจะลักษณะ\nกิตติคุณ\nกิตติศัพท์\nกินขาด\nกินใจ\nกินดอง\nกินโต๊ะ\nกินแบ่ง\nกินเปล่า\nกินเลี้ยง\nกินเส้น\nกินแหนง\nกิโลกรัม\nกิโลเมตร\nกิโลลิตร\nกิโลเฮิรตซ์\nกีดกัน\nกีดกั้น\nกีดขวาง\nกึกก้อง\nกึกกัก\nกึกกือ\nกึ่งกลาง\nกุกกัก\nกุ๊กกิ๊ก\nกุ้งฝอย\nกุ้งมังกร\nกุ้งแห้ง\nกุ้งเต้น\nกุ้งยิง\nกุญแจผี\nกุญแจมือ\nกุญแจเลื่อน\nกุญแจเสียง\nกุลธิดา\nกุลบุตร\nกุลสตรี\nกู้ยืม\nเก้งก้าง\nเก็บกวาด\nเก็บเกี่ยว\nเก็บงำ\nเก็บตก\nเกรงกลัว\nเกรงใจ\nเกรงขาม\nเกรียงไกร\nเกรียมกรม\nเกรี้ยวกราด\nเกล็ดเลือด\nเกลี้ยกล่อม\nเกลี้ยงเกลา\nเกลือกกลั้ว\nเกลือกกลิ้ง\nเกลื่อนกล่น\nเกลื่อนกลาด\nเกศธาตุ\nเกษตรกร\nเกษตรกรรม\nเกษตรศาสตร์\nเกษมสันต์\nเกษียรสมุทร\nเก้อเขิน\nเกาะแกะ\nเกี่ยงงอน\nเกียจคร้าน\nเกียรติคุณ\nเกียรตินิยม\nเกียรติประวัติ\nเกียรติภูมิ\nเกียรติยศ\nเกียรติศักดิ์\nเกียรติมุข\nเกี่ยวข้อง\nเกี่ยวดอง\nเกี่ยวพัน\nเกี่ยวโยง\nเกี้ยวพาน\nเกี้ยวพาราสี\nแก่แดด\nแก้ขัด\nแก้ไข\nแก้ตัว\nแก้เผ็ด\nแก้ลำ\nแกงคั่ว\nแกงจืด\nแกงบวด\nแกงป่า\nแกงเผ็ด\nแกงส้ม\nแก่งแย่ง\nแก่นแก้ว\nแก่นสาร\nแก้วตา\nแก้วหู\nแกว่งกวัด\nแกว่งไกว\nแกะรอย\nโก้เก๋\nโกรธเกรี้ยว\nโกรธขึ้ง\nไก่เขี่ย\nไก่ชน\nไก่บ้าน\nไก่ป่า\nไก่ฟ้า\nไกล่เกลี่ย\nขจัดขจาย\nขนเพชร\nขนสัตว์\nขนหนู\nขนส่ง\nขนมจีน\nขบขัน\nขบวนการ\nข่มขี่\nข่มขู่\nข่มขืน\nข่มเหง\nขมหิน\nขมิ้นชัน\nขมุบขมิบ\nขยะแขยง\nขยักขย่อน\nขยักขย้อน\nขยับขยาย\nขยับเขยื้อน\nขวดโหล\nขวยเขิน\nขวัญใจ\nขวัญตา\nขวัญอ่อน\nขวากหนาม\nขวางโลก\nของ้าว\nขอสับ\nขอขมา\nขอทาน\nขอโทษ\nขอร้อง\nขออภัย\nข้อเขียน\nข้อความ\nข้อเท็จจริง\nของกลาง\nของขวัญ\nของแข็ง\nของชำ\nของลับ\nของเล่น\nของว่าง\nของเหลว\nของไหล\nของไหว้\nข้องใจ\nข้องแวะ\nขอบข่าย\nขอบเขต\nขอบคุณ\nขอบใจ\nขอบพระคุณ\nข้อมูล\nข้อแม้\nข้อหา\nข้อสังเกต\nขัดข้อง\nขัดขืน\nขัดเขิน\nขัดจังหวะ\nขัดดอก\nขัดแตะ\nขัดยอก\nขัดแย้ง\nขัดสมาธิ\nขันหมาก\nขันอาสา\nขับขี่\nขับเคี่ยว\nขั้วโลก\nขาจร\nขาประจำ\nข้าราชการ\nข้าศึก\nข้าหลวง\nขาดแคลน\nขาดใจ\nขาดตอน\nขาดตัว\nขาดทุน\nขาดเหลือ\nขายหน้า\nข่าวกรอง\nข่าวคราว\nข่าวล่า\nข่าวลือ\nข่าวสาร\nข้าวเกรียบ\nข้าวแกง\nข้าวของ\nข้าวจี่\nข้าวเจ้า\nข้าวซอย\nข้าวต้ม\nข้าวตอก\nข้าวตัง\nข้าวแตน\nข้าวทิพย์\nข้าวบิณฑ์\nข้าวเปลือก\nข้าวโพด\nข้าวฟ่าง\nข้าวเม่า\nข้าวสวย\nข้าวสาร\nข้าวเหนียว\nข้าวหมาก\nข้าวหลาม\nขี้เกียจ\nขี้ข้า\nขี้ครอก\nขี้คร้าน\nขี้คุก\nขี้ไคล\nขี้เซา\nขีดขั้น\nขีดคร่อม\nขีดคั่น\nขีดฆ่า\nขี้ตา\nขี้ตืด\nขี้เถ้า\nขี้ทูด\nขี้ปะติ๋ว\nขี้ผึ้ง\nขี้มูก\nขี้ยา\nขี้แย\nขี้ริ้ว\nขี้เรื้อน\nขี้เล็บ\nขี้หู\nขี้หน้า\nขี้เหนียว\nขี้เหล็ก\nขี้เหร่\nขึงขัง\nขึงพืด\nขึ้งโกรธ\nขึ้นใจ\nขึ้นชื่อ\nขึ้นมือ\nขืนใจ\nขื่นขม\nขุดคุ้ย\nขุนทอง\nขุนนาง\nขุนพล\nขุนศึก\nขุนหลวง\nขูดรีด\nเข็มกลัด\nเข็มขัด\nเข้มข้น\nเข้มแข็ง\nเข้มงวด\nเข็มทิศ\nเข็มหมุด\nเข้าขา\nเข้าเค้า\nเข้าใจ\nเข้าชื่อ\nเข้าตัว\nเข้าถึง\nเข้าทรง\nเข้าท่า\nเข้าที\nเข้าเนื้อ\nเข้าเล่ม\nเขียวเสวย\nเขียวหวาน\nแขกเต้า\nแข็งกร้าว\nแข็งกล้า\nแข็งแกร่ง\nแข็งข้อ\nแข็งขัน\nแข่งขัน\nแข็งใจ\nแข็งตัว\nแข็งเมือง\nแข็งแรง\nแขวนลอย\nโขกสับ\nโขยกเขยก\nไขข้อ\nไขควง\nไขมัน\nไข่มุก\nไขว่ห้าง\nไขสันหลัง\nไขสือ\nไข่เค็ม\nไข่เยี่ยวม้า\nไข่หงส์\nไข่เหี้ย\nคงกระพัน\nคงตัว\nคงทน\nคงที่\nคชลักษณ์\nคชสาร\nคชสีห์\nคชราช\nคณิตศาสตร์\nคดเคี้ยว\nคติธรรม\nคติพจน์\nคนกลาง\nคนไข้\nคนใช้\nคนทรง\nคบไฟ\nคบเพลิง\nคบค้า\nคบคิด\nคบหา\nคมคาย\nครบครัน\nครบถ้วน\nครอบครอง\nครอบคลุม\nครอบครัว\nครอบงำ\nครอบจักรวาล\nคริสตกาล\nคริสตจักร\nคริสต์มาส\nคริสต์ศตวรรษ\nคริสต์ศักราช\nคริสตัง\nคริสเตียน\nครุกรรม\nครุภัณฑ์\nครุศาสตร์\nครุฑพ่าห์\nครุ่นคิด\nคลอเคลีย\nคล่องแคล่ว\nคล่องตัว\nคล่องมือ\nคลั่งไคล้\nคลาคล่ำ\nคลาไคล\nคลาดเคลื่อน\nคลาดแคล้ว\nคลี่คลาย\nคลึงเคล้น\nคลึงเคล้า\nคลื่นไส้\nคลื่นเหียน\nคลุกคลี\nคลุกคลาน\nคลุมเครือ\nคลุมโปง\nคลุ้มคลั่ง\nควงสว่าน\nควบคุม\nควบคู่\nควบแน่น\nควันหลง\nความคิด\nความหลัง\nความเห็น\nคอหอย\nคอห่าน\nคอแห้ง\nค่อนขอด\nค่อนแคะ\nค้อนควัก\nคั่งค้าง\nคั่งแค้น\nคัดค้าน\nคัดง้าง\nคัดท้าย\nคัดเลือก\nคันจาม\nคันฉ่อง\nคันฉาย\nคันชัก\nคันชั่ง\nคันไถ\nคันนา\nคันเร่ง\nคับขัน\nคับคั่ง\nคับแค้น\nคับแคบ\nคางทูม\nคางหมู\nค้างคืน\nค้างปี\nคาดคั้น\nคาดเชือก\nคาดโทษ\nคาดหมาย\nคานหาม\nคาบเกี่ยว\nคาบศิลา\nคาบสมุทร\nคำขาด\nคำนำ\nคิดค้น\nคืนดี\nคืนตัว\nคุกเข่า\nคุณค่า\nคุณชาย\nคุณธรรม\nคุณนาย\nคุณภาพ\nคุณลักษณะ\nคุณวุฒิ\nคุณศัพท์\nคุณสมบัติ\nคุณหญิง\nคุณากร\nคุณูปการ\nคุโณปการ\nคุมเชิง\nคุ้มกัน\nคุยเขื่อง\nคุยโต\nคุ้ยเขี่ย\nคุ้มครอง\nคู่กรณี\nคู่กัด\nคู่ขา\nคู่แข่ง\nคู่ครอง\nคู่ควร\nคู่คิด\nคู่คี่\nคู่ใจ\nคู่ชีพ\nคู่ชีวิต\nคู่บารมี\nคู่บุญ\nคู่ปรปักษ์\nคู่ปรับ\nคู่ผสม\nคู่มือ\nคู่รัก\nคู่ลำดับ\nคู่สาย\nคู่หมั้น\nคู่หู\nคู่อริ\nคู่อาฆาต\nเคมีภัณฑ์\nเคยตัว\nเคร่งขรึม\nเคร่งครัด\nเคร่งเครียด\nเครดิตฟองซิเอร์\nเครื่องกล\nเครื่องกัณฑ์\nเครื่องแกง\nเครื่องเขิน\nเครื่องครัว\nเครื่องเคียง\nเครื่องเงิน\nเครื่องจักร\nเครื่องเซ่น\nเครื่องดนตรี\nเครื่องต้น\nเครื่องทุ่นแรง\nเครื่องเทศ\nเครื่องใน\nเครื่องบิน\nเครื่องบูชา\nเครื่องแบบ\nเครื่องประดับ\nเครื่องปรุง\nเครื่องปรุงรส\nเครื่องมือ\nเครื่องยนต์\nเครื่องร่อน\nเครื่องราง\nเครื่องเรือน\nเครื่องล่าง\nเครื่องเล่น\nเครื่องสาย\nเครื่องสำอาง\nเครื่องสุกำศพ\nเครื่องหมาย\nเครือรัฐ\nเคลียคลอ\nเคลื่อนที่\nเคลื่อนไหว\nเคลือบแคลง\nเคลือบแฝง\nเคลือบฟัน\nเคว้งคว้าง\nเคหสถาน\nเค้าโครง\nเคียดแค้น\nเคี่ยวเข็ญ\nเคี้ยวเอื้อง\nเคืองขุ่น\nโคนม\nโคบาล\nโคมูตร\nโคมลอย\nโครงการ\nโครงเรื่อง\nโครงงาน\nโครงสร้าง\nโครมคราม\nโคลงเคลง\nฆ้องกระแต\nฆ้องชัย\nฆ้องวง\nฆ้องหุ่ย\nฆ้องเหม่ง\nฆ้องโหม่ง\nฆาตกร\nฆาตกรรม\nฆานประสาท\nงงงวย\nงงงัน\nงดเว้น\nงบดุล\nงบประมาณ\nงมโข่ง\nงมงาย\nง่วงงุน\nง่วงเหงา\nงอหาย\nง้องอน\nงอนง้อ\nงอมแงม\nงาช้าง\nง่าเงย\nงานการ\nง่ายดาย\nงึมงำ\nเงินเดือน\nเงินตรา\nเงินยวง\nเงียบกริบ\nเงียบเชียบ\nเงียบเหงา\nเงื่องหงอย\nเงื่อนไข\nเงื่อนงำ\nเงื่อนเวลา\nเงื้อมมือ\nแง่งอน\nจงใจ\nจงรัก\nจดจ่อ\nจดจำ\nจดหมาย\nจดหมายเหตุ\nจรจัด\nจรรยาบรรณ\nจริงจัง\nจริงใจ\nจอมขวัญ\nจอมใจ\nจอมทัพ\nจอมปลวก\nจอมพล\nจ๊ะเอ๋\nจักสาน\nจักรพรรดิ\nจักรภพ\nจักรยาน\nจักรยานยนต์\nจักรราศี\nจักรวรรดิ\nจักรวรรดินิยม\nจักรวาล\nจังหนับ\nจัดการ\nจัดจ้าน\nจัดเจน\nจัดแจง\nจัดตั้ง\nจัดสรร\nจับกุม\nจับจด\nจับเจ่า\nจ่าหน้า\nจาตุทสี\nจาตุมหาราช\nจาตุมหาราชิก\nจาตุมหาราชิกา\nจาตุรงคสันนิบาต\nจาตุรราชการ\nจานเชิง\nจานบิน\nจานผี\nจานเสียง\nจาบจ้วง\nจำเป็น\nจำพรรษา\nจำวัด\nจ้ำจี้จ้ำไช\nจำเลาะตา\nจิงโจ้น้ำ\nจิตใจ\nจิตตภาวนา\nจิตตัง\nจิตตานุปัสสนา\nจิตนิยม\nจิตบำบัด\nจิตแพทย์\nจิตวิสัย\nจิตรกร\nจิตรกรรม\nจิตรลดา\nจิตวิทยา\nจิตเวช\nจิตเวชศาสตร์\nจินตกวี\nจินตนา\nจินตนาการ\nจินตภาพ\nจุฑามณี\nจุฑามาศ\nจุฑารัตน์\nจุนเจือ\nจุ้นจ้าน\nจุลชีพ\nจุลชีวัน\nจุลชีวิน\nจุลทรรศน์\nจุลภาค\nจุลวรรค\nจุลศักราช\nจุลสาร\nจุลินทรีย์\nจุฬามณี\nจุฬาลักษณ์\nเจตคติ\nเจตจำนง\nเจตนารมณ์\nเจตภูต\nเจริญพร\nเจ้ากรม\nเจ้ากรรม\nเจ้าของ\nเจ้าขา\nเจ้าข้า\nเจ้าคณะ\nเจ้าค่ะ\nเจ้าจอม\nเจ้าชู้\nเจ้าตัว\nเจ้าถิ่น\nเจ้าท่า\nเจ้าที่\nเจ้าทุกข์\nเจ้านาย\nเจ้าเนื้อ\nเจ้าบ้าน\nเจ้าบ่าว\nเจ้าประคุณ\nเจ้าประคู้น\nเจ้าพนักงาน\nเจ้าพระคุณ\nเจ้าพระยา\nเจ้าพ่อ\nเจ้าพายุ\nเจ้าฟ้า\nเจ้าภาพ\nเจ้ามือ\nเจ้าแม่\nเจ้าเรือน\nเจ้าสังกัด\nเจ้าสัว\nเจ้าสาว\nเจ้าหน้าที่\nเจ้าหนี้\nเจ้าอาวาส\nเจาะจง\nเจือจาง\nเจือจาน\nเจือปน\nเจื้อยแจ้ว\nแจกจ่าย\nแจ่มแจ้ง\nแจ่มใส\nโจงกระเบน\nโจมตี\nโจรกรรม\nโจรสลัด\nใจความ\nใจคอ\nฉกฉวย\nฉกชิง\nฉลองได\nฉ้อฉล\nฉัตรมงคล\nฉันทลักษณ์\nฉายาลักษณ์\nฉิบหาย\nฉุกเฉิน\nฉุกละหุก\nฉุนเฉียว\nฉุปศาสตร์\nเฉไฉ\nเฉยเมย\nเฉาโฉด\nเฉิดฉัน\nเฉิดฉาย\nเฉิดฉิน\nเฉียบขาด\nเฉียบพลัน\nเฉียบแหลม\nเฉื่อยชา\nแฉะแบะ\nโฉดเฉา\nโฉมงาม\nโฉมฉาย\nโฉมเฉลา\nโฉมตรู\nโฉมยง\nโฉมศรี\nโฉมหน้า\nชดช้อย\nชดเชย\nชดใช้\nชนบท\nชนินทร์\nชนกกรรม\nชนมพรรษา\nชนมายุ\nชมเชย\nชมพูทวีป\nชมพูนท\nชมพูนุท\nชราธรรม\nชราภาพ\nชลจร\nชลธาร\nชลธี\nชลนัยน์\nชลนา\nชลเนตร\nชลประทาน\nชลมารค\nชลาธาร\nชลาลัย\nชลาศัย\nชลาสินธุ์\nชโลทร\nช่วงชิง\nช่วงใช้\nชวนชม\nชวนหัว\nช่วยเหลือ\nช่อฟ้า\nช่อม่วง\nชอกช้ำ\nช่องเขา\nช่องแคบ\nช่องไฟ\nช่องว่าง\nช้องนาง\nชอบกล\nชอบใจ\nชอบธรรม\nชอบพอ\nชักโครก\nชักเงา\nชักจูง\nชักชวน\nชักนำ\nชักเนื้อ\nชักพระ\nชักเย่อ\nชักใย\nชั่งใจ\nชังฆวิหาร\nชัดเจน\nชั้นเชิง\nชั่วคน\nชั่วคราว\nชั่วช้า\nชั่วโมง\nชั่วแล่น\nชาเย็น\nช้านาน\nช่างเครื่อง\nช่างฝีมือ\nช่างฟิต\nช่างไฟ\nช้างน้ำ\nช้างเผือก\nช้างพลาย\nช้างพัง\nช้างสาร\nช้างสีดอ\nชาติธรรม\nชาตินิยม\nชาติพันธุ์\nชาติพันธุ์วิทยา\nชาติภูมิ\nชานชาลา\nชายชาตรี\nชายคา\nชายฝั่ง\nชายทะเล\nชาวเล\nชาววัง\nช้ำใจ\nช้ำชอก\nชิงชัง\nชิงพลบ\nชินชา\nชินบุตร\nชิ้นเอก\nชิมลาง\nชีเปลือย\nชี้ขาด\nชี้แจง\nชี้นำ\nชี้แนะ\nชี้ฟ้า\nชีพจร\nชีพิตักษัย\nชื่นชม\nชื่นบาน\nชื่นมื่น\nชื่อย่อ\nชื่อรอง\nชื่อเล่น\nชื่อเสียง\nชุกชุม\nชุติมา\nชุบตัว\nชุบเลี้ยง\nชุมชน\nชุมทาง\nชุมสาย\nชุ่มใจ\nชุ่มชื่น\nชุ่มชื้น\nชุมนุมชน\nชูชีพ\nชูโรง\nชู้สาว\nเชยชม\nเชลยศักดิ์\nเชลยศึก\nเช่าซื้อ\nเช้าตรู่\nเช้ามืด\nเชิงกราน\nเชิงกล\nเชิงชั้น\nเชิงชาย\nเชิงซ้อน\nเชิงเดียว\nเชิงเดี่ยว\nเชิงตะกอน\nเชิงเทิน\nเชิงมุม\nเชิดชู\nเชิงอรรถ\nเชี่ยนหมาก\nเชี่ยวชาญ\nเชื่องช้า\nเชื่อใจ\nเชื่อถือ\nเชื่อฟัง\nเชื่อมือ\nเชื้อชาติ\nเชื้อเพลิง\nเชื้อไฟ\nเชื้อโรค\nเชื้อสาย\nเชื้อเชิญ\nเชื่องช้า\nเชือนแช\nเชื่อวัน\nแช่เย็น\nแช่อิ่ม\nแช่มช้อย\nแช่มชื่น\nโชกโชน\nโชติช่วง\nโชติรส\nใช้สอย\nซบเซา\nซมซาน\nซวนเซ\nซอกซอน\nซอกแซก\nซ่องสุม\nซ่องเสพ\nซ่องแซ่ง\nซ่อนรูป\nซ่อนเร้น\nซ่อนหา\nซ่อนกลิ่น\nซ่อนทราย\nซ่อมแซม\nซักค้าน\nซักซ้อม\nซักไซ้\nซักฟอก\nซักแห้ง\nซังกะตาย\nซังตาย\nซัดเซ\nซัดทอด\nซับซ้อน\nซับใน\nซับพระพักตร์\nซากศพ\nซ่านเซ็น\nซ้ำซ้อน\nซ้ำซาก\nซ้ำเติม\nซ้ำร้าย\nซี่โครง\nซีดเซียว\nซึมกะทือ\nซึมซาบ\nซึมเซา\nซึมทราบ\nซึมเศร้า\nซื่อตรง\nซื่อสัตย์\nซื้อขาย\nซุกซน\nซุกซ่อน\nซุบซิบ\nซู่ซ่า\nเซซัง\nเซ่อซ่า\nแซ่ซ้อง\nโซดาไฟ\nญาณทัสนะ\nญาณวิทยา\nญาณศาสตร์\nญาติกา\nฐานราก\nดกดื่น\nดงดิบ\nดลใจ\nดลบันดาล\nดวงแก้ว\nดวงใจ\nดวงเดือน\nดวงตรา\nดวงตา\nดวงสมร\nดอกจัน\nดอกจิก\nดอกบัว\nดอกเบี้ย\nดอกฟ้า\nดอกไม้\nดอกยาง\nดอกเล็บ\nดอกทอง\nดอกสร้อย\nดองยา\nดักคอ\nดักฟัง\nดังนั้น\nดังนี้\nดังหนึ่ง\nดั้งเดิม\nดัดจริต\nดัดแปลง\nดันทุรัง\nดับขันธ์\nดับจิต\nดับชีพ\nด่าทอ\nด่างทับทิม\nด่างพร้อย\nดาดฟ้า\nดาราศาสตร์\nดาลเดือด\nดาวกระจาย\nดาวเคราะห์\nดาวตก\nดาวเทียม\nดาวรุ่ง\nดาวเรือง\nดาวฤกษ์\nดาวหาง\nดาวเหนือ\nดาษดื่น\nดินขาว\nดินดาน\nดินดำ\nดินประสิว\nดินปืน\nดินระเบิด\nดินสอ\nดินสอพอง\nดิ้นรน\nดิบดี\nดีเกลือ\nดีใจ\nดีซ่าน\nดีดัก\nดีเดือด\nดีฝ่อ\nดีดดิ้น\nดึกดำบรรพ์\nดึกดื่น\nดึงดัน\nดึงดูด\nดื่มด่ำ\nดื้อด้าน\nดื้อดึง\nดื้อแพ่ง\nดื้อยา\nดื้อรั้น\nดุดัน\nดุเดือด\nดุร้าย\nดุลการค้า\nดุลพินิจ\nดุลภาค\nดุลยพินิจ\nดุลยภาพ\nดุษฎีนิพนธ์\nดุษฎีบัณฑิต\nดุษณีภาพ\nดูแคลน\nดูถูก\nดูดาย\nดูเบา\nดูแล\nดูหมิ่น\nดูเหมือน\nดูดดื่ม\nเด็ดขาด\nเด็ดดวง\nเด็ดเดี่ยว\nเดนตาย\nเดาสวด\nเดาสุ่ม\nเดินทาง\nเดินสะพัด\nเดินสาย\nเดินเหิน\nเดิมพัน\nเดียงสา\nเดียดฉันท์\nเดียวกัน\nเดียวดาย\nเดี๋ยวเดียว\nเดี๋ยวนี้\nเดือดดาล\nเดือดร้อน\nเดือนมืด\nเดือนหงาย\nแดดาล\nแดดิ้น\nแดกดัน\nโด่เด่\nโด่งดัง\nโดดเดี่ยว\nโดยสาร\nได้การ\nได้แก่\nได้ใจ\nได้ที\nได้ยิน\nได้เสีย\nตกเขียว\nตกค้าง\nตกใจ\nตกต่ำ\nตกแต่ง\nตกทอด\nตกฟาก\nตกมัน\nตกยาก\nตกลง\nตกหล่น\nต้นขั้ว\nต้นคิด\nต้นฉบับ\nต้นตอ\nต้นตำรับ\nต้นทุน\nต้นแบบ\nต้นเพลิง\nต้นมือ\nต้นไม้\nต้นร่าง\nต้นเรื่อง\nต้นสังกัด\nต้นหน\nต้นเหตุ\nตบตา\nตบแต่ง\nตบแผละ\nตบมือ\nต้มข่า\nต้มโคล้ง\nต้มยำ\nต้มส้ม\nตรมตรอม\nตรรกวิทยา\nตรรกศาสตร์\nตรวจการ\nตรวจการณ์\nตรวจตรา\nตระบัดสัตย์\nตรัสรู้\nตราตั้ง\nตราบาป\nตรายาง\nตราสาร\nตริตรอง\nตรีกฏุก\nตรีกาย\nตรีโกณ\nตรีโกณมิติ\nตรีคูณ\nตรีทูต\nตรีปิฎก\nตรีภพ\nตรีมูรติ\nตรึกตรอง\nตรึงตรา\nตรุษจีน\nตฤณชาติ\nตฤณมัย\nตลกบาตร\nตลบตะแลง\nตลบหลัง\nตลาดนัด\nตลาดน้ำ\nตลาดมืด\nตลาดสด\nต่อตี\nต่อเติม\nต่อว่า\nต่อสู้\nต่อกร\nต่อต้าน\nต่อแย้ง\nต้องการ\nต้องโทษ\nต้องหา\nต้อนรับ\nตอบโต้\nตอบแทน\nต่อยหอย\nตะพาบน้ำ\nตักตวง\nตักบาตร\nตั้งเข็ม\nตั้งไข่\nตั้งเค้า\nตั้งแง่\nตั้งใจ\nตั้งต้น\nตั้งแต่\nตั้งท้อง\nตัดขาด\nตัดใจ\nตัดเชือก\nตัดตอน\nตัดทอน\nตัดบท\nตัดพ้อ\nตัดรอน\nตัดสิน\nตับเต่า\nตับแลบ\nตับอ่อน\nตัวกลาง\nตัวการ\nตัวเก็ง\nตัวดี\nตัวตั้ง\nตัวเต็ง\nตัวถัง\nตัวแทน\nตัวประกอบ\nตัวประกัน\nตัวแปร\nตัวผู้\nตัวพิมพ์\nตัวเมีย\nตัวยืน\nตัวเลข\nตัวอย่าง\nตั๋วเงิน\nตั๋วแลกเงิน\nตากล้อง\nตาไก่\nตาข่าย\nตาชั่ง\nตาตุ่ม\nตาทวด\nตาปลา\nตาราง\nต่างหาก\nต้านทาน\nตามใจ\nตายใจ\nตายซาก\nตายด้าน\nตายตัว\nตายทั้งกลม\nตายห่า\nตายโหง\nตาลปัตร\nต่ำช้า\nต่ำต้อย\nตำส้ม\nติเตียน\nติณชาติ\nติดขัด\nติดใจ\nติดต่อ\nติดตั้ง\nติดตาม\nติดตื้น\nติดพัน\nติดลม\nติดอ่าง\nตีเกลียว\nตีขลุม\nตีความ\nตีคู่\nตีจาก\nตีตื้น\nตีแผ่\nตีรวน\nตีลังกา\nตีวง\nตีเสมอ\nตีนกา\nตีนคู้\nตีนจก\nตีนตะขาบ\nตีนผี\nตีนเหยียด\nตึกแถว\nตึกระฟ้า\nตึงเครียด\nตึงตัง\nตื้นตัน\nตื่นตัว\nตื่นตูม\nตื่นเต้น\nตุ๊ต๊ะ\nตุ้บตั้บ\nตุ้มหู\nตุลาการ\nตุลาคม\nตู้นิรภัย\nตูมตาม\nตู้เสบียง\nเตโชธาตุ\nเตร็ดเตร่\nเต้นรำ\nเตาแก๊ส\nเตาผิง\nเตาฟู่\nเตาไฟ\nเตารีด\nเตาสูบ\nเต่าทอง\nเต้ารับ\nเต้าส่วน\nเต้าเสียบ\nเต้าหู้ยี้\nแต่ละ\nแตกคอ\nแตกคอก\nแตกฉาน\nแตกดับ\nแตกตื่น\nแตกพาน\nแตกแยก\nแตกร้าว\nแตกหัก\nแต่งงาน\nแต่งตั้ง\nแต้มคู\nแต้มต่อ\nแตรงอน\nแตรเดี่ยว\nแตรฝรั่ง\nแตรฟันฟาร์\nแตรวง\nโต้ตอบ\nโต้เถียง\nโต้แย้ง\nโต๊ะหมู่\nโต๊ะอิหม่าม\nใต้ถุน\nไต้ก๋ง\nไต่คู้\nไต่เต้า\nไต่ถาม\nไต้ฝุ่น\nไตรจักร\nไตรจีวร\nไตรตรึงษ์\nไตรทวาร\nไตรปิฎก\nไตรเพท\nไตรภพ\nไตรภูมิ\nไตรภาคี\nไตรยางศ์\nไตรรงค์\nไตรรัตน์\nไตรลักษณ์\nไตรโลก\nไตรสรณคมน์\nไตรสิกขา\nไต่สวน\nถกเถียง\nถดถอย\nถนัดถนี่\nถนิมสร้อย\nถมถืด\nถมเถ\nถมไป\nถลากไถล\nถ้วนถี่\nถ้วยฟู\nถ่องแท้\nถอดถอน\nถ้อยคำ\nถ้อยแถลง\nถากถาง\nถ่านไฟฉาย\nถ่านหิน\nถามไถ่\nถ่ายทอด\nถ่ายทุกข์\nถ่ายเท\nถาวรวัตถุ\nถ้ำมอง\nถี่ถ้วน\nถึงใจ\nถูกใจ\nถูกชะตา\nเถรวาท\nเถ้าแก่\nเถ้าแก่เนี้ย\nแถมพก\nแถลงการณ์\nไถ่ถอน\nไถ่ถาม\nทดแทน\nทดรอง\nทดลอง\nทดสอบ\nทนทาน\nทนายความ\nทบทวน\nทแยงมุม\nทรงกลด\nทรงเครื่อง\nทรงเจ้า\nทรัพย์สิน\nทรามชม\nทรามเชย\nทรามวัย\nทรามสงวน\nทรามสวาท\nทรุดโทรม\nทฤษฎีบท\nท้วงติง\nท่วมท้น\nทวาทศ\nทวาทศมาส\nทวาบรยุค\nทวารบาล\nทวิบถ\nทวิบท\nทวิบาท\nทวิภาค\nทวิภาคี\nทวีคูณ\nทศกัณฐ์\nทศชาติ\nทศทิศ\nทศนิยม\nทศพร\nทศพล\nทศพิธราชธรรม\nทศมาส\nทศวรรษ\nท่อไอเสีย\nท้อถอย\nท้อแท้\nทองขาว\nทองคำ\nทองคำขาว\nทองคำเปลว\nทองเค\nทองแดง\nทองบรอนซ์\nทองม้วน\nทองย้อย\nทองสัมฤทธิ์\nทองหยอด\nทองหยิบ\nทองเหลือง\nทองเอก\nท่องเที่ยว\nท้องตรา\nท้องถิ่น\nท้องที่\nท้องน้อย\nท้องร่อง\nท้องเรื่อง\nทอดมัน\nทอดทิ้ง\nทอดน่อง\nทอดยอด\nทอดหุ่ย\nทอยกอง\nทะเบียนบ้าน\nทะเลทราย\nทะเลสาบ\nทะเลหลวง\nทักขิณาวัฏ\nทักท้วง\nทักทาย\nทักษิณาวรรต\nทักษิณาทาน\nทักษิณานุประทาน\nทั้งกลม\nทั้งคน\nทั้งดุ้น\nทั้งที\nทั้งนั้น\nทั้งนี้\nทั้งปวง\nทั้งผอง\nทั้งเพ\nทั้งมวล\nทั้งสิ้น\nทั้งหมด\nทั้งหลาย\nทัณฑ์บน\nทัดทาน\nทัดเทียม\nทันควัน\nทันใจ\nทันใด\nทันตา\nทันสมัย\nทันที\nทับถม\nทับทรวง\nทับศัพท์\nทั่วถึง\nทั่วไป\nท่าทาง\nท่าที\nท้าทาย\nทางการ\nทางข้าม\nทางด่วน\nทางเท้า\nทางโท\nทางใน\nทางผ่าน\nทางม้าลาย\nทางหลวง\nทางออก\nทางเอก\nทานกัณฑ์\nทานตะวัน\nท่านชาย\nทานบารมี\nท่านผู้หญิง\nท่านหญิง\nทาบทาม\nท้ายทอย\nทารุณกรรม\nทำคลอด\nทำใจ\nทำซ้ำ\nทำท่า\nทำที\nทำแท้ง\nทำโทษ\nทำบาป\nทำบุญ\nทำพิษ\nทำฟัน\nทำร้าย\nทำวัตร\nทำสาว\nทำเสน่ห์\nทำหมัน\nทำให้\nทิ้งขว้าง\nทิ้งทวน\nทิ้งท้าย\nทินกร\nทิพจักขุ\nทิพโสต\nทิพยจักษุ\nทิพยญาณ\nทิพยเนตร\nทิพยรส\nทิพากร\nทิ่มตำ\nทิ่มแทง\nทิวากร\nทิวากาล\nทิศทาง\nทีเด็ด\nทีท่า\nทีนี้\nทีหลัง\nทีฆนิกาย\nทีฆสระ\nที่ดิน\nที่นอน\nที่นั่ง\nที่ปรึกษา\nที่พึ่ง\nที่มั่น\nที่ราบ\nที่ว่าการ\nที่สุด\nที่หมาย\nที่ไหน\nทุกที\nทุกเมื่อ\nทุกข์สุข\nทุนทรัพย์\nทุนนิยม\nทุนรอน\nทุนสำรอง\nทุ่มเถียง\nทุ่มเท\nทูนหัว\nทูลกระหม่อม\nเทกระจาด\nเทครัว\nเทพเจ้า\nเทพดา\nเทพธิดา\nเทพนม\nเทพนิยม\nเทพนิยาย\nเทพบุตร\nเทพสังหรณ์\nเทศกาล\nเทศนาโวหาร\nเทศบัญญัติ\nเทศบาล\nเทศมนตรี\nเทห์ฟากฟ้า\nเท่ากับ\nเท่าใด\nเท่าตัว\nเท่าทัน\nเท่าทุน\nเท่าเทียม\nเท่านั้น\nเท่าไร\nเท้าช้าง\nเทิดทูน\nเที่ยงตรง\nเที่ยงแท้\nเที่ยงธรรม\nเทียนชนวน\nเทียนพรรษา\nเทียบเคียง\nเทียบเท่า\nเทือกเขา\nเทือกเถา\nแท็งก์น้ำ\nแท่นพิมพ์\nแท่นมณฑล\nแท่นหมึก\nแทรกซอน\nแทรกซ้อน\nแทรกซึม\nแทรกแซง\nแทะโลม\nไทยดำ\nไทยทาน\nไทยธรรม\nไทยน้อย\nไทยใหญ่\nธงชัย\nธงชาติ\nธงทิว\nธรณีวิทยา\nธรณีสงฆ์\nธรรมกาย\nธรรมการ\nธรรมเกษตร\nธรรมขันธ์\nธรรมคุณ\nธรรมจรรยา\nธรรมจริยา\nธรรมจักร\nธรรมจักษุ\nธรรมจาคะ\nธรรมจารี\nธรรมชาติ\nธรรมดา\nธรรมเนียม\nธรรมราชา\nธรรมศาสตร์\nธรรมสภา\nธรรมสังเวช\nธัญพืช\nธารพระกร\nธีรภาพ\nธีรราช\nนกเขา\nนกต่อ\nนกยูง\nนกรู้\nนกหวีด\nนครบาล\nนครรัฐ\nนงคราญ\nนงนุช\nนงพะงา\nนงเยาว์\nนงราม\nนงลักษณ์\nนบนอบ\nนพเก้า\nนพคุณ\nนพเคราะห์\nนพปฎล\nนพพล\nนพรัตน์\nนพศก\nนพศูล\nนมข้น\nนมผง\nนมไม้\nนมนาน\nนมหนู\nนมแมว\nนรีเวช\nนรีเวชวิทยา\nนวดฟั้น\nนวยนาด\nนวลระหง\nนวลลออ\nนวลละออง\nนวลจันทร์\nนอกครู\nนอกคอก\nนอกจาก\nนอกใจ\nนอกชาน\nนอกรีต\nนอกเหนือ\nนองเนือง\nนองเลือด\nนอนก้น\nนอนใจ\nนอบนบ\nนอนเล่น\nนอบน้อม\nน้อมนำ\nน้อยใจ\nน้อยหน้า\nนักการ\nนักการเมือง\nนักกีฬา\nนักข่าว\nนักท่องเที่ยว\nนักเทศน์\nนักโทษ\nนักธรรม\nนักบวช\nนักบิน\nนักบุญ\nนักปราชญ์\nนักพรต\nนักรบ\nนักเรียน\nนักเลง\nนักวิชาการ\nนักศึกษา\nนักสิทธิ์\nนักสืบ\nนักหนา\nนั่งเทียน\nนั่งร้าน\nนัดแนะ\nนัดหมาย\nนั่นแหละ\nนั่นเอง\nนับถือ\nนับประสา\nนัยน์ตา\nนาดำ\nนาปรัง\nนาปี\nนาสวน\nนาหว่าน\nนาคบาศ\nนาคปรก\nนาคราช\nนางกวัก\nนางกำนัล\nนางงาม\nนางใน\nนางบำเรอ\nนางแบบ\nนางพญา\nนางฟ้า\nนางไม้\nนางโลม\nนางสาว\nนางห้าม\nนางเอก\nนาฏกรรม\nนาฏดนตรี\nนาฏศิลป์\nนานนม\nน่านน้ำ\nน่านฟ้า\nนามกร\nนามธรรม\nนามไธย\nนามบัตร\nนามปากกา\nนามแฝง\nนามสกุล\nนามสงเคราะห์\nนามสมญา\nนายทะเบียน\nนายท่า\nนายท้าย\nนายทุน\nนายประกัน\nนายหน้า\nนายอำเภอ\nนารายณ์หัตถ์\nนารีผล\nนาวิกโยธิน\nนำจับ\nนำพา\nนำทาง\nนำร่อง\nนำสืบ\nนำแสดง\nน้ำกรด\nน้ำกาม\nน้ำเกลือ\nน้ำข้าว\nน้ำแข็ง\nน้ำแข็งไส\nน้ำแข็งแห้ง\nน้ำครำ\nน้ำคร่ำ\nน้ำค้าง\nน้ำค้างแข็ง\nน้ำคาวปลา\nน้ำคำ\nน้ำเค็ม\nน้ำเคย\nน้ำเงิน\nน้ำเงี้ยว\nน้ำจัณฑ์\nน้ำจิ้ม\nน้ำใจ\nน้ำเชื้อ\nน้ำเชื่อม\nน้ำซาวข้าว\nน้ำดอกไม้\nน้ำดี\nน้ำตก\nน้ำตา\nน้ำตาล\nน้ำท่า\nน้ำนม\nน้ำนวล\nน้ำบาดาล\nน้ำประสานทอง\nน้ำประปา\nน้ำปลา\nน้ำป่า\nน้ำผึ้ง\nน้ำพริก\nน้ำพริกเผา\nน้ำพี้\nน้ำพุ\nน้ำมนต์\nน้ำมนตร์\nน้ำมัน\nน้ำมือ\nน้ำมูก\nน้ำเมา\nน้ำย่อย\nน้ำยา\nน้ำรัก\nน้ำแร่\nน้ำลาย\nน้ำเลี้ยง\nน้ำสต๊อก\nน้ำส้ม\nน้ำส้มสายชู\nน้ำสังข์\nน้ำสาบาน\nน้ำเสียง\nน้ำหนวก\nน้ำหนอง\nน้ำหนัก\nน้ำหน้า\nน้ำหนึ่ง\nน้ำหมึก\nน้ำหอม\nน้ำเหลือง\nน้ำอบ\nน้ำอ้อย\nน้ำอัดลม\nนิ่งเฉย\nนิจศีล\nนิดเดียว\nนิดหน่อย\nนิติกร\nนิติกรรม\nนิติธรรม\nนิตินัย\nนิติบัญญัติ\nนิติบุคคล\nนิติภาวะ\nนิติวิทยาศาสตร์\nนิติเวช\nนิติเวชศาสตร์\nนิติศาสตร์\nนิเทศศาสตร์\nนิ่มนวล\nนิรุกติศาสตร์\nนิเวศวิทยา\nนิศากร\nนิศากาล\nนิศาชล\nนิศารัตน์\nนี่แน่ะ\nนี่แหละ\nนี่เอง\nนึกคิด\nนุงถุง\nนุ่งห่ม\nนุ่มนวล\nนุ่มนิ่ม\nเนตรนารี\nเนติบัณฑิต\nเนยเทียม\nเนยใส\nเนิ่นนาน\nเนิบนาบ\nเนื้อความ\nเนื้อคู่\nเนื้อเค็ม\nเนื้องอก\nเนื้อตัว\nเนื้อตาย\nเนื้อที่\nเนื้อแท้\nเนื้อเปื่อย\nเนื้อผ้า\nเนื้อเพลง\nเนื้อไม้\nเนื้อเยื่อ\nเนื้อร้อง\nเนื้อร้าย\nเนื้อเรื่อง\nเนื้อหา\nเนืองนอง\nเนืองนิตย์\nเนืองแน่น\nแน่ใจ\nแน่ชัด\nแน่แท้\nแน่นอน\nแน่นิ่ง\nแน่แน่ว\nแน่นแฟ้น\nแน่นหนา\nแนบเนียน\nแนบแน่น\nแนวคิด\nแนวทาง\nแนวโน้ม\nแนวป่า\nแนวรบ\nแนวร่วม\nแนวหน้า\nแนวหลัง\nแน่วแน่\nแนะนำ\nแนะแนว\nโน้มน้าว\nในหลวง\nบกพร่อง\nบงกช\nบงการ\nบดบัง\nบทกลอน\nบทกวี\nบทความ\nบทคัดย่อ\nบทเฉพาะกาล\nบทนำ\nบทบัญญัติ\nบทบาท\nบทประพันธ์\nบทเพลง\nบทร้อง\nบทเรียน\nบทลงโทษ\nบทสนทนา\nบทอัศจรรย์\nบทจร\nบทบงสุ์\nบทมาลย์\nบทรัช\nบทเรศ\nบทวลัญช์\nบนบาน\nบรมครู\nบรมธาตุ\nบรมบพิตร\nบรมวงศานุวงศ์\nบรมอัฐิ\nบรรณพิภพ\nบรรณศาลา\nบรรณาการ\nบรรณาธิการ\nบรรณานุกรม\nบรรณารักษ์\nบรรณารักษศาสตร์\nบรรดามี\nบรรดาศักดิ์\nบรรทัดฐาน\nบรรพบุรุษ\nบรรลัยกัลป์\nบรรลัยจักร\nบริคณห์สนธิ\nบวงสรวง\nบ่วงบาศ\nบ้วนพระโอษฐ์\nบ่อเกิด\nบอกกล่าว\nบอกบท\nบอกบุญ\nบอกใบ้\nบอกปัด\nบ้องกัญชา\nบ้องตื้น\nบ้องไฟ\nบ้องหู\nบอดสี\nบ่อนทำลาย\nบอบช้ำ\nบอบบาง\nบอบแบบ\nบังโกลน\nบังโคลน\nบังใบ\nบั้งไฟ\nบังคับการ\nบังคับบัญชา\nบัญชาการ\nบัณฑุกัมพล\nบัดดล\nบัดเดี๋ยว\nบัดนั้น\nบัดนี้\nบัดสีบัดเถลิง\nบัตรเครดิต\nบัตรพลี\nบัตรสนเท่ห์\nบัตรสินเชื่อ\nบั่นทอน\nบั้นท้าย\nบั้นปลาย\nบั้นพระองค์\nบั้นเอว\nบันไดลิง\nบันไดเลื่อน\nบันเทิงคดี\nบัวลอย\nบัวบก\nบ้าจี้\nบ้าดีเดือด\nบ้าน้ำลาย\nบ้าบิ่น\nบ้าระห่ำ\nบ้าเลือด\nบ้าหอบฟาง\nบากบั่น\nบากหน้า\nบางตา\nบางเบา\nบางที\nบาดเจ็บ\nบาดแผล\nบาดหมาง\nบาตรใหญ่\nบาทบงกช\nบาทบงสุ์\nบาทบริจาริกา\nบาทวิถี\nบานเกล็ด\nบานตะเกียง\nบานตะไท\nบานเบอะ\nบานปลาย\nบานแผละ\nบานพับ\nบ้านจัดสรร\nบ้านช่อง\nบ้านนอก\nบ้านพัก\nบ้านเมือง\nบ้านรับรอง\nบ้านเรือน\nบาปกรรม\nบายศรี\nบ่ายเบี่ยง\nบ่ายหน้า\nบ่าวไพร่\nบิดเบี้ยว\nบิดเบือน\nบิดพลิ้ว\nบี้แบน\nบีบคั้น\nบีบรัด\nบึ้งตึง\nบึ้งบูด\nบุกบั่น\nบุกเบิก\nบุกรุก\nบุคลิกภาพ\nบุคลิกลักษณะ\nบุญธรรม\nบุญนิธิ\nบุญฤทธิ์\nบุบสลาย\nบุ้ยใบ้\nบุรุษเพศ\nบุหงารำไป\nบู้บี้\nบูชายัญ\nบูดบึ้ง\nบูดเบี้ยว\nเบาความ\nเบาใจ\nเบาบาง\nเบาปัญญา\nเบามือ\nเบาแรง\nเบาสมอง\nเบาหวาน\nเบาโหวง\nเบ้าตา\nเบาะแส\nเบิกความ\nเบิกบาน\nเบี้ยล่าง\nเบี้ยเลี้ยง\nเบี้ยหวัด\nเบี่ยงบ่าย\nเบียดบัง\nเบียดเบียน\nเบียดเสียด\nเบื้องต้น\nเบื้องบน\nเบื้องหน้า\nเบื้องหลัง\nแบกะดิน\nแบเบาะ\nแบ่งเบา\nแบ่งปัน\nแบ่งแยก\nแบบฉบับ\nแบบแปลน\nแบบแผน\nแบบฝึกหัด\nแบบพิมพ์\nแบบสอบถาม\nแบบอย่าง\nแบะแฉะ\nแบะท่า\nโบแดง\nโบราณคดี\nโบราณวัตถุ\nโบราณสถาน\nใบขับขี่\nใบจอง\nใบตอง\nใบแทรก\nใบบอก\nใบบุญ\nใบเบิกทาง\nใบปลิว\nใบพัด\nใบโพ\nใบไม้\nใบระกา\nใบรับรอง\nใบลา\nใบเลี้ยง\nใบสั่ง\nใบสำคัญ\nใบสุทธิ\nใบเสร็จ\nใบหน้า\nใบอนุญาต\nใบระกา\nปกครอง\nปกคลุม\nปกป้อง\nปกปิด\nปฏิบัติการ\nปฏิบัติบูชา\nปฐพีวิทยา\nปฐมฌาน\nปฐมทัศน์\nปฐมเทศนา\nปฐมนิเทศ\nปฐมพยาบาล\nปฐมยาม\nปฐมฤกษ์\nปฐมวัย\nปฐมสมโพธิ\nปนเป\nป่นปี้\nปมเขื่อง\nปมเด่น\nปมด้อย\nปรนเปรอ\nปรบไก่\nปรบมือ\nปรสิตวิทยา\nประโปรย\nประพรม\nประกันชีวิต\nประกันภัย\nประจักษ์พยาน\nประจัญบาน\nประจันหน้า\nประจำการ\nประจำเดือน\nประจำเมือง\nประจำยาม\nประชดประชัน\nประชากร\nประชากรศาสตร์\nประชาคม\nประชาชน\nประชาราษฎร์\nประชาชาติ\nประชาชี\nประชาทัณฑ์\nประชาบาล\nประชาพิจารณ์\nประชาภิบาล\nประชามติ\nประชาสงเคราะห์\nประชาสัมพันธ์\nประดับประดา\nประดามี\nประดาน้ำ\nประเดี๋ยวเดียว\nประเดี๋ยวนี้\nประทับใจ\nประทุษร้าย\nประเทศราช\nประพาสต้น\nประเพณีนิยม\nประลัยกัลป์\nประวัติการณ์\nประวัติศาสตร์\nประสบการณ์\nประสบการณ์นิยม\nประสาทการ\nประสูติการ\nประสูติกาล\nประเส\nปรับทุกข์\nปรับโทษ\nปรับปรุง\nปรากฏการณ์\nปราดเปรียว\nปราดเปรื่อง\nปราบปราม\nปริญญาบัตร\nปรัยัติธรรม\nปรุโปร่ง\nปลงใจ\nปลงตก\nปลดทุกข์\nปลดปลง\nปลดปล่อย\nปลดเปลื้อง\nปลดระวาง\nปลดแอก\nปล้นสะดม\nปลอกกระสุน\nปลอกคอ\nปลอดโปร่ง\nปลอดภัย\nปลอมปน\nปลอมแปลง\nปลอบโยน\nปล่อยใจ\nปล่อยตัว\nปล่อยปละ\nปลั๊กไฟ\nปลากริม\nปลาเค็ม\nปลาจ่อม\nปลาเจ่า\nปลาแดก\nปลาตู้\nปลาทอง\nปลาร้า\nปลาส้ม\nปลาดาว\nปลาบิน\nปลาฝา\nปลาวาฬ\nปลาหมึก\nปลาบปลื้ม\nปลายข้าว\nปลายแถว\nปลายทาง\nปลิ้นปลอก\nปลิ้นปล้อน\nปลีกตัว\nปลีกย่อย\nปลุกใจ\nปลุกปล้ำ\nปลุกปั่น\nปลุกระดม\nปลุกเสก\nปลูกฝัง\nปลูกสร้าง\nปวดถ่วง\nปวดมวน\nปวดร้าว\nป่วนปั่น\nป่วยการ\nปอกลอก\nป้องกัน\nปักใจ\nปักดำ\nปักหลัก\nปัจเจกบุคคล\nปัจเจกพุทธะ\nปัจเจกโพธิ\nปัจฉิมชน\nปัจฉิมทิศ\nปัจฉิมภาค\nปัจฉิมยาม\nปัจฉิมลิขิต\nปัจฉิมวัย\nปัจฉิมวาจา\nปัญญาชน\nปัญญาวิมุติ\nปัญญาอ่อน\nปัดเป่า\nปันส่วน\nปั่นป่วน\nปั่นแปะ\nปั่นหัว\nปั้นจิ้ม\nปั้นเจ๋อ\nปั้นปึ่ง\nปั้นสิบ\nปั๊มน้ำมัน\nป่าช้า\nป่าชายเลน\nป่าดง\nป่าดงดิบ\nป่าดิบ\nป่าเถื่อน\nป่าเบญจพรรณ\nป่าละเมาะ\nปากกา\nปากขอ\nปากแข็ง\nปากคอ\nปากคำ\nปากคีบ\nปากจัด\nปากน้ำ\nปากเปล่า\nปากเสียง\nปานกลาง\nป่านนี้\nป้านลม\nป้ายสี\nป่าวร้อง\nปิดฉาก\nปิดบัง\nปิตุฆาต\nปิตุภูมิ\nปีมะโว้\nปีแสง\nปี่กลาง\nปี่ไฉน\nปี่ชวา\nปี่นอก\nปี่ใน\nปี่พาทย์\nปี่อ้อ\nปีกกา\nปีนเกลียว\nปีนป่าย\nปึกแผ่น\nปึงปัง\nปืนกล\nปืนครก\nปืนพก\nปืนยา\nปืนยาว\nปืนลม\nปืนเล็ก\nปืนเล็กยาว\nปืนสั้น\nปืนใหญ่\nปุบปับ\nปุ๊บปั๊บ\nปุ่มเปือก\nปุยฝ้าย\nปุ๋ยคอก\nปุ๋ยเคมี\nปุ๋ยวิทยาศาสตร์\nปุ๋ยหมัก\nปุ๋ยอินทรีย์\nปูจ๋า\nปูเสฉวน\nปู่เจ้า\nปู่ทวด\nปูนขาว\nปูนซีเมนต์\nปูนดิบ\nปูนแดง\nปูนปลาสเตอร์\nปูนปั้น\nเป็ดเทศ\nเป็ดน้ำ\nเป็นกลาง\nเป็นใจ\nเป็นต้น\nเป็นต่อ\nเป็นรอง\nเป็นไร\nเป็นลม\nเป็นห่วง\nเป็นอยู่\nเปรมปรีดิ์\nเปรอะเปื้อน\nเปรียบเทียบ\nเปรียบเปรย\nเปรี้ยวปาก\nเปรี้ยวหวาน\nเปรื่องปราด\nเปลญวน\nเปล่งปลั่ง\nเปล่าดาย\nเปล่าเปลี่ยว\nเปลี่ยนใจ\nเปลี่ยนตัว\nเปลี่ยนแปลง\nเปลี่ยนมือ\nเปลี่ยนหน้า\nเป๋อเหลอ\nเปะปะ\nเป่ากบ\nเป้านิ่ง\nเป้าหมาย\nเปิดฉาก\nเปิดเปิง\nเปิดโปง\nเปิดผนึก\nเปิดเผย\nเปียกปูน\nแป้งสาลี\nแป้งนวล\nแป้งเปียก\nแป้งมัน\nแป้งฝุ่น\nแป้งร่ำ\nแป้งสิงคโปร์\nแป้งหมี่\nแปดปน\nแปดเปื้อน\nแปรปรวน\nแปรผัน\nแปรพักตร์\nแปรรูป\nแปรอักษร\nแปลกปลอม\nแปะโป้ง\nโป้ปด\nโปร่งแสง\nโปร่งใส\nโปรดปราน\nโปรยทาน\nโปรยปราย\nโปโลน้ำ\nผกผัน\nผกากรอง\nผงขาว\nผงชูรส\nผงซักฟอก\nผงฟู\nผดุงครรภ์\nผมไฟ\nผลพลอยได้\nผลลัพธ์\nผลัดเปลี่ยน\nผลิตผล\nผลิตภัณฑ์\nผลุบโผล่\nผสมเทียม\nผสมผสาน\nผสมผเส\nผสมพันธุ์\nผสมโรง\nผสมเสร็จ\nผ่องแผ้ว\nผ่องใส\nผ่อนคลาย\nผ่อนชำระ\nผ่อนปรน\nผ่อนผัน\nผ่อนส่ง\nผอมโซ\nผอมแห้ง\nผักชี\nผักตบชวา\nผักบุ้ง\nผังเมือง\nผัดผ่อน\nผันแปร\nผันผวน\nผ่าตัด\nผ่าเผย\nผ่าหมาก\nผ่าเหล่า\nผ้าขนหนู\nผ้าขาวม้า\nผ้าขี้ริ้ว\nผ้าเช็ดตัว\nผ้าเช็ดปาก\nผ้าเช็ดมือ\nผ้าเช็ดหน้า\nผ้าดิบ\nผ้าต่วน\nผ้าไตร\nผ้าถุง\nผ้าแถบ\nผ้านวม\nผ้านุ่ง\nผ้าใบ\nผ้าป่า\nผ้าป่าน\nผ้าผ่อน\nผ้าพันคอ\nผ้าพันแผล\nผ้าแพร\nผ้าโพกหัว\nผ้ามัดหมี่\nผ้ายาง\nผ้าลูกไม้\nผ้าเหลือง\nผ้าอนามัย\nผ้าอ้อม\nผาดโผน\nผาติกรรม\nผิดหวัง\nผิวเผิน\nผิวพรรณ\nผิวหนัง\nผีกระสือ\nผีกระหัง\nผีกองกอย\nผีโขมด\nผีดิบ\nผีตองเหลือง\nผีถ้วยแก้ว\nผีแถน\nผีทะเล\nผีบุญ\nผีปอบ\nผีพุ่งไต้\nผีฟ้า\nผีเรือน\nผีสาง\nผีเสื้อ\nผีห่า\nผึ่งผาย\nผุดผ่อง\nผุดผาด\nผู้คน\nผู้คุม\nผู้จัดการ\nผู้ชาย\nผู้เชี่ยวชาญ\nผู้ดี\nผู้โดยสาร\nผู้ต้องขัง\nผู้ต้องหา\nผู้แทน\nผู้น้อย\nผู้บริโภค\nผู้บังคับบัญชา\nผู้ปกครอง\nผู้ประกอบการ\nผู้ป่วย\nผู้พิพากษา\nผู้เยาว์\nผู้ร้าย\nผู้วิเศษ\nผู้สื่อข่าว\nผู้เสียหาย\nผู้หญิง\nผู้ใหญ่\nผู้ใหญ่บ้าน\nผูกขวัญ\nผูกขาด\nผูกพัน\nผูกมัด\nเผชิญหน้า\nเผด็จการ\nเผด็จศึก\nเผยแผ่\nเผยแพร่\nเผละผละ\nเผ่าพันธุ์\nเผื่อแผ่\nแผงลอย\nแผนการ\nแผนงาน\nแผนที่\nแผนผัง\nแผนภาพ\nแผนภูมิ\nแผ่นดิน\nแผ่นเสียง\nแผ้วพาน\nโผงผาง\nฝนทอง\nฝอยทอง\nฝักแค\nฝักบัว\nฝักฝ่าย\nฝักใฝ่\nฝังใจ\nฝังหัว\nฝาชี\nฝาแฝด\nฝาละมี\nฝ่าพระบาท\nฝ่าฝืน\nฝ่าฟัน\nฝ้าฟาง\nฝากตัว\nฝากฝัง\nฝีดาษ\nฝีมะม่วง\nฝีจักร\nฝีเท้า\nฝีปาก\nฝีพาย\nฝีมือ\nฝีเย็บ\nฝึกงาน\nฝึกปรือ\nฝึกฝน\nฝึกสอน\nฝึกหัด\nฝืดเคือง\nใฝ่ฝัน\nพงพี\nพงศ์พันธุ์\nพญาโศก\nพญาไฟ\nพบปะ\nพบพาน\nพรสวรรค์\nพรมคด\nพรมแดน\nพรมมิ\nพรรคพวก\nพรรณราย\nพรวดพราด\nพรหมชาติ\nพรหมลิขิต\nพรหมโลก\nพรหมวิหาร\nพร้อมใจ\nพร้อมพรั่ง\nพร้อมเพรียง\nพร้อมมูล\nพร้อมสรรพ\nพร้อมหน้า\nพระครู\nพระคุณ\nพระเคราะห์\nพระเครื่อง\nพระเจ้า\nพระเจ้าอยู่หัว\nพระชายา\nพระทัย\nพระนาง\nพระนางเจ้า\nพระเป็นเจ้า\nพระผู้เป็นเจ้า\nพระพิมพ์\nพระพุทธเจ้า\nพระพุทธองค์\nพระภูมิ\nพระยา\nพระรอง\nพระสนม\nพระสนมเอก\nพระองค์\nพระองค์เจ้า\nพระเอก\nพรั่งพร้อม\nพรั่งพรู\nพรั่นพรึง\nพร่างพราว\nพรายน้ำ\nพรายแพรว\nพราวแพรว\nพร่ำพลอด\nพร่ำเพรื่อ\nพร่ำเพ้อ\nพริกไทย\nพริ้งพราย\nพริ้งเพรา\nพริ้งเพริศ\nพริบตา\nพริ้มพราย\nพริ้มเพรา\nพรุ่งนี้\nพฤติกรรม\nพฤติการณ์\nพฤตินัย\nพลการ\nพลขับ\nพลความ\nพลเมือง\nพลรบ\nพลร่ม\nพลเรือน\nพลโลก\nพลศึกษา\nพลบค่ำ\nพลอดรัก\nพลังงาน\nพลังเงียบ\nพลังจิต\nพลั้งปาก\nพลั้งเผลอ\nพลั้งพลาด\nพลัดถิ่น\nพลัดพราก\nพลาดท่า\nพลาดพลั้ง\nพลิกแพลง\nพลีกรรม\nพลุ่งพล่าน\nพวกพ้อง\nพวงมาลัย\nพวงมาลา\nพวงหรีด\nพวงคราม\nพวงชมพู\nพวงแสด\nพ่วงพี\nพวยน้ำ\nพวยพุ่ง\nพสกนิกร\nพหุคูณ\nพหุภาคี\nพหูพจน์\nพหูสูต\nพอควร\nพอใจ\nพอใช้\nพอใช้ได้\nพอดี\nพอตัว\nพอทำเนา\nพอประมาณ\nพอเพียง\nพอแรง\nพอสมควร\nพอเหมาะ\nพ่อขุน\nพ่อครัว\nพ่อตา\nพ่อบ้าน\nพ่อพันธุ์\nพ่อม่าย\nพ่อเมือง\nพ่อเลี้ยง\nพ่อสื่อ\nพอกพูน\nพ้องพาน\nพักผ่อน\nพักพิง\nพักฟื้น\nพักร้อน\nพักแรม\nพัดยศ\nพัดลม\nพันพัว\nพับฐาน\nพับเพียบ\nพัวพัน\nพาซื่อ\nพาดพิง\nพิณพาทย์\nพิธีกร\nพิธีกรรม\nพิธีการ\nพิธีรีตอง\nพิธีสาร\nพินัยกรรม\nพิมพ์เขียว\nพิมพ์ใจ\nพิมพ์ดีด\nพิษสง\nพี่น้อง\nพี่เบิ้ม\nพี่เลี้ยง\nพึงใจ\nพึงพอใจ\nพึ่งพา\nพึ่งพิง\nพืชพันธุ์\nพืชมงคล\nพื้นฐาน\nพื้นที่\nพื้นบ้าน\nพื้นเพ\nพื้นเมือง\nพื้นเสีย\nพุพอง\nพุทธกาล\nพุทธคุณ\nพุทธจักร\nพุทธเจดีย์\nพุทธฎีกา\nพุทธปฏิมา\nพุทธปฏิมากร\nพุทธมามกะ\nพุทธศักราช\nพุทธศาสนิกชน\nพุทธองค์\nพุทธชาด\nพุทธรักษา\nพุ่มพวง\nพุ่มไม้\nพู่กัน\nพูดจา\nเพ่งเล็ง\nเพดานบิน\nเพดานปาก\nเพริศพราย\nเพริศพริ้ง\nเพริศแพร้ว\nเพรียกพร้อง\nเพรียวลม\nเพลงเชิด\nเพลงยาว\nเพลิงกัลป์\nเพลินใจ\nเพลินตา\nเพลี่ยงพล้ำ\nเพ้อฝัน\nเพาะกาย\nเพาะชำ\nเพาะปลูก\nเพิกถอน\nเพิกเฉย\nเพิ่มเติม\nเพิ่มพูน\nเพียงตา\nเพียงพอ\nเพียบแประ\nเพียบพร้อม\nเพื่อนเกลอ\nเพื่อนตาย\nเพื่อนบ้าน\nเพื่อนฝูง\nเพื่อนยาก\nแพ้ท้อง\nแพร่หลาย\nแพร่งพราย\nแพรวพราว\nโพธิญาณ\nโพธิบัลลังก์\nโพธิสมภาร\nโพธิสัตว์\nโพ้นทะเล\nโพยภัย\nไพ่ตาย\nไพ่ป๊อก\nไพรวัน\nไพรสณฑ์\nไพรสัณฑ์\nไพร่พล\nไพร่ฟ้า\nไพร่สม\nไพร่ส่วย\nไพร่หลวง\nฟกช้ำ\nฟองเต้าหู้\nฟองน้ำ\nฟองมัน\nฟ้องกลับ\nฟ้องร้อง\nฟอนเฟะ\nฟักทอง\nฟัดเฟียด\nฟันดาบ\nฟันฝ่า\nฟันแท้\nฟันน้ำนม\nฟันปลา\nฟันฟาง\nฟันเฟือง\nฟันม้า\nฟันเลื่อย\nฟันหนู\nฟั่นเฝือ\nฟั่นเฟือน\nฟื้นตัว\nฟื้นฝอย\nฟื้นฟู\nฟุ้งซ่าน\nฟุ้งเฟ้อ\nฟุ้งเฟื่อง\nฟุตบอล\nฟูฟ่อง\nฟูเฟื่อง\nฟูมฟัก\nฟูมฟาย\nเฟะฟะ\nเฟื่องฟ้า\nเฟื่องฟุ้ง\nเฟื่องฟู\nไฟฉาย\nไฟแช็ก\nไฟธาตุ\nไฟฟ้า\nภัตกิจ\nภาคทัณฑ์\nภาคพื้น\nภาคเรียน\nภาคภูมิ\nภาพถ่าย\nภาพนิ่ง\nภาพประกอบ\nภาพพจน์\nภาพยนตร์\nภาพลวงตา\nภาพลักษณ์\nภายนอก\nภายใน\nภายหน้า\nภายหลัง\nภารกิจ\nภารธุระ\nภารโรง\nภารตวิทยา\nภาษาศาสตร์\nภาสกร\nภิญโญภาพ\nภินชาติ\nภูธร\nภูธเรศ\nภูบาล\nภูเบศ\nภูเบศวร์\nภูเขา\nภูเขาไฟ\nภูผา\nภูตคาม\nภูตบดี\nภูตรูป\nภูเตศวร\nภูมินทร์\nภูมิบาล\nภูมิประเทศ\nภูมิภาค\nภูมิรัฐศาสตร์\nภูมิลำเนา\nภูมิศาสตร์\nภูมิอากาศ\nภูมิธรรม\nภูมิปัญญา\nภูมิรู้\nภูมิใจ\nภูมิฐาน\nภูมิคุ้มกัน\nภูมิแพ้\nภูษาโยง\nเภทภัย\nเภสัชกร\nเภสัชกรรม\nเภสัชวิทยา\nเภสัชศาสตร์\nโภคทรัพย์\nโภคภัณฑ์\nโภชนากร\nโภชนาการ\nมกุฎราชกุมาร\nมงคลแฝด\nมงคลสูตร\nมงคลหัตถี\nมณเฑียรบาล\nมดดำ\nมดแดง\nมดเท็จ\nมดยอบ\nมดลูก\nมธุปายาส\nมธุรส\nมนเทียรบาล\nมนุษย์กบ\nมโนกรรม\nมโนคติ\nมโนทุจริต\nมโนธรรม\nมโนภาพ\nมโนมัย\nมโนรถ\nมโนรมย์\nมโนสุจริต\nมรรคนายก\nมรรคผล\nมฤคชาติ\nมฤคทายวัน\nมฤคราช\nมลทิน\nมลพิษ\nมลสาร\nมวกเหล็ก\nม้วนหน้า\nมวยไทย\nมวยปล้ำ\nมวยล้ม\nมวยวัด\nมวยสากล\nมวยหมู่\nมวลสาร\nมอคราม\nมอซอ\nมอหมึก\nมองเมียง\nมอบตัว\nมอบหมาย\nมอมเมา\nมะขามเทศ\nมะขามป้อม\nมะขามเปียก\nมะเขือเทศ\nมะเขือพวง\nมะพร้าวแก้ว\nมักคุ้น\nมักจี่\nมักง่าย\nมักน้อย\nมักมาก\nมักใหญ่\nมั่งคั่ง\nมั่งมี\nมัจจุราช\nมัชฌิมนิกาย\nมัชฌิมประเทศ\nมัชฌิมยาม\nมัชฌิมวัย\nมัดจำ\nมัดหมี่\nมัธยมกาล\nมัธยมศึกษา\nมันแกว\nมันเทศ\nมันฝรั่ง\nมันเปลว\nมันสมอง\nมั่นคง\nมั่นใจ\nมั่นหมาย\nมั่นเหมาะ\nมัวเมา\nมัวหมอง\nมั่วสุม\nม้าเทศ\nม้าน้ำ\nม้ามืด\nม้าเร็ว\nม้าล่อ\nม้าลาย\nมากมาย\nมาตรการ\nมาตรฐาน\nมาตราส่วน\nมาตุคาม\nมาตุฆาต\nมาตุภูมิ\nม่านตา\nม่านบังตา\nมายากร\nมายากล\nมายาการ\nมายาวี\nมารผจญ\nมารวิชัย\nมารสังคม\nมารหัวขน\nมาลาการ\nมิ่งขวัญ\nมิ่งมิตร\nมิจฉาจาร\nมิจฉาชีพ\nมิดชิด\nมิดเมี้ยน\nมิดหมี\nมิตรจิต\nมิตรภาพ\nมิตรสหาย\nมิน่า\nมีหน้า\nมีดโกน\nมีดดาบ\nมีดโต้\nมีดพก\nมีดพับ\nมีดสั้น\nมึนงง\nมึนชา\nมึนตึง\nมึนเมา\nมืดครึ้ม\nมืดมน\nมืดมัว\nมือจับ\nมือดี\nมือเติบ\nมือปืน\nมือเปล่า\nมือมืด\nมือสอง\nมือเสือ\nมือหนึ่ง\nมือใหม่\nมุกตลก\nมุขปาฐะ\nมุขมนตรี\nมุ่งมั่น\nมุ่งมาด\nมุ่งหน้า\nมุ่งหมาย\nมุ่งหวัง\nมุ้งลวด\nมุ้งสายบัว\nมุมก้ม\nมุมกลับ\nมุมเงย\nมุมฉาก\nมุมตรง\nมุมป้าน\nมุมมืด\nมุมแย้ง\nมุมสะท้อน\nมุมหักเห\nมุมแหลม\nมุสาวาท\nมูกเลือด\nมูกมัน\nมูกหลวง\nมูนดิน\nมูลฐาน\nมูลนาย\nมูลนิธิ\nมูลเหตุ\nมูลค่า\nมูลฝอย\nเม็ดเงิน\nเม็ดเลือด\nเม็ดโลหิต\nเม่นทะเล\nเมรุมาศ\nเมรุราช\nเมล์อากาศ\nเมาดิบ\nเมามัน\nเมามัว\nเมามาย\nเมินเฉย\nเมียน้อย\nเมียหลวง\nเมียงมอง\nเมี่ยงลาว\nเมี่ยงส้ม\nเมื่อกี้\nเมื่อตะกี้\nเมื่อใด\nเมื่อไร\nเมื่อไหร่\nเมื่อนั้น\nเมืองขึ้น\nเมืองท่า\nเมืองนอก\nเมืองหลวง\nเมื่อยขบ\nเมื่อยล้า\nแม่กอง\nแม่กุญแจ\nแม่คุณ\nแม่งาน\nแม่เจ้า\nแม่ชี\nแม่ทัพ\nแม่นม\nแม่น้ำ\nแม่บท\nแม่บ้าน\nแม่เบี้ย\nแม่พระ\nแม่พิมพ์\nแม่เพลง\nแม่มด\nแม่ม่าย\nแม่ไม้\nแม่ยก\nแม่ยาย\nแม่ร้าง\nแม่เรือน\nแม่แรง\nแม่เล้า\nแม่เลี้ยง\nแม่สี\nแม่สื่อ\nแม่เหล็ก\nแมงมุม\nแม่นยำ\nแมลงช้าง\nแมลงวัน\nแมลงปอ\nแมลงภู่\nแมลงเม่า\nแมวเซา\nแมวน้ำ\nแมวป่า\nแมวมอง\nไม้กลัด\nไม้กวาด\nไม้กางเขน\nไม้เกาหลัง\nไม้ขีดไฟ\nไม้จิ้มฟัน\nไม้เด็ด\nไม้ตาย\nไม้ตีพริก\nไม้ที\nไม้เท้า\nไม้บรรทัด\nไม้เมตร\nไม้ระแนง\nไม้เรียว\nไม้หมอน\nไม้อัด\nไม้จัตวา\nไม้ตรี\nไม้ไต่คู้\nไม้โท\nไม้ผัด\nไม้มลาย\nไม้ม้วน\nไม้ยมก\nไม้หน้า\nไม้หันอากาศ\nไม้เอก\nยกกลีบ\nยกครู\nยกเครื่อง\nยกเค้า\nยกทรง\nยกฟ้อง\nยกเมฆ\nยกยอ\nยกย่อง\nยกเลิก\nยกเว้น\nย่นย่อ\nยมทูต\nยมบาล\nยมราช\nยมโลก\nยวดยิ่ง\nยวดยาน\nยวนยี\nยวบยาบ\nย่อท้อ\nย่อส่วน\nย่อหน้า\nย่อหย่อน\nยอกย้อน\nยองใย\nย่องเบา\nย่องแย่ง\nยอดเยี่ยม\nยอดอก\nย้อนยอก\nย้อนรอย\nย้อนศร\nย้อนแสง\nย้อนหลัง\nยอบแยบ\nยอมความ\nย่อมเยา\nย่อยยับ\nยักยอก\nยักย้าย\nยักเยื้อง\nยัญกรรม\nยัญพิธี\nยัดเยียด\nยับเยิน\nยับยั้ง\nยั่วยวน\nยั่วยุ\nยั่วเย้า\nยากวาด\nยากันยุง\nยาเขียว\nยาใจ\nยาฉุน\nยาชา\nยาซัด\nยาดอง\nยาแดง\nยาถ่าย\nยาธาตุ\nยานัตถุ์\nยาเบื่อ\nยาโป๊\nยาแฝด\nยาพิษ\nยาระบาย\nยาสลบ\nยาสั่ง\nยาสีฟัน\nยาสูบ\nยาเส้น\nยาเสพติด\nยาหม่อง\nยาเหลือง\nย่าทวด\nย่านาง\nยากแค้น\nยากจน\nยากเย็น\nยากไร้\nยางนอก\nยางใน\nยางมะตอย\nยางมะตูม\nยางลบ\nยางสน\nยางอาย\nย่างกราย\nย่างเยื้อง\nย่างสด\nย่างสามขุม\nย่างเหยียบ\nยานเกราะ\nยานพาหนะ\nยานอวกาศ\nยานคาง\nยายทวด\nยาวเฟื้อย\nยาวยืด\nยาวเหยียด\nยำทวาย\nยำใหญ่\nยำเกรง\nยำเยง\nย่ำต๊อก\nย่ำยี\nย่ำแย่\nยิงเป้า\nยิ่งนัก\nยิ่งยวด\nยิ่งใหญ่\nยินดี\nยินยอม\nยินร้าย\nยิ้มกริ่ม\nยิ้มแฉ่ง\nยิ้มแต้\nยิ้มแป้น\nยิ้มเผล่\nยิ้มเยาะ\nยิ้มแย้ม\nยียวน\nยึดครอง\nยึดถือ\nยึดมั่น\nยึดเหนี่ยว\nยืดยาด\nยืดยาว\nยืดเยื้อ\nยืดหยุ่น\nยืดอก\nยืนกราน\nยืนต้น\nยืนพื้น\nยืนยง\nยืนยัน\nยืนหยัด\nยื้อยุด\nยุยง\nยุแยง\nยุแหย่\nยุคลบาท\nยุคเข็ญ\nยุคทอง\nยุคมืด\nยุ่งขิง\nยุ่งยาก\nยุ่งเหยิง\nยุติธรรม\nยุทธการ\nยุทธนาวี\nยุทธปัจจัย\nยุทธภัณฑ์\nยุทธภูมิ\nยุทธวิธี\nยุทธศาสตร์\nยุทธหัตถี\nยุทธนาการ\nยุทธนาธิการ\nยุบยับ\nยุบยิบ\nยุพราช\nยู่ยี่\nเย็นเจี๊ยบ\nเย็นใจ\nเย็นฉ่ำ\nเย็นเฉียบ\nเย็นชา\nเย็นชืด\nเย็นตา\nเย็นเยียบ\nเย็นเยือก\nเย็นวาบ\nเย็นวูบ\nเย็บกี่\nเย็บจักร\nเย็บด้าย\nเย้ยหยัน\nเย้าหยอก\nเยาะเย้ย\nเยี่ยมกราย\nเยี่ยมเยียน\nเยี่ยมเยือน\nเยี่ยมยอด\nเยื่อเคย\nเยื่อใย\nเยือกเย็น\nเยื้องกราย\nเยื้องยัก\nเยื้องย่าง\nแยกย้าย\nแยกแยะ\nแย่งชิง\nแยบคาย\nแยบยล\nแย้มพราย\nแย้มยิ้ม\nแย้มสรวล\nโยเย\nโย้เย้\nโยกโคลง\nโยกย้าย\nโยกโย้\nโยนกลอง\nใยหิน\nรกชัฏ\nรกร้าง\nรกเรี้ยว\nรกเรื้อ\nรกราก\nรงควัตถุ\nรชนีกร\nรถกระบะ\nรถเก๋ง\nรถเข็น\nรถแข่ง\nรถจักร\nรถจี๊ป\nรถตู้\nรถทัวร์\nรถบรรทุก\nรถพ่วง\nรถพยาบาล\nรถไฟ\nรถไฟฟ้า\nรถม้า\nรถเมล์\nรถยนต์\nรถราง\nรถลาก\nรถสปอร์ต\nรถสิบล้อ\nรบกวน\nรบรา\nรบเร้า\nรมดำ\nร่มเกล้า\nร่มชูชีพ\nร่มเย็น\nร่มรื่น\nร่วงโรย\nรวงผึ้ง\nรวงรัง\nรวดเร็ว\nรวนเร\nรวบยอด\nรวบรวม\nรวบรัด\nรวมพล\nรวมหัว\nร่วมใจ\nร่วมเพศ\nร่วมมือ\nร่วมรัก\nร่วมสมัย\nรวยริน\nรวยรื่น\nรสชาติ\nรสนิยม\nรองท้อง\nรองเท้า\nรองพื้น\nร่องน้ำ\nร่องรอย\nร้องขอ\nร้องทุกข์\nร้องเรียน\nร้องห่ม\nร้องไห้\nรองช้ำ\nรองทรง\nรอดชีวิต\nรอดตัว\nรอดตาย\nรอนแรม\nร่อนเร่\nร้อนใจ\nร้อนตัว\nร้อนรน\nร้อนรุ่ม\nร้อนวิชา\nร้อนอาสน์\nรอบจัด\nรอบเดือน\nรอบรู้\nรอยร้าว\nร่อยหรอ\nร้อยละ\nร้อยกรอง\nร้อยแก้ว\nร้อยหวาย\nระนาดทุ้ม\nระนาดเอก\nระเบิดขวด\nระเบิดมือ\nระเบียบการ\nรักใคร่\nรักษาการ\nรักษาการณ์\nรังไข่\nรังแตน\nรังนก\nรังผึ้ง\nรังเพลิง\nรังมด\nรังสรรค์\nรังสฤษฏ์\nรั้งรอ\nรังสีแพทย์\nรังสีวิทยา\nรัชกาล\nรัชทายาท\nรัชนีกร\nรัฐธรรมนูญ\nรัฐบาล\nรัฐบุรุษ\nรัฐประศาสน์\nรัฐประหาร\nรัฐพิธี\nรัฐมนตรี\nรัฐวิสาหกิจ\nรัฐศาสตร์\nรัฐสภา\nรัดกุม\nรัดเกล้า\nรัดตัว\nรัดประคด\nรัดรึง\nรัดรูป\nรัตติกาล\nรับขวัญ\nรับจ้าง\nรับช่วง\nรับใช้\nรับซื้อ\nรับทราบ\nรับประกัน\nรับประทาน\nรับปาก\nรับผิด\nรับผิดชอบ\nรับฟ้อง\nรับฟัง\nรับมือ\nรับรอง\nรับรู้\nรับสมัคร\nรับสั่ง\nรับหน้า\nรับเหมา\nรั่วไหล\nรามือ\nร่าเริง\nรากแก้ว\nรากขวัญ\nรากฐาน\nรากฟัน\nรากศัพท์\nรากเหง้า\nร่างกาย\nร่างแห\nร้างรา\nราชกรณียกิจ\nราชการ\nราชกิจ\nราชครู\nราชฐาน\nราชทัณฑ์\nราชทินนาม\nราชทูต\nราชธานี\nราชนาวี\nราชบัณฑิต\nราชบัลลังก์\nราชบาตร\nราชบุตร\nราชปะแตน\nราชภัฏ\nราชมัล\nราชยาน\nราชรถ\nราชลัญจกร\nราชเลขาธิการ\nราชเลขานุการ\nราชวงศ์\nราชวัติ\nราชสกุล\nราชสมบัติ\nราชสาส์น\nราชหัตถเลขา\nราชองครักษ์\nราชโองการ\nราชาคณะ\nราชาศัพท์\nราชินีนาถ\nร้านชำ\nร้านรวง\nราบคาบ\nราบรื่น\nราบเรียบ\nรายการ\nรายงาน\nรายจ่าย\nรายได้\nรายทาง\nรายรับ\nรายล้อม\nรายละเอียด\nรายวิชา\nร่ายยาว\nร่ายรำ\nร้ายกาจ\nร้ายแรง\nราวนม\nราวป่า\nร้าวฉาน\nร้าวราน\nรำพัด\nรำแพน\nรำวง\nร่ำไป\nร่ำร้อง\nร่ำเรียน\nร่ำไร\nร่ำลา\nร่ำไห้\nริเริ่ม\nริอ่าน\nริมฝีปาก\nริ้วรอย\nรีบร้อน\nรีบรุด\nรีบเร่ง\nรื่นรมย์\nรื่นเริง\nรื้อถอน\nรื้อฟื้น\nรุกฆาต\nรุกราน\nรุกล้ำ\nรุกไล่\nรุ่งขึ้น\nรุ่งแจ้ง\nรุ่งเช้า\nรุ่งเรือง\nรุ่งโรจน์\nรุ่งสว่าง\nรุ่งสาง\nรุ่งอรุณ\nรุจิเรข\nรุดหน้า\nรุนแรง\nรุมเร้า\nรุมล้อม\nรุ่มรวย\nรุ่มร้อน\nรุ่ยร่าย\nรู้แกว\nรู้ความ\nรู้คุณ\nรู้งาน\nรู้จัก\nรู้แจ้ง\nรู้ใจ\nรู้เชิง\nรู้ตัว\nรู้ทัน\nรู้เท่า\nรู้เรื่อง\nรู้สำนึก\nรู้สึก\nรู้เห็น\nรูปการณ์\nรูปโฉม\nรูปฌาน\nรูปถ่าย\nรูปทรง\nรูปธรรม\nรูปแบบ\nรูปพรรณ\nรูปพรหม\nรูปภพ\nรูปภาพ\nรูปร่าง\nรูปสมบัติ\nเร่ร่อน\nเร่งด่วน\nเร่งมือ\nเร่งรัด\nเร่งรีบ\nเร่งเร้า\nเร้นลับ\nเร่อร่า\nเร่าร้อน\nเราะราย\nเราะร้าย\nเริงใจ\nเริงรมย์\nเริดร้าง\nเริ่มต้น\nเริ่มแรก\nเรี่ยราด\nเรี่ยไร\nเรียกคืน\nเรียกตัว\nเรียกร้อง\nเรียกหา\nเรียบร้อย\nเรียงความ\nเรียงตัว\nเรียงเบอร์\nเรียงพิมพ์\nเรียงเม็ด\nเรียงราย\nเรียนรู้\nเรียบร้อย\nเรียบเรียง\nเรียบวุธ\nเรี่ยมเร้\nเรี่ยวแรง\nเรือกลไฟ\nเรือกอและ\nเรือกำปั่น\nเรือจ้าง\nเรือดำน้ำ\nเรือโดยสาร\nเรือตรวจการณ์\nเรือตังเก\nเรือธง\nเรือนำร่อง\nเรือบด\nเรือบิน\nเรือใบ\nเรือประมง\nเรือพ่วง\nเรือพิฆาต\nเรือยนต์\nเรือยาว\nเรือโยง\nเรือรบ\nเรือลากจูง\nเรือสำปั้น\nเรือสำเภา\nเรือหลวง\nเรือหางยาว\nเรืออีโปง\nเรือเอี้ยมจุ๊น\nเรื้อรัง\nเรือกสวน\nเรืองนาม\nเรืองรอง\nเรืองแสง\nเรื่องราว\nเรื่องสั้น\nเรือนแก้ว\nเรือนจำ\nเรือนเบี้ย\nเรือนแพ\nเรือนหอ\nเรื่อยเจื้อย\nเรื่อยเฉื่อย\nเรื่อยเปื่อย\nแรเงา\nแรกนา\nแรกนาขวัญ\nแรงงาน\nแรงดึงดูด\nแรงเทียน\nแรงม้า\nแรงเหวี่ยง\nแรมรอน\nแรมรา\nแรมโรย\nโรคจิต\nโรงครัว\nโรงงาน\nโรงเจ\nโรงเตี๊ยม\nโรงทาน\nโรงนา\nโรงพยาบาล\nโรงพัก\nโรงพิมพ์\nโรงเรียน\nโรงเรือน\nโรงแรม\nโรงเลี้ยง\nโรงเลื่อย\nโรงสี\nโรงสีข้าว\nโรงอาหาร\nโรมรัน\nโรยรา\nฤชากร\nฤดูกาล\nลงขัน\nลงแขก\nลงคอ\nลงตัว\nลงท้าย\nลงทุน\nลงโทษ\nลงพุง\nลงมือ\nลงรัก\nลงรอย\nลงแรง\nลงโรง\nลงเอย\nลดตัว\nลดละ\nลดเลี้ยว\nลดหย่อน\nลดหลั่น\nลนลาน\nล้นพ้น\nล้นหลาม\nล้นเหลือ\nลบล้าง\nลบเลือน\nลบหลู่\nลมกรด\nลมค้า\nลมงวง\nลมแดด\nลมทะเล\nลมบก\nลมบน\nลมบ้าหมู\nลมปราณ\nลมปาก\nลมพิษ\nลมว่าว\nลมเสีย\nลมหนาว\nลมหายใจ\nล่มจม\nล่มสลาย\nล้มละลาย\nล้มลุก\nล้มเลิก\nล่วงเกิน\nล่วงรู้\nล่วงละเมิด\nล่วงลับ\nล่วงล้ำ\nล่วงเลย\nล่วงหน้า\nลวดลาย\nลวดสปริง\nลวดหนาม\nล้วนแล้ว\nลหุโทษ\nล่อลวง\nล่อหลอก\nล่อแหลม\nล้อต๊อก\nล้อเลื่อน\nล้อเล่น\nล้อเลียน\nล้อหลอก\nลองเชิง\nลองดี\nลองภูมิ\nล่องหน\nลอดช่อง\nล่อนจ้อน\nลอบกัด\nล้อมวง\nลอยแก้ว\nลอยชาย\nลอยตัว\nลอยนวล\nลอยแพ\nลอยลำ\nละทิ้ง\nละเลย\nละเว้น\nละครนอก\nละครใน\nละครเพลง\nละครร้อง\nละครรำ\nละครลิง\nละครสัตว์\nละเอียดอ่อน\nลักไก่\nลักพา\nลักเพศ\nลักยิ้ม\nลักลอบ\nลักลั่น\nลักหลับ\nลัดเลาะ\nลับตา\nลับแล\nลับหลัง\nลาออก\nล่าช้า\nล่าทัพ\nล้าสมัย\nล้าหลัง\nลากข้าง\nล้างบาง\nล้างผลาญ\nลาดเขา\nลาดตระเวน\nลาดเท\nลาดยาง\nลานบิน\nลาภปาก\nลาภลอย\nลามปาม\nลามเลีย\nลายคราม\nลายเซ็น\nลายแทง\nลายน้ำ\nลายพร้อย\nลายมือ\nลายลักษณ์\nลายเส้น\nลำกล้อง\nลำแข้ง\nลำธาร\nลำแสง\nลำไส้\nลำตัด\nลำนำ\nล่ำสัน\nล้ำยุค\nล้ำสมัย\nล้ำลึก\nล้ำเลิศ\nล้ำเส้น\nล้ำหน้า\nลิงจุ่น\nลิงลม\nลิงโลด\nลิดรอน\nลิ้นไก่\nลิ้นชัก\nลิ้นปี่\nลิ้นควาย\nลิ้นงูเห่า\nลิ้นหมา\nลิบลับ\nลิบลิ่ว\nลิ่มเลือด\nลี้ภัย\nลี้ลับ\nลึกซึ้ง\nลึกลับ\nลึกล้ำ\nลืมตน\nลืมต้น\nลืมตัว\nลืมตา\nลืมเลือน\nลือชา\nลือชื่อ\nลือลั่น\nลุล่วง\nลุกลน\nลุกลาม\nลุกลี้ลุกลน\nลุกฮือ\nลุ่มน้ำ\nลุ่มลึก\nลุ่มหลง\nลุ่ทาง\nลูกกรง\nลูกกรอก\nลูกกรุง\nลูกกลอน\nลูกกลิ้ง\nลูกกวาด\nลูกกะจ๊อก\nลูกกุญแจ\nลูกเกด\nลูกแก้ว\nลูกขนไก่\nลูกข่าง\nลูกขุน\nลูกเขย\nลูกครึ่ง\nลูกคลื่น\nลูกความ\nลูกคอ\nลูกค้า\nลูกคิด\nลูกคู่\nลูกจ้าง\nลูกช้าง\nลูกชิด\nลูกชิ้น\nลูกชุบ\nลูกซอง\nลูกโซ่\nลูกดอก\nลูกดิ่ง\nลูกตะกั่ว\nลูกตุ้ม\nลูกเต้า\nลูกเต๋า\nลูกถ้วย\nลูกทุ่ง\nลูกเธอ\nลูกน้อง\nลูกน้ำ\nลูกนิมิต\nลูกบอล\nลูกบ้าน\nลูกบาศก์\nลูกบิด\nลูกเบี้ยว\nลูกประคบ\nลูกประคำ\nลูกปัด\nลูกปืน\nลูกโป่ง\nลูกผสม\nลูกผู้ชาย\nลูกผู้หญิง\nลูกพรรค\nลูกพี่\nลูกฟูก\nลูกไฟ\nลูกมือ\nลูกโม่\nลูกไม้\nลูกยาเธอ\nลูกรอก\nลูกรัง\nลูกเรือ\nลูกล้อ\nลูกลอย\nลูกเล่น\nลูกเลี้ยง\nลูกโลก\nลูกวัด\nลูกศร\nลูกศิษย์\nลูกสมุน\nลูกสะใภ้\nลูกสูบ\nลูกเสือ\nลูกหนัง\nลูกหนี้\nลูกหนู\nลูกหมาก\nลูกหลง\nลูกหลาน\nลูกหาบ\nลูกหิน\nลูกเห็บ\nลูกเหม็น\nลูกแหง่\nลูกอม\nลูกหม้อ\nลูบคม\nลูบคลำ\nลูบไล้\nเล็กน้อย\nเลขคณิต\nเลขผา\nเลขหมาย\nเล็ดลอด\nเล่นงาน\nเล่นแง่\nเล่นชู้\nเล่นตัว\nเล่นลิ้น\nเล่นหัว\nเลนส์นูน\nเลนส์เว้า\nเล็บครุฑ\nเลยเถิด\nเลศนัย\nเล่ห์กล\nเล่ห์เหลี่ยม\nเลอโฉม\nเลอมาน\nเลอเลิศ\nเลอสรวง\nเล่อล่า\nเลอะเลือน\nเล่าเรียน\nเล่าลือ\nเลาะลัด\nเลิกรา\nเลิกร้าง\nเลิกล้ม\nเลิศเลอ\nเลี้ยงชีพ\nเลี้ยงดู\nเลี้ยงต้อย\nเลียบเคียง\nเลี้ยวลด\nเลือกตั้ง\nเลือกเฟ้น\nเลือกสรร\nเลื่องลือ\nเลือดกำเดา\nเลือดเนื้อ\nเลือดฝาด\nเลือดเย็น\nเลือดร้อน\nเลือดหมู\nเลือดอุ่น\nเลือนราง\nเลื่อนเปื้อน\nเลื่อนลอย\nเลื่อมพราย\nเลื่อมใส\nเลื่อยฉลุ\nเลื่อยลันดา\nเลื่อยวงเดือน\nเลื้อยคลาน\nแลเหลียว\nแลกเปลี่ยน\nแล้วกัน\nและเล็ม\nโล่งใจ\nโล่งโถง\nโล่งอก\nโลดเต้น\nโลดโผน\nโลดลิ่ว\nโลดแล่น\nไล่ที่\nไล่เบี้ย\nไล่เลี่ย\nไล่เลียง\nไล่หลัง\nไล่ออก\nวกวน\nวงกบ\nวงกลม\nวงการ\nวงแขน\nวงเงิน\nวงจร\nวงนอก\nวงใน\nวงรี\nวงเล็บ\nวงเวียน\nวงแหวน\nวงศ์วาน\nวจีกรรม\nวจีเภท\nวจีภาค\nวนเวียน\nวอดวาย\nว็อบแว็บ\nวังวน\nวังหน้า\nวังหลวง\nวังหลัง\nวัดราษฎร์\nวัดวา\nวัดหลวง\nวัดผล\nวัดพื้น\nวัตถุนิยม\nวัตถุประสงค์\nวัตรปฏิบัติ\nวันโกน\nวันพระ\nวันเพ็ญ\nวัยรุ่น\nวัยวุฒิ\nว่ากล่าว\nว่าจ้าง\nว่าด้วย\nว่าที่\nวางก้าม\nวางใจ\nวางตัว\nวางตา\nวางโต\nวางท่า\nวางมวย\nวางมาด\nวางมือ\nวางวาย\nว่างเปล่า\nว่างเว้น\nวาดเขียน\nว่านเครือ\nวาบหวาม\nวายชนม์\nวายปราณ\nวายวาง\nวายวอด\nวายร้าย\nวายุภักษ์\nวาววับ\nวาววาม\nวาวแวว\nวาวแสง\nวิกฤตการณ์\nวิกฤติการณ์\nวิกฤตกาล\nวิกฤติกาล\nวิกลจริต\nวิงเวียน\nวิ่งเต้น\nวิ่งผลัด\nวิ่งรอก\nวิ่งราว\nวิจิตรศิลป์\nวิชาการ\nวิชาชีพ\nวิชาธร\nวิญญูชน\nวิดพื้น\nวิตกจริต\nวิถีทาง\nวิทยากร\nวิทยากล\nวิทยาการ\nวิทยาเขต\nวิทยาทาน\nวิทยาธร\nวิทยานิพนธ์\nวิทยาศาสตร์\nวิเทศสัมพันธ์\nวิธีการ\nวินัยธร\nวินัยปิฎก\nวินาศกรรม\nวินาศภัย\nวินาศสันตะโร\nวิภัชพยากรณ์\nวิภัชวาที\nวิไลวรรณ\nวิสัญญีแพทย์\nวิสัญญีภาพ\nวิสัญญีวิทยา\nวุฒิบัตร\nวุฒิสภา\nวุฒิสมาชิก\nวุ่นวาย\nวุ้นเส้น\nวูบวาบ\nเวจกุฎี\nเวจมรรค\nเวชกรรม\nเวชภัณฑ์\nเวชศาสตร์\nเวทมนตร์\nเวนคืน\nเวรกรรม\nเวฬุการ\nเวฬุวัน\nเว้าวอน\nเวิ้งว้าง\nเวียงวัง\nเวียนเทียน\nแว้งกัด\nแวดล้อม\nแวดวง\nแว่นขยาย\nแว่นแคว้น\nแว่นตา\nแวบวับ\nแววตา\nแวววาม\nแวววาว\nแวะเวียน\nโวยวาย\nไวไฟ\nไว้ใจ\nไว้ชื่อ\nไว้ตัว\nไว้ทุกข์\nไว้ลาย\nไว้หน้า\nไว้อาลัย\nศนิวาร\nศอกกลับ\nศอกกำ\nศอกกำมา\nศักดิ์ศรี\nศักดิ์สิทธิ์\nศารทวิษุวัติ\nศาลแขวง\nศาลจังหวัด\nศาลชั้นต้น\nศาลฎีกา\nศาลเตี้ย\nศาลทหาร\nศาลปกครอง\nศาลพระภูมิ\nศาลเพียงตา\nศาลแพ่ง\nศาลรัฐธรรมนูญ\nศาลแรงงาน\nศาลล้มละลาย\nศาลโลก\nศาลสูง\nศาลสูงสุด\nศาลอาญา\nศาลอุทธรณ์\nศาลากลาง\nศาลาดิน\nศาลาราย\nศาลาวัด\nศิลาฤกษ์\nศิลาแลง\nศิษย์เก่า\nศิษย์เอก\nศีลจุ่ม\nศีลธรรม\nศีลวัต\nศีลอด\nศูนย์กลาง\nศูนย์การค้า\nศูนย์ถ่วง\nศูนย์สูตร\nศูนย์หน้า\nเศร้าใจ\nเศร้าโศก\nเศร้าสร้อย\nเศร้าสลด\nเศร้าหมอง\nเศวตฉัตร\nเศษเกิน\nเศษซ้อน\nเศษวรรค\nเศษส่วน\nเศษเหล็ก\nโศกนาฏกรรม\nโศกศัลย์\nโศกเศร้า\nโศกสลด\nสกลโลก\nส่งเดช\nส่งท้าย\nส่งเสริม\nส่งเสีย\nส่งเสียง\nสงบเงียบ\nสงบเสงี่ยม\nสง่างาม\nสง่าราศี\nสดชื่น\nสดใส\nสตรีเพศ\nสติปัญญา\nสถลมารค\nสถานกงสุล\nสถานที่\nสถานทูต\nสถานการณ์\nสถานภาพ\nสถิติศาสตร์\nสนตะพาย\nสนใจ\nส้นตีน\nสนธิสัญญา\nสนนราคา\nสนับแข้ง\nสนับเพลา\nสนับมือ\nสนามบิน\nสนามเพลาะ\nสนิทสนม\nสนิมขุม\nสนิมสร้อย\nสนุกสนาน\nสบประมาท\nสบายใจ\nสภาพธรรม\nสมควร\nสมจริง\nสมใจ\nสมนัย\nสมน้ำหน้า\nสมประกอบ\nสมส่วน\nสมหวัง\nสมคบ\nสมทบ\nสมยอม\nสมรัก\nสมรู้\nสมสู่\nส้มฉุน\nส้มตำ\nส้มลิ้ม\nส้มกุ้ง\nส้มเช้า\nสมญานาม\nสมมติฐาน\nสมมุติฐาน\nสมมติเทพ\nสมรภูมิ\nสมัครใจ\nสมัยนิยม\nสมุทรศาสตร์\nสมุทรเสนา\nสยดสยอง\nสยองขวัญ\nสยามรัฐ\nสรรหา\nสรวมชีพ\nสรวลเส\nสร้อยเศร้า\nสร้างสรรค์\nสร้างเสริม\nสลดใจ\nสลบไสล\nสละสลวย\nสลาเหิน\nสลากภัต\nสวนครัว\nสวนป่า\nสวนสนุก\nสวนหย่อม\nส่วนกลาง\nส่วนเกิน\nส่วนตัว\nส่วนบุญ\nส่วนแบ่ง\nส่วนประกอบ\nส่วนพระองค์\nส่วนผสม\nส่วนรวม\nส่วนร่วม\nส่วนลด\nส่วนสัด\nสวมกอด\nสวมเขา\nสวมรอย\nสวยมภู\nสว่างไสว\nสวามิภักดิ์\nสวิงสวาย\nสสารนิยม\nส่อเสียด\nสอดคล้อง\nสอดแทรก\nสอดแนม\nสอบถาม\nสอบทาน\nสอบไล่\nสอบสวน\nส้อมเสียง\nสะสวย\nสะแกวัลย์\nสะแกแสง\nสะใจ\nสะเด็ดยาด\nสะเทือนใจ\nสะบัดช่อ\nสั่งสม\nสั่งสอน\nสั่งเสีย\nสังเกตการณ์\nสังคมนิยม\nสังคมวิทยา\nสังคมศาสตร์\nสังคมศึกษา\nสังคมสงเคราะห์\nสัญญาบัตร\nสัดส่วน\nสัตการ\nสัตบุรุษ\nสัตบริภัณฑ์\nสัตภัณฑ์\nสัตมหาสถาน\nสัตโลหะ\nสันเขา\nสันดอน\nสันหลัง\nสั่นเทา\nสั่นเทิ้ม\nสันติบาล\nสันติภาพ\nสันติวิธี\nสันติสุข\nสับเปลี่ยน\nสับสน\nสับหลีก\nสับหว่าง\nสัมมาคารวะ\nสัมมาชีพ\nส่าเหล้า\nสากกะเบือ\nสาทิสลักษณ์\nสาธุการ\nสาธุชน\nสาบเสือ\nสาปสรร\nสาปแช่ง\nสาปส่ง\nสามง่าม\nสามล้อ\nสามเหลี่ยม\nสามเวท\nสามัญชน\nสามัญสำนึก\nสายดิ่ง\nสายดิน\nสายตรวจ\nสายน้ำ\nสายบัว\nสายพาน\nสายฟ้า\nสายยาง\nสายยู\nสายใย\nสายรก\nสายรุ้ง\nสายล่อฟ้า\nสายลับ\nสายเลือด\nสายโลหิต\nสายวัด\nสายส่ง\nสายสวาท\nสายสะดือ\nสายสะพาย\nสายสัมพันธ์\nสายสิญจน์\nสายสืบ\nสายไหม\nสายอากาศ\nสายตา\nสายหยุด\nสารตรา\nสารประกอบ\nสารละลาย\nสารส้ม\nสารหนู\nสารทฤดู\nสาวใช้\nสาวน้อย\nสาวใหญ่\nสำนักงาน\nสำนักพิมพ์\nสำนักสงฆ์\nสำมะโนครัว\nสำเร็จรูป\nสิกขาบท\nสิงสถิต\nสิงสู่\nสิ่งก่อสร้าง\nสิ่งของ\nสิ่งปฏิกูล\nสิ่งพิมพ์\nสิ่งแวดล้อม\nสิ่งศักดิ์สิทธิ์\nสิทธิกร\nสิทธิ์ขาด\nสิทธิชัย\nสิทธิโชค\nสิทธิบัตร\nสินค้า\nสินจ้าง\nสินเชื่อ\nสินไถ่\nสินทรัพย์\nสินน้ำใจ\nสินบน\nสินแร่\nสินสมรส\nสินสอด\nสินไหม\nสิ้นเชิง\nสิ้นสุด\nสีผึ้ง\nสีลม\nสีชอล์ก\nสีถ่าน\nสีเทียน\nสีน้ำ\nสีน้ำมัน\nสีโปสเตอร์\nสีฝุ่น\nสี่เหลี่ยม\nสีหน้า\nสึกหรอ\nสืบทอด\nสืบค้น\nสืบสวน\nสืบสาว\nสืบเสาะ\nสื่อผสม\nสื่อมวลชน\nสื่อสาร\nสุกงอม\nสุกดิบ\nสุกปลั่ง\nสุกใส\nสุขนาฏกรรม\nสุขภัณฑ์\nสุขภาพ\nสุขลักษณะ\nสุขวิทยา\nสุขศาลา\nสุขศึกษา\nสุดท้าย\nสุตกวี\nสุนทรพจน์\nสุภาพชน\nสู่ขอ\nสู่รู้\nสู่สม\nสูงส่ง\nสูญเปล่า\nสูญสิ้น\nสูญเสีย\nสูญหาย\nเสสรวล\nเสแสร้ง\nเสกสรร\nเสถียรภาพ\nเส้นชัย\nเส้นตรง\nเส้นตาย\nเส้นทาง\nเส้นใย\nเส้นรุ้ง\nเส้นเลือด\nเส้นแวง\nเส้นสาย\nเส้นเสียง\nเส้นหมี่\nเส้นเอ็น\nเสบียงกรัง\nเสมอภาค\nเสมอหน้า\nเสมอเหมือน\nเสมียนตรา\nเสร็จสรรพ\nเสร็จสิ้น\nเสริมส่ง\nเสริมสร้าง\nเสริมสวย\nเสรีไทย\nเสรีธรรม\nเสรีนิยม\nเสรีภาพ\nเสาเข็ม\nเสาธง\nเสียใจ\nเสียเชิง\nเสียดาย\nเสียที\nเสียเที่ยว\nเสียเปรียบ\nเสียเปล่า\nเสียรู้\nเสียแรง\nเสียสละ\nเสียหลัก\nเสียหาย\nเสี่ยงทาย\nเสียดแทง\nเสียดแทรก\nเสียดสี\nเสี้ยนศึก\nเสี้ยนหนาม\nเสี้ยมสอน\nเสียวซ่าน\nเสียวไส้\nเสือดาว\nเสือดำ\nเสือปลา\nเสือป่า\nเสือไฟ\nเสื่อกก\nเสื่อกระจูด\nเสื่อน้ำมัน\nเสื่อลำแพน\nเสื้อกล้าม\nเสื้อกั๊ก\nเสื้อเกราะ\nเสื้อครุย\nเสื้อแสง\nเสื้อเมือง\nเสือกคลาน\nเสือกสน\nเสือกไส\nเสื่อมคลาย\nเสื่อมถอย\nเสื่อมทราม\nเสื่อมโทรม\nเสื่อมสลาย\nเสื่อมสูญ\nเสื่อมเสีย\nเสือหมอบ\nแสกหน้า\nแสดงออก\nแสเถา\nแสนกล\nแสนรู้\nแสร้งว่า\nใส่ความ\nใส่ไคล้\nใส่ใจ\nใส่ไฟ\nไส้กรอก\nไส้ไก่\nไส้ติ่ง\nไส้ศึก\nไส้อั่ว\nไส้เดือน\nไส้ตัน\nไสยเวท\nไสยศาสตร์\nหกล้ม\nหงส์หยก\nหงอนไก่\nหงอยก๋อย\nหงอยเหงา\nหงายท้อง\nหงายหลัง\nหงำเหงอะ\nหงำเหงือก\nหดหาย\nหดหู่\nหนทาง\nหนวกหู\nหน่วงเหนี่ยว\nหน่วยก้าน\nหน่อไม้\nหนองใน\nหนองแซง\nหนักข้อ\nหนักใจ\nหนักแน่น\nหนักหน่วง\nหนักหนา\nหนังกลับ\nหนังตะลุง\nหนังเรียด\nหนังสด\nหนังใหญ่\nหนังสือพิมพ์\nหนาแน่น\nหน้ากระดาน\nหน้ากาก\nหน้ากาฬ\nหน้าแข้ง\nหน้าจั่ว\nหน้าฉาน\nหน้าตัก\nหน้าตา\nหน้าต่าง\nหน้าท้อง\nหน้าทับ\nหน้าที่\nหน้าที่นั่ง\nหน้าบัน\nหน้าปัด\nหน้าผา\nหน้าผาก\nหน้าม้า\nหน้ามุข\nหน้าไม้\nหน้าเลือด\nหน้าอก\nหนามเตย\nหน่ายหนี\nหน่ายแหนง\nหนาวเหน็บ\nหนำใจ\nหนี้สิน\nหนี้ส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น\nกณิกนันต์\nกณิการ์\nกด\nกตเวทิตา\nกตเวที\nกตัญชลี\nกตัญญุตา\nกตัญญู\nกตาธิการ\nกตาภินิหาร\nกติกา\nกถา\nกถิกาจารย์\nกทลี\nกน\nก่น\nก้น\nกนก\nกนิษฐ์\nกนิษฐา\nกบ\nกบฏ\nกบดาน\nกบทู\nกบาล\nกบินทร์\nกบิล\nกบี่\nกบูร\nกเบนทร์\nกม\nก้ม\nกมณฑลาภิเษก\nกมณฑโลทก\nกมล\nกมลา\nกมลาศ\nกมลาสน์\nกมเลศ\nกมัณฑลุ\nกมุท\nกร\nกรกฎ\nกรกฎาคม\nกรกฏ\nกรง\nกรชกาย\nกรณฑ์\nกรณิการ์\nกรณี\nกรณีย์\nกรณียกิจ\nกรณียะ\nกรด\nกรน\nกรบ\nกรบูร\nกรพินธุ์\nกรม\nกรรกฎ\nกรรกศ\nกรรเกด\nกรรไกร\nกรรเจียก\nกรรชิง\nกรรเชียง\nกรรโชก\nกรรฐ์\nกรรฐา\nกรรณ\nกรรณา\nกรรณิกา\nกรรณิการ์\nกรรดิ\nกรรดิก\nกรรดึก\nกรรตุ\nกรรไตร\nกรรทบ\nกรรแทก\nกรรบิด\nกรรบูร\nกรรภิรมย์\nกรรม\nกรรม์\nกรรม์ภิรมย์\nกรรมชวาต\nกรรมัชวาต\nกรรมาชีพ\nกรรมาธิการ\nกรรมาร\nกรรษก\nกรรสะ\nกรรแสง\nกรวด\nกรวบ\nกรวม\nกร้วม\nกรวย\nกรวิก\nกรสาปน์\nกรสุทธิ์\nกรอ\nกร้อ\nกรอก\nกร็อกกร๋อย\nกรอกแกรก\nกรอง\nกรองกรอย\nกรอด\nกร่อน\nกรอบ\nกรอม\nกร่อย\nกระ\nกระกร\nกระกรุ่น\nกระกลับกลอก\nกระกี้\nกระเกรอก\nกระเกริก\nกระเกริ่น\nกระคน\nกระคาย\nกระงกกระเงิ่น\nกระง่องกระแง่ง\nกระง่อนกระแง่น\nกระเง้ากระงอด\nกระโงก\nกระจก\nกระจง\nกระจร\nกระจอก\nกระจองหง่อง\nกระจ๋องหง่อง\nกระจองอแง\nกระจ้อน\nกระจอนหู\nกระจ้อย\nกระจ๋อหวอ\nกระจะ\nกระจัก\nกระจัง\nกระจัด\nกระจับ\nกระจ่า\nกระจ่าง\nกระจาด\nกระจาน\nกระจาบ\nกระจาม\nกระจาย\nกระจาว\nกระจิก\nกระจิ๋ง\nกระจิด\nกระจิบ\nกระจิ๋ม\nกระจิริด\nกระจิ๋ว\nกระจี้\nกระจี๋\nกระจุก\nกระจุ๋งกระจิ๋ง\nกระจุบ\nกระจุ๊บ\nกระจุ๋มกระจิ๋ม\nกระจุย\nกระจู้\nกระจู๋กระจี๋\nกระจูด\nกระเจอะกระเจิง\nกระเจา\nกระเจ่า\nกระเจ้า\nกระเจาะ\nกระเจิง\nกระเจิดกระเจิง\nกระเจี้ยง\nกระเจี๊ยบ\nกระเจียว\nกระเจี๊ยว\nกระแจะ\nกระโจน\nกระโจม\nกระฉอก\nกระฉ่อน\nกระฉับกระเฉง\nกระฉิ่ง\nกระฉีก\nกระฉูด\nกระเฉก\nกระเฉด\nกระแฉก\nกระโฉกกระเฉก\nกระโฉม\nกระชดกระช้อย\nกระชอน\nกระชอมดอก\nกระช้อย\nกระชัง\nกระชั้น\nกระชับ\nกระชาก\nกระชาย\nกระชิง\nกระชิด\nกระชุ\nกระชุก\nกระชุ่มกระชวย\nกระเชอ\nกระเชา\nกระเช้า\nกระเชียง\nกระแชง\nกระแชะ\nกระโชก\nกระซ่องกระแซ่ง\nกระซับ\nกระซาบ\nกระซิก\nกระซิบ\nกระซี้\nกระซุง\nกระซุบกระซิบ\nกระซุ้ม\nกระซู่\nกระเซ็น\nกระเซอ\nกระเซอะกระเซอ\nกระเซอะกระเซิง\nกระเซ้า\nกระเซิง\nกระแซ\nกระแซะ\nกระโซกระเซ\nกระฎี\nกระฎุมพี\nกระดก\nกระด้ง\nกระดนโด่\nกระดวง\nกระดวน\nกระด้วมกระเดี้ยม\nกระดอ\nกระดอง\nกระดองหาย\nกระดอน\nกระดอม\nกระดักกระเดี้ย\nกระดังงัว\nกระดังงา\nกระดาก\nกระด้าง\nกระดางลาง\nกระดาด\nกระดาดขาว\nกระดาน\nกระดานพน\nกระดาษ\nกระดำกระด่าง\nกระดิก\nกระดิ่ง\nกระดิ้ง\nกระดิบ\nกระดี่\nกระดี้กระเดียม\nกระดึง\nกระดืบ\nกระดุ\nกระดุกกระดิก\nกระดุ้งกระดิ้ง\nกระดุบ\nกระดุบกระดิบ\nกระดุม\nกระดูก\nกระเดก\nกระเด้ง\nกระเด็น\nกระเด้า\nกระเดาะ\nกระเดิด\nกระเดี้ย\nกระเดียด\nกระเดือก\nกระเดื่อง\nกระแด็ก\nกระแด้ง\nกระแด้แร่\nกระแด่ว\nกระแดะ\nกระโดก\nกระโดง\nกระโดด\nกระโดน\nกระได\nกระตรับ\nกระตราก\nกระตรุด\nกระตรุม\nกระต้วมกระเตี้ยม\nกระต้อ\nกระต่องกระแต่ง\nกระต๊อบ\nกระต้อยตีวิด\nกระตัก\nกระตั้ว\nกระต่าย\nกระติก\nกระติ๊ด\nกระติบ\nกระตือรือร้น\nกระตุก\nกระตุ้งกระติ้ง\nกระตุ่น\nกระตุ้น\nกระตูบ\nกระเตง\nกระเต็น\nกระเตอะ\nกระเตาะ\nกระเตาะกระแตะ\nกระเตื้อง\nกระแต\nกระแตแต้แว้ด\nกระโตกกระตาก\nกระโตน\nกระถด\nกระถอบ\nกระถั่ว\nกระถาง\nกระถิก\nกระถิน\nกระเถิบ\nกระโถน\nกระทก\nกระทง\nกระทบ\nกระทรวง\nกระทอก\nกระท้อน\nกระท่อนกระแท่น\nกระท่อม\nกระท้อมกระแท้ม\nกระทะ\nกระทั่ง\nกระทั้น\nกระทา\nกระทาย\nกระทาสี\nกระทาหอง\nกระทำ\nกระทิกกระทวย\nกระทิง\nกระทึง\nกระทืบ\nกระทุ\nกระทุง\nกระทุ้ง\nกระทุ่ม\nกระทู้\nกระเท่\nกระเทียบ\nกระเทียม\nกระเทือน\nกระเทื้อม\nกระแทก\nกระแท่น\nกระแทะ\nกระไทชาย\nกระน่อง\nกระนั้น\nกระนี้\nกระแนะกระแหน\nกระโน้น\nกระไน\nกระบก\nกระบม\nกระบวน\nกระบวย\nกระบวร\nกระบอก\nกระบอง\nกระบะ\nกระบัด\nกระบั้วกระเบี้ย\nกระบ่า\nกระบ้า\nกระบาก\nกระบาย\nกระบาล\nกระบิ\nกระบิ้ง\nกระบิด\nกระบิล\nกระบี่\nกระบือ\nกระบุง\nกระบุ่มกระบ่าม\nกระบู้กระบี้\nกระบูน\nกระบูร\nกระเบง\nกระเบญ\nกระเบ็ดกระบวน\nกระเบน\nกระเบา\nกระเบิก\nกระเบียด\nกระเบียน\nกระเบื้อง\nกระแบก\nกระแบะ\nกระโบม\nกระปมกระปำ\nกระปมกระเปา\nกระปรอก\nกระปรอกว่าว\nกระปรี้กระเปร่า\nกระปอก\nกระป้อกระแป้\nกระป่อง\nกระป๋อง\nกระปอดกระแปด\nกระป๋อหลอ\nกระปั้วกระเปี้ย\nกระป่ำ\nกระปุก\nกระปุ๊กลุก\nกระปุ่ม\nกระปุ่มกระป่ำ\nกระปุ่มกระปิ่ม\nกระเป๋า\nกระเปาะ\nกระโปก\nกระโปรง\nกระผม\nกระผลีกระผลาม\nกระผาน\nกระผีก\nกระพรวน\nกระพริ้ม\nกระพอก\nกระพอง\nกระพ้อม\nกระพัก\nกระพัง\nกระพังเหิร\nกระพังโหม\nกระพัด\nกระพัตร\nกระพัน\nกระพั่น\nกระพา\nกระพาก\nกระพี้\nกระพือ\nกระพุ้ง\nกระพุ่ม\nกระเพาะ\nกระเพิง\nกระเพื่อม\nกระแพ้ง\nกระฟัดกระเฟียด\nกระฟูมกระฟาย\nกระมล\nกระมอบ\nกระมอมกระแมม\nกระมัง\nกระมัน\nกระมิดกระเมี้ยน\nกระมุท\nกระเมาะ\nกระย่อง\nกระย่องกระแย่ง\nกระย่อน\nกระย่อม\nกระยา\nกระยาง\nกระยาจก\nกระยาหงัน\nกระยิก\nกระยิ้มกระย่อง\nกระยึกกระยือ\nกระยืดกระยาด\nกระเย้อกระแหย่ง\nกระรอก\nกระเรียน\nกระโรกน้ำข้าว\nกระโรกใหญ่\nกระไร\nกระลด\nกระลบ\nกระลอก\nกระลอม\nกระละหล่ำ\nกระลัด\nกระลับ\nกระลัมพร\nกระลา\nกระลำ\nกระลำพัก\nกระลำพุก\nกระลิง\nกระลี\nกระลุมพาง\nกระลุมพุก\nกระลุมพู\nกระลูน\nกระลู่น์\nกระเล็น\nกระเลียด\nกระเลือก\nกระโลง\nกระวน\nกระวัด\nกระวาด\nกระวาน\nกระวาย\nกระวิน\nกระวี\nกระวีกระวาด\nกระวูดกระวาด\nกระเวน\nกระเวยกระวาย\nกระแวน\nกระโวยกระวาย\nกระษัย\nกระษาปณ์\nกระสง\nกระสบ\nกระสม\nกระสรวล\nกระสร้อย\nกระสวน\nกระสวย\nกระสอบ\nกระสะ\nกระสัง\nกระสัน\nกระสับกระส่าย\nกระสา\nกระสานติ์\nกระสาบ\nกระสาย\nกระสือ\nกระสุงกระสิง\nกระส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ทิง\nกากะเยีย\nกากี\nกาง\nก้าง\nกางเกง\nกางเขน\nก๊าซ\nกาซะลอง\nกาญจนา\nกาฐ\nกาด\nก๊าด\nกาน\nก่าน\nก้าน\nก๊าน\nกานดา\nกานต์\nกานน\nก้านพร้าว\nกานพลู\nกาน้า\nกาบ\nก้าบ\nกาบู\nกาพย์\nกาเฟอีน\nกาแฟ\nกาม\nก้าม\nกามารมณ์\nกามินี\nกาเมสุมิจฉาจาร\nกาย\nก่าย\nกาเยน\nการ\nการณ์\nการ์ด\nการ์ตูน\nการบูร\nการย์\nการวิก\nการเวก\nการะเกด\nการะบุหนิง\nการัณย์\nการันต์\nการางหัวขวาน\nการิตการก\nการิตวาจก\nการุญ\nการุณย์\nกาเรการ่อน\nกาล\nกาลกรรณี\nกาลกิณี\nกาลจักร\nกาลัญญุตา\nกาลัญญู\nกาลัด\nกาลานุกาล\nกาลิก\nกาลี\nกาแล\nกาแล็กซี\nกาแล็กโทส\nกาว\nก้าว\nกาววาว\nกาวาง\nกาแวน\nกาศิก\nกาษฐะ\nกาษา\nกาสร\nกาสะ\nกาสา\nกาสาร\nกาสาวะ\nกาสิโน\nกาหล\nกาหลง\nกาหลา\nกาเหว่า\nกาไหล่\nกาฬ\nกาฬาวก\nกาฮัง\nกำ\nก่ำ\nกำกวม\nกำกัด\nกำกับ\nก้ำกึ่ง\nกำกูน\nก้ำเกิน\nกำเกียง\nกำคูน\nกำจร\nกำจัด\nกำจาย\nกำชับ\nกำชำ\nกำซาบ\nกำซำ\nกำด้น\nกำดัด\nกำดาล\nกำเดา\nกำธร\nกำนล\nกำนัน\nกำนัล\nกำเนิด\nกำบัง\nก่ำบึ้ง\nกำเบ้อ\nกำปอ\nกำปั่น\nกำผลา\nกำพง\nกำพด\nกำพต\nกำพร้า\nกำพราก\nกำพวด\nกำพอง\nกำพืด\nกำพุด\nกำพู\nกำเพลิง\nกำแพง\nกำภู\nกำมลาศน์\nกำมเลศ\nกำมะถัน\nกำมะลอ\nกำมะหยี่\nกำมะหริด\nกำมังละการ\nกำมังวิลิต\nกำมัชพล\nกำยาน\nกำยำ\nกำรอ\nกำราบ\nกำราล\nกำเริบ\nกำไร\nกำลัง\nกำลุง\nกำเลา\nกำไล\nกำสรด\nกำสรวล\nกำหนด\nกำหนัด\nกำเหน็จ\nกำแหง\nกิก\nกิ๊ก\nกิ่ง\nกิ้งก่า\nกิ้งกือ\nกิ้งโครง\nกิจ\nกิจจะ\nกิจจา\nกิดาการ\nกิดาหยัน\nกิตติ\nกิตติมศักดิ์\nกิน\nกินนร\nกินปลี\nกินเปี้ยว\nกินริน\nกินรี\nกิ๊บ\nกิมตึ๋ง\nกิมิชาติ\nกิมิวิทยา\nกิโมโน\nกิโยตีน\nกิระ\nกิริณี\nกิรินท\nกิริเนศวร\nกิริยา\nกิเลน\nกิเลส\nกิโล\nกิโลมกะ\nกิ่ว\nกิ๋ว\nกี\nกี่\nกี้\nกี๊\nกี๋\nกีฏวิทยา\nกีด\nกีตาร์\nกีบ\nกีรติ\nกีฬา\nกึก\nกึง\nกึ่ง\nกึ๋น\nกุ\nกุก\nกุ๊ก\nกุกกุฏ\nกุกกุร\nกุกรรม\nกุ้ง\nกุงอน\nกุงาน\nกุโงก\nกุจี\nกุญแจ\nกุญชร\nกุฎ\nกุฎา\nกุฎี\nกุฎุมพี\nกุฏฐัง\nกุฏิ\nกุณฑ์\nกุณฑล\nกุณฑี\nกุณโฑ\nกุณาล\nกุณี\nกุด\nกุดัง\nกุดั่น\nกุดา\nกุทัณฑ์\nกุน\nกุ๊น\nกุนเชียง\nกุนที\nกุโนกามอ\nกุบ\nกุบกับ\nกุม\nกุ่ม\nกุมฝอย\nกุมภ์\nกุมภนิยา\nกุมภัณฑ์\nกุมภา\nกุมภิล\nกุมภีล์\nกุมเหง\nกุมาร\nกุมารา\nกุมารี\nกุมุท\nกุย\nกุ๊ย\nกุ๋ย\nกุยช่าย\nกุยเฮง\nกุรระ\nกุรุพินท์\nกุเรา\nกุล\nกุลา\nกุลาหล\nกุลี\nกุลีกุจอ\nกุเลา\nกุแล\nกุเวร\nกุศราช\nกุศล\nกุศโลบาย\nกุสุม\nกุสุมภ์\nกุสุมา\nกุสุมาลย์\nกุสุมิตลดาเวลลิตา\nกุหนี\nกุหนุง\nกุหร่า\nกุหล่า\nกุหลาบ\nกุแหละ\nกู\nกู่\nกู้\nกู๊ก\nกูฏ\nกูฏา\nกูณฑ์\nกูด\nกูบ\nกูปรี\nกูรมะ\nกูรมาวตาร\nเก\nเก้\nเก๊\nเก๋\nเกก\nเก๊ก\nเกกมะเหรก\nเก๊กฮวย\nเก้กัง\nเก็ง\nเก่ง\nเก้ง\nเก๋ง\nเกงกอย\nเก่งกาจ\nเกงเขง\nเก๋งเคง\nเก็จ\nเกจิอาจารย์\nเกชา\nเกณฑ์\nเกด\nเก็ด\nเกตุ\nเกน\nเก็บ\nเกม\nเกย\nเกยูร\nเกรง\nเกร็ง\nเกร็ด\nเกรน\nเกร่อ\nเกรอะ\nเกราะ\nเกริก\nเกริน\nเกริ่น\nเกรียก\nเกรียง\nเกรียด\nเกรียน\nเกรียบ\nเกรียม\nเกรียว\nเกรี้ยว\nเกเร\nเกล็ด\nเกลศ\nเกลอ\nเกลา\nเกล้า\nเกลาะ\nเกลี่ย\nเกลี้ย\nเกลียง\nเกลี้ยง\nเกลียด\nเกลียว\nเกลือ\nเกลื้อ\nเกลือก\nเกลื่อน\nเกลื้อน\nเกวัฏ\nเกวียน\nเกศ\nเกศว\nเกศวะ\nเกศา\nเกศินี\nเกศี\nเกษตร\nเกษม\nเกษียณ\nเกษียน\nเกษียร\nเกส\nเกสร\nเกสรี\nเกสา\nเกสี\nเก้อ\nเกอิชา\nเกะ\nเกะกะ\nเกา\nเก่า\nเก้า\nเก๋า\nเกาต์\nเกาทัณฑ์\nเกาบิล\nเกาลัด\nเกาลิน\nเกาไศย\nเกาหลี\nเกาเหลา\nเกาเหลียง\nเก้าอี้\nเกาะ\nเกิ้ง\nเกิด\nเกิน\nเกิบ\nเกีย\nเกียกกาย\nเกียง\nเกี่ยง\nเกี๋ยง\nเกียจ\nเกียด\nเกียน\nเกี้ยมไฉ่\nเกี้ยมอี๋\nเกียร์\nเกียรติ\nเกียรติ์\nเกี่ยว\nเกี้ยว\nเกี๊ยว\nเกี๊ยะ\nเกื้อ\nเกือก\nเกื้อกูล\nเกือบ\nแก\nแก่\nแก้\nแกง\nแก่ง\nแก้ง\nแก๊ง\nแกงได\nแกงแนง\nแกโดลิเนียม\nแกน\nแก่น\nแก๊ป\nแกม\nแก้ม\nแกมมา\nแกรก\nแกร่ง\nแกร็น\nแกรนิต\nแกรไฟต์\nแกร่ว\nแกระ\nแกล\nแกล้ง\nแกลน\nแกลบ\nแกล้ม\nแกลลอน\nแกลเลียม\nแกล้ว\nแกละ\nแกแล\nแกว\nแก้ว\nแกว่ง\nแก๊ส\nแกะ\nโก\nโก่\nโก้\nโก๋\nโกก\nโกกนุท\nโกกิลา\nโกโก้\nโกง\nโก่ง\nโกงกาง\nโก้งเก้ง\nโกงโก้\nโก้งโค้ง\nโกเชาว์\nโกญจนาท\nโกญจา\nโกฏิ\nโกฐ\nโกฐาส\nโกณะ\nโกดัง\nโกทัณฑ์\nโกน\nโก่น\nโก๋น\nโกมล\nโกมุท\nโกเมน\nโกเมศ\nโกย\nโกรก\nโกรกกราก\nโกรง\nโกร่ง\nโกร่งกร่าง\nโกรงเกรง\nโกร๋งเกร๋ง\nโกรญจ\nโกรต๋น\nโกรธ\nโกรธา\nโกร๋น\nโกรม\nโกรย\nโกรศ\nโกโรโกเต\nโกโรโกโรก\nโกโรโกโส\nโกลน\nโกลาหล\nโกไล\nโกวิท\nโกศ\nโกศล\nโกษม\nโกสน\nโกสัช\nโกสินทร์\nโกสีย์\nโกสุม\nโกไสย\nโกหก\nใกล้\nไก\nไก่\nไก๊\nไก๋\nไก่กอม\nไกพัล\nไกร\nไกรพ\nไกรลาส\nไกรศร\nไกรศรี\nไกรสร\nไกรสรี\nไกรสิทธิ\nไกล\nไกล่\nไกลาส\nไกว\nไกวัล\nขงจื๊อ\nขจร\nขจรจบ\nขจัด\nขจ่าง\nขจาย\nขจาว\nขจิต\nขจี\nขจุย\nขเจา\nขณะ\nขด\nขน\nข้น\nขนง\nขนด\nขนบ\nขนม\nขนอง\nขนอน\nขนอบ\nขนัด\nขนัน\nขนาก\nขนาง\nขนาด\nขนาน\nขนาบ\nขนาย\nขนำ\nขนิษฐ\nขนิษฐา\nขนุน\nขนุนนก\nขบ\nขบถ\nขบวน\nขบวร\nขม\nข่ม\nขมงโกรย\nขมวด\nขมวน\nขมอง\nขม่อม\nขมัง\nขมับ\nขมา\nขม้ำ\nขมิ้น\nขมิบ\nขมีขมัน\nขมึง\nขมึงทึง\nขมุ\nขมุกขมัว\nขมุบ\nขโมย\nขยด\nขยม\nขย่ม\nขยอก\nขยอง\nขย่อน\nขย้อน\nขยะ\nขยัก\nขยัน\nขยั้น\nขยับ\nขยาด\nขยาย\nขยำ\nขย้ำ\nขยิก\nขยิบ\nขยิ่ม\nขยี้\nขยุกขยิก\nขยุกขยุย\nขยุบ\nขยุบขยิบ\nขยุม\nขยุ้ม\nขยุย\nขรม\nขรรค์\nขรัว\nขริบ\nขรี\nขรึม\nขรุขระ\nขลบ\nขล้อ\nขลัง\nขลับ\nขลาด\nขลาย\nขลิบ\nขลุก\nขลุกขลัก\nขลุกขลิก\nขลุบ\nขลุม\nขลุ่ย\nขลู\nขลู่\nขวง\nข่วง\nขวด\nข่วน\nขวนขวาย\nขวบ\nขวย\nขวักไขว่\nขวัญ\nขวั้น\nขวับ\nขวับเขวียว\nขวา\nขวาก\nขวาง\nขว้าง\nขวาด\nขวาน\nขวายขวน\nขวาว\nขว้าว\nขวิด\nขอ\nข่อ\nข้อ\nของ\nข้อง\nขอด\nขอน\nข้อน\nขอบ\nขอม\nข่อย\nข้อย\nข่อยหยอง\nขะแจะ\nขะเน็ด\nขะมอมขะแมม\nขะมักเขม้น\nขะมุกขะมอม\nขะยิก\nขะยุก\nขะเย้อแขย่ง\nขัค\nขัง\nขังขอก\nขัช\nขัณฑสกร\nขัณฑสีมา\nขัด\nขัดมอน\nขัตติยมานะ\nขัน\nขั้น\nขันติ\nขันตี\nขันโตก\nขันที\nขันธ์\nขันธาวาร\nขับ\nขัว\nขั้ว\nขา\nข่า\nข้า\nขาก\nขาก๊วย\nขาง\nข่าง\nข้าง\nขาณุ\nขาด\nขาทนียะ\nขาน\nขาบ\nข้าพเจ้า\nขาม\nข่าม\nข้าม\nขาย\nข่าย\nขาล\nขาว\nข่าว\nข้าว\nข้าวอังกุลี\nขำ\nขิก\nขิง\nขิงแกลง\nขิงแครง\nขิด\nขิปสัทโท\nขิม\nขี่\nขี้\nขี้เข็บ\nขีณาสพ\nขีด\nขี้ตังนี\nขีปนาวุธ\nขี้ยอก\nขีระ\nขึง\nขึ้ง\nขึ้น\nขึ้นฉ่าย\nขืน\nขื่น\nขื่อ\nขุก\nขุด\nขุน\nขุ่น\nขุนเพ็ด\nขุม\nขุย\nขู่\nขูด\nเข\nเข้\nเขก\nเข็ง\nเข่ง\nเขจร\nเข็ญ\nเข็ด\nเขดา\nเขต\nเขน\nเข็น\nเข่น\nเขนง\nเขน็ด\nเขนย\nเขบ็จขบวน\nเขบ็ต\nเขม\nเข็ม\nเข้ม\nเข้มขาบ\nเขม็ง\nเขม็ดแขม่\nเขม่น\nเขม้น\nเขม้นขะมัก\nเขมร\nเขมา\nเขม่า\nเขมือบ\nเขย\nเขยก\nเขย่ง\nเขย้อแขย่ง\nเขย่า\nเขยิน\nเขยิบ\nเขยื้อน\nเขรอะ\nเขลง\nเขลอะ\nเขละ\nเขลา\nเขลาะ\nเขว\nเขษม\nเขฬะ\nเขะขะ\nเขา\nเข่า\nเข้า\nเขิง\nเขิน\nเขิบ\nเขี่ย\nเขียง\nเขียด\nเขียดตะปาด\nเขียน\nเขี่ยน\nเขียม\nเขียว\nเขี้ยว\nเขียะ\nเขือ\nเขือง\nเขื่อง\nเขื่อน\nเขือม\nแข\nแข้\nแขก\nแข็ง\nแข่ง\nแข้ง\nแขน\nแข่น\nแข้น\nแขนง\nแขม\nแขม็บ\nแขม่ว\nแขยง\nแขย่ง\nแขวก\nแขวง\nแขวน\nแขวะ\nโข\nโขก\nโขง\nโข่ง\nโขด\nโขดง\nโขน\nโขนง\nโขม\nโขมง\nโขมด\nโขยก\nโขยง\nโขย่ง\nโขยด\nโขลก\nโขลง\nโขลน\nโขษม\nไข\nไข่\nไข้\nไขว่\nไขว้\nคคนะ\nคคนัมพร\nคคนางค์\nคคนานต์\nคง\nคงคา\nคงไคย\nคช\nคชาชาติ\nคชาชีพ\nคชาธาร\nคชาภรณ์\nคณนา\nคณบดี\nคณะ\nคณาจารย์\nคณาธิการ\nคณาธิปไตย\nคณานับ\nคณิกา\nคณิต\nคเณศ\nคด\nคดี\nคติ\nคทา\nคน\nค้น\nคนทา\nคนทิสอ\nคนที\nคนโท\nคนธ์\nคันธ์\nคนธรรพ์\nคเนจร\nคบ\nคม\nคมน์\nคมนาการ\nคมนาคม\nคมิกภัต\nครก\nครบ\nครรชิต\nครรภ\nครรภ์\nครรลอง\nครรโลง\nครรไล\nครวญ\nครวี\nครหา\nครอก\nครอง\nครองแครง\nคร่อเงาะ\nคร่อเทียน\nครอบ\nคร่อม\nคระเมิม\nคระแลง\nคระไล\nคระแวง\nคระหน\nคระหวน\nคระหาย\nคระโหย\nครั่ง\nครั้ง\nครัดเคร่ง\nครัน\nครั่น\nครั้น\nครับ\nครัว\nครา\nคร่า\nคราก\nคราง\nคราญ\nคราด\nคร้าน\nคราบ\nคราม\nคร้าม\nครามครัน\nคราว\nคร่าว\nคราส\nครำ\nคร่ำ\nคร่ำเคร่ง\nคริปทอน\nคริสต์\nครีบ\nครีม\nครีษมายัน\nครึ\nครึกครื้น\nครึกโครม\nครึ่ง\nครึ่ด\nครึน\nครึ้ม\nครืด\nครืน\nครื้น\nครืนครั่น\nครื้นครั่น\nครื้นครึก\nครื้นเครง\nครือ\nครุ\nครุคระ\nครุฑ\nครุ่น\nครุมเครือ\nครุย\nครุวนา\nครู\nครู่\nครูด\nคฤโฆษ\nคฤนถ์\nคฤหบดี\nคฤหัสถ์\nคฤหาสน์\nคลวง\nคลอ\nคล้อ\nคลอก\nคลอง\nคล่อง\nคล้อง\nคลอด\nคลอน\nคล้อย\nคลอรีน\nคลอโรฟอร์ม\nคลอโรฟีลล์\nคละ\nคละคลุ้ง\nคลัก\nคลั่ก\nคลัง\nคลั่ง\nคลัตช์\nคลับคล้าย\nคลับคลา\nคลา\nคล้า\nคลางแคลง\nคลาด\nคลาน\nคลาย\nคล้าย\nคล้ายคลึง\nคล่าว\nคลำ\nคล่ำ\nคล้ำ\nคลิ้งโคลง\nคลิด\nคลินิก\nคลี\nคลี่\nคลึง\nคลื่น\nคลุก\nคลุ้ง\nคลุบ\nคลุม\nคลุ่ม\nคลุ้ม\nควง\nควณ\nควน\nควบ\nควย\nควร\nควัก\nควั่ก\nควั่งคว้าง\nควัน\nควั่น\nคว้า\nควาก\nคว้าง\nควาญ\nควาน\nคว้าน\nความ\nควาย\nคว่าว\nคว่ำ\nควินิน\nควิวคว่าง\nคหกรรม\nคหกรรมศาสตร์\nคหบดี\nคหัฐ\nคอ\nค้อ\nคอก\nค็อกคัส\nค็อกเทล\nคอเคซอยด์\nค่องอ้อย\nคอด\nคอแดง\nคอน\nค่อน\nค้อน\nคอนกรีต\nคอนเดนเซอร์\nคอนแวนต์\nคอนเสิร์ต\nคอม\nค่อม\nค้อม\nคอมพิวเตอร์\nคอมมานโด\nคอมมิวนิสต์\nคอย\nค่อย\nค้อย\nคอยล์\nคอร์ด\nคอแลน\nคอสติกโซดา\nคะ\nค่ะ\nคะไขว่\nคะค้อย\nคะคาน\nคะนน\nคะนอง\nคะน้า\nคะนึง\nคะเน\nคะเนงร้าย\nคะเน็ด\nคะแนน\nคะมำ\nคะยั้นคะยอ\nคะเยอ\nคัก\nคั่ก\nคัคนะ\nคัคนัมพร\nคัคนางค์\nคัคนานต์\nคั่ง\nคังไคย\nคัจฉ\nคัณฑมาลา\nคัณฑสูตร\nคัด\nคัดเค้า\nคัดมอน\nคัดเม็ง\nคัทลียา\nคัน\nคั่น\nคั้น\nคันถรจนาจารย์\nคันธกุฎี\nคันธมาทน์\nคันธารราษฎร์\nคับ\nคับค้อน\nคับคา\nคับแค\nคัพภ์\nคัมภีร์\nคัมภีรภาพ\nคัล\nคั่ว\nคา\nค่า\nค้า\nค่าคบ\nคาง\nค่าง\nค้าง\nคางคก\nค้างคาว\nคาด\nคาถา\nคาทอลิก\nคาน\nค้าน\nคาบ\nคาพยุต\nคาม\nคามวาสี\nคามณีย์\nคามภีร์\nคาย\nค่าย\nคาร์บอน\nคาร์บอเนต\nคาร์บอลิก\nคาร์บูเรเตอร์\nคาร์โบรันดัม\nคาร์โบไฮเดรต\nคารม\nคารวะ\nคาราเต้\nคาราวาน\nคาว\nค่าว\nค้าว\nคาวตอง\nคาวี\nคาวุต\nคาส\nคำ\nค่ำ\nค้ำ\nคำนวณ\nคำนวร\nคำนับ\nคำนัล\nคำนึง\nคำนูณ\nคำฝอย\nคำเพลิง\nคำรน\nคำรบ\nคำราม\nคำแสด\nคำแหง\nคำโอง\nคิก\nคิง\nคิด\nคิมหันต์\nคิรี\nคิลาน\nคิลานะ\nคิว\nคิ้ว\nคี่\nคีต\nคีบ\nคีม\nคีรี\nคีรีบูน\nคึก\nคึ่ก\nคึกคัก\nคืน\nคืบ\nคือ\nคุ\nคุก\nคุกกี้\nคุกคาม\nคุกพาทย์\nคุ้ง\nคุณ\nคุด\nคุดทะราด\nคุต\nคุตติ\nคุ่น\nคุ้น\nคุป\nคุปต์\nคุปติ\nคุม\nคุ่ม\nคุ้ม\nคุย\nคุ้ย\nคุยหฐาน\nคุยหประเทศ\nคุรุ\nคุลา\nคุลิก่า\nคุลีการ\nคุหา\nคู\nคู่\nคู้\nคูณ\nคูถ\nคูน\nคูปอง\nคูเรียม\nคูหา\nเค้ก\nเค้เก้\nเค้ง\nเคณฑะ\nเคด\nเค็ด\nเคน\nเค้น\nเคเบิล\nเค็ม\nเคมี\nเคย\nเครง\nเคร่ง\nเครงครา\nเครงครำ\nเครดิต\nเครน\nเครา\nเคร่า\nเคราหณี\nเคราะห์\nเครียด\nเครียว\nเครือ\nเครื่อง\nเคล้ง\nเคล็ด\nเคล้น\nเคล้า\nเคล่าคล่อง\nเคลิบเคลิ้ม\nเคลิ้ม\nเคลีย\nเคลื่อน\nเคลือบ\nเคว้ง\nเคหะ\nเคหา\nเคอะ\nเค้า\nเคาน์เตอร์\nเคารพ\nเคาะ\nเคาะแคะ\nเคียง\nเคียด\nเคียน\nเคียม\nเคี่ยม\nเคียร\nเคียว\nเคี่ยว\nเคี้ยว\nเคือง\nแค\nแค่\nแค้\nแคแล\nแคดเมียม\nแค็ตตาล็อก\nแคแตร\nแคโทด\nแคน\nแค่น\nแค้น\nแคบ\nแคบหมู\nแคปซูล\nแคม\nแคร่\nแครก\nแครง\nแคระ\nแคลคูลัส\nแคลง\nแคลเซียม\nแคลน\nแคล้ว\nแคล่วคล่อง\nแคลอรี\nแคลิฟอร์เนียม\nแคว\nแควก\nแคว้น\nแคแสด\nแคะ\nโค\nโคก\nโคเคน\nโค่ง\nโค้ง\nโคจร\nโคเซแคนต์\nโคไซน์\nโคตร\nโคแทนเจนต์\nโคธา\nโคน\nโค่น\nโคบอลต์\nโคปผกะ\nโคม\nโคม่า\nโครก\nโครกคราก\nโครง\nโคร่ง\nโคร่งคร่าง\nโครม\nโครเมียม\nโครโมโซม\nโคราช\nโครำ\nโคล\nโคลง\nโคลน\nโควตา\nโคออร์ดิเนต\nใคร\nใคร่\nใคร่ครวญ\nไค\nไค้\nไคร้\nไคร้เครือ\nไคล\nไคล้\nฆนะ\nฆราวาส\nฆ้อง\nฆ่า\nฆาต\nฆาน\nฆานินทรีย์\nเฆี่ยน\nโฆรวิส\nโฆษก\nโฆษณา\nโฆษะ\nโฆษ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nเฉ่ง\nเฉด\nเฉท\nเฉนียน\nเฉพาะ\nเฉย\nเฉลย\nเฉลว\nเฉลา\nเฉลิม\nเฉลี่ย\nเฉลียง\nเฉลี่ยง\nเฉลียบ\nเฉลียว\nเฉวียง\nเฉวียน\nเฉอะแฉะ\nเฉา\nเฉาก๊วย\nเฉาฮื้อ\nเฉาะ\nเฉิด\nเฉิบ\nเฉียง\nเฉียงพร้านางแอ\nเฉียด\nเฉียบ\nเฉียว\nเฉี่ยว\nเฉือน\nเฉื่อย\nแฉ\nแฉ่\nแฉก\nแฉง\nแฉ่ง\nแฉลบ\nแฉล้ม\nแฉะ\nโฉ\nโฉ่\nโฉเก\nโฉ่งฉ่าง\nโฉงเฉง\nโฉด\nโฉนด\nโฉบ\nโฉเบ๊\nโฉม\nโฉลก\nไฉน\nไฉไล\nชก\nชคัตตรัย\nชง\nชงโค\nชงฆ์\nชงฆา\nชงโลง\nชฎา\nชฎามังษี\nชฎามังสี\nชฎิล\nชด\nชน\nชนก\nชนนี\nชนม์\nชนวน\nชนะ\nชนัก\nชนา\nชนาง\nชนิด\nชเนตตี\nชบา\nชม\nชมดชม้อย\nชมนาด\nชมพู\nชมพู่\nชมรม\nชม้อย\nชม้าย\nชไม\nชยา\nชโย\nชรทึง\nชรริน\nชรอุ่ม\nชระล้ำ\nชระลุ\nชระอาบ\nชระเอม\nชรัด\nชรา\nชล\nชโลง\nชโลม\nช่วง\nชวด\nชวน\nชวย\nช่วย\nชวร\nชวลิต\nชวา\nชวาล\nชวาลา\nช่อ\nชอก\nช็อก\nช็อกโกเลต\nช็อกโกแลต\nชอง\nช่อง\nช้อง\nชองระอา\nชอน\nช่อน\nช้อน\nชอบ\nชอม\nช้อย\nชอล์ก\nชอ่ำ\nชอุ่ม\nชะ\nชะคราม\nชะงอก\nชะง่อน\nชะงัก\nชะงัด\nชะง้ำ\nชะงุ้ม\nชะเง้อ\nชะเงื้อม\nชะแง้\nชะโงก\nชะฉ่า\nชะช่อง\nชะชะ\nชะช้า\nชะโด\nชะตา\nชะต้า\nชะนี\nชะเนาะ\nชะเนียง\nชะพลู\nชะเพลิง\nชะมด\nชะมบ\nชะมวง\nชะมัง\nชะมัด\nชะแม่\nชะรอย\nชะลอ\nชะลอม\nชะล่า\nชะลาน\nชะลิน\nชะลูด\nชะเลง\nชะเลย\nชะแล็ก\nชะแลง\nชะวาก\nชะวาด\nชะเวิกชะวาก\nชะแวง\nชะอม\nชะอ้อน\nชะเอม\nชะโอน\nชัก\nชักคราม\nชักช้า\nชัง\nชั่ง\nชังคา\nชังฆ\nชัชวาล\nชัฏ\nชัด\nชัดช้า\nชัน\nชั้น\nชันกาด\nชันชี\nชันตุ\nชันนะตุ\nชันนุ\nชันโรง\nชันษา\nชันสูตร\nชัปนะ\nชัพ\nชัมพูนท\nชัย\nชัยพฤกษ์\nชัยภูมิ\nชัลลุกา\nชั่ว\nชั้ว\nชัวชม\nชา\nช้า\nชาคระ\nชาคริต\nชาคริยานุโยค\nช่าง\nช้าง\nช้าช่อน\nชาญ\nชาด\nชาดก\nชาต\nชาตบุษย์\nชาตรี\nชาตะ\nชาตา\nชาติ\nชาน\nชานุ\nช้าปี่\nชาปีไหน\nช้าแป้น\nช้าพลู\nชาม\nชามพูนท\nชามาดร\nชามาดา\nชามาตุ\nชาย\nชายา\nชาล\nชาลา\nชาลินี\nช้าเลือด\nชาว\nชาวี\nชำ\nช่ำ\nช้ำ\nชำงัด\nชำงาย\nช่ำชอง\nชำนะ\nชำนัญ\nชำนัน\nชำนาญ\nชำนิ\nชำเนียร\nชำมะนาด\nชำมะเลียง\nชำร่วย\nชำระ\nช้ำรั่ว\nชำรุด\nชำเรา\nชำเราะ\nชำแรก\nชำแระ\nชำเลือง\nชำแหละ\nชิ\nชิง\nชิ่ง\nชิงชัน\nชิงช้า\nชิงช้าชาลี\nชิงชี่\nชิงฮื้อ\nชิชะ\nชิชิ\nชิณณะ\nชิด\nชิเดนทรีย์\nชิต\nชิตินทรีย์\nชิน\nชิ้น\nชินโต\nชิโนรส\nชิม\nชิมแปนซี\nชิยา\nชิรณะ\nชิระ\nชิวหา\nชิสา\nชี\nชี่\nชี้\nชีปะขาว\nชีผะขาว\nชีผ้าขาว\nชีพ\nชีฟอง\nชีรณ\nชีระ\nชีวเคมี\nชีวประวัติ\nชีวภาพ\nชีววิทยา\nชีวะ\nชีวัน\nชีวา\nชีวาตม์\nชีวาลัย\nชีวิต\nชีวิตักษัย\nชีวิน\nชีวี\nชืด\nชื่น\nชื้น\nชื่อ\nชุก\nชุกชี\nชุ้ง\nชุณห\nชุด\nชุติ\nชุน\nชุบ\nชุม\nชุ่ม\nชุมนุม\nชุมพร\nชุมพา\nชุมแพรก\nชุมรุม\nชุมแสง\nชุมเห็ด\nชุ่ย\nชุลมุน\nชุลี\nชุษณะ\nชู\nชู้\nเช็ค\nเช้ง\nเช้งวับ\nเชงเลง\nเช็ด\nเชน\nเช่น\nเชย\nเชลง\nเชลย\nเชลแล็ก\nเชลียง\nเชวง\nเชษฐะ\nเชษฐา\nเชอ\nเช่า\nเช้า\nเชาว์\nเชาวน์\nเชิง\nเชิญ\nเชิด\nเชิ้ต\nเชียง\nเชี่ยน\nเชียบ\nเชี่ยม\nเชียร\nเชียร์\nเชียว\nเชี่ยว\nเชื่อ\nเชื้อ\nเชือก\nเชื่อง\nเชือด\nเชือน\nเชื่อม\nแช\nแช่\nแช่ง\nแชงมา\nแชบ๊วย\nแช่ม\nแชร์\nแชล่ม\nแชสซี\nแชะ\nโชก\nโชค\nโชงโลง\nโชดก\nโชดึก\nโชต\nโชตก\nโชติ\nโชติก\nโชน\nโชมโรม\nโชย\nโชยงการ\nโชยชาย\nโชยติส\nโชว์\nใช่\nใช้\nไช\nไชนะ\nไชย\nไชโย\nซก\nซ่ก\nซงดำ\nซ่งฮื้อ\nซด\nซน\nซ้น\nซบ\nซม\nซวดเซ\nซวน\nซวย\nซอ\nซอก\nซอง\nซ่อง\nซ้อง\nซองแมว\nซ้องแมว\nซอน\nซ่อน\nซ้อน\nซอม\nซ่อม\nซ้อม\nซอมซ่อ\nซอย\nซอส\nซัก\nซักส้าว\nซัง\nซั้ง\nซัด\nซับ\nซัลฟา\nซั้ว\nซา\nซ่า\nซาก\nซาง\nซ่าง\nซาด\nซาน\nซ่าน\nซาบซ่าน\nซาบซึ้ง\nซ่าโบะ\nซาแมเรียม\nซ้าย\nซาลาเปา\nซาว\nซ่าหริ่ม\nซำ\nซ้ำ\nซิ\nซี\nซิก\nซิกข์\nซิกซี้\nซิกแซ็ก\nซิการ์\nซิงโคนา\nซิ่น\nซินนามิก\nซินแส\nซิบ\nซิป\nซิฟิลิส\nซิลิคอน\nซิว\nซี่\nซีก\nซีเซียม\nซีด\nซี้ด\nซีนอน\nซีป่าย\nซีเมนต์\nซีเรียม\nซีลีเนียม\nซีอิ๊ว\nซึก\nซึง\nซึ่ง\nซึ้ง\nซึม\nซื่อ\nซื้อ\nซุก\nซุง\nซุน\nซุบ\nซุป\nซุ่ม\nซุ้ม\nซุ่มซ่าม\nซุย\nซู่\nซูโครส\nซูด\nซู้ด\nซูดซาด\nซูบ\nเซ\nเซ็ก\nเซแคนต์\nเซ็ง\nเซ่ง\nเซ้ง\nเซ็งแซ่\nเซต\nเซน\nเซ็น\nเซ่น\nเซนติกรัม\nเซนติเกรด\nเซนติเมตร\nเซนติลิตร\nเซปักตะกร้อ\nเซราะ\nเซรุ่ม\nเซลเซียส\nเซลล์\nเซลลูลอยด์\nเซลลูโลส\nเซ่อ\nเซอร์โคเนียม\nเซอะ\nเซา\nเซ้าซี้\nเซาะ\nเซิง\nเซิ้ง\nเซียน\nเซียบ\nเซียมซี\nเซียว\nเซี่ยว\nเซี่ยวกาง\nเซื่อง\nแซ\nแซ่\nแซ็กคาริน\nแซง\nแซงแซว\nแซด\nแซบ\nแซม\nแซยิด\nแซ่ว\nแซะ\nโซ\nโซ่\nโซก\nโซ่ง\nโซงโขดง\nโซเซ\nโซดา\nโซเดียม\nโซม\nโซรม\nโซลา\nไซ\nไซ้\nไซเกิล\nไซโคลน\nไซน์\nไซยาไนด์\nไซร้\nไซเรน\nไซโล\nฌาน\nฌาปน\nฌาปนกิจ\nฌาปนสถาน\nเฌอ\nเฌอเอม\nญวน\nญัตติ\nญาณ\nญาติ\nญานาซะฮ์\nญิบ\nญี่ปุ่น\nเญยธรรม\nไญยธรรม\nฎีกา\nฐกัด\nฐากูร\nฐาน\nฐานะ\nฐานันดร\nฐานานุกรม\nฐานานุรูป\nฐานานุศักดิ์\nฐานียะ\nฐาปน\nฐาปนา\nฐายี\nฐิต\nฐิติ\nฑาหก\nฑาหะ\nเฒ่า\nณรงค์\nเณร\nดก\nดง\nด้ง\nด้น\nดนโด่\nดนตรี\nดนัย\nดนุ\nดนู\nดบัสวิน\nดบัสวี\nดม\nดรงค์\nดรณี\nดรรชนี\nดราฟต์\nดรุณ\nดรุณี\nดล\nดวง\nด้วง\nดวด\nด่วน\nด้วน\nด้วย\nดอก\nดอง\nด่อง\nด้อง\nดองฉาย\nดองดึง\nดอด\nดอน\nด่อน\nดอม\nด้อม\nดอย\nด้อย\nดอลลาร์\nดะ\nดะโต๊ะ\nดะหมัง\nดัก\nดักดาน\nดักแด้\nดัง\nดั่ง\nดั้ง\nดัชนี\nดัด\nดัตช์\nดัน\nดั้น\nดับ\nดัมพ์\nดั้วเดี้ย\nดัสกร\nดา\nด่า\nดาก\nด่าง\nด้าง\nดาด\nดาน\nด่าน\nด้าน\nดาบ\nดาบส\nดาม\nด้าม\nด้ามจิ้ว\nดามพ์\nดาย\nด้าย\nดารกะ\nดารณี\nดารดาษ\nดาระ\nดารา\nดาล\nดาลัด\nดาว\nด่าว\nด้าว\nดาวดึงส์\nดาวบส\nดาษ\nดาษดา\nดำ\nด่ำ\nด้ำ\nดำกล\nดำเกิง\nดำแคง\nดำดง\nดำนาณ\nดำเนิน\nดำบล\nดำรง\nดำรวจ\nดำรัส\nดำริ\nดำรี\nดำรู\nดำฤษณา\nดำเลิง\nดิก\nดิ่ง\nดิฉัน\nดิฐ\nดิตถ์\nดิถี\nดิน\nดิ้น\nดิบ\nดิรัจฉาน\nดิลก\nดิ่ว\nดิ้ว\nดิ้วเดี้ยว\nดิษฐ์\nดิสโพรเซียม\nดี\nดีเซล\nดีด\nดีดีที\nดีบุก\nดีปลี\nดีเปรสชัน\nดีหมี\nดีหลี\nดึก\nดึง\nดึ่ง\nดึ่ม\nดื่น\nดื่ม\nดือ\nดื้อ\nดุ\nดุก\nดุกดิก\nดุกทะเล\nดุ้ง\nดุ้งดิ้ง\nดุจ\nดุด\nดุน\nดุ้น\nดุบ\nดุม\nดุ่ม\nดุ่ย\nดุรงค์\nดุริยะ\nดุริยางค์\nดุริยางคศาสตร์\nดุริยางคศิลป์\nดุล\nดุษฎี\nดุษณี\nดุษณีภาพ\nดุษิต\nดุสิต\nดุเหว่า\nดู\nดูกค่าง\nดูกร\nดูด\nดูรา\nดูแล\nเด\nเด่\nเดก\nเด็ก\nเดกซ์โทรส\nเดคากรัม\nเดคาเมตร\nเดคาลิตร\nเด้ง\nเด็จ\nเดช\nเดชน์\nเดชนะ\nเดชะ\nเดโช\nเดซิกรัม\nเดซิเมตร\nเดซิลิตร\nเด็ด\nเดน\nเด่น\nเดนมาร์ก\nเดรัจฉาน\nเด๋อ\nเด๋อด๋า\nเดา\nเด้า\nเดาะ\nเดิน\nเดิ่น\nเดิม\nเดียง\nเดียด\nเดียรดาษ\nเดียรถีย์\nเดียรัจฉาน\nเดียว\nเดี่ยว\nเดี๋ยว\nเดียะ\nเดื่อ\nเดือก\nเดื่อง\nเดือด\nเดือน\nเดือย\nแด\nแด่\nแดก\nแด็ก\nแดกงา\nแดกแด้\nแดง\nแดด\nแดน\nแด่น\nแด่ว\nแดะ\nแดะแด๋\nโด\nโด่\nโดกเดก\nโด่ง\nโดด\nโดน\nโดม\nโดมร\nโดย\nโดรณ\nใด\nได\nได้\nไดแซ็กคาไรด์\nไดนาโม\nไดนาไมต์\nไดโนเสาร์\nไดเรกตริกซ์\nตก\nต๊กโต\nตง\nต๋ง\nตงฉิน\nตงิด\nตงุ่น\nตด\nตติย\nตถาคต\nตน\nต้น\nตนัย\nตนุ\nตบ\nตบะ\nตปนียะ\nตม\nต้ม\nตมูก\nตยาคี\nตรง\nตรณี\nตรม\nตรรก\nตรรกะ\nตรลบ\nตรลอด\nตรลาด\nตรวจ\nตรวน\nตรอก\nตรอง\nตรอมใจ\nตรอมตรม\nตระ\nตระกล\nตระกวน\nตระกอง\nตระการ\nตระกูล\nตระคัร\nตระเตรียม\nตระนาว\nตระบก\nตระบอก\nตระบอง\nตระบัด\nตระบัน\nตระเบ็ง\nตระแบก\nตระแบง\nตระโบม\nตระพอง\nตระพัง\nตระลาการ\nตระวัน\nตระเว็ด\nตระเวน\nตระสัก\nตระหง่าน\nตระหนก\nตระหนัก\nตระหน่ำ\nตระหนี่\nตรัง\nตรังค์\nตรับ\nตรับฟัง\nตรัย\nตรัยตรึงศ์\nตรัส\nตรัสสา\nตรา\nตรากตรำ\nตราชู\nตราบ\nตราสัง\nตรำ\nตริ\nตริว\nตรี\nตรีปวาย\nตรีพิธพรรณ\nตรียัมปวาย\nตรึก\nตรึง\nตรุ\nตรุณ\nตรุณะ\nตรุษ\nตรู\nตรู่\nตฤณ\nตฤตีย\nตฤษณา\nตลก\nตลบ\nตลอด\nตลับ\nตลาด\nตลิ่ง\nตลึง\nตวง\nต่วน\nต้วมเตี้ยม\nตวัก\nตวัด\nตวาด\nตอ\nตอม่อ\nต่อ\nต้อ\nตอก\nต๊อก\nต๊อกต๋อย\nตอง\nต้อง\nตองกราย\nต้องเต\nตองแตก\nต่องแต่ง\nตองเหลือง\nตอด\nตอน\nต้อน\nตอบ\nตอเบา\nตอม\nต่อม\nต๋อม\nต่อย\nต้อย\nต้อยตริ่ง\nต้อยติ่ง\nต้อยตีวิด\nตอแย\nตอร์ปิโด\nต่อไส้\nตอแหล\nตะ\nตะกรน\nตะกร้อ\nตะกรัน\nตะกรับ\nตะกร้า\nตะกราม\nตะกรุด\nตะกรุม\nตะกรุมตะกราม\nตะกละ\nตะกลาม\nตะกวด\nตะกอ\nตะกอน\nตะกัง\nตะกั่ว\nตะกาง\nตะกาด\nตะกาย\nตะกาว\nตะกุกตะกัก\nตะกุย\nตะกู\nตะกูด\nตะเกียกตะกาย\nตะเกียง\nตะเกียบ\nตะแก\nตะแก่\nตะแกรง\nตะโก\nตะโก้\nตะโกก\nตะโกน\nตะโกรง\nตะโกรม\nตะไกร\nตะขบ\nตะขอ\nตะขาบ\nตะขิดตะขวง\nตะเข้\nตะเข็บ\nตะโขง\nตะคร้อ\nตะครอง\nตะครั่นตะครอ\nตะคร้ำ\nตะคริว\nตะคิว\nตะครุบ\nตะคอก\nตะคัน\nตะค้า\nตะคาก\nตะค้าน\nตะคุ่ม\nตะเครียว\nตะเคียว\nตะเคียน\nตะแคง\nตะไคร่\nตะไคร้\nตะเฆ่\nตะนอย\nตะนาว\nตะบม\nตะบอง\nตะบอย\nตะบัน\nตะบิ้ง\nตะบิด\nตะบิดตะบอย\nตะบี้ตะบัน\nตะบึง\nตะบูน\nตะเบ็ง\nตะเบ็งมาน\nตะเบ๊ะ\nตะแบก\nตะแบง\nตะโบม\nตะไบ\nตะปบ\nตะปลิง\nตะปิ้ง\nตะปุ่มตะป่ำ\nตะปู\nตะพง\nตะพด\nตะพอง\nตะพัก\nตะพัง\nตะพัด\nตะพั้น\nตะพาก\nตะพาน\nตะพาบ\nตะพาย\nตะพึด\nตะพึดตะพือ\nตะพุ่น\nตะเพรา\nตะเพิง\nตะเพิด\nตะเพียน\nตะโพก\nตะโพง\nตะโพน\nตะเภา\nตะใภ้\nตะม่อ\nตะมอย\nตะรังกะนู\nตะรังตังกวาง\nตะรังตังช้าง\nตะราง\nตะลอง\nตะลอน\nตะล่อม\nตะละ\nตะลาน\nตะลิงปลิง\nตะลิบ\nตะลีตะลาน\nตะลึง\nตะลึงพรึงเพริด\nตะลุง\nตะลุ่ม\nตะลุ่มนก\nตะลุมบอน\nตะลุ่มโปง\nตะลุมพอ\nตะลุมพุก\nตะลุย\nตะเลง\nตะแลงแกง\nตะไล\nตะวัน\nตะเวน\nตะหลิว\nตะหลุก\nตะหลุง\nตะแหง่ว\nตะแหมะแขะ\nตะโหงก\nตัก\nตักกะ\nตักเตือน\nตั๊กแตน\nตักษัย\nตัง\nตั่ง\nตั้ง\nตังเก\nตังฉ่าย\nตังเม\nตังวาย\nตังโอ๋\nตัจฉก\nตัจฉนี\nตัณฑุล\nตัณหา\nตัด\nตัน\nตันตระ\nตันติ\nตันหยง\nตับ\nตับปิ้ง\nตัว\nตั๋ว\nตัวจี๊ด\nตัวตืด\nตั้วโผ\nตั้วเหี่ย\nตา\nตาก\nตากวาง\nต่าง\nตาด\nตาน\nต่าน\nต้าน\nตานนกกด\nตานี\nตาบ\nตาม\nตามะแน\nตามิน\nตาย\nตาราไต\nตาล\nตาลุ\nต๋าว\nตาเสือ\nตาหนู\nตาฬ\nตำ\nต่ำ\nตำนาน\nตำบล\nตำแบ\nตำแย\nตำรวจ\nตำรับ\nตำรา\nตำรุ\nตำลึง\nตำเสา\nตำหนัก\nตำหนิ\nตำแหน่ง\nติ\nติก\nติ๊ก\nติกะ\nติกาหรัง\nติง\nติ่ง\nติ๋ง\nติ่งตั่ง\nติ๋งต่าง\nติงส\nติงสติ\nติณ\nติด\nติตติกะ\nติตติร\nติตถ\nติตถะ\nติถี\nติมิงคละ\nติรัจฉาน\nติลก\nติละ\nติ้ว\nตี\nตี่\nตีน\nตีบ\nตีรถะ\nตีระ\nตึ\nตึก\nตึ้ก\nตึ้กตั้ก\nตึง\nตึดตื๋อ\nตึ๊ดตื๋อ\nตืด\nตื่น\nตื้น\nตื้อ\nตื๊อ\nตื๋อ\nตุ\nตุ๊\nตุ๊กแก\nตุ๊กตา\nตุ๊กต่ำ\nตุกติก\nตุ๊กติ๊ก\nตุ๊กตุ่น\nตุ๊กตุ๋ย\nตุง\nตุ้งก่า\nตุ้งติ้ง\nตุ๊ดตู่\nตุน\nตุ่น\nตุ๋น\nตุนาหงัน\nตุบ\nตุ้บ\nตุ๊บป่อง\nตุปัดตุป่อง\nตุปัดตุเป๋\nตุ่ม\nตุ้ม\nตุ๋ม\nตุ้มกว้าว\nตุมกา\nตุ้มแซะ\nตุมตัง\nตุ้มเต๋น\nตุ้มปี่\nตุมพะ\nตุ่ย\nตุ้ย\nตุ๊ย\nตุ๋ยตุ่ย\nตุรคะ\nตุรงค์\nตุล\nตุลา\nตุหรัดตุเหร่\nตู\nตู่\nตู้\nตูก\nตูด\nตูบ\nตูม\nเต๊ก\nเต็ง\nเต่ง\nเตช\nเตโช\nเต้น\nเต็นท์\nเต็ม\nเตย\nเตร่\nเตร็ด\nเตรตา\nเตรียม\nเตรียมตรม\nเตละ\nเตลิด\nเตว็ด\nเต่อ\nเตอะ\nเตะ\nเตา\nเต่า\nเต้า\nเต๋า\nเต่าเกียด\nเต้าเจี้ยว\nเต้าทึง\nเต้าหู้\nเต้าฮวย\nเต๊าะ\nเตาะแตะ\nเติ่ง\nเติบ\nเติม\nเตี้ย\nเตียง\nเตียน\nเตียบ\nเตี๋ยม\nเตียรถ์\nเตียว\nเตี่ยว\nเตือน\nแต่\nแต้\nแตก\nแตง\nแต่ง\nแตงเม\nแต้จิ๋ว\nแตด\nแต๊ดแต๋\nแตน\nแต้ม\nแตร\nแตระ\nแต้ว\nแต้วแร้ว\nแต้วแล้ว\nแต่ว่า\nแต้แว้ด\nแตะ\nโต\nโต้\nโตก\nโต่ง\nโต้ง\nโตงเตง\nโตฎก\nโต๊ด\nโตน\nโตนด\nโต้โผ\nโตมร\nโตย\nโตรก\nโต๊ะ\nใต้\nไต\nไต่\nไต้\nไต๋\nไตร\nไตรกิศยา\nไตรดายุค\nไตร่ตรอง\nไตรย\nไต้หวัน\nถก\nถกล\nถงาด\nถด\nถนน\nถนอม\nถนัด\nถนัน\nถนำ\nถนิม\nถม\nถ่ม\nถมอ\nถมึงทึง\nถลก\nถลกบาตร\nถลน\nถล่ม\nถลอก\nถลัน\nถลา\nถลาก\nถลาย\nถลำ\nถลึงตา\nถลุง\nถ่วง\nถ้วน\nถ้วย\nถวัล\nถวัลย์\nถวาย\nถวิน\nถวิล\nถ่อ\nถ้อ\nถอก\nถอง\nถ่อง\nถ้อง\nถอด\nถอน\nถอบ\nถอบแถบ\nถ่อม\nถอย\nถ่อย\nถ้อย\nถะ\nถะถั่น\nถะมัดถะแมง\nถัก\nถัง\nถั่ง\nถัด\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นยา\nบิลเลียด\nบิวเรตต์\nบิสมัท\nบี้\nบีฑา\nบีตา\nบีบ\nบีเยศ\nบึก\nบึกบึน\nบึง\nบึ่ง\nบึ้ง\nบุ\nบุก\nบุคคล\nบุคลากร\nบุคลาธิษฐาน\nบุคลิก\nบุง\nบุ่ง\nบุ้ง\nบุ้งกี๋\nบุญ\nบุญญาธิการ\nบุญญานุภาพ\nบุญญาภินิหาร\nบุญญาภิสังขาร\nบุณฑริก\nบุณมี\nบุณย์\nบุตร\nบุตรี\nบุถุชน\nบุทคล\nบุนนาค\nบุบ\nบุบบิบ\nบุปผชาติ\nบุพกรรม\nบุพการี\nบุพกิจ\nบุพชาติ\nบุพทักษิณ\nบุพนิมิต\nบุพบท\nบุพพาจารย์\nบุพเพสันนิวาส\nบุพโพ\nบุ๋ม\nบุ่มบ่าม\nบุ้ย\nบุรณะ\nบุรพทิศ\nบุรพบท\nบุรพาจารย์\nบูรพาจารย์\nบุระ\nบุราณ\nบุรินทร์\nบุริมทิศ\nบุริมพรรษา\nบุริมสิทธิ\nบุรี\nบุรุษ\nบุโรทั่ง\nบุษกร\nบุษบก\nบุษบง\nบุษบัน\nบุษบา\nบุษบามินตรา\nบุษปราค\nบุษปะ\nบุษย์\nบุษย์น้ำทอง\nบุษยมาส\nบุษยะ\nบุษราคัม\nบุหงัน\nบุหงา\nบุหรง\nบุหรี่\nบุหลัน\nบู่\nบู้\nบูชนียสถาน\nบูชา\nบูชิต\nบูด\nบูดู\nบูร\nบูรณ์\nบูรณภาพ\nบูรณมี\nบูรณะ\nบูรณาการ\nบูรพ์\nบูรพะ\nบูรพา\nเบ้\nเบ่ง\nเบ๊จี๋\nเบญกานี\nเบญจกัลยาณี\nเบญจกามคุณ\nเบญจขันธ์\nเบญจดุริยางค์\nเบญจธรรม\nเบญจบรรพต\nเบญจพรรณ\nเบญจเพส\nเบญจมาศ\nเบญจรงค์\nเบญจศก\nเบญจศีล\nเบญจะ\nเบญจา\nเบญจางค์\nเบญจางคประดิษฐ์\nเบญญา\nเบญพาด\nเบ็ด\nเบ็ดเตล็ด\nเบ็ดเสร็จ\nเบน\nเบนซิน\nเบรก\nเบริลเลียม\nเบส\nเบ้อ\nเบอร์\nเบอร์คีเลียม\nเบ้อเร่อ\nเบ้อเร่อเท่อ\nเบ้อเริ่ม\nเบ้อเริ่มเทิ่ม\nเบอะ\nเบอะบะ\nเบะ\nเบา\nเบ้า\nเบาราณ\nเบาะ\nเบิก\nเบิ่ง\nเบี้ย\nเบี่ยง\nเบียด\nเบียน\nเบียร์\nเบี้ยว\nเบือ\nเบื่อ\nเบื้อ\nเบื้อง\nเบือน\nแบ\nแบ้\nแบก\nแบคทีเรีย\nแบ่ง\nแบงก์\nแบดมินตัน\nแบตเตอรี่\nแบน\nแบนโจ\nแบบ\nแบ็บ\nแบเรียม\nแบหลา\nแบะ\nโบ\nโบ้\nโบ๋\nโบก\nโบกขรณี\nโบกขรพรรษ\nโบชุก\nโบต\nโบนัส\nโบ๊เบ๊\nโบย\nโบรมีน\nโบรอน\nโบราณ\nโบสถ์\nใบ\nใบ้\nไบ่\nปก\nปกติ\nปกรณ์\nปกรณัม\nปกิณกะ\nปกีรณัม\nปโกฏิ\nปง\nป่ง\nปงช้าง\nปฎล\nปฏัก\nปฏิกรณ์\nปฏิกรรม\nปฏิการะ\nปฏิกิริยา\nปฏิกูล\nปฏิคม\nปฏิคหิต\nปฏิคาหก\nปฏิฆะ\nปฏิชีวนะ\nปฏิญญา\nปฏิญาณ\nปฏิทิน\nปฏิบถ\nปฏิบัติ\nปฏิปทา\nปฏิปักษ์\nปฏิปัน\nปฏิปุจฉาพยากรณ์\nปฏิปุจฉาวาที\nปฏิพัทธ์\nปฏิพากย์\nปฏิภาค\nปฏิภาณ\nปฏิมา\nปฏิมากร\nปฏิยุทธ์\nปฏิรพ\nปฏิรูป\nปฏิโลม\nปฏิวัติ\nปฏิวาต\nปฏิวาท\nปฏิเวธ\nปฏิสนธิ\nปฏิสวะ\nปฏิสังขรณ์\nปฏิสันถาร\nปฏิสัมภิทา\nปฏิเสธ\nปฐพี\nปฐม\nปฐวี\nปณต\nปณาม\nปณิธาน\nปณิธิ\nปณีต\nปด\nปดิวรัดา\nปติ\nปถพี\nปถมัง\nปถวี\nปทัฏฐาน\nปทัสถาน\nปทานุกรม\nปทีป\nปทุม\nปน\nป่น\nปนัดดา\nปปัญจะ\nปม\nปรนัย\nปรปักษ์\nปรโลก\nปรวาที\nปรก\nปรกติ\nปรง\nปรตยักษ์\nปรน\nปรนนิบัติ\nปรนิมมิตวสวัตดี\nปรบ\nปรปักษ์\nปรมัตถ์\nปรมาจารย์\nปรมาณู\nปรมาภิไธย\nปรมาภิเษก\nปรมินทร์\nบรเมนทร์\nปรเมศวร์\nปรเมษฐ์\nปรวด\nปรวนแปร\nปรศุ\nปรสิต\nปร๋อ\nปรองดอง\nปรอด\nปรอท\nปรอย\nประ\nประกบ\nประกฤต\nประกฤติ\nประกล\nประกวด\nประกวดประขัน\nประกอบ\nประกัน\nประกับ\nประกาย\nประกายพรึก\nประการ\nประกาศ\nประกาศนียบัตร\nประกาศิต\nประกำ\nประกิด\nประกิต\nประคด\nประคนธรรพ\nประคนธรรพ์\nประคบ\nประคบประหงม\nประคอง\nประคับประคอง\nประคัลภ์\nประคำ\nประคิ่น\nประคุณ\nประเคน\nประเคราะห์\nประโคน\nประโคนธรรพ\nประโคนธรรพ์\nประโคม\nประจง\nประจญ\nประจบ\nประจบประแจง\nประจวบ\nประจ๋อประแจ๋\nประจักษ์\nประจักษนิยม\nประจัญ\nประจัน\nประจาก\nประจาค\nประจาน\nประจำ\nประจิม\nประจิ้มประเจ๋อ\nประจุ\nประจุคมน์\nประจุบัน\nประเจก\nประเจิด\nประเจิดประเจ้อ\nประเจียด\nประแจ\nประชด\nประชน\nประชวม\nประชวร\nประชัน\nประชา\nประชาธิปไตย\nประชิด\nประชี\nประชุม\nประเชิญ\nประณต\nประณม\nประณาม\nประณิธาน\nประณิธิ\nประณีต\nประณุท\nประดง\nประดน\nประดวน\nประดอน\nประดอย\nประดัก\nประดักประเดิด\nประดัง\nประดับ\nประดา\nประดาก\nประดาป\nประดาษ\nประดิชญา\nประดิดประดอย\nประดิทิน\nประดิษฐ์\nประดิษฐกรรม\nประดิษฐาน\nประดุง\nประดุจ\nประดู่\nประเด\nประเด็น\nประเดิม\nประเดียง\nประเดี๋ยว\nประเดี๋ยวประด๋าว\nประแดง\nประแดะ\nประโดง\nประโดย\nประตง\nประตัก\nประตาป\nประติชญา\nประติญาณ\nประติทิน\nประติมากร\nประติมากรรม\nประติรพ\nประตู\nประถม\nประถมจินดา\nประทม\nประท้วง\nประทวน\nประทักษ์\nประทักษิณ\nประทัง\nประทัด\nประทับ\nประทาน\nประทาย\nประทาศี\nประทิน\nประทิ่น\nประทีป\nประทุฐ\nประทุน\nประทุษ\nประทุษฐ์\nประเทศ\nประเทา\nประเทียด\nประเทียบ\nประเทือง\nประธาน\nประธานาธิบดี\nประนม\nประนอ\nประนอม\nประนัง\nประนัปดา\nประนีประนอม\nประปราน\nประปราย\nประปา\nประเปรี้ยง\nประเปรียว\nประพจน์\nประพนธ์\nประพรม\nประพฤติ\nประพฤทธิ์\nประพัด\nประพัทธ์\nประพันธ์\nประพาต\nประพาส\nประพาสมหรณพ\nประพาฬ\nประพิณ\nประพิมพ์ประพาย\nประพุทธ์\nประเพณี\nประโพธ\nประไพ\nประไพร\nประภพ\nประภัสสร\nประภา\nประภาคาร\nประภาพ\nประภาษ\nประภาส\nประเภท\nประมง\nประมวล\nประมาณ\nประมาท\nประมุข\nประมุท\nประมูล\nประเมิน\nประโมง\nประโมทย์\nประยงค์\nประยุกต์\nประยุทธ์\nประยุร\nประยูร\nประโยค\nประโยชน์\nประโรหิต\nประลมพ์\nประลอง\nประลัย\nประลาต\nประลาย\nประลุ\nประเล่ห์\nประเล้าประโลม\nประโลม\nประวรรต\nประวรรตน์\nประวัติ\nประวาล\nประวาลปัทม์\nประวาส\nประวิง\nประวิช\nประวิตร\nประวิน\nประวีณ\nประเวณี\nประเวศ\nประเวศน์\nประศม\nประศาสน์\nประศุ\nประสก\nประสงค์\nประสบ\nประสพ\nประสม\nประสะ\nประสัก\nประสันนาการ\nประสัยห์\nประสา\nประสาท\nประสาธน์\nประสาน\nประสาร\nประสิทธิ์\nประสิทธิผล\nประสิทธิภาพ\nประสีประสา\nประสูต\nประสูติ\nประเสบัน\nประเสบันอากง\nประเสริฐ\nประหนึ่ง\nประหม่า\nประหยัด\nประหลาด\nประหล่ำ\nประหวัด\nประหวั่น\nประหัตประหาร\nประหาณ\nประหาร\nประเหล\nประเหส\nประไหมสุหรี\nประอบ\nประอร\nปรัก\nปรักปรำ\nปรักมะ\nปรัง\nปรัชญา\nปรัตถจริยา\nปรัตยุบัน\nปรัน\nปรับ\nปรัมปรา\nปรัศจิม\nปรัศนา\nปรัศนี\nปรัศว์\nปรัสสบท\nปร่า\nปรากฏ\nปรากรม\nปรากฤต\nปราการ\nปราง\nปรางค์\nปราจีน\nปราชญ์\nปราชญา\nปราชัย\nปราณ\nปราณี\nปราด\nปราน\nปรานี\nปราบ\nปราบดาภิเษก\nปราปต์\nปราม\nปรามาส\nปราโมช\nปราโมทย์\nปราย\nปรารถนา\nปรารภ\nปรารมภ์\nปราศ\nปราศจาก\nปราศรัย\nปราษณี\nปราษาณ\nปราสัย\nปราสาท\nปรำ\nปริ\nปริก\nปริกขาร\nปริกรรม\nปริกัป\nปริคณห์\nปริจาค\nปริจาริกา\nปริเฉท\nปริชน\nปริซึม\nปริญญา\nปริณาม\nปริณายก\nปริต\nปริตตะ\nปริตโตทก\nปริตร\nปริทรรศน์\nปริทัยหัคคี\nปริทัศน์\nปริเทพ\nปริเทวะ\nปรินิพพาน\nปริบ\nปริบท\nปริปันถ์\nปริพนธ์\nปริพัตร\nปริพันธ์\nปริพาชก\nปริภัณฑ์\nปริภาษ\nปริภูมิ\nปริโภค\nปริ่ม\nปริมณฑล\nปริมาณ\nปริมาตร\nปริยัติ\nปริยานุช\nปริยาย\nปริเยศ\nปริโยสาน\nปริวรรต\nปริวัตร\nปริวาร\nปริวาส\nปริวิตก\nปริเวณ\nปริศนา\nปริษัท\nปริสัญญู\nปริสุทธิ\nปริหาน\nปริหาร\nปรี่\nปรีชญา\nปรีชา\nปรี๊ด\nปรีดา\nปรีดิ\nปรีดิ์\nปรีดี\nปรีติ\nปรียะ\nปรียา\nปรึก\nปรึกษา\nปรึง\nปรือ\nปรื๋อ\nปรุ\nปรุง\nปรู\nปรู๋\nปรูด\nปรู๊ด\nปรู๊ดปร๊าด\nปรู๊ฟ\nปฤงคพ\nปฤจฉา\nปฤษฎางค์\nปฤษฐ\nปลก\nปลกเปลี้ย\nปลง\nปล่ง\nปลด\nปล้น\nปลวก\nปลอก\nปล่อง\nปล้อง\nปลอด\nปล้อน\nปลอบ\nปลอม\nปล่อย\nปละ\nปลัก\nปลั๊ก\nปลัง\nปลั่ง\nปลัด\nปลัดขิก\nปลา\nปลาต\nปลาบ\nปลาย\nปลาสเตอร์\nปลาสนาการ\nปล้ำ\nปลิง\nปลิด\nปลิ้น\nปลิโพธ\nปลิม\nปลิ่ม\nปลิว\nปลี\nปลีก\nปลื้ม\nปลุก\nปลูก\nปวกเปียก\nปวง\nป่วง\nปวด\nป่วน\nป้วน\nป้วนเปี้ยน\nป่วย\nปวัตน์\nปวารณา\nปวาล\nปวาส\nปวาฬ\nปวิช\nปวิตร\nปวิธ\nปวิเวก\nปวีณ\nปวุติ\nปเวณี\nปเวส\nปเวสน์\nปศุ\nปสันนะ\nปสันนาการ\nปสาท\nปสาน\nปสาสน์\nปสุ\nปสุต\nปสูติ\nปหังสนะ\nปหาน\nปหาร\nปหาส\nปอ\nป้อ\nป๋อ\nปอก\nปอง\nป่อง\nป้อง\nปอด\nปอน\nป้อน\nปอนด์\nปอเนาะ\nปอบ\nป้อแป้\nปอม\nป้อม\nป๋อม\nปอย\nป้อย\nป้อยอ\nปะ\nปะกน\nปะกัง\nปะการัง\nปะกำ\nปะขาว\nปะงาบ\nปะตาปา\nปะตาระกาหลา\nปะติดปะต่อ\nปะติยาน\nปะทะ\nปะทะปะทัง\nปะทุ\nปะทุน\nปะปน\nปะมง\nปะราลี\nปะรำ\nปะไร\nปะลอม\nปะเลง\nปะแล่ม\nปะโลง\nปะวะหล่ำ\nปะหงับ\nปะหนัน\nปะหัง\nปะเหลาะ\nปัก\nปักข์\nปักเป้า\nปักษ์\nปักษา\nปักษิน\nปักษี\nปัง\nปั๋ง\nปังสุ์\nปังสุกุล\nปัจจัตตะ\nปัจจันต์\nปัจจันตคาม\nปัจจันตชนบท\nปัจจันตประเทศ\nปัจจัย\nปัจจามิตร\nปัจจุคมน์\nปัจจุทธรณ์\nปัจจุบัน\nปัจจุสมัย\nปัจเจก\nปัจโจปการกิจ\nปัจฉา\nปัจฉิม\nปัจถรณ์\nปัจนึก\nปัจยาการ\nปัจเวกขณ์\nปัชชุน\nปัญจนที\nปัญจวัคคีย์\nปัญจก\nปัญจกะ\nปัญจมี\nปัญจวีสติ\nปัญญัติ\nปัญญา\nปัญหา\nปัฏ\nปัฏนะ\nปัฐยาวัต\nปัณฑรหัตถี\nปัณณะ\nปัณณาส\nปัณณาสก์\nปัณรสี\nปัณหิ\nปัด\nปัตคาด\nปัตตะ\nปัตตานึก\nปัตตานุโมทนา\nปัตตาเวีย\nปัตติ\nปัตติก\nปัตถร\nปัตถะ\nปัตนิ\nปัตนี\nปัตหล่า\nปัถพี\nปัถวี\nปัทม์\nปัทมะ\nปัทมาสน์\nปัน\nปั่น\nปั้น\nปั้นจั่น\nปันจุเหร็จ\nปั้นลม\nปั้นหยา\nปั้นเหน่ง\nปับ\nปั๊บ\nปัปผาสะ\nปัพพาชนียกรรม\nปัพภาระ\nปั๊ม\nปัยกา\nปัยยิกา\nปัวเปีย\nปัศจิม\nปัศตัน\nปัสสาวะ\nปัสสาสะ\nปา\nป่า\nป้า\nปาก\nปาง\nป้าง\nปาจรีย์\nปาจิตตีย์\nปาจีน\nปาฏลิ\nปาฏิบท\nปาฏิบุคลิก\nปาฏิโภค\nปาฏิหาริย์\nปาฐก\nปาฐกถา\nปาฐะ\nปาณกชาติ\nปาณะ\nปาณาติบาต\nปาณิ\nปาณี\nปาด\nปาติโมกข์\nปาตี\nปาเต๊ะ\nปาท่องโก๋\nปาทังกา\nปาทาน\nปาทุกา\nปาน\nป่าน\nป้าน\nปานะ\nปานียะ\nป้าบ\nป๊าบ\nปาพจน์\nปาม\nปาโมกข์\nป่าย\nป้าย\nปายาส\nปาร์เกต์\nปารมี\nปารเมศ\nปาราชิก\nปาริฉัตร\nปาริชาต\nปารุสกวัน\nปาล\nปาล์ม\nปาลิไลยก์\nปาลี\nปาว\nป่าว\nป๊าว\nปาวาร\nปาษาณ\nปาส\nปาสาณ\nปาสาทิกะ\nปาหนัน\nปาหี่\nปำ\nป้ำ\nป้ำเป๋อ\nปิกนิก\nปิ้ง\nปิงคละ\nปิงปอง\nปิฎก\nปิฏฐะ\nปิฐิ\nปิณฑะ\nปิด\nปิตตะ\nปิตา\nปิตามหัยกา\nปิตามหัยยิกา\nปิตุ\nปิตุจฉา\nปิตุภูมิ\nปิตุลา\nปิโตรเลียม\nปิ่น\nปิ่นแก้ว\nปิ่นโต\nปิปผลี\nปิ่ม\nปิ้ม\nปิยะ\nปิยังคุ\nปิโยรส\nปิลันธน์\nปิ๋ว\nปิศาจ\nปิหกะ\nปี\nปี่\nปี้\nปี๋\nปีก\nปีฐะ\nปี๊ด\nปีติ\nปีน\nปีบ\nปี๊บ\nปีศาจ\nปีฬกะ\nปึก\nปึง\nปึ่ง\nปึ๋ง\nปึ้ด\nปึมปื้อ\nปืน\nปื้น\nปือ\nปื้อ\nปื๋อ\nปุ\nปุ๊\nปุก\nปุกปุย\nปุคละ\nปุ้งกี๋\nปุงควะ\nปุงลิงค์\nปุงลึงค์\nปุจฉา\nปุฏะ\nปุณฑริก\nปุด\nปุตตะ\nปุถุชน\nปุนนาค\nปุนภพ\nปุนัพสุ\nปุบ\nปุ๊บ\nปุปผะ\nปุปะ\nปุพพะ\nปุ่ม\nปุ่มป่ำ\nปุ้ม\nปุ๋ม\nปุย\nปุ้ย\nปุ๋ย\nปุรณะ\nปุระ\nปุราณ\nปุราณะ\nปุริมพรรษา\nปุเรจาริก\nปุโรหิต\nปุลลิงค์\nปุลลึงค์\nปุลินท์\nปุโลปุเล\nปุษยะ\nปุสสะ\nปู\nปู่\nปูชกะ\nปูชนียบุคคล\nปูชนียวัตถุ\nปูชนียะ\nปูชา\nปูชิต\nปูด\nปูน\nปูม\nปู้ยี่ปู้ยำ\nปูระ\nปูลู\nเป้\nเป๋\nเปก\nเป๊ก\nเป่ง\nเป้ง\nเป๋ง\nเป็ด\nเปตพลี\nเปตอง\nเป็น\nเปยยาล\nเปร็ง\nเปรต\nเปรม\nเปรย\nเปรอ\nเปรอะ\nเปราะ\nเปรียง\nเปรี้ยง\nเปรี้ยงปร้าง\nเปรียญ\nเปรียบ\nเปรี่ยม\nเปรียว\nเปรี้ยว\nเปรียะ\nเปรี๊ยะ\nเปรื่อง\nเปรื้อย\nเปล\nเปล่ง\nเปลว\nเปลา\nเปล่า\nเปล้า\nเปลาะ\nเปลี้ย\nเปลี่ยน\nเปลี่ยม\nเปลี่ยว\nเปลือก\nเปลือง\nเปลื้อง\nเปลือย\nเปศะ\nเปศัส\nเปสการ\nเปสละ\nเปสุญวาท\nเป๋อ\nเป้อเย้อ\nเปอร์\nเปอร์เซ็นต์\nเปะ\nเปา\nเป่า\nเป้า\nเป๊า\nเป๋า\nเป๋าฮื้อ\nเปาะ\nเป๊าะ\nเปาะเปี๊ยะ\nเปาะแปะ\nเปิก\nเปิง\nเปิงมาง\nเปิด\nเปิ่น\nเปิบ\nเปิ๊บ\nเปีย\nเปียก\nเปี๊ยก\nเปี๊ยบ\nเปี่ยม\nเปี้ยว\nเปี๊ยว\nเปียะ\nเปี๊ยะ\nเปือก\nเปื้อน\nเปื่อย\nแป\nแป้ง\nแป๋ง\nแปด\nแป๊ด\nแปทู\nแป้น\nแป๊น\nแปบ\nแป๊บ\nแปม\nแปร\nแปร๋\nแปรก\nแปรง\nแปร่ง\nแปร๊ด\nแปร้น\nแปร๋น\nแปรปรวน\nแประ\nแปล\nแปล้\nแปลก\nแปลง\nแปลน\nแปลบ\nแปล๊บ\nแป้ว\nแป๊ว\nแปะ\nแป๊ะ\nแป๊ะซะ\nโป\nโป่\nโป้\nโป๊\nโปก\nโป๊ก\nโป๊กเกอร์\nโปกขรณี\nโปกขรพรรษ\nโปเก\nโปง\nโป่ง\nโป้ง\nโป่งข่าม\nโปงลาง\nโป่งวิด\nโปฐบท\nโปฐปทมาส\nโปดก\nโปตถกะ\nโปน\nโป๊ป\nโป๊ยเซียน\nโปรแกรม\nโปรง\nโปร่ง\nโปรด\nโปรตอน\nโปรตีน\nโปรเตสแตนต์\nโปรแทรกเตอร์\nโปรย\nโปล่ง\nโปลิโอ\nโปโล\nโปสก\nโปสต์การ์ด\nโปะ\nโป๊ะ\nโป๊ะจ้าย\nไป\nไป่\nไป๋\nไปยาล\nไปรษณีย์\nไปรษณียบัตร\nไปรษณียภัณฑ์\nไปรษณียากร\nไปล่\nไปศาจ\nผก\nผกา\nผกาย\nผคม\nผง\nผงก\nผงม\nผงะ\nผงาด\nผง่าน\nผจง\nผจญ\nผจัญ\nผจาน\nผชุม\nผณิน\nผณินทร\nผณิศวร\nผด\nผดุง\nผเดิน\nผทม\nผนวก\nผนวช\nผนัง\nผนิด\nผนึก\nผม\nผยอง\nผรณาปีติ\nผรสุ\nผริต\nผรุสวาท\nผล\nผลคุน\nผลคุนี\nผล็อย\nผละ\nผลัก\nผลัด\nผลับ\nผลัวะ\nผลา\nผลาญ\nผลานิสงส์\nผลาผล\nผลาหาร\nผลิ\nผลิกะ\nผลิต\nผลิน\nผลี\nผลีผลาม\nผลึก\nผลึ่ง\nผลือ\nผลุ\nผลุง\nผลุด\nผลุน\nผลุนผลัน\nผลุบ\nผลุบผลับ\nผลุย\nผลู\nผวน\nผวย\nผวา\nผสม\nผสาน\nผอง\nผ่อง\nผ่อน\nผอบ\nผอม\nผ็อย\nผอูน\nผะ\nผะดา\nผะสา\nผัก\nผัคคุณ\nผัคคุณี\nผัง\nผัด\nผัน\nผับ\nผัว\nผัวะ\nผัสสะ\nผัสสาหาร\nผา\nผ่า\nผ้า\nผาก\nผาง\nผ่าง\nผาณิต\nผาด\nผาติ\nผ่าน\nผาม\nผาย\nผ่ายผอม\nผาล\nผาลคุน\nผาลา\nผ่าว\nผาสุก\nผ้าฮาด\nผำ\nผ้ำ\nผิ\nผิง\nผิด\nผิตะ\nผิน\nผิว\nผี\nผี้ว์\nผึง\nผึ่ง\nผึ้ง\nผึ้งรวง\nผืน\nผื่น\nผุ\nผุด\nผุยผง\nผุสราคา\nผู้\nผูก\nเผ\nเผง\nเผชิญ\nเผ็ด\nเผด็จ\nเผดิม\nเผดียง\nเผ่น\nเผนิก\nเผย\nเผยอ\nเผยิบ\nเผยิบผยาบ\nเผล\nเผล่\nเผล็ด\nเผลอ\nเผลอไผล\nเผละ\nเผลาะ\nเผลาะแผละ\nเผลียง\nเผอเรอ\nเผอิญ\nเผอิล\nเผะ\nเผา\nเผ่า\nเผ้า\nเผาะ\nเผิ้ง\nเผิน\nเผียน\nเผือ\nเผื่อ\nเผือก\nเผือด\nเผือน\nเผื่อน\nแผ่\nแผก\nแผง\nแผด\nแผน\nแผ่น\nแผนก\nแผล\nแผลง\nแผล็บ\nแผล็ว\nแผละ\nแผ่ว\nแผ้ว\nโผ\nโผง\nโผฏฐัพพะ\nโผน\nโผเผ\nโผย\nโผล่\nโผลกเผลก\nโผละ\nโผอน\nโผะ\nไผ\nไผ่\nไผท\nฝน\nฝรั่ง\nฝรั่งเศส\nฝ่อ\nฝอย\nฝัก\nฝัง\nฝั่ง\nฝัด\nฝัน\nฝา\nฝ่า\nฝ้า\nฝาก\nฝาง\nฝาด\nฝาน\nฝาย\nฝ่าย\nฝ้าย\nฝิ่น\nฝี\nฝี่\nฝีก\nฝึก\nฝืด\nฝืน\nฝุ่น\nฝูง\nเฝ้า\nเฝือ\nเฝือก\nเฝือง\nเฝื่อน\nแฝก\nแฝง\nแฝด\nใฝ่\nไฝ\nพก\nพกุล\nพง\nพงศ์\nพงศกร\nพงศธร\nพงศา\nพงศาวดาร\nพจน์\nพจนา\nพจนานุกรม\nพจนารถ\nพจนีย์\nพจมาน\nพจี\nพชระ\nพญา\nพญาลอ\nพณิช\nพณิชย์\nพดด้วง\nพธู\nพนันดร\nพนาดร\nพนาดอน\nพนาราม\nพนาลัย\nพนาลี\nพนาวาส\nพนาเวศ\nพนาศรม\nพนาสณฑ์\nพนาสัณฑ์\nพเนจร\nพ่น\nพ้น\nพนม\nพนอง\nพนอม\nพนัก\nพนักงาน\nพนัง\nพนัน\nพนัส\nพนา\nพนาย\nพนิดา\nพนิต\nพบ\nพม่า\nพยนต์\nพยศ\nพยัก\nพยักพเยิด\nพยัคฆ์\nพยัคฆา\nพยัคฆิน\nพยัคฆี\nพยัชน์\nพยัญชนะ\nพยัต\nพยับ\nพยากรณ์\nพยาฆร์\nพยางค์\nพยาธิ\nพยาน\nพยาบาท\nพยาบาล\nพยาม\nพยามะ\nพยายาม\nพยุ\nพยุง\nพยุหยาตรา\nพยุหโยธา\nพยุหเสนา\nพยุหะ\nพยู่ห์\nพเยีย\nพร\nพรต\nพรม\nพรรค\nพรรค์\nพรรคานต์\nพรรณ\nพรรณนา\nพรรดึก\nพรรลาย\nพรรษ\nพรรษา\nพรรเอิญ\nพรวด\nพรวน\nพรหม\nพรหมจรรย์\nพรหมจาริณี\nพรหมจารี\nพรหมา\nพรหมาสตร์\nพรหมินทร์\nพรอก\nพร่อง\nพร้อง\nพรอด\nพร้อม\nพร้อมพรัก\nพร่อย\nพร้อย\nพระ\nพระนอม\nพระนาด\nพระฮาม\nพรักพร้อม\nพรั่ง\nพรัด\nพรั่น\nพรับ\nพร่า\nพร้า\nพราก\nพราง\nพร่าง\nพราด\nพราน\nพราย\nพราว\nพราหมณ์\nพราหมณะ\nพราหมณี\nพราหมี\nพรำ\nพร่ำ\nพริก\nพริ้ง\nพริบ\nพริ้ม\nพรึง\nพรึน\nพรึบ\nพรึ่บ\nพรืด\nพรุ\nพรุ่ง\nพรุน\nพรู\nพรูด\nพฤกษ์\nพฤกษชาติ\nพฤกษเทวดา\nพฤกษราช\nพฤกษศาสตร์\nพฤกษา\nพฤฒ\nพฤฒา\nพฤฒาจารย์\nพฤฒิ\nพฤต\nพฤติ\nพฤทธ์\nพฤทธิ์\nพฤนต์\nพฤนท์\nพฤศจิก\nพฤศจิกายน\nพฤษภ\nพฤษภาคม\nพฤหัสบดี\nพล\nพละ\nพลากร\nพลาดิศัย\nพลาธิการ\nพลานามัย\nพลบ\nพลวก\nพลวง\nพลวัต\nพลศาสตร์\nพลอ\nพล้อ\nพลอง\nพลอด\nพลอน\nพลอมแพลม\nพลอย\nพล่อย\nพลั่ก\nพลัง\nพลั่ง\nพลั้ง\nพลัด\nพลัน\nพลับ\nพลับพลา\nพลับพลึง\nพลั่ว\nพล่า\nพลาง\nพลาญ\nพลาด\nพล่าน\nพลาม\nพล่าม\nพลาย\nพลาสติก\nพลาสมา\nพลาหก\nพลำ\nพล้ำ\nพลำภัง\nพลิก\nพลิพัท\nพลิ้ว\nพลี\nพลุ\nพลุก\nพลุ่ก\nพลุกพล่าน\nพลุ่ง\nพลุ้ย\nพลู\nพลูโต\nพลูโทเนียม\nพวก\nพวง\nพ่วง\nพวน\nพวย\nพสก\nพสุ\nพสุธา\nพสุสงกรานต์\nพหล\nพหุ\nพหุล\nพหู\nพอ\nพ่อ\nพ้อ\nพอก\nพอง\nพ้อง\nพอน\nพ้อม\nพอโลเนียม\nพะ\nพะงา\nพะงาบ\nพะจง\nพะทำมะรง\nพะนอ\nพะเน้าพะนอ\nพะเนิน\nพะเนียง\nพะแนง\nพะพาน\nพะพิง\nพะเพิง\nพะยอม\nพ่ะย่ะค่ะ\nพะยูง\nพะยูน\nพะเยิบ\nพะเยิบพะยาบ\nพะรุงพะรัง\nพะโล้\nพะไล\nพะวง\nพะวักพะวน\nพะวา\nพะว้าพะวัง\nพะอง\nพะอากพะอำ\nพะอืดพะอม\nพัก\nพักตร์\nพักตรา\nพักร\nพัง\nพังกา\nพังงา\nพังผืด\nพังพวย\nพังพอน\nพังพาน\nพังพาบ\nพังเพย\nพัช\nพัชนี\nพัชระ\nพัญจน์\nพัฒนะ\nพัฒนา\nพัฒนากร\nพัฒนาการ\nพัด\nพัดชา\nพัดดึงส์\nพัตร\nพัทธ์\nพัทธยา\nพัทธสีมา\nพัทร\nพัน\nพันตู\nพันทาง\nพันธ์\nพันธะ\nพันธกรณี\nพันธบัตร\nพันธมิตร\nพันธนะ\nพันธนาคาร\nพันธนาการ\nพันธุ์\nพันธุกรรม\nพันลึก\nพันลือ\nพันเลิศ\nพันเอิญ\nพับ\nพัลลภ\nพัลวัน\nพัว\nพัวะ\nพัศดี\nพัสดุ\nพัสตร์\nพัสถาน\nพา\nพาก\nพากเพียร\nพากย์\nพาง\nพ่าง\nพาชี\nพาณิช\nพาณิชย์\nพาณิชยกรรม\nพาณิชยการ\nพาณิชยศาสตร์\nพาณิชยศิลป์\nพาณินี\nพาณี\nพาณีนี\nพาด\nพาท\nพาทย์\nพาธ\nพาธา\nพาน\nพ่าน\nพานร\nพานรินทร์\nพาม\nพาย\nพ่าย\nพายม้า\nพายัพ\nพายุ\nพาร์เซก\nพารณ\nพารา\nพาราฟิน\nพาล\nพาลา\nพาลี\nพาลุก\nพาโล\nพาไล\nพาส\nพาสน์\nพาสนา\nพาสุกรี\nพ่าห์\nพาหนะ\nพาหะ\nพาหา\nพาหิรกะ\nพาหิระ\nพาหุ\nพาหุรัด\nพาหุสัจจะ\nพาเหียร\nพาฬ\nพำ\nพำนัก\nพำพึม\nพำลา\nพิกล\nพิกสิต\nพิกัด\nพิกัติ\nพิกัน\nพิการ\nพิกุล\nพิเคราะห์\nพิฆน์\nพิฆเนศ\nพิฆเนศวร\nพิฆาต\nพิง\nพิจัย\nพิจาร\nพิจารณ์\nพิจารณา\nพิจิก\nพิจิต\nพิจิตร\nพิชญ์\nพิชัย\nพิชาน\nพิชิต\nพิเชฐ\nพิเชียร\nพิฑูรย์\nพิณ\nพิดทูล\nพิดาน\nพิโดร\nพิตร\nพิถี\nพิถีพิถัน\nพิทย\nพิทย์\nพิทยา\nพิทยาคม\nพิทยาคาร\nพิทยาธร\nพิทยาลัย\nพิทักษ์\nพิทูร\nพิเทศ\nพิธาน\nพิธี\nพิธุ\nพินทุ\nพินอบพิเทา\nพินัย\nพินาศ\nพินิจ\nพินิต\nพินิศ\nพินิศจัย\nพิเนต\nพิบัติ\nพิบุล\nพิบูล\nพิปริต\nพิปลาส\nพิพรรธ\nพิพรรธน์\nพิพักพิพ่วน\nพิพัฒ\nพิพัฒน์\nพิพากษ์\nพิพากษา\nพิพาท\nพิพิธ\nพิพิธภัณฑ์\nพิพิธภัณฑสถาน\nพิภพ\nพิภัช\nพิภาค\nพิภูษณะ\nพิเภก\nพิมปะการัง\nพิมพ์\nพิมพการัง\nพิมพา\nพิมพาภรณ์\nพิมล\nพิมเสน\nพิมาน\nพิมุข\nพิโมกข์\nพิโมกษ์\nพิโยกพิเกน\nพิโยค\nพิรอด\nพิระ\nพิรากล\nพิราบ\nพิราม\nพิราลัย\nพิริยะ\nพิรี้พิไร\nพิรุณ\nพิรุธ\nพิรุฬห์\nพิเรนทร์\nพิเราะ\nพิโรธ\nพิไร\nพิลังกาสา\nพิลาป\nพิลาส\nพิลิปดา\nพิลึก\nพิลึกกึกกือ\nพิลึกพิลั่น\nพิโลน\nพิไล\nพิศ\nพิศวง\nพิศวาส\nพิศาล\nพิศุทธ์\nพิศุทธิ์\nพิเศษ\nพิษ\nพิษฐาน\nพิษนาศน์\nพิสดาร\nพิสมร\nพิสมัย\nพิสัง\nพิสัช\nพิสัย\nพิสิฐ\nพิสุทธิ์\nพิสูจน์\nพิหค\nพิหาร\nพิฬาร\nพี\nพี่\nพี้\nพีชคณิต\nพีระมิด\nพึง\nพึ่ง\nพึ่บ\nพึ่บพั่บ\nพึม\nพึมพำ\nพืช\nพืด\nพื้น\nพุ\nพุก\nพุกาม\nพุง\nพุ่ง\nพุงดอ\nพุฒ\nพุฒิ\nพุด\nพุดตาน\nพุทธ\nพุทธะ\nพุทธังกูร\nพุทธางกูร\nพุทธันดร\nพุทธาภิเษก\nพุทธาวาส\nพุทธิ\nพุทโธ่\nพุทรา\nพุธ\nพุ่ม\nพุมเรียง\nพุ้ย\nพู\nพูพอน\nพู่\nพูด\nพูน\nพู้น\nพู่ระหง\nเพ\nเพ็ก\nเพกา\nเพคะ\nเพ็ง\nเพ่ง\nเพ็จ\nเพชฉลูกรรม\nเพชฌฆาต\nเพชร\nเพชรดา\nเพชรปาณี\nเพชรฤกษ์\nเพชรายุธ\nเพชรกลับ\nเพชรสังฆาต\nเพชรหลีก\nเพชรหึง\nเพ็ญ\nเพฑูริย์\nเพณี\nเพ็ดทูล\nเพดาน\nเพท\nเพทนา\nเพทาย\nเพทุบาย\nเพโทบาย\nเพ่นพ่าน\nเพนียด\nเพไนย\nเพ้ย\nเพรง\nเพรซีโอดิเมียม\nเพรา\nเพราะ\nเพริด\nเพริศ\nเพรียก\nเพรียง\nเพรียบ\nเพรี้ยม\nเพรียว\nเพรื่อ\nเพรือง\nเพล\nเพลง\nเพล็ด\nเพล้โพล้\nเพลา\nเพลาะ\nเพลิง\nเพลิดเพลิน\nเพลิน\nเพลีย\nเพลี้ย\nเพลี่ยง\nเพศ\nเพส\nเพสลาด\nเพ่อ\nเพ้อ\nเพ้อเจ้อ\nเพอิญ\nเพะ\nเพา\nเพาะ\nเพิก\nเพิง\nเพิ่ง\nเพิดเพ้ย\nเพิ่ม\nเพี้ย\nเพียง\nเพี้ยง\nเพียงออ\nเพี้ยน\nเพียบ\nเพียร\nเพื่อ\nเพื่อน\nแพ\nแพ้\nแพง\nแพ่ง\nแพงพวย\nแพทย์\nแพทยศาสตร์\nแพน\nแพ่น\nแพนก\nแพนงเชิง\nแพร\nแพร่\nแพรก\nแพร่ง\nแพรว\nแพร้ว\nแพลง\nแพลทินัม\nแพล็บ\nแพลม\nแพลเลเดียม\nแพละ\nแพละโลม\nแพว\nแพ้ว\nแพศย์\nแพศยา\nแพะ\nโพ\nโพก\nโพกพาย\nโพง\nโพงพาง\nโพชฌงค์\nโพซิตรอน\nโพด\nโพทะเล\nโพแทสเซียม\nโพธ\nโพธิ\nโพธิ์\nโพน\nโพ้น\nโพนทะนา\nโพบาย\nโพย\nโพยก๊วน\nโพยม\nโพรก\nโพรง\nโพรโทแอกทิเนียม\nโพรมีเทียม\nโพระดก\nโพล่\nโพลง\nโพล่ง\nโพล้ง\nโพลน\nโพล้เพล้\nโพละ\nโพสพ\nไพ\nไพ่\nไพจิตร\nไพชน\nไพชยนต์\nไพฑูรย์\nไพที\nไพบูลย์\nไพพรรณ\nไพร\nไพร่\nไพรจิตร\nไพรชน\nไพรชยนต์\nไพรฑูรย์\nไพรที\nไพรบูลย์\nไพรเราะ\nไพรัช\nไพรำ\nไพริน\nไพรินทร์\nไพรี\nไพเราะ\nไพโรจน์\nไพล\nไพล่\nไพศาขะ\nไพศาล\nไพเศษ\nไพสพ\nไพสิฐ\nไพหาร\nฟก\nฟ้ง\nฟรักโทส\nฟรี\nฟลูออรีน\nฟ่อ\nฟ้อ\nฟอก\nฟอง\nฟ่อง\nฟ้อง\nฟอด\nฟอน\nฟ่อน\nฟ้อน\nฟ้อแฟ้\nฟอร์มาลดีไฮด์\nฟอร์มาลิน\nฟอสฟอรัส\nฟอสเฟต\nฟัก\nฟักฟุ้น\nฟัง\nฟังก์ชัน\nฟัด\nฟัน\nฟั่น\nฟั้น\nฟ้า\nฟาก\nฟาง\nฟ่าง\nฟาด\nฟาทอม\nฟาน\nฟ่าม\nฟาย\nฟาร์ม\nฟาสซิสต์\nฟิด\nฟิต\nฟิบ\nฟิล์ม\nฟิวส์\nฟิสิกส์\nฟี่\nฟี้\nฟืดฟาด\nฟืน\nฟื้น\nฟืม\nฟุ\nฟุ้ง\nฟุต\nฟุน\nฟุบ\nฟุ่บ\nฟุ่มเฟือย\nฟุลสแก๊ป\nฟู\nฟู่\nฟูก\nฟูด\nฟูม\nเฟ็ด\nเฟ้น\nเฟลด์สปาร์\nเฟ้อ\nเฟอร์เมียม\nเฟอะ\nเฟอะฟะ\nเฟะ\nเฟะฟะ\nเฟิน\nเฟี้ยม\nเฟี้ยว\nเฟือ\nเฟื้อ\nเฟือง\nเฟื่อง\nเฟื้อง\nเฟือน\nเฟือย\nเฟื้อย\nแฟ่\nแฟง\nแฟชั่น\nแฟน\nแฟบ\nแฟ้ม\nแฟรนเซียม\nแฟลกซ์\nแฟลต\nแฟะ\nโฟกัส\nไฟ\nภควดี\nภควัต\nภควันต์\nภควัม\nภควา\nภควาน\nภคะ\nภคันทลา\nภคินี\nภณะ\nภณิดา\nภพ\nภมร\nภมริน\nภมรี\nภมุกา\nภยันตราย\nภยาคติ\nภระ\nภรณี\nภรต\nภรรดร\nภรรดา\nภรรยา\nภระมร\nภระมรี\nภราดร\nภราดรภาพ\nภราดา\nภริยา\nภฤศ\nภวะ\nภวตัณหา\nภวนะ\nภวังค์\nภวังคจิต\nภักดี\nภักตะ\nภักติ\nภักษ์\nภักษา\nภักษาหาร\nภัค\nภัคน์\nภังคะ\nภังคี\nภัจ\nภัณฑ์\nภัณฑาคาร\nภัณฑาคาริก\nภัณฑารักษ์\nภัณฑนะ\nภัณฑู\nภัต\nภัตตาคาร\nภัตตาหาร\nภัตร\nภัทระ\nภัทรกัป\nภัพ\nภัย\nภัสดา\nภัสตรา\nภัสมะ\nภัสสร\nภา\nภาค\nภาคย์\nภาคยานุวัติ\nภาคินี\nภาคิไนย\nภาคี\nภาคียะ\nภาชนะ\nภาชี\nภาณ\nภาณวาร\nภาณกะ\nภาณี\nภาณุ\nภาดร\nภาดา\nภาตระ\nภาตา\nภาตุ\nภาติกะ\nภาติยะ\nภาพ\nภาพย์\nภาม\nภาย\nภาร\nภาระ\nภารดี\nภารต\nภารตี\nภารยทรัพย์\nภารยา\nภารา\nภาวนา\nภาวะ\nภาษ\nภาษณ์\nภาษา\nภาษิต\nภาษี\nภาส\nภาสน์\nภาสวร\nภาสา\nภาสุระ\nภิกขา\nภิกขาจาร\nภิกขุ\nภิกขุนี\nภิกษา\nภิกษาจาร\nภิกษาหาร\nภิกษุ\nภิกษุณี\nภิงคาร\nภิญโญ\nภิตติ\nภินท์\nภินทนาการ\nภิยโย\nภิรมย์\nภิรมย์สุรางค์\nภิษัช\nภิสัก\nภีตะ\nภีมะ\nภีรุ\nภุกต์\nภุขัน\nภุช\nภุชงค์\nภุต\nภุมมะ\nภุมรัตน์\nภุมวาร\nภุมรา\nภุมริน\nภุมรี\nภุมเรศ\nภู\nภู่\nภูต\nภูติ\nภูม\nภูมิ\nภูมี\nภูริ\nภูรี\nภูวดล\nภูวนาถ\nภูวเนตร\nภูวไนย\nภูษา\nภูษิต\nเภกะ\nเภตรา\nเภท\nเภทุบาย\nเภรี\nเภสัช\nโภค\nโภคะ\nโภคิน\nโภคี\nโภไคย\nโภไคศวรรย์\nโภช\nโภชย์\nโภชก\nโภชนะ\nโภชนา\nโภชนาหาร\nโภชนียะ\nไภริน\nไภรี\nไภษัชคุรุ\nไภษัชย์\nมกร\nมกราคม\nมกุฎ\nมคธ\nมฆวัน\nมฆะ\nมฆา\nม่ง\nมงกุฎ\nมงโกรย\nมงคล\nมงคลวาร\nมณฑ์\nมณฑก\nมณฑนะ\nมณฑป\nมณฑล\nมณฑา\nมณฑารพ\nมณฑิระ\nมณเฑียร\nมณี\nมด\nมตะ\nมตกภัต\nมติ\nมทนะ\nมทะ\nมธุ\nมธุกร\nมธุการี\nมธุลีห์\nมธุระ\nมธุรพจน์\nมน\nมนินทรีย์\nม่น\nมนต์\nมนตร์\nมนตรี\nมนท์\nมนทิราลัย\nมนเทียร\nมนสิการ\nมนัส\nมนัสวี\nมนินทรีย์\nมนิมนา\nมนิลา\nมนุญ\nมนุษย์\nมนุษยชาติ\nมนุษยธรรม\nมนุษย์มนา\nมนุษยโลก\nมนุษยศาสตร์\nมนุษยสัมพันธ์\nมนุสาร\nมนู\nมนูสาร\nมโน\nมโนช\nมโนชญ์\nมโนราห์\nมโนสาเร่\nมโนห์รา\nมมังการ\nมยุรฉัตร\nมยุระ\nมยุรา\nมยุรี\nมยุเรศ\nมยูร\nมรกต\nมรคา\nมรฑป\nมรณ์\nมรณะ\nมรณกรรม\nมรณบัตร\nมรณภัย\nมรณภาพ\nมรดก\nมรรค\nมรรคา\nมรรตัย\nมรรยาท\nมรรษ\nมรสุม\nมริจ\nมริยาท\nมรีจิ\nมรุต\nมฤค\nมฤคย์\nมฤคศิระ\nมฤคศิรมาส\nมฤคเศียร\nมฤคินทร์\nมฤเคนทร์\nมฤดก\nมฤต\nมฤตยู\nมฤทุ\nมล\nมละ\nมลัก\nมลังเมลือง\nมล้าง\nมลาย\nมลายู\nมวก\nม่วง\nมวน\nม่วน\nม้วน\nม้วนต้วน\nมวย\nม้วย\nมวล\nมหกรรม\nมหรณพ\nมหรรณพ\nมหรสพ\nมหัจฉริยะ\nมหัต\nมหัทธนะ\nมหันต์\nมหันตโทษ\nมหัพภาค\nมหัศจรรย์\nมหา\nมหากฐิน\nมหากาฬ\nมหาขันธกะ\nมหาจักร\nมหาชน\nมหาชัย\nมหาชาติ\nมหาโชตรัต\nมหาดไทย\nมหาดเล็ก\nมหาตมะ\nมหาไถ่\nมหาเทพ\nมหาเทพี\nมหาเทวี\nมหาธาตุ\nมหานิกาย\nมหานิล\nมหาบพิตร\nมหาบัณฑิต\nมหาพน\nมหาพรหม\nมหาภารตะ\nมหาภิเนษกรมณ์\nมหาภูต\nมหาเมฆ\nมหายาน\nมหายุค\nมหาราช\nมหาฤกษ์\nมหาละลวย\nมหาละลาย\nมหาวงศ์\nมหาวรรค\nมหาวิทยาลัย\nมหาศักราช\nมหาศาล\nมหาสงกรานต์\nมหาสดมภ์\nมหาสดำ\nมหาสมุทร\nมหาสาวก\nมหาหงส์\nมหาหิงคุ์\nมหาอำนาจ\nมหาอุจ\nมหาอุด\nมหาอุปรากร\nมหาอุปราช\nมหิ\nมหิดล\nมหิธร\nมหิป\nมหิงส์\nมหิทธิ\nมหินท์\nมหิมา\nมหิศร\nมหิศวร\nมหิษ\nมหิษี\nมหึมา\nมเหยงค์\nมเหศ\nมเหศวร\nมเหศักดิ์\nมเหสักข์\nมเหสิ\nมเหสี\nมเหาฬาร\nมโหรสพ\nมโหระทึก\nมโหรี\nมโหฬาร\nมไหศวรรย์\nมอ\nมอง\nมองโกลอยด์\nมองโกเลีย\nมองคร่อ\nมอญ\nมอด\nม่อต้อ\nมอเตอร์\nมอเตอร์ไซค์\nมอน\nม่อน\nมอบ\nมอม\nมอมแมม\nม่อย\nมอร์ฟีน\nมอระกู่\nมอลโทส\nม่อลอกม่อแลก\nม่อห้อม\nม่อฮ่อม\nมะ\nมะกรูด\nมะกล่ำ\nมะกอก\nมะก่อง\nมะกะโรนี\nมะกา\nมะเกลือ\nมะเกี๋ยง\nมะข่วง\nมะขวิด\nมะขาม\nมะเขือ\nมะแข่น\nมะคังแดง\nมะค่า\nมะคำไก่\nมะคำดีควาย\nมะงั่ว\nมะงุมมะงาหรา\nมะซัก\nมะซาง\nมะดัน\nมะดีหวี\nมะดูก\nมะเดหวี\nมะเดื่อ\nมะต้อง\nมะตะบะ\nมะตาด\nมะตาหะรี\nมะตึ่ง\nมะตื๋น\nมะตูม\nมะแตก\nมะโต\nมะนาว\nมะปราง\nมะปริง\nมะฝ่อ\nมะพร้าว\nมะพลับ\nมะพูด\nมะแพน\nมะแพร้ว\nมะเฟือง\nมะแฟน\nมะไฟ\nมะม่วง\nมะม่าว\nมะมี่\nมะมื่น\nมะมุด\nมะเมอ\nมะเมีย\nมะเมื่อย\nมะแม\nมะยง\nมะยม\nมะระ\nมะริด\nมะรืน\nมะรุม\nมะรุมมะตุ้ม\nมะเร็ง\nมะเรื่อง\nมะโรง\nมะลอกมะแลก\nมะละกอ\nมะลิ\nมะลื่น\nมะลืมดำ\nมะลุลี\nมะแว้ง\nมะสัง\nมะเส็ง\nมะหวด\nมะหะหมัด\nมะหาด\nมะหิ่ง\nมะเหงก\nมะอึก\nมะฮอกกานี\nมัก\nมักกะโรนี\nมักกะลีผล\nมักกะสัน\nมักขะ\nมั่กขั้ก\nมักขิกา\nมัค\nมัคคะ\nมัคคุเทศก์\nมัคนายก\nมัฆวาน\nมั่ง\nมังกง\nมังกร\nมังกุ\nมังคละ\nมังค่า\nมังคุด\nมังตาน\nมังสวิรัติ\nมังสะ\nมังสี\nมัจจะ\nมัจจุ\nมัจฉริยะ\nมัจฉรี\nมัจฉะ\nมัจฉา\nมัชชะ\nมัชวิรัติ\nมัชชาระ\nมัชฌันติกสมัย\nมัชฌิม\nมัชฌิมา\nมัญจา\nมัญชิษฐา\nมัญชุ\nมัญชุสา\nมัญชูสา\nมัญเชฏฐะ\nมัฏฐะ\nมัณฑนศิลป์\nมัณฑนา\nมัด\nมัตตะ\nมัตตัญญู\nมัตตา\nมัตติกา\nมัตถกะ\nมัตถลุงค์\nมัตสยะ\nมัตสยา\nมัตสระ\nมัตสริน\nมัททวะ\nมัทนะ\nมัทยะ\nมัธยฐาน\nมัธยม\nมัธยันห์\nมัธยัสถ์\nมัน\nมั่น\nมันตา\nมันถะ\nมันทิระ\nมันทิราลัย\nมับ\nมั้ม\nมัมมี่\nมัย\nมัลละ\nมัลลิกา\nมัว\nมัวซัว\nมั่ว\nมัศยา\nมัสตุ\nมัสตาร์ด\nมัสมั่น\nมัสยิด\nมัสรู่\nมัสลิน\nมัสสุ\nมา\nม้า\nมาก\nมาคสิระ\nมาฆบูชา\nมาฆะ\nม้าง\nมางสะ\nมาณพ\nมาณวิกา\nมาด\nมาดา\nมาตงค์\nมาตร\nมาตรา\nมาตฤ\nมาตังคะ\nมาตา\nมาตามหัยกะ\nมาตามหัยกา\nมาตามหัยยิกา\nมาติกะ\nมาติกา\nมาตุ\nมาตุจฉา\nมาตุรงค์\nมาตุเรศ\nมาตุละ\nมาตุลา\nมาตุลานี\nมาทะ\nมาธยมิก\nมาธยมิกะ\nมาธุระ\nมาธุสร\nมาธูระ\nมาน\nม่าน\nม้าน\nมานพ\nมานะ\nมานัต\nมานัส\nมานิต\nมานี\nมานุษ\nมานุษยวิทยา\nมาโนชญ์\nมาบ\nมาภา\nม้าม\nม่าย\nมายา\nมาร\nมาราธิราช\nมารค\nมารดร\nมารดา\nมารยา\nมารยาท\nมารศรี\nมารษา\nมาริต\nมารุต\nมาลย์\nมาลัย\nมาลา\nมาลาตี\nมาลาเรีย\nมาลินี\nมาลี\nมาลุต\nมาศ\nมาส\nมาสก\nมาห์\nม่าห์\nมาหิส\nม่าเหมี่ยว\nมาฬก\nมิ\nมิค\nมิคสัญญี\nมิ่ง\nมิจฉา\nมิด\nมิตร\nมิติ\nมิเตอร์\nมิถยา\nมิถุน\nมิถุนายน\nมิทธะ\nมินตรา\nมินตา\nมินหม้อ\nมิ่ม\nมิ้ม\nมิไย\nมิรันตี\nมิลลิกรัม\nมิลลิบาร์\nมิลลิเมตร\nมิลลิลิตร\nมิลักขะ\nมิลักขู\nมิส\nมิสกรี\nมิสกวัน\nมิสซา\nมี\nมี่\nมีด\nมีเทน\nมีน\nมีนาคม\nมี่สั้ว\nมึง\nมึน\nมืด\nมืน\nมื่น\nมือ\nมื้อ\nมุ\nมุก\nมุกดา\nมุกดาหาร\nมุกุระ\nมุข\nมุขเด็จ\nมุขยประโยค\nมุโขโลกนะ\nมุคคะ\nมุง\nมุ่ง\nมุ้ง\nมุจฉา\nมุจนะ\nมุจลินท์\nมุญจนะ\nมุญชะ\nมุฐิ\nมุณฑกะ\nมุณฑะ\nมุด\nมุตกิด\nมุตฆาต\nมุตตะ\nมุตตา\nมุตติ\nมุตะ\nมุติ\nมุททา\nมุทธชะ\nมุทธา\nมุทธาภิเษก\nมุทรา\nมุทริกา\nมุทะลุ\nมุทา\nมุทิกา\nมุทิงค์\nมุทิตา\nมุทุ\nมุทุตา\nมุ่น\nมุนิ\nมุนินทร์\nมุนี\nมุบ\nมุบมิบ\nมุม\nมุ้ม\nมุ่ย\nมุรธา\nมุรธาภิเษก\nมุสละ\nมุสลิม\nมุสา\nมุสิก\nมุหงิด\nมุหน่าย\nมุหุต\nมุฮัมมัด\nมูก\nมูเซอ\nมูตร\nมู่ทู่\nมูน\nมูมมาม\nมูรติ\nมูรธา\nมูรธาภิเษก\nมูล\nมูละ\nมูลา\nมูลิกากร\nมู่ลี่\nมู่เล่\nมูสัง\nมูสิก\nมูสิกะ\nมูสิกทันต์\nเม\nเม็ก\nเมกะเฮิรตซ์\nเมขลา\nเมฆ\nเมฆา\nเมฆินทร์\nเมฆี\nเม็ง\nเม็ด\nเมตตา\nเมตไตรย\nเมตร\nเมตริก\nเมตริกตัน\nเมถุน\nเมท\nเมโท\nเมทนี\nเมทินี\nเมทนีดล\nเมทานอล\nเมทิลแอลกอฮอล์\nเมธ\nเมธา\nเมธาวี\nเมธี\nเมน\nเม่น\nเม้น\nเมนเดลีเวียม\nเมนทอล\nเม้ม\nเมรัย\nเมริเดียน\nเมรุ\nเมล์\nเมล็ด\nเมลือง\nเมษ\nเมษายน\nเมห์\nเมหนะ\nเมหะ\nเมะ\nเมา\nเม่า\nเม้า\nเมารี\nเมาลี\nเมาฬี\nเมาะ\nเมิง\nเมิน\nเมิล\nเมีย\nเมียง\nเมี่ยง\nเมี้ยน\nเมือ\nเมื้อ\nเมื่อ\nเมือก\nเมือง\nเมือบ\nเมื่อย\nแม่\nแม้\nแมก\nแมกนีเซียม\nแมง\nแมงกะพรุน\nแมงกานิน\nแมงกานีส\nแมงคา\nแมงคาเรือง\nแมงช้าง\nแมงดา\nแมงลัก\nแม่ตะงาว\nแมน\nแม่น\nแม้น\nแมลง\nแมลบ\nแมว\nแม้ว\nแมะ\nโม\nโม่\nโม้\nโมก\nโมกข์\nโมกษะ\nโมฆกรรม\nโมฆสัญญา\nโมฆะ\nโมฆียกรรม\nโมฆียะ\nโมง\nโม่ง\nโมงครุ่ม\nโมทนา\nโมโนแซ็กคาไรด์\nโมไนย\nโมเม\nโมเมนต์\nโมเย\nโมรา\nโมรี\nโมเรส\nโมลิบดีนัม\nโมลี\nโมเลกุล\nโมเสก\nโมเสส\nโม่ห์\nโมหะ\nโมหันธ์\nโมหาคติ\nโมโห\nไม่\nไม้\nไมกา\nไมครอน\nไมโครกรัม\nไมโครฟิล์ม\nไมโครโฟน\nไมโครมิเตอร์\nไมโครเมตร\nไมโครลิตร\nไมโครเวฟ\nไมตรี\nไมยราบ\nไมล์\nยก\nยกกระบัตร\nยกนะ\nยง\nยงโย่\nยชุรเวท\nยติ\nยติภังค์\nยถากรรม\nยถาภูตญาณ\nย่น\nยนต์\nยนตร์\nยม\nยมก\nยมโดย\nยมนา\nยมล\nยมะ\nยรรยง\nยล\nยวง\nยวด\nยวน\nยวบ\nย้วย\nยวรยาตร\nยศ\nยโส\nยอ\nย่อ\nยอก\nย็อกแย็ก\nยอง\nย่อง\nย้อง\nยอด\nยอน\nย้อน\nยอบ\nยอม\nย่อม\nย้อม\nย่อย\nย้อย\nย้อแย้\nยะ\nย่ะ\nยะยอบ\nยะยับ\nยัก\nยักข์\nยักขินี\nยักษ์\nยักษา\nยักษิณี\nยักษี\nยัง\nยั้ง\nยั่งยืน\nยัชโญปวีต\nยัญ\nยัญญะ\nยัด\nยัติภังค์\nยัน\nยั่น\nยันต์\nยันตร\nยันตร์\nยันตรกรรม\nยั่นตะนี\nยับ\nยั่ว\nยั้ว\nยั้วเยี้ย\nยัวรยาตร\nยัวะ\nยัษฏิ\nยา\nย่า\nยาก\nยาคุ\nยาคู\nยาง\nย่าง\nยางพารา\nยาจก\nยาจนา\nยาไฉน\nยาด\nยาดา\nยาตร\nยาตรา\nยาน\nย่าน\nย่านพาโหม\nยานมาศ\nยานุมาศ\nยานี\nยาม\nย่าม\nยามะ\nยามักการ\nยามา\nยาย\nย้าย\nยายี\nยาว\nย้าว\nยาวกาลิก\nยาวชีวิก\nยาสูบ\nย่าหยา\nยาหยี\nยำ\nย่ำ\nย้ำ\nยำเยีย\nยิก\nยิง\nยิ่ง\nยิฏฐะ\nยิน\nยิบ\nยิบหยี\nยิปซัม\nยิปซี\nยิ้ม\nยิมนาสติก\nยิหวา\nยี\nยี่\nยี้\nยี่ก่า\nยี่เก\nยี่เข่ง\nยี่โถ\nยีน\nยี่โป้\nยี่ภู่\nยีราฟ\nยี่สก\nยี่สง\nยี่สน\nยี่สาน\nยี่สุ่น\nยี่หระ\nยี่หร่า\nยี่ห้อ\nยี่หุบ\nยึกยัก\nยึกยือ\nยึด\nยืด\nยืน\nยื่น\nยืม\nยื้อ\nยุ\nยุกกระบัตร\nยุกดิ\nยุกติ\nยุกติธรรม\nยุกต์\nยุค\nยุคนธร\nยุคล\nยุคันต์\nยุคันธร\nยุคุนธร\nยุง\nยุ่ง\nยุ้ง\nยุด\nยุต\nยุติ\nยุทธ\nยุทธ์\nยุทธนา\nยุทโธปกรณ์\nยุบ\nยุ่บ\nยุ่บยั่บ\nยุบล\nยุพดี\nยุพเรศ\nยุพา\nยุพาน\nยุพาพาล\nยุพาพิน\nยุ่มย่าม\nยุ่ย\nยุ้ย\nยุรยาตร\nยูรยาตร\nยุวชน\nยุวดี\nยุวราช\nยุวา\nยุวาน\nยู\nยู่\nยูง\nยูโด\nยูถะ\nยูถิกา\nยูริก\nยูเรนัส\nยูเรเนียม\nยูโรเพียม\nเย\nเย้\nเยง\nเยซู\nเย็ด\nเย็น\nเย็นตาโฟ\nเย็นเตาโฟ\nเย็บ\nเย้ย\nเยอ\nเย่อ\nเยอรมัน\nเยอว\nเย่อหยิ่ง\nเยอะ\nเยอะแยะ\nเยา\nเย้า\nเยาว์\nเยาวชน\nเยาวมาลย์\nเยาวยอด\nเยาวราช\nเยาวเรศ\nเยาวลักษณ์\nเยาวพา\nเยาวพาณี\nเยาวพาน\nเยาะ\nเยิง\nเยิน\nเยิ่น\nเยิ่นเย้อ\nเยินยอ\nเยิบ\nเยิบยาบ\nเยิ้ม\nเยีย\nเยี่ยง\nเยี่ยงอย่าง\nเยียงผา\nเยียดยัด\nเยียน\nเยียบ\nเยี่ยม\nเยียรบับ\nเยียรยง\nเยียว\nเยี่ยว\nเยียวยา\nเยือ\nเยื่อ\nเยื้อ\nเยือก\nเยือง\nเยื่อง\nเยื้อง\nเยือน\nเยื้อน\nแย่\nแย้\nแยก\nแยง\nแย่ง\nแย้ง\nแยงแย่\nแยงแย้\nแยบ\nแย็บ\nแยม\nแย้ม\nแยแส\nแยะ\nโย\nโย้\nโยก\nโยกเยก\nโยคาพจร\nโยคาวจร\nโยคเกณฑ์\nโยคยะ\nโยคะ\nโยคิน\nโยคี\nโยง\nโย่ง\nโย่งเย่ง\nโยงโย่\nโยชน์\nโยชนา\nโยถิกะ\nโยทะกา\nโยธวาทิต\nโยธา\nโยธิน\nโยน\nโยนก\nโยนิโส\nโยนี\nโยม\nโยโส\nใย\nไย\nไย่\nไยดี\nไยไพ\nรก\nรง\nรงค์\nรงควัตถุ\nรงรอง\nรจนา\nรจเรข\nรจเลข\nรจิต\nรชตะ\nรชนิ\nรชนี\nรชะ\nรณรงค์\nรด\nรดี\nรตนะ\nรตะ\nรติ\nรถ\nรน\nร่น\nรบ\nรบาญ\nรพี\nรม\nร่ม\nรมณี\nรมณีย์\nรมณียสถาน\nรมย์\nรมเยศ\nรยางค์\nรวก\nรวง\nร่วง\nรวด\nรวน\nรวนเร\nร่วน\nรวบ\nรวม\nร่วม\nรวย\nรวิ\nรวิวาร\nรวี\nรศนา\nรส\nรสนา\nรสสุคนธ์\nรสายนเวท\nรสิก\nรหัท\nรหัส\nรโห\nรโหฐาน\nรอ\nร่อ\nรอก\nรอง\nร่อง\nร้อง\nรองเง็ง\nร่องแร่ง\nรอด\nรอน\nร่อน\nร้อน\nรอบ\nรอบคอบ\nรอม\nรอมชอม\nรอมร่อ\nรอย\nร่อย\nร้อย\nร่อแร่\nระ\nระกะ\nระกา\nระกำ\nระเกะระกะ\nระคน\nระคาง\nระคาย\nระแคะ\nระฆัง\nระงม\nระงับ\nระแง้\nระโงกหิน\nระชวย\nระดม\nระดะ\nระดับ\nระดา\nระด่าว\nระดู\nระเด่น\nระเดียง\nระแด\nระตู\nระทก\nระทด\nระทม\nระทวย\nระทา\nระทึก\nระแทะ\nระนาด\nระนาบ\nระนาม\nระนาว\nระเนน\nระเนระนาด\nระเนียด\nระแนง\nระแนะ\nระบบ\nระบม\nระบอบ\nระบัด\nระบับ\nระบาด\nระบาย\nระบำ\nระบิล\nระบือ\nระบุ\nระเบง\nระเบ็ง\nระเบิด\nระเบียง\nระเบียน\nระเบียบ\nระแบบ\nระมัดระวัง\nระมาด\nระเมียร\nระย่อ\nระย่อม\nระยะ\nระยั้ง\nระยับ\nระย้า\nระยาบ\nระยำ\nระยิบระยับ\nระโยง\nระโยงระยาง\nระรวย\nระรอง\nระร่อน\nระรัว\nระราน\nระร่าย\nระริก\nระรี่\nระรึง\nระรื่น\nระรื้น\nระเร้ง\nระเริง\nระเรียง\nระเรื่อย\nระแรง\nระลวง\nระลอก\nระลึก\nระวัง\nระวาง\nระวาย\nระวิง\nระแวง\nระแวดระวัง\nระไว\nระส่ำระสาย\nระหกระเหิน\nระหง\nระหวย\nระหว่าง\nระหองระแหง\nระหัด\nระหาย\nระเห็จ\nระเหย\nระเหระหน\nระเหหน\nระเหิด\nระเหินระหก\nระแหง\nระโหย\nระอา\nระอิดระอา\nระอุ\nรัก\nรักข์\nรักขิต\nรักตะ\nรักบี้\nรักเร่\nรักแร้\nรักษ์\nรักษา\nรัง\nรั้ง\nรังเกียจ\nรังแก\nรังค์\nรังควาน\nรังแค\nรังรอง\nรังวัด\nรังสิ\nรังสี\nรังสิมันตุ์\nรังสิมา\nรัจฉา\nรัช\nรัชชูปการ\nรัชมังคลาภิเษก\nรัชชุ\nรัชฎาภิเษก\nรัชดาภิเษก\nรัชนะ\nรัชนี\nรัญจวน\nรัฏฐาภิปาลโนบาย\nรัฐ\nรัฐประศาสโนบาย\nรัฐประศาสนศาสตร์\nรัด\nรัต\nรัตกัมพล\nรัตมณี\nรัตคน\nรัตจันทน์\nรัตตัญญู\nรัตติ\nรัตน์\nรัตนะ\nรัตนโกสินทร์\nรัตนโกสินทรศก\nรัตนชาติ\nรัตนตรัย\nรัตนบัลลังก์\nรัตนวราภรณ์\nรัตนสิงหาสน์\nรัตนา\nรัตนากร\nรัตนาภรณ์\nรัตนาวลี\nรัตมา\nรัถ\nรัถยา\nรัทเทอร์ฟอร์เดียม\nรัน\nรั้น\nรันทด\nรันทวย\nรับ\nรัมณียสถาน\nรัมภา\nรัมมี่\nรัมย์\nรัย\nรัว\nรั่ว\nรั้ว\nรัศมิมัต\nรัศมิมาน\nรัศมี\nรัษฎากร\nรัสเซีย\nรัสสะ\nรัสสระ\nรา\nร่า\nร้า\nราก\nรากษส\nรากสาด\nราคะ\nราคจริต\nราคา\nราคิน\nราคี\nราง\nร่าง\nร้าง\nรางจืด\nรางชาง\nรางวัล\nราช\nราชกิจจานุเบกษา\nราชนิกุล\nราชวโรงการ\nราชญี\nราชดัด\nราชพฤกษ์\nราชมาณพ\nราชมาษ\nราชมาส\nราชย์\nราชสีห์\nราชะ\nราชัน\nราชันย์\nราชัย\nราชา\nราชาธิปไตย\nราชาธิราช\nราชาภิเษก\nราชายตนะ\nราชาวดี\nราชี\nราชินิกุล\nราชินีกุล\nราชินี\nราชินูปถัมภ์\nราชูปถัมภ์\nราชูปโภค\nราเชน\nราเชนทร์\nราเชนทรยาน\nราโชวาท\nราไชศวรรย์\nราญ\nราญรอน\nราด\nราต\nราตร\nราตรี\nราน\nร่าน\nร้าน\nราบ\nราพณ์\nราพณาสูร\nราม\nรามเกียรติ์\nรามสูร\nรามัญ\nรามา\nราย\nร่าย\nร้าย\nราว\nร้าว\nราวี\nราศี\nราษฎร\nราษฎร์\nราษตรี\nราษราตรี\nราหุ\nราหู\nรำ\nร่ำ\nรำคาญ\nรำงับ\nรำจวน\nรำบาญ\nรำพัน\nรำพาย\nรำพึง\nรำเพย\nรำไพ\nรำมะนา\nรำมะนาด\nรำมะร่อ\nร่ำรวย\nร่ำร่ำ\nรำไร\nรำลึก\nรำหัด\nรำหัส\nริ\nริก\nริดสีดวง\nริน\nริ้น\nริบ\nริบบิ้น\nริบรี่\nริบหรี่\nริปุ\nริปู\nริม\nริ้ว\nริษยา\nรี\nรี่\nรี้พล\nรีด\nรีดักชัน\nรีต\nรีเนียม\nรีบ\nรีม\nรีรอ\nรี้ริก\nรึง\nรึ้ง\nรื่น\nรื้น\nรื้อ\nรุ\nรุก\nรุกข์\nรุกขชาติ\nรุกขเทวดา\nรุกขมูล\nรุกขา\nรุกรุย\nรุ่ง\nรุ้ง\nรุงรัง\nรุ่งริ่ง\nรุจ\nรุจา\nรุจนะ\nรุจิ\nรุจี\nรุจิระ\nรุจิรา\nรุด\nรุต\nรุทธ์\nรุทระ\nรุธิร\nรุธิระ\nรุเธียร\nรุน\nรุ่น\nรุบรู่\nรุม\nรุ่ม\nรุ่มร่าม\nรุย\nรุ่ย\nรุรุ\nรุหะ\nรู\nรู่\nรู้\nรูจี\nรูด\nรูทีเนียม\nรูบิเดียม\nรูป\nรูปิยะ\nรูปี\nรูเล็ตต์\nเร่\nเรข\nเรขา\nเรขาคณิต\nเร็ง\nเร่ง\nเร้ง\nเรณุ\nเรณู\nเรดอน\nเรดาร์\nเรเดียม\nเร้น\nเรรวน\nเรไร\nเร็ว\nเร่ว\nเรวดี\nเรอ\nเร่อ\nเรา\nเร่า\nเร้า\nเราะ\nเริง\nเริด\nเริม\nเริ่ม\nเริ้ม\nเริศร้าง\nเรี่ย\nเรี้ย\nเรียก\nเรียง\nเรียด\nเรียน\nเรียบ\nเรียม\nเรี่ยม\nเรียว\nเรี่ยว\nเรี้ยวรก\nเรือ\nเรื่อ\nเรื้อ\nเรือก\nเรือง\nเรื่อง\nเรื้อง\nเรืองรอง\nเรือด\nเรือน\nเรื้อน\nเรื่อย\nแร\nแร่\nแรก\nแร็กเกต\nแรง\nแร่ง\nแร้ง\nแรด\nแร้นแค้น\nแรม\nแร้ว\nแระ\nโร\nโร่\nโรค\nโรคา\nโรคาพาธ\nโรง\nโรจ\nโรจน์\nโรเดียม\nโรตี\nโรท\nโรธ\nโรม\nโรมัน\nโรเมอร์\nโรย\nโรเร\nโรหิณี\nโรหิต\nไร\nไร่\nไร้\nไรย์\nฤกษ์\nฤกษณะ\nฤคเวท\nฤชา\nฤชุ\nฤณ\nฤดี\nฤดียา\nฤดู\nฤต\nฤติยา\nฤตุ\nฤทธา\nฤทธิ์\nฤทัย\nฤษภ\nฤษยา\nฤษี\nฤๅ\nฤๅดี\nฤๅทัย\nฤๅษี\nฤๅสาย\nลก\nล่ก\nลฆุ\nลง\nล่ง\nลงกา\nล้งเล้ง\nลด\nลดา\nลดาวัลย์\nลน\nล้น\nลบ\nลบอง\nลพ\nลพุช\nลม\nล่ม\nล้ม\nลมาด\nลรรลุง\nลลนา\nลลิต\nลวก\nลวง\nล่วง\nล้วง\nลวณะ\nลวด\nล้วน\nลวนลาม\nลวนะ\nล่วม\nลวะ\nลวิตร\nลหุ\nลหุกาบัติ\nล่อ\nล้อ\nลอก\nล็อก\nล็อกเกต\nลอกแลก\nลอการิทึม\nลอง\nล่อง\nลองกอง\nลองจิจูด\nลองไน\nลอด\nลอตเตอรี่\nลอน\nล่อน\nลอบ\nลอม\nล้อม\nลอมชอม\nลอมพอก\nลอย\nล่อย\nล่อแล่\nลอว์เรนเซียม\nลออ\nละ\nล่ะ\nละคร\nละติจูด\nละบม\nละบอง\nละบือ\nละเบ็ง\nละโบม\nละม่อม\nละมั่ง\nละมาน\nละม้าย\nละมุ\nละมุด\nละมุน\nละเมอ\nละเมาะ\nละเมิด\nละเมียด\nละแมะ\nละโมก\nละโมบ\nละไม\nละลวย\nละลอก\nละล้า\nละล้าละลัง\nละลาน\nละลาบละล้วง\nละลาย\nละล้าว\nละล่ำละลัก\nละลิบ\nละลุม\nละเลง\nละเล้า\nละเลาะ\nละเลิง\nละเลียด\nละเลียบ\nละไล้\nละว้า\nละวาด\nละเวง\nละแวก\nละโว้\nละหมาด\nละห้อย\nละหาน\nละหาร\nละหุ่ง\nละเหย\nละเหี่ย\nละอง\nละออง\nละอาย\nละเอียด\nละแอน\nลัก\nลักขณะ\nลักขณา\nลักขะ\nลักขี\nลักจั่น\nลักปิดลักเปิด\nลักษณ์\nลักษณนาม\nลักษณะ\nลักษณาการ\nลักษมณ์\nลักษมาณา\nลักษมี\nลักษะ\nลัคคะ\nลัคน์\nลัคนา\nลัง\nลั่ง\nลังกา\nลังคี\nลังถึง\nลังลอง\nลังเล\nลังสาด\nลัชชา\nลัชชี\nลัญจ์\nลัญจกร\nลัญฉกร\nลัญฉน์\nลัฐิ\nลัฐิกา\nลัด\nลัดา\nลัทธ์\nลัทธิ\nลัน\nลั่น\nลันเต\nลันเตา\nลันไต\nลั่นทม\nลันโทม\nลับ\nลัพธ์\nลัพธิ\nลัภ\nลัภนะ\nลัภย์\nลัมพ์\nลัย\nลา\nล่า\nล้า\nลาก\nลาง\nล่าง\nล้าง\nลางลิง\nลางสาด\nลาช\nลาชะ\nลาชา\nลาญ\nลาด\nลาดเลา\nล้าต้า\nล่าเตียง\nลาน\nล่าน\nล้าน\nลาบ\nลาพอน\nลาภ\nลาม\nล่าม\nลามก\nลาย\nล้าย\nลายสือ\nลาลา\nลาว\nลาวัณย์\nลาวา\nลำ\nล่ำ\nล้ำ\nลำเข็ญ\nลำแข\nลำเค็ญ\nลำเจียก\nลำดวน\nลำดับ\nลำเนา\nลำบอง\nลำบาก\nลำปำ\nลำพวน\nลำพอง\nลำพัง\nลำพู\nลำเพ็ญ\nลำเพา\nลำแพน\nลำโพง\nลำไพ่\nลำภุขัน\nลำมะลอก\nลำยอง\nลำไย\nลำลอง\nล่ำลา\nลำลาบ\nลำลึก\nลำเลาะ\nลำเลิก\nลำเลียง\nลำเวียง\nลำเอียก\nลำเอียง\nลิ\nลิกขา\nลิกไนต์\nลิกู\nลิเก\nลิขนะ\nลิขสิทธิ์\nลิขิต\nลิง\nลิงค์\nลิด\nลิต\nลิตมัส\nลิตร\nลิเทียม\nลิ่น\nลิ้น\nลินจง\nลิ้นจี่\nลินลา\nลินสีด\nลิ่นฮื้อ\nลินิน\nลิบ\nลิปดา\nลิปสติก\nลิปิ\nลิฟต์\nลิเภา\nลิ่ม\nลิ้ม\nลิมป์\nลิมปนะ\nลิลิต\nลิว\nลิ่ว\nลิสง\nลี\nลี่\nลี้\nลีซอ\nลีบ\nลีลา\nลีลาศ\nลีฬหา\nลึก\nลึงค์\nลืด\nลื่น\nลื้น\nลืบ\nลืม\nลือ\nลื่อ\nลื้อ\nลุ\nลุก\nลุง\nลุ้ง\nลุ่น\nลุ้น\nลุพธ์\nลุ่ม\nลุมพี\nลุมพู\nลุย\nลุ่ย\nลุ้ย\nลู่\nลูก\nลูกระมาศ\nลูกเอ็น\nลูขะ\nลูทีเชียม\nลูบ\nเลก\nเล็ก\nเลข\nเลขา\nเลขาธิการ\nเลขานุการ\nเล็ง\nเล้ง\nเล่งฮื้อ\nเลเซอร์\nเลฑฑุ\nเลณฑุ\nเลณะ\nเล็ด\nเลน\nเล็น\nเล่น\nเลนส์\nเล็บ\nเลบง\nเลปกร\nเลปน์\nเลเป\nเลเพ\nเล็ม\nเล่ม\nเลย\nเลว\nเลวง\nเลวูโลส\nเลศ\nเลษฏุ\nเล่ห์\nเล่ห์กระเท่ห์\nเลหลัง\nเลหะ\nเลอ\nเล่อ\nเลอะ\nเลอะเทอะ\nเละ\nเละเทะ\nเลา\nเล่า\nเล้า\nเลากัย\nเล้าโลม\nเลาะ\nเลิก\nเลิ่กลั่ก\nเลิง\nเลิ้ง\nเลินเล่อ\nเลิศ\nเลีย\nเลียง\nเลี่ยง\nเลี้ยง\nเลียงผา\nเลียงฝ้าย\nเลียงมัน\nเลียน\nเลี่ยน\nเลียนไฟ\nเลียบ\nเลี่ยม\nเลียว\nเลี้ยว\nเลือก\nเลือง\nเลื่อง\nเลือด\nเลือน\nเลื่อน\nเลื่อม\nเลื่อย\nเลื้อย\nเลื่อยล้า\nแล\nแล่\nแล้\nแลก\nแล็กเกอร์\nแล็กโทส\nแลง\nแล่ง\nแล้ง\nแลน\nแล่น\nแลนทานัม\nแลบ\nแล้ว\nและ\nโล่\nโล้\nโลก\nโลกเชษฐ์\nโลกธรรม\nโลกธาตุ\nโลกนาถ\nโลกบาล\nโลกย์\nโลกัย\nโลกวัชชะ\nโลกวิทู\nโลกัตถจริยา\nโลกันตร์\nโลกา\nโลกาธิบดี\nโลกาธิปไตย\nโลกานุวัตร\nโลกาภิวัตน์\nโลกามิส\nโลกายัต\nโลกาวินาศ\nโลกิยะ\nโลกีย์\nโลกียวัตร\nโลกียวิสัย\nโลกียสุข\nโลกุตระ\nโลกุตรธรรม\nโลกุตรภูมิ\nโลง\nโล่ง\nโล้ง\nโล่งโจ้ง\nโล่งโต้ง\nโล้งโต้ง\nโลจนะ\nโลณะ\nโลด\nโล่ติ๊น\nโลโต\nโลท\nโลน\nโล้น\nโลภ\nโลม\nโลมเล้า\nโลมะ\nโลมา\nโลลุป\nโลเล\nโลโล\nโลโล้\nโลหะ\nโลหกุมภี\nโลหัช\nโลหิต\nไล่\nไล้\nไลย\nไลลา\nไล่เลี่ย\nฦๅ\nฦๅชา\nฦๅสาย\nวก\nวง\nวงก์\nวงกต\nวงศ์\nวงศกร\nวงศา\nวงษ์\nวจนะ\nวจี\nวชิระ\nวชิรปาณี\nวชิรหัตถ์\nวชิราวุธ\nวฏะ\nวฏาการ\nวณิช\nวณิชชา\nวณิชย์\nวณิชยา\nวณิพก\nวดี\nวทนะ\nวทัญญุตา\nวทัญญู\nวธุกา\nวธู\nวน\nวนศาสตร์\nวนสณฑ์\nวนสัณฑ์\nวนอุทยาน\nวนัส\nวนัสบดี\nวนา\nวนาดร\nวนาดอน\nวนานต์\nวนาลัย\nวนาลี\nวนาวาส\nวนาศรม\nวนาสณฑ์\nวนาสัณฑ์\nวนิดา\nวนิพก\nวเนจร\nวโนทยาน\nวยัคฆ์\nวยากรณ์\nวรดนู\nวรทาน\nวรมหาวิหาร\nวรงค์\nวรณะ\nวรรค\nวรรคย์\nวรรช\nวรรชย์\nวรรณะ\nวรรณกรรม\nวรรณคดี\nวรรณยุกต์\nวรรณยุต\nวรรณศิลป์\nวรรณนา\nวรรณพฤติ\nวรรณึก\nวรรธกะ\nวรรธนะ\nวรรษ\nวรรษา\nวรวิหาร\nวรัญญู\nวรางคณา\nวรางคนา\nวราห์\nวราหะ\nวรุณ\nวโรดม\nวฤก\nวลัช\nวลัญช์\nวลัญชน์\nวลัย\nวลาหก\nวลี\nวศค\nวศะ\nวศิน\nวสนะ\nวสภะ\nวสละ\nวสวัดดี\nวสวัตตี\nวสะ\nวสันต์\nวสันตดิลก\nวสันตฤดู\nวสันตวิษุวัต\nวสา\nวสี\nวสุ\nวสุธา\nวสุนธรา\nวสุมดี\nวหะ\nวอ\nวอก\nวอกแวก\nว่องไว\nวอด\nวอน\nว่อน\nว็อบ\nวอมแวม\nวอลเลย์บอล\nวอแว\nวะ\nวัก\nวักกะ\nวัคคีย์\nวัคคุ\nวัคซีน\nวัง\nวังก์\nวังชา\nวังเวง\nวังศะ\nวังสะ\nวัจจะ\nวัจกุฎี\nวัจฉละ\nวัจน์\nวัช\nวัชชะ\nวัชพืช\nวัชฌ์\nวัชระ\nวัชรปาณี\nวัชรยาน\nวัชรอาสน์\nวัชราสน์\nวัชรินทร์\nวัชรี\nวัชเรนทร์\nวัฏ\nวัฏฏะ\nวัฏจักร\nวัฏทุกข์\nวัฏสงสาร\nวัฏกะ\nวัฏฏิ\nวัฒกะ\nวัฒกี\nวัฒนธรรม\nวัฒนะ\nวัฒนา\nวัณ\nวัณโรค\nวัณฏ์\nวัณณะ\nวัณนา\nวัด\nวัต\nวัตต์\nวัตตา\nวัตถ์\nวัตถาภรณ์\nวัตถาลังการ\nวัตถุ\nวัตนะ\nวัตร\nวัตสดร\nวัตสะ\nวัติ\nวัทน์\nวัน\nวันต์\nวันทนา\nวันทนาการ\nวันทนีย์\nวันทยหัตถ์\nวันทยาวุธ\nวันทา\nวันทิ\nวับ\nวับวาบ\nวับวาม\nวับแวบ\nวับแวม\nวัปปะ\nวัมมิกะ\nวัย\nวัลก์\nวัลคุ\nวัลย์\nวัลลภ\nวัลลี\nวัว\nวัสสะ\nวัสโสทก\nวัสดุ\nวัสตร์\nวัสน์\nวัสนะ\nวัสสานะ\nวัสสานฤดู\nวา\nว่า\nว้า\nว้าเหว่\nวาก\nว้าก\nวากยสัมพันธ์\nวากยะ\nวาง\nว่าง\nว้าง\nวาจก\nวาจา\nวาจาไปยะ\nวาจาล\nวาชเปยะ\nวาณิช\nวาณิชกะ\nวาณิชย์\nวาณี\nวาด\nวาต\nวาตะ\nวาตภัย\nวาท\nวาทศาสตร์\nวาทศิลป์\nวาทกะ\nวาทนะ\nวาทย์\nวาทยกร\nวาทิต\nวาทิน\nวาที\nวาน\nวานซืน\nว่าน\nวานร\nวานรินทร์\nวาเนเดียม\nวาบ\nวาปี\nวาม\nวามน\nวามนาวตาร\nวามะ\nวาย\nว่าย\nว้าย\nวายะ\nวาโย\nวายามะ\nวายุ\nวายุกูล\nวาร\nวาระ\nวารสาร\nวารสารศาสตร์\nวาริ\nวารี\nวาริช\nวารีช\nวาริท\nวาริธร\nวารุณ\nวารุณี\nวาล\nวาลวีชนี\nวาล์ว\nวาลิกา\nวาลุกา\nวาว\nว่าว\nว้าว่อน\nว้าวุ่น\nวาสนะ\nวาสนา\nวาสพ\nวาสะ\nวาสิน\nวาสี\nวาสุกรี\nวาสุกี\nวาสุเทพ\nวาหนะ\nวาหะ\nวาหินี\nวาฬ\nวิกขัมภ์\nวิกขัมภนะ\nวิกเขป\nวิกรม\nวิกรัย\nวิกรานต์\nวิกฤต\nวิกฤติ\nวิกล\nวิกสิต\nวิกัต\nวิกัติ\nวิกัติการก\nวิกัป\nวิกัย\nวิการ\nวิกาล\nวิกาลโภชน์\nวิคหะ\nวิเคราะห์\nวิฆเนศ\nวิฆเนศวร\nวิฆาต\nวิง\nวิ่ง\nวิ่งเปี้ยว\nวิงวอน\nวิจฉิกะ\nวิจล\nวิจักขณ์\nวิจักษ์\nวิจักษณ์\nวิจัย\nวิจาร\nวิจารณ์\nวิจารณญาณ\nวิจิ\nวิจิกิจฉา\nวิจิต\nวิจิตร\nวิจิน\nวิจุณ\nวิจุรณ\nวิชชา\nวิชชุ\nวิชชุดา\nวิชชุตา\nวิชชุลดา\nวิชญะ\nวิชน\nวิชนี\nวิชย\nวิชัย\nวิชา\nวิชานนะ\nวิชิต\nวิเชียร\nวิญญัตติ\nวิญญาณ\nวิญญาณกทรัพย์\nวิญญู\nวิฑูรย์\nวิด\nวิตก\nวิตถาร\nวิตามิน\nวิถี\nวิทธะ\nวิทยฐานะ\nวิทยา\nวิทยาคม\nวิทยาคาร\nวิทยาลัย\nวิทยุ\nวิทยุต\nวิทวัส\nวิทัตถิ\nวิทัศน์\nวิทารณ์\nวิทิต\nวิทู\nวิทูร\nวิเทศ\nวิเทโศบาย\nวิธ\nวิธวา\nวิธาน\nวิธี\nวิธุระ\nวิธู\nวิธูปนะ\nวิ่น\nวินตกะ\nวินัย\nวินาที\nวินายก\nวินาศ\nวินิจ\nวินิจฉัย\nวินิต\nวินิบาต\nวินิปาติก\nวิเนต\nวิบัติ\nวิบาก\nวิบุล\nวิบุลย์\nวิบูล\nวิบูลย์\nวิปการ\nวิปฏิสาร\nวิปโยค\nวิประโยค\nวิปริต\nวิปลาส\nวิปวาส\nวิปักษ์\nวิปัสสก\nวิปัสสนา\nวิปัสสนายานิก\nวิพากษ์\nวิพิธทัศนา\nวิพุธ\nวิภว\nวิภวตัณหา\nวิภังค์\nวิภัช\nวิภัตติ\nวิภา\nวิภาค\nวิภาช\nวิภาดา\nวิภาวี\nวิภาษ\nวิภาส\nวิภู\nวิภูษณะ\nวิภูษา\nวิภูษิต\nวิมน\nวิมล\nวิมลัก\nวิมังสา\nวิมัติ\nวิมาน\nวิมุข\nวิมุต\nวิมุตติ\nวิเมลือง\nวิโมกข์\nวิโยค\nวิระ\nวิรงรอง\nวิรังรอง\nวิรัช\nวิรัต\nวิรัติ\nวิราคะ\nวิราม\nวิริยภาพ\nวิริยะ\nวิรุธ\nวิรุฬห์\nวิรุฬหก\nวิรูป\nวิรูปักษ์\nวิเรนทร์\nวิโรจ\nวิโรจน์\nวิโรฒ\nวิโรธ\nวิลันดา\nวิลัย\nวิลาด\nวิลาศ\nวิลาป\nวิลาวัณย์\nวิลาส\nวิลาสินี\nวิลิปดา\nวิลิศมาหรา\nวิเลป\nวิเลปนะ\nวิโลกนะ\nวิโลม\nวิไล\nวิไลวรรณ\nวิวรณ์\nวิวรรธน์\nวิวัฏ\nวิวัฒน์\nวิวัฒนาการ\nวิวัน\nวิวาท\nวิวาห์\nวิวาหมงคล\nวิวาหะ\nวิวิต\nวิวิธ\nวิเวก\nวิศรุต\nวิศว\nวิศวกร\nวิศวกรรม\nวิศวกรรมศาสตร์\nวิศัลย์\nวิศาขบูชา\nวิศาขา\nวิศาล\nวิศิษฏ์\nวิศุทธ์\nวิศุทธิ์\nวิเศษ\nวิเศษณ์\nวิษณุ\nวิษณุกรรม\nวิษธร\nวิษัย\nวิษาณ\nวิษุวัต\nวิสกี้\nวิสรรชนีย์\nวิสฤต\nวิสสุกรรม\nวิสัชนา\nวิสัญญี\nวิสัย\nวิสัยทัศน์\nวิสาขบูชา\nวิสาขะ\nวิสาขา\nวิสามัญ\nวิสามานยนาม\nวิสาร\nวิสารทะ\nวิสาล\nวิสาสะ\nวิสาหกิจ\nวิสิฐ\nวิสุงคามสีมา\nวิสุทธ์\nวิสุทธิ์\nวิสูตร\nวิเสท\nวิหค\nวิหลั่น\nวิหาร\nวิหิงสา\nวิเหสา\nวิฬาร\nวิฬาร์\nวี\nวีจิ\nวีชนี\nวีณา\nวี้ด\nวีรกรรม\nวีรชน\nวีรบุรุษ\nวีรสตรี\nวี่วัน\nวี่แวว\nวีสะ\nวุ้ง\nวุฐิ\nวุฒ\nวุฒิ\nวุด\nวุ่น\nวุ้น\nวุบ\nวุ้ย\nวุลแฟรม\nวู้\nวูดวาด\nวูบ\nวู่วาม\nเว้\nเวค\nเวคิน\nเวคี\nเวจ\nเวช\nเวชยันต์\nเวฐน์\nเวณิ\nเวณิก\nเวณุ\nเวตน์\nเวตร\nเวตาล\nเวท\nเวทคู\nเวทนา\nเวทย์\nเวทัลละ\nเวทางค์\nเวทางคศาสตร์\nเวทานต์\nเวทานตะ\nเวทิ\nเวที\nเวธะ\nเวน\nเว้น\nเวนไตย\nเวไนย\nเวมัต\nเว้ย\nเวยยากรณะ\nเวร\nเวรมณี\nเวรี\nเวโรจน์\nเวลา\nเวเลนซี\nเวศม์\nเวศย์\nเวศยา\nเวสน์\nเวสภู\nเวสม์\nเวสวัณ\nเวสสะ\nเวสสันดร\nเวสสุกรรม\nเวสสุวัณ\nเวสารัช\nเวสิยา\nเวหน\nเวหะ\nเวหา\nเวหาส\nเวฬุ\nเวฬุริยะ\nเว่อ\nเว้า\nเวิก\nเวิ้ง\nเวี่ย\nเวียง\nเวียด\nเวียดนาม\nเวียน\nเวียร\nเวี่ยว\nแว้\nแวง\nแว้ง\nแวด\nแว้ด\nแวน\nแว่น\nแวนดา\nแวบ\nแว็บ\nแวม\nแว็ม\nแวว\nแว่ว\nแวะ\nโว\nโว่\nโวการ\nโว่ง\nโวทาน\nโวย\nโว้ย\nโว้เว้\nโวหาร\nไว\nไว้\nไวกูณฐ์\nไวฑูรย์\nไวทย์\nไวน์\nไวพจน์\nไวยากรณ์\nไวยาวัจกร\nไวยาวัจมัย\nไวรัส\nไววรรณ\nไวษณพ\nไวโอลิน\nศก\nศกุน\nศกุนต์\nศกุนิ\nศกุนี\nศจี\nศตะ\nศตภิษัช\nศตวรรษ\nศตพรรษ\nศตกะ\nศนิ\nศพ\nศมนะ\nศมะ\nศยาม\nศยามล\nศร\nศรายุธ\nศราวรณ์\nศรรกรา\nศรวณะ\nศรวณีย์\nศรวิษฐา\nศรัณย์\nศรัณยู\nศรัท\nศรัทธา\nศรัย\nศราทธ์\nศราทธพรต\nศราพก\nศราวก\nศราวณะ\nศรี\nศรีตรัง\nศรุติ\nศฤคาล\nศฤงค์\nศฤงคาร\nศฤงคาริน\nศฤงคารี\nศลิษฏ์\nศลิษา\nศวะ\nศวัส\nศวา\nศวาน\nศศะ\nศศธร\nศศพินทุ์\nศศลักษณ์\nศศิ\nศศิน\nศศี\nศศิขัณฑ์\nศศิธร\nศศิมณฑล\nศศิวิมล\nศอ\nศอก\nศักดา\nศักดิ\nศักดิ์\nศักดินา\nศักติ\nศักย\nศักยภาพ\nศักย์\nศักยะ\nศักร\nศักรินทร์\nศักเรนทร์\nศักราช\nศังกร\nศัตรู\nศันสนะ\nศันสนีย์\nศัพท์\nศัยยา\nศัล\nศัลย์\nศัลยกรรม\nศัลยแพทย์\nศัลยศาสตร์\nศัสดร\nศัสตร\nศัสตรศาสตร์\nศัสตรา\nศัสตราวุธ\nศากตะ\nศากย\nศากยะ\nศากยพุทธ\nศากยมุนี\nศาฎก\nศาณ\nศานต์\nศานติ\nศาป\nศารท\nศารทูล\nศาริกา\nศาล\nศาลา\nศาศวัต\nศาสดา\nศาสตร์\nศาสตรา\nศาสตราจารย์\nศาสนา\nศาสนกิจ\nศาสนจักร\nศาสนธรรม\nศาสนบุคคล\nศาสนพิธี\nศาสนวัตถุ\nศาสนศาสตร์\nศาสนสถาน\nศาสนสมบัติ\nศาสนิกชน\nศาสนีย์\nศาสนูปถัมภก\nศาสน์\nศิกษก\nศิการ\nศิขร\nศิขริน\nศิขรี\nศิขัณฑ์\nศิคาล\nศิงขร\nศิงขริน\nศิตะ\nศิถี\nศิพิระ\nศิระ\nศิรประภา\nศิราภรณ์\nศิโรรัตน์\nศิโรเวฐน์\nศิรา\nศิรามพุช\nศิโรราบ\nศิลป\nศิลป์\nศิลปะ\nศิลปกร\nศิลปกรรม\nศิลปกิจ\nศิลปวัตถุ\nศิลปวิทยา\nศิลปศาสตร์\nศิลปศึกษา\nศิลปหัตถกรรม\nศิลปิน\nศิลปี\nศิลา\nศิวะ\nศิวโมกข์\nศิวลึงค์\nศิวเวท\nศิวาลัย\nศิศีระ\nศิษฎิ\nศิษฏ์\nศิษย์\nศิษยานุศิษย์\nศีขร\nศีต\nศีตกาล\nศีรษะ\nศีล\nศึก\nศึกษา\nศึกษาธิการ\nศึกษานิเทศก์\nศุกร์\nศุกรวรรณ\nศุกรวาร\nศุกระ\nศุกล\nศุกลปักษ์\nศุจิ\nศุทธะ\nศุทธิ\nศุนะ\nศุนิ\nศุภกร\nศุภเคราะห์\nศุภนิมิต\nศุภมัสดุ\nศุภมาตรา\nศุภมาส\nศุภอักษร\nศุภางค์\nศูกร\nศุลกากร\nศุลการักษ์\nศุลี\nศุษิร\nศูทร\nศูนย์\nศูนยวาท\nศูละ\nศูลิน\nเศรณี\nเศรษฐ\nเศรษฐ์\nเศรษฐกิจ\nเศรษฐศาสตร์\nเศรษฐี\nเศร้า\nเศลษ\nเศวต\nเศวตร\nเศวตัมพร\nเศษ\nเศาจ\nเศาร์\nเศารยะ\nเศิก\nเศียร\nโศก\nโศกา\nโศกาดูร\nโศกาลัย\nโศกี\nโศจิ\nโศธนะ\nโศภน\nโศภะ\nโศภา\nโศภิต\nโศภิน\nโศภิษฐ์\nโศภี\nโศรดา\nโศรตร\nโศลก\nไศล\nไศวะ\nษมา\nษัฏ\nษัฑ\nษัณ\nษัษ\nษัษฐะ\nษัษฐี\nโษฑศัน\nสก\nสกวาที\nสกฏะ\nสกทาคามิผล\nสกิทาคามิผล\nสกทาคามิมรรค\nสกิทาคามิมรรค\nสกทาคามี\nสกิทาคามี\nสกนธ์\nสกปรก\nสกรณีย์\nสกรรจ์\nสกรรมกริยา\nสกล\nสกลมหาสังฆปริณายก\nสกัด\nสกา\nสกาว\nสกี\nสกุณ\nสกุณา\nสกุณี\nสกุน\nสกุนต์\nสกุล\nสเกต\nสแกนเดียม\nสขะ\nสง\nส่ง\nสงกร\nสงกรานต์\nสงกา\nสงค์\nสงคร\nสงคราม\nสงเคราะห์\nสงฆ์\nสงบ\nสงวน\nส่งสการ\nสงสัย\nสงสาร\nสงสารวัฏ\nสงัด\nสง่า\nสฐะ\nสณฑ์\nสด\nสดมภ์\nสดับ\nสดับปกรณ์\nสดำ\nสดุดี\nสตะ\nสตน\nสตภิสชะ\nสตรอนเชียม\nสตริกนิน\nสตรี\nสตัฟฟ์\nสตัมภ์\nสตางค์\nสติ\nสติปัฏฐาน\nสตี\nสตู\nสตูป\nสเต๊ก\nสถบดี\nสถล\nสถวีระ\nสถาน\nสถานะ\nสถานี\nสถาบัน\nสถาปนา\nสถาปนิก\nสถาปัตยกรรม\nสถาปัตยกรรมศาสตร์\nสถาปัตยเรขา\nสถาปัตยเวท\nสถาพร\nสถาวร\nสถิต\nสถิตยศาสตร์\nสถิติ\nสถิร\nสถีรวาท\nสถุล\nสถูป\nสทิง\nสทึง\nสทุม\nสธนะ\nสาธุสะ\nสน\nส้น\nสนทนา\nสนทรรศ\nสนทรรศน์\nสนเทศ\nสนเท่ห์\nสนธยา\nสนธิ\nสนน\nสนม\nสนวน\nสนอง\nสนอบ\nสนอม\nสนะ\nสนัด\nสนั่น\nสนับ\nสนับทึบ\nสนับสนุน\nสนาน\nสนาม\nสนายุ\nสนิกะ\nสนิท\nสนิธ\nสนิม\nสนุก\nสนุกเกอร์\nสนุข\nสนุต\nสนุ่น\nสบ\nสบง\nสบถ\nสบัน\nสบาย\nสบู่\nสไบ\nสปริง\nสปอร์\nสปาเกตตี\nสเปกตรัม\nสเปกโทรสโกป\nสไปริลลัม\nสพาบ\nสภา\nสภาพ\nสภาวการณ์\nสภาวะ\nสม\nสมการ\nสมจารี\nสมดุล\nสมมูล\nส้ม\nสมญา\nสมณะ\nสมณบริขาร\nสมณศักดิ์\nสมณสารูป\nสมเด็จ\nสมถะ\nสมถยานิก\nสมถวิปัสสนา\nสมนาคุณ\nสมบัติ\nสมบุกสมบัน\nสมบูรณ์\nสมบูรณาญาสิทธิราชย์\nสมประดี\nสมปฤดี\nสมปฤๅดี\nส้มป่อย\nสมปัก\nสมผุส\nสมพง\nสมพงศ์\nสมพล\nสมพัตสร\nสมพาส\nสมเพช\nสมโพธน์\nสมโพธิ\nสมภพ\nสมภาร\nสมโภค\nสมโภช\nสมมต\nสมมติ\nสมมุติ\nสมมาตร\nส้มมือ\nสมโมท\nสมโยค\nสมร\nสมรด\nสมรรถ\nสมรรถนะ\nสมรรถภาพ\nสมรส\nสมฤดี\nสมฤติ\nสมวายะ\nสมเสร็จ\nสมอ\nสมอง\nสมะ\nสมัคร\nสมังคี\nสมัช\nสมัชชา\nสมัญญา\nสมัต\nสมัน\nสมันต์\nสมัย\nสมา\nสมาคม\nสมาจาร\nสมาชิก\nสมาทาน\nสมาธิ\nสมาน\nสมานฉันท์\nสมาบัติ\nสมาพันธรัฐ\nสมาส\nสม่ำเสมอ\nสมิง\nสมิต\nสมิติ\nสมิทธ์\nสมิทธิ\nสมี\nสมุก\nสมุจจัย\nสมุจเฉท\nสมุฏฐาน\nสมุด\nสมุทร\nสมุทรโคดม\nสมุทัย\nสมุน\nสมุนไพร\nสมุลแว้ง\nสมุห\nสมุห์\nสมุหกลาโหม\nสมุหเทศาภิบาล\nสมุหนาม\nสมุหนายก\nสโมธาน\nสโมสร\nสยด\nสยนะ\nสยบ\nสยมพร\nสยมภู\nสยอง\nสยอน\nสยัมวรา\nสยาม\nสยามานุสติ\nสยามินทร์\nสยาย\nสยิว\nสยิ้ว\nสยุมพร\nสยุมภู\nสร\nสรง\nสร่ง\nสรณะ\nสรณคมน์\nสรณาคมน์\nสรณตรัย\nสรตะ\nสรทะ\nสรนุก\nสรเนาะ\nสรไน\nสรเพชญ\nสรภะ\nสรภัญญะ\nสรภู\nสรม\nสรร\nสรรค์\nสรรพ\nสรรพคุณ\nสรรพนาม\nสรรพสามิต\nสรรพัชญ\nสรรพากร\nสรรพางค์\nสรรเพชญ\nสรรเพชุดา\nสรรเสริญ\nสรลอน\nสรเลข\nสรวง\nสรวม\nสรวล\nสรเสริญ\nสร้อย\nสระ\nสระกอ\nสระท้อน\nสระพรั่ง\nสระอาด\nสรั่ง\nสรัสวดี\nสร่าง\nสร้าง\nสราญ\nสรี้\nสรีระ\nสรีรกิจ\nสรีรธาตุ\nสรีรวิทยา\nสรีรศาสตร์\nสรีรังคาร\nสรีรางคาร\nสรุป\nสโรช\nสโรชะ\nสฤก\nสฤต\nสฤษฎิ\nสฤษฎี\nสฤษฏ์\nสฤษดิ์\nสลด\nสลบ\nสลวน\nสลวย\nสลอด\nสลอน\nสลอย\nสละ\nสลัก\nสลัด\nสลัดได\nสลับ\nสลัว\nสลา\nสลาก\nสลาง\nสล้าง\nสลาด\nสลาตัน\nสลาบ\nสลาย\nสลิด\nสลิล\nสลึก\nสลึง\nสลุต\nสลุบ\nสลุมพร\nสแลง\nสวการย์\nสวภาพ\nสวราชย์\nสวก\nส้วง\nสวด\nสวน\nสวนะ\nสวนาการ\nส่วน\nสวนิต\nสวบ\nสวม\nส้วม\nสวย\nส่วย\nส้วย\nสวยม\nสวรรค\nสวรรค์\nสวรรคต\nสวรรคาลัย\nสวรรยา\nสวระ\nสวะ\nสวัสดิ\nสวัสดิ์\nสวัสดิการ\nสวัสดิภาพ\nสวัสดิมงคล\nสวัสดี\nสวัสติ\nสวาตี\nสวัสติกะ\nสวา\nสวาปาม\nสวาคตะ\nสวาง\nสว่าง\nสวาด\nสวาดิ\nสวาท\nสว่าน\nสว้าน\nสวาบ\nสวามิ\nสวามี\nสวามินี\nสวาย\nสวาสดิ์\nสวาหะ\nสวิง\nสวิญญาณกทรัพย์\nสวิตช์\nสสาร\nสสุระ\nสสุรี\nสหกรณ์\nสหการ\nสหจร\nสหชาต\nสหชาติ\nสหธรรม\nสหธรรมิก\nสหประชาชาติ\nสหพันธ์\nสหพันธรัฐ\nสหภาพ\nสหศึกษา\nสหัช\nสหัมบดี\nสหัส\nสหัสสะ\nสหัสธารา\nสหัสนัยน์\nสหัสเนตร\nสหัสรังสี\nสหัสา\nสหาย\nสอ\nส่อ\nสอง\nส่อง\nส้อง\nสอด\nสอน\nส่อน\nสอบ\nสอพลอ\nส้อม\nสอย\nสะ\nสะกด\nสะกอ\nสะกาง\nสะการะ\nสะกิด\nสะกิดสะเกา\nสะเก็ด\nสะแก\nสะคร้อ\nสะคราญ\nสะค้าน\nสะเงาะสะแงะ\nสะดม\nสะดวก\nสะดิ้ง\nสะดึง\nสะดือ\nสะดุ้ง\nสะดุด\nสะเด็ด\nสะเดา\nสะเดาะ\nสะตอ\nสะตาหมัน\nสะตึ\nสะตือ\nสะตุ\nสะเต๊ะ\nสะโตก\nสะทก\nสะท้อน\nสะท้าน\nสะทึก\nสะเทิน\nสะเทิ้น\nสะเทือน\nสะเทื้อน\nสะบะ\nสะบัก\nสะบักสะบอม\nสะบัด\nสะบัดสะบิ้ง\nสะบั้น\nสะบันงา\nสะบ้า\nสะบู\nสะแบง\nสะเปะสะปะ\nสะพรั่ง\nสะพรึงกลัว\nสะพรึบ\nสะพรึ่บ\nสะพัก\nสะพัง\nสะพัด\nสะพั้น\nสะพาน\nสะพาย\nสะเพร่า\nสะโพก\nสะเภา\nสะใภ้\nสะโมง\nสะระตะ\nสะระแหน่\nสะลาง\nสะลาบ\nสะลึมสะลือ\nสะวี้ดสะว้าด\nสะสม\nสะสวย\nสะสาง\nสะเหล่อ\nสะอาง\nสะอาด\nสะอ้าน\nสะอิ้ง\nสะอิดสะเอียน\nสะอึก\nสะอื้น\nสะเอ้ง\nสะเอว\nสะเออะ\nสะโอดสะอง\nสะไอ\nสัก\nสักกะ\nสักยะ\nสักกัจจะ\nสักกายทิฐิ\nสักการ\nสักการะ\nสักขี\nสักวา\nสักหลาด\nสัค\nสัคคะ\nสั่ง\nสังกร\nสังกรณี\nสังกรประโยค\nสังกะตัง\nสังกะวัง\nสังกะวาด\nสังกะสี\nสังกัด\nสังกัปปะ\nสังกา\nสังการ\nสังกาศ\nสังกิเลส\nสังเกต\nสังข์\nสังขกร\nสังขตธรรม\nสังขตะ\nสังขยา\nสังขลิก\nสังขลิกา\nสังขาร\nสังขารา\nสังเขป\nสังค์\nสังคญาติ\nสังคม\nสังคหะ\nสังคัง\nสังคายนา\nสังคายนาย\nสังคีต\nสังคีติ\nสังเค็ด\nสังเคราะห์\nสังฆกรรม\nสังฆการี\nสังฆเถระ\nสังฆทาน\nสังฆนายก\nสังฆปาโมกข์\nสังฆภัต\nสังฆเภท\nสังฆมณฑล\nสังฆมนตรี\nสังฆราช\nสังฆสภา\nสังฆาณัติ\nสังฆาฏิ\nสังฆาทิเสส\nสังฆาธิการ\nสังฆานุสติ\nสังฆาวาส\nสังยุตนิกาย\nสังโยค\nสังโยชน์\nสังวร\nสังวัจฉระ\nสังวัธยาย\nสังวาล\nสังวาส\nสังเวคะ\nสังเวช\nสังเวชนียสถาน\nสังเวย\nสังเวียน\nสังสกฤต\nสังสการ\nสังสนทนา\nสั่งสนทนา\nสังสรรค์\nสังสารวัฏ\nสังสิทธิ\nสังสุทธ์\nสังสุทธิ\nสังหร\nสังหรณ์\nสังหาร\nสังหาริมทรัพย์\nสังหาริมะ\nสังหิต\nสัจ\nสัจกิริยา\nสัจจะ\nสัจญาณ\nสัจธรรม\nสัจนิยม\nสัจพจน์\nสัชฌะ\nสัชฌุ\nสัญจร\nสัญเจตนา\nสัญชาตญาณ\nสัญชาติ\nสัญฌา\nสัญญา\nสัญญาณ\nสัญญี\nสัญโญชน์\nสัญนิยม\nสัญประกาศ\nสัญลักษณ์\nสัฐิ\nสัณฐาน\nสัณฐิติ\nสัณฑ์\nสัณห์\nสัด\nสัดจอง\nสัต\nสัตตะ\nสัตตาหกรณียะ\nสัตตาหกาลิก\nสัตมวาร\nสัตสดก\nสัตตบงกช\nสัตตบรรณ\nสัตตบุษย์\nสัตตู\nสัตถันดร\nสัตถา\nสัตถิ\nสัตถุ\nสัตถุศาสนา\nสัตบรรณ\nสัตย์\nสัตยพรต\nสัตยวาที\nสัตยาเคราะห์\nสัตยาธิษฐาน\nสัตยาบัน\nสัตว์\nสัตวชาติ\nสัตวบาล\nสัตวแพทย์\nสัตววิทยา\nสัตวา\nสัทธรรม\nสัทธา\nสัทธาจริต\nสัทธาธิกะ\nสัทธินทรีย์\nสัทธิงวิหาริก\nสัทธิวิหาริก\nสัทวิทยา\nสัทศาสตร์\nสัทอักษร\nสัน\nสั่น\nสั้น\nสันดาน\nสันดาป\nสันโดษ\nสันต์\nสันตติ\nสันตะปาปา\nสันตะวา\nสันติ\nสันตุฏฐี\nสันถวไมตรี\nสันถวะ\nสันถัต\nสันถาร\nสันทนะ\nสันทะ\nสันทัด\nสันทัสนะ\nสันทาน\nสันทิฐิก\nสันทิส\nสันเทหะ\nสันธาน\nสันนิธิ\nสันนิบาต\nสันนิวาส\nสันนิษฐาน\nสันสกฤต\nสับ\nสับปลับ\nสับปลี้\nสับปะรด\nสัปคับ\nสัปดาห์\nสัปดาหะ\nสัปดน\nสัปตศก\nสัปทน\nสัปปะ\nสัปปิ\nสัปปุริส\nสัปปุรุษ\nสัประยุทธ์\nสัปหงก\nสัปเหร่อ\nสัพ\nสัพพะ\nสัพพัญญู\nสัพเพเหระ\nสัพยอก\nสัมบูรณ์\nสัมปชัญญะ\nสัมปทา\nสัมปทาน\nสัมปยุต\nสัมปโยค\nสัมประสิทธิ์\nสัมประหาร\nสัมปรายภพ\nสัมปรายิกภพ\nสัมปัตติ\nสัมผัปลาป\nสัมผัปลาปะ\nสัมผัส\nสัมพล\nสัมพหุลา\nสัมพัจฉรฉินท์\nสัมพัตสร\nสัมพัทธ์\nสัมพันธ์\nสัมพันธน์\nสัมพันธภาพ\nสัมพันธมิตร\nสัมพันธไมตรี\nสัมพาหะ\nสัมพุทธ\nสัมพุทธะ\nสัมโพธิ\nสัมภวะ\nสัมภเวสี\nสัมภัต\nสัมภัตตะ\nสัมภาระ\nสัมภาษณ์\nสัมโภคกาย\nสัมมนา\nสัมมัปธาน\nสัมมา\nสัมโมทนียกถา\nสัมฤทธิ\nสัมฤทธิ์\nสัมฤทธิศก\nสัยน์\nสัลเลข\nสัสดี\nสัสตทิฐิ\nสัสสะ\nสัสสุ\nสัสสู\nสา\nส่า\nสาก\nสากรรจ์\nสากล\nสากัจฉา\nสากัลย์\nสากิยะ\nสาเก\nสาขา\nสาคร\nสาคเรศ\nสาคู\nสาง\nส้าง\nสาชล\nสาฎก\nสาฏิก\nสาณี\nสาด\nสาไถย\nสาทร\nสาทิส\nสาทุ\nสาโท\nสาธก\nสาธยะ\nสาธยาย\nสาธารณะ\nสาธารณชน\nสาธารณประโยชน์\nสาธารณภัย\nสาธารณรัฐ\nสาธารณสถาน\nสาธารณสมบัติ\nสาธารณสุข\nสาธารณูปการ\nสาธารณูปโภค\nสาธารณ์\nสาธิต\nสาธุ\nสาน\nส่าน\nสานุ\nสานู\nสานุศิษย์\nสาบ\nสาบสูญ\nสาบาน\nสาป\nสาปไตย\nสาม\nสามชุก\nสามเณร\nสามเณรี\nสามนต์\nสามนตราช\nสามยทรัพย์\nสามล\nสามหาว\nสามะ\nสามัคคี\nสามัญ\nสามัตถิยะ\nสามานย์\nสามานยนาม\nสามารถ\nสามิต\nสามินี\nสามิภักดิ์\nสามี\nสามีจิกรรม\nสาย\nส่าย\nส้าย\nสายชู\nสายัณห์\nสายาห์\nสาร\nสารคดี\nสารธรรม\nสารนิเทศ\nสารบบ\nสารบรรณ\nสารบัญ\nสารบาญ\nสารบาญชี\nสารประโยชน์\nสารสนเทศ\nสารทุกข์\nสารถี\nสารท\nสารพัด\nสารพัน\nสารพางค์\nสารภาพ\nสารภี\nสารวัตร\nสาระ\nสาระแน\nสาระพา\nสาระยำ\nสาระวารี\nสาระสะมา\nสารัตถประโยชน์\nสารัตถศึกษา\nสารัตถะ\nสารัทธ์\nสารัมภ์\nสาราณียกร\nสาราณียธรรม\nสาราณียะ\nสารานุกรม\nสารีริกธาตุ\nสารูป\nสาโรช\nสาละ\nสาละวน\nสาลิ\nสาลิกา\nสาลินี\nสาลี\nสาลี่\nสาลู\nสาโลหิต\nสาว\nสาวก\nสาวิกา\nสาวิตร\nสาวิตรี\nสาสน\nสาสน์\nสาส์น\nสาสนา\nสาสม\nสาหร่าย\nสาหรี\nส่าหรี\nสาหัส\nสาเหตุ\nสาแหรก\nสำ\nสำส่อน\nส่ำ\nสำคัญ\nสำซ่าง\nสำแดง\nสำทับ\nสำนวน\nสำนอง\nสำนัก\nสำนาน\nสำนึก\nสำนึง\nสำเนา\nสำเนียง\nสำบัด\nสำปะลอ\nสำปะหลัง\nสำปั้น\nสำปันนี\nสำเภา\nสำมะงา\nสำมะโน\nสำมะลอ\nสำมะเลเทเมา\nสำมะหา\nสำรด\nสำรวจ\nสำรวม\nสำรวย\nสำรวล\nสำรอก\nสำรอง\nสำรับ\nสำราก\nสำราญ\nสำริด\nสำเร็จ\nสำเรา\nสำเริง\nสำโรง\nสำลัก\nสำลาน\nสำลี\nสำแลง\nสำหรวด\nสำหรับ\nสำหา\nสำเหนียก\nสำเหร่\nสำออย\nสำอาง\nสิ\nสิกข์\nสิข\nสิกขมานา\nสิกขา\nสิขร\nสิขรี\nสิขเรศ\nสิขา\nสิขานล\nสิขี\nสิคาล\nสิง\nสิ่ง\nสิงขร\nสิงค์\nสิงคลิ้ง\nสิงคลี\nสิงคาร\nสิงคาล\nสิงคี\nสิงโต\nสิงห์\nสิงหนาท\nสิงหบัญชร\nสิงหรา\nสิงหราช\nสิงหาคม\nสิงหาสน์\nสิงหล\nสิญจน์\nสิตะ\nสิตางศุ์\nสิถิล\nสิทธ์\nสิทธัตถะ\nสิทธา\nสิทธาจารย์\nสิทธารถ\nสิทธิ\nสิทธิ์\nสิทธิการิยะ\nสิธยะ\nสิน\nสิ้น\nสินเทา\nสินธพ\nสินธุ\nสินธุ์\nสินธุระ\nสินธู\nสินเธาว์\nสินาด\nสินิทธ์\nสินี\nสิเนรุ\nสิเนหก\nสิเนหะ\nสิเนหา\nสิเน่หา\nสิบ\nสิปปะ\nสิมพลี\nสิระ\nสิโรดม\nสิโรตม์\nสิริ\nสิรี\nสิลา\nสิว\nสิ่ว\nสิวะ\nสิวาลัย\nสิวิกา\nสี\nสี่\nสี้\nสีกา\nสีกุน\nสีข้าง\nสีด\nสีดอ\nสีดา\nสีตลรัศมี\nสีตโลทก\nสีโตทก\nสีทันดร\nสีมันต์\nสีมา\nสีละมัน\nสีวิกา\nสีสอ\nสีสะ\nสีสา\nสีสุก\nสีเสียด\nสีห์\nสีหนาท\nสีหบัญชร\nสีหราช\nสีหไสยา\nสีหไสยาสน์\nสีหะ\nสึก\nสึง\nสืบ\nสื่อ\nสุ\nสุก\nสุกข์\nสุกร\nสุกรม\nสุกำศพ\nสุกียากี้\nสุข\nสุขา\nสุขาภิบาล\nสุขารมณ์\nสุขาวดี\nสุขิน\nสุขี\nสุขุม\nสุขุมาล\nสุโข\nสุคต\nสุคติ\nสุคนธ\nสุคนธ์\nสุคนธชาติ\nสุคนธรส\nสุคันธ์\nสุคันธรส\nสุงกะ\nสุงกากร\nสุงสิง\nสุงสุมาร\nสุจริต\nสุจหนี่\nสุจิ\nสุจิต\nสุจิตร\nสุชน\nสุชัมบดี\nสุชา\nสุชาดา\nสุญ\nสุญญากาศ\nสุญตา\nสุญนิยม\nสุณ\nสุณิสา\nสุด\nสุดา\nสุต\nสุตตนิบาต\nสุตตะ\nสุตตันตปิฎก\nสุตตันตะ\nสุติ\nสุทธ\nสุทธ์\nสุทธาวาส\nสุทธิ\nสุทรรศน์\nสุทัศน์\nสุธา\nสุธาโภชน์\nสุธารส\nสุธาสินี\nสุธาสี\nสุธี\nสุนทร\nสุนทรี\nสุนทรียภาพ\nสุนทรียศาสตร์\nสุนทรียะ\nสุนัข\nสุนันท์\nสุโนก\nสุบดี\nสุบรรณ\nสุบิน\nสุปรีดิ์\nสุปรีย์\nสุปาณี\nสุพพัต\nสุพรรณ\nสุพรรณบัฏ\nสุพรรณภาชน์\nสุพรรณราช\nสุพรรณศรี\nสุพรรณถัน\nสุพรรณิการ์\nสุภร\nสุภัค\nสุภา\nสุภาพ\nสุภาษิต\nสุม\nสุ่ม\nสุมทุม\nสุมน\nสุมนะ\nสุมนัส\nสุมนา\nสุ้มเสียง\nสุมะ\nสุมาลี\nสุเมธ\nสุเมรุ\nสุรคต\nสุรเชษฐ์\nสุรบดี\nสุรภาพ\nสุรโลก\nสุรสีหนาท\nสุรเสียง\nสุรงค์\nสุรังค์\nสุรภี\nสุรัติ\nสุรัสวดี\nสุรา\nสุรางค์จำเรียง\nสุรางคนา\nสุรางคนางค์\nสุรารักษ์\nสุราลัย\nสุรินทร์\nสุรินทราหู\nสุริยะ\nสุริยกันต์\nสุริยกานต์\nสุริยการ\nสุริยกาล\nสุริยคติ\nสุริยคราส\nสุริยมณฑล\nสุริยวงศ์\nสุริยง\nสุริยา\nสุริเยนทร์\nสุริเยศ\nสุริโย\nสุริยน\nสุริยัน\nสุริยุปราคา\nสุรีย์\nสุรุ่ยสุร่าย\nสุลต่าน\nสุวคนธ์\nสุวภาพ\nสุวรรณ\nสุวรรณภูมิ\nสุวะ\nสุวาน\nสุวินัย\nสุวิมล\nสุษิระ\nสุสาน\nสุหนัต\nสุหร่ง\nสุหร่าย\nสุหฤท\nสุหัท\nสุเหร่า\nสู\nสู่\nสู้\nสูง\nสูจิ\nสูจิบัตร\nสูญ\nสูด\nสูต\nสูตร\nสูติ\nสูติกรรม\nสูตินรีเวช\nสูติบัตร\nสูติแพทย์\nสูติศาสตร์\nสูท\nสูทกรรม\nสูทศาสตร์\nสูบ\nสูปะ\nสูร\nสูรย์\nสูรยกานต์\nสูริ\nสูสี\nเส\nเสก\nเสกขบุคคล\nเสกขะ\nเสขบุคคล\nเสขะ\nเส็ง\nเส้ง\nเส็งเคร็ง\nเสงี่ยม\nเสฏฐี\nเสณี\nเสด\nเสด็จ\nเสตุ\nเสถียร\nเสทะ\nเสโท\nเสน\nเส้น\nเสนง\nเสน่ง\nเสน่ห์\nเสนหา\nเสน่หา\nเสนอ\nเสนะ\nเสนา\nเสนาธิการ\nเสนาบดี\nเสน่า\nเสนากุฎ\nเสนางค์\nเสนางคนิกร\nเสนานี\nเสนาสนะ\nเสนาะ\nเสนี\nเสนีย์\nเสนียะ\nเสนียด\nเสบย\nเสบียง\nเสพ\nเสพย์\nเสเพล\nเสภา\nเสม็ด\nเสมหะ\nเสมอ\nเสมา\nเสมียน\nเสมือน\nเสย\nเสร็จ\nเสริด\nเสริม\nเสรี\nเสลด\nเสลบรรพต\nเสลา\nเสลี่ยง\nเสลือกสลน\nเสโล\nเสวก\nเสวกามาตย์\nเสวนะ\nเสวนา\nเสวย\nเสวียน\nเสสรวง\nเสสรวล\nเสา\nเส้า\nเสาร์\nเสารภย์\nเสารี\nเสาวคนธ์\nเสาวธาร\nเสาวภา\nเสาวภาคย์\nเสาวภาพ\nเสาวรภย์\nเสาวรส\nเสาวลักษณ์\nเสาวณิต\nเสาวนะ\nเสาวนา\nเสาวนีย์\nเสาหฤท\nเสาะ\nเสาะแสะ\nเสิร์จ\nเสิร์ฟ\nเสีย\nเสียง\nเสี่ยง\nเสียด\nเสี้ยน\nเสียบ\nเสียม\nเสี่ยม\nเสี้ยม\nเสียว\nเสี่ยว\nเสี้ยว\nเสือ\nเสื่อ\nเสื้อ\nเสือก\nเสื่อม\nแส\nแส่\nแส้\nแสก\nแสง\nแสด\nแสดง\nแสตมป์\nแสน\nแสนย์\nแสนยากร\nแสนยานุภาพ\nแสบ\nแสม\nแสยก\nแสยง\nแสยะ\nแสรก\nแสร้ง\nแสลง\nแสล้ม\nแสวง\nแสะ\nโสก\nโสกโดก\nโสกันต์\nโสโครก\nโสณฑ์\nโสณิ\nโสณี\nโสด\nโสดก\nโสดม\nโสดา\nโสดาบัน\nโสดาปัตติผล\nโสดาปัตติมรรค\nโสต\nโสตทัศนวัสดุ\nโสตทัศนอุปกรณ์\nโสตทัศนูปกรณ์\nโสตินทรีย์\nโสตถิ\nโสทก\nโสทร\nโสธก\nโสธนะ\nโสน\nโสภณ\nโสภา\nโสภี\nโสภิณี\nโสเภณี\nโสม\nโสมนัส\nโสมม\nโสมย์\nโสร่ง\nโสรจ\nโสรวาร\nโสโร\nโสวรรณ\nโสหุ้ย\nโสฬส\nใส\nใส่\nไส\nไส้\nไสย\nไสยา\nไสยาสน์\nไสร้\nไสว\nหก\nหกคะเมน\nหง\nหงก\nหงส์\nหงสบาท\nหงสรถ\nหงอ\nหงอก\nหง่อง\nหงองแหงง\nหงอด\nหงอน\nหง่อม\nหงอย\nหง่อย\nหงัก\nหงับ\nหง่าง\nหงาย\nหง่าว\nหงำ\nหงิก\nหงิง\nหงิม\nหงึก\nหงุงหงิง\nหงุดหงิด\nหงุบ\nหงุ่ย\nหญ้า\nหญ้าฝรั่น\nหญ้ายายเภา\nหญิง\nหญิบ\nหด\nหตะ\nหทัย\nหน\nหนวก\nหน่วง\nหนวด\nหน่วย\nหน่วยกิต\nหนอ\nหน่อ\nหนอก\nหนอง\nหนอน\nหนอนตายหยาก\nหน่อย\nหน็อยแน่\nหนัก\nหนัง\nหนังสติ๊ก\nหนังสือ\nหนั่น\nหนับ\nหนา\nหน้า\nหน่าง\nหนาด\nหนาน\nหนาม\nหน่าย\nหนาว\nหนำ\nหนำเลี้ยบ\nหนี\nหนี้\nหนีบ\nหนึก\nหนึ่ง\nหนึบ\nหนืด\nหนุ\nหนุน\nหนุบ\nหนุ่ม\nหนุ่ย\nหนู\nห่ม\nหมก\nหมด\nหม่น\nหมวก\nหมวด\nหมวน\nหมอ\nหม่อ\nหม้อ\nหมอก\nหมอง\nหม่อง\nหมอน\nหม่อน\nหมอบ\nหม่อม\nหมอย\nหม้อห้อม\nหมัก\nหมักหมม\nหมัด\nหมัน\nหมั่น\nหมั้น\nหมับ\nหมา\nหม่า\nหมาก\nหมากฮอส\nหมาง\nหมาด\nหมามุ่ย\nหมามุ้ย\nหมาย\nหม้าย\nหมาร่า\nหม่ำ\nหม้ำ\nหมิ่น\nหมี\nหมี่\nหมึก\nหมืน\nหมื่น\nหมุด\nหมุน\nหมุบ\nหมุบหมับ\nหมุบหมิบ\nหมุ่ย\nหมุยขาว\nหมู\nหมู่\nหมูหริ่ง\nหยก\nหย่ง\nหยด\nหยวก\nหยวบ\nหยอก\nหยอกเอิน\nหย็อกหย็อย\nหยอง\nหย็อง\nหย่อง\nหย็องกรอด\nหย็องแหย็ง\nหยอด\nหยอน\nหย่อน\nหย่อม\nหย็อมแหย็ม\nหย็อย\nหย่อย\nหยัก\nหยักไย่\nหยักเหยา\nหยัง\nหยั่ง\nหยังหยัง\nหยัด\nหยัน\nหยับ\nหยั่วเมือง\nหย่า\nหยากเยื่อ\nหยากไย่\nหยาด\nหยาบ\nหยาม\nหยาว\nหย้าว\nหยำเป\nหยำเหยอะ\nหยำแหยะ\nหยิก\nหยิ่ง\nหยิบ\nหยิม\nหยี\nหยี่\nหยุกหยิก\nหยุด\nหยุ่น\nหยุบ\nหยุมหยิม\nหยูกยา\nหโยดม\nหรคุณ\nหรณะ\nหรดาล\nหรดี\nหรรษ์\nหรรษา\nหรอ\nหรอก\nหร็อมแหร็ม\nหรอย\nหระ\nหรับ\nหรา\nหริ\nหริ่ง\nหริณะ\nหริต\nหริตกี\nหรีตกี\nหรี่\nหรีด\nหรือ\nหรุบ\nหรุบรู่\nหรุบหรู่\nหรุ่ม\nหรู\nหรูหรา\nหฤทัย\nหฤทย์\nหฤษฎ์\nหฤษฎี\nหฤหรรษ์\nหฤโหด\nหลง\nหลงใหล\nหลงจู๊\nหลด\nหลน\nหล่น\nหลบ\nหล่ม\nหลวง\nหลวม\nหลอ\nหล่อ\nหลอก\nหลอด\nหลอน\nหล็อน\nหล่อน\nหลอม\nหละ\nหละหลวม\nหลัก\nหลัง\nหลั่ง\nหลัด\nหลั่น\nหลับ\nหลัว\nหลา\nหล้า\nหลาก\nหลาน\nหลาบ\nหลาม\nหลาย\nหลาว\nหลิ่ง\nหลิท\nหลิน\nหลิม\nหลิว\nหลิ่ว\nหลี\nหลีก\nหลีโก\nหลีบ\nหลีฮื้อ\nหลืบ\nหลุกหลิก\nหลุด\nหลุน\nหลุบ\nหลุม\nหลุมพอ\nหลุมพี\nหลู่\nหวง\nห่วง\nห้วง\nหวด\nหวน\nห้วน\nหวย\nห้วย\nหวอ\nหวอด\nหวะ\nหวัง\nหวัด\nหวั่น\nหวันยิหวา\nหวัว\nหวัวร่อ\nหวัวเราะ\nหวา\nหว่า\nหว้า\nหวาก\nหว่าง\nหวาด\nหวาน\nหว่าน\nหวาม\nหวาย\nหวำ\nหวิด\nหวิว\nหวี\nหวี่\nหวีด\nหวือ\nหวุดหวิด\nหวูด\nหอ\nห่อ\nห้อ\nหอก\nหอง\nห้อง\nหอน\nห่อน\nหอบ\nหอม\nห้อม\nหอย\nห้อย\nหะ\nหะยี\nหะหาย\nหัก\nหัจญ์\nหัจญี\nหัช\nหัฏฐะ\nหัด\nหัต\nหัตถ์\nหัตถกรรม\nหัตถการ\nหัตถกิจ\nหัตถบาส\nหัตถพันธ์\nหัตถาภรณ์\nหัตถศาสตร์\nหัตถศิลป์\nหัตถศึกษา\nหัตถาจารย์\nหัตถินี\nหัตถี\nหัน\nหั่น\nหั้น\nหันตรา\nหับ\nหัย\nหัว\nหัวร่อ\nหัวเราะ\nหัส\nหัสดิน\nหัสดี\nหัสต์\nหัสตะ\nหา\nห่า\nห้า\nหาก\nหาง\nห่าง\nห้าง\nหาญ\nหาด\nห่าน\nหาบ\nหาม\nห่าม\nห้าม\nหาย\nหายใจ\nหายนะ\nหาร\nหารือ\nหาว\nห้าว\nหาสะ\nหำ\nห้ำ\nหิ้ง\nหิงคุ\nหิงสา\nหิงห้อย\nหิ่งห้อย\nหิ่งหาย\nหิด\nหิต\nหิตานุหิตประโยชน์\nหิน\nหิมพาน\nหิมพานต์\nหิมวัต\nหิมวันต์\nหิมวา\nหิมะ\nหิมาลัย\nหิรัญ\nหิรัญญิการ์\nหิรัญบัฏ\nหิรัณย์\nหิรัณยรัศมี\nหิริ\nหิว\nหิ้ว\nหี\nหีด\nหีนยาน\nหีบ\nหึ\nหึง\nหึ่ง\nหึงสา\nหืด\nหืน\nหื่น\nหือ\nหื้อ\nหุง\nหุน\nหุ่น\nหุ้น\nหุนหัน\nหุบ\nหุ้ม\nหุยฮา\nหู\nหู่\nหูก\nหูด\nเห\nเห่\nเหง\nเหง่ง\nเหงา\nเหง้า\nเหงื่อ\nเหงือก\nเห็จ\nเห็ด\nเหติ\nเหตุ\nเห็น\nเหน่ง\nเหนงนายพราน\nเหน็ดเหนื่อย\nเหน็บ\nเหน่อ\nเห็นอ้ม\nเหนอะ\nเหนอะหนะ\nเหน้า\nเหนาะ\nเหนียง\nเหนี่ยง\nเหนี่ยน\nเหนียม\nเหนียว\nเหนี่ยว\nเหนือ\nเหนื่อย\nเห็บ\nเหม\nเหม่\nเหม็ง\nเหม่ง\nเหม็น\nเหมวดี\nเหม่อ\nเหมันต์\nเหมันตฤดู\nเหมา\nเหมายัน\nเหมาะ\nเหมียว\nเหมี่ยว\nเหมือง\nเหมือด\nเหมือน\nเหมื่อย\nเหย\nเหยง\nเหย่อย\nเหยา\nเหย่า\nเหย้า\nเหยาะ\nเหยาะแหยะ\nเหยิง\nเหยิบ\nเหยียด\nเหยียบ\nเหยี่ยว\nเหยื่อ\nเหยือก\nเหรอ\nเหรอะ\nเหรัญญิก\nเหรา\nเหราะ\nเหรียญ\nเหล่\nเหล็ก\nเหลน\nเหลว\nเหลอ\nเหลา\nเหล่า\nเหล้า\nเหลาะแหละ\nเหลิง\nเหลิงเจิ้ง\nเหลียน\nเหลี่ยม\nเหลียว\nเหลือ\nเหลือก\nเหลือง\nเหลือบ\nเหลือม\nเหลื่อม\nเหว\nเหว่\nเหวง\nเหวย\nเหวอะ\nเหวอะหวะ\nเหวี่ยง\nเห่อ\nเหอะ\nเหะ\nเหะหะ\nเหา\nเห่า\nเหาะ\nเหิน\nเหิม\nเหี้ย\nเหียง\nเหียน\nเหี้ยน\nเหี้ยม\nเหี่ยว\nเหื่อ\nเหือด\nแห\nแห่\nแห้\nแหก\nแหง\nแหง่\nแห่ง\nแห้ง\nแหงแก๋\nแหง่ง\nแหงน\nแหน\nแห้น\nแหนง\nแหนบ\nแหนม\nแหบ\nแหม\nแหม่\nแหม่ม\nแหมะ\nแหย\nแหย่\nแหยง\nแหย่ง\nแหยม\nแหย็ม\nแหยะ\nแหล่\nแหลก\nแหล่ง\nแหลน\nแหลม\nแหละ\nแหว\nแห้ว\nแหวก\nแหว่ง\nแหวด\nแหวน\nแหวะ\nแหะ\nโห่\nโหง\nโหด\nโหน\nโหนก\nโหน่ง\nโหม\nโหม่ง\nโหมด\nโหย\nโหยกเหยก\nโหยง\nโหย่ง\nโหร\nโหรง\nโหรงเหรง\nโหรดาจารย์\nโหระพา\nโหรา\nโหราจารย์\nโหราศาสตร์\nโหล\nโหล่\nโหลงโจ้ง\nโหว\nโหว่\nโหว้\nโหวกเหวก\nโหวง\nโหวด\nโหวต\nให้\nใหญ่\nใหม่\nไห\nไห่\nไห้\nไหน\nไหม\nไหม้\nไหรณย์\nไหล\nไหล่\nไหว\nไหว้\nไหหลำ\nอก\nอกตเวทิตา\nอกตเวที\nอกตัญญุตา\nอกตัญญู\nอกนิษฐ์\nอกรณีย์\nอกรรมกริยา\nอกัปปิยวัตถุ\nอกัปปิยะ\nอกุศล\nอคติ\nอคาธ\nอโฆษะ\nองก์\nองค์\nองคชาต\nองคมนตรี\nองครักษ์\nองคาพยพ\nองคุลี\nองศ์\nองศา\nองอาจ\nองุ่น\nอจลา\nอจินตา\nอจินไตย\nอจิระ\nอเจลก\nอเจละ\nอชะ\nอชิน\nอชินี\nอชิระ\nอฏวี\nอณิ\nอณู\nอโณทัย\nอด\nอดิถี\nอดิเทพ\nอดิเรก\nอดิศร\nอดิศวร\nอดิศัย\nอดีต\nอดุล\nอดุลย์\nอติ\nอติชาต\nอติมานะ\nอติราช\nอติเรก\nอติสาร\nอถรรพเวท\nอาถรรพเวท\nอทระ\nอทินนาทาน\nอธรรม\nอธิ\nอธิกมาส\nอธิกรณ์\nอธิกวาร\nอธิกสุรทิน\nอธิการ\nอธิคม\nอธิฏฐาน\nอธิบดี\nอธิบาย\nอธิป\nอธิปไตย\nอธิมาตร\nอธิมุตติ\nอธิโมกข์\nอธิราช\nอธิวาส\nอธิวาสนะ\nอธิศีล\nอธิษฐาน\nอธึก\nอ้น\nอนงค์\nอนงคณะ\nอนงคเลขา\nอนธการ\nอนนต์\nอนยะ\nอนรรฆ\nอนรรถ\nอนล\nอนวัช\nอนัญ\nอนัตตา\nอนันต์\nอนันตริยกรรม\nอนัม\nอนาคต\nอนาคามิผล\nอนาคามิมรรค\nอนาคามี\nอนาจาร\nอนาถ\nอนาถา\nอนาทร\nอนาธิปไตย\nอนามัย\nอนามิกา\nอนารยชน\nอนารยธรรม\nอนารยะ\nอนาลัย\nอนำ\nอนิจ\nอนิจจัง\nอนิจจา\nอนิฏฐารมณ์\nอนิยต\nอนิยม\nอนิล\nอนิวรรต\nอนิวรรตน์\nอนีกะ\nอนีจะ\nอนึก\nอนึ่ง\nอนุ\nอนุกร\nอนุกรม\nอนุกรรมการ\nอนุกระเบียด\nอนุกาชาด\nอนุการ\nอนุกูล\nอนุคามิก\nอนุเคราะห์\nอนุจร\nอนุช\nอนุชน\nอนุชา\nอนุชาต\nอนุชิต\nอนุญาต\nอนุญาโตตุลาการ\nอนุตร\nอนุเถระ\nอนุทิน\nอนุบท\nอนุบาล\nอนุประโยค\nอนุปริญญา\nอนุปสัมบัน\nอนุปัสนา\nอนุพงศ์\nอนุพัทธ์\nอนุพันธ์\nอนุโพธ\nอนุภรรยา\nอนุภริยา\nอนุภาค\nอนุภาษ\nอนุมัติ\nอนุมาตรา\nอนุมาน\nอนุมูล\nอนุโมทนา\nอนุโยค\nอนุรักษ์\nอนุรักษนิยม\nอนุราช\nอนุราธ\nอนุราธะ\nอนุราธา\nอนุรูป\nอนุโลม\nอนุวงศ์\nอนุวรรตน์\nอนุวัต\nอนุวัตน์\nอนุวัตร\nอนุวัติ\nอนุวาต\nอนุศาสก\nอนุศาสน์\nอนุศาสนาจารย์\nอนุศิษฏ์\nอนุสติ\nอนุสนธิ\nอนุสร\nอนุสรณ์\nอนุสัญญา\nอนุสัย\nอนุสาวรีย์\nอนุสาสนี\nอเนก\nอเนกคุณ\nอเนกรรถประโยค\nอเนจอนาถ\nอโนชา\nอโนดาต\nอบ\nอบเชย\nอบาย\nอปจายนธรรม\nอปจายนมัย\nอปมงคล\nอปยศ\nอประไมย\nอปรัณณชาติ\nอปรา\nอปราชัย\nอปราชิต\nอปริมาณ\nอปลักษณ์\nอปโลกน์\nอปวาท\nอเปหิ\nอพพะ\nอพยพ\nอภัพ\nอภัย\nอภิ\nอภิฆาต\nอภิชฌา\nอภิชน\nอภิชนาธิปไตย\nอภิชัย\nอภิชาต\nอภิชิต\nอภิญญา\nอภิญญาณ\nอภิธรรม\nอภิธาน\nอภิไธย\nอภินันท์\nอภินันทนาการ\nอภินัย\nอภินิหาร\nอภิเนษกรมณ์\nอภิบาล\nอภิปรัชญา\nอภิปราย\nอภิมหาอำนาจ\nอภิมานะ\nอภิมุข\nอภิรดี\nอภิรติ\nอภิรมย์\nอภิรักษ์\nอภิราม\nอภิรุต\nอภิรุม\nอภิรูป\nอภิลักขิต\nอภิลักขิตสมัย\nอภิเลปน์\nอภิวันท์\nอภิวาท\nอภิวาทน์\nอภิเษก\nอภิสมโพธิ\nอภิสมัย\nอภิสมาจาร\nอภิสัมโพธิ\nอภิสัมโพธิญาณ\nอภิสิต\nอภิสิทธิ์\nอภูตะ\nอม\nอมพะนำ\nอ้ม\nอมตธรรม\nอมตบท\nอมตะ\nอมนุษย์\nอมร\nอมรา\nอมราวดี\nอมรินทร์\nอมเรนทร์\nอมเรศ\nอมฤต\nอมัตร\nอมาตย์\nอมาวสี\nอมาวสุ\nอมาวาสี\nอมิตร\nอเมริกัน\nอย่า\nอยาก\nอย่าง\nอยุทธ์\nอยู่\nอร\nอรชร\nอรชุน\nอรดี\nอรติ\nอรทัย\nอรไท\nอรนุช\nอรพินท์\nอรพิม\nอรรค\nอรรฆ\nอรรฆย์\nอรรจน์\nอรรณพ\nอรรถ\nอรรถกถา\nอรรถกถาจารย์\nอรรถาธิบาย\nอรรธ\nอรสุม\nอรหะ\nอรหัง\nอรหัต\nอรหัตผล\nอรหัตมรรค\nอรหัน\nอรหันต์\nอรหันตฆาต\nอร่อย\nอรัญ\nอรัญญิก\nอรัญวาส\nอรัญวาสี\nอรัณย์\nอราดี\nอร่าม\nอริ\nอรินทร์\nอริน\nอริยกะ\nอริยทรัพย์\nอริยบุคคล\nอริยผล\nอริยมรรค\nอริยสั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nโฮก\nโฮ่ง\nโฮ้ง\nโฮเต็ล\nโฮลเมียม\nโฮะ\nไฮ้\nไฮโกรมิเตอร์\nไฮดรา\nไฮโดร\nไฮโดรคาร์บอน\nไฮโดรเจน\nไฮโดรมิเตอร์\nไฮไฟ\nไฮโล\nไฮฮี\n"
+      words = "ก.ก.\nก.ก.น.\nก.ข.ค.\nก.ค.\nก.จ.\nก.ช.น.\nก.ฌ.\nก.ต.\nก.ต.ง.\nก.ต.ช.\nก.ตร.\nก.ท.\nก.น.ช.\nก.บช.\nก.บถ.\nก.ป.ส.\nก.พ.\nก.ม.\nก.ย.\nก.ร.\nก.ล.ต.\nก.ว.\nก.ศ.ว.\nก.ส.ท.\nก.ส.ธ.\nก.ส.อ.\nก.อ.\nกก.ตชด.\nกก.ตร.น.\nกก.ภ.จว.\nกก.รสช.\nกกบ.ขส.ทบ.\nกกล.รพน.\nกง.กห.\nกง.ทบ.\nกง.ทร.\nกซข.ป.\nกซม.ป.\nกทม.กรุงเทพมหานคร\nกบ.ทบ.\nกป.สป.\nกพ.ทบ.\nกพ.ทร.\nกพ.ทหาร\nกร.ทบ.\nกรป.กลาง\nกรอ.พอ.\nกศ.ด.\nกศ.บ.\nกศ.บป.\nกศ.ม.\nกษ.ด.\nกษ.บ.\nกษ.ม.\nกส.ด.\nกส.ทบ.\nกส.บ.\nกส.ม.\nกอ.ปค.\nกอ.รพน.\nกอ.รมน.\nกอ.รสต.\nข.ต.ว.\nขว.ทบ.\nขว.ทร.\nขว.ทหาร\nขส.ทบ.\nขส.ทร.\nขส.ทอ.\nค.ด.\nค.บ.\nค.พ.ศ.\nค.ม.\nค.ร.น.\nค.ร.ฟ.\nค.ร.ม.\nค.ศ.\nค.อ.ด.\nค.อ.บ.\nค.อ.ม.\nคศ.ด.\nคศ.บ.\nคศ.ม.\nง.ด.\nจ.จ.\nจ.จ.จ.\nจ.ช.\nจ.ต.\nจ.ท.\nจ.ป.ร.\nจ.ม.\nจ.ศ.\nจ.ส.ต.\nจ.ส.ท.\nจ.ส.อ.\nจ.อ.\nจ.อ.ร.\nจ.๑๘\nจก.ธน.\nจก.สน.\nช.ค.\nช.ค.บ.\nช.พ.ค.\nช.ส.\nช.ส.ค.\nฌ.ป.ค.\nฌ.ศ.ร.\nฌ.ส.อ.\nฐท.สห.\nด.ช.\nด.ญ.\nด.ต.\nด.ศ.ค.\nด.ศ.ร.\nดย.ทร.\nต.ก.\nต.ค.\nต.จ.\nต.จ.ว.\nต.ช.\nต.ต.\nต.บ.\nต.ม.\nต.ร.\nต.ศ.ร.\nต.ห.\nต.อ.\nต.อ.จ.\nตร.กม.\nตร.ซม.\nตร.ต.\nตร.ทล.\nตร.น.\nตร.ปม.\nตร.ภ.\nตร.ม.\nตร.รฟ.\nตร.ว.\nตร.ส.\nตร.สข.\nท.จ.\nท.จ.ว.\nท.ช.\nท.ญ.\nท.ด.\nท.ท.ท.\nท.ทบ.\nท.บ.\nท.พ.\nท.ม.\nท.ศ.\nทก.ด.\nทก.บ.\nทก.ม.\nทส.ปช.\nทส.รมว.กห.\nทุ.ส.นิ.ม.\nธ.ก.ส.\nธ.ค.\nธ.ญ\nธ.บ.\nน.ช.\nน.ญ.\nน.ด.\nน.ต.\nน.ท.\nน.น.\nน.บ.\nน.บ.ท.\nน.ป.ท.\nน.พ.\nน.ม.\nน.ร.\nน.ว.\nน.ศ.\nน.ส.\nน.ส.พ.\nน.ส.๓\nน.สพ.\nน.อ.\nนปพ.ภ.\nนศ.ด.\nนศ.บ.\nนศ.ม.\nบ.ก.\nบ.ข.ส.\nบ.ช.\nบ.ด.ท.\nบ.ตร.\nบ.ภ.\nบ.ม.\nบก.จร.\nบก.ตชด.\nบก.ตม.\nบก.ทล.\nบก.น.\nบก.ป.\nบก.ปค.\nบก.ปม.\nบก.ภ.เขต\nบก.รน.\nบก.รฟ.\nบก.ร้อย.ตชด.\nบก.ส.\nบกข.ป.\nบจพ.ป.\nบช.ก.\nบช.ด.\nบช.ตชด.\nบช.น.\nบช.บ.\nบช.ปส.\nบช.ภ.\nบช.ม.\nบชท.ป.\nบชน.ป.\nบชส.ป.\nบธ.ด.\nบธ.บ.\nบธ.ม.\nบนท.ป.\nบนอ.ป.\nบปช.ป.\nป.กท.\nป.กศ.\nป.กศ.สูง\nป.จ.\nป.จ.ว.\nป.ช.\nป.ธ.\nป.ป.\nป.ป.ก.\nป.ป.ช.\nป.ป.ป.\nป.ป.ร.\nป.ป.ส.\nป.พ.\nป.พ.พ.\nป.พย.\nป.ม.\nป.ม.ก.\nป.ม.ช.\nป.ม.ธ.\nป.ม.ศ.\nป.ม.อ.\nป.ร.ร.๔\nป.ร.ร.๕\nป.ร.ร.๖\nป.ล.\nป.ว.พ.\nป.วิ.อ.\nป.ส.ส.\nป.อ.\nป.อ.ร.ส.\nป.๑\nปม.วส.\nปอ.พ.\nผกก.ภ.\nผช.ผอ.\nผต.มท.\nผบ.ตร.\nผบ.ทบ.\nผบ.ทร.\nผบ.ทสส.\nผบ.ทอ.\nผบก.น.\nผบก.ป.\nผบก.ปค.\nผบก.ปม.\nผบก.ภ.\nผบช.ก.\nผบช.ตชด.\nผบช.น.\nผบช.ภ.\nผว.กทม.\nผอ.ปจ.\nพ.ก.ง.\nพ.กศ.\nพ.ข.ต.\nพ.ค.\nพ.ค.ช.\nพ.ค.ว.\nพ.ค.ศ.\nพ.จ.ต.\nพ.จ.ท.\nพ.จ.อ.\nพ.ช.\nพ.ช.ค.\nพ.ด.\nพ.ต.\nพ.ต.ต.\nพ.ต.ท.\nพ.ต.อ.\nพ.ต.อ.พิเศษ\nพ.ท.\nพ.บ.\nพ.ป.\nพ.ภ.ม.\nพ.ม.\nพ.ม.ช.\nพ.ย.\nพ.ร.ก.\nพ.ร.ฎ.\nพ.ร.ต.\nพ.ร.ธ.\nพ.ร.บ.\nพ.ศ.\nพ.ศ.บ.\nพ.ส.ร.\nพ.ส.ล.\nพ.อ.\nพ.อ.ต.\nพ.อ.ท.\nพ.อ.พิเศษ\nพ.อ.อ.\nพณ.ด.\nพณ.บ.\nพณ.ม.\nพธ.ด.\nพธ.บ.\nพธ.ม.\nพบ.ด.\nพบ.บ.\nพบ.ม.\nพย.ด.\nพย.บ.\nพย.ม.\nพล.จ.\nพล.ต.\nพล.ต.จ.\nพล.ต.ต.\nพล.ต.ท.\nพล.ต.อ.\nพล.ท.\nพล.ปตอ.\nพล.ม.\nพล.ม.๒\nพล.ร.จ.\nพล.ร.ต.\nพล.ร.ท.\nพล.ร.อ.\nพล.อ.\nพล.อ.จ.\nพล.อ.ต.\nพล.อ.ท.\nพล.อ.อ.\nพลา.ทร.\nพศ.ด.\nพศ.บ.\nพศ.ม.\nพอ.สว.\nภ.ง.ด.\nภ.ง.ด.๙\nภ.ด.\nภ.บ.\nภ.บ.ท.๕\nภ.ป.ร.\nภ.พ.\nภ.ม.\nภ.สถ.บ.\nม.ค.\nม.จ.\nม.ป.ท.\nม.ป.ป.\nม.ป.พ.\nม.ร.ว.\nม.ศ.\nม.อ.\nม.อ.ปัตตานี\nมิ.ย.\nมี.ค.\nยศ.ทบ.\nยศ.ทร.\nยศ.ทอ.\nร.ง.\nร.ด.\nร.ต.\nร.ต.ต.\nร.ต.ท.\nร.ต.อ.\nร.ท.\nร.น.\nร.บ.\nร.พ.\nร.ฟ.ล.\nร.ย.ล.\nร.ย.ส.ท.\nร.ล.\nร.ศ.\nร.ส.พ.\nร.อ.\nรป.ม.\nรร.จปร.\nรร.จอ.\nรร.ชท.\nรร.ตท.\nรร.นร.\nรร.นรต.\nรร.นอ.\nล.ญ.\nล.ว.\nลส.ชบ.\nว.ค.\nว.ฉ.\nว.ช.\nว.ด.ป.\nว.ป.ถ.\nวท.บ.\nศ.บ.\nศ.ป.ก.\nศ.ศ.ป.\nศฝร.ภ.\nศศ.บ.\nศษ.บ.\nศส.บ.\nส.ก.\nส.ก.ศ.ท.\nส.ค.\nส.ค.1\nส.ค.ร.\nส.ค.ส.\nส.ต.\nส.ต.ต.\nส.ต.ท.\nส.ต.อ.\nส.ท.\nส.ทร.\nส.ป.ช.\nส.ป.ส.ท.\nส.ป.อ.\nส.ร.\nส.ล.น.\nส.ว.\nส.ว.ท.\nส.ว.ส.ท.\nส.ส.\nส.ส.ท.\nส.ส.ร.\nส.ห.\nส.อ.\nสถ.บ.\nสนง.สสอ.\nสพ.ญ.\nสพ.บ.\nสว.จร.\nสว.ธร.\nสว.ส.\nสว.สป.\nสว.สส.\nสว.อก.\nสส.บ.\nสุ.จิ.ปุ.ลิ.\nห.ร.ม.\nอ.ก.ค.\nอ.ก.จ.\nอ.จ.\nอ.ช.พ.\nอ.ตร.\nอ.บ.\nอ.ส.ท.\nอ.ส.ม.ท.\nอ.ส.ย.\nอ.อ.ป.\nอส.รด.\nอุ.อา.ก.ส.\nฮ.จ.\nฮ.ท.\nฮ.ฝ.\nฮ.ล.\nฮ.ศ.\nเม.ย.\n\nกรีนิช\nกลันตัน\nกัลกัตตา\nกัวลาลัมเปอร์\nกัศมีร์\nกาฐมาณฑุ\nโกลกาตา\nควิเบก\nคอนเนตทิคัต\nคาบูล\nคุชราต\nคุนหมิง\nเคนตักกี\nเคนทักกี\nเคมบริดจ์\nแคชเมียร์\nแคนซัส\nแคนเบอร์รา\nแคโรไลนา\nแคลิฟอร์เนีย\nโคเปนเฮเกน\nโคลัมโบ\nโคโลราโด\nไครสต์เชิร์ช\nไคโร\nจาการ์ตา\nจำปาศักดิ์\nเจนไน\nเจนีวา\nเจ้อเจียง\nฉงชิ่ง\nเฉิงตู\nชานตง\nชิคาโก\nเชนไน\nเชอร์โนบิล\nซัปโปโร\nซานมารีโน\nซาบาห์\nซาราเยโว\nซาราวัก\nซิดนีย์\nซีอาน\nซีแอตเทิล\nซูริก\nซูริค\nเซเชลส์\nเซนได\nเซี่ยงไฮ้\nโซเฟีย\nโซล\nโซโลมอน\nไซ่ง่อน\nไซบีเรีย\nดัลลัส\nดาโคตา\nดานัง\nดีทรอยต์\nดูไบ\nเดนเวอร์\nเดลาแวร์\nเดียนเบียนฟู\nโดเวอร์\nโดฮา\nไดฟุกุ\nไดฟูกุ\nตรังกานู\nตริโปลี\nตูวาลู\nเตหะราน\nโตเกียว\nโตรอนโต\nทมิฬนาฑู\nทริโปลี\nทิเบต\nเทกซัส\nเท็กซัส\nเทนเนสซี\nเทลอาวีฟ\nแทสเมเนีย\nโทรอนโต\nไทเป\nธากา\nนางาซากิ\nนาริตะ\nนิวเจอร์ซีย์\nนิวเดลี\nนิวยอร์ก\nนิวยอร์ค\nนิวแฮมป์เชียร์\nเนบราสกา\nเนแบรสกา\nเนวาดา\nบรัสเซลส์\nบราซิเลีย\nบอมเบย์\nบอสตัน\nบังกาลอร์\nบังคาลอร์\nบูคาเรสต์\nบูดาเปสต์\nเบงกาซี\nเบนกาซี\nเบรุต\nเบลเกรด\nเบอร์ลิน\nแบกแดด\nปอยเปต\nปะลิส\nปะหัง\nปักกิ่ง\nปัญจาบ\nปัฏนา\nปารีส\nปีนัง\nเประ\nเปียงยาง\nพนมเปญ\nพระตะบอง\nพะโค\nพะสิม\nพาราณสี\nพิหารี\nเพนซิลวาเนีย\nเพนซิลเวเนีย\nฟรานซ์\nฟลอริดา\nฟิลาเดลเฟีย\nฟุกุชิมะ\nฟุกุชิมา\nฟุกุโอกะ\nฟูกูโอกะ\nแฟรงก์เฟิร์ต\nมอสโก\nมะนิลา\nมะละแหม่ง\nมัณฑะเลย์\nมัทราส\nมาดริด\nมิชิแกน\nมินนิโซตา\nมิยางิ\nมิลาน\nมิวนิก\nมิสซูรี\nมุมไบ\nเมน\nเมลเบิร์น\nเมาะตะมะ\nเมาะลำเลิง\nแมนจูเรีย\nแมนเชสเตอร์\nแมนฮัตตัน\nแมริแลนด์\nแมรีแลนด์\nแมสซาชูเซตส์\nยะไข่\nย่างกุ้ง\nยูทาห์\nยูนนาน\nเยรูซาเล็ม\nโยโกฮามา\nริยาด\nรีโอเดจาเนโร\nโรดไอแลนด์\nลอนดอน\nลอสแองเจลิส\nลาปาซ\nลาสเวกัส\nลิสบอน\nลุยเซียนา\nโลซาน\nโลซานน์\nวอชิงตัน\nวอร์ซอ\nวิสคอนซิน\nเวนิส\nเวลส์\nเวอร์จิเนีย\nเวอร์มอนต์\nเวียงจันทน์\nเวียนนา\nแวนคูเวอร์\nไวโอมิง\nสกอตแลนด์\nสก็อตแลนด์\nสตอกโฮล์ม\nสลังงอร์\nเสฉวน\nเสียมราฐ\nเสียมเรียบ\nหงสา\nหงสาวดี\nหนานไห่\nหลวงพระบาง\nหูเป่ย\nหูเป่ย์\nหูหนาน\nเหอเป่ย\nเหอเป่ย์\nเหอหนาน\nอชันตา\nอลาสกา\nอวันตี\nออริกอน\nออสโล\nอะแลสกา\nอัตตะปือ\nอัมมาน\nอัมสเตอร์ดัม\nอัสสัม\nอาบูดาบี\nอาร์คันซอ\nอินเดียนา\nอิบารากิ\nอิลลินอยส์\nอิสตันบูล\nอิสลามาบัด\nอุรุมชี\nอูลานบาตอร์\nเอดินบะระ\nเอเธนส์\nแอตแลนตา\nแอริโซนา\nแอลเจียร์\nโอคลาโฮมา\nโอค็อตสค์\nโอกินาวา\nโอซากา\nโอริสสา\nโอเรกอน\nโอไฮโอ\nไอดาโฮ\nไอโอวา\nฮอนโนลูลู\nฮานอย\nฮาเนดะ\nฮาราเร\nฮาวาย\nฮิโรชิมา\nฮุสตัน\nเฮลซิงกิ\n\nมกรา\nกุมภา\nมีนา\nเมษา\nพฤษภา\nมิถุนา\nกรกฎา\nสิงหา\nกันยา\nตุลา\nพฤศจิกา\nธันวา\nเอ\nบี\nซี\nดี\nอี\nเอฟ\nจี\nเอช\nไอ\nเจ\nเค\nแอล\nเอ็ม\nเอ็น\nโอ\nพี\nคิว\nอาร์\nเอส\nที\nยู\nวี\nดับเบิล\nดับบลิว\nเอ็กซ์\nเอ๊กซ์\nวาย\nแซด\nแอลฟา\nแอลฟ่า\nเบตา\nเบต้า\nแกมมา\nแกมม่า\nเดลตา\nเดลต้า\nโอเมกา\nโอเมก้า\nเมกะ\nกิกะ\nนาโน\nไมโคร\n\nกรรมาชน\nกรอบรูป\nกระดี๊กระด๊า\nกระบับ\nกราวนด์\nกรีน\nกรุ๊ป\nกฤษณ์\nกลาส\nก๊วน\nกษัตริยา\nกษัตริยาธิราช\nก่อนหน้า\nกะบับ\nกับดัก\nกัมมันตะ\nก๊าก\nก๋ากั่น\nกาญจน์\nกาญจนาภิเษก\nกามิกาเซ่\nการันตี\nกาหลิบ\nกิฟท์\nกิมจิ\nกีวี\nกึ๊ก\nกึ๋ย\nกุนซือ\nกุมภาพันธ์\nกู๋\nเกจิ\nเกมส์\nเกย์\nเกรด\nเกรย์\nเกสต์เฮาส์\nเก๊ะ\nเก๋ากี้\nเกิร์ล\nแกงค์\nแกรนด์\nแกสโซฮอล์\nแก๊สโซฮอล์\nโกเต็กซ์\nโกลด์\nโกะ\nโก๊ะ\nไกด์\nขั้นตอน\nเขวี้ยง\nคณาญาติ\nครัวซอง\nครัวซองต์\nคร่ำครวญ\nครีเอทีฟ\nครูเสด\nคลับ\nคลาสสิก\nคลิตอริส\nคลิป\nความหมาย\nควิก\nควีน\nคองเกรส\nคอนซูเมอร์\nคอนเซปต์\nคอนเซ็ปต์\nคอนโด\nคอนโดมิเนียม\nคอนเทนเนอร์\nคอนแทค\nคอนแท็ค\nคอนโทรล\nคอนเฟิร์ม\nคอปเตอร์\nคอมพ์\nคอมเพล็กซ์\nคอมมอนส์\nคอมเมนท์\nคอมเมนต์\nคอร์ป\nคอร์ปอเรชั่น\nคอร์รัปชัน\nคอร์รัปชั่น\nคอรัปชัน\nคอรัปชั่น\nคอร์ส\nคอลเล็กชั่น\nคอลัมน์\nคอลัมนิสต์\nคัตเอาต์\nคันคาก\nคันถธุระ\nคันธาระ\nคันยิ\nคัสตาร์ด\nคาราโอเกะ\nคีตกวี\nคีตปฏิภาณ\nคีตราชัน\nคาปูชิโน\nคามิคาเซ่\nคาเฟ่\nคาร์\nคาร์โก้\nคาราเมล\nคาแรกเตอร์\nคาแร็กเตอร์\nคาแรคเตอร์\nคาแร็คเตอร์\nคาวบอย\nคาสิโน\nคิกขุ\nคิวบิก\nคูลเลอร์\nเคบับ\nเครป\nเคลม\nเคลียร์\nเคลื่อนย้าย\nเคส\nเคอร์ฟิว\nแคชเชียร์\nแคทวอล์ค\nแคนดิเดต\nแคนตาลูป\nแคนยอน\nแคนู\nแคป\nแคมป์\nแคมปัส\nแคมเปญ\nแคร์\nแครกเกอร์\nแคร็กเกอร์\nแครอท\nแคสต์\nแคสติง\nแคสติ้ง\nโค้ก\nโค้ช\nโคโยตี\nโคโยตี้\nโครนา\nโคอะล่า\nโคอาลา\nโคอาล่า\nไคลแมกซ์\nไคลแม็กซ์\nงั้น\nง่าว\nงี้\nเง็ง\nโง่เขลา\nไง\nจตุคาม\nจ๊อกกี้\nจอหงวน\nจังโก้\nจัมโบ้\nจ๊าบ\nจารกรรม\nจารชน\nจิ๊ก\nจิ๊กโก๋\nจิ๊กซอว์\nจิตพิสัย\nจิตเภท\nจีดีพี\nจึ๊ก\nจุ๊ย\nจูน\nจูเนียร์\nเจ๊\nเจได\nเจ็ต\nเจล\nเจ๊าะแจ๊ะ\nเจี๊ยว\nแจ็กเก็ต\nแจ๊กเก็ต\nแจ็กพอต\nแจ็กพ็อต\nแจ๊กพอต\nแจ๊กพ็อต\nแจม\nแจ๊ส\nโจ๋\nฉลุย\nเฉิ่ม\nชนะเลิศ\nช็อค\nช็อต\nช็อป\nช็อปปิ้ง\nช็อปเปอร์\nชะโนด\nชัตเตอร์\nชัวร์\nชาร์จ\nชาร์ต\nชาร์ป\nชินบัญชร\nชิฟฟอน\nชีส\nชีอะห์\nเช็ก\nเช็งเม้ง\nเชฟ\nเชลียร์\nเชอร์รี่\nแชเชือน\nแช่แข็ง\nแชมป์\nแชมปิยอง\nแชมเปญ\nแชมเปี้ยน\nแชมพู\nโชว์รูม\nโชห่วย\nใช้งาน\nไชน่า\nซ้อ\nซอมบี้\nซะ\nซังเต\nซันตาคลอส\nซัพพลาย\nซัพพลายเออร์\nซัมเมอร์\nซากุระ\nซาดิสต์\nซาดิสม์\nซาตาน\nซานตาคลอส\nซาฟารี\nซาบะ\nซามูไร\nซาร์\nซาร์ดีน\nซาเล้ง\nซิง\nซิ่ง\nซิงเกิล\nซิตี\nซิตี้\nซินโดรม\nซิม\nซิ้ม\nซิมโฟนี\nซิมโฟนี่\nซิลเวอร์\nซี้\nซี้ซั้ว\nซีดาน\nซีน\nซีนีเพล็กซ์\nซีเนียร์\nซีร็อกซ์\nซีรีส์\nซีเรียส\nซีอีโอ\nซื่อบื้อ\nซุนหนี่\nซุปเปอร์\nซูชิ\nซูเปอร์\nซูม\nซูโม่\nซูเอี๋ย\nซูฮก\nเซ็กซ์\nเซ็กซี่\nเซ็กส์\nเซนเซอร์\nเซ็นเซอร์\nเซนเตอร์\nเซ็นเตอร์\nเซ็นทรัล\nเซนส์\nเซ่นไหว้\nเซฟตี้\nเซรามิก\nเซลส์\nเซลส์แมน\nเซอร์\nเซอร์ไพรส์\nเซอร์วิส\nเซาท์\nเซี้ยว\nแซ็ก\nแซกโซโฟน\nแซ็กโซโฟน\nแซนด์วิช\nแซมบ้า\nแซลมอน\nแซว\nโซเชียล\nโซน\nโซนี่\nโซลาร์\nโซโล\nโซโล่\nญาณทัสสนะ\nดยุก\nดยุค\nดร็อป\nดรัมเมเยอร์\nดรามา\nดราม่า\nดอกเตอร์\nด็อกเตอร์\nดัมพ์\nดั๊มพ์\nดาวน์\nดิกชันนารี\nดิสเครดิต\nดีกรี\nดีเจ\nดีไซน์\nดีไซน์เนอร์\nดีไซเนอร์\nดีเบต\nดีพาร์ตเมนต์\nดีพาร์ตเมนท์\nดีพาร์ทเมนต์\nดีพาร์ทเมนท์\nดีมานด์\nดีล\nดีลเลอร์\nดีเลย์\nเดชานุภาพ\nเดบิต\nเดโม\nเดย์\nเด้อ\nเดอะ\nเด๊ะ\nเดี้ยง\nเดี๊ยะ\nแดนซ์\nแดนเซอร์\nแดรี่\nโดนัท\nโดมิโน\nโดรายากิ\nไดเอ็ต\nตถตา\nตนเอง\nตรวจทาน\nตรวจสอบ\nตอกย้ำ\nต๊อง\nต่อยอด\nต่อรอง\nตะหงิด\nตังค์\nตันเถียน\nตัวตน\nตัวเอง\nตาปรือ\nต้าอ่วย\nติงต๊อง\nติ๋ม\nติ่มซำ\nติว\nติวเตอร์\nตี๋\nตื้บ\nตุ๊ก\nตุ๊กตุ๊ก\nตุ๊ด\nตุ๋ย\nตู้เซฟ\nเต๊ะ\nเตี๊ยม\nแตงกวา\nแตงโม\nแต๋ว\nโต๋เต๋\nโต๊ะจีน\nไตรมาส\nถ่ายทำ\nถูกต้อง\nทงคัตสึ\nทริป\nทรู\nทอม\nท็อป\nทอร์นาโด\nทอล์ค\nทักซิโด\nทันตกรรม\nทันตแพทยศาสตร์\nทับซ้อน\nทัวร์\nทัวร์นาเมนต์\nทัวร์นาเมนท์\nทัวริสต์\nทาเลนต์\nทาวน์\nทาวน์เฮาส์\nทำงาน\nทิป\nทิพยสมบัติ\nทิวลิป\nทีรามิสุ\nทีวี\nทูน่า\nเท็กซ์\nเทค\nเทคโน\nเทคโนแครต\nเทควันโด\nเทป\nเทรด\nเทรนด์\nเทรนเนอร์\nเทรลเลอร์\nเทรลเล่อร์\nเทเลกราฟ\nเทวบัญชา\nเทวบุตร\nเทวา\nเทวาธิราช\nเทโวโรหนะ\nเทอร์โบ\nเที่ยงคืน\nเที่ยงวัน\nเทียมทาน\nแทกติค\nแทคติค\nแทงกั๊ก\nแทงโก้\nโทมาฮอก\nโทมาฮอว์ก\nโทมาฮอว์ค\nโทร\nโทรโข่ง\nไทม์\nไทยแลนด์\nไทเฮา\nธรรมา\nธรรมาภิบาล\nธัมโม\nธีม\nธุรกรรม\nธุหร่ำ\nเธค\nนพมาศ\nนรีแพทย์\nน็อก\nน็อค\nน้องใหม่\nนอมินี\nนอร์ท\nน่ะ\nนางแบบ\nนาฏยศาลา\nนายแบบ\nนายพราน\nนินจา\nนิรันดร์\nนิว\nนิวส์\nนู้ด\nเนอะ\nเนิร์สเซอรี\nเนิร์สเซอรี่\nเนี้ยบ\nโนติส\nไนท์\nไนน์\nบรรพชน\nบร็อกโคลี\nบร็อคโคลี\nบรา\nบริกร\nบริวเวอรี่ส์\nบลอนด์\nบลูเบอร์รี\nบลูเบอร์รี่\nบ๊วย\nบอกซ์\nบ็อกซ์\nบ๊อกซ์\nบอดี้\nบอนด์\nบ๊อบ\nบอมบ์\nบ๋อย\nบอยคอต\nบอยคอตต์\nบอร์ด\nบังเกอร์\nบัตเตอร์\nบัลลาสต์\nบัส\nบาบูน\nบาร์บีคิว\nบาร์บี้\nบาลานซ์\nบิ๊ก\nบิล\nบึม\nบึ้ม\nบุญคุณ\nบุ๋น\nบุปผา\nบู๊\nบูชิโด\nบูติก\nบูติค\nบูม\nเบเกอรี่\nเบญจมบพิตร\nเบตาดีน\nเบนโตะ\nเบนโล\nเบบี้\nเบลอ\nเบอร์เกอร์\nเบอร์รี\nเบิร์ด\nเบิร์น\nแบ็กโฮ\nแบคโฮ\nแบด\nแบต\nแบนเนอร์\nแบรนด์\nแบล็ก\nแบล็ค\nไบโอ\nโบกี้\nโบตั๋น\nโบ้ย\nโบรกเกอร์\nโบรชัวร์\nโบว์\nโบว์ลิ่ง\nไบเบิล\nปฏิสัมพันธ์\nป๊อก\nปอดแหก\nป๊อป\nป๋อหลอ\nปักขคณนา\nปัจเจกชน\nปัจฉิมนิเทศ\nป๊า\nป๋า\nป่าไม้\nปาร์ตี้\nปาสกาล\nปาสคาล\nปาสเตอร์\nปิกอัพ\nปิ๊ง\nปิโตรเคมี\nปิยมิตร\nปึ้ก\nปูอัด\nเปโซ\nเป็นไง\nเปปเปอร์มินต์\nเปเปอร์\nเปราะบาง\nเป๊ะ\nเป่ายิงฉุบ\nเป่ายิ้งฉุบ\nเปียโน\nแป้ก\nแป๋ว\nแป๊ะเจี๊ยะ\nโปร\nโปรเจกต์\nโปรเจ็กต์\nโปรเจกเตอร์\nโปรเจ็กเตอร์\nโปรเจคท์\nโปรเจ็คท์\nโปรดักชั่น\nโปรดิวเซอร์\nโปรโมชั่น\nโปรโมต\nโปรโมเตอร์\nโปรโมท\nโปลิศ\nโปสเตอร์\nผลไม้\nผลักดัน\nผ้าห่ม\nผิดพลาด\nผู้นำ\nแผดเผา\nเฝอ\nพงษ์\nพริตตี้\nพรีเซนต์\nพรีเซ็นเตอร์\nพรีเมียม\nพรีเมียร์\nพฤหัส\nพล็อต\nพลาซ่า\nพลานุภาพ\nพ่อค้า\nพอเพียง\nพะเรอ\nพันธกิจ\nพันธุวิศวกรรม\nพาร์\nพาร์ตเนอร์\nพาร์ทเนอร์\nพาวเวอร์\nพาสเจอร์ไรส์\nพาสตา\nพาสต้า\nพาสปอร์ต\nพาเหรด\nพิซซ่า\nพีเรียด\nพุดดิ้ง\nพุทธภูมิ\nพุทธศตวรรษ\nพุทโธ\nพูล\nเพทนาการ\nเพนกวิน\nเพนตากอน\nเพรส\nเพรียวบาง\nเพลซ\nเพลท\nเพลย์บอย\nเพียบแปร้\nเพียว\nเพาเวอร์\nแพกเกจ\nแพ็ค\nแพตเทิร์น\nแพทเทิร์น\nแพทยสภา\nแพนงเชิญ\nแพนดา\nแพนด้า\nแพลน\nโพลล์\nโพลารอยด์\nโพสต์\nไพลิน\nฟยอร์ด\nฟรังก์\nฟรุต\nฟลอร์\nฟลุก\nฟลุค\nฟลุต\nฟลุท\nฟอยล์\nฟอร์ม\nฟันด์\nฟาวล์\nฟาสต์ฟู้ด\nฟินิกซ์\nฟิวเจอร์\nฟีด\nฟีเวอร์\nฟุตบาท\nเฟรช\nเฟรชชี่\nเฟรม\nเฟมินิสต์\nเฟส\nเฟอร์นิเจอร์\nเฟอร์รี่\nเฟิร์ม\nเฟี้ยวฟ้าว\nแฟกซ์\nแฟ็กซ์\nแฟนซี\nแฟนตาซี\nแฟ้บ\nแฟร์\nแฟรนไชส์\nแฟรี\nแฟรี่\nแฟลช\nแฟล็ต\nโฟน\nโฟม\nโฟล์ค\nไฟต์\nไฟแนนซ์\nไฟลต์\nไฟลท์\nภควัทคีตา\nภควัมบดี\nภควัมปติ\nภคันทลาพาธ\nภววิสัย\nภารตะ\nภูมิทัศน์\nม้ง\nมวลชน\nมยุราภิรมย์\nมลภาวะ\nมหภาค\nมหาอุปราชา\nมอคคา\nมอคค่า\nมอนสเตอร์\nม็อบ\nมอบตัว\nมอยส์เจอไรเซอร์\nมอลล์\nมะกัน\nมั้ง\nมัฟฟิน\nมั้ย\nม้านั่ง\nมาเฟีย\nมาม่า\nมายองเนส\nมายาคติ\nมาร์ก\nมาร์เก็ต\nมาร์เก็ตติ้ง\nมาร์ค\nมาร์จิน\nมาร์ช\nมาร์ต\nมาร์ท\nมาราธอน\nม้าหินอ่อน\nมินต์\nมินท์\nมินิ\nมิลค์\nมิวสิค\nมิสซัง\nมิสไซล์\nมิสเตอร์\nมือถือ\nมุมมอง\nเมคอัพ\nเมจิก\nเมจิค\nเมทัล\nเมเปิล\nเมาท์\nเมี่ยงคำ\nแมกกาซีน\nแม็กกาซีน\nแมคเคอเรล\nแม่ค้า\nแมชชีน\nแมชีน\nแมนชั่น\nแมมบ้า\nแมมโบ้\nโมจิ\nโมเดล\nโมเดิร์น\nโมเต็ล\nโมโนเรล\nโมหจริต\nไมค์\nไมเกรน\nยนตรกรรม\nยอมรับ\nยะเยือก\nยังไง\nยากูซ่า\nยาวี\nยิม\nยิว\nยุวทูต\nยูโทเปีย\nยูโร\nยูวี\nเยน\nเยลลี่\nเย้ว\nเยอบีรา\nเยอบีร่า\nเยอร์บีรา\nเยอร์บีร่า\nแยมโรล\nโยเกิร์ต\nโยโย่\nรวมมิตร\nร็อค\nร็อคเก็ต\nรองรับ\nรอมฎอน\nรอยัลตี้\nระโงก\nรันเวย์\nรัม\nรากหญ้า\nราชบัณฑิตยสถาน\nราชานุญาต\nราชานุสาวรีย์\nรามเทพ\nรามาธิบดี\nรามายณะ\nราเม็ง\nราเมน\nรายชื่อ\nราสเบอร์รี\nริกเตอร์\nริคเตอร์\nรีไซเคิล\nรีดไถ\nรีทัช\nรีเทิร์น\nรีไทร์\nรีแบรนด์\nรีพอร์ท\nรีโมต\nรีโมท\nรีวิว\nรีสอร์ต\nรีสอร์ท\nรีเสิร์ช\nรุมบ้า\nรุสโซ\nรูบิก\nรูบิค\nเรซิน\nเรซิ่น\nเรดิโอ\nเรต\nเรตติ้ง\nแรงใจ\nแรงดูด\nแรงผลัก\nแรลลี\nแรลลี่\nโรดแมป\nโรเนียว\nโรแมนติก\nโรแมนติค\nโรล\nโรลออน\nไรเฟิล\nล็อกเกอร์\nลอจิสติกส์\nล็อต\nล็อบบี้\nลอร์ด\nล้มเหลว\nละติน\nละอ่อน\nลาซานญ่า\nลาติน\nลาเต้\nลานีญา\nลามะ\nลิมิต\nลิมูซีน\nลิสต์\nลีก\nลีด\nลีดเดอร์\nลีเมอร์\nลีลาวดี\nลุค\nลูกชาย\nลูกสาว\nเลกเชอร์\nเลคเชอร์\nเลดี้\nเลสเบี้ยน\nเลิฟ\nแลนด์\nแล็บ\nโลโก้\nโลชั่น\nไลท์\nไลน์\nไลฟ์\nวนาราม\nวราราม\nวโรกาส\nว้อดก้า\nวอเตอร์\nวอฟเฟิล\nว้อย\nวอร์ม\nวอร์มอัพ\nวอร์รูม\nวอล์ก\nวอล์ค\nวอลซ์\nวอลนัต\nวอลนัท\nวอลล์\nว่ะ\nวันเวย์\nวัสสา\nวาซาบิ\nวาทกรรม\nวาทะ\nวานิลลา\nวานิลา\nวาฟเฟิล\nวาริชศาสตร์\nว้าว\nวัคค์\nวัจนะ\nวาไรตี้\nวิก\nวิดีโอ\nวิทย์\nวิน\nวิป\nวิปปิ้ง\nวิภัชภาค\nวิว\nวิลล์\nวิลเลจ\nวีเจ\nวีซ่า\nวีดิทัศน์\nวีน\nวีไอพี\nวืด\nเวณิกา\nเวเฟอร์\nเวสต์\nเวอร์\nเวิร์ก\nเวิร์กช็อป\nเวิร์ค\nเวิร์ลด์\nเวิลด์\nแวมไพร์\nไวกิ้ง\nไวเบรเตอร์\nไวอะกร้า\nไวอากร้า\nศากยบุตร\nศิรินทร์\nศิลปวัฒนธรรม\nศิลปากร\nศิวิไลซ์\nศึกษาศาสตร์\nสกรัม\nสกาย\nสกู๊ป\nสเกตช์\nสเก็ตช์\nสคริปต์\nสแควร์\nสงบสุข\nสจ๊วต\nสตรอเบอร์รี\nสตรอเบอรี\nสตรอว์เบอร์รี\nสตริง\nสต็อก\nสต๊อก\nสต็อค\nสต๊อค\nสตอรี\nสตาร์\nสตาร์ท\nสติกเกอร์\nสติ๊กเกอร์\nสตีล\nสตูดิโอ\nสเตชัน\nสเตชั่น\nสเตเดียม\nสเตนเลส\nสเต็ป\nสเตย์\nสเตริโอ\nสเตอริโอ\nสแตนดาร์ด\nสแตนเลส\nสโตน\nสโตร์\nสไตรค์\nสไตล์\nสถาปัตย์\nสไนเปอร์\nสปอต\nสป็อต\nสปอนเซอร์\nสปอร์ต\nสปา\nสปาย\nสปิริต\nสเปก\nสเปค\nสไปเดอร์\nสมณพราหมณ์\nสมาพันธ์\nสมิติเวช\nสโรชา\nสลัม\nสแล็ก\nสโลแกน\nสโลว์\nสไลด์\nสวีท\nสหรัฐ\nสหัชญาณ\nสหัสวรรษ\nสะกอม\nสะเด่า\nสะบึม\nสะบึมส์\nสะออน\nสังโฆ\nสะโหลสะเหล\nสันทนาการ\nสัมนา\nสามช่า\nสามแยก\nสารขัณฑ์\nสี่แยก\nสึนามิ\nสุนทรีย์\nสุริยยาตร\nสุริยยาตร์\nสุหนี่\nเสกสรรค์\nเสพติด\nเสือโคร่ง\nหงวน\nหน่อมแน้ม\nหมวย\nหมั่นโถว\nหม่านโถว\nหมายปอง\nหมิง\nหยวน\nหลวงตา\nหลวงปู่\nหลวงพี่\nหล่อฮังก้วย\nหลินจือ\nห่วย\nเห็นด้วย\nเหมย\nเห่ย\nเหี่ยวย่น\nแหม็บ\nแหวว\nโหงว\nโหงวเฮ้ง\nโหลน\nโหลยโท่ย\nไหง\nไหร่\nอพาร์ตเมนต์\nอพาร์ตเมนท์\nอพาร์ทเมนต์\nอพาร์ทเมนท์\nอมาตยาธิปไตย\nอยุติธรรม\nอริยสงฆ์\nอ่วม\nอวอร์ด\nออกแบบ\nออดิชั่น\nออดิทอเรียม\nออเดอร์\nออโต้\nออทิสติก\nอ่อนด้อย\nออฟ\nออยล์\nออร์แกน\nออร์แกนิก\nออร์แกนิค\nออร์เดอร์\nออรัล\nออสซี่\nอะ\nอัตลักษณ์\nอัตวิสัย\nอันเดอร์\nอันตรกิริยา\nอัลตรา\nอัลไซเมอร์\nอัลบัม\nอัลบั้ม\nอัลมอนด์\nอาข่า\nอาโนเนะ\nอาฟเตอร์\nอาร์ติสต์\nอาร์พีจี\nอาว์\nอาสวะ\nอิกัวนา\nอินดอร์\nอินดัสตรีส์\nอินเตอร์\nอิ่มแปร้\nอิมพีเรียล\nอิเล็กทริก\nอิเล็กทริค\nอิเลียด\nอิสรชน\nอิเหนา\nอิออน\nอีแต๋น\nอีโรติก\nอีเวนท์\nอีสต์\nอีสเตอร์\nอึ๊บ\nอึ้ม\nอึ๋ม\nอึมครึม\nอุด้ง\nอุตสาหการ\nอุเทน\nอุปการคุณ\nอุปทาน\nอุปนายก\nอุปนายิกา\nอุปสงค์\nอุปัทวเหตุ\nอุรังคธาตุ\nอูคูเลเล่\nอู้ฟู่\nเอ๋\nเอ็กซ์โป\nเอ็กซ์เพรส\nเอ็กโซเซต์\nเอ็กโซเซ่ต์\nเอเซีย\nเอ็นจีโอ\nเอ็นเตอร์เทน\nเอนทรานซ์\nเอ็นทรานซ์\nเอฟเฟ็กต์\nเอเยนต์\nเอลนีโญ\nเอสเปรสโซ\nเอสเพรสโซ\nเอ๋อ\nเอาต์\nเอาท์\nเอาท์ดอร์\nเอ๊าะ\nแอ็กชั่น\nแอ็คชั่น\nแอคทีฟ\nแอดมิชชั่น\nแอดมิสชัน\nแอนด์\nแอ๊บแบ๊ว\nแอปเปิล\nแอปเปิ้ล\nแอปพริคอท\nแอพพริคอท\nแอพริคอต\nแอร์\nแอโรบิก\nแอโรบิค\nแอลมอนด์\nแอสเตอร์\nโอเค\nโอเปอเรเตอร์\nโอเปร่า\nโอเพ่น\nโอ้ย\nโอยัวะ\nโอรสาธิราช\nโอเลี้ยง\nโอวัลติน\nโอเวอร์\nไอซ์\nไอซียู\nไอดอล\nไอเดีย\nไอติม\nฮวงจุ้ย\nฮ่องเต้\nฮองเฮา\nฮอต\nฮ็อต\nฮอตดอก\nฮ็อตด็อก\nฮันนีมูน\nฮัม\nฮัลโลวีน\nฮัลโหล\nฮากกา\nฮาร์ด\nฮาราคีรี\nฮาลาล\nฮาโลวีน\nฮิ\nฮิต\nฮิบรู\nฮิปโป\nฮิปฮอป\nฮีโร่\nฮูลาฮูป\nฮูล่าฮูป\nเฮฟวี\nเฮฟวี่\nเฮอร์ริเคน\nเฮีย\nแฮนด์\nแฮปปี้\nแฮมเบอร์เกอร์\nโฮป\nโฮม\nโฮลดิงส์\nโฮลวีต\nโฮสเตส\nไฮกุ\nไฮแจ็ค\nไฮโซ\nไฮเทค\nไฮบริด\nไฮเปอร์\nไฮไลต์\nไฮไลท์\nไฮเวย์\nไฮสคูล\nไฮเอนด์\n\nกรีซ\nกัมพูชา\nกัวเตมาลา\nกาตาร์\nกานา\nกาบอง\nกายอานา\nกินี\nเกรนาดีนส์\nเกรเนดา\nเกาหลี\nแกมเบีย\nโกตดิวัวร์\nคองโก\nคอโมโรส\nคอสตาริกา\nคาซัคสถาน\nคิตส์\nคิริบาตี\nคิริบาส\nคิวบา\nคีร์กีซสถาน\nคูเวต\nเคนยา\nเคปเวิร์ด\nเคย์แมน\nแคนาดา\nแคเมอรูน\nโครเอเชีย\nโคลอมเบีย\nจอร์เจีย\nจอร์แดน\nจาเมกา\nจิบูตี\nจีน\nชาด\nชิลี\nเช็ก\nซามัว\nซาอุ\nซิมบับเว\nซีเรีย\nซูดาน\nซูรินาเม\nเซนต์\nเซเนกัล\nเซอร์เบีย\nเซาตูเม\nเซียร์รา\nแซมเบีย\nโซมาเลีย\nโซเวียต\nไซปรัส\nญี่ปุ่น\nดารุสซาลาม\nเดนมาร์ก\nโดมินิกัน\nโดมินิกา\nตรินิแดด\nตองกา\nติมอร์\nตุรกี\nตูนิเซีย\nเติร์กเมนิสถาน\nโตโก\nโตเบโก\nไต้หวัน\nทาจิกิสถาน\nแทนซาเนีย\nนอร์เวย์\nนามิเบีย\nนาอูรู\nนิการากัว\nนิวซีแลนด์\nเนเธอร์แลนด์\nเนปาล\nเนวิส\nไนจีเรีย\nไนเจอร์\nบราซิล\nบริติช\nบริเตน\nบรูไน\nบอตสวานา\nบอสเนีย\nบังกลาเทศ\nบังคลาเทศ\nบัลแกเรีย\nบาร์บูดา\nบาร์เบโดส\nบาห์เรน\nบาฮามาส\nบิสเซา\nบุรุนดี\nบูร์กินาฟาโซ\nเบนิน\nเบลเยียม\nเบลารุส\nเบลีซ\nเบอร์มิวดา\nโบลิเวีย\nปรินซิปี\nปากีสถาน\nปานามา\nปาปัวนิวกินี\nปารากวัย\nปาเลสไตน์\nปาเลา\nเปรู\nเปอร์เซีย\nเปอร์โตริโก\nโปรตุเกส\nโปแลนด์\nฝรั่งเศส\nพม่า\nฟิจิ\nฟินแลนด์\nฟิลิปปินส์\nเฟรนช์\nภูฏาน\nภูฐาน\nมองโกเลีย\nมอนเตเนโกร\nมอนแทนา\nมอริเชียส\nมอริเตเนีย\nมอลโดวา\nมอลตา\nมัลดีฟส์\nมาเก๊า\nมาซิโดเนีย\nมาดากัสการ์\nมาร์แชลล์\nมาลาวี\nมาลี\nมาเลเซีย\nเม็กซิโก\nเมียนมาร์\nโมซัมบิก\nโมนาโก\nโมนาโค\nโมร็อกโก\nไมโครนีเซีย\nยูกันดา\nยูโกสลาเวีย\nยูเครน\nเยเมน\nเยอรมนี\nรวันดา\nรัสเซีย\nโรมาเนีย\nลักเซมเบิร์ก\nลัตเวีย\nลาว\nลิกเตนสไตน์\nลิทัวเนีย\nลิเบีย\nลีโอน\nลูเซีย\nเลโซโท\nเลบานอน\nเลสเต\nไลบีเรีย\nวาติกัน\nวานูอาตู\nวินเซนต์\nเวเนซุเอลา\nเวียดนาม\nศรีลังกา\nสเปน\nสโลวะเกีย\nสโลวัก\nสโลวีเนีย\nสวาซิแลนด์\nสวิตเซอร์แลนด์\nสวีเดน\nสหรัฐ\nสหราชอาณาจักร\nสิกขิม\nสิงคโปร์\nอเมริกา\nออสเตรเลีย\nออสเตรีย\nอันดอร์รา\nอัฟกานิสถาน\nอาเซอร์ไบจาน\nอาร์เจนตินา\nอาร์เมเนีย\nอาระเบีย\nอิเควทอเรียล\nอิตาลี\nอินเดีย\nอินโดนีเซีย\nอิรัก\nอิสราเอล\nอิหร่าน\nอียิปต์\nอุซเบกิสถาน\nอุรุกวัย\nเอกวาดอร์\nเอธิโอเปีย\nเอมิเรตส์\nเอริเทรีย\nเอลซัลวาดอร์\nเอสโตเนีย\nแองโกลา\nแอนติกา\nแอลจีเรีย\nแอลเบเนีย\nโอมาน\nไอซ์แลนด์\nไอร์แลนด์\nฮ่องกง\nฮอนดูรัส\nฮังการี\nเฮติ\nเฮอร์เซโกวีนา\n\nกระบี่\nกรุงเทพ\nกาญจนบุรี\nกาฬสินธุ์\nกำแพงเพชร\nขอนแก่น\nจันทบุรี\nฉะเชิงเทรา\nชลบุรี\nชัยนาท\nชัยภูมิ\nชุมพร\nเชียงราย\nเชียงใหม่\nตรัง\nตราด\nตาก\nนครนายก\nนครปฐม\nนครพนม\nนครราชสีมา\nนครศรีธรรมราช\nนครสวรรค์\nนนทบุรี\nนราธิวาส\nน่าน\nบึงกาฬ\nบุรีรัมย์\nปทุมธานี\nประจวบคีรีขันธ์\nปราจีนบุรี\nปัตตานี\nพะเยา\nพังงา\nพัทลุง\nพิจิตร\nพิษณุโลก\nเพชรบุรี\nเพชรบูรณ์\nแพร่\nภูเก็ต\nมหาสารคาม\nมุกดาหาร\nแม่ฮ่องสอน\nยโสธร\nยะลา\nร้อยเอ็ด\nระนอง\nระยอง\nราชบุรี\nลพบุรี\nลำปาง\nลำพูน\nเลย\nศรีสะเกษ\nสกลนคร\nสงขลา\nสตูล\nสมุทรปราการ\nสมุทรสงคราม\nสมุทรสาคร\nสระแก้ว\nสระบุรี\nสิงห์บุรี\nสุโขทัย\nสุพรรณบุรี\nสุราษฎร์\nสุราษฎร์ธานี\nสุรินทร์\nหนองคาย\nหนองบัวลำภู\nอยุธยา\nอ่างทอง\nอำนาจเจริญ\nอุดรธานี\nอุตรดิตถ์\nอุทัยธานี\nอุบลราชธานี\nกันทรลักษ์\nจตุจักร\nไชยา\nซีคอน\nดอนเมือง\nถลาง\nไทรโยค\nธนบุรี\nธัญบุรี\nบางกอก\nบางปะกง\nบางระจัน\nปะทิว\nปาย\nพญาไท\nพัฒน์พงษ์\nพัทยา\nพารากอน\nภูมิซรอล\nรัตนาธิเบศร์\nรังสิต\nลันตา\nลาดพร้าว\nวโรรส\nวิภาวดี\nสตึก\nสมุย\nสัตหีบ\nสิมิลัน\nสุขุมวิท\nสุไหง\nเสลภูมิ\nอังรีดูนังต์\nอ่างขาง\nอินทนนท์\nเอ็มโพเรียม\n\nคิวชู\nแคริบเบียน\nแคสเปียน\nดานูบ\nตะนาวศรี\nนอร์วีเจียน\nนิโคบาร์\nเนรัญชรา\nไนล์\nบอร์เนียว\nบอลติก\nเบงกอล\nปิง\nแปซิฟิก\nมะละกา\nมินดาเนา\nมิสซิสซิปปี\nเมดิเตอร์เรเนียน\nเมโสโปเตเมีย\nยมุนา\nยุโรป\nยูเรเชีย\nยูเรเซีย\nแยงซี\nแยงซีเกียง\nสแกนดิเนเวีย\nสะโตง\nสาละวิน\nสุมาตรา\nสุเอซ\nอะเมซอน\nอันดามัน\nอัลไต\nอาร์กติก\nอาหรับ\nอินโดจีน\nอิรวดี\nอิระวดี\nอีเจียน\nอุษาคเณย์\nอูราล\nเอเชีย\nเอเดรียติก\nเอเวอเรสต์\nแอตแลนติก\nแอนตาร์กติก\nแอนตาร์กติกา\nแอฟริกา\nโอลิมปัส\nไอโอเนียน\nฮวงโห\nฮอกไกโด\nฮอนชู\n\nกบิลพัสดุ์\nกุสินารา\nโกลิยะ\nโกสัมพี\nโคตรบอง\nโคตรบูรณ์\nตองอู\nทรอย\nทวารวดี\nทวาราวดี\nเทวทหะ\nไทรบุรี\nนาลันทา\nไบแซนไทน์\nปรัสเซีย\nปัลลวะ\nปาฏลีบุตร\nพุทธคยา\nมถุรา\nมายัน\nมิถิลา\nราชคฤห์\nล้านช้าง\nล้านนา\nลุมพินี\nวัชชี\nเวสาลี\nสารนาถ\nสาวัตถี\nหริภุญชัย\nอโยธยา\nออตโตมัน\nอังวะ\nอินทปัตถ์\nอุชเชนี\n\nกราฟิก\nกราฟิกส์\nกราฟิค\nกริด\nกิกะไบต์\nกูเกิล\nกูเกิ้ล\nเกตเวย์\nโกลบอล\nคลัสเตอร์\nคลาส\nคลิก\nคลิปอาร์ต\nคอนโซล\nคอนเทนต์\nคอมพิวติ้ง\nคอมไพล์\nคอมไพเลอร์\nคอมมูนิเคชั่น\nคอร์\nคีย์\nคีย์บอร์ด\nเครือข่าย\nเคอร์เซอร์\nเคอร์เนล\nแคช\nแคมฟรอก\nแคมฟร็อก\nแคร็ก\nโค้ด\nจาวา\nจีพีเอส\nชิป\nชิพ\nเชลล์\nแช็ต\nแชนเนล\nแชนแนล\nซ็อกเก็ต\nซอฟต์แวร์\nซอฟท์แวร์\nซอร์ส\nซัพพอร์ต\nซัพพอร์ท\nซีดี\nซีดีรอม\nซีเนอร์\nเซิร์ฟเวอร์\nโซลูชัน\nโซลูชั่น\nไซต์\nไซเบอร์\nทรานแซกชัน\nทรานแซกชั่น\nทรานแซ็กชัน\nทรานแซ็กชั่น\nทรานแซคชัน\nทรานแซคชั่น\nทรานแซ็คชัน\nทรานแซ็คชั่น\nทวิตเตอร์\nทวีต\nทัชแพด\nเทมเพลต\nเทอร์มินัล\nแท็ก\nแท็บ\nแทบเล็ต\nโทรจัน\nเน็ต\nเน็ตบุ๊ก\nเน็ตบุค\nเน็ตบุ๊ค\nเน็ตเวิร์ก\nเน็ตเวิร์ค\nโน้ตบุ๊ก\nโน้ตบุค\nโน้ตบุ๊ค\nดอส\nดาวน์เกรด\nดาวน์โหลด\nดิจิตอล\nดิจิทัล\nดีบั๊ก\nดีวีดี\nดีไวซ์\nเดเบียน\nเดลไฟ\nเดสก์ท็อป\nโดเมน\nไดรว์\nไดรเวอร์\nไดเรกทอรี\nไดโอด\nเทเลคอม\nบล็อกเกอร์\nบรอดแบนด์\nบราวเซอร์\nบลูทูท\nบลูทูธ\nบลูเรย์\nบั๊ก\nบัฟเฟอร์\nบิต\nบิท\nบูต\nเบราว์เซอร์\nแบนด์วิดท์\nไบต์\nไบนารี\nโปรแกรมเมอร์\nโปรเซส\nโปรเซสเซอร์\nโปรโตคอล\nพร็อกซี\nพอร์ต\nพอร์ท\nพาเนล\nพาร์ทิชัน\nพารามิเตอร์\nพาสเวิร์ด\nพิกเซล\nเพจ\nเพจเจอร์\nแพกเก็ต\nแพตช์\nแพลตฟอร์ม\nโพรเซส\nโพรเซสเซอร์\nโพรโทคอล\nไพธอน\nฟล็อปส์\nฟอนต์\nฟอร์แมต\nฟอร์เวิร์ด\nฟอรัม\nฟีเจอร์\nเฟซบุ๊ก\nเฟิร์มแวร์\nแฟล็ก\nโฟลเดอร์\nไฟร์ฟอกซ์\nไฟร์วอลล์\nไฟล์\nมอดูล\nมอนิเตอร์\nมัลติ\nมัลติทัช\nมัลติเพล็กซ์\nมัลแวร์\nมาสเตอร์\nมีเดีย\nเมนู\nเมมโมรี\nเมล\nเมาส์\nแมค\nโมดูล\nโมเด็ม\nโมบาย\nโมบายล์\nโมไบล์\nไมโครซอฟท์\nยูนิกซ์\nยูนิโคด\nยูนิโค้ด\nริงโทน\nรีเฟรช\nรีเลย์\nเราเตอร์\nเรียลไทม์\nลิงก์\nลินุกซ์\nลีนุกซ์\nลูป\nเลเยอร์\nแล็ปท็อป\nไลเซนส์\nไลบรารี\nวิกิ\nวิกิพีเดีย\nวินโดวส์\nวินโดว์ส\nเว็บ\nเวอร์ชวล\nเวอร์ชัน\nเวอร์ชั่น\nเวิร์กสเตชัน\nเวิร์กสเตชั่น\nเวิร์คสเตชัน\nเวิร์คสเตชั่น\nเวิร์ด\nเวิร์ม\nไวแมกซ์\nสกรีน\nสแกน\nสแกนเนอร์\nสแต็ก\nสนิฟเฟอร์\nสปายแวร์\nสเปซ\nสแปม\nสมาร์ท\nสล็อต\nเสิร์ช\nโหลด\nออนไลน์\nออปติก\nออปติคอล\nออปติคัล\nออฟไลน์\nออราเคิล\nอัพเกรด\nอัพเดต\nอัพโหลด\nอัปเกรด\nอัปเดต\nอัปโหลด\nอัลกอริทึม\nอาร์กิวเมนต์\nอินเตอร์เน็ต\nอินทิเกรเตอร์\nอินเทอร์เน็ต\nอินเทอร์เฟซ\nอินเทล\nอินพุต\nอินพุท\nอีเมล\nอีเมล์\nอูบุนตู\nอูบันตู\nเอนจิน\nเอ็นจิน\nเอาต์พุต\nเอาต์พุท\nเอาท์พุต\nเอาท์พุท\nแอนะล็อก\nแอนิเมชัน\nแอนิเมชั่น\nแอปพลิเคชัน\nแอปพลิเคชั่น\nแอพพลิเคชัน\nแอพพลิเคชั่น\nแอสเซมบลี\nแอสเซมเบลอร์\nโอเพน\nไอคอน\nไอซี\nไอพอด\nไอพ็อด\nไอแพด\nไอโฟน\nฮับ\nฮาร์ดดิสก์\nฮาร์ดแวร์\nแฮกเกอร์\nแฮ็กเกอร์\nแฮนด์เฮลด์\nโฮสต์\n\nกรีก\nกัณณาฑ\nกัศมีรี\nคันจิ\nคาตาคานะ\nคุชราตี\nคุรุมุขี\nซีริลลิก\nซูลู\nเซลติก\nเซิร์บ\nตากาล็อก\nเตลุคู\nเติร์ก\nทราวิฑ\nเบงกาลี\nปัญจาบี\nพินอิน\nมลยาฬัม\nมองโกล\nมาราฐี\nมาเลย์\nเม็กซิกัน\nแมนจู\nแมนดาริน\nไมถิลี\nเยอรมัน\nรัสเซียน\nสวาฮิลี\nสวิส\nสินธี\nอูรดู\nอัสสมี\nอารบิก\nอิตาเลียน\nอุยกูร์\nแอฟริกัน\nโอริยา\nไอริช\nฮันกึล\nฮินดี\nฮิรางานะ\nฮีบรู\n\nกรีนพีซ\nกรือเซะ\nกวนอิม\nกวนอู\nกัดดาฟี\nกัลยาณวัตร\nกัสสปะ\nกาลิเลโอ\nกินเนส\nกุมภกรรณ\nกูเตนเบิร์ก\nเกตส์\nเกษมณี\nโกณฑัญญะ\nขงเบ้ง\nคริสโตเฟอร์\nคองคอร์ด\nคอลเกต\nคานธี\nคาเบรียล\nคาร์ฟูร์\nคาร์สัน\nคาราบาว\nคาสิโอ\nคิริน\nคุนลุ้น\nคูโบต้า\nเครมลิน\nแคทรีนา\nโคตมะ\nโคตมี\nโคลัมบัส\nไคฟง\nไครสเลอร์\nง้อไบ๊\nจตุพร\nจ็อบส์\nจอห์น\nจิ้น\nจิม\nจิ๋ม\nจิว\nจุฬาภรณ์\nจุฬาลงกรณ์\nเจมส์\nแจ็กสัน\nโจเซฟ\nโจว\nชมัยมรุเชฐ\nชมัยมรุเชษฐ์\nชเวดากอง\nชาร์ลส์\nชินราช\nชินวัตร\nชุนชิว\nเช็ง\nเชตวัน\nเชฟรอน\nเชฟโรเลต\nเชลซี\nไชยานุชิต\nซ่ง\nซังฮี้\nซัดดัม\nซันซิล\nซัมซุง\nซัวเจ๋ง\nซินหัว\nซีซาร์\nซีแพค\nซูซาน\nซูซูกิ\nซูบารุ\nเซ็นทารา\nเซเวน\nเซเว่น\nโซฟิเทล\nโซยุซ\nโซยูซ\nณัฐวุฒิ\nดาร์ลี่\nดาวโจนส์\nดิสนีย์\nดีแทค\nดูปองท์\nเดโมแครต\nเดลล์\nเดลินิวส์\nเดวิด\nแดวู\nโดนัลด์\nโดราเอมอน\nโดเรมอน\nต๋อง\nตั๊กม้อ\nตากสิน\nตาเมือน\nตาลีบัน\nตูน\nเตมีย์\nโต๋\nโตชิบา\nโตโยต้า\nถังซัมจั๋ง\nถังซำจั๋ง\nทรพา\nทราเวล\nทรูมูฟ\nทีปังกร\nเทปโก\nเทพรัตน\nเทวทัต\nเทสโก้\nโทมัส\nไททานิก\nไททานิค\nไทยรัฐ\nธีออส\nนครินทรา\nนโปเลียน\nนพดล\nนราดูร\nนเรนทร\nนอสตราดามุส\nนาซา\nนาซ่า\nนาซี\nนาโต\nนาโต้\nนาลแก\nนิคอน\nนิโคลัส\nนิด้า\nนินเทนโด\nนิปปอน\nนิวตัน\nนิสสัน\nเนคเทค\nเนชั่น\nเนชันแนล\nเนชั่นแนล\nเนวิน\nเนสเล่\nเนสาด\nแนท\nแนสแดค\nโนเกีย\nโนเบล\nโนเวลล์\nโนโวเทล\nไนเม็กซ์\nบรอดเวย์\nบรัดเลย์\nบรู๊ซ\nบัลเมอร์\nบารัก\nบารัค\nบู๊ตึ๊ง\nเบญกาย\nเบนซ์\nเบ็นซ์\nเบนจามิน\nโบตัน\nไบโอเทค\nประชาธิปัตย์\nปวีณา\nปอเต็กตึ๊ง\nปอเต๊กตึ๊ง\nป่อเต็กตึ๊ง\nปัตตะโชติ\nปารุสก์\nปีเตอร์\nปูติน\nเป๊ปซี่\nเป้ย\nเปอร์โยต์\nเปาบุ้นจิ้น\nโปเกมอน\nโป๊ยก่าย\nพรหมทัต\nพลาโต\nพอลล่า\nพานาโซนิค\nพานาโซนิก\nพิทยานุกูล\nพิมพิสาร\nเพนแทกซ์\nเพลโต\nไพโอเนียร์\nฟรอยด์\nฟรังซิส\nฟรานซิส\nฟลอเรนซ์\nฟอร์ด\nฟิลิปส์\nฟูจิ\nแฟซ่า\nโฟร์โมสต์\nภูมิพล\nภูริทัต\nมงฟอร์ต\nมณโฑ\nมติชน\nมหิตลาธิเบศร\nมโหสถ\nมัจฉานุ\nมาร์กาเร็ต\nมาร์ติน\nมาสด้า\nมิตซูบิชิ\nมิราเคิล\nมุสโสลินี\nเม้ง\nเมจิ\nเมอร์ซีเดส\nเมอร์เซเดส\nแมกซ์เวลล์\nแมกไซไซ\nแมคอินทอช\nแมชีนเนอรี่\nโมคคัลลานะ\nโมโตโรลา\nโมโตโรล่า\nโมเนีย\nไมเคิล\nไมยราพณ์\nยโสธรา\nยะโฮวา\nยะโฮวาห์\nยามาฮ่า\nยาเวห์\nยาฮู\nยูนิเซฟ\nยูเนสโก\nยูไล\nเยโฮวาห์\nรอยเตอร์\nรอยัล\nรัชดา\nรัสปูติน\nราฟาเอล\nรามาวตาร\nราเมศวร\nราหุล\nริชาร์ด\nรีพับลิกัน\nรูนีย์\nเรนโบว์\nแรมโบ้\nโรตารี\nโรนัลโด\nโรนัลโด้\nโรบินสัน\nโรเบิร์ต\nล็อกซเล่ย์\nลิงคอล์น\nลิจฉวี\nลิไท\nลิไทย\nลินคอล์น\nลิเวอร์พูล\nเลโนโว\nเลียดก๊ก\nโลตัส\nวชิราลงกรณ์\nวลาดิเมียร์\nวอลล์สตรีท\nวาเลนไทน์\nวิกตอเรีย\nวิทยานุสรณ์\nวิทยายน\nวิมเบิลดัน\nวิลเลียม\nวีระ\nวุฒิชัย\nเวียดกง\nไวตามิลค์\nศกุนตลา\nศรีวิชัย\nศิริพงษ์\nศิริราช\nศุภชลาศัย\nสดกก๊อกธม\nสดายุ\nสตาลิน\nสตีฟ\nสแตนฟอร์ด\nสวรินทิรา\nสังกัจจายน์\nสาทิตย์\nสารีบุตร\nสิริกิติ์\nสิรินธร\nสิหิงค์\nสีวลี\nสีหนุ\nสีหมุนี\nสีหโมนี\nสุครีพ\nสุทโธทนะ\nสุเทพ\nสุนทราภรณ์\nสุนีย์\nสุรนารี\nสุรยุทธ์\nสุริยาสน์\nเส้าหลิน\nโสกราตีส\nโสภิต\nหนุมาน\nหลินฮุ่ย\nหลุยส์\nเห้งเจีย\nไหหม่า\nองคต\nองคุลิมาล\nอชาตศัตรู\nอดุลยเดช\nอพอลโล\nอริสโตเติล\nอริสมันต์\nอลิซาเบธ\nอ๋อม\nออร์คิด\nออสการ์\nอะพอลโล\nอับราฮัม\nอั้ม\nอัลกออิดะห์\nอัลคาเทล\nอัลจาซีราห์\nอัลเฟรด\nอัลเลาะห์\nอัสซุส\nอัสสชิ\nอัสสัมชัญ\nอาเซม\nอาเซ็ม\nอาเซียน\nอาฟต้า\nอาร์เซนอล\nอินทรชิต\nอินทราทิตย์\nอีซูซุ\nอีเลฟเวน\nอีเลฟเว่น\nอุณรุท\nอุบลรัตน์\nอุบาลี\nอุ๋ย\nเอกทัศน์\nเอเซอร์\nเอ็ดเวิร์ด\nเอดิสัน\nเอแบค\nเอลิซาเบธ\nเอสพลานาด\nเอสพลานาร์ด\nแอคคอร์\nแอคคอร์ด\nแองเจลิน่า\nแอตแลนติส\nแอน\nแอ๋ม\nแอมบาสซาเดอร์\nแอมบาสเดอร์\nแอมเวย์\nแอ๋ว\nโอดีสซีย์\nโอเดียน\nโอบามา\nโอรสาราม\nโอลิมเปีย\nโออิชิ\nไอน์สไตน์\nฮอนด้า\nฮอปกินส์\nฮอลลีวูด\nฮอลลีวู้ด\nฮานามิ\nฮามาส\nฮิตเลอร์\nฮิตาชิ\nฮุนเซน\nฮุนเซ็น\nฮุนได\nฮุสเซ็น\nเฮนรี\nเฮนรี่\nเฮเลน\nโฮจิมินห์\nโฮปเวลล์\nโฮเมอร์\n\nกลีเซอรีน\nกำทอน\nแกนีมีด\nครอส\nคริสตัล\nคลอโรพลาสต์\nคลอไรด์\nควอนตัม\nคอนดักเตอร์\nคอปเปอร์\nคอลลาเจน\nคอเลสเตอรอล\nคอสมิก\nคูลอมบ์\nเคอราติน\nแคโรทีน\nแคสสินี\nโครมาโทกราฟี\nไคโตซาน\nจีโนม\nจุลชีววิทยา\nชิคุนกุนยา\nซัลฟิวริก\nซัลเฟต\nซัลไฟด์\nซิงค์\nซิริอุส\nซิลิกา\nซิลิเกต\nซิลิโคน\nซีเทน\nซีเวิร์ต\nเซ็กเตอร์\nเซ็กเมนต์\nเซมิ\nโซนาร์\nไซบอร์ก\nดอปเปลอร์\nดอปเพลอร์\nไดนามิก\nไดนามิกส์\nไดนามิค\nไดนามิคส์\nไดออกไซด์\nทรานส์\nทามิฟลู\nเทฟลอน\nเทสโทสเตอโรน\nเทอร์โม\nแทนนิน\nไททัน\nไทฟอยด์\nไทรอยด์\nธาลัสซีเมีย\nเนกาตีฟ\nโนวา\nบอแรกซ์\nโบทอกซ์\nโบท็อกซ์\nไบโอติน\nปฏิยานุพันธ์\nโปรเจสเตอโรน\nพอลิเมอร์\nพันธุศาสตร์\nพัลซาร์\nพาร์กินสัน\nพาราเซตามอล\nพาราโบลา\nเพอร์ออกไซด์\nโพรเจสเทอโรน\nโพลาไรซ์\nโพลิเมอร์\nโพลีเอทิลีน\nไพรเมต\nฟลาโวนอยด์\nฟลูออเรสเซนซ์\nฟลูออไรด์\nฟอสซิล\nฟิชชัน\nฟิวชัน\nฟีโรโมน\nไฟเบอร์\nมอนอกไซด์\nมิราจ\nเมตริกซ์\nเมทริกซ์\nเมลานิน\nเมลามีน\nโมเมนตัม\nไมโตคอนเดรีย\nไมโทคอนเดรีย\nยีสต์\nยูริก\nยูเรีย\nรูมาตอยด์\nวีก้า\nเวกเตอร์\nเวก้า\nเวสิเคิล\nโวลต์\nสเกล\nสเกลาร์\nสเต็ม\nสเตียรอยด์\nสปีชีส์\nสเปิร์ม\nสัมพัทธภาพ\nสุริยจักรวาล\nออกเทน\nออโรรา\nออโรร่า\nอะคริลิก\nอะครีลิก\nอะซีติก\nอะซีโตน\nอะมิโน\nอะลูมินา\nอันโดรเมดา\nอัลคาไลน์\nอัลตราซาวด์\nอัลตราซาวนด์\nอัลลอยด์\nอินทิกรัล\nอินทิเกรต\nอีโบลา\nอีโบล่า\nเอ็กซ์โพเนนเชียล\nเอทานอล\nเอทิลีน\nเอนโทรปี\nเอสเตอร์\nเอสโตรเจน\nเอสโทรเจน\nแอนดรอยด์\nแอนแทร็กซ์\nแอมพลิจูด\nแอมโมเนียม\nแอลกอฮอลิซึม\nแอสพาร์แตม\nโอเซลทามิเวียร์\nฮับเบิล\nฮิวมัส\nไฮดรอกไซด์\nไฮดรอลิก\nไฮโดรลิก\nไฮเพอร์โบลา\n\nกงเต็ก\nกฎุมพี\nกฏ\nกฏหมาย\nกบฎ\nกราไฟต์\nก๊อปปี้\nกะทะ\nกังวาล\nกุฎฐัง\nกุฏุมพี\nฃวด\nคฑา\nคลินิค\nคลีนิก\nคลีนิค\nคาทอลิค\nคึ่นฉ่าย\nแคตตาล็อก\nโควต้า\nฅน\nจุมพฎ\nช็อคโกแลต\nแซ่ด\nดัทช์\nทนง\nทรมาณ\nทราก\nทะแยง\nทิฏฐิ\nบล็อค\nบ๊องแบ๊ว\nบัลเล่ต์\nเบรค\nแบงค์\nปรากฎ\nปัคคหะ\nปาฏิโมกข์\nปิติ\nปิรามิด\nโปรเตสแตนท์\nพนิช\nพยักเพยิด\nพริ้ว\nพลูโตเนียม\nพากษ์\nเฟิร์น\nยากี้\nเยภุยยสิกา\nรุสเซีย\nฤาษี\nล็อค\nล็อคเกอร์\nวันทยาหัตถ์\nวานิช\nวิญญาน\nวิศิษฐ์\nศรีษะ\nสเปกโทรสโคป\nสฤษฎ์\nสลิ่ม\nสัตตสดก\nสาราณียากร\nสุกี้\nสุกี้ยากี้\nสูญญากาศ\nหยอมแหยม\nหยอย\nหล่ะ\nอะดรีนาลีน\nอะหลั่ย\nอัตคัต\nอัฟริกา\nอานิสงค์\nอาฟริกา\nอิริยาบท\nอิเลคโทรนิคส์\nอีรุงตุงนัง\nอุตรายัน\nอุลตรา\nอุลตร้า\nโอกาศ\n\nกกหู\nกงการ\nกงจักร\nกฎเกณฑ์\nกฎบัตร\nกฎหมาย\nกฎหมู่\nกดขี่\nกดดัน\nก้นกบ\nก้นบึ้ง\nก้นปล่อง\nกนิษฐภคินี\nกนิษฐภาดา\nกรงเล็บ\nกรมการ\nกรมท่า\nกรมธรรม์\nกรมนา\nกรมวัง\nกรรมกร\nกรรมการ\nกรรมฐาน\nกรรมบถ\nกรรมพันธุ์\nกรรมวิธี\nกรรมสิทธิ์\nกระจกเงา\nกระจกตา\nกระจกนูน\nกระจกเว้า\nกระจอกชวา\nกระจอกเทศ\nกระจ้อยร่อย\nกระจับบก\nกระจับปิ้ง\nกระจับปี่\nกระโชกโฮกฮาก\nกระดานดำ\nกระดานหก\nกระดาษแก้ว\nกระดาษไข\nกระดาษทราย\nกระดาษสา\nกระดูกงู\nกระทาชาย\nกระเท่เร่\nกระบวนการ\nกระบองเพชร\nกระผีกริ้น\nกระยาทิพย์\nกระยาสารท\nกระยาหาร\nกระสอบทราย\nกระสุนปืน\nกระแสจิต\nกระแสน้ำ\nกระแสลม\nกรับคู่\nกรับพวง\nกรับเสภา\nกราดเกรี้ยว\nกราวรูด\nกริ่งเกรง\nกรีฑาสถาน\nกรีดกราย\nกรี๊ดกร๊าด\nกลไก\nกลบท\nกลเม็ด\nกลยุทธ์\nกลวิธี\nกลศาสตร์\nกลอักษร\nกลบเกลื่อน\nกลมกล่อม\nกลมกลืน\nกลมเกลียว\nกล้วยแขก\nกล้วยไม้\nกล่องเสียง\nกล่อมเกลา\nกล่อมเกลี้ยง\nกลัดกลุ้ม\nกลัดมัน\nกลั่นกรอง\nกลั่นแกล้ง\nกลับกลอก\nกลางคน\nกลางคัน\nกลางค่ำ\nกลางคืน\nกลางแจ้ง\nกลางแปลง\nกลางวัน\nกลาดเกลื่อน\nกล่าวขวัญ\nกล่าวโทษ\nกล่าวหา\nกล้ำกราย\nกล้ำกลืน\nกลิ้งกลอก\nกลิ้งเกลือก\nกลิ่นอาย\nกลียุค\nกวดขัน\nกวัดแกว่ง\nกวัดไกว\nกวีนิพนธ์\nก่อกวน\nก่อการ\nก่อตั้ง\nก่อสร้าง\nก่อหวอด\nก้อร่อก้อติก\nกองกลาง\nกองเกิน\nกองโจร\nกองทัพ\nกองทุน\nกองพล\nกองพัน\nกองฟอน\nกองร้อย\nกองหนุน\nกอบโกย\nกะเกณฑ์\nกะบังลม\nกักกัน\nกักขัง\nกักตัว\nกักตุน\nกัณฑ์เทศน์\nกัดฟัน\nกันชน\nกันท่า\nกันสาด\nกันเอง\nกับแกล้ม\nกับข้าว\nกับระเบิด\nกากเพชร\nกากหมู\nกาชาด\nกาญจนาภิเษก\nก้านคอ\nกาฝาก\nก้ามกราม\nกามกิจ\nกามคุณ\nกามเทพ\nกามโรค\nก้ามปู\nกายกรรม\nกายบริหาร\nกายภาพ\nกายวิภาค\nกายสิทธิ์\nก่ายกอง\nการคลัง\nการเงิน\nการบ้าน\nการเปรียญ\nการเมือง\nการเรือน\nการละเล่น\nกาลกิริยา\nกาลเทศะ\nก้าวก่าย\nก้าวร้าว\nก้าวหน้า\nกาสาวพัสตร์\nกาฬพฤกษ์\nกาฬโรค\nกำปั้น\nกำมือ\nกำแพงขาว\nกำลังใจ\nกำลังม้า\nกี่งอำเภอ\nกิจกรรม\nกิจการ\nกิจวัตร\nกิจจะลักษณะ\nกิตติคุณ\nกิตติศัพท์\nกินขาด\nกินใจ\nกินดอง\nกินโต๊ะ\nกินแบ่ง\nกินเปล่า\nกินเลี้ยง\nกินเส้น\nกินแหนง\nกิโลกรัม\nกิโลเมตร\nกิโลลิตร\nกิโลเฮิรตซ์\nกีดกัน\nกีดกั้น\nกีดขวาง\nกึกก้อง\nกึกกัก\nกึกกือ\nกึ่งกลาง\nกุกกัก\nกุ๊กกิ๊ก\nกุ้งฝอย\nกุ้งมังกร\nกุ้งแห้ง\nกุ้งเต้น\nกุ้งยิง\nกุญแจผี\nกุญแจมือ\nกุญแจเลื่อน\nกุญแจเสียง\nกุลธิดา\nกุลบุตร\nกุลสตรี\nกู้ยืม\nเก้งก้าง\nเก็บกวาด\nเก็บเกี่ยว\nเก็บงำ\nเก็บตก\nเกรงกลัว\nเกรงใจ\nเกรงขาม\nเกรียงไกร\nเกรียมกรม\nเกรี้ยวกราด\nเกล็ดเลือด\nเกลี้ยกล่อม\nเกลี้ยงเกลา\nเกลือกกลั้ว\nเกลือกกลิ้ง\nเกลื่อนกล่น\nเกลื่อนกลาด\nเกศธาตุ\nเกษตรกร\nเกษตรกรรม\nเกษตรศาสตร์\nเกษมสันต์\nเกษียรสมุทร\nเก้อเขิน\nเกาะแกะ\nเกี่ยงงอน\nเกียจคร้าน\nเกียรติคุณ\nเกียรตินิยม\nเกียรติประวัติ\nเกียรติภูมิ\nเกียรติยศ\nเกียรติศักดิ์\nเกียรติมุข\nเกี่ยวข้อง\nเกี่ยวดอง\nเกี่ยวพัน\nเกี่ยวโยง\nเกี้ยวพาน\nเกี้ยวพาราสี\nแก่แดด\nแก้ขัด\nแก้ไข\nแก้ตัว\nแก้เผ็ด\nแก้ลำ\nแกงคั่ว\nแกงจืด\nแกงบวด\nแกงป่า\nแกงเผ็ด\nแกงส้ม\nแก่งแย่ง\nแก่นแก้ว\nแก่นสาร\nแก้วตา\nแก้วหู\nแกว่งกวัด\nแกว่งไกว\nแกะรอย\nโก้เก๋\nโกรธเกรี้ยว\nโกรธขึ้ง\nไก่เขี่ย\nไก่ชน\nไก่บ้าน\nไก่ป่า\nไก่ฟ้า\nไกล่เกลี่ย\nขจัดขจาย\nขนเพชร\nขนสัตว์\nขนหนู\nขนส่ง\nขนมจีน\nขบขัน\nขบวนการ\nข่มขี่\nข่มขู่\nข่มขืน\nข่มเหง\nขมหิน\nขมิ้นชัน\nขมุบขมิบ\nขยะแขยง\nขยักขย่อน\nขยักขย้อน\nขยับขยาย\nขยับเขยื้อน\nขวดโหล\nขวยเขิน\nขวัญใจ\nขวัญตา\nขวัญอ่อน\nขวากหนาม\nขวางโลก\nของ้าว\nขอสับ\nขอขมา\nขอทาน\nขอโทษ\nขอร้อง\nขออภัย\nข้อเขียน\nข้อความ\nข้อเท็จจริง\nของกลาง\nของขวัญ\nของแข็ง\nของชำ\nของลับ\nของเล่น\nของว่าง\nของเหลว\nของไหล\nของไหว้\nข้องใจ\nข้องแวะ\nขอบข่าย\nขอบเขต\nขอบคุณ\nขอบใจ\nขอบพระคุณ\nข้อมูล\nข้อแม้\nข้อหา\nข้อสังเกต\nขัดข้อง\nขัดขืน\nขัดเขิน\nขัดจังหวะ\nขัดดอก\nขัดแตะ\nขัดยอก\nขัดแย้ง\nขัดสมาธิ\nขันหมาก\nขันอาสา\nขับขี่\nขับเคี่ยว\nขั้วโลก\nขาจร\nขาประจำ\nข้าราชการ\nข้าศึก\nข้าหลวง\nขาดแคลน\nขาดใจ\nขาดตอน\nขาดตัว\nขาดทุน\nขาดเหลือ\nขายหน้า\nข่าวกรอง\nข่าวคราว\nข่าวล่า\nข่าวลือ\nข่าวสาร\nข้าวเกรียบ\nข้าวแกง\nข้าวของ\nข้าวจี่\nข้าวเจ้า\nข้าวซอย\nข้าวต้ม\nข้าวตอก\nข้าวตัง\nข้าวแตน\nข้าวทิพย์\nข้าวบิณฑ์\nข้าวเปลือก\nข้าวโพด\nข้าวฟ่าง\nข้าวเม่า\nข้าวสวย\nข้าวสาร\nข้าวเหนียว\nข้าวหมาก\nข้าวหลาม\nขี้เกียจ\nขี้ข้า\nขี้ครอก\nขี้คร้าน\nขี้คุก\nขี้ไคล\nขี้เซา\nขีดขั้น\nขีดคร่อม\nขีดคั่น\nขีดฆ่า\nขี้ตา\nขี้ตืด\nขี้เถ้า\nขี้ทูด\nขี้ปะติ๋ว\nขี้ผึ้ง\nขี้มูก\nขี้ยา\nขี้แย\nขี้ริ้ว\nขี้เรื้อน\nขี้เล็บ\nขี้หู\nขี้หน้า\nขี้เหนียว\nขี้เหล็ก\nขี้เหร่\nขึงขัง\nขึงพืด\nขึ้งโกรธ\nขึ้นใจ\nขึ้นชื่อ\nขึ้นมือ\nขืนใจ\nขื่นขม\nขุดคุ้ย\nขุนทอง\nขุนนาง\nขุนพล\nขุนศึก\nขุนหลวง\nขูดรีด\nเข็มกลัด\nเข็มขัด\nเข้มข้น\nเข้มแข็ง\nเข้มงวด\nเข็มทิศ\nเข็มหมุด\nเข้าขา\nเข้าเค้า\nเข้าใจ\nเข้าชื่อ\nเข้าตัว\nเข้าถึง\nเข้าทรง\nเข้าท่า\nเข้าที\nเข้าเนื้อ\nเข้าเล่ม\nเขียวเสวย\nเขียวหวาน\nแขกเต้า\nแข็งกร้าว\nแข็งกล้า\nแข็งแกร่ง\nแข็งข้อ\nแข็งขัน\nแข่งขัน\nแข็งใจ\nแข็งตัว\nแข็งเมือง\nแข็งแรง\nแขวนลอย\nโขกสับ\nโขยกเขยก\nไขข้อ\nไขควง\nไขมัน\nไข่มุก\nไขว่ห้าง\nไขสันหลัง\nไขสือ\nไข่เค็ม\nไข่เยี่ยวม้า\nไข่หงส์\nไข่เหี้ย\nคงกระพัน\nคงตัว\nคงทน\nคงที่\nคชลักษณ์\nคชสาร\nคชสีห์\nคชราช\nคณิตศาสตร์\nคดเคี้ยว\nคติธรรม\nคติพจน์\nคนกลาง\nคนไข้\nคนใช้\nคนทรง\nคบไฟ\nคบเพลิง\nคบค้า\nคบคิด\nคบหา\nคมคาย\nครบครัน\nครบถ้วน\nครอบครอง\nครอบคลุม\nครอบครัว\nครอบงำ\nครอบจักรวาล\nคริสตกาล\nคริสตจักร\nคริสต์มาส\nคริสต์ศตวรรษ\nคริสต์ศักราช\nคริสตัง\nคริสเตียน\nครุกรรม\nครุภัณฑ์\nครุศาสตร์\nครุฑพ่าห์\nครุ่นคิด\nคลอเคลีย\nคล่องแคล่ว\nคล่องตัว\nคล่องมือ\nคลั่งไคล้\nคลาคล่ำ\nคลาไคล\nคลาดเคลื่อน\nคลาดแคล้ว\nคลี่คลาย\nคลึงเคล้น\nคลึงเคล้า\nคลื่นไส้\nคลื่นเหียน\nคลุกคลี\nคลุกคลาน\nคลุมเครือ\nคลุมโปง\nคลุ้มคลั่ง\nควงสว่าน\nควบคุม\nควบคู่\nควบแน่น\nควันหลง\nความคิด\nความหลัง\nความเห็น\nคอหอย\nคอห่าน\nคอแห้ง\nค่อนขอด\nค่อนแคะ\nค้อนควัก\nคั่งค้าง\nคั่งแค้น\nคัดค้าน\nคัดง้าง\nคัดท้าย\nคัดเลือก\nคันจาม\nคันฉ่อง\nคันฉาย\nคันชัก\nคันชั่ง\nคันไถ\nคันนา\nคันเร่ง\nคับขัน\nคับคั่ง\nคับแค้น\nคับแคบ\nคางทูม\nคางหมู\nค้างคืน\nค้างปี\nคาดคั้น\nคาดเชือก\nคาดโทษ\nคาดหมาย\nคานหาม\nคาบเกี่ยว\nคาบศิลา\nคาบสมุทร\nคำขาด\nคำนำ\nคิดค้น\nคืนดี\nคืนตัว\nคุกเข่า\nคุณค่า\nคุณชาย\nคุณธรรม\nคุณนาย\nคุณภาพ\nคุณลักษณะ\nคุณวุฒิ\nคุณศัพท์\nคุณสมบัติ\nคุณหญิง\nคุณากร\nคุณูปการ\nคุโณปการ\nคุมเชิง\nคุ้มกัน\nคุยเขื่อง\nคุยโต\nคุ้ยเขี่ย\nคุ้มครอง\nคู่กรณี\nคู่กัด\nคู่ขา\nคู่แข่ง\nคู่ครอง\nคู่ควร\nคู่คิด\nคู่คี่\nคู่ใจ\nคู่ชีพ\nคู่ชีวิต\nคู่บารมี\nคู่บุญ\nคู่ปรปักษ์\nคู่ปรับ\nคู่ผสม\nคู่มือ\nคู่รัก\nคู่ลำดับ\nคู่สาย\nคู่หมั้น\nคู่หู\nคู่อริ\nคู่อาฆาต\nเคมีภัณฑ์\nเคยตัว\nเคร่งขรึม\nเคร่งครัด\nเคร่งเครียด\nเครดิตฟองซิเอร์\nเครื่องกล\nเครื่องกัณฑ์\nเครื่องแกง\nเครื่องเขิน\nเครื่องครัว\nเครื่องเคียง\nเครื่องเงิน\nเครื่องจักร\nเครื่องเซ่น\nเครื่องดนตรี\nเครื่องต้น\nเครื่องทุ่นแรง\nเครื่องเทศ\nเครื่องใน\nเครื่องบิน\nเครื่องบูชา\nเครื่องแบบ\nเครื่องประดับ\nเครื่องปรุง\nเครื่องปรุงรส\nเครื่องมือ\nเครื่องยนต์\nเครื่องร่อน\nเครื่องราง\nเครื่องเรือน\nเครื่องล่าง\nเครื่องเล่น\nเครื่องสาย\nเครื่องสำอาง\nเครื่องสุกำศพ\nเครื่องหมาย\nเครือรัฐ\nเคลียคลอ\nเคลื่อนที่\nเคลื่อนไหว\nเคลือบแคลง\nเคลือบแฝง\nเคลือบฟัน\nเคว้งคว้าง\nเคหสถาน\nเค้าโครง\nเคียดแค้น\nเคี่ยวเข็ญ\nเคี้ยวเอื้อง\nเคืองขุ่น\nโคนม\nโคบาล\nโคมูตร\nโคมลอย\nโครงการ\nโครงเรื่อง\nโครงงาน\nโครงสร้าง\nโครมคราม\nโคลงเคลง\nฆ้องกระแต\nฆ้องชัย\nฆ้องวง\nฆ้องหุ่ย\nฆ้องเหม่ง\nฆ้องโหม่ง\nฆาตกร\nฆาตกรรม\nฆานประสาท\nงงงวย\nงงงัน\nงดเว้น\nงบดุล\nงบประมาณ\nงมโข่ง\nงมงาย\nง่วงงุน\nง่วงเหงา\nงอหาย\nง้องอน\nงอนง้อ\nงอมแงม\nงาช้าง\nง่าเงย\nงานการ\nง่ายดาย\nงึมงำ\nเงินเดือน\nเงินตรา\nเงินยวง\nเงียบกริบ\nเงียบเชียบ\nเงียบเหงา\nเงื่องหงอย\nเงื่อนไข\nเงื่อนงำ\nเงื่อนเวลา\nเงื้อมมือ\nแง่งอน\nจงใจ\nจงรัก\nจดจ่อ\nจดจำ\nจดหมาย\nจดหมายเหตุ\nจรจัด\nจรรยาบรรณ\nจริงจัง\nจริงใจ\nจอมขวัญ\nจอมใจ\nจอมทัพ\nจอมปลวก\nจอมพล\nจ๊ะเอ๋\nจักสาน\nจักรพรรดิ\nจักรภพ\nจักรยาน\nจักรยานยนต์\nจักรราศี\nจักรวรรดิ\nจักรวรรดินิยม\nจักรวาล\nจังหนับ\nจัดการ\nจัดจ้าน\nจัดเจน\nจัดแจง\nจัดตั้ง\nจัดสรร\nจับกุม\nจับจด\nจับเจ่า\nจ่าหน้า\nจาตุทสี\nจาตุมหาราช\nจาตุมหาราชิก\nจาตุมหาราชิกา\nจาตุรงคสันนิบาต\nจาตุรราชการ\nจานเชิง\nจานบิน\nจานผี\nจานเสียง\nจาบจ้วง\nจำเป็น\nจำพรรษา\nจำวัด\nจ้ำจี้จ้ำไช\nจำเลาะตา\nจิงโจ้น้ำ\nจิตใจ\nจิตตภาวนา\nจิตตัง\nจิตตานุปัสสนา\nจิตนิยม\nจิตบำบัด\nจิตแพทย์\nจิตวิสัย\nจิตรกร\nจิตรกรรม\nจิตรลดา\nจิตวิทยา\nจิตเวช\nจิตเวชศาสตร์\nจินตกวี\nจินตนา\nจินตนาการ\nจินตภาพ\nจุฑามณี\nจุฑามาศ\nจุฑารัตน์\nจุนเจือ\nจุ้นจ้าน\nจุลชีพ\nจุลชีวัน\nจุลชีวิน\nจุลทรรศน์\nจุลภาค\nจุลวรรค\nจุลศักราช\nจุลสาร\nจุลินทรีย์\nจุฬามณี\nจุฬาลักษณ์\nเจตคติ\nเจตจำนง\nเจตนารมณ์\nเจตภูต\nเจริญพร\nเจ้ากรม\nเจ้ากรรม\nเจ้าของ\nเจ้าขา\nเจ้าข้า\nเจ้าคณะ\nเจ้าค่ะ\nเจ้าจอม\nเจ้าชู้\nเจ้าตัว\nเจ้าถิ่น\nเจ้าท่า\nเจ้าที่\nเจ้าทุกข์\nเจ้านาย\nเจ้าเนื้อ\nเจ้าบ้าน\nเจ้าบ่าว\nเจ้าประคุณ\nเจ้าประคู้น\nเจ้าพนักงาน\nเจ้าพระคุณ\nเจ้าพระยา\nเจ้าพ่อ\nเจ้าพายุ\nเจ้าฟ้า\nเจ้าภาพ\nเจ้ามือ\nเจ้าแม่\nเจ้าเรือน\nเจ้าสังกัด\nเจ้าสัว\nเจ้าสาว\nเจ้าหน้าที่\nเจ้าหนี้\nเจ้าอาวาส\nเจาะจง\nเจือจาง\nเจือจาน\nเจือปน\nเจื้อยแจ้ว\nแจกจ่าย\nแจ่มแจ้ง\nแจ่มใส\nโจงกระเบน\nโจมตี\nโจรกรรม\nโจรสลัด\nใจความ\nใจคอ\nฉกฉวย\nฉกชิง\nฉลองได\nฉ้อฉล\nฉัตรมงคล\nฉันทลักษณ์\nฉายาลักษณ์\nฉิบหาย\nฉุกเฉิน\nฉุกละหุก\nฉุนเฉียว\nฉุปศาสตร์\nเฉไฉ\nเฉยเมย\nเฉาโฉด\nเฉิดฉัน\nเฉิดฉาย\nเฉิดฉิน\nเฉียบขาด\nเฉียบพลัน\nเฉียบแหลม\nเฉื่อยชา\nแฉะแบะ\nโฉดเฉา\nโฉมงาม\nโฉมฉาย\nโฉมเฉลา\nโฉมตรู\nโฉมยง\nโฉมศรี\nโฉมหน้า\nชดช้อย\nชดเชย\nชดใช้\nชนบท\nชนินทร์\nชนกกรรม\nชนมพรรษา\nชนมายุ\nชมเชย\nชมพูทวีป\nชมพูนท\nชมพูนุท\nชราธรรม\nชราภาพ\nชลจร\nชลธาร\nชลธี\nชลนัยน์\nชลนา\nชลเนตร\nชลประทาน\nชลมารค\nชลาธาร\nชลาลัย\nชลาศัย\nชลาสินธุ์\nชโลทร\nช่วงชิง\nช่วงใช้\nชวนชม\nชวนหัว\nช่วยเหลือ\nช่อฟ้า\nช่อม่วง\nชอกช้ำ\nช่องเขา\nช่องแคบ\nช่องไฟ\nช่องว่าง\nช้องนาง\nชอบกล\nชอบใจ\nชอบธรรม\nชอบพอ\nชักโครก\nชักเงา\nชักจูง\nชักชวน\nชักนำ\nชักเนื้อ\nชักพระ\nชักเย่อ\nชักใย\nชั่งใจ\nชังฆวิหาร\nชัดเจน\nชั้นเชิง\nชั่วคน\nชั่วคราว\nชั่วช้า\nชั่วโมง\nชั่วแล่น\nชาเย็น\nช้านาน\nช่างเครื่อง\nช่างฝีมือ\nช่างฟิต\nช่างไฟ\nช้างน้ำ\nช้างเผือก\nช้างพลาย\nช้างพัง\nช้างสาร\nช้างสีดอ\nชาติธรรม\nชาตินิยม\nชาติพันธุ์\nชาติพันธุ์วิทยา\nชาติภูมิ\nชานชาลา\nชายชาตรี\nชายคา\nชายฝั่ง\nชายทะเล\nชาวเล\nชาววัง\nช้ำใจ\nช้ำชอก\nชิงชัง\nชิงพลบ\nชินชา\nชินบุตร\nชิ้นเอก\nชิมลาง\nชีเปลือย\nชี้ขาด\nชี้แจง\nชี้นำ\nชี้แนะ\nชี้ฟ้า\nชีพจร\nชีพิตักษัย\nชื่นชม\nชื่นบาน\nชื่นมื่น\nชื่อย่อ\nชื่อรอง\nชื่อเล่น\nชื่อเสียง\nชุกชุม\nชุติมา\nชุบตัว\nชุบเลี้ยง\nชุมชน\nชุมทาง\nชุมสาย\nชุ่มใจ\nชุ่มชื่น\nชุ่มชื้น\nชุมนุมชน\nชูชีพ\nชูโรง\nชู้สาว\nเชยชม\nเชลยศักดิ์\nเชลยศึก\nเช่าซื้อ\nเช้าตรู่\nเช้ามืด\nเชิงกราน\nเชิงกล\nเชิงชั้น\nเชิงชาย\nเชิงซ้อน\nเชิงเดียว\nเชิงเดี่ยว\nเชิงตะกอน\nเชิงเทิน\nเชิงมุม\nเชิดชู\nเชิงอรรถ\nเชี่ยนหมาก\nเชี่ยวชาญ\nเชื่องช้า\nเชื่อใจ\nเชื่อถือ\nเชื่อฟัง\nเชื่อมือ\nเชื้อชาติ\nเชื้อเพลิง\nเชื้อไฟ\nเชื้อโรค\nเชื้อสาย\nเชื้อเชิญ\nเชื่องช้า\nเชือนแช\nเชื่อวัน\nแช่เย็น\nแช่อิ่ม\nแช่มช้อย\nแช่มชื่น\nโชกโชน\nโชติช่วง\nโชติรส\nใช้สอย\nซบเซา\nซมซาน\nซวนเซ\nซอกซอน\nซอกแซก\nซ่องสุม\nซ่องเสพ\nซ่องแซ่ง\nซ่อนรูป\nซ่อนเร้น\nซ่อนหา\nซ่อนกลิ่น\nซ่อนทราย\nซ่อมแซม\nซักค้าน\nซักซ้อม\nซักไซ้\nซักฟอก\nซักแห้ง\nซังกะตาย\nซังตาย\nซัดเซ\nซัดทอด\nซับซ้อน\nซับใน\nซับพระพักตร์\nซากศพ\nซ่านเซ็น\nซ้ำซ้อน\nซ้ำซาก\nซ้ำเติม\nซ้ำร้าย\nซี่โครง\nซีดเซียว\nซึมกะทือ\nซึมซาบ\nซึมเซา\nซึมทราบ\nซึมเศร้า\nซื่อตรง\nซื่อสัตย์\nซื้อขาย\nซุกซน\nซุกซ่อน\nซุบซิบ\nซู่ซ่า\nเซซัง\nเซ่อซ่า\nแซ่ซ้อง\nโซดาไฟ\nญาณทัสนะ\nญาณวิทยา\nญาณศาสตร์\nญาติกา\nฐานราก\nดกดื่น\nดงดิบ\nดลใจ\nดลบันดาล\nดวงแก้ว\nดวงใจ\nดวงเดือน\nดวงตรา\nดวงตา\nดวงสมร\nดอกจัน\nดอกจิก\nดอกบัว\nดอกเบี้ย\nดอกฟ้า\nดอกไม้\nดอกยาง\nดอกเล็บ\nดอกทอง\nดอกสร้อย\nดองยา\nดักคอ\nดักฟัง\nดังนั้น\nดังนี้\nดังหนึ่ง\nดั้งเดิม\nดัดจริต\nดัดแปลง\nดันทุรัง\nดับขันธ์\nดับจิต\nดับชีพ\nด่าทอ\nด่างทับทิม\nด่างพร้อย\nดาดฟ้า\nดาราศาสตร์\nดาลเดือด\nดาวกระจาย\nดาวเคราะห์\nดาวตก\nดาวเทียม\nดาวรุ่ง\nดาวเรือง\nดาวฤกษ์\nดาวหาง\nดาวเหนือ\nดาษดื่น\nดินขาว\nดินดาน\nดินดำ\nดินประสิว\nดินปืน\nดินระเบิด\nดินสอ\nดินสอพอง\nดิ้นรน\nดิบดี\nดีเกลือ\nดีใจ\nดีซ่าน\nดีดัก\nดีเดือด\nดีฝ่อ\nดีดดิ้น\nดึกดำบรรพ์\nดึกดื่น\nดึงดัน\nดึงดูด\nดื่มด่ำ\nดื้อด้าน\nดื้อดึง\nดื้อแพ่ง\nดื้อยา\nดื้อรั้น\nดุดัน\nดุเดือด\nดุร้าย\nดุลการค้า\nดุลพินิจ\nดุลภาค\nดุลยพินิจ\nดุลยภาพ\nดุษฎีนิพนธ์\nดุษฎีบัณฑิต\nดุษณีภาพ\nดูแคลน\nดูถูก\nดูดาย\nดูเบา\nดูแล\nดูหมิ่น\nดูเหมือน\nดูดดื่ม\nเด็ดขาด\nเด็ดดวง\nเด็ดเดี่ยว\nเดนตาย\nเดาสวด\nเดาสุ่ม\nเดินทาง\nเดินสะพัด\nเดินสาย\nเดินเหิน\nเดิมพัน\nเดียงสา\nเดียดฉันท์\nเดียวกัน\nเดียวดาย\nเดี๋ยวเดียว\nเดี๋ยวนี้\nเดือดดาล\nเดือดร้อน\nเดือนมืด\nเดือนหงาย\nแดดาล\nแดดิ้น\nแดกดัน\nโด่เด่\nโด่งดัง\nโดดเดี่ยว\nโดยสาร\nได้การ\nได้แก่\nได้ใจ\nได้ที\nได้ยิน\nได้เสีย\nตกเขียว\nตกค้าง\nตกใจ\nตกต่ำ\nตกแต่ง\nตกทอด\nตกฟาก\nตกมัน\nตกยาก\nตกลง\nตกหล่น\nต้นขั้ว\nต้นคิด\nต้นฉบับ\nต้นตอ\nต้นตำรับ\nต้นทุน\nต้นแบบ\nต้นเพลิง\nต้นมือ\nต้นไม้\nต้นร่าง\nต้นเรื่อง\nต้นสังกัด\nต้นหน\nต้นเหตุ\nตบตา\nตบแต่ง\nตบแผละ\nตบมือ\nต้มข่า\nต้มโคล้ง\nต้มยำ\nต้มส้ม\nตรมตรอม\nตรรกวิทยา\nตรรกศาสตร์\nตรวจการ\nตรวจการณ์\nตรวจตรา\nตระบัดสัตย์\nตรัสรู้\nตราตั้ง\nตราบาป\nตรายาง\nตราสาร\nตริตรอง\nตรีกฏุก\nตรีกาย\nตรีโกณ\nตรีโกณมิติ\nตรีคูณ\nตรีทูต\nตรีปิฎก\nตรีภพ\nตรีมูรติ\nตรึกตรอง\nตรึงตรา\nตรุษจีน\nตฤณชาติ\nตฤณมัย\nตลกบาตร\nตลบตะแลง\nตลบหลัง\nตลาดนัด\nตลาดน้ำ\nตลาดมืด\nตลาดสด\nต่อตี\nต่อเติม\nต่อว่า\nต่อสู้\nต่อกร\nต่อต้าน\nต่อแย้ง\nต้องการ\nต้องโทษ\nต้องหา\nต้อนรับ\nตอบโต้\nตอบแทน\nต่อยหอย\nตะพาบน้ำ\nตักตวง\nตักบาตร\nตั้งเข็ม\nตั้งไข่\nตั้งเค้า\nตั้งแง่\nตั้งใจ\nตั้งต้น\nตั้งแต่\nตั้งท้อง\nตัดขาด\nตัดใจ\nตัดเชือก\nตัดตอน\nตัดทอน\nตัดบท\nตัดพ้อ\nตัดรอน\nตัดสิน\nตับเต่า\nตับแลบ\nตับอ่อน\nตัวกลาง\nตัวการ\nตัวเก็ง\nตัวดี\nตัวตั้ง\nตัวเต็ง\nตัวถัง\nตัวแทน\nตัวประกอบ\nตัวประกัน\nตัวแปร\nตัวผู้\nตัวพิมพ์\nตัวเมีย\nตัวยืน\nตัวเลข\nตัวอย่าง\nตั๋วเงิน\nตั๋วแลกเงิน\nตากล้อง\nตาไก่\nตาข่าย\nตาชั่ง\nตาตุ่ม\nตาทวด\nตาปลา\nตาราง\nต่างหาก\nต้านทาน\nตามใจ\nตายใจ\nตายซาก\nตายด้าน\nตายตัว\nตายทั้งกลม\nตายห่า\nตายโหง\nตาลปัตร\nต่ำช้า\nต่ำต้อย\nตำส้ม\nติเตียน\nติณชาติ\nติดขัด\nติดใจ\nติดต่อ\nติดตั้ง\nติดตาม\nติดตื้น\nติดพัน\nติดลม\nติดอ่าง\nตีเกลียว\nตีขลุม\nตีความ\nตีคู่\nตีจาก\nตีตื้น\nตีแผ่\nตีรวน\nตีลังกา\nตีวง\nตีเสมอ\nตีนกา\nตีนคู้\nตีนจก\nตีนตะขาบ\nตีนผี\nตีนเหยียด\nตึกแถว\nตึกระฟ้า\nตึงเครียด\nตึงตัง\nตื้นตัน\nตื่นตัว\nตื่นตูม\nตื่นเต้น\nตุ๊ต๊ะ\nตุ้บตั้บ\nตุ้มหู\nตุลาการ\nตุลาคม\nตู้นิรภัย\nตูมตาม\nตู้เสบียง\nเตโชธาตุ\nเตร็ดเตร่\nเต้นรำ\nเตาแก๊ส\nเตาผิง\nเตาฟู่\nเตาไฟ\nเตารีด\nเตาสูบ\nเต่าทอง\nเต้ารับ\nเต้าส่วน\nเต้าเสียบ\nเต้าหู้ยี้\nแต่ละ\nแตกคอ\nแตกคอก\nแตกฉาน\nแตกดับ\nแตกตื่น\nแตกพาน\nแตกแยก\nแตกร้าว\nแตกหัก\nแต่งงาน\nแต่งตั้ง\nแต้มคู\nแต้มต่อ\nแตรงอน\nแตรเดี่ยว\nแตรฝรั่ง\nแตรฟันฟาร์\nแตรวง\nโต้ตอบ\nโต้เถียง\nโต้แย้ง\nโต๊ะหมู่\nโต๊ะอิหม่าม\nใต้ถุน\nไต้ก๋ง\nไต่คู้\nไต่เต้า\nไต่ถาม\nไต้ฝุ่น\nไตรจักร\nไตรจีวร\nไตรตรึงษ์\nไตรทวาร\nไตรปิฎก\nไตรเพท\nไตรภพ\nไตรภูมิ\nไตรภาคี\nไตรยางศ์\nไตรรงค์\nไตรรัตน์\nไตรลักษณ์\nไตรโลก\nไตรสรณคมน์\nไตรสิกขา\nไต่สวน\nถกเถียง\nถดถอย\nถนัดถนี่\nถนิมสร้อย\nถมถืด\nถมเถ\nถมไป\nถลากไถล\nถ้วนถี่\nถ้วยฟู\nถ่องแท้\nถอดถอน\nถ้อยคำ\nถ้อยแถลง\nถากถาง\nถ่านไฟฉาย\nถ่านหิน\nถามไถ่\nถ่ายทอด\nถ่ายทุกข์\nถ่ายเท\nถาวรวัตถุ\nถ้ำมอง\nถี่ถ้วน\nถึงใจ\nถูกใจ\nถูกชะตา\nเถรวาท\nเถ้าแก่\nเถ้าแก่เนี้ย\nแถมพก\nแถลงการณ์\nไถ่ถอน\nไถ่ถาม\nทดแทน\nทดรอง\nทดลอง\nทดสอบ\nทนทาน\nทนายความ\nทบทวน\nทแยงมุม\nทรงกลด\nทรงเครื่อง\nทรงเจ้า\nทรัพย์สิน\nทรามชม\nทรามเชย\nทรามวัย\nทรามสงวน\nทรามสวาท\nทรุดโทรม\nทฤษฎีบท\nท้วงติง\nท่วมท้น\nทวาทศ\nทวาทศมาส\nทวาบรยุค\nทวารบาล\nทวิบถ\nทวิบท\nทวิบาท\nทวิภาค\nทวิภาคี\nทวีคูณ\nทศกัณฐ์\nทศชาติ\nทศทิศ\nทศนิยม\nทศพร\nทศพล\nทศพิธราชธรรม\nทศมาส\nทศวรรษ\nท่อไอเสีย\nท้อถอย\nท้อแท้\nทองขาว\nทองคำ\nทองคำขาว\nทองคำเปลว\nทองเค\nทองแดง\nทองบรอนซ์\nทองม้วน\nทองย้อย\nทองสัมฤทธิ์\nทองหยอด\nทองหยิบ\nทองเหลือง\nทองเอก\nท่องเที่ยว\nท้องตรา\nท้องถิ่น\nท้องที่\nท้องน้อย\nท้องร่อง\nท้องเรื่อง\nทอดมัน\nทอดทิ้ง\nทอดน่อง\nทอดยอด\nทอดหุ่ย\nทอยกอง\nทะเบียนบ้าน\nทะเลทราย\nทะเลสาบ\nทะเลหลวง\nทักขิณาวัฏ\nทักท้วง\nทักทาย\nทักษิณาวรรต\nทักษิณาทาน\nทักษิณานุประทาน\nทั้งกลม\nทั้งคน\nทั้งดุ้น\nทั้งที\nทั้งนั้น\nทั้งนี้\nทั้งปวง\nทั้งผอง\nทั้งเพ\nทั้งมวล\nทั้งสิ้น\nทั้งหมด\nทั้งหลาย\nทัณฑ์บน\nทัดทาน\nทัดเทียม\nทันควัน\nทันใจ\nทันใด\nทันตา\nทันสมัย\nทันที\nทับถม\nทับทรวง\nทับศัพท์\nทั่วถึง\nทั่วไป\nท่าทาง\nท่าที\nท้าทาย\nทางการ\nทางข้าม\nทางด่วน\nทางเท้า\nทางโท\nทางใน\nทางผ่าน\nทางม้าลาย\nทางหลวง\nทางออก\nทางเอก\nทานกัณฑ์\nทานตะวัน\nท่านชาย\nทานบารมี\nท่านผู้หญิง\nท่านหญิง\nทาบทาม\nท้ายทอย\nทารุณกรรม\nทำคลอด\nทำใจ\nทำซ้ำ\nทำท่า\nทำที\nทำแท้ง\nทำโทษ\nทำบาป\nทำบุญ\nทำพิษ\nทำฟัน\nทำร้าย\nทำวัตร\nทำสาว\nทำเสน่ห์\nทำหมัน\nทำให้\nทิ้งขว้าง\nทิ้งทวน\nทิ้งท้าย\nทินกร\nทิพจักขุ\nทิพโสต\nทิพยจักษุ\nทิพยญาณ\nทิพยเนตร\nทิพยรส\nทิพากร\nทิ่มตำ\nทิ่มแทง\nทิวากร\nทิวากาล\nทิศทาง\nทีเด็ด\nทีท่า\nทีนี้\nทีหลัง\nทีฆนิกาย\nทีฆสระ\nที่ดิน\nที่นอน\nที่นั่ง\nที่ปรึกษา\nที่พึ่ง\nที่มั่น\nที่ราบ\nที่ว่าการ\nที่สุด\nที่หมาย\nที่ไหน\nทุกที\nทุกเมื่อ\nทุกข์สุข\nทุนทรัพย์\nทุนนิยม\nทุนรอน\nทุนสำรอง\nทุ่มเถียง\nทุ่มเท\nทูนหัว\nทูลกระหม่อม\nเทกระจาด\nเทครัว\nเทพเจ้า\nเทพดา\nเทพธิดา\nเทพนม\nเทพนิยม\nเทพนิยาย\nเทพบุตร\nเทพสังหรณ์\nเทศกาล\nเทศนาโวหาร\nเทศบัญญัติ\nเทศบาล\nเทศมนตรี\nเทห์ฟากฟ้า\nเท่ากับ\nเท่าใด\nเท่าตัว\nเท่าทัน\nเท่าทุน\nเท่าเทียม\nเท่านั้น\nเท่าไร\nเท้าช้าง\nเทิดทูน\nเที่ยงตรง\nเที่ยงแท้\nเที่ยงธรรม\nเทียนชนวน\nเทียนพรรษา\nเทียบเคียง\nเทียบเท่า\nเทือกเขา\nเทือกเถา\nแท็งก์น้ำ\nแท่นพิมพ์\nแท่นมณฑล\nแท่นหมึก\nแทรกซอน\nแทรกซ้อน\nแทรกซึม\nแทรกแซง\nแทะโลม\nไทยดำ\nไทยทาน\nไทยธรรม\nไทยน้อย\nไทยใหญ่\nธงชัย\nธงชาติ\nธงทิว\nธรณีวิทยา\nธรณีสงฆ์\nธรรมกาย\nธรรมการ\nธรรมเกษตร\nธรรมขันธ์\nธรรมคุณ\nธรรมจรรยา\nธรรมจริยา\nธรรมจักร\nธรรมจักษุ\nธรรมจาคะ\nธรรมจารี\nธรรมชาติ\nธรรมดา\nธรรมเนียม\nธรรมราชา\nธรรมศาสตร์\nธรรมสภา\nธรรมสังเวช\nธัญพืช\nธารพระกร\nธีรภาพ\nธีรราช\nนกเขา\nนกต่อ\nนกยูง\nนกรู้\nนกหวีด\nนครบาล\nนครรัฐ\nนงคราญ\nนงนุช\nนงพะงา\nนงเยาว์\nนงราม\nนงลักษณ์\nนบนอบ\nนพเก้า\nนพคุณ\nนพเคราะห์\nนพปฎล\nนพพล\nนพรัตน์\nนพศก\nนพศูล\nนมข้น\nนมผง\nนมไม้\nนมนาน\nนมหนู\nนมแมว\nนรีเวช\nนรีเวชวิทยา\nนวดฟั้น\nนวยนาด\nนวลระหง\nนวลลออ\nนวลละออง\nนวลจันทร์\nนอกครู\nนอกคอก\nนอกจาก\nนอกใจ\nนอกชาน\nนอกรีต\nนอกเหนือ\nนองเนือง\nนองเลือด\nนอนก้น\nนอนใจ\nนอบนบ\nนอนเล่น\nนอบน้อม\nน้อมนำ\nน้อยใจ\nน้อยหน้า\nนักการ\nนักการเมือง\nนักกีฬา\nนักข่าว\nนักท่องเที่ยว\nนักเทศน์\nนักโทษ\nนักธรรม\nนักบวช\nนักบิน\nนักบุญ\nนักปราชญ์\nนักพรต\nนักรบ\nนักเรียน\nนักเลง\nนักวิชาการ\nนักศึกษา\nนักสิทธิ์\nนักสืบ\nนักหนา\nนั่งเทียน\nนั่งร้าน\nนัดแนะ\nนัดหมาย\nนั่นแหละ\nนั่นเอง\nนับถือ\nนับประสา\nนัยน์ตา\nนาดำ\nนาปรัง\nนาปี\nนาสวน\nนาหว่าน\nนาคบาศ\nนาคปรก\nนาคราช\nนางกวัก\nนางกำนัล\nนางงาม\nนางใน\nนางบำเรอ\nนางแบบ\nนางพญา\nนางฟ้า\nนางไม้\nนางโลม\nนางสาว\nนางห้าม\nนางเอก\nนาฏกรรม\nนาฏดนตรี\nนาฏศิลป์\nนานนม\nน่านน้ำ\nน่านฟ้า\nนามกร\nนามธรรม\nนามไธย\nนามบัตร\nนามปากกา\nนามแฝง\nนามสกุล\nนามสงเคราะห์\nนามสมญา\nนายทะเบียน\nนายท่า\nนายท้าย\nนายทุน\nนายประกัน\nนายหน้า\nนายอำเภอ\nนารายณ์หัตถ์\nนารีผล\nนาวิกโยธิน\nนำจับ\nนำพา\nนำทาง\nนำร่อง\nนำสืบ\nนำแสดง\nน้ำกรด\nน้ำกาม\nน้ำเกลือ\nน้ำข้าว\nน้ำแข็ง\nน้ำแข็งไส\nน้ำแข็งแห้ง\nน้ำครำ\nน้ำคร่ำ\nน้ำค้าง\nน้ำค้างแข็ง\nน้ำคาวปลา\nน้ำคำ\nน้ำเค็ม\nน้ำเคย\nน้ำเงิน\nน้ำเงี้ยว\nน้ำจัณฑ์\nน้ำจิ้ม\nน้ำใจ\nน้ำเชื้อ\nน้ำเชื่อม\nน้ำซาวข้าว\nน้ำดอกไม้\nน้ำดี\nน้ำตก\nน้ำตา\nน้ำตาล\nน้ำท่า\nน้ำนม\nน้ำนวล\nน้ำบาดาล\nน้ำประสานทอง\nน้ำประปา\nน้ำปลา\nน้ำป่า\nน้ำผึ้ง\nน้ำพริก\nน้ำพริกเผา\nน้ำพี้\nน้ำพุ\nน้ำมนต์\nน้ำมนตร์\nน้ำมัน\nน้ำมือ\nน้ำมูก\nน้ำเมา\nน้ำย่อย\nน้ำยา\nน้ำรัก\nน้ำแร่\nน้ำลาย\nน้ำเลี้ยง\nน้ำสต๊อก\nน้ำส้ม\nน้ำส้มสายชู\nน้ำสังข์\nน้ำสาบาน\nน้ำเสียง\nน้ำหนวก\nน้ำหนอง\nน้ำหนัก\nน้ำหน้า\nน้ำหนึ่ง\nน้ำหมึก\nน้ำหอม\nน้ำเหลือง\nน้ำอบ\nน้ำอ้อย\nน้ำอัดลม\nนิ่งเฉย\nนิจศีล\nนิดเดียว\nนิดหน่อย\nนิติกร\nนิติกรรม\nนิติธรรม\nนิตินัย\nนิติบัญญัติ\nนิติบุคคล\nนิติภาวะ\nนิติวิทยาศาสตร์\nนิติเวช\nนิติเวชศาสตร์\nนิติศาสตร์\nนิเทศศาสตร์\nนิ่มนวล\nนิรุกติศาสตร์\nนิเวศวิทยา\nนิศากร\nนิศากาล\nนิศาชล\nนิศารัตน์\nนี่แน่ะ\nนี่แหละ\nนี่เอง\nนึกคิด\nนุงถุง\nนุ่งห่ม\nนุ่มนวล\nนุ่มนิ่ม\nเนตรนารี\nเนติบัณฑิต\nเนยเทียม\nเนยใส\nเนิ่นนาน\nเนิบนาบ\nเนื้อความ\nเนื้อคู่\nเนื้อเค็ม\nเนื้องอก\nเนื้อตัว\nเนื้อตาย\nเนื้อที่\nเนื้อแท้\nเนื้อเปื่อย\nเนื้อผ้า\nเนื้อเพลง\nเนื้อไม้\nเนื้อเยื่อ\nเนื้อร้อง\nเนื้อร้าย\nเนื้อเรื่อง\nเนื้อหา\nเนืองนอง\nเนืองนิตย์\nเนืองแน่น\nแน่ใจ\nแน่ชัด\nแน่แท้\nแน่นอน\nแน่นิ่ง\nแน่แน่ว\nแน่นแฟ้น\nแน่นหนา\nแนบเนียน\nแนบแน่น\nแนวคิด\nแนวทาง\nแนวโน้ม\nแนวป่า\nแนวรบ\nแนวร่วม\nแนวหน้า\nแนวหลัง\nแน่วแน่\nแนะนำ\nแนะแนว\nโน้มน้าว\nในหลวง\nบกพร่อง\nบงกช\nบงการ\nบดบัง\nบทกลอน\nบทกวี\nบทความ\nบทคัดย่อ\nบทเฉพาะกาล\nบทนำ\nบทบัญญัติ\nบทบาท\nบทประพันธ์\nบทเพลง\nบทร้อง\nบทเรียน\nบทลงโทษ\nบทสนทนา\nบทอัศจรรย์\nบทจร\nบทบงสุ์\nบทมาลย์\nบทรัช\nบทเรศ\nบทวลัญช์\nบนบาน\nบรมครู\nบรมธาตุ\nบรมบพิตร\nบรมวงศานุวงศ์\nบรมอัฐิ\nบรรณพิภพ\nบรรณศาลา\nบรรณาการ\nบรรณาธิการ\nบรรณานุกรม\nบรรณารักษ์\nบรรณารักษศาสตร์\nบรรดามี\nบรรดาศักดิ์\nบรรทัดฐาน\nบรรพบุรุษ\nบรรลัยกัลป์\nบรรลัยจักร\nบริคณห์สนธิ\nบวงสรวง\nบ่วงบาศ\nบ้วนพระโอษฐ์\nบ่อเกิด\nบอกกล่าว\nบอกบท\nบอกบุญ\nบอกใบ้\nบอกปัด\nบ้องกัญชา\nบ้องตื้น\nบ้องไฟ\nบ้องหู\nบอดสี\nบ่อนทำลาย\nบอบช้ำ\nบอบบาง\nบอบแบบ\nบังโกลน\nบังโคลน\nบังใบ\nบั้งไฟ\nบังคับการ\nบังคับบัญชา\nบัญชาการ\nบัณฑุกัมพล\nบัดดล\nบัดเดี๋ยว\nบัดนั้น\nบัดนี้\nบัดสีบัดเถลิง\nบัตรเครดิต\nบัตรพลี\nบัตรสนเท่ห์\nบัตรสินเชื่อ\nบั่นทอน\nบั้นท้าย\nบั้นปลาย\nบั้นพระองค์\nบั้นเอว\nบันไดลิง\nบันไดเลื่อน\nบันเทิงคดี\nบัวลอย\nบัวบก\nบ้าจี้\nบ้าดีเดือด\nบ้าน้ำลาย\nบ้าบิ่น\nบ้าระห่ำ\nบ้าเลือด\nบ้าหอบฟาง\nบากบั่น\nบากหน้า\nบางตา\nบางเบา\nบางที\nบาดเจ็บ\nบาดแผล\nบาดหมาง\nบาตรใหญ่\nบาทบงกช\nบาทบงสุ์\nบาทบริจาริกา\nบาทวิถี\nบานเกล็ด\nบานตะเกียง\nบานตะไท\nบานเบอะ\nบานปลาย\nบานแผละ\nบานพับ\nบ้านจัดสรร\nบ้านช่อง\nบ้านนอก\nบ้านพัก\nบ้านเมือง\nบ้านรับรอง\nบ้านเรือน\nบาปกรรม\nบายศรี\nบ่ายเบี่ยง\nบ่ายหน้า\nบ่าวไพร่\nบิดเบี้ยว\nบิดเบือน\nบิดพลิ้ว\nบี้แบน\nบีบคั้น\nบีบรัด\nบึ้งตึง\nบึ้งบูด\nบุกบั่น\nบุกเบิก\nบุกรุก\nบุคลิกภาพ\nบุคลิกลักษณะ\nบุญธรรม\nบุญนิธิ\nบุญฤทธิ์\nบุบสลาย\nบุ้ยใบ้\nบุรุษเพศ\nบุหงารำไป\nบู้บี้\nบูชายัญ\nบูดบึ้ง\nบูดเบี้ยว\nเบาความ\nเบาใจ\nเบาบาง\nเบาปัญญา\nเบามือ\nเบาแรง\nเบาสมอง\nเบาหวาน\nเบาโหวง\nเบ้าตา\nเบาะแส\nเบิกความ\nเบิกบาน\nเบี้ยล่าง\nเบี้ยเลี้ยง\nเบี้ยหวัด\nเบี่ยงบ่าย\nเบียดบัง\nเบียดเบียน\nเบียดเสียด\nเบื้องต้น\nเบื้องบน\nเบื้องหน้า\nเบื้องหลัง\nแบกะดิน\nแบเบาะ\nแบ่งเบา\nแบ่งปัน\nแบ่งแยก\nแบบฉบับ\nแบบแปลน\nแบบแผน\nแบบฝึกหัด\nแบบพิมพ์\nแบบสอบถาม\nแบบอย่าง\nแบะแฉะ\nแบะท่า\nโบแดง\nโบราณคดี\nโบราณวัตถุ\nโบราณสถาน\nใบขับขี่\nใบจอง\nใบตอง\nใบแทรก\nใบบอก\nใบบุญ\nใบเบิกทาง\nใบปลิว\nใบพัด\nใบโพ\nใบไม้\nใบระกา\nใบรับรอง\nใบลา\nใบเลี้ยง\nใบสั่ง\nใบสำคัญ\nใบสุทธิ\nใบเสร็จ\nใบหน้า\nใบอนุญาต\nใบระกา\nปกครอง\nปกคลุม\nปกป้อง\nปกปิด\nปฏิบัติการ\nปฏิบัติบูชา\nปฐพีวิทยา\nปฐมฌาน\nปฐมทัศน์\nปฐมเทศนา\nปฐมนิเทศ\nปฐมพยาบาล\nปฐมยาม\nปฐมฤกษ์\nปฐมวัย\nปฐมสมโพธิ\nปนเป\nป่นปี้\nปมเขื่อง\nปมเด่น\nปมด้อย\nปรนเปรอ\nปรบไก่\nปรบมือ\nปรสิตวิทยา\nประโปรย\nประพรม\nประกันชีวิต\nประกันภัย\nประจักษ์พยาน\nประจัญบาน\nประจันหน้า\nประจำการ\nประจำเดือน\nประจำเมือง\nประจำยาม\nประชดประชัน\nประชากร\nประชากรศาสตร์\nประชาคม\nประชาชน\nประชาราษฎร์\nประชาชาติ\nประชาชี\nประชาทัณฑ์\nประชาบาล\nประชาพิจารณ์\nประชาภิบาล\nประชามติ\nประชาสงเคราะห์\nประชาสัมพันธ์\nประดับประดา\nประดามี\nประดาน้ำ\nประเดี๋ยวเดียว\nประเดี๋ยวนี้\nประทับใจ\nประทุษร้าย\nประเทศราช\nประพาสต้น\nประเพณีนิยม\nประลัยกัลป์\nประวัติการณ์\nประวัติศาสตร์\nประสบการณ์\nประสบการณ์นิยม\nประสาทการ\nประสูติการ\nประสูติกาล\nประเส\nปรับทุกข์\nปรับโทษ\nปรับปรุง\nปรากฏการณ์\nปราดเปรียว\nปราดเปรื่อง\nปราบปราม\nปริญญาบัตร\nปรัยัติธรรม\nปรุโปร่ง\nปลงใจ\nปลงตก\nปลดทุกข์\nปลดปลง\nปลดปล่อย\nปลดเปลื้อง\nปลดระวาง\nปลดแอก\nปล้นสะดม\nปลอกกระสุน\nปลอกคอ\nปลอดโปร่ง\nปลอดภัย\nปลอมปน\nปลอมแปลง\nปลอบโยน\nปล่อยใจ\nปล่อยตัว\nปล่อยปละ\nปลั๊กไฟ\nปลากริม\nปลาเค็ม\nปลาจ่อม\nปลาเจ่า\nปลาแดก\nปลาตู้\nปลาทอง\nปลาร้า\nปลาส้ม\nปลาดาว\nปลาบิน\nปลาฝา\nปลาวาฬ\nปลาหมึก\nปลาบปลื้ม\nปลายข้าว\nปลายแถว\nปลายทาง\nปลิ้นปลอก\nปลิ้นปล้อน\nปลีกตัว\nปลีกย่อย\nปลุกใจ\nปลุกปล้ำ\nปลุกปั่น\nปลุกระดม\nปลุกเสก\nปลูกฝัง\nปลูกสร้าง\nปวดถ่วง\nปวดมวน\nปวดร้าว\nป่วนปั่น\nป่วยการ\nปอกลอก\nป้องกัน\nปักใจ\nปักดำ\nปักหลัก\nปัจเจกบุคคล\nปัจเจกพุทธะ\nปัจเจกโพธิ\nปัจฉิมชน\nปัจฉิมทิศ\nปัจฉิมภาค\nปัจฉิมยาม\nปัจฉิมลิขิต\nปัจฉิมวัย\nปัจฉิมวาจา\nปัญญาชน\nปัญญาวิมุติ\nปัญญาอ่อน\nปัดเป่า\nปันส่วน\nปั่นป่วน\nปั่นแปะ\nปั่นหัว\nปั้นจิ้ม\nปั้นเจ๋อ\nปั้นปึ่ง\nปั้นสิบ\nปั๊มน้ำมัน\nป่าช้า\nป่าชายเลน\nป่าดง\nป่าดงดิบ\nป่าดิบ\nป่าเถื่อน\nป่าเบญจพรรณ\nป่าละเมาะ\nปากกา\nปากขอ\nปากแข็ง\nปากคอ\nปากคำ\nปากคีบ\nปากจัด\nปากน้ำ\nปากเปล่า\nปากเสียง\nปานกลาง\nป่านนี้\nป้านลม\nป้ายสี\nป่าวร้อง\nปิดฉาก\nปิดบัง\nปิตุฆาต\nปิตุภูมิ\nปีมะโว้\nปีแสง\nปี่กลาง\nปี่ไฉน\nปี่ชวา\nปี่นอก\nปี่ใน\nปี่พาทย์\nปี่อ้อ\nปีกกา\nปีนเกลียว\nปีนป่าย\nปึกแผ่น\nปึงปัง\nปืนกล\nปืนครก\nปืนพก\nปืนยา\nปืนยาว\nปืนลม\nปืนเล็ก\nปืนเล็กยาว\nปืนสั้น\nปืนใหญ่\nปุบปับ\nปุ๊บปั๊บ\nปุ่มเปือก\nปุยฝ้าย\nปุ๋ยคอก\nปุ๋ยเคมี\nปุ๋ยวิทยาศาสตร์\nปุ๋ยหมัก\nปุ๋ยอินทรีย์\nปูจ๋า\nปูเสฉวน\nปู่เจ้า\nปู่ทวด\nปูนขาว\nปูนซีเมนต์\nปูนดิบ\nปูนแดง\nปูนปลาสเตอร์\nปูนปั้น\nเป็ดเทศ\nเป็ดน้ำ\nเป็นกลาง\nเป็นใจ\nเป็นต้น\nเป็นต่อ\nเป็นรอง\nเป็นไร\nเป็นลม\nเป็นห่วง\nเป็นอยู่\nเปรมปรีดิ์\nเปรอะเปื้อน\nเปรียบเทียบ\nเปรียบเปรย\nเปรี้ยวปาก\nเปรี้ยวหวาน\nเปรื่องปราด\nเปลญวน\nเปล่งปลั่ง\nเปล่าดาย\nเปล่าเปลี่ยว\nเปลี่ยนใจ\nเปลี่ยนตัว\nเปลี่ยนแปลง\nเปลี่ยนมือ\nเปลี่ยนหน้า\nเป๋อเหลอ\nเปะปะ\nเป่ากบ\nเป้านิ่ง\nเป้าหมาย\nเปิดฉาก\nเปิดเปิง\nเปิดโปง\nเปิดผนึก\nเปิดเผย\nเปียกปูน\nแป้งสาลี\nแป้งนวล\nแป้งเปียก\nแป้งมัน\nแป้งฝุ่น\nแป้งร่ำ\nแป้งสิงคโปร์\nแป้งหมี่\nแปดปน\nแปดเปื้อน\nแปรปรวน\nแปรผัน\nแปรพักตร์\nแปรรูป\nแปรอักษร\nแปลกปลอม\nแปะโป้ง\nโป้ปด\nโปร่งแสง\nโปร่งใส\nโปรดปราน\nโปรยทาน\nโปรยปราย\nโปโลน้ำ\nผกผัน\nผกากรอง\nผงขาว\nผงชูรส\nผงซักฟอก\nผงฟู\nผดุงครรภ์\nผมไฟ\nผลพลอยได้\nผลลัพธ์\nผลัดเปลี่ยน\nผลิตผล\nผลิตภัณฑ์\nผลุบโผล่\nผสมเทียม\nผสมผสาน\nผสมผเส\nผสมพันธุ์\nผสมโรง\nผสมเสร็จ\nผ่องแผ้ว\nผ่องใส\nผ่อนคลาย\nผ่อนชำระ\nผ่อนปรน\nผ่อนผัน\nผ่อนส่ง\nผอมโซ\nผอมแห้ง\nผักชี\nผักตบชวา\nผักบุ้ง\nผังเมือง\nผัดผ่อน\nผันแปร\nผันผวน\nผ่าตัด\nผ่าเผย\nผ่าหมาก\nผ่าเหล่า\nผ้าขนหนู\nผ้าขาวม้า\nผ้าขี้ริ้ว\nผ้าเช็ดตัว\nผ้าเช็ดปาก\nผ้าเช็ดมือ\nผ้าเช็ดหน้า\nผ้าดิบ\nผ้าต่วน\nผ้าไตร\nผ้าถุง\nผ้าแถบ\nผ้านวม\nผ้านุ่ง\nผ้าใบ\nผ้าป่า\nผ้าป่าน\nผ้าผ่อน\nผ้าพันคอ\nผ้าพันแผล\nผ้าแพร\nผ้าโพกหัว\nผ้ามัดหมี่\nผ้ายาง\nผ้าลูกไม้\nผ้าเหลือง\nผ้าอนามัย\nผ้าอ้อม\nผาดโผน\nผาติกรรม\nผิดหวัง\nผิวเผิน\nผิวพรรณ\nผิวหนัง\nผีกระสือ\nผีกระหัง\nผีกองกอย\nผีโขมด\nผีดิบ\nผีตองเหลือง\nผีถ้วยแก้ว\nผีแถน\nผีทะเล\nผีบุญ\nผีปอบ\nผีพุ่งไต้\nผีฟ้า\nผีเรือน\nผีสาง\nผีเสื้อ\nผีห่า\nผึ่งผาย\nผุดผ่อง\nผุดผาด\nผู้คน\nผู้คุม\nผู้จัดการ\nผู้ชาย\nผู้เชี่ยวชาญ\nผู้ดี\nผู้โดยสาร\nผู้ต้องขัง\nผู้ต้องหา\nผู้แทน\nผู้น้อย\nผู้บริโภค\nผู้บังคับบัญชา\nผู้ปกครอง\nผู้ประกอบการ\nผู้ป่วย\nผู้พิพากษา\nผู้เยาว์\nผู้ร้าย\nผู้วิเศษ\nผู้สื่อข่าว\nผู้เสียหาย\nผู้หญิง\nผู้ใหญ่\nผู้ใหญ่บ้าน\nผูกขวัญ\nผูกขาด\nผูกพัน\nผูกมัด\nเผชิญหน้า\nเผด็จการ\nเผด็จศึก\nเผยแผ่\nเผยแพร่\nเผละผละ\nเผ่าพันธุ์\nเผื่อแผ่\nแผงลอย\nแผนการ\nแผนงาน\nแผนที่\nแผนผัง\nแผนภาพ\nแผนภูมิ\nแผ่นดิน\nแผ่นเสียง\nแผ้วพาน\nโผงผาง\nฝนทอง\nฝอยทอง\nฝักแค\nฝักบัว\nฝักฝ่าย\nฝักใฝ่\nฝังใจ\nฝังหัว\nฝาชี\nฝาแฝด\nฝาละมี\nฝ่าพระบาท\nฝ่าฝืน\nฝ่าฟัน\nฝ้าฟาง\nฝากตัว\nฝากฝัง\nฝีดาษ\nฝีมะม่วง\nฝีจักร\nฝีเท้า\nฝีปาก\nฝีพาย\nฝีมือ\nฝีเย็บ\nฝึกงาน\nฝึกปรือ\nฝึกฝน\nฝึกสอน\nฝึกหัด\nฝืดเคือง\nใฝ่ฝัน\nพงพี\nพงศ์พันธุ์\nพญาโศก\nพญาไฟ\nพบปะ\nพบพาน\nพรสวรรค์\nพรมคด\nพรมแดน\nพรมมิ\nพรรคพวก\nพรรณราย\nพรวดพราด\nพรหมชาติ\nพรหมลิขิต\nพรหมโลก\nพรหมวิหาร\nพร้อมใจ\nพร้อมพรั่ง\nพร้อมเพรียง\nพร้อมมูล\nพร้อมสรรพ\nพร้อมหน้า\nพระครู\nพระคุณ\nพระเคราะห์\nพระเครื่อง\nพระเจ้า\nพระเจ้าอยู่หัว\nพระชายา\nพระทัย\nพระนาง\nพระนางเจ้า\nพระเป็นเจ้า\nพระผู้เป็นเจ้า\nพระพิมพ์\nพระพุทธเจ้า\nพระพุทธองค์\nพระภูมิ\nพระยา\nพระรอง\nพระสนม\nพระสนมเอก\nพระองค์\nพระองค์เจ้า\nพระเอก\nพรั่งพร้อม\nพรั่งพรู\nพรั่นพรึง\nพร่างพราว\nพรายน้ำ\nพรายแพรว\nพราวแพรว\nพร่ำพลอด\nพร่ำเพรื่อ\nพร่ำเพ้อ\nพริกไทย\nพริ้งพราย\nพริ้งเพรา\nพริ้งเพริศ\nพริบตา\nพริ้มพราย\nพริ้มเพรา\nพรุ่งนี้\nพฤติกรรม\nพฤติการณ์\nพฤตินัย\nพลการ\nพลขับ\nพลความ\nพลเมือง\nพลรบ\nพลร่ม\nพลเรือน\nพลโลก\nพลศึกษา\nพลบค่ำ\nพลอดรัก\nพลังงาน\nพลังเงียบ\nพลังจิต\nพลั้งปาก\nพลั้งเผลอ\nพลั้งพลาด\nพลัดถิ่น\nพลัดพราก\nพลาดท่า\nพลาดพลั้ง\nพลิกแพลง\nพลีกรรม\nพลุ่งพล่าน\nพวกพ้อง\nพวงมาลัย\nพวงมาลา\nพวงหรีด\nพวงคราม\nพวงชมพู\nพวงแสด\nพ่วงพี\nพวยน้ำ\nพวยพุ่ง\nพสกนิกร\nพหุคูณ\nพหุภาคี\nพหูพจน์\nพหูสูต\nพอควร\nพอใจ\nพอใช้\nพอใช้ได้\nพอดี\nพอตัว\nพอทำเนา\nพอประมาณ\nพอเพียง\nพอแรง\nพอสมควร\nพอเหมาะ\nพ่อขุน\nพ่อครัว\nพ่อตา\nพ่อบ้าน\nพ่อพันธุ์\nพ่อม่าย\nพ่อเมือง\nพ่อเลี้ยง\nพ่อสื่อ\nพอกพูน\nพ้องพาน\nพักผ่อน\nพักพิง\nพักฟื้น\nพักร้อน\nพักแรม\nพัดยศ\nพัดลม\nพันพัว\nพับฐาน\nพับเพียบ\nพัวพัน\nพาซื่อ\nพาดพิง\nพิณพาทย์\nพิธีกร\nพิธีกรรม\nพิธีการ\nพิธีรีตอง\nพิธีสาร\nพินัยกรรม\nพิมพ์เขียว\nพิมพ์ใจ\nพิมพ์ดีด\nพิษสง\nพี่น้อง\nพี่เบิ้ม\nพี่เลี้ยง\nพึงใจ\nพึงพอใจ\nพึ่งพา\nพึ่งพิง\nพืชพันธุ์\nพืชมงคล\nพื้นฐาน\nพื้นที่\nพื้นบ้าน\nพื้นเพ\nพื้นเมือง\nพื้นเสีย\nพุพอง\nพุทธกาล\nพุทธคุณ\nพุทธจักร\nพุทธเจดีย์\nพุทธฎีกา\nพุทธปฏิมา\nพุทธปฏิมากร\nพุทธมามกะ\nพุทธศักราช\nพุทธศาสนิกชน\nพุทธองค์\nพุทธชาด\nพุทธรักษา\nพุ่มพวง\nพุ่มไม้\nพู่กัน\nพูดจา\nเพ่งเล็ง\nเพดานบิน\nเพดานปาก\nเพริศพราย\nเพริศพริ้ง\nเพริศแพร้ว\nเพรียกพร้อง\nเพรียวลม\nเพลงเชิด\nเพลงยาว\nเพลิงกัลป์\nเพลินใจ\nเพลินตา\nเพลี่ยงพล้ำ\nเพ้อฝัน\nเพาะกาย\nเพาะชำ\nเพาะปลูก\nเพิกถอน\nเพิกเฉย\nเพิ่มเติม\nเพิ่มพูน\nเพียงตา\nเพียงพอ\nเพียบแประ\nเพียบพร้อม\nเพื่อนเกลอ\nเพื่อนตาย\nเพื่อนบ้าน\nเพื่อนฝูง\nเพื่อนยาก\nแพ้ท้อง\nแพร่หลาย\nแพร่งพราย\nแพรวพราว\nโพธิญาณ\nโพธิบัลลังก์\nโพธิสมภาร\nโพธิสัตว์\nโพ้นทะเล\nโพยภัย\nไพ่ตาย\nไพ่ป๊อก\nไพรวัน\nไพรสณฑ์\nไพรสัณฑ์\nไพร่พล\nไพร่ฟ้า\nไพร่สม\nไพร่ส่วย\nไพร่หลวง\nฟกช้ำ\nฟองเต้าหู้\nฟองน้ำ\nฟองมัน\nฟ้องกลับ\nฟ้องร้อง\nฟอนเฟะ\nฟักทอง\nฟัดเฟียด\nฟันดาบ\nฟันฝ่า\nฟันแท้\nฟันน้ำนม\nฟันปลา\nฟันฟาง\nฟันเฟือง\nฟันม้า\nฟันเลื่อย\nฟันหนู\nฟั่นเฝือ\nฟั่นเฟือน\nฟื้นตัว\nฟื้นฝอย\nฟื้นฟู\nฟุ้งซ่าน\nฟุ้งเฟ้อ\nฟุ้งเฟื่อง\nฟุตบอล\nฟูฟ่อง\nฟูเฟื่อง\nฟูมฟัก\nฟูมฟาย\nเฟะฟะ\nเฟื่องฟ้า\nเฟื่องฟุ้ง\nเฟื่องฟู\nไฟฉาย\nไฟแช็ก\nไฟธาตุ\nไฟฟ้า\nภัตกิจ\nภาคทัณฑ์\nภาคพื้น\nภาคเรียน\nภาคภูมิ\nภาพถ่าย\nภาพนิ่ง\nภาพประกอบ\nภาพพจน์\nภาพยนตร์\nภาพลวงตา\nภาพลักษณ์\nภายนอก\nภายใน\nภายหน้า\nภายหลัง\nภารกิจ\nภารธุระ\nภารโรง\nภารตวิทยา\nภาษาศาสตร์\nภาสกร\nภิญโญภาพ\nภินชาติ\nภูธร\nภูธเรศ\nภูบาล\nภูเบศ\nภูเบศวร์\nภูเขา\nภูเขาไฟ\nภูผา\nภูตคาม\nภูตบดี\nภูตรูป\nภูเตศวร\nภูมินทร์\nภูมิบาล\nภูมิประเทศ\nภูมิภาค\nภูมิรัฐศาสตร์\nภูมิลำเนา\nภูมิศาสตร์\nภูมิอากาศ\nภูมิธรรม\nภูมิปัญญา\nภูมิรู้\nภูมิใจ\nภูมิฐาน\nภูมิคุ้มกัน\nภูมิแพ้\nภูษาโยง\nเภทภัย\nเภสัชกร\nเภสัชกรรม\nเภสัชวิทยา\nเภสัชศาสตร์\nโภคทรัพย์\nโภคภัณฑ์\nโภชนากร\nโภชนาการ\nมกุฎราชกุมาร\nมงคลแฝด\nมงคลสูตร\nมงคลหัตถี\nมณเฑียรบาล\nมดดำ\nมดแดง\nมดเท็จ\nมดยอบ\nมดลูก\nมธุปายาส\nมธุรส\nมนเทียรบาล\nมนุษย์กบ\nมโนกรรม\nมโนคติ\nมโนทุจริต\nมโนธรรม\nมโนภาพ\nมโนมัย\nมโนรถ\nมโนรมย์\nมโนสุจริต\nมรรคนายก\nมรรคผล\nมฤคชาติ\nมฤคทายวัน\nมฤคราช\nมลทิน\nมลพิษ\nมลสาร\nมวกเหล็ก\nม้วนหน้า\nมวยไทย\nมวยปล้ำ\nมวยล้ม\nมวยวัด\nมวยสากล\nมวยหมู่\nมวลสาร\nมอคราม\nมอซอ\nมอหมึก\nมองเมียง\nมอบตัว\nมอบหมาย\nมอมเมา\nมะขามเทศ\nมะขามป้อม\nมะขามเปียก\nมะเขือเทศ\nมะเขือพวง\nมะพร้าวแก้ว\nมักคุ้น\nมักจี่\nมักง่าย\nมักน้อย\nมักมาก\nมักใหญ่\nมั่งคั่ง\nมั่งมี\nมัจจุราช\nมัชฌิมนิกาย\nมัชฌิมประเทศ\nมัชฌิมยาม\nมัชฌิมวัย\nมัดจำ\nมัดหมี่\nมัธยมกาล\nมัธยมศึกษา\nมันแกว\nมันเทศ\nมันฝรั่ง\nมันเปลว\nมันสมอง\nมั่นคง\nมั่นใจ\nมั่นหมาย\nมั่นเหมาะ\nมัวเมา\nมัวหมอง\nมั่วสุม\nม้าเทศ\nม้าน้ำ\nม้ามืด\nม้าเร็ว\nม้าล่อ\nม้าลาย\nมากมาย\nมาตรการ\nมาตรฐาน\nมาตราส่วน\nมาตุคาม\nมาตุฆาต\nมาตุภูมิ\nม่านตา\nม่านบังตา\nมายากร\nมายากล\nมายาการ\nมายาวี\nมารผจญ\nมารวิชัย\nมารสังคม\nมารหัวขน\nมาลาการ\nมิ่งขวัญ\nมิ่งมิตร\nมิจฉาจาร\nมิจฉาชีพ\nมิดชิด\nมิดเมี้ยน\nมิดหมี\nมิตรจิต\nมิตรภาพ\nมิตรสหาย\nมิน่า\nมีหน้า\nมีดโกน\nมีดดาบ\nมีดโต้\nมีดพก\nมีดพับ\nมีดสั้น\nมึนงง\nมึนชา\nมึนตึง\nมึนเมา\nมืดครึ้ม\nมืดมน\nมืดมัว\nมือจับ\nมือดี\nมือเติบ\nมือปืน\nมือเปล่า\nมือมืด\nมือสอง\nมือเสือ\nมือหนึ่ง\nมือใหม่\nมุกตลก\nมุขปาฐะ\nมุขมนตรี\nมุ่งมั่น\nมุ่งมาด\nมุ่งหน้า\nมุ่งหมาย\nมุ่งหวัง\nมุ้งลวด\nมุ้งสายบัว\nมุมก้ม\nมุมกลับ\nมุมเงย\nมุมฉาก\nมุมตรง\nมุมป้าน\nมุมมืด\nมุมแย้ง\nมุมสะท้อน\nมุมหักเห\nมุมแหลม\nมุสาวาท\nมูกเลือด\nมูกมัน\nมูกหลวง\nมูนดิน\nมูลฐาน\nมูลนาย\nมูลนิธิ\nมูลเหตุ\nมูลค่า\nมูลฝอย\nเม็ดเงิน\nเม็ดเลือด\nเม็ดโลหิต\nเม่นทะเล\nเมรุมาศ\nเมรุราช\nเมล์อากาศ\nเมาดิบ\nเมามัน\nเมามัว\nเมามาย\nเมินเฉย\nเมียน้อย\nเมียหลวง\nเมียงมอง\nเมี่ยงลาว\nเมี่ยงส้ม\nเมื่อกี้\nเมื่อตะกี้\nเมื่อใด\nเมื่อไร\nเมื่อไหร่\nเมื่อนั้น\nเมืองขึ้น\nเมืองท่า\nเมืองนอก\nเมืองหลวง\nเมื่อยขบ\nเมื่อยล้า\nแม่กอง\nแม่กุญแจ\nแม่คุณ\nแม่งาน\nแม่เจ้า\nแม่ชี\nแม่ทัพ\nแม่นม\nแม่น้ำ\nแม่บท\nแม่บ้าน\nแม่เบี้ย\nแม่พระ\nแม่พิมพ์\nแม่เพลง\nแม่มด\nแม่ม่าย\nแม่ไม้\nแม่ยก\nแม่ยาย\nแม่ร้าง\nแม่เรือน\nแม่แรง\nแม่เล้า\nแม่เลี้ยง\nแม่สี\nแม่สื่อ\nแม่เหล็ก\nแมงมุม\nแม่นยำ\nแมลงช้าง\nแมลงวัน\nแมลงปอ\nแมลงภู่\nแมลงเม่า\nแมวเซา\nแมวน้ำ\nแมวป่า\nแมวมอง\nไม้กลัด\nไม้กวาด\nไม้กางเขน\nไม้เกาหลัง\nไม้ขีดไฟ\nไม้จิ้มฟัน\nไม้เด็ด\nไม้ตาย\nไม้ตีพริก\nไม้ที\nไม้เท้า\nไม้บรรทัด\nไม้เมตร\nไม้ระแนง\nไม้เรียว\nไม้หมอน\nไม้อัด\nไม้จัตวา\nไม้ตรี\nไม้ไต่คู้\nไม้โท\nไม้ผัด\nไม้มลาย\nไม้ม้วน\nไม้ยมก\nไม้หน้า\nไม้หันอากาศ\nไม้เอก\nยกกลีบ\nยกครู\nยกเครื่อง\nยกเค้า\nยกทรง\nยกฟ้อง\nยกเมฆ\nยกยอ\nยกย่อง\nยกเลิก\nยกเว้น\nย่นย่อ\nยมทูต\nยมบาล\nยมราช\nยมโลก\nยวดยิ่ง\nยวดยาน\nยวนยี\nยวบยาบ\nย่อท้อ\nย่อส่วน\nย่อหน้า\nย่อหย่อน\nยอกย้อน\nยองใย\nย่องเบา\nย่องแย่ง\nยอดเยี่ยม\nยอดอก\nย้อนยอก\nย้อนรอย\nย้อนศร\nย้อนแสง\nย้อนหลัง\nยอบแยบ\nยอมความ\nย่อมเยา\nย่อยยับ\nยักยอก\nยักย้าย\nยักเยื้อง\nยัญกรรม\nยัญพิธี\nยัดเยียด\nยับเยิน\nยับยั้ง\nยั่วยวน\nยั่วยุ\nยั่วเย้า\nยากวาด\nยากันยุง\nยาเขียว\nยาใจ\nยาฉุน\nยาชา\nยาซัด\nยาดอง\nยาแดง\nยาถ่าย\nยาธาตุ\nยานัตถุ์\nยาเบื่อ\nยาโป๊\nยาแฝด\nยาพิษ\nยาระบาย\nยาสลบ\nยาสั่ง\nยาสีฟัน\nยาสูบ\nยาเส้น\nยาเสพติด\nยาหม่อง\nยาเหลือง\nย่าทวด\nย่านาง\nยากแค้น\nยากจน\nยากเย็น\nยากไร้\nยางนอก\nยางใน\nยางมะตอย\nยางมะตูม\nยางลบ\nยางสน\nยางอาย\nย่างกราย\nย่างเยื้อง\nย่างสด\nย่างสามขุม\nย่างเหยียบ\nยานเกราะ\nยานพาหนะ\nยานอวกาศ\nยานคาง\nยายทวด\nยาวเฟื้อย\nยาวยืด\nยาวเหยียด\nยำทวาย\nยำใหญ่\nยำเกรง\nยำเยง\nย่ำต๊อก\nย่ำยี\nย่ำแย่\nยิงเป้า\nยิ่งนัก\nยิ่งยวด\nยิ่งใหญ่\nยินดี\nยินยอม\nยินร้าย\nยิ้มกริ่ม\nยิ้มแฉ่ง\nยิ้มแต้\nยิ้มแป้น\nยิ้มเผล่\nยิ้มเยาะ\nยิ้มแย้ม\nยียวน\nยึดครอง\nยึดถือ\nยึดมั่น\nยึดเหนี่ยว\nยืดยาด\nยืดยาว\nยืดเยื้อ\nยืดหยุ่น\nยืดอก\nยืนกราน\nยืนต้น\nยืนพื้น\nยืนยง\nยืนยัน\nยืนหยัด\nยื้อยุด\nยุยง\nยุแยง\nยุแหย่\nยุคลบาท\nยุคเข็ญ\nยุคทอง\nยุคมืด\nยุ่งขิง\nยุ่งยาก\nยุ่งเหยิง\nยุติธรรม\nยุทธการ\nยุทธนาวี\nยุทธปัจจัย\nยุทธภัณฑ์\nยุทธภูมิ\nยุทธวิธี\nยุทธศาสตร์\nยุทธหัตถี\nยุทธนาการ\nยุทธนาธิการ\nยุบยับ\nยุบยิบ\nยุพราช\nยู่ยี่\nเย็นเจี๊ยบ\nเย็นใจ\nเย็นฉ่ำ\nเย็นเฉียบ\nเย็นชา\nเย็นชืด\nเย็นตา\nเย็นเยียบ\nเย็นเยือก\nเย็นวาบ\nเย็นวูบ\nเย็บกี่\nเย็บจักร\nเย็บด้าย\nเย้ยหยัน\nเย้าหยอก\nเยาะเย้ย\nเยี่ยมกราย\nเยี่ยมเยียน\nเยี่ยมเยือน\nเยี่ยมยอด\nเยื่อเคย\nเยื่อใย\nเยือกเย็น\nเยื้องกราย\nเยื้องยัก\nเยื้องย่าง\nแยกย้าย\nแยกแยะ\nแย่งชิง\nแยบคาย\nแยบยล\nแย้มพราย\nแย้มยิ้ม\nแย้มสรวล\nโยเย\nโย้เย้\nโยกโคลง\nโยกย้าย\nโยกโย้\nโยนกลอง\nใยหิน\nรกชัฏ\nรกร้าง\nรกเรี้ยว\nรกเรื้อ\nรกราก\nรงควัตถุ\nรชนีกร\nรถกระบะ\nรถเก๋ง\nรถเข็น\nรถแข่ง\nรถจักร\nรถจี๊ป\nรถตู้\nรถทัวร์\nรถบรรทุก\nรถพ่วง\nรถพยาบาล\nรถไฟ\nรถไฟฟ้า\nรถม้า\nรถเมล์\nรถยนต์\nรถราง\nรถลาก\nรถสปอร์ต\nรถสิบล้อ\nรบกวน\nรบรา\nรบเร้า\nรมดำ\nร่มเกล้า\nร่มชูชีพ\nร่มเย็น\nร่มรื่น\nร่วงโรย\nรวงผึ้ง\nรวงรัง\nรวดเร็ว\nรวนเร\nรวบยอด\nรวบรวม\nรวบรัด\nรวมพล\nรวมหัว\nร่วมใจ\nร่วมเพศ\nร่วมมือ\nร่วมรัก\nร่วมสมัย\nรวยริน\nรวยรื่น\nรสชาติ\nรสนิยม\nรองท้อง\nรองเท้า\nรองพื้น\nร่องน้ำ\nร่องรอย\nร้องขอ\nร้องทุกข์\nร้องเรียน\nร้องห่ม\nร้องไห้\nรองช้ำ\nรองทรง\nรอดชีวิต\nรอดตัว\nรอดตาย\nรอนแรม\nร่อนเร่\nร้อนใจ\nร้อนตัว\nร้อนรน\nร้อนรุ่ม\nร้อนวิชา\nร้อนอาสน์\nรอบจัด\nรอบเดือน\nรอบรู้\nรอยร้าว\nร่อยหรอ\nร้อยละ\nร้อยกรอง\nร้อยแก้ว\nร้อยหวาย\nระนาดทุ้ม\nระนาดเอก\nระเบิดขวด\nระเบิดมือ\nระเบียบการ\nรักใคร่\nรักษาการ\nรักษาการณ์\nรังไข่\nรังแตน\nรังนก\nรังผึ้ง\nรังเพลิง\nรังมด\nรังสรรค์\nรังสฤษฏ์\nรั้งรอ\nรังสีแพทย์\nรังสีวิทยา\nรัชกาล\nรัชทายาท\nรัชนีกร\nรัฐธรรมนูญ\nรัฐบาล\nรัฐบุรุษ\nรัฐประศาสน์\nรัฐประหาร\nรัฐพิธี\nรัฐมนตรี\nรัฐวิสาหกิจ\nรัฐศาสตร์\nรัฐสภา\nรัดกุม\nรัดเกล้า\nรัดตัว\nรัดประคด\nรัดรึง\nรัดรูป\nรัตติกาล\nรับขวัญ\nรับจ้าง\nรับช่วง\nรับใช้\nรับซื้อ\nรับทราบ\nรับประกัน\nรับประทาน\nรับปาก\nรับผิด\nรับผิดชอบ\nรับฟ้อง\nรับฟัง\nรับมือ\nรับรอง\nรับรู้\nรับสมัคร\nรับสั่ง\nรับหน้า\nรับเหมา\nรั่วไหล\nรามือ\nร่าเริง\nรากแก้ว\nรากขวัญ\nรากฐาน\nรากฟัน\nรากศัพท์\nรากเหง้า\nร่างกาย\nร่างแห\nร้างรา\nราชกรณียกิจ\nราชการ\nราชกิจ\nราชครู\nราชฐาน\nราชทัณฑ์\nราชทินนาม\nราชทูต\nราชธานี\nราชนาวี\nราชบัณฑิต\nราชบัลลังก์\nราชบาตร\nราชบุตร\nราชปะแตน\nราชภัฏ\nราชมัล\nราชยาน\nราชรถ\nราชลัญจกร\nราชเลขาธิการ\nราชเลขานุการ\nราชวงศ์\nราชวัติ\nราชสกุล\nราชสมบัติ\nราชสาส์น\nราชหัตถเลขา\nราชองครักษ์\nราชโองการ\nราชาคณะ\nราชาศัพท์\nราชินีนาถ\nร้านชำ\nร้านรวง\nราบคาบ\nราบรื่น\nราบเรียบ\nรายการ\nรายงาน\nรายจ่าย\nรายได้\nรายทาง\nรายรับ\nรายล้อม\nรายละเอียด\nรายวิชา\nร่ายยาว\nร่ายรำ\nร้ายกาจ\nร้ายแรง\nราวนม\nราวป่า\nร้าวฉาน\nร้าวราน\nรำพัด\nรำแพน\nรำวง\nร่ำไป\nร่ำร้อง\nร่ำเรียน\nร่ำไร\nร่ำลา\nร่ำไห้\nริเริ่ม\nริอ่าน\nริมฝีปาก\nริ้วรอย\nรีบร้อน\nรีบรุด\nรีบเร่ง\nรื่นรมย์\nรื่นเริง\nรื้อถอน\nรื้อฟื้น\nรุกฆาต\nรุกราน\nรุกล้ำ\nรุกไล่\nรุ่งขึ้น\nรุ่งแจ้ง\nรุ่งเช้า\nรุ่งเรือง\nรุ่งโรจน์\nรุ่งสว่าง\nรุ่งสาง\nรุ่งอรุณ\nรุจิเรข\nรุดหน้า\nรุนแรง\nรุมเร้า\nรุมล้อม\nรุ่มรวย\nรุ่มร้อน\nรุ่ยร่าย\nรู้แกว\nรู้ความ\nรู้คุณ\nรู้งาน\nรู้จัก\nรู้แจ้ง\nรู้ใจ\nรู้เชิง\nรู้ตัว\nรู้ทัน\nรู้เท่า\nรู้เรื่อง\nรู้สำนึก\nรู้สึก\nรู้เห็น\nรูปการณ์\nรูปโฉม\nรูปฌาน\nรูปถ่าย\nรูปทรง\nรูปธรรม\nรูปแบบ\nรูปพรรณ\nรูปพรหม\nรูปภพ\nรูปภาพ\nรูปร่าง\nรูปสมบัติ\nเร่ร่อน\nเร่งด่วน\nเร่งมือ\nเร่งรัด\nเร่งรีบ\nเร่งเร้า\nเร้นลับ\nเร่อร่า\nเร่าร้อน\nเราะราย\nเราะร้าย\nเริงใจ\nเริงรมย์\nเริดร้าง\nเริ่มต้น\nเริ่มแรก\nเรี่ยราด\nเรี่ยไร\nเรียกคืน\nเรียกตัว\nเรียกร้อง\nเรียกหา\nเรียบร้อย\nเรียงความ\nเรียงตัว\nเรียงเบอร์\nเรียงพิมพ์\nเรียงเม็ด\nเรียงราย\nเรียนรู้\nเรียบร้อย\nเรียบเรียง\nเรียบวุธ\nเรี่ยมเร้\nเรี่ยวแรง\nเรือกลไฟ\nเรือกอและ\nเรือกำปั่น\nเรือจ้าง\nเรือดำน้ำ\nเรือโดยสาร\nเรือตรวจการณ์\nเรือตังเก\nเรือธง\nเรือนำร่อง\nเรือบด\nเรือบิน\nเรือใบ\nเรือประมง\nเรือพ่วง\nเรือพิฆาต\nเรือยนต์\nเรือยาว\nเรือโยง\nเรือรบ\nเรือลากจูง\nเรือสำปั้น\nเรือสำเภา\nเรือหลวง\nเรือหางยาว\nเรืออีโปง\nเรือเอี้ยมจุ๊น\nเรื้อรัง\nเรือกสวน\nเรืองนาม\nเรืองรอง\nเรืองแสง\nเรื่องราว\nเรื่องสั้น\nเรือนแก้ว\nเรือนจำ\nเรือนเบี้ย\nเรือนแพ\nเรือนหอ\nเรื่อยเจื้อย\nเรื่อยเฉื่อย\nเรื่อยเปื่อย\nแรเงา\nแรกนา\nแรกนาขวัญ\nแรงงาน\nแรงดึงดูด\nแรงเทียน\nแรงม้า\nแรงเหวี่ยง\nแรมรอน\nแรมรา\nแรมโรย\nโรคจิต\nโรงครัว\nโรงงาน\nโรงเจ\nโรงเตี๊ยม\nโรงทาน\nโรงนา\nโรงพยาบาล\nโรงพัก\nโรงพิมพ์\nโรงเรียน\nโรงเรือน\nโรงแรม\nโรงเลี้ยง\nโรงเลื่อย\nโรงสี\nโรงสีข้าว\nโรงอาหาร\nโรมรัน\nโรยรา\nฤชากร\nฤดูกาล\nลงขัน\nลงแขก\nลงคอ\nลงตัว\nลงท้าย\nลงทุน\nลงโทษ\nลงพุง\nลงมือ\nลงรัก\nลงรอย\nลงแรง\nลงโรง\nลงเอย\nลดตัว\nลดละ\nลดเลี้ยว\nลดหย่อน\nลดหลั่น\nลนลาน\nล้นพ้น\nล้นหลาม\nล้นเหลือ\nลบล้าง\nลบเลือน\nลบหลู่\nลมกรด\nลมค้า\nลมงวง\nลมแดด\nลมทะเล\nลมบก\nลมบน\nลมบ้าหมู\nลมปราณ\nลมปาก\nลมพิษ\nลมว่าว\nลมเสีย\nลมหนาว\nลมหายใจ\nล่มจม\nล่มสลาย\nล้มละลาย\nล้มลุก\nล้มเลิก\nล่วงเกิน\nล่วงรู้\nล่วงละเมิด\nล่วงลับ\nล่วงล้ำ\nล่วงเลย\nล่วงหน้า\nลวดลาย\nลวดสปริง\nลวดหนาม\nล้วนแล้ว\nลหุโทษ\nล่อลวง\nล่อหลอก\nล่อแหลม\nล้อต๊อก\nล้อเลื่อน\nล้อเล่น\nล้อเลียน\nล้อหลอก\nลองเชิง\nลองดี\nลองภูมิ\nล่องหน\nลอดช่อง\nล่อนจ้อน\nลอบกัด\nล้อมวง\nลอยแก้ว\nลอยชาย\nลอยตัว\nลอยนวล\nลอยแพ\nลอยลำ\nละทิ้ง\nละเลย\nละเว้น\nละครนอก\nละครใน\nละครเพลง\nละครร้อง\nละครรำ\nละครลิง\nละครสัตว์\nละเอียดอ่อน\nลักไก่\nลักพา\nลักเพศ\nลักยิ้ม\nลักลอบ\nลักลั่น\nลักหลับ\nลัดเลาะ\nลับตา\nลับแล\nลับหลัง\nลาออก\nล่าช้า\nล่าทัพ\nล้าสมัย\nล้าหลัง\nลากข้าง\nล้างบาง\nล้างผลาญ\nลาดเขา\nลาดตระเวน\nลาดเท\nลาดยาง\nลานบิน\nลาภปาก\nลาภลอย\nลามปาม\nลามเลีย\nลายคราม\nลายเซ็น\nลายแทง\nลายน้ำ\nลายพร้อย\nลายมือ\nลายลักษณ์\nลายเส้น\nลำกล้อง\nลำแข้ง\nลำธาร\nลำแสง\nลำไส้\nลำตัด\nลำนำ\nล่ำสัน\nล้ำยุค\nล้ำสมัย\nล้ำลึก\nล้ำเลิศ\nล้ำเส้น\nล้ำหน้า\nลิงจุ่น\nลิงลม\nลิงโลด\nลิดรอน\nลิ้นไก่\nลิ้นชัก\nลิ้นปี่\nลิ้นควาย\nลิ้นงูเห่า\nลิ้นหมา\nลิบลับ\nลิบลิ่ว\nลิ่มเลือด\nลี้ภัย\nลี้ลับ\nลึกซึ้ง\nลึกลับ\nลึกล้ำ\nลืมตน\nลืมต้น\nลืมตัว\nลืมตา\nลืมเลือน\nลือชา\nลือชื่อ\nลือลั่น\nลุล่วง\nลุกลน\nลุกลาม\nลุกลี้ลุกลน\nลุกฮือ\nลุ่มน้ำ\nลุ่มลึก\nลุ่มหลง\nลุ่ทาง\nลูกกรง\nลูกกรอก\nลูกกรุง\nลูกกลอน\nลูกกลิ้ง\nลูกกวาด\nลูกกะจ๊อก\nลูกกุญแจ\nลูกเกด\nลูกแก้ว\nลูกขนไก่\nลูกข่าง\nลูกขุน\nลูกเขย\nลูกครึ่ง\nลูกคลื่น\nลูกความ\nลูกคอ\nลูกค้า\nลูกคิด\nลูกคู่\nลูกจ้าง\nลูกช้าง\nลูกชิด\nลูกชิ้น\nลูกชุบ\nลูกซอง\nลูกโซ่\nลูกดอก\nลูกดิ่ง\nลูกตะกั่ว\nลูกตุ้ม\nลูกเต้า\nลูกเต๋า\nลูกถ้วย\nลูกทุ่ง\nลูกเธอ\nลูกน้อง\nลูกน้ำ\nลูกนิมิต\nลูกบอล\nลูกบ้าน\nลูกบาศก์\nลูกบิด\nลูกเบี้ยว\nลูกประคบ\nลูกประคำ\nลูกปัด\nลูกปืน\nลูกโป่ง\nลูกผสม\nลูกผู้ชาย\nลูกผู้หญิง\nลูกพรรค\nลูกพี่\nลูกฟูก\nลูกไฟ\nลูกมือ\nลูกโม่\nลูกไม้\nลูกยาเธอ\nลูกรอก\nลูกรัง\nลูกเรือ\nลูกล้อ\nลูกลอย\nลูกเล่น\nลูกเลี้ยง\nลูกโลก\nลูกวัด\nลูกศร\nลูกศิษย์\nลูกสมุน\nลูกสะใภ้\nลูกสูบ\nลูกเสือ\nลูกหนัง\nลูกหนี้\nลูกหนู\nลูกหมาก\nลูกหลง\nลูกหลาน\nลูกหาบ\nลูกหิน\nลูกเห็บ\nลูกเหม็น\nลูกแหง่\nลูกอม\nลูกหม้อ\nลูบคม\nลูบคลำ\nลูบไล้\nเล็กน้อย\nเลขคณิต\nเลขผา\nเลขหมาย\nเล็ดลอด\nเล่นงาน\nเล่นแง่\nเล่นชู้\nเล่นตัว\nเล่นลิ้น\nเล่นหัว\nเลนส์นูน\nเลนส์เว้า\nเล็บครุฑ\nเลยเถิด\nเลศนัย\nเล่ห์กล\nเล่ห์เหลี่ยม\nเลอโฉม\nเลอมาน\nเลอเลิศ\nเลอสรวง\nเล่อล่า\nเลอะเลือน\nเล่าเรียน\nเล่าลือ\nเลาะลัด\nเลิกรา\nเลิกร้าง\nเลิกล้ม\nเลิศเลอ\nเลี้ยงชีพ\nเลี้ยงดู\nเลี้ยงต้อย\nเลียบเคียง\nเลี้ยวลด\nเลือกตั้ง\nเลือกเฟ้น\nเลือกสรร\nเลื่องลือ\nเลือดกำเดา\nเลือดเนื้อ\nเลือดฝาด\nเลือดเย็น\nเลือดร้อน\nเลือดหมู\nเลือดอุ่น\nเลือนราง\nเลื่อนเปื้อน\nเลื่อนลอย\nเลื่อมพราย\nเลื่อมใส\nเลื่อยฉลุ\nเลื่อยลันดา\nเลื่อยวงเดือน\nเลื้อยคลาน\nแลเหลียว\nแลกเปลี่ยน\nแล้วกัน\nและเล็ม\nโล่งใจ\nโล่งโถง\nโล่งอก\nโลดเต้น\nโลดโผน\nโลดลิ่ว\nโลดแล่น\nไล่ที่\nไล่เบี้ย\nไล่เลี่ย\nไล่เลียง\nไล่หลัง\nไล่ออก\nวกวน\nวงกบ\nวงกลม\nวงการ\nวงแขน\nวงเงิน\nวงจร\nวงนอก\nวงใน\nวงรี\nวงเล็บ\nวงเวียน\nวงแหวน\nวงศ์วาน\nวจีกรรม\nวจีเภท\nวจีภาค\nวนเวียน\nวอดวาย\nว็อบแว็บ\nวังวน\nวังหน้า\nวังหลวง\nวังหลัง\nวัดราษฎร์\nวัดวา\nวัดหลวง\nวัดผล\nวัดพื้น\nวัตถุนิยม\nวัตถุประสงค์\nวัตรปฏิบัติ\nวันโกน\nวันพระ\nวันเพ็ญ\nวัยรุ่น\nวัยวุฒิ\nว่ากล่าว\nว่าจ้าง\nว่าด้วย\nว่าที่\nวางก้าม\nวางใจ\nวางตัว\nวางตา\nวางโต\nวางท่า\nวางมวย\nวางมาด\nวางมือ\nวางวาย\nว่างเปล่า\nว่างเว้น\nวาดเขียน\nว่านเครือ\nวาบหวาม\nวายชนม์\nวายปราณ\nวายวาง\nวายวอด\nวายร้าย\nวายุภักษ์\nวาววับ\nวาววาม\nวาวแวว\nวาวแสง\nวิกฤตการณ์\nวิกฤติการณ์\nวิกฤตกาล\nวิกฤติกาล\nวิกลจริต\nวิงเวียน\nวิ่งเต้น\nวิ่งผลัด\nวิ่งรอก\nวิ่งราว\nวิจิตรศิลป์\nวิชาการ\nวิชาชีพ\nวิชาธร\nวิญญูชน\nวิดพื้น\nวิตกจริต\nวิถีทาง\nวิทยากร\nวิทยากล\nวิทยาการ\nวิทยาเขต\nวิทยาทาน\nวิทยาธร\nวิทยานิพนธ์\nวิทยาศาสตร์\nวิเทศสัมพันธ์\nวิธีการ\nวินัยธร\nวินัยปิฎก\nวินาศกรรม\nวินาศภัย\nวินาศสันตะโร\nวิภัชพยากรณ์\nวิภัชวาที\nวิไลวรรณ\nวิสัญญีแพทย์\nวิสัญญีภาพ\nวิสัญญีวิทยา\nวุฒิบัตร\nวุฒิสภา\nวุฒิสมาชิก\nวุ่นวาย\nวุ้นเส้น\nวูบวาบ\nเวจกุฎี\nเวจมรรค\nเวชกรรม\nเวชภัณฑ์\nเวชศาสตร์\nเวทมนตร์\nเวนคืน\nเวรกรรม\nเวฬุการ\nเวฬุวัน\nเว้าวอน\nเวิ้งว้าง\nเวียงวัง\nเวียนเทียน\nแว้งกัด\nแวดล้อม\nแวดวง\nแว่นขยาย\nแว่นแคว้น\nแว่นตา\nแวบวับ\nแววตา\nแวววาม\nแวววาว\nแวะเวียน\nโวยวาย\nไวไฟ\nไว้ใจ\nไว้ชื่อ\nไว้ตัว\nไว้ทุกข์\nไว้ลาย\nไว้หน้า\nไว้อาลัย\nศนิวาร\nศอกกลับ\nศอกกำ\nศอกกำมา\nศักดิ์ศรี\nศักดิ์สิทธิ์\nศารทวิษุวัติ\nศาลแขวง\nศาลจังหวัด\nศาลชั้นต้น\nศาลฎีกา\nศาลเตี้ย\nศาลทหาร\nศาลปกครอง\nศาลพระภูมิ\nศาลเพียงตา\nศาลแพ่ง\nศาลรัฐธรรมนูญ\nศาลแรงงาน\nศาลล้มละลาย\nศาลโลก\nศาลสูง\nศาลสูงสุด\nศาลอาญา\nศาลอุทธรณ์\nศาลากลาง\nศาลาดิน\nศาลาราย\nศาลาวัด\nศิลาฤกษ์\nศิลาแลง\nศิษย์เก่า\nศิษย์เอก\nศีลจุ่ม\nศีลธรรม\nศีลวัต\nศีลอด\nศูนย์กลาง\nศูนย์การค้า\nศูนย์ถ่วง\nศูนย์สูตร\nศูนย์หน้า\nเศร้าใจ\nเศร้าโศก\nเศร้าสร้อย\nเศร้าสลด\nเศร้าหมอง\nเศวตฉัตร\nเศษเกิน\nเศษซ้อน\nเศษวรรค\nเศษส่วน\nเศษเหล็ก\nโศกนาฏกรรม\nโศกศัลย์\nโศกเศร้า\nโศกสลด\nสกลโลก\nส่งเดช\nส่งท้าย\nส่งเสริม\nส่งเสีย\nส่งเสียง\nสงบเงียบ\nสงบเสงี่ยม\nสง่างาม\nสง่าราศี\nสดชื่น\nสดใส\nสตรีเพศ\nสติปัญญา\nสถลมารค\nสถานกงสุล\nสถานที่\nสถานทูต\nสถานการณ์\nสถานภาพ\nสถิติศาสตร์\nสนตะพาย\nสนใจ\nส้นตีน\nสนธิสัญญา\nสนนราคา\nสนับแข้ง\nสนับเพลา\nสนับมือ\nสนามบิน\nสนามเพลาะ\nสนิทสนม\nสนิมขุม\nสนิมสร้อย\nสนุกสนาน\nสบประมาท\nสบายใจ\nสภาพธรรม\nสมควร\nสมจริง\nสมใจ\nสมนัย\nสมน้ำหน้า\nสมประกอบ\nสมส่วน\nสมหวัง\nสมคบ\nสมทบ\nสมยอม\nสมรัก\nสมรู้\nสมสู่\nส้มฉุน\nส้มตำ\nส้มลิ้ม\nส้มกุ้ง\nส้มเช้า\nสมญานาม\nสมมติฐาน\nสมมุติฐาน\nสมมติเทพ\nสมรภูมิ\nสมัครใจ\nสมัยนิยม\nสมุทรศาสตร์\nสมุทรเสนา\nสยดสยอง\nสยองขวัญ\nสยามรัฐ\nสรรหา\nสรวมชีพ\nสรวลเส\nสร้อยเศร้า\nสร้างสรรค์\nสร้างเสริม\nสลดใจ\nสลบไสล\nสละสลวย\nสลาเหิน\nสลากภัต\nสวนครัว\nสวนป่า\nสวนสนุก\nสวนหย่อม\nส่วนกลาง\nส่วนเกิน\nส่วนตัว\nส่วนบุญ\nส่วนแบ่ง\nส่วนประกอบ\nส่วนพระองค์\nส่วนผสม\nส่วนรวม\nส่วนร่วม\nส่วนลด\nส่วนสัด\nสวมกอด\nสวมเขา\nสวมรอย\nสวยมภู\nสว่างไสว\nสวามิภักดิ์\nสวิงสวาย\nสสารนิยม\nส่อเสียด\nสอดคล้อง\nสอดแทรก\nสอดแนม\nสอบถาม\nสอบทาน\nสอบไล่\nสอบสวน\nส้อมเสียง\nสะสวย\nสะแกวัลย์\nสะแกแสง\nสะใจ\nสะเด็ดยาด\nสะเทือนใจ\nสะบัดช่อ\nสั่งสม\nสั่งสอน\nสั่งเสีย\nสังเกตการณ์\nสังคมนิยม\nสังคมวิทยา\nสังคมศาสตร์\nสังคมศึกษา\nสังคมสงเคราะห์\nสัญญาบัตร\nสัดส่วน\nสัตการ\nสัตบุรุษ\nสัตบริภัณฑ์\nสัตภัณฑ์\nสัตมหาสถาน\nสัตโลหะ\nสันเขา\nสันดอน\nสันหลัง\nสั่นเทา\nสั่นเทิ้ม\nสันติบาล\nสันติภาพ\nสันติวิธี\nสันติสุข\nสับเปลี่ยน\nสับสน\nสับหลีก\nสับหว่าง\nสัมมาคารวะ\nสัมมาชีพ\nส่าเหล้า\nสากกะเบือ\nสาทิสลักษณ์\nสาธุการ\nสาธุชน\nสาบเสือ\nสาปสรร\nสาปแช่ง\nสาปส่ง\nสามง่าม\nสามล้อ\nสามเหลี่ยม\nสามเวท\nสามัญชน\nสามัญสำนึก\nสายดิ่ง\nสายดิน\nสายตรวจ\nสายน้ำ\nสายบัว\nสายพาน\nสายฟ้า\nสายยาง\nสายยู\nสายใย\nสายรก\nสายรุ้ง\nสายล่อฟ้า\nสายลับ\nสายเลือด\nสายโลหิต\nสายวัด\nสายส่ง\nสายสวาท\nสายสะดือ\nสายสะพาย\nสายสัมพันธ์\nสายสิญจน์\nสายสืบ\nสายไหม\nสายอากาศ\nสายตา\nสายหยุด\nสารตรา\nสารประกอบ\nสารละลาย\nสารส้ม\nสารหนู\nสารทฤดู\nสาวใช้\nสาวน้อย\nสาวใหญ่\nสำนักงาน\nสำนักพิมพ์\nสำนักสงฆ์\nสำมะโนครัว\nสำเร็จรูป\nสิกขาบท\nสิงสถิต\nสิงสู่\nสิ่งก่อสร้าง\nสิ่งของ\nสิ่งปฏิกูล\nสิ่งพิมพ์\nสิ่งแวดล้อม\nสิ่งศักดิ์สิทธิ์\nสิทธิกร\nสิทธิ์ขาด\nสิทธิชัย\nสิทธิโชค\nสิทธิบัตร\nสินค้า\nสินจ้าง\nสินเชื่อ\nสินไถ่\nสินทรัพย์\nสินน้ำใจ\nสินบน\nสินแร่\nสินสมรส\nสินสอด\nสินไหม\nสิ้นเชิง\nสิ้นสุด\nสีผึ้ง\nสีลม\nสีชอล์ก\nสีถ่าน\nสีเทียน\nสีน้ำ\nสีน้ำมัน\nสีโปสเตอร์\nสีฝุ่น\nสี่เหลี่ยม\nสีหน้า\nสึกหรอ\nสืบทอด\nสืบค้น\nสืบสวน\nสืบสาว\nสืบเสาะ\nสื่อผสม\nสื่อมวลชน\nสื่อสาร\nสุกงอม\nสุกดิบ\nสุกปลั่ง\nสุกใส\nสุขนาฏกรรม\nสุขภัณฑ์\nสุขภาพ\nสุขลักษณะ\nสุขวิทยา\nสุขศาลา\nสุขศึกษา\nสุดท้าย\nสุตกวี\nสุนทรพจน์\nสุภาพชน\nสู่ขอ\nสู่รู้\nสู่สม\nสูงส่ง\nสูญเปล่า\nสูญสิ้น\nสูญเสีย\nสูญหาย\nเสสรวล\nเสแสร้ง\nเสกสรร\nเสถียรภาพ\nเส้นชัย\nเส้นตรง\nเส้นตาย\nเส้นทาง\nเส้นใย\nเส้นรุ้ง\nเส้นเลือด\nเส้นแวง\nเส้นสาย\nเส้นเสียง\nเส้นหมี่\nเส้นเอ็น\nเสบียงกรัง\nเสมอภาค\nเสมอหน้า\nเสมอเหมือน\nเสมียนตรา\nเสร็จสรรพ\nเสร็จสิ้น\nเสริมส่ง\nเสริมสร้าง\nเสริมสวย\nเสรีไทย\nเสรีธรรม\nเสรีนิยม\nเสรีภาพ\nเสาเข็ม\nเสาธง\nเสียใจ\nเสียเชิง\nเสียดาย\nเสียที\nเสียเที่ยว\nเสียเปรียบ\nเสียเปล่า\nเสียรู้\nเสียแรง\nเสียสละ\nเสียหลัก\nเสียหาย\nเสี่ยงทาย\nเสียดแทง\nเสียดแทรก\nเสียดสี\nเสี้ยนศึก\nเสี้ยนหนาม\nเสี้ยมสอน\nเสียวซ่าน\nเสียวไส้\nเสือดาว\nเสือดำ\nเสือปลา\nเสือป่า\nเสือไฟ\nเสื่อกก\nเสื่อกระจูด\nเสื่อน้ำมัน\nเสื่อลำแพน\nเสื้อกล้าม\nเสื้อกั๊ก\nเสื้อเกราะ\nเสื้อครุย\nเสื้อแสง\nเสื้อเมือง\nเสือกคลาน\nเสือกสน\nเสือกไส\nเสื่อมคลาย\nเสื่อมถอย\nเสื่อมทราม\nเสื่อมโทรม\nเสื่อมสลาย\nเสื่อมสูญ\nเสื่อมเสีย\nเสือหมอบ\nแสกหน้า\nแสดงออก\nแสเถา\nแสนกล\nแสนรู้\nแสร้งว่า\nใส่ความ\nใส่ไคล้\nใส่ใจ\nใส่ไฟ\nไส้กรอก\nไส้ไก่\nไส้ติ่ง\nไส้ศึก\nไส้อั่ว\nไส้เดือน\nไส้ตัน\nไสยเวท\nไสยศาสตร์\nหกล้ม\nหงส์หยก\nหงอนไก่\nหงอยก๋อย\nหงอยเหงา\nหงายท้อง\nหงายหลัง\nหงำเหงอะ\nหงำเหงือก\nหดหาย\nหดหู่\nหนทาง\nหนวกหู\nหน่วงเหนี่ยว\nหน่วยก้าน\nหน่อไม้\nหนองใน\nหนองแซง\nหนักข้อ\nหนักใจ\nหนักแน่น\nหนักหน่วง\nหนักหนา\nหนังกลับ\nหนังตะลุง\nหนังเรียด\nหนังสด\nหนังใหญ่\nหนังสือพิมพ์\nหนาแน่น\nหน้ากระดาน\nหน้ากาก\nหน้ากาฬ\nหน้าแข้ง\nหน้าจั่ว\nหน้าฉาน\nหน้าตัก\nหน้าตา\nหน้าต่าง\nหน้าท้อง\nหน้าทับ\nหน้าที่\nหน้าที่นั่ง\nหน้าบัน\nหน้าปัด\nหน้าผา\nหน้าผาก\nหน้าม้า\nหน้ามุข\nหน้าไม้\nหน้าเลือด\nหน้าอก\nหนามเตย\nหน่ายหนี\nหน่ายแหนง\nหนาวเหน็บ\nหนำใจ\nหนี้สิน\nหนี้ส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น\nกณิกนันต์\nกณิการ์\nกด\nกตเวทิตา\nกตเวที\nกตัญชลี\nกตัญญุตา\nกตัญญู\nกตาธิการ\nกตาภินิหาร\nกติกา\nกถา\nกถิกาจารย์\nกทลี\nกน\nก่น\nก้น\nกนก\nกนิษฐ์\nกนิษฐา\nกบ\nกบฏ\nกบดาน\nกบทู\nกบาล\nกบินทร์\nกบิล\nกบี่\nกบูร\nกเบนทร์\nกม\nก้ม\nกมณฑลาภิเษก\nกมณฑโลทก\nกมล\nกมลา\nกมลาศ\nกมลาสน์\nกมเลศ\nกมัณฑลุ\nกมุท\nกร\nกรกฎ\nกรกฎาคม\nกรกฏ\nกรง\nกรชกาย\nกรณฑ์\nกรณิการ์\nกรณี\nกรณีย์\nกรณียกิจ\nกรณียะ\nกรด\nกรน\nกรบ\nกรบูร\nกรพินธุ์\nกรม\nกรรกฎ\nกรรกศ\nกรรเกด\nกรรไกร\nกรรเจียก\nกรรชิง\nกรรเชียง\nกรรโชก\nกรรฐ์\nกรรฐา\nกรรณ\nกรรณา\nกรรณิกา\nกรรณิการ์\nกรรดิ\nกรรดิก\nกรรดึก\nกรรตุ\nกรรไตร\nกรรทบ\nกรรแทก\nกรรบิด\nกรรบูร\nกรรภิรมย์\nกรรม\nกรรม์\nกรรม์ภิรมย์\nกรรมชวาต\nกรรมัชวาต\nกรรมาชีพ\nกรรมาธิการ\nกรรมาร\nกรรษก\nกรรสะ\nกรรแสง\nกรวด\nกรวบ\nกรวม\nกร้วม\nกรวย\nกรวิก\nกรสาปน์\nกรสุทธิ์\nกรอ\nกร้อ\nกรอก\nกร็อกกร๋อย\nกรอกแกรก\nกรอง\nกรองกรอย\nกรอด\nกร่อน\nกรอบ\nกรอม\nกร่อย\nกระ\nกระกร\nกระกรุ่น\nกระกลับกลอก\nกระกี้\nกระเกรอก\nกระเกริก\nกระเกริ่น\nกระคน\nกระคาย\nกระงกกระเงิ่น\nกระง่องกระแง่ง\nกระง่อนกระแง่น\nกระเง้ากระงอด\nกระโงก\nกระจก\nกระจง\nกระจร\nกระจอก\nกระจองหง่อง\nกระจ๋องหง่อง\nกระจองอแง\nกระจ้อน\nกระจอนหู\nกระจ้อย\nกระจ๋อหวอ\nกระจะ\nกระจัก\nกระจัง\nกระจัด\nกระจับ\nกระจ่า\nกระจ่าง\nกระจาด\nกระจาน\nกระจาบ\nกระจาม\nกระจาย\nกระจาว\nกระจิก\nกระจิ๋ง\nกระจิด\nกระจิบ\nกระจิ๋ม\nกระจิริด\nกระจิ๋ว\nกระจี้\nกระจี๋\nกระจุก\nกระจุ๋งกระจิ๋ง\nกระจุบ\nกระจุ๊บ\nกระจุ๋มกระจิ๋ม\nกระจุย\nกระจู้\nกระจู๋กระจี๋\nกระจูด\nกระเจอะกระเจิง\nกระเจา\nกระเจ่า\nกระเจ้า\nกระเจาะ\nกระเจิง\nกระเจิดกระเจิง\nกระเจี้ยง\nกระเจี๊ยบ\nกระเจียว\nกระเจี๊ยว\nกระแจะ\nกระโจน\nกระโจม\nกระฉอก\nกระฉ่อน\nกระฉับกระเฉง\nกระฉิ่ง\nกระฉีก\nกระฉูด\nกระเฉก\nกระเฉด\nกระแฉก\nกระโฉกกระเฉก\nกระโฉม\nกระชดกระช้อย\nกระชอน\nกระชอมดอก\nกระช้อย\nกระชัง\nกระชั้น\nกระชับ\nกระชาก\nกระชาย\nกระชิง\nกระชิด\nกระชุ\nกระชุก\nกระชุ่มกระชวย\nกระเชอ\nกระเชา\nกระเช้า\nกระเชียง\nกระแชง\nกระแชะ\nกระโชก\nกระซ่องกระแซ่ง\nกระซับ\nกระซาบ\nกระซิก\nกระซิบ\nกระซี้\nกระซุง\nกระซุบกระซิบ\nกระซุ้ม\nกระซู่\nกระเซ็น\nกระเซอ\nกระเซอะกระเซอ\nกระเซอะกระเซิง\nกระเซ้า\nกระเซิง\nกระแซ\nกระแซะ\nกระโซกระเซ\nกระฎี\nกระฎุมพี\nกระดก\nกระด้ง\nกระดนโด่\nกระดวง\nกระดวน\nกระด้วมกระเดี้ยม\nกระดอ\nกระดอง\nกระดองหาย\nกระดอน\nกระดอม\nกระดักกระเดี้ย\nกระดังงัว\nกระดังงา\nกระดาก\nกระด้าง\nกระดางลาง\nกระดาด\nกระดาดขาว\nกระดาน\nกระดานพน\nกระดาษ\nกระดำกระด่าง\nกระดิก\nกระดิ่ง\nกระดิ้ง\nกระดิบ\nกระดี่\nกระดี้กระเดียม\nกระดึง\nกระดืบ\nกระดุ\nกระดุกกระดิก\nกระดุ้งกระดิ้ง\nกระดุบ\nกระดุบกระดิบ\nกระดุม\nกระดูก\nกระเดก\nกระเด้ง\nกระเด็น\nกระเด้า\nกระเดาะ\nกระเดิด\nกระเดี้ย\nกระเดียด\nกระเดือก\nกระเดื่อง\nกระแด็ก\nกระแด้ง\nกระแด้แร่\nกระแด่ว\nกระแดะ\nกระโดก\nกระโดง\nกระโดด\nกระโดน\nกระได\nกระตรับ\nกระตราก\nกระตรุด\nกระตรุม\nกระต้วมกระเตี้ยม\nกระต้อ\nกระต่องกระแต่ง\nกระต๊อบ\nกระต้อยตีวิด\nกระตัก\nกระตั้ว\nกระต่าย\nกระติก\nกระติ๊ด\nกระติบ\nกระตือรือร้น\nกระตุก\nกระตุ้งกระติ้ง\nกระตุ่น\nกระตุ้น\nกระตูบ\nกระเตง\nกระเต็น\nกระเตอะ\nกระเตาะ\nกระเตาะกระแตะ\nกระเตื้อง\nกระแต\nกระแตแต้แว้ด\nกระโตกกระตาก\nกระโตน\nกระถด\nกระถอบ\nกระถั่ว\nกระถาง\nกระถิก\nกระถิน\nกระเถิบ\nกระโถน\nกระทก\nกระทง\nกระทบ\nกระทรวง\nกระทอก\nกระท้อน\nกระท่อนกระแท่น\nกระท่อม\nกระท้อมกระแท้ม\nกระทะ\nกระทั่ง\nกระทั้น\nกระทา\nกระทาย\nกระทาสี\nกระทาหอง\nกระทำ\nกระทิกกระทวย\nกระทิง\nกระทึง\nกระทืบ\nกระทุ\nกระทุง\nกระทุ้ง\nกระทุ่ม\nกระทู้\nกระเท่\nกระเทียบ\nกระเทียม\nกระเทือน\nกระเทื้อม\nกระแทก\nกระแท่น\nกระแทะ\nกระไทชาย\nกระน่อง\nกระนั้น\nกระนี้\nกระแนะกระแหน\nกระโน้น\nกระไน\nกระบก\nกระบม\nกระบวน\nกระบวย\nกระบวร\nกระบอก\nกระบอง\nกระบะ\nกระบัด\nกระบั้วกระเบี้ย\nกระบ่า\nกระบ้า\nกระบาก\nกระบาย\nกระบาล\nกระบิ\nกระบิ้ง\nกระบิด\nกระบิล\nกระบี่\nกระบือ\nกระบุง\nกระบุ่มกระบ่าม\nกระบู้กระบี้\nกระบูน\nกระบูร\nกระเบง\nกระเบญ\nกระเบ็ดกระบวน\nกระเบน\nกระเบา\nกระเบิก\nกระเบียด\nกระเบียน\nกระเบื้อง\nกระแบก\nกระแบะ\nกระโบม\nกระปมกระปำ\nกระปมกระเปา\nกระปรอก\nกระปรอกว่าว\nกระปรี้กระเปร่า\nกระปอก\nกระป้อกระแป้\nกระป่อง\nกระป๋อง\nกระปอดกระแปด\nกระป๋อหลอ\nกระปั้วกระเปี้ย\nกระป่ำ\nกระปุก\nกระปุ๊กลุก\nกระปุ่ม\nกระปุ่มกระป่ำ\nกระปุ่มกระปิ่ม\nกระเป๋า\nกระเปาะ\nกระโปก\nกระโปรง\nกระผม\nกระผลีกระผลาม\nกระผาน\nกระผีก\nกระพรวน\nกระพริ้ม\nกระพอก\nกระพอง\nกระพ้อม\nกระพัก\nกระพัง\nกระพังเหิร\nกระพังโหม\nกระพัด\nกระพัตร\nกระพัน\nกระพั่น\nกระพา\nกระพาก\nกระพี้\nกระพือ\nกระพุ้ง\nกระพุ่ม\nกระเพาะ\nกระเพิง\nกระเพื่อม\nกระแพ้ง\nกระฟัดกระเฟียด\nกระฟูมกระฟาย\nกระมล\nกระมอบ\nกระมอมกระแมม\nกระมัง\nกระมัน\nกระมิดกระเมี้ยน\nกระมุท\nกระเมาะ\nกระย่อง\nกระย่องกระแย่ง\nกระย่อน\nกระย่อม\nกระยา\nกระยาง\nกระยาจก\nกระยาหงัน\nกระยิก\nกระยิ้มกระย่อง\nกระยึกกระยือ\nกระยืดกระยาด\nกระเย้อกระแหย่ง\nกระรอก\nกระเรียน\nกระโรกน้ำข้าว\nกระโรกใหญ่\nกระไร\nกระลด\nกระลบ\nกระลอก\nกระลอม\nกระละหล่ำ\nกระลัด\nกระลับ\nกระลัมพร\nกระลา\nกระลำ\nกระลำพัก\nกระลำพุก\nกระลิง\nกระลี\nกระลุมพาง\nกระลุมพุก\nกระลุมพู\nกระลูน\nกระลู่น์\nกระเล็น\nกระเลียด\nกระเลือก\nกระโลง\nกระวน\nกระวัด\nกระวาด\nกระวาน\nกระวาย\nกระวิน\nกระวี\nกระวีกระวาด\nกระวูดกระวาด\nกระเวน\nกระเวยกระวาย\nกระแวน\nกระโวยกระวาย\nกระษัย\nกระษาปณ์\nกระสง\nกระสบ\nกระสม\nกระสรวล\nกระสร้อย\nกระสวน\nกระสวย\nกระสอบ\nกระสะ\nกระสัง\nกระสัน\nกระสับกระส่าย\nกระสา\nกระสานติ์\nกระสาบ\nกระสาย\nกระสือ\nกระสุงกระสิง\nกระส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ทิง\nกากะเยีย\nกากี\nกาง\nก้าง\nกางเกง\nกางเขน\nก๊าซ\nกาซะลอง\nกาญจนา\nกาฐ\nกาด\nก๊าด\nกาน\nก่าน\nก้าน\nก๊าน\nกานดา\nกานต์\nกานน\nก้านพร้าว\nกานพลู\nกาน้า\nกาบ\nก้าบ\nกาบู\nกาพย์\nกาเฟอีน\nกาแฟ\nกาม\nก้าม\nกามารมณ์\nกามินี\nกาเมสุมิจฉาจาร\nกาย\nก่าย\nกาเยน\nการ\nการณ์\nการ์ด\nการ์ตูน\nการบูร\nการย์\nการวิก\nการเวก\nการะเกด\nการะบุหนิง\nการัณย์\nการันต์\nการางหัวขวาน\nการิตการก\nการิตวาจก\nการุญ\nการุณย์\nกาเรการ่อน\nกาล\nกาลกรรณี\nกาลกิณี\nกาลจักร\nกาลัญญุตา\nกาลัญญู\nกาลัด\nกาลานุกาล\nกาลิก\nกาลี\nกาแล\nกาแล็กซี\nกาแล็กโทส\nกาว\nก้าว\nกาววาว\nกาวาง\nกาแวน\nกาศิก\nกาษฐะ\nกาษา\nกาสร\nกาสะ\nกาสา\nกาสาร\nกาสาวะ\nกาสิโน\nกาหล\nกาหลง\nกาหลา\nกาเหว่า\nกาไหล่\nกาฬ\nกาฬาวก\nกาฮัง\nกำ\nก่ำ\nกำกวม\nกำกัด\nกำกับ\nก้ำกึ่ง\nกำกูน\nก้ำเกิน\nกำเกียง\nกำคูน\nกำจร\nกำจัด\nกำจาย\nกำชับ\nกำชำ\nกำซาบ\nกำซำ\nกำด้น\nกำดัด\nกำดาล\nกำเดา\nกำธร\nกำนล\nกำนัน\nกำนัล\nกำเนิด\nกำบัง\nก่ำบึ้ง\nกำเบ้อ\nกำปอ\nกำปั่น\nกำผลา\nกำพง\nกำพด\nกำพต\nกำพร้า\nกำพราก\nกำพวด\nกำพอง\nกำพืด\nกำพุด\nกำพู\nกำเพลิง\nกำแพง\nกำภู\nกำมลาศน์\nกำมเลศ\nกำมะถัน\nกำมะลอ\nกำมะหยี่\nกำมะหริด\nกำมังละการ\nกำมังวิลิต\nกำมัชพล\nกำยาน\nกำยำ\nกำรอ\nกำราบ\nกำราล\nกำเริบ\nกำไร\nกำลัง\nกำลุง\nกำเลา\nกำไล\nกำสรด\nกำสรวล\nกำหนด\nกำหนัด\nกำเหน็จ\nกำแหง\nกิก\nกิ๊ก\nกิ่ง\nกิ้งก่า\nกิ้งกือ\nกิ้งโครง\nกิจ\nกิจจะ\nกิจจา\nกิดาการ\nกิดาหยัน\nกิตติ\nกิตติมศักดิ์\nกิน\nกินนร\nกินปลี\nกินเปี้ยว\nกินริน\nกินรี\nกิ๊บ\nกิมตึ๋ง\nกิมิชาติ\nกิมิวิทยา\nกิโมโน\nกิโยตีน\nกิระ\nกิริณี\nกิรินท\nกิริเนศวร\nกิริยา\nกิเลน\nกิเลส\nกิโล\nกิโลมกะ\nกิ่ว\nกิ๋ว\nกี\nกี่\nกี้\nกี๊\nกี๋\nกีฏวิทยา\nกีด\nกีตาร์\nกีบ\nกีรติ\nกีฬา\nกึก\nกึง\nกึ่ง\nกึ๋น\nกุ\nกุก\nกุ๊ก\nกุกกุฏ\nกุกกุร\nกุกรรม\nกุ้ง\nกุงอน\nกุงาน\nกุโงก\nกุจี\nกุญแจ\nกุญชร\nกุฎ\nกุฎา\nกุฎี\nกุฎุมพี\nกุฏฐัง\nกุฏิ\nกุณฑ์\nกุณฑล\nกุณฑี\nกุณโฑ\nกุณาล\nกุณี\nกุด\nกุดัง\nกุดั่น\nกุดา\nกุทัณฑ์\nกุน\nกุ๊น\nกุนเชียง\nกุนที\nกุโนกามอ\nกุบ\nกุบกับ\nกุม\nกุ่ม\nกุมฝอย\nกุมภ์\nกุมภนิยา\nกุมภัณฑ์\nกุมภา\nกุมภิล\nกุมภีล์\nกุมเหง\nกุมาร\nกุมารา\nกุมารี\nกุมุท\nกุย\nกุ๊ย\nกุ๋ย\nกุยช่าย\nกุยเฮง\nกุรระ\nกุรุพินท์\nกุเรา\nกุล\nกุลา\nกุลาหล\nกุลี\nกุลีกุจอ\nกุเลา\nกุแล\nกุเวร\nกุศราช\nกุศล\nกุศโลบาย\nกุสุม\nกุสุมภ์\nกุสุมา\nกุสุมาลย์\nกุสุมิตลดาเวลลิตา\nกุหนี\nกุหนุง\nกุหร่า\nกุหล่า\nกุหลาบ\nกุแหละ\nกู\nกู่\nกู้\nกู๊ก\nกูฏ\nกูฏา\nกูณฑ์\nกูด\nกูบ\nกูปรี\nกูรมะ\nกูรมาวตาร\nเก\nเก้\nเก๊\nเก๋\nเกก\nเก๊ก\nเกกมะเหรก\nเก๊กฮวย\nเก้กัง\nเก็ง\nเก่ง\nเก้ง\nเก๋ง\nเกงกอย\nเก่งกาจ\nเกงเขง\nเก๋งเคง\nเก็จ\nเกจิอาจารย์\nเกชา\nเกณฑ์\nเกด\nเก็ด\nเกตุ\nเกน\nเก็บ\nเกม\nเกย\nเกยูร\nเกรง\nเกร็ง\nเกร็ด\nเกรน\nเกร่อ\nเกรอะ\nเกราะ\nเกริก\nเกริน\nเกริ่น\nเกรียก\nเกรียง\nเกรียด\nเกรียน\nเกรียบ\nเกรียม\nเกรียว\nเกรี้ยว\nเกเร\nเกล็ด\nเกลศ\nเกลอ\nเกลา\nเกล้า\nเกลาะ\nเกลี่ย\nเกลี้ย\nเกลียง\nเกลี้ยง\nเกลียด\nเกลียว\nเกลือ\nเกลื้อ\nเกลือก\nเกลื่อน\nเกลื้อน\nเกวัฏ\nเกวียน\nเกศ\nเกศว\nเกศวะ\nเกศา\nเกศินี\nเกศี\nเกษตร\nเกษม\nเกษียณ\nเกษียน\nเกษียร\nเกส\nเกสร\nเกสรี\nเกสา\nเกสี\nเก้อ\nเกอิชา\nเกะ\nเกะกะ\nเกา\nเก่า\nเก้า\nเก๋า\nเกาต์\nเกาทัณฑ์\nเกาบิล\nเกาลัด\nเกาลิน\nเกาไศย\nเกาหลี\nเกาเหลา\nเกาเหลียง\nเก้าอี้\nเกาะ\nเกิ้ง\nเกิด\nเกิน\nเกิบ\nเกีย\nเกียกกาย\nเกียง\nเกี่ยง\nเกี๋ยง\nเกียจ\nเกียด\nเกียน\nเกี้ยมไฉ่\nเกี้ยมอี๋\nเกียร์\nเกียรติ\nเกียรติ์\nเกี่ยว\nเกี้ยว\nเกี๊ยว\nเกี๊ยะ\nเกื้อ\nเกือก\nเกื้อกูล\nเกือบ\nแก\nแก่\nแก้\nแกง\nแก่ง\nแก้ง\nแก๊ง\nแกงได\nแกงแนง\nแกโดลิเนียม\nแกน\nแก่น\nแก๊ป\nแกม\nแก้ม\nแกมมา\nแกรก\nแกร่ง\nแกร็น\nแกรนิต\nแกรไฟต์\nแกร่ว\nแกระ\nแกล\nแกล้ง\nแกลน\nแกลบ\nแกล้ม\nแกลลอน\nแกลเลียม\nแกล้ว\nแกละ\nแกแล\nแกว\nแก้ว\nแกว่ง\nแก๊ส\nแกะ\nโก\nโก่\nโก้\nโก๋\nโกก\nโกกนุท\nโกกิลา\nโกโก้\nโกง\nโก่ง\nโกงกาง\nโก้งเก้ง\nโกงโก้\nโก้งโค้ง\nโกเชาว์\nโกญจนาท\nโกญจา\nโกฏิ\nโกฐ\nโกฐาส\nโกณะ\nโกดัง\nโกทัณฑ์\nโกน\nโก่น\nโก๋น\nโกมล\nโกมุท\nโกเมน\nโกเมศ\nโกย\nโกรก\nโกรกกราก\nโกรง\nโกร่ง\nโกร่งกร่าง\nโกรงเกรง\nโกร๋งเกร๋ง\nโกรญจ\nโกรต๋น\nโกรธ\nโกรธา\nโกร๋น\nโกรม\nโกรย\nโกรศ\nโกโรโกเต\nโกโรโกโรก\nโกโรโกโส\nโกลน\nโกลาหล\nโกไล\nโกวิท\nโกศ\nโกศล\nโกษม\nโกสน\nโกสัช\nโกสินทร์\nโกสีย์\nโกสุม\nโกไสย\nโกหก\nใกล้\nไก\nไก่\nไก๊\nไก๋\nไก่กอม\nไกพัล\nไกร\nไกรพ\nไกรลาส\nไกรศร\nไกรศรี\nไกรสร\nไกรสรี\nไกรสิทธิ\nไกล\nไกล่\nไกลาส\nไกว\nไกวัล\nขงจื๊อ\nขจร\nขจรจบ\nขจัด\nขจ่าง\nขจาย\nขจาว\nขจิต\nขจี\nขจุย\nขเจา\nขณะ\nขด\nขน\nข้น\nขนง\nขนด\nขนบ\nขนม\nขนอง\nขนอน\nขนอบ\nขนัด\nขนัน\nขนาก\nขนาง\nขนาด\nขนาน\nขนาบ\nขนาย\nขนำ\nขนิษฐ\nขนิษฐา\nขนุน\nขนุนนก\nขบ\nขบถ\nขบวน\nขบวร\nขม\nข่ม\nขมงโกรย\nขมวด\nขมวน\nขมอง\nขม่อม\nขมัง\nขมับ\nขมา\nขม้ำ\nขมิ้น\nขมิบ\nขมีขมัน\nขมึง\nขมึงทึง\nขมุ\nขมุกขมัว\nขมุบ\nขโมย\nขยด\nขยม\nขย่ม\nขยอก\nขยอง\nขย่อน\nขย้อน\nขยะ\nขยัก\nขยัน\nขยั้น\nขยับ\nขยาด\nขยาย\nขยำ\nขย้ำ\nขยิก\nขยิบ\nขยิ่ม\nขยี้\nขยุกขยิก\nขยุกขยุย\nขยุบ\nขยุบขยิบ\nขยุม\nขยุ้ม\nขยุย\nขรม\nขรรค์\nขรัว\nขริบ\nขรี\nขรึม\nขรุขระ\nขลบ\nขล้อ\nขลัง\nขลับ\nขลาด\nขลาย\nขลิบ\nขลุก\nขลุกขลัก\nขลุกขลิก\nขลุบ\nขลุม\nขลุ่ย\nขลู\nขลู่\nขวง\nข่วง\nขวด\nข่วน\nขวนขวาย\nขวบ\nขวย\nขวักไขว่\nขวัญ\nขวั้น\nขวับ\nขวับเขวียว\nขวา\nขวาก\nขวาง\nขว้าง\nขวาด\nขวาน\nขวายขวน\nขวาว\nขว้าว\nขวิด\nขอ\nข่อ\nข้อ\nของ\nข้อง\nขอด\nขอน\nข้อน\nขอบ\nขอม\nข่อย\nข้อย\nข่อยหยอง\nขะแจะ\nขะเน็ด\nขะมอมขะแมม\nขะมักเขม้น\nขะมุกขะมอม\nขะยิก\nขะยุก\nขะเย้อแขย่ง\nขัค\nขัง\nขังขอก\nขัช\nขัณฑสกร\nขัณฑสีมา\nขัด\nขัดมอน\nขัตติยมานะ\nขัน\nขั้น\nขันติ\nขันตี\nขันโตก\nขันที\nขันธ์\nขันธาวาร\nขับ\nขัว\nขั้ว\nขา\nข่า\nข้า\nขาก\nขาก๊วย\nขาง\nข่าง\nข้าง\nขาณุ\nขาด\nขาทนียะ\nขาน\nขาบ\nข้าพเจ้า\nขาม\nข่าม\nข้าม\nขาย\nข่าย\nขาล\nขาว\nข่าว\nข้าว\nข้าวอังกุลี\nขำ\nขิก\nขิง\nขิงแกลง\nขิงแครง\nขิด\nขิปสัทโท\nขิม\nขี่\nขี้\nขี้เข็บ\nขีณาสพ\nขีด\nขี้ตังนี\nขีปนาวุธ\nขี้ยอก\nขีระ\nขึง\nขึ้ง\nขึ้น\nขึ้นฉ่าย\nขืน\nขื่น\nขื่อ\nขุก\nขุด\nขุน\nขุ่น\nขุนเพ็ด\nขุม\nขุย\nขู่\nขูด\nเข\nเข้\nเขก\nเข็ง\nเข่ง\nเขจร\nเข็ญ\nเข็ด\nเขดา\nเขต\nเขน\nเข็น\nเข่น\nเขนง\nเขน็ด\nเขนย\nเขบ็จขบวน\nเขบ็ต\nเขม\nเข็ม\nเข้ม\nเข้มขาบ\nเขม็ง\nเขม็ดแขม่\nเขม่น\nเขม้น\nเขม้นขะมัก\nเขมร\nเขมา\nเขม่า\nเขมือบ\nเขย\nเขยก\nเขย่ง\nเขย้อแขย่ง\nเขย่า\nเขยิน\nเขยิบ\nเขยื้อน\nเขรอะ\nเขลง\nเขลอะ\nเขละ\nเขลา\nเขลาะ\nเขว\nเขษม\nเขฬะ\nเขะขะ\nเขา\nเข่า\nเข้า\nเขิง\nเขิน\nเขิบ\nเขี่ย\nเขียง\nเขียด\nเขียดตะปาด\nเขียน\nเขี่ยน\nเขียม\nเขียว\nเขี้ยว\nเขียะ\nเขือ\nเขือง\nเขื่อง\nเขื่อน\nเขือม\nแข\nแข้\nแขก\nแข็ง\nแข่ง\nแข้ง\nแขน\nแข่น\nแข้น\nแขนง\nแขม\nแขม็บ\nแขม่ว\nแขยง\nแขย่ง\nแขวก\nแขวง\nแขวน\nแขวะ\nโข\nโขก\nโขง\nโข่ง\nโขด\nโขดง\nโขน\nโขนง\nโขม\nโขมง\nโขมด\nโขยก\nโขยง\nโขย่ง\nโขยด\nโขลก\nโขลง\nโขลน\nโขษม\nไข\nไข่\nไข้\nไขว่\nไขว้\nคคนะ\nคคนัมพร\nคคนางค์\nคคนานต์\nคง\nคงคา\nคงไคย\nคช\nคชาชาติ\nคชาชีพ\nคชาธาร\nคชาภรณ์\nคณนา\nคณบดี\nคณะ\nคณาจารย์\nคณาธิการ\nคณาธิปไตย\nคณานับ\nคณิกา\nคณิต\nคเณศ\nคด\nคดี\nคติ\nคทา\nคน\nค้น\nคนทา\nคนทิสอ\nคนที\nคนโท\nคนธ์\nคันธ์\nคนธรรพ์\nคเนจร\nคบ\nคม\nคมน์\nคมนาการ\nคมนาคม\nคมิกภัต\nครก\nครบ\nครรชิต\nครรภ\nครรภ์\nครรลอง\nครรโลง\nครรไล\nครวญ\nครวี\nครหา\nครอก\nครอง\nครองแครง\nคร่อเงาะ\nคร่อเทียน\nครอบ\nคร่อม\nคระเมิม\nคระแลง\nคระไล\nคระแวง\nคระหน\nคระหวน\nคระหาย\nคระโหย\nครั่ง\nครั้ง\nครัดเคร่ง\nครัน\nครั่น\nครั้น\nครับ\nครัว\nครา\nคร่า\nคราก\nคราง\nคราญ\nคราด\nคร้าน\nคราบ\nคราม\nคร้าม\nครามครัน\nคราว\nคร่าว\nคราส\nครำ\nคร่ำ\nคร่ำเคร่ง\nคริปทอน\nคริสต์\nครีบ\nครีม\nครีษมายัน\nครึ\nครึกครื้น\nครึกโครม\nครึ่ง\nครึ่ด\nครึน\nครึ้ม\nครืด\nครืน\nครื้น\nครืนครั่น\nครื้นครั่น\nครื้นครึก\nครื้นเครง\nครือ\nครุ\nครุคระ\nครุฑ\nครุ่น\nครุมเครือ\nครุย\nครุวนา\nครู\nครู่\nครูด\nคฤโฆษ\nคฤนถ์\nคฤหบดี\nคฤหัสถ์\nคฤหาสน์\nคลวง\nคลอ\nคล้อ\nคลอก\nคลอง\nคล่อง\nคล้อง\nคลอด\nคลอน\nคล้อย\nคลอรีน\nคลอโรฟอร์ม\nคลอโรฟีลล์\nคละ\nคละคลุ้ง\nคลัก\nคลั่ก\nคลัง\nคลั่ง\nคลัตช์\nคลับคล้าย\nคลับคลา\nคลา\nคล้า\nคลางแคลง\nคลาด\nคลาน\nคลาย\nคล้าย\nคล้ายคลึง\nคล่าว\nคลำ\nคล่ำ\nคล้ำ\nคลิ้งโคลง\nคลิด\nคลินิก\nคลี\nคลี่\nคลึง\nคลื่น\nคลุก\nคลุ้ง\nคลุบ\nคลุม\nคลุ่ม\nคลุ้ม\nควง\nควณ\nควน\nควบ\nควย\nควร\nควัก\nควั่ก\nควั่งคว้าง\nควัน\nควั่น\nคว้า\nควาก\nคว้าง\nควาญ\nควาน\nคว้าน\nความ\nควาย\nคว่าว\nคว่ำ\nควินิน\nควิวคว่าง\nคหกรรม\nคหกรรมศาสตร์\nคหบดี\nคหัฐ\nคอ\nค้อ\nคอก\nค็อกคัส\nค็อกเทล\nคอเคซอยด์\nค่องอ้อย\nคอด\nคอแดง\nคอน\nค่อน\nค้อน\nคอนกรีต\nคอนเดนเซอร์\nคอนแวนต์\nคอนเสิร์ต\nคอม\nค่อม\nค้อม\nคอมพิวเตอร์\nคอมมานโด\nคอมมิวนิสต์\nคอย\nค่อย\nค้อย\nคอยล์\nคอร์ด\nคอแลน\nคอสติกโซดา\nคะ\nค่ะ\nคะไขว่\nคะค้อย\nคะคาน\nคะนน\nคะนอง\nคะน้า\nคะนึง\nคะเน\nคะเนงร้าย\nคะเน็ด\nคะแนน\nคะมำ\nคะยั้นคะยอ\nคะเยอ\nคัก\nคั่ก\nคัคนะ\nคัคนัมพร\nคัคนางค์\nคัคนานต์\nคั่ง\nคังไคย\nคัจฉ\nคัณฑมาลา\nคัณฑสูตร\nคัด\nคัดเค้า\nคัดมอน\nคัดเม็ง\nคัทลียา\nคัน\nคั่น\nคั้น\nคันถรจนาจารย์\nคันธกุฎี\nคันธมาทน์\nคันธารราษฎร์\nคับ\nคับค้อน\nคับคา\nคับแค\nคัพภ์\nคัมภีร์\nคัมภีรภาพ\nคัล\nคั่ว\nคา\nค่า\nค้า\nค่าคบ\nคาง\nค่าง\nค้าง\nคางคก\nค้างคาว\nคาด\nคาถา\nคาทอลิก\nคาน\nค้าน\nคาบ\nคาพยุต\nคาม\nคามวาสี\nคามณีย์\nคามภีร์\nคาย\nค่าย\nคาร์บอน\nคาร์บอเนต\nคาร์บอลิก\nคาร์บูเรเตอร์\nคาร์โบรันดัม\nคาร์โบไฮเดรต\nคารม\nคารวะ\nคาราเต้\nคาราวาน\nคาว\nค่าว\nค้าว\nคาวตอง\nคาวี\nคาวุต\nคาส\nคำ\nค่ำ\nค้ำ\nคำนวณ\nคำนวร\nคำนับ\nคำนัล\nคำนึง\nคำนูณ\nคำฝอย\nคำเพลิง\nคำรน\nคำรบ\nคำราม\nคำแสด\nคำแหง\nคำโอง\nคิก\nคิง\nคิด\nคิมหันต์\nคิรี\nคิลาน\nคิลานะ\nคิว\nคิ้ว\nคี่\nคีต\nคีบ\nคีม\nคีรี\nคีรีบูน\nคึก\nคึ่ก\nคึกคัก\nคืน\nคืบ\nคือ\nคุ\nคุก\nคุกกี้\nคุกคาม\nคุกพาทย์\nคุ้ง\nคุณ\nคุด\nคุดทะราด\nคุต\nคุตติ\nคุ่น\nคุ้น\nคุป\nคุปต์\nคุปติ\nคุม\nคุ่ม\nคุ้ม\nคุย\nคุ้ย\nคุยหฐาน\nคุยหประเทศ\nคุรุ\nคุลา\nคุลิก่า\nคุลีการ\nคุหา\nคู\nคู่\nคู้\nคูณ\nคูถ\nคูน\nคูปอง\nคูเรียม\nคูหา\nเค้ก\nเค้เก้\nเค้ง\nเคณฑะ\nเคด\nเค็ด\nเคน\nเค้น\nเคเบิล\nเค็ม\nเคมี\nเคย\nเครง\nเคร่ง\nเครงครา\nเครงครำ\nเครดิต\nเครน\nเครา\nเคร่า\nเคราหณี\nเคราะห์\nเครียด\nเครียว\nเครือ\nเครื่อง\nเคล้ง\nเคล็ด\nเคล้น\nเคล้า\nเคล่าคล่อง\nเคลิบเคลิ้ม\nเคลิ้ม\nเคลีย\nเคลื่อน\nเคลือบ\nเคว้ง\nเคหะ\nเคหา\nเคอะ\nเค้า\nเคาน์เตอร์\nเคารพ\nเคาะ\nเคาะแคะ\nเคียง\nเคียด\nเคียน\nเคียม\nเคี่ยม\nเคียร\nเคียว\nเคี่ยว\nเคี้ยว\nเคือง\nแค\nแค่\nแค้\nแคแล\nแคดเมียม\nแค็ตตาล็อก\nแคแตร\nแคโทด\nแคน\nแค่น\nแค้น\nแคบ\nแคบหมู\nแคปซูล\nแคม\nแคร่\nแครก\nแครง\nแคระ\nแคลคูลัส\nแคลง\nแคลเซียม\nแคลน\nแคล้ว\nแคล่วคล่อง\nแคลอรี\nแคลิฟอร์เนียม\nแคว\nแควก\nแคว้น\nแคแสด\nแคะ\nโค\nโคก\nโคเคน\nโค่ง\nโค้ง\nโคจร\nโคเซแคนต์\nโคไซน์\nโคตร\nโคแทนเจนต์\nโคธา\nโคน\nโค่น\nโคบอลต์\nโคปผกะ\nโคม\nโคม่า\nโครก\nโครกคราก\nโครง\nโคร่ง\nโคร่งคร่าง\nโครม\nโครเมียม\nโครโมโซม\nโคราช\nโครำ\nโคล\nโคลง\nโคลน\nโควตา\nโคออร์ดิเนต\nใคร\nใคร่\nใคร่ครวญ\nไค\nไค้\nไคร้\nไคร้เครือ\nไคล\nไคล้\nฆนะ\nฆราวาส\nฆ้อง\nฆ่า\nฆาต\nฆาน\nฆานินทรีย์\nเฆี่ยน\nโฆรวิส\nโฆษก\nโฆษณา\nโฆษะ\nโฆษ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nเฉ่ง\nเฉด\nเฉท\nเฉนียน\nเฉพาะ\nเฉย\nเฉลย\nเฉลว\nเฉลา\nเฉลิม\nเฉลี่ย\nเฉลียง\nเฉลี่ยง\nเฉลียบ\nเฉลียว\nเฉวียง\nเฉวียน\nเฉอะแฉะ\nเฉา\nเฉาก๊วย\nเฉาฮื้อ\nเฉาะ\nเฉิด\nเฉิบ\nเฉียง\nเฉียงพร้านางแอ\nเฉียด\nเฉียบ\nเฉียว\nเฉี่ยว\nเฉือน\nเฉื่อย\nแฉ\nแฉ่\nแฉก\nแฉง\nแฉ่ง\nแฉลบ\nแฉล้ม\nแฉะ\nโฉ\nโฉ่\nโฉเก\nโฉ่งฉ่าง\nโฉงเฉง\nโฉด\nโฉนด\nโฉบ\nโฉเบ๊\nโฉม\nโฉลก\nไฉน\nไฉไล\nชก\nชคัตตรัย\nชง\nชงโค\nชงฆ์\nชงฆา\nชงโลง\nชฎา\nชฎามังษี\nชฎามังสี\nชฎิล\nชด\nชน\nชนก\nชนนี\nชนม์\nชนวน\nชนะ\nชนัก\nชนา\nชนาง\nชนิด\nชเนตตี\nชบา\nชม\nชมดชม้อย\nชมนาด\nชมพู\nชมพู่\nชมรม\nชม้อย\nชม้าย\nชไม\nชยา\nชโย\nชรทึง\nชรริน\nชรอุ่ม\nชระล้ำ\nชระลุ\nชระอาบ\nชระเอม\nชรัด\nชรา\nชล\nชโลง\nชโลม\nช่วง\nชวด\nชวน\nชวย\nช่วย\nชวร\nชวลิต\nชวา\nชวาล\nชวาลา\nช่อ\nชอก\nช็อก\nช็อกโกเลต\nช็อกโกแลต\nชอง\nช่อง\nช้อง\nชองระอา\nชอน\nช่อน\nช้อน\nชอบ\nชอม\nช้อย\nชอล์ก\nชอ่ำ\nชอุ่ม\nชะ\nชะคราม\nชะงอก\nชะง่อน\nชะงัก\nชะงัด\nชะง้ำ\nชะงุ้ม\nชะเง้อ\nชะเงื้อม\nชะแง้\nชะโงก\nชะฉ่า\nชะช่อง\nชะชะ\nชะช้า\nชะโด\nชะตา\nชะต้า\nชะนี\nชะเนาะ\nชะเนียง\nชะพลู\nชะเพลิง\nชะมด\nชะมบ\nชะมวง\nชะมัง\nชะมัด\nชะแม่\nชะรอย\nชะลอ\nชะลอม\nชะล่า\nชะลาน\nชะลิน\nชะลูด\nชะเลง\nชะเลย\nชะแล็ก\nชะแลง\nชะวาก\nชะวาด\nชะเวิกชะวาก\nชะแวง\nชะอม\nชะอ้อน\nชะเอม\nชะโอน\nชัก\nชักคราม\nชักช้า\nชัง\nชั่ง\nชังคา\nชังฆ\nชัชวาล\nชัฏ\nชัด\nชัดช้า\nชัน\nชั้น\nชันกาด\nชันชี\nชันตุ\nชันนะตุ\nชันนุ\nชันโรง\nชันษา\nชันสูตร\nชัปนะ\nชัพ\nชัมพูนท\nชัย\nชัยพฤกษ์\nชัยภูมิ\nชัลลุกา\nชั่ว\nชั้ว\nชัวชม\nชา\nช้า\nชาคระ\nชาคริต\nชาคริยานุโยค\nช่าง\nช้าง\nช้าช่อน\nชาญ\nชาด\nชาดก\nชาต\nชาตบุษย์\nชาตรี\nชาตะ\nชาตา\nชาติ\nชาน\nชานุ\nช้าปี่\nชาปีไหน\nช้าแป้น\nช้าพลู\nชาม\nชามพูนท\nชามาดร\nชามาดา\nชามาตุ\nชาย\nชายา\nชาล\nชาลา\nชาลินี\nช้าเลือด\nชาว\nชาวี\nชำ\nช่ำ\nช้ำ\nชำงัด\nชำงาย\nช่ำชอง\nชำนะ\nชำนัญ\nชำนัน\nชำนาญ\nชำนิ\nชำเนียร\nชำมะนาด\nชำมะเลียง\nชำร่วย\nชำระ\nช้ำรั่ว\nชำรุด\nชำเรา\nชำเราะ\nชำแรก\nชำแระ\nชำเลือง\nชำแหละ\nชิ\nชิง\nชิ่ง\nชิงชัน\nชิงช้า\nชิงช้าชาลี\nชิงชี่\nชิงฮื้อ\nชิชะ\nชิชิ\nชิณณะ\nชิด\nชิเดนทรีย์\nชิต\nชิตินทรีย์\nชิน\nชิ้น\nชินโต\nชิโนรส\nชิม\nชิมแปนซี\nชิยา\nชิรณะ\nชิระ\nชิวหา\nชิสา\nชี\nชี่\nชี้\nชีปะขาว\nชีผะขาว\nชีผ้าขาว\nชีพ\nชีฟอง\nชีรณ\nชีระ\nชีวเคมี\nชีวประวัติ\nชีวภาพ\nชีววิทยา\nชีวะ\nชีวัน\nชีวา\nชีวาตม์\nชีวาลัย\nชีวิต\nชีวิตักษัย\nชีวิน\nชีวี\nชืด\nชื่น\nชื้น\nชื่อ\nชุก\nชุกชี\nชุ้ง\nชุณห\nชุด\nชุติ\nชุน\nชุบ\nชุม\nชุ่ม\nชุมนุม\nชุมพร\nชุมพา\nชุมแพรก\nชุมรุม\nชุมแสง\nชุมเห็ด\nชุ่ย\nชุลมุน\nชุลี\nชุษณะ\nชู\nชู้\nเช็ค\nเช้ง\nเช้งวับ\nเชงเลง\nเช็ด\nเชน\nเช่น\nเชย\nเชลง\nเชลย\nเชลแล็ก\nเชลียง\nเชวง\nเชษฐะ\nเชษฐา\nเชอ\nเช่า\nเช้า\nเชาว์\nเชาวน์\nเชิง\nเชิญ\nเชิด\nเชิ้ต\nเชียง\nเชี่ยน\nเชียบ\nเชี่ยม\nเชียร\nเชียร์\nเชียว\nเชี่ยว\nเชื่อ\nเชื้อ\nเชือก\nเชื่อง\nเชือด\nเชือน\nเชื่อม\nแช\nแช่\nแช่ง\nแชงมา\nแชบ๊วย\nแช่ม\nแชร์\nแชล่ม\nแชสซี\nแชะ\nโชก\nโชค\nโชงโลง\nโชดก\nโชดึก\nโชต\nโชตก\nโชติ\nโชติก\nโชน\nโชมโรม\nโชย\nโชยงการ\nโชยชาย\nโชยติส\nโชว์\nใช่\nใช้\nไช\nไชนะ\nไชย\nไชโย\nซก\nซ่ก\nซงดำ\nซ่งฮื้อ\nซด\nซน\nซ้น\nซบ\nซม\nซวดเซ\nซวน\nซวย\nซอ\nซอก\nซอง\nซ่อง\nซ้อง\nซองแมว\nซ้องแมว\nซอน\nซ่อน\nซ้อน\nซอม\nซ่อม\nซ้อม\nซอมซ่อ\nซอย\nซอส\nซัก\nซักส้าว\nซัง\nซั้ง\nซัด\nซับ\nซัลฟา\nซั้ว\nซา\nซ่า\nซาก\nซาง\nซ่าง\nซาด\nซาน\nซ่าน\nซาบซ่าน\nซาบซึ้ง\nซ่าโบะ\nซาแมเรียม\nซ้าย\nซาลาเปา\nซาว\nซ่าหริ่ม\nซำ\nซ้ำ\nซิ\nซี\nซิก\nซิกข์\nซิกซี้\nซิกแซ็ก\nซิการ์\nซิงโคนา\nซิ่น\nซินนามิก\nซินแส\nซิบ\nซิป\nซิฟิลิส\nซิลิคอน\nซิว\nซี่\nซีก\nซีเซียม\nซีด\nซี้ด\nซีนอน\nซีป่าย\nซีเมนต์\nซีเรียม\nซีลีเนียม\nซีอิ๊ว\nซึก\nซึง\nซึ่ง\nซึ้ง\nซึม\nซื่อ\nซื้อ\nซุก\nซุง\nซุน\nซุบ\nซุป\nซุ่ม\nซุ้ม\nซุ่มซ่าม\nซุย\nซู่\nซูโครส\nซูด\nซู้ด\nซูดซาด\nซูบ\nเซ\nเซ็ก\nเซแคนต์\nเซ็ง\nเซ่ง\nเซ้ง\nเซ็งแซ่\nเซต\nเซน\nเซ็น\nเซ่น\nเซนติกรัม\nเซนติเกรด\nเซนติเมตร\nเซนติลิตร\nเซปักตะกร้อ\nเซราะ\nเซรุ่ม\nเซลเซียส\nเซลล์\nเซลลูลอยด์\nเซลลูโลส\nเซ่อ\nเซอร์โคเนียม\nเซอะ\nเซา\nเซ้าซี้\nเซาะ\nเซิง\nเซิ้ง\nเซียน\nเซียบ\nเซียมซี\nเซียว\nเซี่ยว\nเซี่ยวกาง\nเซื่อง\nแซ\nแซ่\nแซ็กคาริน\nแซง\nแซงแซว\nแซด\nแซบ\nแซม\nแซยิด\nแซ่ว\nแซะ\nโซ\nโซ่\nโซก\nโซ่ง\nโซงโขดง\nโซเซ\nโซดา\nโซเดียม\nโซม\nโซรม\nโซลา\nไซ\nไซ้\nไซเกิล\nไซโคลน\nไซน์\nไซยาไนด์\nไซร้\nไซเรน\nไซโล\nฌาน\nฌาปน\nฌาปนกิจ\nฌาปนสถาน\nเฌอ\nเฌอเอม\nญวน\nญัตติ\nญาณ\nญาติ\nญานาซะฮ์\nญิบ\nญี่ปุ่น\nเญยธรรม\nไญยธรรม\nฎีกา\nฐกัด\nฐากูร\nฐาน\nฐานะ\nฐานันดร\nฐานานุกรม\nฐานานุรูป\nฐานานุศักดิ์\nฐานียะ\nฐาปน\nฐาปนา\nฐายี\nฐิต\nฐิติ\nฑาหก\nฑาหะ\nเฒ่า\nณรงค์\nเณร\nดก\nดง\nด้ง\nด้น\nดนโด่\nดนตรี\nดนัย\nดนุ\nดนู\nดบัสวิน\nดบัสวี\nดม\nดรงค์\nดรณี\nดรรชนี\nดราฟต์\nดรุณ\nดรุณี\nดล\nดวง\nด้วง\nดวด\nด่วน\nด้วน\nด้วย\nดอก\nดอง\nด่อง\nด้อง\nดองฉาย\nดองดึง\nดอด\nดอน\nด่อน\nดอม\nด้อม\nดอย\nด้อย\nดอลลาร์\nดะ\nดะโต๊ะ\nดะหมัง\nดัก\nดักดาน\nดักแด้\nดัง\nดั่ง\nดั้ง\nดัชนี\nดัด\nดัตช์\nดัน\nดั้น\nดับ\nดัมพ์\nดั้วเดี้ย\nดัสกร\nดา\nด่า\nดาก\nด่าง\nด้าง\nดาด\nดาน\nด่าน\nด้าน\nดาบ\nดาบส\nดาม\nด้าม\nด้ามจิ้ว\nดามพ์\nดาย\nด้าย\nดารกะ\nดารณี\nดารดาษ\nดาระ\nดารา\nดาล\nดาลัด\nดาว\nด่าว\nด้าว\nดาวดึงส์\nดาวบส\nดาษ\nดาษดา\nดำ\nด่ำ\nด้ำ\nดำกล\nดำเกิง\nดำแคง\nดำดง\nดำนาณ\nดำเนิน\nดำบล\nดำรง\nดำรวจ\nดำรัส\nดำริ\nดำรี\nดำรู\nดำฤษณา\nดำเลิง\nดิก\nดิ่ง\nดิฉัน\nดิฐ\nดิตถ์\nดิถี\nดิน\nดิ้น\nดิบ\nดิรัจฉาน\nดิลก\nดิ่ว\nดิ้ว\nดิ้วเดี้ยว\nดิษฐ์\nดิสโพรเซียม\nดี\nดีเซล\nดีด\nดีดีที\nดีบุก\nดีปลี\nดีเปรสชัน\nดีหมี\nดีหลี\nดึก\nดึง\nดึ่ง\nดึ่ม\nดื่น\nดื่ม\nดือ\nดื้อ\nดุ\nดุก\nดุกดิก\nดุกทะเล\nดุ้ง\nดุ้งดิ้ง\nดุจ\nดุด\nดุน\nดุ้น\nดุบ\nดุม\nดุ่ม\nดุ่ย\nดุรงค์\nดุริยะ\nดุริยางค์\nดุริยางคศาสตร์\nดุริยางคศิลป์\nดุล\nดุษฎี\nดุษณี\nดุษณีภาพ\nดุษิต\nดุสิต\nดุเหว่า\nดู\nดูกค่าง\nดูกร\nดูด\nดูรา\nดูแล\nเด\nเด่\nเดก\nเด็ก\nเดกซ์โทรส\nเดคากรัม\nเดคาเมตร\nเดคาลิตร\nเด้ง\nเด็จ\nเดช\nเดชน์\nเดชนะ\nเดชะ\nเดโช\nเดซิกรัม\nเดซิเมตร\nเดซิลิตร\nเด็ด\nเดน\nเด่น\nเดนมาร์ก\nเดรัจฉาน\nเด๋อ\nเด๋อด๋า\nเดา\nเด้า\nเดาะ\nเดิน\nเดิ่น\nเดิม\nเดียง\nเดียด\nเดียรดาษ\nเดียรถีย์\nเดียรัจฉาน\nเดียว\nเดี่ยว\nเดี๋ยว\nเดียะ\nเดื่อ\nเดือก\nเดื่อง\nเดือด\nเดือน\nเดือย\nแด\nแด่\nแดก\nแด็ก\nแดกงา\nแดกแด้\nแดง\nแดด\nแดน\nแด่น\nแด่ว\nแดะ\nแดะแด๋\nโด\nโด่\nโดกเดก\nโด่ง\nโดด\nโดน\nโดม\nโดมร\nโดย\nโดรณ\nใด\nได\nได้\nไดแซ็กคาไรด์\nไดนาโม\nไดนาไมต์\nไดโนเสาร์\nไดเรกตริกซ์\nตก\nต๊กโต\nตง\nต๋ง\nตงฉิน\nตงิด\nตงุ่น\nตด\nตติย\nตถาคต\nตน\nต้น\nตนัย\nตนุ\nตบ\nตบะ\nตปนียะ\nตม\nต้ม\nตมูก\nตยาคี\nตรง\nตรณี\nตรม\nตรรก\nตรรกะ\nตรลบ\nตรลอด\nตรลาด\nตรวจ\nตรวน\nตรอก\nตรอง\nตรอมใจ\nตรอมตรม\nตระ\nตระกล\nตระกวน\nตระกอง\nตระการ\nตระกูล\nตระคัร\nตระเตรียม\nตระนาว\nตระบก\nตระบอก\nตระบอง\nตระบัด\nตระบัน\nตระเบ็ง\nตระแบก\nตระแบง\nตระโบม\nตระพอง\nตระพัง\nตระลาการ\nตระวัน\nตระเว็ด\nตระเวน\nตระสัก\nตระหง่าน\nตระหนก\nตระหนัก\nตระหน่ำ\nตระหนี่\nตรัง\nตรังค์\nตรับ\nตรับฟัง\nตรัย\nตรัยตรึงศ์\nตรัส\nตรัสสา\nตรา\nตรากตรำ\nตราชู\nตราบ\nตราสัง\nตรำ\nตริ\nตริว\nตรี\nตรีปวาย\nตรีพิธพรรณ\nตรียัมปวาย\nตรึก\nตรึง\nตรุ\nตรุณ\nตรุณะ\nตรุษ\nตรู\nตรู่\nตฤณ\nตฤตีย\nตฤษณา\nตลก\nตลบ\nตลอด\nตลับ\nตลาด\nตลิ่ง\nตลึง\nตวง\nต่วน\nต้วมเตี้ยม\nตวัก\nตวัด\nตวาด\nตอ\nตอม่อ\nต่อ\nต้อ\nตอก\nต๊อก\nต๊อกต๋อย\nตอง\nต้อง\nตองกราย\nต้องเต\nตองแตก\nต่องแต่ง\nตองเหลือง\nตอด\nตอน\nต้อน\nตอบ\nตอเบา\nตอม\nต่อม\nต๋อม\nต่อย\nต้อย\nต้อยตริ่ง\nต้อยติ่ง\nต้อยตีวิด\nตอแย\nตอร์ปิโด\nต่อไส้\nตอแหล\nตะ\nตะกรน\nตะกร้อ\nตะกรัน\nตะกรับ\nตะกร้า\nตะกราม\nตะกรุด\nตะกรุม\nตะกรุมตะกราม\nตะกละ\nตะกลาม\nตะกวด\nตะกอ\nตะกอน\nตะกัง\nตะกั่ว\nตะกาง\nตะกาด\nตะกาย\nตะกาว\nตะกุกตะกัก\nตะกุย\nตะกู\nตะกูด\nตะเกียกตะกาย\nตะเกียง\nตะเกียบ\nตะแก\nตะแก่\nตะแกรง\nตะโก\nตะโก้\nตะโกก\nตะโกน\nตะโกรง\nตะโกรม\nตะไกร\nตะขบ\nตะขอ\nตะขาบ\nตะขิดตะขวง\nตะเข้\nตะเข็บ\nตะโขง\nตะคร้อ\nตะครอง\nตะครั่นตะครอ\nตะคร้ำ\nตะคริว\nตะคิว\nตะครุบ\nตะคอก\nตะคัน\nตะค้า\nตะคาก\nตะค้าน\nตะคุ่ม\nตะเครียว\nตะเคียว\nตะเคียน\nตะแคง\nตะไคร่\nตะไคร้\nตะเฆ่\nตะนอย\nตะนาว\nตะบม\nตะบอง\nตะบอย\nตะบัน\nตะบิ้ง\nตะบิด\nตะบิดตะบอย\nตะบี้ตะบัน\nตะบึง\nตะบูน\nตะเบ็ง\nตะเบ็งมาน\nตะเบ๊ะ\nตะแบก\nตะแบง\nตะโบม\nตะไบ\nตะปบ\nตะปลิง\nตะปิ้ง\nตะปุ่มตะป่ำ\nตะปู\nตะพง\nตะพด\nตะพอง\nตะพัก\nตะพัง\nตะพัด\nตะพั้น\nตะพาก\nตะพาน\nตะพาบ\nตะพาย\nตะพึด\nตะพึดตะพือ\nตะพุ่น\nตะเพรา\nตะเพิง\nตะเพิด\nตะเพียน\nตะโพก\nตะโพง\nตะโพน\nตะเภา\nตะใภ้\nตะม่อ\nตะมอย\nตะรังกะนู\nตะรังตังกวาง\nตะรังตังช้าง\nตะราง\nตะลอง\nตะลอน\nตะล่อม\nตะละ\nตะลาน\nตะลิงปลิง\nตะลิบ\nตะลีตะลาน\nตะลึง\nตะลึงพรึงเพริด\nตะลุง\nตะลุ่ม\nตะลุ่มนก\nตะลุมบอน\nตะลุ่มโปง\nตะลุมพอ\nตะลุมพุก\nตะลุย\nตะเลง\nตะแลงแกง\nตะไล\nตะวัน\nตะเวน\nตะหลิว\nตะหลุก\nตะหลุง\nตะแหง่ว\nตะแหมะแขะ\nตะโหงก\nตัก\nตักกะ\nตักเตือน\nตั๊กแตน\nตักษัย\nตัง\nตั่ง\nตั้ง\nตังเก\nตังฉ่าย\nตังเม\nตังวาย\nตังโอ๋\nตัจฉก\nตัจฉนี\nตัณฑุล\nตัณหา\nตัด\nตัน\nตันตระ\nตันติ\nตันหยง\nตับ\nตับปิ้ง\nตัว\nตั๋ว\nตัวจี๊ด\nตัวตืด\nตั้วโผ\nตั้วเหี่ย\nตา\nตาก\nตากวาง\nต่าง\nตาด\nตาน\nต่าน\nต้าน\nตานนกกด\nตานี\nตาบ\nตาม\nตามะแน\nตามิน\nตาย\nตาราไต\nตาล\nตาลุ\nต๋าว\nตาเสือ\nตาหนู\nตาฬ\nตำ\nต่ำ\nตำนาน\nตำบล\nตำแบ\nตำแย\nตำรวจ\nตำรับ\nตำรา\nตำรุ\nตำลึง\nตำเสา\nตำหนัก\nตำหนิ\nตำแหน่ง\nติ\nติก\nติ๊ก\nติกะ\nติกาหรัง\nติง\nติ่ง\nติ๋ง\nติ่งตั่ง\nติ๋งต่าง\nติงส\nติงสติ\nติณ\nติด\nติตติกะ\nติตติร\nติตถ\nติตถะ\nติถี\nติมิงคละ\nติรัจฉาน\nติลก\nติละ\nติ้ว\nตี\nตี่\nตีน\nตีบ\nตีรถะ\nตีระ\nตึ\nตึก\nตึ้ก\nตึ้กตั้ก\nตึง\nตึดตื๋อ\nตึ๊ดตื๋อ\nตืด\nตื่น\nตื้น\nตื้อ\nตื๊อ\nตื๋อ\nตุ\nตุ๊\nตุ๊กแก\nตุ๊กตา\nตุ๊กต่ำ\nตุกติก\nตุ๊กติ๊ก\nตุ๊กตุ่น\nตุ๊กตุ๋ย\nตุง\nตุ้งก่า\nตุ้งติ้ง\nตุ๊ดตู่\nตุน\nตุ่น\nตุ๋น\nตุนาหงัน\nตุบ\nตุ้บ\nตุ๊บป่อง\nตุปัดตุป่อง\nตุปัดตุเป๋\nตุ่ม\nตุ้ม\nตุ๋ม\nตุ้มกว้าว\nตุมกา\nตุ้มแซะ\nตุมตัง\nตุ้มเต๋น\nตุ้มปี่\nตุมพะ\nตุ่ย\nตุ้ย\nตุ๊ย\nตุ๋ยตุ่ย\nตุรคะ\nตุรงค์\nตุล\nตุลา\nตุหรัดตุเหร่\nตู\nตู่\nตู้\nตูก\nตูด\nตูบ\nตูม\nเต๊ก\nเต็ง\nเต่ง\nเตช\nเตโช\nเต้น\nเต็นท์\nเต็ม\nเตย\nเตร่\nเตร็ด\nเตรตา\nเตรียม\nเตรียมตรม\nเตละ\nเตลิด\nเตว็ด\nเต่อ\nเตอะ\nเตะ\nเตา\nเต่า\nเต้า\nเต๋า\nเต่าเกียด\nเต้าเจี้ยว\nเต้าทึง\nเต้าหู้\nเต้าฮวย\nเต๊าะ\nเตาะแตะ\nเติ่ง\nเติบ\nเติม\nเตี้ย\nเตียง\nเตียน\nเตียบ\nเตี๋ยม\nเตียรถ์\nเตียว\nเตี่ยว\nเตือน\nแต่\nแต้\nแตก\nแตง\nแต่ง\nแตงเม\nแต้จิ๋ว\nแตด\nแต๊ดแต๋\nแตน\nแต้ม\nแตร\nแตระ\nแต้ว\nแต้วแร้ว\nแต้วแล้ว\nแต่ว่า\nแต้แว้ด\nแตะ\nโต\nโต้\nโตก\nโต่ง\nโต้ง\nโตงเตง\nโตฎก\nโต๊ด\nโตน\nโตนด\nโต้โผ\nโตมร\nโตย\nโตรก\nโต๊ะ\nใต้\nไต\nไต่\nไต้\nไต๋\nไตร\nไตรกิศยา\nไตรดายุค\nไตร่ตรอง\nไตรย\nไต้หวัน\nถก\nถกล\nถงาด\nถด\nถนน\nถนอม\nถนัด\nถนัน\nถนำ\nถนิม\nถม\nถ่ม\nถมอ\nถมึงทึง\nถลก\nถลกบาตร\nถลน\nถล่ม\nถลอก\nถลัน\nถลา\nถลาก\nถลาย\nถลำ\nถลึงตา\nถลุง\nถ่วง\nถ้วน\nถ้วย\nถวัล\nถวัลย์\nถวาย\nถวิน\nถวิล\nถ่อ\nถ้อ\nถอก\nถอง\nถ่อง\nถ้อง\nถอด\nถอน\nถอบ\nถอบแถบ\nถ่อม\nถอย\nถ่อย\nถ้อย\nถะ\nถะถั่น\nถะมัดถะแมง\nถัก\nถัง\nถั่ง\nถัด\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นยา\nบิลเลียด\nบิวเรตต์\nบิสมัท\nบี้\nบีฑา\nบีตา\nบีบ\nบีเยศ\nบึก\nบึกบึน\nบึง\nบึ่ง\nบึ้ง\nบุ\nบุก\nบุคคล\nบุคลากร\nบุคลาธิษฐาน\nบุคลิก\nบุง\nบุ่ง\nบุ้ง\nบุ้งกี๋\nบุญ\nบุญญาธิการ\nบุญญานุภาพ\nบุญญาภินิหาร\nบุญญาภิสังขาร\nบุณฑริก\nบุณมี\nบุณย์\nบุตร\nบุตรี\nบุถุชน\nบุทคล\nบุนนาค\nบุบ\nบุบบิบ\nบุปผชาติ\nบุพกรรม\nบุพการี\nบุพกิจ\nบุพชาติ\nบุพทักษิณ\nบุพนิมิต\nบุพบท\nบุพพาจารย์\nบุพเพสันนิวาส\nบุพโพ\nบุ๋ม\nบุ่มบ่าม\nบุ้ย\nบุรณะ\nบุรพทิศ\nบุรพบท\nบุรพาจารย์\nบูรพาจารย์\nบุระ\nบุราณ\nบุรินทร์\nบุริมทิศ\nบุริมพรรษา\nบุริมสิทธิ\nบุรี\nบุรุษ\nบุโรทั่ง\nบุษกร\nบุษบก\nบุษบง\nบุษบัน\nบุษบา\nบุษบามินตรา\nบุษปราค\nบุษปะ\nบุษย์\nบุษย์น้ำทอง\nบุษยมาส\nบุษยะ\nบุษราคัม\nบุหงัน\nบุหงา\nบุหรง\nบุหรี่\nบุหลัน\nบู่\nบู้\nบูชนียสถาน\nบูชา\nบูชิต\nบูด\nบูดู\nบูร\nบูรณ์\nบูรณภาพ\nบูรณมี\nบูรณะ\nบูรณาการ\nบูรพ์\nบูรพะ\nบูรพา\nเบ้\nเบ่ง\nเบ๊จี๋\nเบญกานี\nเบญจกัลยาณี\nเบญจกามคุณ\nเบญจขันธ์\nเบญจดุริยางค์\nเบญจธรรม\nเบญจบรรพต\nเบญจพรรณ\nเบญจเพส\nเบญจมาศ\nเบญจรงค์\nเบญจศก\nเบญจศีล\nเบญจะ\nเบญจา\nเบญจางค์\nเบญจางคประดิษฐ์\nเบญญา\nเบญพาด\nเบ็ด\nเบ็ดเตล็ด\nเบ็ดเสร็จ\nเบน\nเบนซิน\nเบรก\nเบริลเลียม\nเบส\nเบ้อ\nเบอร์\nเบอร์คีเลียม\nเบ้อเร่อ\nเบ้อเร่อเท่อ\nเบ้อเริ่ม\nเบ้อเริ่มเทิ่ม\nเบอะ\nเบอะบะ\nเบะ\nเบา\nเบ้า\nเบาราณ\nเบาะ\nเบิก\nเบิ่ง\nเบี้ย\nเบี่ยง\nเบียด\nเบียน\nเบียร์\nเบี้ยว\nเบือ\nเบื่อ\nเบื้อ\nเบื้อง\nเบือน\nแบ\nแบ้\nแบก\nแบคทีเรีย\nแบ่ง\nแบงก์\nแบดมินตัน\nแบตเตอรี่\nแบน\nแบนโจ\nแบบ\nแบ็บ\nแบเรียม\nแบหลา\nแบะ\nโบ\nโบ้\nโบ๋\nโบก\nโบกขรณี\nโบกขรพรรษ\nโบชุก\nโบต\nโบนัส\nโบ๊เบ๊\nโบย\nโบรมีน\nโบรอน\nโบราณ\nโบสถ์\nใบ\nใบ้\nไบ่\nปก\nปกติ\nปกรณ์\nปกรณัม\nปกิณกะ\nปกีรณัม\nปโกฏิ\nปง\nป่ง\nปงช้าง\nปฎล\nปฏัก\nปฏิกรณ์\nปฏิกรรม\nปฏิการะ\nปฏิกิริยา\nปฏิกูล\nปฏิคม\nปฏิคหิต\nปฏิคาหก\nปฏิฆะ\nปฏิชีวนะ\nปฏิญญา\nปฏิญาณ\nปฏิทิน\nปฏิบถ\nปฏิบัติ\nปฏิปทา\nปฏิปักษ์\nปฏิปัน\nปฏิปุจฉาพยากรณ์\nปฏิปุจฉาวาที\nปฏิพัทธ์\nปฏิพากย์\nปฏิภาค\nปฏิภาณ\nปฏิมา\nปฏิมากร\nปฏิยุทธ์\nปฏิรพ\nปฏิรูป\nปฏิโลม\nปฏิวัติ\nปฏิวาต\nปฏิวาท\nปฏิเวธ\nปฏิสนธิ\nปฏิสวะ\nปฏิสังขรณ์\nปฏิสันถาร\nปฏิสัมภิทา\nปฏิเสธ\nปฐพี\nปฐม\nปฐวี\nปณต\nปณาม\nปณิธาน\nปณิธิ\nปณีต\nปด\nปดิวรัดา\nปติ\nปถพี\nปถมัง\nปถวี\nปทัฏฐาน\nปทัสถาน\nปทานุกรม\nปทีป\nปทุม\nปน\nป่น\nปนัดดา\nปปัญจะ\nปม\nปรนัย\nปรปักษ์\nปรโลก\nปรวาที\nปรก\nปรกติ\nปรง\nปรตยักษ์\nปรน\nปรนนิบัติ\nปรนิมมิตวสวัตดี\nปรบ\nปรปักษ์\nปรมัตถ์\nปรมาจารย์\nปรมาณู\nปรมาภิไธย\nปรมาภิเษก\nปรมินทร์\nบรเมนทร์\nปรเมศวร์\nปรเมษฐ์\nปรวด\nปรวนแปร\nปรศุ\nปรสิต\nปร๋อ\nปรองดอง\nปรอด\nปรอท\nปรอย\nประ\nประกบ\nประกฤต\nประกฤติ\nประกล\nประกวด\nประกวดประขัน\nประกอบ\nประกัน\nประกับ\nประกาย\nประกายพรึก\nประการ\nประกาศ\nประกาศนียบัตร\nประกาศิต\nประกำ\nประกิด\nประกิต\nประคด\nประคนธรรพ\nประคนธรรพ์\nประคบ\nประคบประหงม\nประคอง\nประคับประคอง\nประคัลภ์\nประคำ\nประคิ่น\nประคุณ\nประเคน\nประเคราะห์\nประโคน\nประโคนธรรพ\nประโคนธรรพ์\nประโคม\nประจง\nประจญ\nประจบ\nประจบประแจง\nประจวบ\nประจ๋อประแจ๋\nประจักษ์\nประจักษนิยม\nประจัญ\nประจัน\nประจาก\nประจาค\nประจาน\nประจำ\nประจิม\nประจิ้มประเจ๋อ\nประจุ\nประจุคมน์\nประจุบัน\nประเจก\nประเจิด\nประเจิดประเจ้อ\nประเจียด\nประแจ\nประชด\nประชน\nประชวม\nประชวร\nประชัน\nประชา\nประชาธิปไตย\nประชิด\nประชี\nประชุม\nประเชิญ\nประณต\nประณม\nประณาม\nประณิธาน\nประณิธิ\nประณีต\nประณุท\nประดง\nประดน\nประดวน\nประดอน\nประดอย\nประดัก\nประดักประเดิด\nประดัง\nประดับ\nประดา\nประดาก\nประดาป\nประดาษ\nประดิชญา\nประดิดประดอย\nประดิทิน\nประดิษฐ์\nประดิษฐกรรม\nประดิษฐาน\nประดุง\nประดุจ\nประดู่\nประเด\nประเด็น\nประเดิม\nประเดียง\nประเดี๋ยว\nประเดี๋ยวประด๋าว\nประแดง\nประแดะ\nประโดง\nประโดย\nประตง\nประตัก\nประตาป\nประติชญา\nประติญาณ\nประติทิน\nประติมากร\nประติมากรรม\nประติรพ\nประตู\nประถม\nประถมจินดา\nประทม\nประท้วง\nประทวน\nประทักษ์\nประทักษิณ\nประทัง\nประทัด\nประทับ\nประทาน\nประทาย\nประทาศี\nประทิน\nประทิ่น\nประทีป\nประทุฐ\nประทุน\nประทุษ\nประทุษฐ์\nประเทศ\nประเทา\nประเทียด\nประเทียบ\nประเทือง\nประธาน\nประธานาธิบดี\nประนม\nประนอ\nประนอม\nประนัง\nประนัปดา\nประนีประนอม\nประปราน\nประปราย\nประปา\nประเปรี้ยง\nประเปรียว\nประพจน์\nประพนธ์\nประพรม\nประพฤติ\nประพฤทธิ์\nประพัด\nประพัทธ์\nประพันธ์\nประพาต\nประพาส\nประพาสมหรณพ\nประพาฬ\nประพิณ\nประพิมพ์ประพาย\nประพุทธ์\nประเพณี\nประโพธ\nประไพ\nประไพร\nประภพ\nประภัสสร\nประภา\nประภาคาร\nประภาพ\nประภาษ\nประภาส\nประเภท\nประมง\nประมวล\nประมาณ\nประมาท\nประมุข\nประมุท\nประมูล\nประเมิน\nประโมง\nประโมทย์\nประยงค์\nประยุกต์\nประยุทธ์\nประยุร\nประยูร\nประโยค\nประโยชน์\nประโรหิต\nประลมพ์\nประลอง\nประลัย\nประลาต\nประลาย\nประลุ\nประเล่ห์\nประเล้าประโลม\nประโลม\nประวรรต\nประวรรตน์\nประวัติ\nประวาล\nประวาลปัทม์\nประวาส\nประวิง\nประวิช\nประวิตร\nประวิน\nประวีณ\nประเวณี\nประเวศ\nประเวศน์\nประศม\nประศาสน์\nประศุ\nประสก\nประสงค์\nประสบ\nประสพ\nประสม\nประสะ\nประสัก\nประสันนาการ\nประสัยห์\nประสา\nประสาท\nประสาธน์\nประสาน\nประสาร\nประสิทธิ์\nประสิทธิผล\nประสิทธิภาพ\nประสีประสา\nประสูต\nประสูติ\nประเสบัน\nประเสบันอากง\nประเสริฐ\nประหนึ่ง\nประหม่า\nประหยัด\nประหลาด\nประหล่ำ\nประหวัด\nประหวั่น\nประหัตประหาร\nประหาณ\nประหาร\nประเหล\nประเหส\nประไหมสุหรี\nประอบ\nประอร\nปรัก\nปรักปรำ\nปรักมะ\nปรัง\nปรัชญา\nปรัตถจริยา\nปรัตยุบัน\nปรัน\nปรับ\nปรัมปรา\nปรัศจิม\nปรัศนา\nปรัศนี\nปรัศว์\nปรัสสบท\nปร่า\nปรากฏ\nปรากรม\nปรากฤต\nปราการ\nปราง\nปรางค์\nปราจีน\nปราชญ์\nปราชญา\nปราชัย\nปราณ\nปราณี\nปราด\nปราน\nปรานี\nปราบ\nปราบดาภิเษก\nปราปต์\nปราม\nปรามาส\nปราโมช\nปราโมทย์\nปราย\nปรารถนา\nปรารภ\nปรารมภ์\nปราศ\nปราศจาก\nปราศรัย\nปราษณี\nปราษาณ\nปราสัย\nปราสาท\nปรำ\nปริ\nปริก\nปริกขาร\nปริกรรม\nปริกัป\nปริคณห์\nปริจาค\nปริจาริกา\nปริเฉท\nปริชน\nปริซึม\nปริญญา\nปริณาม\nปริณายก\nปริต\nปริตตะ\nปริตโตทก\nปริตร\nปริทรรศน์\nปริทัยหัคคี\nปริทัศน์\nปริเทพ\nปริเทวะ\nปรินิพพาน\nปริบ\nปริบท\nปริปันถ์\nปริพนธ์\nปริพัตร\nปริพันธ์\nปริพาชก\nปริภัณฑ์\nปริภาษ\nปริภูมิ\nปริโภค\nปริ่ม\nปริมณฑล\nปริมาณ\nปริมาตร\nปริยัติ\nปริยานุช\nปริยาย\nปริเยศ\nปริโยสาน\nปริวรรต\nปริวัตร\nปริวาร\nปริวาส\nปริวิตก\nปริเวณ\nปริศนา\nปริษัท\nปริสัญญู\nปริสุทธิ\nปริหาน\nปริหาร\nปรี่\nปรีชญา\nปรีชา\nปรี๊ด\nปรีดา\nปรีดิ\nปรีดิ์\nปรีดี\nปรีติ\nปรียะ\nปรียา\nปรึก\nปรึกษา\nปรึง\nปรือ\nปรื๋อ\nปรุ\nปรุง\nปรู\nปรู๋\nปรูด\nปรู๊ด\nปรู๊ดปร๊าด\nปรู๊ฟ\nปฤงคพ\nปฤจฉา\nปฤษฎางค์\nปฤษฐ\nปลก\nปลกเปลี้ย\nปลง\nปล่ง\nปลด\nปล้น\nปลวก\nปลอก\nปล่อง\nปล้อง\nปลอด\nปล้อน\nปลอบ\nปลอม\nปล่อย\nปละ\nปลัก\nปลั๊ก\nปลัง\nปลั่ง\nปลัด\nปลัดขิก\nปลา\nปลาต\nปลาบ\nปลาย\nปลาสเตอร์\nปลาสนาการ\nปล้ำ\nปลิง\nปลิด\nปลิ้น\nปลิโพธ\nปลิม\nปลิ่ม\nปลิว\nปลี\nปลีก\nปลื้ม\nปลุก\nปลูก\nปวกเปียก\nปวง\nป่วง\nปวด\nป่วน\nป้วน\nป้วนเปี้ยน\nป่วย\nปวัตน์\nปวารณา\nปวาล\nปวาส\nปวาฬ\nปวิช\nปวิตร\nปวิธ\nปวิเวก\nปวีณ\nปวุติ\nปเวณี\nปเวส\nปเวสน์\nปศุ\nปสันนะ\nปสันนาการ\nปสาท\nปสาน\nปสาสน์\nปสุ\nปสุต\nปสูติ\nปหังสนะ\nปหาน\nปหาร\nปหาส\nปอ\nป้อ\nป๋อ\nปอก\nปอง\nป่อง\nป้อง\nปอด\nปอน\nป้อน\nปอนด์\nปอเนาะ\nปอบ\nป้อแป้\nปอม\nป้อม\nป๋อม\nปอย\nป้อย\nป้อยอ\nปะ\nปะกน\nปะกัง\nปะการัง\nปะกำ\nปะขาว\nปะงาบ\nปะตาปา\nปะตาระกาหลา\nปะติดปะต่อ\nปะติยาน\nปะทะ\nปะทะปะทัง\nปะทุ\nปะทุน\nปะปน\nปะมง\nปะราลี\nปะรำ\nปะไร\nปะลอม\nปะเลง\nปะแล่ม\nปะโลง\nปะวะหล่ำ\nปะหงับ\nปะหนัน\nปะหัง\nปะเหลาะ\nปัก\nปักข์\nปักเป้า\nปักษ์\nปักษา\nปักษิน\nปักษี\nปัง\nปั๋ง\nปังสุ์\nปังสุกุล\nปัจจัตตะ\nปัจจันต์\nปัจจันตคาม\nปัจจันตชนบท\nปัจจันตประเทศ\nปัจจัย\nปัจจามิตร\nปัจจุคมน์\nปัจจุทธรณ์\nปัจจุบัน\nปัจจุสมัย\nปัจเจก\nปัจโจปการกิจ\nปัจฉา\nปัจฉิม\nปัจถรณ์\nปัจนึก\nปัจยาการ\nปัจเวกขณ์\nปัชชุน\nปัญจนที\nปัญจวัคคีย์\nปัญจก\nปัญจกะ\nปัญจมี\nปัญจวีสติ\nปัญญัติ\nปัญญา\nปัญหา\nปัฏ\nปัฏนะ\nปัฐยาวัต\nปัณฑรหัตถี\nปัณณะ\nปัณณาส\nปัณณาสก์\nปัณรสี\nปัณหิ\nปัด\nปัตคาด\nปัตตะ\nปัตตานึก\nปัตตานุโมทนา\nปัตตาเวีย\nปัตติ\nปัตติก\nปัตถร\nปัตถะ\nปัตนิ\nปัตนี\nปัตหล่า\nปัถพี\nปัถวี\nปัทม์\nปัทมะ\nปัทมาสน์\nปัน\nปั่น\nปั้น\nปั้นจั่น\nปันจุเหร็จ\nปั้นลม\nปั้นหยา\nปั้นเหน่ง\nปับ\nปั๊บ\nปัปผาสะ\nปัพพาชนียกรรม\nปัพภาระ\nปั๊ม\nปัยกา\nปัยยิกา\nปัวเปีย\nปัศจิม\nปัศตัน\nปัสสาวะ\nปัสสาสะ\nปา\nป่า\nป้า\nปาก\nปาง\nป้าง\nปาจรีย์\nปาจิตตีย์\nปาจีน\nปาฏลิ\nปาฏิบท\nปาฏิบุคลิก\nปาฏิโภค\nปาฏิหาริย์\nปาฐก\nปาฐกถา\nปาฐะ\nปาณกชาติ\nปาณะ\nปาณาติบาต\nปาณิ\nปาณี\nปาด\nปาติโมกข์\nปาตี\nปาเต๊ะ\nปาท่องโก๋\nปาทังกา\nปาทาน\nปาทุกา\nปาน\nป่าน\nป้าน\nปานะ\nปานียะ\nป้าบ\nป๊าบ\nปาพจน์\nปาม\nปาโมกข์\nป่าย\nป้าย\nปายาส\nปาร์เกต์\nปารมี\nปารเมศ\nปาราชิก\nปาริฉัตร\nปาริชาต\nปารุสกวัน\nปาล\nปาล์ม\nปาลิไลยก์\nปาลี\nปาว\nป่าว\nป๊าว\nปาวาร\nปาษาณ\nปาส\nปาสาณ\nปาสาทิกะ\nปาหนัน\nปาหี่\nปำ\nป้ำ\nป้ำเป๋อ\nปิกนิก\nปิ้ง\nปิงคละ\nปิงปอง\nปิฎก\nปิฏฐะ\nปิฐิ\nปิณฑะ\nปิด\nปิตตะ\nปิตา\nปิตามหัยกา\nปิตามหัยยิกา\nปิตุ\nปิตุจฉา\nปิตุภูมิ\nปิตุลา\nปิโตรเลียม\nปิ่น\nปิ่นแก้ว\nปิ่นโต\nปิปผลี\nปิ่ม\nปิ้ม\nปิยะ\nปิยังคุ\nปิโยรส\nปิลันธน์\nปิ๋ว\nปิศาจ\nปิหกะ\nปี\nปี่\nปี้\nปี๋\nปีก\nปีฐะ\nปี๊ด\nปีติ\nปีน\nปีบ\nปี๊บ\nปีศาจ\nปีฬกะ\nปึก\nปึง\nปึ่ง\nปึ๋ง\nปึ้ด\nปึมปื้อ\nปืน\nปื้น\nปือ\nปื้อ\nปื๋อ\nปุ\nปุ๊\nปุก\nปุกปุย\nปุคละ\nปุ้งกี๋\nปุงควะ\nปุงลิงค์\nปุงลึงค์\nปุจฉา\nปุฏะ\nปุณฑริก\nปุด\nปุตตะ\nปุถุชน\nปุนนาค\nปุนภพ\nปุนัพสุ\nปุบ\nปุ๊บ\nปุปผะ\nปุปะ\nปุพพะ\nปุ่ม\nปุ่มป่ำ\nปุ้ม\nปุ๋ม\nปุย\nปุ้ย\nปุ๋ย\nปุรณะ\nปุระ\nปุราณ\nปุราณะ\nปุริมพรรษา\nปุเรจาริก\nปุโรหิต\nปุลลิงค์\nปุลลึงค์\nปุลินท์\nปุโลปุเล\nปุษยะ\nปุสสะ\nปู\nปู่\nปูชกะ\nปูชนียบุคคล\nปูชนียวัตถุ\nปูชนียะ\nปูชา\nปูชิต\nปูด\nปูน\nปูม\nปู้ยี่ปู้ยำ\nปูระ\nปูลู\nเป้\nเป๋\nเปก\nเป๊ก\nเป่ง\nเป้ง\nเป๋ง\nเป็ด\nเปตพลี\nเปตอง\nเป็น\nเปยยาล\nเปร็ง\nเปรต\nเปรม\nเปรย\nเปรอ\nเปรอะ\nเปราะ\nเปรียง\nเปรี้ยง\nเปรี้ยงปร้าง\nเปรียญ\nเปรียบ\nเปรี่ยม\nเปรียว\nเปรี้ยว\nเปรียะ\nเปรี๊ยะ\nเปรื่อง\nเปรื้อย\nเปล\nเปล่ง\nเปลว\nเปลา\nเปล่า\nเปล้า\nเปลาะ\nเปลี้ย\nเปลี่ยน\nเปลี่ยม\nเปลี่ยว\nเปลือก\nเปลือง\nเปลื้อง\nเปลือย\nเปศะ\nเปศัส\nเปสการ\nเปสละ\nเปสุญวาท\nเป๋อ\nเป้อเย้อ\nเปอร์\nเปอร์เซ็นต์\nเปะ\nเปา\nเป่า\nเป้า\nเป๊า\nเป๋า\nเป๋าฮื้อ\nเปาะ\nเป๊าะ\nเปาะเปี๊ยะ\nเปาะแปะ\nเปิก\nเปิง\nเปิงมาง\nเปิด\nเปิ่น\nเปิบ\nเปิ๊บ\nเปีย\nเปียก\nเปี๊ยก\nเปี๊ยบ\nเปี่ยม\nเปี้ยว\nเปี๊ยว\nเปียะ\nเปี๊ยะ\nเปือก\nเปื้อน\nเปื่อย\nแป\nแป้ง\nแป๋ง\nแปด\nแป๊ด\nแปทู\nแป้น\nแป๊น\nแปบ\nแป๊บ\nแปม\nแปร\nแปร๋\nแปรก\nแปรง\nแปร่ง\nแปร๊ด\nแปร้น\nแปร๋น\nแปรปรวน\nแประ\nแปล\nแปล้\nแปลก\nแปลง\nแปลน\nแปลบ\nแปล๊บ\nแป้ว\nแป๊ว\nแปะ\nแป๊ะ\nแป๊ะซะ\nโป\nโป่\nโป้\nโป๊\nโปก\nโป๊ก\nโป๊กเกอร์\nโปกขรณี\nโปกขรพรรษ\nโปเก\nโปง\nโป่ง\nโป้ง\nโป่งข่าม\nโปงลาง\nโป่งวิด\nโปฐบท\nโปฐปทมาส\nโปดก\nโปตถกะ\nโปน\nโป๊ป\nโป๊ยเซียน\nโปรแกรม\nโปรง\nโปร่ง\nโปรด\nโปรตอน\nโปรตีน\nโปรเตสแตนต์\nโปรแทรกเตอร์\nโปรย\nโปล่ง\nโปลิโอ\nโปโล\nโปสก\nโปสต์การ์ด\nโปะ\nโป๊ะ\nโป๊ะจ้าย\nไป\nไป่\nไป๋\nไปยาล\nไปรษณีย์\nไปรษณียบัตร\nไปรษณียภัณฑ์\nไปรษณียากร\nไปล่\nไปศาจ\nผก\nผกา\nผกาย\nผคม\nผง\nผงก\nผงม\nผงะ\nผงาด\nผง่าน\nผจง\nผจญ\nผจัญ\nผจาน\nผชุม\nผณิน\nผณินทร\nผณิศวร\nผด\nผดุง\nผเดิน\nผทม\nผนวก\nผนวช\nผนัง\nผนิด\nผนึก\nผม\nผยอง\nผรณาปีติ\nผรสุ\nผริต\nผรุสวาท\nผล\nผลคุน\nผลคุนี\nผล็อย\nผละ\nผลัก\nผลัด\nผลับ\nผลัวะ\nผลา\nผลาญ\nผลานิสงส์\nผลาผล\nผลาหาร\nผลิ\nผลิกะ\nผลิต\nผลิน\nผลี\nผลีผลาม\nผลึก\nผลึ่ง\nผลือ\nผลุ\nผลุง\nผลุด\nผลุน\nผลุนผลัน\nผลุบ\nผลุบผลับ\nผลุย\nผลู\nผวน\nผวย\nผวา\nผสม\nผสาน\nผอง\nผ่อง\nผ่อน\nผอบ\nผอม\nผ็อย\nผอูน\nผะ\nผะดา\nผะสา\nผัก\nผัคคุณ\nผัคคุณี\nผัง\nผัด\nผัน\nผับ\nผัว\nผัวะ\nผัสสะ\nผัสสาหาร\nผา\nผ่า\nผ้า\nผาก\nผาง\nผ่าง\nผาณิต\nผาด\nผาติ\nผ่าน\nผาม\nผาย\nผ่ายผอม\nผาล\nผาลคุน\nผาลา\nผ่าว\nผาสุก\nผ้าฮาด\nผำ\nผ้ำ\nผิ\nผิง\nผิด\nผิตะ\nผิน\nผิว\nผี\nผี้ว์\nผึง\nผึ่ง\nผึ้ง\nผึ้งรวง\nผืน\nผื่น\nผุ\nผุด\nผุยผง\nผุสราคา\nผู้\nผูก\nเผ\nเผง\nเผชิญ\nเผ็ด\nเผด็จ\nเผดิม\nเผดียง\nเผ่น\nเผนิก\nเผย\nเผยอ\nเผยิบ\nเผยิบผยาบ\nเผล\nเผล่\nเผล็ด\nเผลอ\nเผลอไผล\nเผละ\nเผลาะ\nเผลาะแผละ\nเผลียง\nเผอเรอ\nเผอิญ\nเผอิล\nเผะ\nเผา\nเผ่า\nเผ้า\nเผาะ\nเผิ้ง\nเผิน\nเผียน\nเผือ\nเผื่อ\nเผือก\nเผือด\nเผือน\nเผื่อน\nแผ่\nแผก\nแผง\nแผด\nแผน\nแผ่น\nแผนก\nแผล\nแผลง\nแผล็บ\nแผล็ว\nแผละ\nแผ่ว\nแผ้ว\nโผ\nโผง\nโผฏฐัพพะ\nโผน\nโผเผ\nโผย\nโผล่\nโผลกเผลก\nโผละ\nโผอน\nโผะ\nไผ\nไผ่\nไผท\nฝน\nฝรั่ง\nฝรั่งเศส\nฝ่อ\nฝอย\nฝัก\nฝัง\nฝั่ง\nฝัด\nฝัน\nฝา\nฝ่า\nฝ้า\nฝาก\nฝาง\nฝาด\nฝาน\nฝาย\nฝ่าย\nฝ้าย\nฝิ่น\nฝี\nฝี่\nฝีก\nฝึก\nฝืด\nฝืน\nฝุ่น\nฝูง\nเฝ้า\nเฝือ\nเฝือก\nเฝือง\nเฝื่อน\nแฝก\nแฝง\nแฝด\nใฝ่\nไฝ\nพก\nพกุล\nพง\nพงศ์\nพงศกร\nพงศธร\nพงศา\nพงศาวดาร\nพจน์\nพจนา\nพจนานุกรม\nพจนารถ\nพจนีย์\nพจมาน\nพจี\nพชระ\nพญา\nพญาลอ\nพณิช\nพณิชย์\nพดด้วง\nพธู\nพนันดร\nพนาดร\nพนาดอน\nพนาราม\nพนาลัย\nพนาลี\nพนาวาส\nพนาเวศ\nพนาศรม\nพนาสณฑ์\nพนาสัณฑ์\nพเนจร\nพ่น\nพ้น\nพนม\nพนอง\nพนอม\nพนัก\nพนักงาน\nพนัง\nพนัน\nพนัส\nพนา\nพนาย\nพนิดา\nพนิต\nพบ\nพม่า\nพยนต์\nพยศ\nพยัก\nพยักพเยิด\nพยัคฆ์\nพยัคฆา\nพยัคฆิน\nพยัคฆี\nพยัชน์\nพยัญชนะ\nพยัต\nพยับ\nพยากรณ์\nพยาฆร์\nพยางค์\nพยาธิ\nพยาน\nพยาบาท\nพยาบาล\nพยาม\nพยามะ\nพยายาม\nพยุ\nพยุง\nพยุหยาตรา\nพยุหโยธา\nพยุหเสนา\nพยุหะ\nพยู่ห์\nพเยีย\nพร\nพรต\nพรม\nพรรค\nพรรค์\nพรรคานต์\nพรรณ\nพรรณนา\nพรรดึก\nพรรลาย\nพรรษ\nพรรษา\nพรรเอิญ\nพรวด\nพรวน\nพรหม\nพรหมจรรย์\nพรหมจาริณี\nพรหมจารี\nพรหมา\nพรหมาสตร์\nพรหมินทร์\nพรอก\nพร่อง\nพร้อง\nพรอด\nพร้อม\nพร้อมพรัก\nพร่อย\nพร้อย\nพระ\nพระนอม\nพระนาด\nพระฮาม\nพรักพร้อม\nพรั่ง\nพรัด\nพรั่น\nพรับ\nพร่า\nพร้า\nพราก\nพราง\nพร่าง\nพราด\nพราน\nพราย\nพราว\nพราหมณ์\nพราหมณะ\nพราหมณี\nพราหมี\nพรำ\nพร่ำ\nพริก\nพริ้ง\nพริบ\nพริ้ม\nพรึง\nพรึน\nพรึบ\nพรึ่บ\nพรืด\nพรุ\nพรุ่ง\nพรุน\nพรู\nพรูด\nพฤกษ์\nพฤกษชาติ\nพฤกษเทวดา\nพฤกษราช\nพฤกษศาสตร์\nพฤกษา\nพฤฒ\nพฤฒา\nพฤฒาจารย์\nพฤฒิ\nพฤต\nพฤติ\nพฤทธ์\nพฤทธิ์\nพฤนต์\nพฤนท์\nพฤศจิก\nพฤศจิกายน\nพฤษภ\nพฤษภาคม\nพฤหัสบดี\nพล\nพละ\nพลากร\nพลาดิศัย\nพลาธิการ\nพลานามัย\nพลบ\nพลวก\nพลวง\nพลวัต\nพลศาสตร์\nพลอ\nพล้อ\nพลอง\nพลอด\nพลอน\nพลอมแพลม\nพลอย\nพล่อย\nพลั่ก\nพลัง\nพลั่ง\nพลั้ง\nพลัด\nพลัน\nพลับ\nพลับพลา\nพลับพลึง\nพลั่ว\nพล่า\nพลาง\nพลาญ\nพลาด\nพล่าน\nพลาม\nพล่าม\nพลาย\nพลาสติก\nพลาสมา\nพลาหก\nพลำ\nพล้ำ\nพลำภัง\nพลิก\nพลิพัท\nพลิ้ว\nพลี\nพลุ\nพลุก\nพลุ่ก\nพลุกพล่าน\nพลุ่ง\nพลุ้ย\nพลู\nพลูโต\nพลูโทเนียม\nพวก\nพวง\nพ่วง\nพวน\nพวย\nพสก\nพสุ\nพสุธา\nพสุสงกรานต์\nพหล\nพหุ\nพหุล\nพหู\nพอ\nพ่อ\nพ้อ\nพอก\nพอง\nพ้อง\nพอน\nพ้อม\nพอโลเนียม\nพะ\nพะงา\nพะงาบ\nพะจง\nพะทำมะรง\nพะนอ\nพะเน้าพะนอ\nพะเนิน\nพะเนียง\nพะแนง\nพะพาน\nพะพิง\nพะเพิง\nพะยอม\nพ่ะย่ะค่ะ\nพะยูง\nพะยูน\nพะเยิบ\nพะเยิบพะยาบ\nพะรุงพะรัง\nพะโล้\nพะไล\nพะวง\nพะวักพะวน\nพะวา\nพะว้าพะวัง\nพะอง\nพะอากพะอำ\nพะอืดพะอม\nพัก\nพักตร์\nพักตรา\nพักร\nพัง\nพังกา\nพังงา\nพังผืด\nพังพวย\nพังพอน\nพังพาน\nพังพาบ\nพังเพย\nพัช\nพัชนี\nพัชระ\nพัญจน์\nพัฒนะ\nพัฒนา\nพัฒนากร\nพัฒนาการ\nพัด\nพัดชา\nพัดดึงส์\nพัตร\nพัทธ์\nพัทธยา\nพัทธสีมา\nพัทร\nพัน\nพันตู\nพันทาง\nพันธ์\nพันธะ\nพันธกรณี\nพันธบัตร\nพันธมิตร\nพันธนะ\nพันธนาคาร\nพันธนาการ\nพันธุ์\nพันธุกรรม\nพันลึก\nพันลือ\nพันเลิศ\nพันเอิญ\nพับ\nพัลลภ\nพัลวัน\nพัว\nพัวะ\nพัศดี\nพัสดุ\nพัสตร์\nพัสถาน\nพา\nพาก\nพากเพียร\nพากย์\nพาง\nพ่าง\nพาชี\nพาณิช\nพาณิชย์\nพาณิชยกรรม\nพาณิชยการ\nพาณิชยศาสตร์\nพาณิชยศิลป์\nพาณินี\nพาณี\nพาณีนี\nพาด\nพาท\nพาทย์\nพาธ\nพาธา\nพาน\nพ่าน\nพานร\nพานรินทร์\nพาม\nพาย\nพ่าย\nพายม้า\nพายัพ\nพายุ\nพาร์เซก\nพารณ\nพารา\nพาราฟิน\nพาล\nพาลา\nพาลี\nพาลุก\nพาโล\nพาไล\nพาส\nพาสน์\nพาสนา\nพาสุกรี\nพ่าห์\nพาหนะ\nพาหะ\nพาหา\nพาหิรกะ\nพาหิระ\nพาหุ\nพาหุรัด\nพาหุสัจจะ\nพาเหียร\nพาฬ\nพำ\nพำนัก\nพำพึม\nพำลา\nพิกล\nพิกสิต\nพิกัด\nพิกัติ\nพิกัน\nพิการ\nพิกุล\nพิเคราะห์\nพิฆน์\nพิฆเนศ\nพิฆเนศวร\nพิฆาต\nพิง\nพิจัย\nพิจาร\nพิจารณ์\nพิจารณา\nพิจิก\nพิจิต\nพิจิตร\nพิชญ์\nพิชัย\nพิชาน\nพิชิต\nพิเชฐ\nพิเชียร\nพิฑูรย์\nพิณ\nพิดทูล\nพิดาน\nพิโดร\nพิตร\nพิถี\nพิถีพิถัน\nพิทย\nพิทย์\nพิทยา\nพิทยาคม\nพิทยาคาร\nพิทยาธร\nพิทยาลัย\nพิทักษ์\nพิทูร\nพิเทศ\nพิธาน\nพิธี\nพิธุ\nพินทุ\nพินอบพิเทา\nพินัย\nพินาศ\nพินิจ\nพินิต\nพินิศ\nพินิศจัย\nพิเนต\nพิบัติ\nพิบุล\nพิบูล\nพิปริต\nพิปลาส\nพิพรรธ\nพิพรรธน์\nพิพักพิพ่วน\nพิพัฒ\nพิพัฒน์\nพิพากษ์\nพิพากษา\nพิพาท\nพิพิธ\nพิพิธภัณฑ์\nพิพิธภัณฑสถาน\nพิภพ\nพิภัช\nพิภาค\nพิภูษณะ\nพิเภก\nพิมปะการัง\nพิมพ์\nพิมพการัง\nพิมพา\nพิมพาภรณ์\nพิมล\nพิมเสน\nพิมาน\nพิมุข\nพิโมกข์\nพิโมกษ์\nพิโยกพิเกน\nพิโยค\nพิรอด\nพิระ\nพิรากล\nพิราบ\nพิราม\nพิราลัย\nพิริยะ\nพิรี้พิไร\nพิรุณ\nพิรุธ\nพิรุฬห์\nพิเรนทร์\nพิเราะ\nพิโรธ\nพิไร\nพิลังกาสา\nพิลาป\nพิลาส\nพิลิปดา\nพิลึก\nพิลึกกึกกือ\nพิลึกพิลั่น\nพิโลน\nพิไล\nพิศ\nพิศวง\nพิศวาส\nพิศาล\nพิศุทธ์\nพิศุทธิ์\nพิเศษ\nพิษ\nพิษฐาน\nพิษนาศน์\nพิสดาร\nพิสมร\nพิสมัย\nพิสัง\nพิสัช\nพิสัย\nพิสิฐ\nพิสุทธิ์\nพิสูจน์\nพิหค\nพิหาร\nพิฬาร\nพี\nพี่\nพี้\nพีชคณิต\nพีระมิด\nพึง\nพึ่ง\nพึ่บ\nพึ่บพั่บ\nพึม\nพึมพำ\nพืช\nพืด\nพื้น\nพุ\nพุก\nพุกาม\nพุง\nพุ่ง\nพุงดอ\nพุฒ\nพุฒิ\nพุด\nพุดตาน\nพุทธ\nพุทธะ\nพุทธังกูร\nพุทธางกูร\nพุทธันดร\nพุทธาภิเษก\nพุทธาวาส\nพุทธิ\nพุทโธ่\nพุทรา\nพุธ\nพุ่ม\nพุมเรียง\nพุ้ย\nพู\nพูพอน\nพู่\nพูด\nพูน\nพู้น\nพู่ระหง\nเพ\nเพ็ก\nเพกา\nเพคะ\nเพ็ง\nเพ่ง\nเพ็จ\nเพชฉลูกรรม\nเพชฌฆาต\nเพชร\nเพชรดา\nเพชรปาณี\nเพชรฤกษ์\nเพชรายุธ\nเพชรกลับ\nเพชรสังฆาต\nเพชรหลีก\nเพชรหึง\nเพ็ญ\nเพฑูริย์\nเพณี\nเพ็ดทูล\nเพดาน\nเพท\nเพทนา\nเพทาย\nเพทุบาย\nเพโทบาย\nเพ่นพ่าน\nเพนียด\nเพไนย\nเพ้ย\nเพรง\nเพรซีโอดิเมียม\nเพรา\nเพราะ\nเพริด\nเพริศ\nเพรียก\nเพรียง\nเพรียบ\nเพรี้ยม\nเพรียว\nเพรื่อ\nเพรือง\nเพล\nเพลง\nเพล็ด\nเพล้โพล้\nเพลา\nเพลาะ\nเพลิง\nเพลิดเพลิน\nเพลิน\nเพลีย\nเพลี้ย\nเพลี่ยง\nเพศ\nเพส\nเพสลาด\nเพ่อ\nเพ้อ\nเพ้อเจ้อ\nเพอิญ\nเพะ\nเพา\nเพาะ\nเพิก\nเพิง\nเพิ่ง\nเพิดเพ้ย\nเพิ่ม\nเพี้ย\nเพียง\nเพี้ยง\nเพียงออ\nเพี้ยน\nเพียบ\nเพียร\nเพื่อ\nเพื่อน\nแพ\nแพ้\nแพง\nแพ่ง\nแพงพวย\nแพทย์\nแพทยศาสตร์\nแพน\nแพ่น\nแพนก\nแพนงเชิง\nแพร\nแพร่\nแพรก\nแพร่ง\nแพรว\nแพร้ว\nแพลง\nแพลทินัม\nแพล็บ\nแพลม\nแพลเลเดียม\nแพละ\nแพละโลม\nแพว\nแพ้ว\nแพศย์\nแพศยา\nแพะ\nโพ\nโพก\nโพกพาย\nโพง\nโพงพาง\nโพชฌงค์\nโพซิตรอน\nโพด\nโพทะเล\nโพแทสเซียม\nโพธ\nโพธิ\nโพธิ์\nโพน\nโพ้น\nโพนทะนา\nโพบาย\nโพย\nโพยก๊วน\nโพยม\nโพรก\nโพรง\nโพรโทแอกทิเนียม\nโพรมีเทียม\nโพระดก\nโพล่\nโพลง\nโพล่ง\nโพล้ง\nโพลน\nโพล้เพล้\nโพละ\nโพสพ\nไพ\nไพ่\nไพจิตร\nไพชน\nไพชยนต์\nไพฑูรย์\nไพที\nไพบูลย์\nไพพรรณ\nไพร\nไพร่\nไพรจิตร\nไพรชน\nไพรชยนต์\nไพรฑูรย์\nไพรที\nไพรบูลย์\nไพรเราะ\nไพรัช\nไพรำ\nไพริน\nไพรินทร์\nไพรี\nไพเราะ\nไพโรจน์\nไพล\nไพล่\nไพศาขะ\nไพศาล\nไพเศษ\nไพสพ\nไพสิฐ\nไพหาร\nฟก\nฟ้ง\nฟรักโทส\nฟรี\nฟลูออรีน\nฟ่อ\nฟ้อ\nฟอก\nฟอง\nฟ่อง\nฟ้อง\nฟอด\nฟอน\nฟ่อน\nฟ้อน\nฟ้อแฟ้\nฟอร์มาลดีไฮด์\nฟอร์มาลิน\nฟอสฟอรัส\nฟอสเฟต\nฟัก\nฟักฟุ้น\nฟัง\nฟังก์ชัน\nฟัด\nฟัน\nฟั่น\nฟั้น\nฟ้า\nฟาก\nฟาง\nฟ่าง\nฟาด\nฟาทอม\nฟาน\nฟ่าม\nฟาย\nฟาร์ม\nฟาสซิสต์\nฟิด\nฟิต\nฟิบ\nฟิล์ม\nฟิวส์\nฟิสิกส์\nฟี่\nฟี้\nฟืดฟาด\nฟืน\nฟื้น\nฟืม\nฟุ\nฟุ้ง\nฟุต\nฟุน\nฟุบ\nฟุ่บ\nฟุ่มเฟือย\nฟุลสแก๊ป\nฟู\nฟู่\nฟูก\nฟูด\nฟูม\nเฟ็ด\nเฟ้น\nเฟลด์สปาร์\nเฟ้อ\nเฟอร์เมียม\nเฟอะ\nเฟอะฟะ\nเฟะ\nเฟะฟะ\nเฟิน\nเฟี้ยม\nเฟี้ยว\nเฟือ\nเฟื้อ\nเฟือง\nเฟื่อง\nเฟื้อง\nเฟือน\nเฟือย\nเฟื้อย\nแฟ่\nแฟง\nแฟชั่น\nแฟน\nแฟบ\nแฟ้ม\nแฟรนเซียม\nแฟลกซ์\nแฟลต\nแฟะ\nโฟกัส\nไฟ\nภควดี\nภควัต\nภควันต์\nภควัม\nภควา\nภควาน\nภคะ\nภคันทลา\nภคินี\nภณะ\nภณิดา\nภพ\nภมร\nภมริน\nภมรี\nภมุกา\nภยันตราย\nภยาคติ\nภระ\nภรณี\nภรต\nภรรดร\nภรรดา\nภรรยา\nภระมร\nภระมรี\nภราดร\nภราดรภาพ\nภราดา\nภริยา\nภฤศ\nภวะ\nภวตัณหา\nภวนะ\nภวังค์\nภวังคจิต\nภักดี\nภักตะ\nภักติ\nภักษ์\nภักษา\nภักษาหาร\nภัค\nภัคน์\nภังคะ\nภังคี\nภัจ\nภัณฑ์\nภัณฑาคาร\nภัณฑาคาริก\nภัณฑารักษ์\nภัณฑนะ\nภัณฑู\nภัต\nภัตตาคาร\nภัตตาหาร\nภัตร\nภัทระ\nภัทรกัป\nภัพ\nภัย\nภัสดา\nภัสตรา\nภัสมะ\nภัสสร\nภา\nภาค\nภาคย์\nภาคยานุวัติ\nภาคินี\nภาคิไนย\nภาคี\nภาคียะ\nภาชนะ\nภาชี\nภาณ\nภาณวาร\nภาณกะ\nภาณี\nภาณุ\nภาดร\nภาดา\nภาตระ\nภาตา\nภาตุ\nภาติกะ\nภาติยะ\nภาพ\nภาพย์\nภาม\nภาย\nภาร\nภาระ\nภารดี\nภารต\nภารตี\nภารยทรัพย์\nภารยา\nภารา\nภาวนา\nภาวะ\nภาษ\nภาษณ์\nภาษา\nภาษิต\nภาษี\nภาส\nภาสน์\nภาสวร\nภาสา\nภาสุระ\nภิกขา\nภิกขาจาร\nภิกขุ\nภิกขุนี\nภิกษา\nภิกษาจาร\nภิกษาหาร\nภิกษุ\nภิกษุณี\nภิงคาร\nภิญโญ\nภิตติ\nภินท์\nภินทนาการ\nภิยโย\nภิรมย์\nภิรมย์สุรางค์\nภิษัช\nภิสัก\nภีตะ\nภีมะ\nภีรุ\nภุกต์\nภุขัน\nภุช\nภุชงค์\nภุต\nภุมมะ\nภุมรัตน์\nภุมวาร\nภุมรา\nภุมริน\nภุมรี\nภุมเรศ\nภู\nภู่\nภูต\nภูติ\nภูม\nภูมิ\nภูมี\nภูริ\nภูรี\nภูวดล\nภูวนาถ\nภูวเนตร\nภูวไนย\nภูษา\nภูษิต\nเภกะ\nเภตรา\nเภท\nเภทุบาย\nเภรี\nเภสัช\nโภค\nโภคะ\nโภคิน\nโภคี\nโภไคย\nโภไคศวรรย์\nโภช\nโภชย์\nโภชก\nโภชนะ\nโภชนา\nโภชนาหาร\nโภชนียะ\nไภริน\nไภรี\nไภษัชคุรุ\nไภษัชย์\nมกร\nมกราคม\nมกุฎ\nมคธ\nมฆวัน\nมฆะ\nมฆา\nม่ง\nมงกุฎ\nมงโกรย\nมงคล\nมงคลวาร\nมณฑ์\nมณฑก\nมณฑนะ\nมณฑป\nมณฑล\nมณฑา\nมณฑารพ\nมณฑิระ\nมณเฑียร\nมณี\nมด\nมตะ\nมตกภัต\nมติ\nมทนะ\nมทะ\nมธุ\nมธุกร\nมธุการี\nมธุลีห์\nมธุระ\nมธุรพจน์\nมน\nมนินทรีย์\nม่น\nมนต์\nมนตร์\nมนตรี\nมนท์\nมนทิราลัย\nมนเทียร\nมนสิการ\nมนัส\nมนัสวี\nมนินทรีย์\nมนิมนา\nมนิลา\nมนุญ\nมนุษย์\nมนุษยชาติ\nมนุษยธรรม\nมนุษย์มนา\nมนุษยโลก\nมนุษยศาสตร์\nมนุษยสัมพันธ์\nมนุสาร\nมนู\nมนูสาร\nมโน\nมโนช\nมโนชญ์\nมโนราห์\nมโนสาเร่\nมโนห์รา\nมมังการ\nมยุรฉัตร\nมยุระ\nมยุรา\nมยุรี\nมยุเรศ\nมยูร\nมรกต\nมรคา\nมรฑป\nมรณ์\nมรณะ\nมรณกรรม\nมรณบัตร\nมรณภัย\nมรณภาพ\nมรดก\nมรรค\nมรรคา\nมรรตัย\nมรรยาท\nมรรษ\nมรสุม\nมริจ\nมริยาท\nมรีจิ\nมรุต\nมฤค\nมฤคย์\nมฤคศิระ\nมฤคศิรมาส\nมฤคเศียร\nมฤคินทร์\nมฤเคนทร์\nมฤดก\nมฤต\nมฤตยู\nมฤทุ\nมล\nมละ\nมลัก\nมลังเมลือง\nมล้าง\nมลาย\nมลายู\nมวก\nม่วง\nมวน\nม่วน\nม้วน\nม้วนต้วน\nมวย\nม้วย\nมวล\nมหกรรม\nมหรณพ\nมหรรณพ\nมหรสพ\nมหัจฉริยะ\nมหัต\nมหัทธนะ\nมหันต์\nมหันตโทษ\nมหัพภาค\nมหัศจรรย์\nมหา\nมหากฐิน\nมหากาฬ\nมหาขันธกะ\nมหาจักร\nมหาชน\nมหาชัย\nมหาชาติ\nมหาโชตรัต\nมหาดไทย\nมหาดเล็ก\nมหาตมะ\nมหาไถ่\nมหาเทพ\nมหาเทพี\nมหาเทวี\nมหาธาตุ\nมหานิกาย\nมหานิล\nมหาบพิตร\nมหาบัณฑิต\nมหาพน\nมหาพรหม\nมหาภารตะ\nมหาภิเนษกรมณ์\nมหาภูต\nมหาเมฆ\nมหายาน\nมหายุค\nมหาราช\nมหาฤกษ์\nมหาละลวย\nมหาละลาย\nมหาวงศ์\nมหาวรรค\nมหาวิทยาลัย\nมหาศักราช\nมหาศาล\nมหาสงกรานต์\nมหาสดมภ์\nมหาสดำ\nมหาสมุทร\nมหาสาวก\nมหาหงส์\nมหาหิงคุ์\nมหาอำนาจ\nมหาอุจ\nมหาอุด\nมหาอุปรากร\nมหาอุปราช\nมหิ\nมหิดล\nมหิธร\nมหิป\nมหิงส์\nมหิทธิ\nมหินท์\nมหิมา\nมหิศร\nมหิศวร\nมหิษ\nมหิษี\nมหึมา\nมเหยงค์\nมเหศ\nมเหศวร\nมเหศักดิ์\nมเหสักข์\nมเหสิ\nมเหสี\nมเหาฬาร\nมโหรสพ\nมโหระทึก\nมโหรี\nมโหฬาร\nมไหศวรรย์\nมอ\nมอง\nมองโกลอยด์\nมองโกเลีย\nมองคร่อ\nมอญ\nมอด\nม่อต้อ\nมอเตอร์\nมอเตอร์ไซค์\nมอน\nม่อน\nมอบ\nมอม\nมอมแมม\nม่อย\nมอร์ฟีน\nมอระกู่\nมอลโทส\nม่อลอกม่อแลก\nม่อห้อม\nม่อฮ่อม\nมะ\nมะกรูด\nมะกล่ำ\nมะกอก\nมะก่อง\nมะกะโรนี\nมะกา\nมะเกลือ\nมะเกี๋ยง\nมะข่วง\nมะขวิด\nมะขาม\nมะเขือ\nมะแข่น\nมะคังแดง\nมะค่า\nมะคำไก่\nมะคำดีควาย\nมะงั่ว\nมะงุมมะงาหรา\nมะซัก\nมะซาง\nมะดัน\nมะดีหวี\nมะดูก\nมะเดหวี\nมะเดื่อ\nมะต้อง\nมะตะบะ\nมะตาด\nมะตาหะรี\nมะตึ่ง\nมะตื๋น\nมะตูม\nมะแตก\nมะโต\nมะนาว\nมะปราง\nมะปริง\nมะฝ่อ\nมะพร้าว\nมะพลับ\nมะพูด\nมะแพน\nมะแพร้ว\nมะเฟือง\nมะแฟน\nมะไฟ\nมะม่วง\nมะม่าว\nมะมี่\nมะมื่น\nมะมุด\nมะเมอ\nมะเมีย\nมะเมื่อย\nมะแม\nมะยง\nมะยม\nมะระ\nมะริด\nมะรืน\nมะรุม\nมะรุมมะตุ้ม\nมะเร็ง\nมะเรื่อง\nมะโรง\nมะลอกมะแลก\nมะละกอ\nมะลิ\nมะลื่น\nมะลืมดำ\nมะลุลี\nมะแว้ง\nมะสัง\nมะเส็ง\nมะหวด\nมะหะหมัด\nมะหาด\nมะหิ่ง\nมะเหงก\nมะอึก\nมะฮอกกานี\nมัก\nมักกะโรนี\nมักกะลีผล\nมักกะสัน\nมักขะ\nมั่กขั้ก\nมักขิกา\nมัค\nมัคคะ\nมัคคุเทศก์\nมัคนายก\nมัฆวาน\nมั่ง\nมังกง\nมังกร\nมังกุ\nมังคละ\nมังค่า\nมังคุด\nมังตาน\nมังสวิรัติ\nมังสะ\nมังสี\nมัจจะ\nมัจจุ\nมัจฉริยะ\nมัจฉรี\nมัจฉะ\nมัจฉา\nมัชชะ\nมัชวิรัติ\nมัชชาระ\nมัชฌันติกสมัย\nมัชฌิม\nมัชฌิมา\nมัญจา\nมัญชิษฐา\nมัญชุ\nมัญชุสา\nมัญชูสา\nมัญเชฏฐะ\nมัฏฐะ\nมัณฑนศิลป์\nมัณฑนา\nมัด\nมัตตะ\nมัตตัญญู\nมัตตา\nมัตติกา\nมัตถกะ\nมัตถลุงค์\nมัตสยะ\nมัตสยา\nมัตสระ\nมัตสริน\nมัททวะ\nมัทนะ\nมัทยะ\nมัธยฐาน\nมัธยม\nมัธยันห์\nมัธยัสถ์\nมัน\nมั่น\nมันตา\nมันถะ\nมันทิระ\nมันทิราลัย\nมับ\nมั้ม\nมัมมี่\nมัย\nมัลละ\nมัลลิกา\nมัว\nมัวซัว\nมั่ว\nมัศยา\nมัสตุ\nมัสตาร์ด\nมัสมั่น\nมัสยิด\nมัสรู่\nมัสลิน\nมัสสุ\nมา\nม้า\nมาก\nมาคสิระ\nมาฆบูชา\nมาฆะ\nม้าง\nมางสะ\nมาณพ\nมาณวิกา\nมาด\nมาดา\nมาตงค์\nมาตร\nมาตรา\nมาตฤ\nมาตังคะ\nมาตา\nมาตามหัยกะ\nมาตามหัยกา\nมาตามหัยยิกา\nมาติกะ\nมาติกา\nมาตุ\nมาตุจฉา\nมาตุรงค์\nมาตุเรศ\nมาตุละ\nมาตุลา\nมาตุลานี\nมาทะ\nมาธยมิก\nมาธยมิกะ\nมาธุระ\nมาธุสร\nมาธูระ\nมาน\nม่าน\nม้าน\nมานพ\nมานะ\nมานัต\nมานัส\nมานิต\nมานี\nมานุษ\nมานุษยวิทยา\nมาโนชญ์\nมาบ\nมาภา\nม้าม\nม่าย\nมายา\nมาร\nมาราธิราช\nมารค\nมารดร\nมารดา\nมารยา\nมารยาท\nมารศรี\nมารษา\nมาริต\nมารุต\nมาลย์\nมาลัย\nมาลา\nมาลาตี\nมาลาเรีย\nมาลินี\nมาลี\nมาลุต\nมาศ\nมาส\nมาสก\nมาห์\nม่าห์\nมาหิส\nม่าเหมี่ยว\nมาฬก\nมิ\nมิค\nมิคสัญญี\nมิ่ง\nมิจฉา\nมิด\nมิตร\nมิติ\nมิเตอร์\nมิถยา\nมิถุน\nมิถุนายน\nมิทธะ\nมินตรา\nมินตา\nมินหม้อ\nมิ่ม\nมิ้ม\nมิไย\nมิรันตี\nมิลลิกรัม\nมิลลิบาร์\nมิลลิเมตร\nมิลลิลิตร\nมิลักขะ\nมิลักขู\nมิส\nมิสกรี\nมิสกวัน\nมิสซา\nมี\nมี่\nมีด\nมีเทน\nมีน\nมีนาคม\nมี่สั้ว\nมึง\nมึน\nมืด\nมืน\nมื่น\nมือ\nมื้อ\nมุ\nมุก\nมุกดา\nมุกดาหาร\nมุกุระ\nมุข\nมุขเด็จ\nมุขยประโยค\nมุโขโลกนะ\nมุคคะ\nมุง\nมุ่ง\nมุ้ง\nมุจฉา\nมุจนะ\nมุจลินท์\nมุญจนะ\nมุญชะ\nมุฐิ\nมุณฑกะ\nมุณฑะ\nมุด\nมุตกิด\nมุตฆาต\nมุตตะ\nมุตตา\nมุตติ\nมุตะ\nมุติ\nมุททา\nมุทธชะ\nมุทธา\nมุทธาภิเษก\nมุทรา\nมุทริกา\nมุทะลุ\nมุทา\nมุทิกา\nมุทิงค์\nมุทิตา\nมุทุ\nมุทุตา\nมุ่น\nมุนิ\nมุนินทร์\nมุนี\nมุบ\nมุบมิบ\nมุม\nมุ้ม\nมุ่ย\nมุรธา\nมุรธาภิเษก\nมุสละ\nมุสลิม\nมุสา\nมุสิก\nมุหงิด\nมุหน่าย\nมุหุต\nมุฮัมมัด\nมูก\nมูเซอ\nมูตร\nมู่ทู่\nมูน\nมูมมาม\nมูรติ\nมูรธา\nมูรธาภิเษก\nมูล\nมูละ\nมูลา\nมูลิกากร\nมู่ลี่\nมู่เล่\nมูสัง\nมูสิก\nมูสิกะ\nมูสิกทันต์\nเม\nเม็ก\nเมกะเฮิรตซ์\nเมขลา\nเมฆ\nเมฆา\nเมฆินทร์\nเมฆี\nเม็ง\nเม็ด\nเมตตา\nเมตไตรย\nเมตร\nเมตริก\nเมตริกตัน\nเมถุน\nเมท\nเมโท\nเมทนี\nเมทินี\nเมทนีดล\nเมทานอล\nเมทิลแอลกอฮอล์\nเมธ\nเมธา\nเมธาวี\nเมธี\nเมน\nเม่น\nเม้น\nเมนเดลีเวียม\nเมนทอล\nเม้ม\nเมรัย\nเมริเดียน\nเมรุ\nเมล์\nเมล็ด\nเมลือง\nเมษ\nเมษายน\nเมห์\nเมหนะ\nเมหะ\nเมะ\nเมา\nเม่า\nเม้า\nเมารี\nเมาลี\nเมาฬี\nเมาะ\nเมิง\nเมิน\nเมิล\nเมีย\nเมียง\nเมี่ยง\nเมี้ยน\nเมือ\nเมื้อ\nเมื่อ\nเมือก\nเมือง\nเมือบ\nเมื่อย\nแม่\nแม้\nแมก\nแมกนีเซียม\nแมง\nแมงกะพรุน\nแมงกานิน\nแมงกานีส\nแมงคา\nแมงคาเรือง\nแมงช้าง\nแมงดา\nแมงลัก\nแม่ตะงาว\nแมน\nแม่น\nแม้น\nแมลง\nแมลบ\nแมว\nแม้ว\nแมะ\nโม\nโม่\nโม้\nโมก\nโมกข์\nโมกษะ\nโมฆกรรม\nโมฆสัญญา\nโมฆะ\nโมฆียกรรม\nโมฆียะ\nโมง\nโม่ง\nโมงครุ่ม\nโมทนา\nโมโนแซ็กคาไรด์\nโมไนย\nโมเม\nโมเมนต์\nโมเย\nโมรา\nโมรี\nโมเรส\nโมลิบดีนัม\nโมลี\nโมเลกุล\nโมเสก\nโมเสส\nโม่ห์\nโมหะ\nโมหันธ์\nโมหาคติ\nโมโห\nไม่\nไม้\nไมกา\nไมครอน\nไมโครกรัม\nไมโครฟิล์ม\nไมโครโฟน\nไมโครมิเตอร์\nไมโครเมตร\nไมโครลิตร\nไมโครเวฟ\nไมตรี\nไมยราบ\nไมล์\nยก\nยกกระบัตร\nยกนะ\nยง\nยงโย่\nยชุรเวท\nยติ\nยติภังค์\nยถากรรม\nยถาภูตญาณ\nย่น\nยนต์\nยนตร์\nยม\nยมก\nยมโดย\nยมนา\nยมล\nยมะ\nยรรยง\nยล\nยวง\nยวด\nยวน\nยวบ\nย้วย\nยวรยาตร\nยศ\nยโส\nยอ\nย่อ\nยอก\nย็อกแย็ก\nยอง\nย่อง\nย้อง\nยอด\nยอน\nย้อน\nยอบ\nยอม\nย่อม\nย้อม\nย่อย\nย้อย\nย้อแย้\nยะ\nย่ะ\nยะยอบ\nยะยับ\nยัก\nยักข์\nยักขินี\nยักษ์\nยักษา\nยักษิณี\nยักษี\nยัง\nยั้ง\nยั่งยืน\nยัชโญปวีต\nยัญ\nยัญญะ\nยัด\nยัติภังค์\nยัน\nยั่น\nยันต์\nยันตร\nยันตร์\nยันตรกรรม\nยั่นตะนี\nยับ\nยั่ว\nยั้ว\nยั้วเยี้ย\nยัวรยาตร\nยัวะ\nยัษฏิ\nยา\nย่า\nยาก\nยาคุ\nยาคู\nยาง\nย่าง\nยางพารา\nยาจก\nยาจนา\nยาไฉน\nยาด\nยาดา\nยาตร\nยาตรา\nยาน\nย่าน\nย่านพาโหม\nยานมาศ\nยานุมาศ\nยานี\nยาม\nย่าม\nยามะ\nยามักการ\nยามา\nยาย\nย้าย\nยายี\nยาว\nย้าว\nยาวกาลิก\nยาวชีวิก\nยาสูบ\nย่าหยา\nยาหยี\nยำ\nย่ำ\nย้ำ\nยำเยีย\nยิก\nยิง\nยิ่ง\nยิฏฐะ\nยิน\nยิบ\nยิบหยี\nยิปซัม\nยิปซี\nยิ้ม\nยิมนาสติก\nยิหวา\nยี\nยี่\nยี้\nยี่ก่า\nยี่เก\nยี่เข่ง\nยี่โถ\nยีน\nยี่โป้\nยี่ภู่\nยีราฟ\nยี่สก\nยี่สง\nยี่สน\nยี่สาน\nยี่สุ่น\nยี่หระ\nยี่หร่า\nยี่ห้อ\nยี่หุบ\nยึกยัก\nยึกยือ\nยึด\nยืด\nยืน\nยื่น\nยืม\nยื้อ\nยุ\nยุกกระบัตร\nยุกดิ\nยุกติ\nยุกติธรรม\nยุกต์\nยุค\nยุคนธร\nยุคล\nยุคันต์\nยุคันธร\nยุคุนธร\nยุง\nยุ่ง\nยุ้ง\nยุด\nยุต\nยุติ\nยุทธ\nยุทธ์\nยุทธนา\nยุทโธปกรณ์\nยุบ\nยุ่บ\nยุ่บยั่บ\nยุบล\nยุพดี\nยุพเรศ\nยุพา\nยุพาน\nยุพาพาล\nยุพาพิน\nยุ่มย่าม\nยุ่ย\nยุ้ย\nยุรยาตร\nยูรยาตร\nยุวชน\nยุวดี\nยุวราช\nยุวา\nยุวาน\nยู\nยู่\nยูง\nยูโด\nยูถะ\nยูถิกา\nยูริก\nยูเรนัส\nยูเรเนียม\nยูโรเพียม\nเย\nเย้\nเยง\nเยซู\nเย็ด\nเย็น\nเย็นตาโฟ\nเย็นเตาโฟ\nเย็บ\nเย้ย\nเยอ\nเย่อ\nเยอรมัน\nเยอว\nเย่อหยิ่ง\nเยอะ\nเยอะแยะ\nเยา\nเย้า\nเยาว์\nเยาวชน\nเยาวมาลย์\nเยาวยอด\nเยาวราช\nเยาวเรศ\nเยาวลักษณ์\nเยาวพา\nเยาวพาณี\nเยาวพาน\nเยาะ\nเยิง\nเยิน\nเยิ่น\nเยิ่นเย้อ\nเยินยอ\nเยิบ\nเยิบยาบ\nเยิ้ม\nเยีย\nเยี่ยง\nเยี่ยงอย่าง\nเยียงผา\nเยียดยัด\nเยียน\nเยียบ\nเยี่ยม\nเยียรบับ\nเยียรยง\nเยียว\nเยี่ยว\nเยียวยา\nเยือ\nเยื่อ\nเยื้อ\nเยือก\nเยือง\nเยื่อง\nเยื้อง\nเยือน\nเยื้อน\nแย่\nแย้\nแยก\nแยง\nแย่ง\nแย้ง\nแยงแย่\nแยงแย้\nแยบ\nแย็บ\nแยม\nแย้ม\nแยแส\nแยะ\nโย\nโย้\nโยก\nโยกเยก\nโยคาพจร\nโยคาวจร\nโยคเกณฑ์\nโยคยะ\nโยคะ\nโยคิน\nโยคี\nโยง\nโย่ง\nโย่งเย่ง\nโยงโย่\nโยชน์\nโยชนา\nโยถิกะ\nโยทะกา\nโยธวาทิต\nโยธา\nโยธิน\nโยน\nโยนก\nโยนิโส\nโยนี\nโยม\nโยโส\nใย\nไย\nไย่\nไยดี\nไยไพ\nรก\nรง\nรงค์\nรงควัตถุ\nรงรอง\nรจนา\nรจเรข\nรจเลข\nรจิต\nรชตะ\nรชนิ\nรชนี\nรชะ\nรณรงค์\nรด\nรดี\nรตนะ\nรตะ\nรติ\nรถ\nรน\nร่น\nรบ\nรบาญ\nรพี\nรม\nร่ม\nรมณี\nรมณีย์\nรมณียสถาน\nรมย์\nรมเยศ\nรยางค์\nรวก\nรวง\nร่วง\nรวด\nรวน\nรวนเร\nร่วน\nรวบ\nรวม\nร่วม\nรวย\nรวิ\nรวิวาร\nรวี\nรศนา\nรส\nรสนา\nรสสุคนธ์\nรสายนเวท\nรสิก\nรหัท\nรหัส\nรโห\nรโหฐาน\nรอ\nร่อ\nรอก\nรอง\nร่อง\nร้อง\nรองเง็ง\nร่องแร่ง\nรอด\nรอน\nร่อน\nร้อน\nรอบ\nรอบคอบ\nรอม\nรอมชอม\nรอมร่อ\nรอย\nร่อย\nร้อย\nร่อแร่\nระ\nระกะ\nระกา\nระกำ\nระเกะระกะ\nระคน\nระคาง\nระคาย\nระแคะ\nระฆัง\nระงม\nระงับ\nระแง้\nระโงกหิน\nระชวย\nระดม\nระดะ\nระดับ\nระดา\nระด่าว\nระดู\nระเด่น\nระเดียง\nระแด\nระตู\nระทก\nระทด\nระทม\nระทวย\nระทา\nระทึก\nระแทะ\nระนาด\nระนาบ\nระนาม\nระนาว\nระเนน\nระเนระนาด\nระเนียด\nระแนง\nระแนะ\nระบบ\nระบม\nระบอบ\nระบัด\nระบับ\nระบาด\nระบาย\nระบำ\nระบิล\nระบือ\nระบุ\nระเบง\nระเบ็ง\nระเบิด\nระเบียง\nระเบียน\nระเบียบ\nระแบบ\nระมัดระวัง\nระมาด\nระเมียร\nระย่อ\nระย่อม\nระยะ\nระยั้ง\nระยับ\nระย้า\nระยาบ\nระยำ\nระยิบระยับ\nระโยง\nระโยงระยาง\nระรวย\nระรอง\nระร่อน\nระรัว\nระราน\nระร่าย\nระริก\nระรี่\nระรึง\nระรื่น\nระรื้น\nระเร้ง\nระเริง\nระเรียง\nระเรื่อย\nระแรง\nระลวง\nระลอก\nระลึก\nระวัง\nระวาง\nระวาย\nระวิง\nระแวง\nระแวดระวัง\nระไว\nระส่ำระสาย\nระหกระเหิน\nระหง\nระหวย\nระหว่าง\nระหองระแหง\nระหัด\nระหาย\nระเห็จ\nระเหย\nระเหระหน\nระเหหน\nระเหิด\nระเหินระหก\nระแหง\nระโหย\nระอา\nระอิดระอา\nระอุ\nรัก\nรักข์\nรักขิต\nรักตะ\nรักบี้\nรักเร่\nรักแร้\nรักษ์\nรักษา\nรัง\nรั้ง\nรังเกียจ\nรังแก\nรังค์\nรังควาน\nรังแค\nรังรอง\nรังวัด\nรังสิ\nรังสี\nรังสิมันตุ์\nรังสิมา\nรัจฉา\nรัช\nรัชชูปการ\nรัชมังคลาภิเษก\nรัชชุ\nรัชฎาภิเษก\nรัชดาภิเษก\nรัชนะ\nรัชนี\nรัญจวน\nรัฏฐาภิปาลโนบาย\nรัฐ\nรัฐประศาสโนบาย\nรัฐประศาสนศาสตร์\nรัด\nรัต\nรัตกัมพล\nรัตมณี\nรัตคน\nรัตจันทน์\nรัตตัญญู\nรัตติ\nรัตน์\nรัตนะ\nรัตนโกสินทร์\nรัตนโกสินทรศก\nรัตนชาติ\nรัตนตรัย\nรัตนบัลลังก์\nรัตนวราภรณ์\nรัตนสิงหาสน์\nรัตนา\nรัตนากร\nรัตนาภรณ์\nรัตนาวลี\nรัตมา\nรัถ\nรัถยา\nรัทเทอร์ฟอร์เดียม\nรัน\nรั้น\nรันทด\nรันทวย\nรับ\nรัมณียสถาน\nรัมภา\nรัมมี่\nรัมย์\nรัย\nรัว\nรั่ว\nรั้ว\nรัศมิมัต\nรัศมิมาน\nรัศมี\nรัษฎากร\nรัสเซีย\nรัสสะ\nรัสสระ\nรา\nร่า\nร้า\nราก\nรากษส\nรากสาด\nราคะ\nราคจริต\nราคา\nราคิน\nราคี\nราง\nร่าง\nร้าง\nรางจืด\nรางชาง\nรางวัล\nราช\nราชกิจจานุเบกษา\nราชนิกุล\nราชวโรงการ\nราชญี\nราชดัด\nราชพฤกษ์\nราชมาณพ\nราชมาษ\nราชมาส\nราชย์\nราชสีห์\nราชะ\nราชัน\nราชันย์\nราชัย\nราชา\nราชาธิปไตย\nราชาธิราช\nราชาภิเษก\nราชายตนะ\nราชาวดี\nราชี\nราชินิกุล\nราชินีกุล\nราชินี\nราชินูปถัมภ์\nราชูปถัมภ์\nราชูปโภค\nราเชน\nราเชนทร์\nราเชนทรยาน\nราโชวาท\nราไชศวรรย์\nราญ\nราญรอน\nราด\nราต\nราตร\nราตรี\nราน\nร่าน\nร้าน\nราบ\nราพณ์\nราพณาสูร\nราม\nรามเกียรติ์\nรามสูร\nรามัญ\nรามา\nราย\nร่าย\nร้าย\nราว\nร้าว\nราวี\nราศี\nราษฎร\nราษฎร์\nราษตรี\nราษราตรี\nราหุ\nราหู\nรำ\nร่ำ\nรำคาญ\nรำงับ\nรำจวน\nรำบาญ\nรำพัน\nรำพาย\nรำพึง\nรำเพย\nรำไพ\nรำมะนา\nรำมะนาด\nรำมะร่อ\nร่ำรวย\nร่ำร่ำ\nรำไร\nรำลึก\nรำหัด\nรำหัส\nริ\nริก\nริดสีดวง\nริน\nริ้น\nริบ\nริบบิ้น\nริบรี่\nริบหรี่\nริปุ\nริปู\nริม\nริ้ว\nริษยา\nรี\nรี่\nรี้พล\nรีด\nรีดักชัน\nรีต\nรีเนียม\nรีบ\nรีม\nรีรอ\nรี้ริก\nรึง\nรึ้ง\nรื่น\nรื้น\nรื้อ\nรุ\nรุก\nรุกข์\nรุกขชาติ\nรุกขเทวดา\nรุกขมูล\nรุกขา\nรุกรุย\nรุ่ง\nรุ้ง\nรุงรัง\nรุ่งริ่ง\nรุจ\nรุจา\nรุจนะ\nรุจิ\nรุจี\nรุจิระ\nรุจิรา\nรุด\nรุต\nรุทธ์\nรุทระ\nรุธิร\nรุธิระ\nรุเธียร\nรุน\nรุ่น\nรุบรู่\nรุม\nรุ่ม\nรุ่มร่าม\nรุย\nรุ่ย\nรุรุ\nรุหะ\nรู\nรู่\nรู้\nรูจี\nรูด\nรูทีเนียม\nรูบิเดียม\nรูป\nรูปิยะ\nรูปี\nรูเล็ตต์\nเร่\nเรข\nเรขา\nเรขาคณิต\nเร็ง\nเร่ง\nเร้ง\nเรณุ\nเรณู\nเรดอน\nเรดาร์\nเรเดียม\nเร้น\nเรรวน\nเรไร\nเร็ว\nเร่ว\nเรวดี\nเรอ\nเร่อ\nเรา\nเร่า\nเร้า\nเราะ\nเริง\nเริด\nเริม\nเริ่ม\nเริ้ม\nเริศร้าง\nเรี่ย\nเรี้ย\nเรียก\nเรียง\nเรียด\nเรียน\nเรียบ\nเรียม\nเรี่ยม\nเรียว\nเรี่ยว\nเรี้ยวรก\nเรือ\nเรื่อ\nเรื้อ\nเรือก\nเรือง\nเรื่อง\nเรื้อง\nเรืองรอง\nเรือด\nเรือน\nเรื้อน\nเรื่อย\nแร\nแร่\nแรก\nแร็กเกต\nแรง\nแร่ง\nแร้ง\nแรด\nแร้นแค้น\nแรม\nแร้ว\nแระ\nโร\nโร่\nโรค\nโรคา\nโรคาพาธ\nโรง\nโรจ\nโรจน์\nโรเดียม\nโรตี\nโรท\nโรธ\nโรม\nโรมัน\nโรเมอร์\nโรย\nโรเร\nโรหิณี\nโรหิต\nไร\nไร่\nไร้\nไรย์\nฤกษ์\nฤกษณะ\nฤคเวท\nฤชา\nฤชุ\nฤณ\nฤดี\nฤดียา\nฤดู\nฤต\nฤติยา\nฤตุ\nฤทธา\nฤทธิ์\nฤทัย\nฤษภ\nฤษยา\nฤษี\nฤๅ\nฤๅดี\nฤๅทัย\nฤๅษี\nฤๅสาย\nลก\nล่ก\nลฆุ\nลง\nล่ง\nลงกา\nล้งเล้ง\nลด\nลดา\nลดาวัลย์\nลน\nล้น\nลบ\nลบอง\nลพ\nลพุช\nลม\nล่ม\nล้ม\nลมาด\nลรรลุง\nลลนา\nลลิต\nลวก\nลวง\nล่วง\nล้วง\nลวณะ\nลวด\nล้วน\nลวนลาม\nลวนะ\nล่วม\nลวะ\nลวิตร\nลหุ\nลหุกาบัติ\nล่อ\nล้อ\nลอก\nล็อก\nล็อกเกต\nลอกแลก\nลอการิทึม\nลอง\nล่อง\nลองกอง\nลองจิจูด\nลองไน\nลอด\nลอตเตอรี่\nลอน\nล่อน\nลอบ\nลอม\nล้อม\nลอมชอม\nลอมพอก\nลอย\nล่อย\nล่อแล่\nลอว์เรนเซียม\nลออ\nละ\nล่ะ\nละคร\nละติจูด\nละบม\nละบอง\nละบือ\nละเบ็ง\nละโบม\nละม่อม\nละมั่ง\nละมาน\nละม้าย\nละมุ\nละมุด\nละมุน\nละเมอ\nละเมาะ\nละเมิด\nละเมียด\nละแมะ\nละโมก\nละโมบ\nละไม\nละลวย\nละลอก\nละล้า\nละล้าละลัง\nละลาน\nละลาบละล้วง\nละลาย\nละล้าว\nละล่ำละลัก\nละลิบ\nละลุม\nละเลง\nละเล้า\nละเลาะ\nละเลิง\nละเลียด\nละเลียบ\nละไล้\nละว้า\nละวาด\nละเวง\nละแวก\nละโว้\nละหมาด\nละห้อย\nละหาน\nละหาร\nละหุ่ง\nละเหย\nละเหี่ย\nละอง\nละออง\nละอาย\nละเอียด\nละแอน\nลัก\nลักขณะ\nลักขณา\nลักขะ\nลักขี\nลักจั่น\nลักปิดลักเปิด\nลักษณ์\nลักษณนาม\nลักษณะ\nลักษณาการ\nลักษมณ์\nลักษมาณา\nลักษมี\nลักษะ\nลัคคะ\nลัคน์\nลัคนา\nลัง\nลั่ง\nลังกา\nลังคี\nลังถึง\nลังลอง\nลังเล\nลังสาด\nลัชชา\nลัชชี\nลัญจ์\nลัญจกร\nลัญฉกร\nลัญฉน์\nลัฐิ\nลัฐิกา\nลัด\nลัดา\nลัทธ์\nลัทธิ\nลัน\nลั่น\nลันเต\nลันเตา\nลันไต\nลั่นทม\nลันโทม\nลับ\nลัพธ์\nลัพธิ\nลัภ\nลัภนะ\nลัภย์\nลัมพ์\nลัย\nลา\nล่า\nล้า\nลาก\nลาง\nล่าง\nล้าง\nลางลิง\nลางสาด\nลาช\nลาชะ\nลาชา\nลาญ\nลาด\nลาดเลา\nล้าต้า\nล่าเตียง\nลาน\nล่าน\nล้าน\nลาบ\nลาพอน\nลาภ\nลาม\nล่าม\nลามก\nลาย\nล้าย\nลายสือ\nลาลา\nลาว\nลาวัณย์\nลาวา\nลำ\nล่ำ\nล้ำ\nลำเข็ญ\nลำแข\nลำเค็ญ\nลำเจียก\nลำดวน\nลำดับ\nลำเนา\nลำบอง\nลำบาก\nลำปำ\nลำพวน\nลำพอง\nลำพัง\nลำพู\nลำเพ็ญ\nลำเพา\nลำแพน\nลำโพง\nลำไพ่\nลำภุขัน\nลำมะลอก\nลำยอง\nลำไย\nลำลอง\nล่ำลา\nลำลาบ\nลำลึก\nลำเลาะ\nลำเลิก\nลำเลียง\nลำเวียง\nลำเอียก\nลำเอียง\nลิ\nลิกขา\nลิกไนต์\nลิกู\nลิเก\nลิขนะ\nลิขสิทธิ์\nลิขิต\nลิง\nลิงค์\nลิด\nลิต\nลิตมัส\nลิตร\nลิเทียม\nลิ่น\nลิ้น\nลินจง\nลิ้นจี่\nลินลา\nลินสีด\nลิ่นฮื้อ\nลินิน\nลิบ\nลิปดา\nลิปสติก\nลิปิ\nลิฟต์\nลิเภา\nลิ่ม\nลิ้ม\nลิมป์\nลิมปนะ\nลิลิต\nลิว\nลิ่ว\nลิสง\nลี\nลี่\nลี้\nลีซอ\nลีบ\nลีลา\nลีลาศ\nลีฬหา\nลึก\nลึงค์\nลืด\nลื่น\nลื้น\nลืบ\nลืม\nลือ\nลื่อ\nลื้อ\nลุ\nลุก\nลุง\nลุ้ง\nลุ่น\nลุ้น\nลุพธ์\nลุ่ม\nลุมพี\nลุมพู\nลุย\nลุ่ย\nลุ้ย\nลู่\nลูก\nลูกระมาศ\nลูกเอ็น\nลูขะ\nลูทีเชียม\nลูบ\nเลก\nเล็ก\nเลข\nเลขา\nเลขาธิการ\nเลขานุการ\nเล็ง\nเล้ง\nเล่งฮื้อ\nเลเซอร์\nเลฑฑุ\nเลณฑุ\nเลณะ\nเล็ด\nเลน\nเล็น\nเล่น\nเลนส์\nเล็บ\nเลบง\nเลปกร\nเลปน์\nเลเป\nเลเพ\nเล็ม\nเล่ม\nเลย\nเลว\nเลวง\nเลวูโลส\nเลศ\nเลษฏุ\nเล่ห์\nเล่ห์กระเท่ห์\nเลหลัง\nเลหะ\nเลอ\nเล่อ\nเลอะ\nเลอะเทอะ\nเละ\nเละเทะ\nเลา\nเล่า\nเล้า\nเลากัย\nเล้าโลม\nเลาะ\nเลิก\nเลิ่กลั่ก\nเลิง\nเลิ้ง\nเลินเล่อ\nเลิศ\nเลีย\nเลียง\nเลี่ยง\nเลี้ยง\nเลียงผา\nเลียงฝ้าย\nเลียงมัน\nเลียน\nเลี่ยน\nเลียนไฟ\nเลียบ\nเลี่ยม\nเลียว\nเลี้ยว\nเลือก\nเลือง\nเลื่อง\nเลือด\nเลือน\nเลื่อน\nเลื่อม\nเลื่อย\nเลื้อย\nเลื่อยล้า\nแล\nแล่\nแล้\nแลก\nแล็กเกอร์\nแล็กโทส\nแลง\nแล่ง\nแล้ง\nแลน\nแล่น\nแลนทานัม\nแลบ\nแล้ว\nและ\nโล่\nโล้\nโลก\nโลกเชษฐ์\nโลกธรรม\nโลกธาตุ\nโลกนาถ\nโลกบาล\nโลกย์\nโลกัย\nโลกวัชชะ\nโลกวิทู\nโลกัตถจริยา\nโลกันตร์\nโลกา\nโลกาธิบดี\nโลกาธิปไตย\nโลกานุวัตร\nโลกาภิวัตน์\nโลกามิส\nโลกายัต\nโลกาวินาศ\nโลกิยะ\nโลกีย์\nโลกียวัตร\nโลกียวิสัย\nโลกียสุข\nโลกุตระ\nโลกุตรธรรม\nโลกุตรภูมิ\nโลง\nโล่ง\nโล้ง\nโล่งโจ้ง\nโล่งโต้ง\nโล้งโต้ง\nโลจนะ\nโลณะ\nโลด\nโล่ติ๊น\nโลโต\nโลท\nโลน\nโล้น\nโลภ\nโลม\nโลมเล้า\nโลมะ\nโลมา\nโลลุป\nโลเล\nโลโล\nโลโล้\nโลหะ\nโลหกุมภี\nโลหัช\nโลหิต\nไล่\nไล้\nไลย\nไลลา\nไล่เลี่ย\nฦๅ\nฦๅชา\nฦๅสาย\nวก\nวง\nวงก์\nวงกต\nวงศ์\nวงศกร\nวงศา\nวงษ์\nวจนะ\nวจี\nวชิระ\nวชิรปาณี\nวชิรหัตถ์\nวชิราวุธ\nวฏะ\nวฏาการ\nวณิช\nวณิชชา\nวณิชย์\nวณิชยา\nวณิพก\nวดี\nวทนะ\nวทัญญุตา\nวทัญญู\nวธุกา\nวธู\nวน\nวนศาสตร์\nวนสณฑ์\nวนสัณฑ์\nวนอุทยาน\nวนัส\nวนัสบดี\nวนา\nวนาดร\nวนาดอน\nวนานต์\nวนาลัย\nวนาลี\nวนาวาส\nวนาศรม\nวนาสณฑ์\nวนาสัณฑ์\nวนิดา\nวนิพก\nวเนจร\nวโนทยาน\nวยัคฆ์\nวยากรณ์\nวรดนู\nวรทาน\nวรมหาวิหาร\nวรงค์\nวรณะ\nวรรค\nวรรคย์\nวรรช\nวรรชย์\nวรรณะ\nวรรณกรรม\nวรรณคดี\nวรรณยุกต์\nวรรณยุต\nวรรณศิลป์\nวรรณนา\nวรรณพฤติ\nวรรณึก\nวรรธกะ\nวรรธนะ\nวรรษ\nวรรษา\nวรวิหาร\nวรัญญู\nวรางคณา\nวรางคนา\nวราห์\nวราหะ\nวรุณ\nวโรดม\nวฤก\nวลัช\nวลัญช์\nวลัญชน์\nวลัย\nวลาหก\nวลี\nวศค\nวศะ\nวศิน\nวสนะ\nวสภะ\nวสละ\nวสวัดดี\nวสวัตตี\nวสะ\nวสันต์\nวสันตดิลก\nวสันตฤดู\nวสันตวิษุวัต\nวสา\nวสี\nวสุ\nวสุธา\nวสุนธรา\nวสุมดี\nวหะ\nวอ\nวอก\nวอกแวก\nว่องไว\nวอด\nวอน\nว่อน\nว็อบ\nวอมแวม\nวอลเลย์บอล\nวอแว\nวะ\nวัก\nวักกะ\nวัคคีย์\nวัคคุ\nวัคซีน\nวัง\nวังก์\nวังชา\nวังเวง\nวังศะ\nวังสะ\nวัจจะ\nวัจกุฎี\nวัจฉละ\nวัจน์\nวัช\nวัชชะ\nวัชพืช\nวัชฌ์\nวัชระ\nวัชรปาณี\nวัชรยาน\nวัชรอาสน์\nวัชราสน์\nวัชรินทร์\nวัชรี\nวัชเรนทร์\nวัฏ\nวัฏฏะ\nวัฏจักร\nวัฏทุกข์\nวัฏสงสาร\nวัฏกะ\nวัฏฏิ\nวัฒกะ\nวัฒกี\nวัฒนธรรม\nวัฒนะ\nวัฒนา\nวัณ\nวัณโรค\nวัณฏ์\nวัณณะ\nวัณนา\nวัด\nวัต\nวัตต์\nวัตตา\nวัตถ์\nวัตถาภรณ์\nวัตถาลังการ\nวัตถุ\nวัตนะ\nวัตร\nวัตสดร\nวัตสะ\nวัติ\nวัทน์\nวัน\nวันต์\nวันทนา\nวันทนาการ\nวันทนีย์\nวันทยหัตถ์\nวันทยาวุธ\nวันทา\nวันทิ\nวับ\nวับวาบ\nวับวาม\nวับแวบ\nวับแวม\nวัปปะ\nวัมมิกะ\nวัย\nวัลก์\nวัลคุ\nวัลย์\nวัลลภ\nวัลลี\nวัว\nวัสสะ\nวัสโสทก\nวัสดุ\nวัสตร์\nวัสน์\nวัสนะ\nวัสสานะ\nวัสสานฤดู\nวา\nว่า\nว้า\nว้าเหว่\nวาก\nว้าก\nวากยสัมพันธ์\nวากยะ\nวาง\nว่าง\nว้าง\nวาจก\nวาจา\nวาจาไปยะ\nวาจาล\nวาชเปยะ\nวาณิช\nวาณิชกะ\nวาณิชย์\nวาณี\nวาด\nวาต\nวาตะ\nวาตภัย\nวาท\nวาทศาสตร์\nวาทศิลป์\nวาทกะ\nวาทนะ\nวาทย์\nวาทยกร\nวาทิต\nวาทิน\nวาที\nวาน\nวานซืน\nว่าน\nวานร\nวานรินทร์\nวาเนเดียม\nวาบ\nวาปี\nวาม\nวามน\nวามนาวตาร\nวามะ\nวาย\nว่าย\nว้าย\nวายะ\nวาโย\nวายามะ\nวายุ\nวายุกูล\nวาร\nวาระ\nวารสาร\nวารสารศาสตร์\nวาริ\nวารี\nวาริช\nวารีช\nวาริท\nวาริธร\nวารุณ\nวารุณี\nวาล\nวาลวีชนี\nวาล์ว\nวาลิกา\nวาลุกา\nวาว\nว่าว\nว้าว่อน\nว้าวุ่น\nวาสนะ\nวาสนา\nวาสพ\nวาสะ\nวาสิน\nวาสี\nวาสุกรี\nวาสุกี\nวาสุเทพ\nวาหนะ\nวาหะ\nวาหินี\nวาฬ\nวิกขัมภ์\nวิกขัมภนะ\nวิกเขป\nวิกรม\nวิกรัย\nวิกรานต์\nวิกฤต\nวิกฤติ\nวิกล\nวิกสิต\nวิกัต\nวิกัติ\nวิกัติการก\nวิกัป\nวิกัย\nวิการ\nวิกาล\nวิกาลโภชน์\nวิคหะ\nวิเคราะห์\nวิฆเนศ\nวิฆเนศวร\nวิฆาต\nวิง\nวิ่ง\nวิ่งเปี้ยว\nวิงวอน\nวิจฉิกะ\nวิจล\nวิจักขณ์\nวิจักษ์\nวิจักษณ์\nวิจัย\nวิจาร\nวิจารณ์\nวิจารณญาณ\nวิจิ\nวิจิกิจฉา\nวิจิต\nวิจิตร\nวิจิน\nวิจุณ\nวิจุรณ\nวิชชา\nวิชชุ\nวิชชุดา\nวิชชุตา\nวิชชุลดา\nวิชญะ\nวิชน\nวิชนี\nวิชย\nวิชัย\nวิชา\nวิชานนะ\nวิชิต\nวิเชียร\nวิญญัตติ\nวิญญาณ\nวิญญาณกทรัพย์\nวิญญู\nวิฑูรย์\nวิด\nวิตก\nวิตถาร\nวิตามิน\nวิถี\nวิทธะ\nวิทยฐานะ\nวิทยา\nวิทยาคม\nวิทยาคาร\nวิทยาลัย\nวิทยุ\nวิทยุต\nวิทวัส\nวิทัตถิ\nวิทัศน์\nวิทารณ์\nวิทิต\nวิทู\nวิทูร\nวิเทศ\nวิเทโศบาย\nวิธ\nวิธวา\nวิธาน\nวิธี\nวิธุระ\nวิธู\nวิธูปนะ\nวิ่น\nวินตกะ\nวินัย\nวินาที\nวินายก\nวินาศ\nวินิจ\nวินิจฉัย\nวินิต\nวินิบาต\nวินิปาติก\nวิเนต\nวิบัติ\nวิบาก\nวิบุล\nวิบุลย์\nวิบูล\nวิบูลย์\nวิปการ\nวิปฏิสาร\nวิปโยค\nวิประโยค\nวิปริต\nวิปลาส\nวิปวาส\nวิปักษ์\nวิปัสสก\nวิปัสสนา\nวิปัสสนายานิก\nวิพากษ์\nวิพิธทัศนา\nวิพุธ\nวิภว\nวิภวตัณหา\nวิภังค์\nวิภัช\nวิภัตติ\nวิภา\nวิภาค\nวิภาช\nวิภาดา\nวิภาวี\nวิภาษ\nวิภาส\nวิภู\nวิภูษณะ\nวิภูษา\nวิภูษิต\nวิมน\nวิมล\nวิมลัก\nวิมังสา\nวิมัติ\nวิมาน\nวิมุข\nวิมุต\nวิมุตติ\nวิเมลือง\nวิโมกข์\nวิโยค\nวิระ\nวิรงรอง\nวิรังรอง\nวิรัช\nวิรัต\nวิรัติ\nวิราคะ\nวิราม\nวิริยภาพ\nวิริยะ\nวิรุธ\nวิรุฬห์\nวิรุฬหก\nวิรูป\nวิรูปักษ์\nวิเรนทร์\nวิโรจ\nวิโรจน์\nวิโรฒ\nวิโรธ\nวิลันดา\nวิลัย\nวิลาด\nวิลาศ\nวิลาป\nวิลาวัณย์\nวิลาส\nวิลาสินี\nวิลิปดา\nวิลิศมาหรา\nวิเลป\nวิเลปนะ\nวิโลกนะ\nวิโลม\nวิไล\nวิไลวรรณ\nวิวรณ์\nวิวรรธน์\nวิวัฏ\nวิวัฒน์\nวิวัฒนาการ\nวิวัน\nวิวาท\nวิวาห์\nวิวาหมงคล\nวิวาหะ\nวิวิต\nวิวิธ\nวิเวก\nวิศรุต\nวิศว\nวิศวกร\nวิศวกรรม\nวิศวกรรมศาสตร์\nวิศัลย์\nวิศาขบูชา\nวิศาขา\nวิศาล\nวิศิษฏ์\nวิศุทธ์\nวิศุทธิ์\nวิเศษ\nวิเศษณ์\nวิษณุ\nวิษณุกรรม\nวิษธร\nวิษัย\nวิษาณ\nวิษุวัต\nวิสกี้\nวิสรรชนีย์\nวิสฤต\nวิสสุกรรม\nวิสัชนา\nวิสัญญี\nวิสัย\nวิสัยทัศน์\nวิสาขบูชา\nวิสาขะ\nวิสาขา\nวิสามัญ\nวิสามานยนาม\nวิสาร\nวิสารทะ\nวิสาล\nวิสาสะ\nวิสาหกิจ\nวิสิฐ\nวิสุงคามสีมา\nวิสุทธ์\nวิสุทธิ์\nวิสูตร\nวิเสท\nวิหค\nวิหลั่น\nวิหาร\nวิหิงสา\nวิเหสา\nวิฬาร\nวิฬาร์\nวี\nวีจิ\nวีชนี\nวีณา\nวี้ด\nวีรกรรม\nวีรชน\nวีรบุรุษ\nวีรสตรี\nวี่วัน\nวี่แวว\nวีสะ\nวุ้ง\nวุฐิ\nวุฒ\nวุฒิ\nวุด\nวุ่น\nวุ้น\nวุบ\nวุ้ย\nวุลแฟรม\nวู้\nวูดวาด\nวูบ\nวู่วาม\nเว้\nเวค\nเวคิน\nเวคี\nเวจ\nเวช\nเวชยันต์\nเวฐน์\nเวณิ\nเวณิก\nเวณุ\nเวตน์\nเวตร\nเวตาล\nเวท\nเวทคู\nเวทนา\nเวทย์\nเวทัลละ\nเวทางค์\nเวทางคศาสตร์\nเวทานต์\nเวทานตะ\nเวทิ\nเวที\nเวธะ\nเวน\nเว้น\nเวนไตย\nเวไนย\nเวมัต\nเว้ย\nเวยยากรณะ\nเวร\nเวรมณี\nเวรี\nเวโรจน์\nเวลา\nเวเลนซี\nเวศม์\nเวศย์\nเวศยา\nเวสน์\nเวสภู\nเวสม์\nเวสวัณ\nเวสสะ\nเวสสันดร\nเวสสุกรรม\nเวสสุวัณ\nเวสารัช\nเวสิยา\nเวหน\nเวหะ\nเวหา\nเวหาส\nเวฬุ\nเวฬุริยะ\nเว่อ\nเว้า\nเวิก\nเวิ้ง\nเวี่ย\nเวียง\nเวียด\nเวียดนาม\nเวียน\nเวียร\nเวี่ยว\nแว้\nแวง\nแว้ง\nแวด\nแว้ด\nแวน\nแว่น\nแวนดา\nแวบ\nแว็บ\nแวม\nแว็ม\nแวว\nแว่ว\nแวะ\nโว\nโว่\nโวการ\nโว่ง\nโวทาน\nโวย\nโว้ย\nโว้เว้\nโวหาร\nไว\nไว้\nไวกูณฐ์\nไวฑูรย์\nไวทย์\nไวน์\nไวพจน์\nไวยากรณ์\nไวยาวัจกร\nไวยาวัจมัย\nไวรัส\nไววรรณ\nไวษณพ\nไวโอลิน\nศก\nศกุน\nศกุนต์\nศกุนิ\nศกุนี\nศจี\nศตะ\nศตภิษัช\nศตวรรษ\nศตพรรษ\nศตกะ\nศนิ\nศพ\nศมนะ\nศมะ\nศยาม\nศยามล\nศร\nศรายุธ\nศราวรณ์\nศรรกรา\nศรวณะ\nศรวณีย์\nศรวิษฐา\nศรัณย์\nศรัณยู\nศรัท\nศรัทธา\nศรัย\nศราทธ์\nศราทธพรต\nศราพก\nศราวก\nศราวณะ\nศรี\nศรีตรัง\nศรุติ\nศฤคาล\nศฤงค์\nศฤงคาร\nศฤงคาริน\nศฤงคารี\nศลิษฏ์\nศลิษา\nศวะ\nศวัส\nศวา\nศวาน\nศศะ\nศศธร\nศศพินทุ์\nศศลักษณ์\nศศิ\nศศิน\nศศี\nศศิขัณฑ์\nศศิธร\nศศิมณฑล\nศศิวิมล\nศอ\nศอก\nศักดา\nศักดิ\nศักดิ์\nศักดินา\nศักติ\nศักย\nศักยภาพ\nศักย์\nศักยะ\nศักร\nศักรินทร์\nศักเรนทร์\nศักราช\nศังกร\nศัตรู\nศันสนะ\nศันสนีย์\nศัพท์\nศัยยา\nศัล\nศัลย์\nศัลยกรรม\nศัลยแพทย์\nศัลยศาสตร์\nศัสดร\nศัสตร\nศัสตรศาสตร์\nศัสตรา\nศัสตราวุธ\nศากตะ\nศากย\nศากยะ\nศากยพุทธ\nศากยมุนี\nศาฎก\nศาณ\nศานต์\nศานติ\nศาป\nศารท\nศารทูล\nศาริกา\nศาล\nศาลา\nศาศวัต\nศาสดา\nศาสตร์\nศาสตรา\nศาสตราจารย์\nศาสนา\nศาสนกิจ\nศาสนจักร\nศาสนธรรม\nศาสนบุคคล\nศาสนพิธี\nศาสนวัตถุ\nศาสนศาสตร์\nศาสนสถาน\nศาสนสมบัติ\nศาสนิกชน\nศาสนีย์\nศาสนูปถัมภก\nศาสน์\nศิกษก\nศิการ\nศิขร\nศิขริน\nศิขรี\nศิขัณฑ์\nศิคาล\nศิงขร\nศิงขริน\nศิตะ\nศิถี\nศิพิระ\nศิระ\nศิรประภา\nศิราภรณ์\nศิโรรัตน์\nศิโรเวฐน์\nศิรา\nศิรามพุช\nศิโรราบ\nศิลป\nศิลป์\nศิลปะ\nศิลปกร\nศิลปกรรม\nศิลปกิจ\nศิลปวัตถุ\nศิลปวิทยา\nศิลปศาสตร์\nศิลปศึกษา\nศิลปหัตถกรรม\nศิลปิน\nศิลปี\nศิลา\nศิวะ\nศิวโมกข์\nศิวลึงค์\nศิวเวท\nศิวาลัย\nศิศีระ\nศิษฎิ\nศิษฏ์\nศิษย์\nศิษยานุศิษย์\nศีขร\nศีต\nศีตกาล\nศีรษะ\nศีล\nศึก\nศึกษา\nศึกษาธิการ\nศึกษานิเทศก์\nศุกร์\nศุกรวรรณ\nศุกรวาร\nศุกระ\nศุกล\nศุกลปักษ์\nศุจิ\nศุทธะ\nศุทธิ\nศุนะ\nศุนิ\nศุภกร\nศุภเคราะห์\nศุภนิมิต\nศุภมัสดุ\nศุภมาตรา\nศุภมาส\nศุภอักษร\nศุภางค์\nศูกร\nศุลกากร\nศุลการักษ์\nศุลี\nศุษิร\nศูทร\nศูนย์\nศูนยวาท\nศูละ\nศูลิน\nเศรณี\nเศรษฐ\nเศรษฐ์\nเศรษฐกิจ\nเศรษฐศาสตร์\nเศรษฐี\nเศร้า\nเศลษ\nเศวต\nเศวตร\nเศวตัมพร\nเศษ\nเศาจ\nเศาร์\nเศารยะ\nเศิก\nเศียร\nโศก\nโศกา\nโศกาดูร\nโศกาลัย\nโศกี\nโศจิ\nโศธนะ\nโศภน\nโศภะ\nโศภา\nโศภิต\nโศภิน\nโศภิษฐ์\nโศภี\nโศรดา\nโศรตร\nโศลก\nไศล\nไศวะ\nษมา\nษัฏ\nษัฑ\nษัณ\nษัษ\nษัษฐะ\nษัษฐี\nโษฑศัน\nสก\nสกวาที\nสกฏะ\nสกทาคามิผล\nสกิทาคามิผล\nสกทาคามิมรรค\nสกิทาคามิมรรค\nสกทาคามี\nสกิทาคามี\nสกนธ์\nสกปรก\nสกรณีย์\nสกรรจ์\nสกรรมกริยา\nสกล\nสกลมหาสังฆปริณายก\nสกัด\nสกา\nสกาว\nสกี\nสกุณ\nสกุณา\nสกุณี\nสกุน\nสกุนต์\nสกุล\nสเกต\nสแกนเดียม\nสขะ\nสง\nส่ง\nสงกร\nสงกรานต์\nสงกา\nสงค์\nสงคร\nสงคราม\nสงเคราะห์\nสงฆ์\nสงบ\nสงวน\nส่งสการ\nสงสัย\nสงสาร\nสงสารวัฏ\nสงัด\nสง่า\nสฐะ\nสณฑ์\nสด\nสดมภ์\nสดับ\nสดับปกรณ์\nสดำ\nสดุดี\nสตะ\nสตน\nสตภิสชะ\nสตรอนเชียม\nสตริกนิน\nสตรี\nสตัฟฟ์\nสตัมภ์\nสตางค์\nสติ\nสติปัฏฐาน\nสตี\nสตู\nสตูป\nสเต๊ก\nสถบดี\nสถล\nสถวีระ\nสถาน\nสถานะ\nสถานี\nสถาบัน\nสถาปนา\nสถาปนิก\nสถาปัตยกรรม\nสถาปัตยกรรมศาสตร์\nสถาปัตยเรขา\nสถาปัตยเวท\nสถาพร\nสถาวร\nสถิต\nสถิตยศาสตร์\nสถิติ\nสถิร\nสถีรวาท\nสถุล\nสถูป\nสทิง\nสทึง\nสทุม\nสธนะ\nสาธุสะ\nสน\nส้น\nสนทนา\nสนทรรศ\nสนทรรศน์\nสนเทศ\nสนเท่ห์\nสนธยา\nสนธิ\nสนน\nสนม\nสนวน\nสนอง\nสนอบ\nสนอม\nสนะ\nสนัด\nสนั่น\nสนับ\nสนับทึบ\nสนับสนุน\nสนาน\nสนาม\nสนายุ\nสนิกะ\nสนิท\nสนิธ\nสนิม\nสนุก\nสนุกเกอร์\nสนุข\nสนุต\nสนุ่น\nสบ\nสบง\nสบถ\nสบัน\nสบาย\nสบู่\nสไบ\nสปริง\nสปอร์\nสปาเกตตี\nสเปกตรัม\nสเปกโทรสโกป\nสไปริลลัม\nสพาบ\nสภา\nสภาพ\nสภาวการณ์\nสภาวะ\nสม\nสมการ\nสมจารี\nสมดุล\nสมมูล\nส้ม\nสมญา\nสมณะ\nสมณบริขาร\nสมณศักดิ์\nสมณสารูป\nสมเด็จ\nสมถะ\nสมถยานิก\nสมถวิปัสสนา\nสมนาคุณ\nสมบัติ\nสมบุกสมบัน\nสมบูรณ์\nสมบูรณาญาสิทธิราชย์\nสมประดี\nสมปฤดี\nสมปฤๅดี\nส้มป่อย\nสมปัก\nสมผุส\nสมพง\nสมพงศ์\nสมพล\nสมพัตสร\nสมพาส\nสมเพช\nสมโพธน์\nสมโพธิ\nสมภพ\nสมภาร\nสมโภค\nสมโภช\nสมมต\nสมมติ\nสมมุติ\nสมมาตร\nส้มมือ\nสมโมท\nสมโยค\nสมร\nสมรด\nสมรรถ\nสมรรถนะ\nสมรรถภาพ\nสมรส\nสมฤดี\nสมฤติ\nสมวายะ\nสมเสร็จ\nสมอ\nสมอง\nสมะ\nสมัคร\nสมังคี\nสมัช\nสมัชชา\nสมัญญา\nสมัต\nสมัน\nสมันต์\nสมัย\nสมา\nสมาคม\nสมาจาร\nสมาชิก\nสมาทาน\nสมาธิ\nสมาน\nสมานฉันท์\nสมาบัติ\nสมาพันธรัฐ\nสมาส\nสม่ำเสมอ\nสมิง\nสมิต\nสมิติ\nสมิทธ์\nสมิทธิ\nสมี\nสมุก\nสมุจจัย\nสมุจเฉท\nสมุฏฐาน\nสมุด\nสมุทร\nสมุทรโคดม\nสมุทัย\nสมุน\nสมุนไพร\nสมุลแว้ง\nสมุห\nสมุห์\nสมุหกลาโหม\nสมุหเทศาภิบาล\nสมุหนาม\nสมุหนายก\nสโมธาน\nสโมสร\nสยด\nสยนะ\nสยบ\nสยมพร\nสยมภู\nสยอง\nสยอน\nสยัมวรา\nสยาม\nสยามานุสติ\nสยามินทร์\nสยาย\nสยิว\nสยิ้ว\nสยุมพร\nสยุมภู\nสร\nสรง\nสร่ง\nสรณะ\nสรณคมน์\nสรณาคมน์\nสรณตรัย\nสรตะ\nสรทะ\nสรนุก\nสรเนาะ\nสรไน\nสรเพชญ\nสรภะ\nสรภัญญะ\nสรภู\nสรม\nสรร\nสรรค์\nสรรพ\nสรรพคุณ\nสรรพนาม\nสรรพสามิต\nสรรพัชญ\nสรรพากร\nสรรพางค์\nสรรเพชญ\nสรรเพชุดา\nสรรเสริญ\nสรลอน\nสรเลข\nสรวง\nสรวม\nสรวล\nสรเสริญ\nสร้อย\nสระ\nสระกอ\nสระท้อน\nสระพรั่ง\nสระอาด\nสรั่ง\nสรัสวดี\nสร่าง\nสร้าง\nสราญ\nสรี้\nสรีระ\nสรีรกิจ\nสรีรธาตุ\nสรีรวิทยา\nสรีรศาสตร์\nสรีรังคาร\nสรีรางคาร\nสรุป\nสโรช\nสโรชะ\nสฤก\nสฤต\nสฤษฎิ\nสฤษฎี\nสฤษฏ์\nสฤษดิ์\nสลด\nสลบ\nสลวน\nสลวย\nสลอด\nสลอน\nสลอย\nสละ\nสลัก\nสลัด\nสลัดได\nสลับ\nสลัว\nสลา\nสลาก\nสลาง\nสล้าง\nสลาด\nสลาตัน\nสลาบ\nสลาย\nสลิด\nสลิล\nสลึก\nสลึง\nสลุต\nสลุบ\nสลุมพร\nสแลง\nสวการย์\nสวภาพ\nสวราชย์\nสวก\nส้วง\nสวด\nสวน\nสวนะ\nสวนาการ\nส่วน\nสวนิต\nสวบ\nสวม\nส้วม\nสวย\nส่วย\nส้วย\nสวยม\nสวรรค\nสวรรค์\nสวรรคต\nสวรรคาลัย\nสวรรยา\nสวระ\nสวะ\nสวัสดิ\nสวัสดิ์\nสวัสดิการ\nสวัสดิภาพ\nสวัสดิมงคล\nสวัสดี\nสวัสติ\nสวาตี\nสวัสติกะ\nสวา\nสวาปาม\nสวาคตะ\nสวาง\nสว่าง\nสวาด\nสวาดิ\nสวาท\nสว่าน\nสว้าน\nสวาบ\nสวามิ\nสวามี\nสวามินี\nสวาย\nสวาสดิ์\nสวาหะ\nสวิง\nสวิญญาณกทรัพย์\nสวิตช์\nสสาร\nสสุระ\nสสุรี\nสหกรณ์\nสหการ\nสหจร\nสหชาต\nสหชาติ\nสหธรรม\nสหธรรมิก\nสหประชาชาติ\nสหพันธ์\nสหพันธรัฐ\nสหภาพ\nสหศึกษา\nสหัช\nสหัมบดี\nสหัส\nสหัสสะ\nสหัสธารา\nสหัสนัยน์\nสหัสเนตร\nสหัสรังสี\nสหัสา\nสหาย\nสอ\nส่อ\nสอง\nส่อง\nส้อง\nสอด\nสอน\nส่อน\nสอบ\nสอพลอ\nส้อม\nสอย\nสะ\nสะกด\nสะกอ\nสะกาง\nสะการะ\nสะกิด\nสะกิดสะเกา\nสะเก็ด\nสะแก\nสะคร้อ\nสะคราญ\nสะค้าน\nสะเงาะสะแงะ\nสะดม\nสะดวก\nสะดิ้ง\nสะดึง\nสะดือ\nสะดุ้ง\nสะดุด\nสะเด็ด\nสะเดา\nสะเดาะ\nสะตอ\nสะตาหมัน\nสะตึ\nสะตือ\nสะตุ\nสะเต๊ะ\nสะโตก\nสะทก\nสะท้อน\nสะท้าน\nสะทึก\nสะเทิน\nสะเทิ้น\nสะเทือน\nสะเทื้อน\nสะบะ\nสะบัก\nสะบักสะบอม\nสะบัด\nสะบัดสะบิ้ง\nสะบั้น\nสะบันงา\nสะบ้า\nสะบู\nสะแบง\nสะเปะสะปะ\nสะพรั่ง\nสะพรึงกลัว\nสะพรึบ\nสะพรึ่บ\nสะพัก\nสะพัง\nสะพัด\nสะพั้น\nสะพาน\nสะพาย\nสะเพร่า\nสะโพก\nสะเภา\nสะใภ้\nสะโมง\nสะระตะ\nสะระแหน่\nสะลาง\nสะลาบ\nสะลึมสะลือ\nสะวี้ดสะว้าด\nสะสม\nสะสวย\nสะสาง\nสะเหล่อ\nสะอาง\nสะอาด\nสะอ้าน\nสะอิ้ง\nสะอิดสะเอียน\nสะอึก\nสะอื้น\nสะเอ้ง\nสะเอว\nสะเออะ\nสะโอดสะอง\nสะไอ\nสัก\nสักกะ\nสักยะ\nสักกัจจะ\nสักกายทิฐิ\nสักการ\nสักการะ\nสักขี\nสักวา\nสักหลาด\nสัค\nสัคคะ\nสั่ง\nสังกร\nสังกรณี\nสังกรประโยค\nสังกะตัง\nสังกะวัง\nสังกะวาด\nสังกะสี\nสังกัด\nสังกัปปะ\nสังกา\nสังการ\nสังกาศ\nสังกิเลส\nสังเกต\nสังข์\nสังขกร\nสังขตธรรม\nสังขตะ\nสังขยา\nสังขลิก\nสังขลิกา\nสังขาร\nสังขารา\nสังเขป\nสังค์\nสังคญาติ\nสังคม\nสังคหะ\nสังคัง\nสังคายนา\nสังคายนาย\nสังคีต\nสังคีติ\nสังเค็ด\nสังเคราะห์\nสังฆกรรม\nสังฆการี\nสังฆเถระ\nสังฆทาน\nสังฆนายก\nสังฆปาโมกข์\nสังฆภัต\nสังฆเภท\nสังฆมณฑล\nสังฆมนตรี\nสังฆราช\nสังฆสภา\nสังฆาณัติ\nสังฆาฏิ\nสังฆาทิเสส\nสังฆาธิการ\nสังฆานุสติ\nสังฆาวาส\nสังยุตนิกาย\nสังโยค\nสังโยชน์\nสังวร\nสังวัจฉระ\nสังวัธยาย\nสังวาล\nสังวาส\nสังเวคะ\nสังเวช\nสังเวชนียสถาน\nสังเวย\nสังเวียน\nสังสกฤต\nสังสการ\nสังสนทนา\nสั่งสนทนา\nสังสรรค์\nสังสารวัฏ\nสังสิทธิ\nสังสุทธ์\nสังสุทธิ\nสังหร\nสังหรณ์\nสังหาร\nสังหาริมทรัพย์\nสังหาริมะ\nสังหิต\nสัจ\nสัจกิริยา\nสัจจะ\nสัจญาณ\nสัจธรรม\nสัจนิยม\nสัจพจน์\nสัชฌะ\nสัชฌุ\nสัญจร\nสัญเจตนา\nสัญชาตญาณ\nสัญชาติ\nสัญฌา\nสัญญา\nสัญญาณ\nสัญญี\nสัญโญชน์\nสัญนิยม\nสัญประกาศ\nสัญลักษณ์\nสัฐิ\nสัณฐาน\nสัณฐิติ\nสัณฑ์\nสัณห์\nสัด\nสัดจอง\nสัต\nสัตตะ\nสัตตาหกรณียะ\nสัตตาหกาลิก\nสัตมวาร\nสัตสดก\nสัตตบงกช\nสัตตบรรณ\nสัตตบุษย์\nสัตตู\nสัตถันดร\nสัตถา\nสัตถิ\nสัตถุ\nสัตถุศาสนา\nสัตบรรณ\nสัตย์\nสัตยพรต\nสัตยวาที\nสัตยาเคราะห์\nสัตยาธิษฐาน\nสัตยาบัน\nสัตว์\nสัตวชาติ\nสัตวบาล\nสัตวแพทย์\nสัตววิทยา\nสัตวา\nสัทธรรม\nสัทธา\nสัทธาจริต\nสัทธาธิกะ\nสัทธินทรีย์\nสัทธิงวิหาริก\nสัทธิวิหาริก\nสัทวิทยา\nสัทศาสตร์\nสัทอักษร\nสัน\nสั่น\nสั้น\nสันดาน\nสันดาป\nสันโดษ\nสันต์\nสันตติ\nสันตะปาปา\nสันตะวา\nสันติ\nสันตุฏฐี\nสันถวไมตรี\nสันถวะ\nสันถัต\nสันถาร\nสันทนะ\nสันทะ\nสันทัด\nสันทัสนะ\nสันทาน\nสันทิฐิก\nสันทิส\nสันเทหะ\nสันธาน\nสันนิธิ\nสันนิบาต\nสันนิวาส\nสันนิษฐาน\nสันสกฤต\nสับ\nสับปลับ\nสับปลี้\nสับปะรด\nสัปคับ\nสัปดาห์\nสัปดาหะ\nสัปดน\nสัปตศก\nสัปทน\nสัปปะ\nสัปปิ\nสัปปุริส\nสัปปุรุษ\nสัประยุทธ์\nสัปหงก\nสัปเหร่อ\nสัพ\nสัพพะ\nสัพพัญญู\nสัพเพเหระ\nสัพยอก\nสัมบูรณ์\nสัมปชัญญะ\nสัมปทา\nสัมปทาน\nสัมปยุต\nสัมปโยค\nสัมประสิทธิ์\nสัมประหาร\nสัมปรายภพ\nสัมปรายิกภพ\nสัมปัตติ\nสัมผัปลาป\nสัมผัปลาปะ\nสัมผัส\nสัมพล\nสัมพหุลา\nสัมพัจฉรฉินท์\nสัมพัตสร\nสัมพัทธ์\nสัมพันธ์\nสัมพันธน์\nสัมพันธภาพ\nสัมพันธมิตร\nสัมพันธไมตรี\nสัมพาหะ\nสัมพุทธ\nสัมพุทธะ\nสัมโพธิ\nสัมภวะ\nสัมภเวสี\nสัมภัต\nสัมภัตตะ\nสัมภาระ\nสัมภาษณ์\nสัมโภคกาย\nสัมมนา\nสัมมัปธาน\nสัมมา\nสัมโมทนียกถา\nสัมฤทธิ\nสัมฤทธิ์\nสัมฤทธิศก\nสัยน์\nสัลเลข\nสัสดี\nสัสตทิฐิ\nสัสสะ\nสัสสุ\nสัสสู\nสา\nส่า\nสาก\nสากรรจ์\nสากล\nสากัจฉา\nสากัลย์\nสากิยะ\nสาเก\nสาขา\nสาคร\nสาคเรศ\nสาคู\nสาง\nส้าง\nสาชล\nสาฎก\nสาฏิก\nสาณี\nสาด\nสาไถย\nสาทร\nสาทิส\nสาทุ\nสาโท\nสาธก\nสาธยะ\nสาธยาย\nสาธารณะ\nสาธารณชน\nสาธารณประโยชน์\nสาธารณภัย\nสาธารณรัฐ\nสาธารณสถาน\nสาธารณสมบัติ\nสาธารณสุข\nสาธารณูปการ\nสาธารณูปโภค\nสาธารณ์\nสาธิต\nสาธุ\nสาน\nส่าน\nสานุ\nสานู\nสานุศิษย์\nสาบ\nสาบสูญ\nสาบาน\nสาป\nสาปไตย\nสาม\nสามชุก\nสามเณร\nสามเณรี\nสามนต์\nสามนตราช\nสามยทรัพย์\nสามล\nสามหาว\nสามะ\nสามัคคี\nสามัญ\nสามัตถิยะ\nสามานย์\nสามานยนาม\nสามารถ\nสามิต\nสามินี\nสามิภักดิ์\nสามี\nสามีจิกรรม\nสาย\nส่าย\nส้าย\nสายชู\nสายัณห์\nสายาห์\nสาร\nสารคดี\nสารธรรม\nสารนิเทศ\nสารบบ\nสารบรรณ\nสารบัญ\nสารบาญ\nสารบาญชี\nสารประโยชน์\nสารสนเทศ\nสารทุกข์\nสารถี\nสารท\nสารพัด\nสารพัน\nสารพางค์\nสารภาพ\nสารภี\nสารวัตร\nสาระ\nสาระแน\nสาระพา\nสาระยำ\nสาระวารี\nสาระสะมา\nสารัตถประโยชน์\nสารัตถศึกษา\nสารัตถะ\nสารัทธ์\nสารัมภ์\nสาราณียกร\nสาราณียธรรม\nสาราณียะ\nสารานุกรม\nสารีริกธาตุ\nสารูป\nสาโรช\nสาละ\nสาละวน\nสาลิ\nสาลิกา\nสาลินี\nสาลี\nสาลี่\nสาลู\nสาโลหิต\nสาว\nสาวก\nสาวิกา\nสาวิตร\nสาวิตรี\nสาสน\nสาสน์\nสาส์น\nสาสนา\nสาสม\nสาหร่าย\nสาหรี\nส่าหรี\nสาหัส\nสาเหตุ\nสาแหรก\nสำ\nสำส่อน\nส่ำ\nสำคัญ\nสำซ่าง\nสำแดง\nสำทับ\nสำนวน\nสำนอง\nสำนัก\nสำนาน\nสำนึก\nสำนึง\nสำเนา\nสำเนียง\nสำบัด\nสำปะลอ\nสำปะหลัง\nสำปั้น\nสำปันนี\nสำเภา\nสำมะงา\nสำมะโน\nสำมะลอ\nสำมะเลเทเมา\nสำมะหา\nสำรด\nสำรวจ\nสำรวม\nสำรวย\nสำรวล\nสำรอก\nสำรอง\nสำรับ\nสำราก\nสำราญ\nสำริด\nสำเร็จ\nสำเรา\nสำเริง\nสำโรง\nสำลัก\nสำลาน\nสำลี\nสำแลง\nสำหรวด\nสำหรับ\nสำหา\nสำเหนียก\nสำเหร่\nสำออย\nสำอาง\nสิ\nสิกข์\nสิข\nสิกขมานา\nสิกขา\nสิขร\nสิขรี\nสิขเรศ\nสิขา\nสิขานล\nสิขี\nสิคาล\nสิง\nสิ่ง\nสิงขร\nสิงค์\nสิงคลิ้ง\nสิงคลี\nสิงคาร\nสิงคาล\nสิงคี\nสิงโต\nสิงห์\nสิงหนาท\nสิงหบัญชร\nสิงหรา\nสิงหราช\nสิงหาคม\nสิงหาสน์\nสิงหล\nสิญจน์\nสิตะ\nสิตางศุ์\nสิถิล\nสิทธ์\nสิทธัตถะ\nสิทธา\nสิทธาจารย์\nสิทธารถ\nสิทธิ\nสิทธิ์\nสิทธิการิยะ\nสิธยะ\nสิน\nสิ้น\nสินเทา\nสินธพ\nสินธุ\nสินธุ์\nสินธุระ\nสินธู\nสินเธาว์\nสินาด\nสินิทธ์\nสินี\nสิเนรุ\nสิเนหก\nสิเนหะ\nสิเนหา\nสิเน่หา\nสิบ\nสิปปะ\nสิมพลี\nสิระ\nสิโรดม\nสิโรตม์\nสิริ\nสิรี\nสิลา\nสิว\nสิ่ว\nสิวะ\nสิวาลัย\nสิวิกา\nสี\nสี่\nสี้\nสีกา\nสีกุน\nสีข้าง\nสีด\nสีดอ\nสีดา\nสีตลรัศมี\nสีตโลทก\nสีโตทก\nสีทันดร\nสีมันต์\nสีมา\nสีละมัน\nสีวิกา\nสีสอ\nสีสะ\nสีสา\nสีสุก\nสีเสียด\nสีห์\nสีหนาท\nสีหบัญชร\nสีหราช\nสีหไสยา\nสีหไสยาสน์\nสีหะ\nสึก\nสึง\nสืบ\nสื่อ\nสุ\nสุก\nสุกข์\nสุกร\nสุกรม\nสุกำศพ\nสุกียากี้\nสุข\nสุขา\nสุขาภิบาล\nสุขารมณ์\nสุขาวดี\nสุขิน\nสุขี\nสุขุม\nสุขุมาล\nสุโข\nสุคต\nสุคติ\nสุคนธ\nสุคนธ์\nสุคนธชาติ\nสุคนธรส\nสุคันธ์\nสุคันธรส\nสุงกะ\nสุงกากร\nสุงสิง\nสุงสุมาร\nสุจริต\nสุจหนี่\nสุจิ\nสุจิต\nสุจิตร\nสุชน\nสุชัมบดี\nสุชา\nสุชาดา\nสุญ\nสุญญากาศ\nสุญตา\nสุญนิยม\nสุณ\nสุณิสา\nสุด\nสุดา\nสุต\nสุตตนิบาต\nสุตตะ\nสุตตันตปิฎก\nสุตตันตะ\nสุติ\nสุทธ\nสุทธ์\nสุทธาวาส\nสุทธิ\nสุทรรศน์\nสุทัศน์\nสุธา\nสุธาโภชน์\nสุธารส\nสุธาสินี\nสุธาสี\nสุธี\nสุนทร\nสุนทรี\nสุนทรียภาพ\nสุนทรียศาสตร์\nสุนทรียะ\nสุนัข\nสุนันท์\nสุโนก\nสุบดี\nสุบรรณ\nสุบิน\nสุปรีดิ์\nสุปรีย์\nสุปาณี\nสุพพัต\nสุพรรณ\nสุพรรณบัฏ\nสุพรรณภาชน์\nสุพรรณราช\nสุพรรณศรี\nสุพรรณถัน\nสุพรรณิการ์\nสุภร\nสุภัค\nสุภา\nสุภาพ\nสุภาษิต\nสุม\nสุ่ม\nสุมทุม\nสุมน\nสุมนะ\nสุมนัส\nสุมนา\nสุ้มเสียง\nสุมะ\nสุมาลี\nสุเมธ\nสุเมรุ\nสุรคต\nสุรเชษฐ์\nสุรบดี\nสุรภาพ\nสุรโลก\nสุรสีหนาท\nสุรเสียง\nสุรงค์\nสุรังค์\nสุรภี\nสุรัติ\nสุรัสวดี\nสุรา\nสุรางค์จำเรียง\nสุรางคนา\nสุรางคนางค์\nสุรารักษ์\nสุราลัย\nสุรินทร์\nสุรินทราหู\nสุริยะ\nสุริยกันต์\nสุริยกานต์\nสุริยการ\nสุริยกาล\nสุริยคติ\nสุริยคราส\nสุริยมณฑล\nสุริยวงศ์\nสุริยง\nสุริยา\nสุริเยนทร์\nสุริเยศ\nสุริโย\nสุริยน\nสุริยัน\nสุริยุปราคา\nสุรีย์\nสุรุ่ยสุร่าย\nสุลต่าน\nสุวคนธ์\nสุวภาพ\nสุวรรณ\nสุวรรณภูมิ\nสุวะ\nสุวาน\nสุวินัย\nสุวิมล\nสุษิระ\nสุสาน\nสุหนัต\nสุหร่ง\nสุหร่าย\nสุหฤท\nสุหัท\nสุเหร่า\nสู\nสู่\nสู้\nสูง\nสูจิ\nสูจิบัตร\nสูญ\nสูด\nสูต\nสูตร\nสูติ\nสูติกรรม\nสูตินรีเวช\nสูติบัตร\nสูติแพทย์\nสูติศาสตร์\nสูท\nสูทกรรม\nสูทศาสตร์\nสูบ\nสูปะ\nสูร\nสูรย์\nสูรยกานต์\nสูริ\nสูสี\nเส\nเสก\nเสกขบุคคล\nเสกขะ\nเสขบุคคล\nเสขะ\nเส็ง\nเส้ง\nเส็งเคร็ง\nเสงี่ยม\nเสฏฐี\nเสณี\nเสด\nเสด็จ\nเสตุ\nเสถียร\nเสทะ\nเสโท\nเสน\nเส้น\nเสนง\nเสน่ง\nเสน่ห์\nเสนหา\nเสน่หา\nเสนอ\nเสนะ\nเสนา\nเสนาธิการ\nเสนาบดี\nเสน่า\nเสนากุฎ\nเสนางค์\nเสนางคนิกร\nเสนานี\nเสนาสนะ\nเสนาะ\nเสนี\nเสนีย์\nเสนียะ\nเสนียด\nเสบย\nเสบียง\nเสพ\nเสพย์\nเสเพล\nเสภา\nเสม็ด\nเสมหะ\nเสมอ\nเสมา\nเสมียน\nเสมือน\nเสย\nเสร็จ\nเสริด\nเสริม\nเสรี\nเสลด\nเสลบรรพต\nเสลา\nเสลี่ยง\nเสลือกสลน\nเสโล\nเสวก\nเสวกามาตย์\nเสวนะ\nเสวนา\nเสวย\nเสวียน\nเสสรวง\nเสสรวล\nเสา\nเส้า\nเสาร์\nเสารภย์\nเสารี\nเสาวคนธ์\nเสาวธาร\nเสาวภา\nเสาวภาคย์\nเสาวภาพ\nเสาวรภย์\nเสาวรส\nเสาวลักษณ์\nเสาวณิต\nเสาวนะ\nเสาวนา\nเสาวนีย์\nเสาหฤท\nเสาะ\nเสาะแสะ\nเสิร์จ\nเสิร์ฟ\nเสีย\nเสียง\nเสี่ยง\nเสียด\nเสี้ยน\nเสียบ\nเสียม\nเสี่ยม\nเสี้ยม\nเสียว\nเสี่ยว\nเสี้ยว\nเสือ\nเสื่อ\nเสื้อ\nเสือก\nเสื่อม\nแส\nแส่\nแส้\nแสก\nแสง\nแสด\nแสดง\nแสตมป์\nแสน\nแสนย์\nแสนยากร\nแสนยานุภาพ\nแสบ\nแสม\nแสยก\nแสยง\nแสยะ\nแสรก\nแสร้ง\nแสลง\nแสล้ม\nแสวง\nแสะ\nโสก\nโสกโดก\nโสกันต์\nโสโครก\nโสณฑ์\nโสณิ\nโสณี\nโสด\nโสดก\nโสดม\nโสดา\nโสดาบัน\nโสดาปัตติผล\nโสดาปัตติมรรค\nโสต\nโสตทัศนวัสดุ\nโสตทัศนอุปกรณ์\nโสตทัศนูปกรณ์\nโสตินทรีย์\nโสตถิ\nโสทก\nโสทร\nโสธก\nโสธนะ\nโสน\nโสภณ\nโสภา\nโสภี\nโสภิณี\nโสเภณี\nโสม\nโสมนัส\nโสมม\nโสมย์\nโสร่ง\nโสรจ\nโสรวาร\nโสโร\nโสวรรณ\nโสหุ้ย\nโสฬส\nใส\nใส่\nไส\nไส้\nไสย\nไสยา\nไสยาสน์\nไสร้\nไสว\nหก\nหกคะเมน\nหง\nหงก\nหงส์\nหงสบาท\nหงสรถ\nหงอ\nหงอก\nหง่อง\nหงองแหงง\nหงอด\nหงอน\nหง่อม\nหงอย\nหง่อย\nหงัก\nหงับ\nหง่าง\nหงาย\nหง่าว\nหงำ\nหงิก\nหงิง\nหงิม\nหงึก\nหงุงหงิง\nหงุดหงิด\nหงุบ\nหงุ่ย\nหญ้า\nหญ้าฝรั่น\nหญ้ายายเภา\nหญิง\nหญิบ\nหด\nหตะ\nหทัย\nหน\nหนวก\nหน่วง\nหนวด\nหน่วย\nหน่วยกิต\nหนอ\nหน่อ\nหนอก\nหนอง\nหนอน\nหนอนตายหยาก\nหน่อย\nหน็อยแน่\nหนัก\nหนัง\nหนังสติ๊ก\nหนังสือ\nหนั่น\nหนับ\nหนา\nหน้า\nหน่าง\nหนาด\nหนาน\nหนาม\nหน่าย\nหนาว\nหนำ\nหนำเลี้ยบ\nหนี\nหนี้\nหนีบ\nหนึก\nหนึ่ง\nหนึบ\nหนืด\nหนุ\nหนุน\nหนุบ\nหนุ่ม\nหนุ่ย\nหนู\nห่ม\nหมก\nหมด\nหม่น\nหมวก\nหมวด\nหมวน\nหมอ\nหม่อ\nหม้อ\nหมอก\nหมอง\nหม่อง\nหมอน\nหม่อน\nหมอบ\nหม่อม\nหมอย\nหม้อห้อม\nหมัก\nหมักหมม\nหมัด\nหมัน\nหมั่น\nหมั้น\nหมับ\nหมา\nหม่า\nหมาก\nหมากฮอส\nหมาง\nหมาด\nหมามุ่ย\nหมามุ้ย\nหมาย\nหม้าย\nหมาร่า\nหม่ำ\nหม้ำ\nหมิ่น\nหมี\nหมี่\nหมึก\nหมืน\nหมื่น\nหมุด\nหมุน\nหมุบ\nหมุบหมับ\nหมุบหมิบ\nหมุ่ย\nหมุยขาว\nหมู\nหมู่\nหมูหริ่ง\nหยก\nหย่ง\nหยด\nหยวก\nหยวบ\nหยอก\nหยอกเอิน\nหย็อกหย็อย\nหยอง\nหย็อง\nหย่อง\nหย็องกรอด\nหย็องแหย็ง\nหยอด\nหยอน\nหย่อน\nหย่อม\nหย็อมแหย็ม\nหย็อย\nหย่อย\nหยัก\nหยักไย่\nหยักเหยา\nหยัง\nหยั่ง\nหยังหยัง\nหยัด\nหยัน\nหยับ\nหยั่วเมือง\nหย่า\nหยากเยื่อ\nหยากไย่\nหยาด\nหยาบ\nหยาม\nหยาว\nหย้าว\nหยำเป\nหยำเหยอะ\nหยำแหยะ\nหยิก\nหยิ่ง\nหยิบ\nหยิม\nหยี\nหยี่\nหยุกหยิก\nหยุด\nหยุ่น\nหยุบ\nหยุมหยิม\nหยูกยา\nหโยดม\nหรคุณ\nหรณะ\nหรดาล\nหรดี\nหรรษ์\nหรรษา\nหรอ\nหรอก\nหร็อมแหร็ม\nหรอย\nหระ\nหรับ\nหรา\nหริ\nหริ่ง\nหริณะ\nหริต\nหริตกี\nหรีตกี\nหรี่\nหรีด\nหรือ\nหรุบ\nหรุบรู่\nหรุบหรู่\nหรุ่ม\nหรู\nหรูหรา\nหฤทัย\nหฤทย์\nหฤษฎ์\nหฤษฎี\nหฤหรรษ์\nหฤโหด\nหลง\nหลงใหล\nหลงจู๊\nหลด\nหลน\nหล่น\nหลบ\nหล่ม\nหลวง\nหลวม\nหลอ\nหล่อ\nหลอก\nหลอด\nหลอน\nหล็อน\nหล่อน\nหลอม\nหละ\nหละหลวม\nหลัก\nหลัง\nหลั่ง\nหลัด\nหลั่น\nหลับ\nหลัว\nหลา\nหล้า\nหลาก\nหลาน\nหลาบ\nหลาม\nหลาย\nหลาว\nหลิ่ง\nหลิท\nหลิน\nหลิม\nหลิว\nหลิ่ว\nหลี\nหลีก\nหลีโก\nหลีบ\nหลีฮื้อ\nหลืบ\nหลุกหลิก\nหลุด\nหลุน\nหลุบ\nหลุม\nหลุมพอ\nหลุมพี\nหลู่\nหวง\nห่วง\nห้วง\nหวด\nหวน\nห้วน\nหวย\nห้วย\nหวอ\nหวอด\nหวะ\nหวัง\nหวัด\nหวั่น\nหวันยิหวา\nหวัว\nหวัวร่อ\nหวัวเราะ\nหวา\nหว่า\nหว้า\nหวาก\nหว่าง\nหวาด\nหวาน\nหว่าน\nหวาม\nหวาย\nหวำ\nหวิด\nหวิว\nหวี\nหวี่\nหวีด\nหวือ\nหวุดหวิด\nหวูด\nหอ\nห่อ\nห้อ\nหอก\nหอง\nห้อง\nหอน\nห่อน\nหอบ\nหอม\nห้อม\nหอย\nห้อย\nหะ\nหะยี\nหะหาย\nหัก\nหัจญ์\nหัจญี\nหัช\nหัฏฐะ\nหัด\nหัต\nหัตถ์\nหัตถกรรม\nหัตถการ\nหัตถกิจ\nหัตถบาส\nหัตถพันธ์\nหัตถาภรณ์\nหัตถศาสตร์\nหัตถศิลป์\nหัตถศึกษา\nหัตถาจารย์\nหัตถินี\nหัตถี\nหัน\nหั่น\nหั้น\nหันตรา\nหับ\nหัย\nหัว\nหัวร่อ\nหัวเราะ\nหัส\nหัสดิน\nหัสดี\nหัสต์\nหัสตะ\nหา\nห่า\nห้า\nหาก\nหาง\nห่าง\nห้าง\nหาญ\nหาด\nห่าน\nหาบ\nหาม\nห่าม\nห้าม\nหาย\nหายใจ\nหายนะ\nหาร\nหารือ\nหาว\nห้าว\nหาสะ\nหำ\nห้ำ\nหิ้ง\nหิงคุ\nหิงสา\nหิงห้อย\nหิ่งห้อย\nหิ่งหาย\nหิด\nหิต\nหิตานุหิตประโยชน์\nหิน\nหิมพาน\nหิมพานต์\nหิมวัต\nหิมวันต์\nหิมวา\nหิมะ\nหิมาลัย\nหิรัญ\nหิรัญญิการ์\nหิรัญบัฏ\nหิรัณย์\nหิรัณยรัศมี\nหิริ\nหิว\nหิ้ว\nหี\nหีด\nหีนยาน\nหีบ\nหึ\nหึง\nหึ่ง\nหึงสา\nหืด\nหืน\nหื่น\nหือ\nหื้อ\nหุง\nหุน\nหุ่น\nหุ้น\nหุนหัน\nหุบ\nหุ้ม\nหุยฮา\nหู\nหู่\nหูก\nหูด\nเห\nเห่\nเหง\nเหง่ง\nเหงา\nเหง้า\nเหงื่อ\nเหงือก\nเห็จ\nเห็ด\nเหติ\nเหตุ\nเห็น\nเหน่ง\nเหนงนายพราน\nเหน็ดเหนื่อย\nเหน็บ\nเหน่อ\nเห็นอ้ม\nเหนอะ\nเหนอะหนะ\nเหน้า\nเหนาะ\nเหนียง\nเหนี่ยง\nเหนี่ยน\nเหนียม\nเหนียว\nเหนี่ยว\nเหนือ\nเหนื่อย\nเห็บ\nเหม\nเหม่\nเหม็ง\nเหม่ง\nเหม็น\nเหมวดี\nเหม่อ\nเหมันต์\nเหมันตฤดู\nเหมา\nเหมายัน\nเหมาะ\nเหมียว\nเหมี่ยว\nเหมือง\nเหมือด\nเหมือน\nเหมื่อย\nเหย\nเหยง\nเหย่อย\nเหยา\nเหย่า\nเหย้า\nเหยาะ\nเหยาะแหยะ\nเหยิง\nเหยิบ\nเหยียด\nเหยียบ\nเหยี่ยว\nเหยื่อ\nเหยือก\nเหรอ\nเหรอะ\nเหรัญญิก\nเหรา\nเหราะ\nเหรียญ\nเหล่\nเหล็ก\nเหลน\nเหลว\nเหลอ\nเหลา\nเหล่า\nเหล้า\nเหลาะแหละ\nเหลิง\nเหลิงเจิ้ง\nเหลียน\nเหลี่ยม\nเหลียว\nเหลือ\nเหลือก\nเหลือง\nเหลือบ\nเหลือม\nเหลื่อม\nเหว\nเหว่\nเหวง\nเหวย\nเหวอะ\nเหวอะหวะ\nเหวี่ยง\nเห่อ\nเหอะ\nเหะ\nเหะหะ\nเหา\nเห่า\nเหาะ\nเหิน\nเหิม\nเหี้ย\nเหียง\nเหียน\nเหี้ยน\nเหี้ยม\nเหี่ยว\nเหื่อ\nเหือด\nแห\nแห่\nแห้\nแหก\nแหง\nแหง่\nแห่ง\nแห้ง\nแหงแก๋\nแหง่ง\nแหงน\nแหน\nแห้น\nแหนง\nแหนบ\nแหนม\nแหบ\nแหม\nแหม่\nแหม่ม\nแหมะ\nแหย\nแหย่\nแหยง\nแหย่ง\nแหยม\nแหย็ม\nแหยะ\nแหล่\nแหลก\nแหล่ง\nแหลน\nแหลม\nแหละ\nแหว\nแห้ว\nแหวก\nแหว่ง\nแหวด\nแหวน\nแหวะ\nแหะ\nโห่\nโหง\nโหด\nโหน\nโหนก\nโหน่ง\nโหม\nโหม่ง\nโหมด\nโหย\nโหยกเหยก\nโหยง\nโหย่ง\nโหร\nโหรง\nโหรงเหรง\nโหรดาจารย์\nโหระพา\nโหรา\nโหราจารย์\nโหราศาสตร์\nโหล\nโหล่\nโหลงโจ้ง\nโหว\nโหว่\nโหว้\nโหวกเหวก\nโหวง\nโหวด\nโหวต\nให้\nใหญ่\nใหม่\nไห\nไห่\nไห้\nไหน\nไหม\nไหม้\nไหรณย์\nไหล\nไหล่\nไหว\nไหว้\nไหหลำ\nอก\nอกตเวทิตา\nอกตเวที\nอกตัญญุตา\nอกตัญญู\nอกนิษฐ์\nอกรณีย์\nอกรรมกริยา\nอกัปปิยวัตถุ\nอกัปปิยะ\nอกุศล\nอคติ\nอคาธ\nอโฆษะ\nองก์\nองค์\nองคชาต\nองคมนตรี\nองครักษ์\nองคาพยพ\nองคุลี\nองศ์\nองศา\nองอาจ\nองุ่น\nอจลา\nอจินตา\nอจินไตย\nอจิระ\nอเจลก\nอเจละ\nอชะ\nอชิน\nอชินี\nอชิระ\nอฏวี\nอณิ\nอณู\nอโณทัย\nอด\nอดิถี\nอดิเทพ\nอดิเรก\nอดิศร\nอดิศวร\nอดิศัย\nอดีต\nอดุล\nอดุลย์\nอติ\nอติชาต\nอติมานะ\nอติราช\nอติเรก\nอติสาร\nอถรรพเวท\nอาถรรพเวท\nอทระ\nอทินนาทาน\nอธรรม\nอธิ\nอธิกมาส\nอธิกรณ์\nอธิกวาร\nอธิกสุรทิน\nอธิการ\nอธิคม\nอธิฏฐาน\nอธิบดี\nอธิบาย\nอธิป\nอธิปไตย\nอธิมาตร\nอธิมุตติ\nอธิโมกข์\nอธิราช\nอธิวาส\nอธิวาสนะ\nอธิศีล\nอธิษฐาน\nอธึก\nอ้น\nอนงค์\nอนงคณะ\nอนงคเลขา\nอนธการ\nอนนต์\nอนยะ\nอนรรฆ\nอนรรถ\nอนล\nอนวัช\nอนัญ\nอนัตตา\nอนันต์\nอนันตริยกรรม\nอนัม\nอนาคต\nอนาคามิผล\nอนาคามิมรรค\nอนาคามี\nอนาจาร\nอนาถ\nอนาถา\nอนาทร\nอนาธิปไตย\nอนามัย\nอนามิกา\nอนารยชน\nอนารยธรรม\nอนารยะ\nอนาลัย\nอนำ\nอนิจ\nอนิจจัง\nอนิจจา\nอนิฏฐารมณ์\nอนิยต\nอนิยม\nอนิล\nอนิวรรต\nอนิวรรตน์\nอนีกะ\nอนีจะ\nอนึก\nอนึ่ง\nอนุ\nอนุกร\nอนุกรม\nอนุกรรมการ\nอนุกระเบียด\nอนุกาชาด\nอนุการ\nอนุกูล\nอนุคามิก\nอนุเคราะห์\nอนุจร\nอนุช\nอนุชน\nอนุชา\nอนุชาต\nอนุชิต\nอนุญาต\nอนุญาโตตุลาการ\nอนุตร\nอนุเถระ\nอนุทิน\nอนุบท\nอนุบาล\nอนุประโยค\nอนุปริญญา\nอนุปสัมบัน\nอนุปัสนา\nอนุพงศ์\nอนุพัทธ์\nอนุพันธ์\nอนุโพธ\nอนุภรรยา\nอนุภริยา\nอนุภาค\nอนุภาษ\nอนุมัติ\nอนุมาตรา\nอนุมาน\nอนุมูล\nอนุโมทนา\nอนุโยค\nอนุรักษ์\nอนุรักษนิยม\nอนุราช\nอนุราธ\nอนุราธะ\nอนุราธา\nอนุรูป\nอนุโลม\nอนุวงศ์\nอนุวรรตน์\nอนุวัต\nอนุวัตน์\nอนุวัตร\nอนุวัติ\nอนุวาต\nอนุศาสก\nอนุศาสน์\nอนุศาสนาจารย์\nอนุศิษฏ์\nอนุสติ\nอนุสนธิ\nอนุสร\nอนุสรณ์\nอนุสัญญา\nอนุสัย\nอนุสาวรีย์\nอนุสาสนี\nอเนก\nอเนกคุณ\nอเนกรรถประโยค\nอเนจอนาถ\nอโนชา\nอโนดาต\nอบ\nอบเชย\nอบาย\nอปจายนธรรม\nอปจายนมัย\nอปมงคล\nอปยศ\nอประไมย\nอปรัณณชาติ\nอปรา\nอปราชัย\nอปราชิต\nอปริมาณ\nอปลักษณ์\nอปโลกน์\nอปวาท\nอเปหิ\nอพพะ\nอพยพ\nอภัพ\nอภัย\nอภิ\nอภิฆาต\nอภิชฌา\nอภิชน\nอภิชนาธิปไตย\nอภิชัย\nอภิชาต\nอภิชิต\nอภิญญา\nอภิญญาณ\nอภิธรรม\nอภิธาน\nอภิไธย\nอภินันท์\nอภินันทนาการ\nอภินัย\nอภินิหาร\nอภิเนษกรมณ์\nอภิบาล\nอภิปรัชญา\nอภิปราย\nอภิมหาอำนาจ\nอภิมานะ\nอภิมุข\nอภิรดี\nอภิรติ\nอภิรมย์\nอภิรักษ์\nอภิราม\nอภิรุต\nอภิรุม\nอภิรูป\nอภิลักขิต\nอภิลักขิตสมัย\nอภิเลปน์\nอภิวันท์\nอภิวาท\nอภิวาทน์\nอภิเษก\nอภิสมโพธิ\nอภิสมัย\nอภิสมาจาร\nอภิสัมโพธิ\nอภิสัมโพธิญาณ\nอภิสิต\nอภิสิทธิ์\nอภูตะ\nอม\nอมพะนำ\nอ้ม\nอมตธรรม\nอมตบท\nอมตะ\nอมนุษย์\nอมร\nอมรา\nอมราวดี\nอมรินทร์\nอมเรนทร์\nอมเรศ\nอมฤต\nอมัตร\nอมาตย์\nอมาวสี\nอมาวสุ\nอมาวาสี\nอมิตร\nอเมริกัน\nอย่า\nอยาก\nอย่าง\nอยุทธ์\nอยู่\nอร\nอรชร\nอรชุน\nอรดี\nอรติ\nอรทัย\nอรไท\nอรนุช\nอรพินท์\nอรพิม\nอรรค\nอรรฆ\nอรรฆย์\nอรรจน์\nอรรณพ\nอรรถ\nอรรถกถา\nอรรถกถาจารย์\nอรรถาธิบาย\nอรรธ\nอรสุม\nอรหะ\nอรหัง\nอรหัต\nอรหัตผล\nอรหัตมรรค\nอรหัน\nอรหันต์\nอรหันตฆาต\nอร่อย\nอรัญ\nอรัญญิก\nอรัญวาส\nอรัญวาสี\nอรัณย์\nอราดี\nอร่าม\nอริ\nอรินทร์\nอริน\nอริยกะ\nอริยทรัพย์\nอริยบุคคล\nอริยผล\nอริยมรรค\nอริยสั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nโฮก\nโฮ่ง\nโฮ้ง\nโฮเต็ล\nโฮลเมียม\nโฮะ\nไฮ้\nไฮโกรมิเตอร์\nไฮดรา\nไฮโดร\nไฮโดรคาร์บอน\nไฮโดรเจน\nไฮโดรมิเตอร์\nไฮไฟ\nไฮโล\nไฮฮี\n"
               .split(/[\r\n]+/)
               .filter(function (w) {
                 return w.length > 1;
diff --git a/assets/javascripts/workers/search.208ed371.min.js b/assets/javascripts/workers/search.16e2a7d4.min.js
similarity index 57%
rename from assets/javascripts/workers/search.208ed371.min.js
rename to assets/javascripts/workers/search.16e2a7d4.min.js
index 8b0e66b673..e0dc159e89 100644
--- a/assets/javascripts/workers/search.208ed371.min.js
+++ b/assets/javascripts/workers/search.16e2a7d4.min.js
@@ -1,4 +1,4 @@
-"use strict";(()=>{var me=Object.create;var U=Object.defineProperty,xe=Object.defineProperties,ve=Object.getOwnPropertyDescriptor,Se=Object.getOwnPropertyDescriptors,Te=Object.getOwnPropertyNames,Y=Object.getOwnPropertySymbols,Qe=Object.getPrototypeOf,X=Object.prototype.hasOwnProperty,Ee=Object.prototype.propertyIsEnumerable;var Z=Math.pow,J=(t,e,r)=>e in t?U(t,e,{enumerable:!0,configurable:!0,writable:!0,value:r}):t[e]=r,A=(t,e)=>{for(var r in e||(e={}))X.call(e,r)&&J(t,r,e[r]);if(Y)for(var r of Y(e))Ee.call(e,r)&&J(t,r,e[r]);return t},q=(t,e)=>xe(t,Se(e));var be=(t,e)=>()=>(e||t((e={exports:{}}).exports,e),e.exports);var Le=(t,e,r,n)=>{if(e&&typeof e=="object"||typeof e=="function")for(let i of Te(e))!X.call(t,i)&&i!==r&&U(t,i,{get:()=>e[i],enumerable:!(n=ve(e,i))||n.enumerable});return t};var we=(t,e,r)=>(r=t!=null?me(Qe(t)):{},Le(e||!t||!t.__esModule?U(r,"default",{value:t,enumerable:!0}):r,t));var B=(t,e,r)=>new Promise((n,i)=>{var s=c=>{try{a(r.next(c))}catch(u){i(u)}},o=c=>{try{a(r.throw(c))}catch(u){i(u)}},a=c=>c.done?n(c.value):Promise.resolve(c.value).then(s,o);a((r=r.apply(t,e)).next())});var te=be((K,ee)=>{/**
+"use strict";(()=>{var ge=Object.create;var W=Object.defineProperty,ye=Object.defineProperties,me=Object.getOwnPropertyDescriptor,ve=Object.getOwnPropertyDescriptors,xe=Object.getOwnPropertyNames,G=Object.getOwnPropertySymbols,Se=Object.getPrototypeOf,X=Object.prototype.hasOwnProperty,Qe=Object.prototype.propertyIsEnumerable;var J=(t,e,r)=>e in t?W(t,e,{enumerable:!0,configurable:!0,writable:!0,value:r}):t[e]=r,M=(t,e)=>{for(var r in e||(e={}))X.call(e,r)&&J(t,r,e[r]);if(G)for(var r of G(e))Qe.call(e,r)&&J(t,r,e[r]);return t},Z=(t,e)=>ye(t,ve(e));var K=(t,e)=>()=>(e||t((e={exports:{}}).exports,e),e.exports);var be=(t,e,r,n)=>{if(e&&typeof e=="object"||typeof e=="function")for(let i of xe(e))!X.call(t,i)&&i!==r&&W(t,i,{get:()=>e[i],enumerable:!(n=me(e,i))||n.enumerable});return t};var H=(t,e,r)=>(r=t!=null?ge(Se(t)):{},be(e||!t||!t.__esModule?W(r,"default",{value:t,enumerable:!0}):r,t));var z=(t,e,r)=>new Promise((n,i)=>{var s=u=>{try{a(r.next(u))}catch(c){i(c)}},o=u=>{try{a(r.throw(u))}catch(c){i(c)}},a=u=>u.done?n(u.value):Promise.resolve(u.value).then(s,o);a((r=r.apply(t,e)).next())});var re=K((ee,te)=>{/**
  * lunr - http://lunrjs.com - A bit like Solr, but much smaller and not as bright - 2.3.9
  * Copyright (C) 2020 Oliver Nightingale
  * @license MIT
@@ -11,18 +11,18 @@
  */t.Set=function(e){if(this.elements=Object.create(null),e){this.length=e.length;for(var r=0;r<this.length;r++)this.elements[e[r]]=!0}else this.length=0},t.Set.complete={intersect:function(e){return e},union:function(){return this},contains:function(){return!0}},t.Set.empty={intersect:function(){return this},union:function(e){return e},contains:function(){return!1}},t.Set.prototype.contains=function(e){return!!this.elements[e]},t.Set.prototype.intersect=function(e){var r,n,i,s=[];if(e===t.Set.complete)return this;if(e===t.Set.empty)return e;this.length<e.length?(r=this,n=e):(r=e,n=this),i=Object.keys(r.elements);for(var o=0;o<i.length;o++){var a=i[o];a in n.elements&&s.push(a)}return new t.Set(s)},t.Set.prototype.union=function(e){return e===t.Set.complete?t.Set.complete:e===t.Set.empty?this:new t.Set(Object.keys(this.elements).concat(Object.keys(e.elements)))},t.idf=function(e,r){var n=0;for(var i in e)i!="_index"&&(n+=Object.keys(e[i]).length);var s=(r-n+.5)/(n+.5);return Math.log(1+Math.abs(s))},t.Token=function(e,r){this.str=e||"",this.metadata=r||{}},t.Token.prototype.toString=function(){return this.str},t.Token.prototype.update=function(e){return this.str=e(this.str,this.metadata),this},t.Token.prototype.clone=function(e){return e=e||function(r){return r},new t.Token(e(this.str,this.metadata),this.metadata)};/*!
  * lunr.tokenizer
  * Copyright (C) 2020 Oliver Nightingale
- */t.tokenizer=function(e,r){if(e==null||e==null)return[];if(Array.isArray(e))return e.map(function(g){return new t.Token(t.utils.asString(g).toLowerCase(),t.utils.clone(r))});for(var n=e.toString().toLowerCase(),i=n.length,s=[],o=0,a=0;o<=i;o++){var c=n.charAt(o),u=o-a;if(c.match(t.tokenizer.separator)||o==i){if(u>0){var l=t.utils.clone(r)||{};l.position=[a,u],l.index=s.length,s.push(new t.Token(n.slice(a,o),l))}a=o+1}}return s},t.tokenizer.separator=/[\s\-]+/;/*!
+ */t.tokenizer=function(e,r){if(e==null||e==null)return[];if(Array.isArray(e))return e.map(function(y){return new t.Token(t.utils.asString(y).toLowerCase(),t.utils.clone(r))});for(var n=e.toString().toLowerCase(),i=n.length,s=[],o=0,a=0;o<=i;o++){var u=n.charAt(o),c=o-a;if(u.match(t.tokenizer.separator)||o==i){if(c>0){var h=t.utils.clone(r)||{};h.position=[a,c],h.index=s.length,s.push(new t.Token(n.slice(a,o),h))}a=o+1}}return s},t.tokenizer.separator=/[\s\-]+/;/*!
  * lunr.Pipeline
  * Copyright (C) 2020 Oliver Nightingale
  */t.Pipeline=function(){this._stack=[]},t.Pipeline.registeredFunctions=Object.create(null),t.Pipeline.registerFunction=function(e,r){r in this.registeredFunctions&&t.utils.warn("Overwriting existing registered function: "+r),e.label=r,t.Pipeline.registeredFunctions[e.label]=e},t.Pipeline.warnIfFunctionNotRegistered=function(e){var r=e.label&&e.label in this.registeredFunctions;r||t.utils.warn(`Function is not registered with pipeline. This may cause problems when serialising the index.
-`,e)},t.Pipeline.load=function(e){var r=new t.Pipeline;return e.forEach(function(n){var i=t.Pipeline.registeredFunctions[n];if(i)r.add(i);else throw new Error("Cannot load unregistered function: "+n)}),r},t.Pipeline.prototype.add=function(){var e=Array.prototype.slice.call(arguments);e.forEach(function(r){t.Pipeline.warnIfFunctionNotRegistered(r),this._stack.push(r)},this)},t.Pipeline.prototype.after=function(e,r){t.Pipeline.warnIfFunctionNotRegistered(r);var n=this._stack.indexOf(e);if(n==-1)throw new Error("Cannot find existingFn");n=n+1,this._stack.splice(n,0,r)},t.Pipeline.prototype.before=function(e,r){t.Pipeline.warnIfFunctionNotRegistered(r);var n=this._stack.indexOf(e);if(n==-1)throw new Error("Cannot find existingFn");this._stack.splice(n,0,r)},t.Pipeline.prototype.remove=function(e){var r=this._stack.indexOf(e);r!=-1&&this._stack.splice(r,1)},t.Pipeline.prototype.run=function(e){for(var r=this._stack.length,n=0;n<r;n++){for(var i=this._stack[n],s=[],o=0;o<e.length;o++){var a=i(e[o],o,e);if(!(a==null||a===""))if(Array.isArray(a))for(var c=0;c<a.length;c++)s.push(a[c]);else s.push(a)}e=s}return e},t.Pipeline.prototype.runString=function(e,r){var n=new t.Token(e,r);return this.run([n]).map(function(i){return i.toString()})},t.Pipeline.prototype.reset=function(){this._stack=[]},t.Pipeline.prototype.toJSON=function(){return this._stack.map(function(e){return t.Pipeline.warnIfFunctionNotRegistered(e),e.label})};/*!
+`,e)},t.Pipeline.load=function(e){var r=new t.Pipeline;return e.forEach(function(n){var i=t.Pipeline.registeredFunctions[n];if(i)r.add(i);else throw new Error("Cannot load unregistered function: "+n)}),r},t.Pipeline.prototype.add=function(){var e=Array.prototype.slice.call(arguments);e.forEach(function(r){t.Pipeline.warnIfFunctionNotRegistered(r),this._stack.push(r)},this)},t.Pipeline.prototype.after=function(e,r){t.Pipeline.warnIfFunctionNotRegistered(r);var n=this._stack.indexOf(e);if(n==-1)throw new Error("Cannot find existingFn");n=n+1,this._stack.splice(n,0,r)},t.Pipeline.prototype.before=function(e,r){t.Pipeline.warnIfFunctionNotRegistered(r);var n=this._stack.indexOf(e);if(n==-1)throw new Error("Cannot find existingFn");this._stack.splice(n,0,r)},t.Pipeline.prototype.remove=function(e){var r=this._stack.indexOf(e);r!=-1&&this._stack.splice(r,1)},t.Pipeline.prototype.run=function(e){for(var r=this._stack.length,n=0;n<r;n++){for(var i=this._stack[n],s=[],o=0;o<e.length;o++){var a=i(e[o],o,e);if(!(a==null||a===""))if(Array.isArray(a))for(var u=0;u<a.length;u++)s.push(a[u]);else s.push(a)}e=s}return e},t.Pipeline.prototype.runString=function(e,r){var n=new t.Token(e,r);return this.run([n]).map(function(i){return i.toString()})},t.Pipeline.prototype.reset=function(){this._stack=[]},t.Pipeline.prototype.toJSON=function(){return this._stack.map(function(e){return t.Pipeline.warnIfFunctionNotRegistered(e),e.label})};/*!
  * lunr.Vector
  * Copyright (C) 2020 Oliver Nightingale
- */t.Vector=function(e){this._magnitude=0,this.elements=e||[]},t.Vector.prototype.positionForIndex=function(e){if(this.elements.length==0)return 0;for(var r=0,n=this.elements.length/2,i=n-r,s=Math.floor(i/2),o=this.elements[s*2];i>1&&(o<e&&(r=s),o>e&&(n=s),o!=e);)i=n-r,s=r+Math.floor(i/2),o=this.elements[s*2];if(o==e||o>e)return s*2;if(o<e)return(s+1)*2},t.Vector.prototype.insert=function(e,r){this.upsert(e,r,function(){throw"duplicate index"})},t.Vector.prototype.upsert=function(e,r,n){this._magnitude=0;var i=this.positionForIndex(e);this.elements[i]==e?this.elements[i+1]=n(this.elements[i+1],r):this.elements.splice(i,0,e,r)},t.Vector.prototype.magnitude=function(){if(this._magnitude)return this._magnitude;for(var e=0,r=this.elements.length,n=1;n<r;n+=2){var i=this.elements[n];e+=i*i}return this._magnitude=Math.sqrt(e)},t.Vector.prototype.dot=function(e){for(var r=0,n=this.elements,i=e.elements,s=n.length,o=i.length,a=0,c=0,u=0,l=0;u<s&&l<o;)a=n[u],c=i[l],a<c?u+=2:a>c?l+=2:a==c&&(r+=n[u+1]*i[l+1],u+=2,l+=2);return r},t.Vector.prototype.similarity=function(e){return this.dot(e)/this.magnitude()||0},t.Vector.prototype.toArray=function(){for(var e=new Array(this.elements.length/2),r=1,n=0;r<this.elements.length;r+=2,n++)e[n]=this.elements[r];return e},t.Vector.prototype.toJSON=function(){return this.elements};/*!
+ */t.Vector=function(e){this._magnitude=0,this.elements=e||[]},t.Vector.prototype.positionForIndex=function(e){if(this.elements.length==0)return 0;for(var r=0,n=this.elements.length/2,i=n-r,s=Math.floor(i/2),o=this.elements[s*2];i>1&&(o<e&&(r=s),o>e&&(n=s),o!=e);)i=n-r,s=r+Math.floor(i/2),o=this.elements[s*2];if(o==e||o>e)return s*2;if(o<e)return(s+1)*2},t.Vector.prototype.insert=function(e,r){this.upsert(e,r,function(){throw"duplicate index"})},t.Vector.prototype.upsert=function(e,r,n){this._magnitude=0;var i=this.positionForIndex(e);this.elements[i]==e?this.elements[i+1]=n(this.elements[i+1],r):this.elements.splice(i,0,e,r)},t.Vector.prototype.magnitude=function(){if(this._magnitude)return this._magnitude;for(var e=0,r=this.elements.length,n=1;n<r;n+=2){var i=this.elements[n];e+=i*i}return this._magnitude=Math.sqrt(e)},t.Vector.prototype.dot=function(e){for(var r=0,n=this.elements,i=e.elements,s=n.length,o=i.length,a=0,u=0,c=0,h=0;c<s&&h<o;)a=n[c],u=i[h],a<u?c+=2:a>u?h+=2:a==u&&(r+=n[c+1]*i[h+1],c+=2,h+=2);return r},t.Vector.prototype.similarity=function(e){return this.dot(e)/this.magnitude()||0},t.Vector.prototype.toArray=function(){for(var e=new Array(this.elements.length/2),r=1,n=0;r<this.elements.length;r+=2,n++)e[n]=this.elements[r];return e},t.Vector.prototype.toJSON=function(){return this.elements};/*!
  * lunr.stemmer
  * Copyright (C) 2020 Oliver Nightingale
  * Includes code from - http://tartarus.org/~martin/PorterStemmer/js.txt
- */t.stemmer=function(){var e={ational:"ate",tional:"tion",enci:"ence",anci:"ance",izer:"ize",bli:"ble",alli:"al",entli:"ent",eli:"e",ousli:"ous",ization:"ize",ation:"ate",ator:"ate",alism:"al",iveness:"ive",fulness:"ful",ousness:"ous",aliti:"al",iviti:"ive",biliti:"ble",logi:"log"},r={icate:"ic",ative:"",alize:"al",iciti:"ic",ical:"ic",ful:"",ness:""},n="[^aeiou]",i="[aeiouy]",s=n+"[^aeiouy]*",o=i+"[aeiou]*",a="^("+s+")?"+o+s,c="^("+s+")?"+o+s+"("+o+")?$",u="^("+s+")?"+o+s+o+s,l="^("+s+")?"+i,g=new RegExp(a),f=new RegExp(u),v=new RegExp(c),m=new RegExp(l),x=/^(.+?)(ss|i)es$/,d=/^(.+?)([^s])s$/,y=/^(.+?)eed$/,b=/^(.+?)(ed|ing)$/,E=/.$/,w=/(at|bl|iz)$/,R=new RegExp("([^aeiouylsz])\\1$"),j=new RegExp("^"+s+i+"[^aeiouwxy]$"),_=/^(.+?[^aeiou])y$/,D=/^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/,N=/^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/,C=/^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/,V=/^(.+?)(s|t)(ion)$/,P=/^(.+?)e$/,z=/ll$/,$=new RegExp("^"+s+i+"[^aeiouwxy]$"),M=function(h){var S,k,L,p,T,O,F;if(h.length<3)return h;if(L=h.substr(0,1),L=="y"&&(h=L.toUpperCase()+h.substr(1)),p=x,T=d,p.test(h)?h=h.replace(p,"$1$2"):T.test(h)&&(h=h.replace(T,"$1$2")),p=y,T=b,p.test(h)){var Q=p.exec(h);p=g,p.test(Q[1])&&(p=E,h=h.replace(p,""))}else if(T.test(h)){var Q=T.exec(h);S=Q[1],T=m,T.test(S)&&(h=S,T=w,O=R,F=j,T.test(h)?h=h+"e":O.test(h)?(p=E,h=h.replace(p,"")):F.test(h)&&(h=h+"e"))}if(p=_,p.test(h)){var Q=p.exec(h);S=Q[1],h=S+"i"}if(p=D,p.test(h)){var Q=p.exec(h);S=Q[1],k=Q[2],p=g,p.test(S)&&(h=S+e[k])}if(p=N,p.test(h)){var Q=p.exec(h);S=Q[1],k=Q[2],p=g,p.test(S)&&(h=S+r[k])}if(p=C,T=V,p.test(h)){var Q=p.exec(h);S=Q[1],p=f,p.test(S)&&(h=S)}else if(T.test(h)){var Q=T.exec(h);S=Q[1]+Q[2],T=f,T.test(S)&&(h=S)}if(p=P,p.test(h)){var Q=p.exec(h);S=Q[1],p=f,T=v,O=$,(p.test(S)||T.test(S)&&!O.test(S))&&(h=S)}return p=z,T=f,p.test(h)&&T.test(h)&&(p=E,h=h.replace(p,"")),L=="y"&&(h=L.toLowerCase()+h.substr(1)),h};return function(I){return I.update(M)}}(),t.Pipeline.registerFunction(t.stemmer,"stemmer");/*!
+ */t.stemmer=function(){var e={ational:"ate",tional:"tion",enci:"ence",anci:"ance",izer:"ize",bli:"ble",alli:"al",entli:"ent",eli:"e",ousli:"ous",ization:"ize",ation:"ate",ator:"ate",alism:"al",iveness:"ive",fulness:"ful",ousness:"ous",aliti:"al",iviti:"ive",biliti:"ble",logi:"log"},r={icate:"ic",ative:"",alize:"al",iciti:"ic",ical:"ic",ful:"",ness:""},n="[^aeiou]",i="[aeiouy]",s=n+"[^aeiouy]*",o=i+"[aeiou]*",a="^("+s+")?"+o+s,u="^("+s+")?"+o+s+"("+o+")?$",c="^("+s+")?"+o+s+o+s,h="^("+s+")?"+i,y=new RegExp(a),g=new RegExp(c),b=new RegExp(u),m=new RegExp(h),Q=/^(.+?)(ss|i)es$/,p=/^(.+?)([^s])s$/,d=/^(.+?)eed$/,w=/^(.+?)(ed|ing)$/,L=/.$/,k=/(at|bl|iz)$/,I=new RegExp("([^aeiouylsz])\\1$"),j=new RegExp("^"+s+i+"[^aeiouwxy]$"),F=/^(.+?[^aeiou])y$/,A=/^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/,N=/^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/,C=/^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/,$=/^(.+?)(s|t)(ion)$/,T=/^(.+?)e$/,V=/ll$/,B=new RegExp("^"+s+i+"[^aeiouwxy]$"),D=function(l){var v,P,E,f,x,R,_;if(l.length<3)return l;if(E=l.substr(0,1),E=="y"&&(l=E.toUpperCase()+l.substr(1)),f=Q,x=p,f.test(l)?l=l.replace(f,"$1$2"):x.test(l)&&(l=l.replace(x,"$1$2")),f=d,x=w,f.test(l)){var S=f.exec(l);f=y,f.test(S[1])&&(f=L,l=l.replace(f,""))}else if(x.test(l)){var S=x.exec(l);v=S[1],x=m,x.test(v)&&(l=v,x=k,R=I,_=j,x.test(l)?l=l+"e":R.test(l)?(f=L,l=l.replace(f,"")):_.test(l)&&(l=l+"e"))}if(f=F,f.test(l)){var S=f.exec(l);v=S[1],l=v+"i"}if(f=A,f.test(l)){var S=f.exec(l);v=S[1],P=S[2],f=y,f.test(v)&&(l=v+e[P])}if(f=N,f.test(l)){var S=f.exec(l);v=S[1],P=S[2],f=y,f.test(v)&&(l=v+r[P])}if(f=C,x=$,f.test(l)){var S=f.exec(l);v=S[1],f=g,f.test(v)&&(l=v)}else if(x.test(l)){var S=x.exec(l);v=S[1]+S[2],x=g,x.test(v)&&(l=v)}if(f=T,f.test(l)){var S=f.exec(l);v=S[1],f=g,x=b,R=B,(f.test(v)||x.test(v)&&!R.test(v))&&(l=v)}return f=V,x=g,f.test(l)&&x.test(l)&&(f=L,l=l.replace(f,"")),E=="y"&&(l=E.toLowerCase()+l.substr(1)),l};return function(O){return O.update(D)}}(),t.Pipeline.registerFunction(t.stemmer,"stemmer");/*!
  * lunr.stopWordFilter
  * Copyright (C) 2020 Oliver Nightingale
  */t.generateStopWordFilter=function(e){var r=e.reduce(function(n,i){return n[i]=i,n},{});return function(n){if(n&&r[n.toString()]!==n.toString())return n}},t.stopWordFilter=t.generateStopWordFilter(["a","able","about","across","after","all","almost","also","am","among","an","and","any","are","as","at","be","because","been","but","by","can","cannot","could","dear","did","do","does","either","else","ever","every","for","from","get","got","had","has","have","he","her","hers","him","his","how","however","i","if","in","into","is","it","its","just","least","let","like","likely","may","me","might","most","must","my","neither","no","nor","not","of","off","often","on","only","or","other","our","own","rather","said","say","says","she","should","since","so","some","than","that","the","their","them","then","there","these","they","this","tis","to","too","twas","us","wants","was","we","were","what","when","where","which","while","who","whom","why","will","with","would","yet","you","your"]),t.Pipeline.registerFunction(t.stopWordFilter,"stopWordFilter");/*!
@@ -31,12 +31,18 @@
  */t.trimmer=function(e){return e.update(function(r){return r.replace(/^\W+/,"").replace(/\W+$/,"")})},t.Pipeline.registerFunction(t.trimmer,"trimmer");/*!
  * lunr.TokenSet
  * Copyright (C) 2020 Oliver Nightingale
- */t.TokenSet=function(){this.final=!1,this.edges={},this.id=t.TokenSet._nextId,t.TokenSet._nextId+=1},t.TokenSet._nextId=1,t.TokenSet.fromArray=function(e){for(var r=new t.TokenSet.Builder,n=0,i=e.length;n<i;n++)r.insert(e[n]);return r.finish(),r.root},t.TokenSet.fromClause=function(e){return"editDistance"in e?t.TokenSet.fromFuzzyString(e.term,e.editDistance):t.TokenSet.fromString(e.term)},t.TokenSet.fromFuzzyString=function(e,r){for(var n=new t.TokenSet,i=[{node:n,editsRemaining:r,str:e}];i.length;){var s=i.pop();if(s.str.length>0){var o=s.str.charAt(0),a;o in s.node.edges?a=s.node.edges[o]:(a=new t.TokenSet,s.node.edges[o]=a),s.str.length==1&&(a.final=!0),i.push({node:a,editsRemaining:s.editsRemaining,str:s.str.slice(1)})}if(s.editsRemaining!=0){if("*"in s.node.edges)var c=s.node.edges["*"];else{var c=new t.TokenSet;s.node.edges["*"]=c}if(s.str.length==0&&(c.final=!0),i.push({node:c,editsRemaining:s.editsRemaining-1,str:s.str}),s.str.length>1&&i.push({node:s.node,editsRemaining:s.editsRemaining-1,str:s.str.slice(1)}),s.str.length==1&&(s.node.final=!0),s.str.length>=1){if("*"in s.node.edges)var u=s.node.edges["*"];else{var u=new t.TokenSet;s.node.edges["*"]=u}s.str.length==1&&(u.final=!0),i.push({node:u,editsRemaining:s.editsRemaining-1,str:s.str.slice(1)})}if(s.str.length>1){var l=s.str.charAt(0),g=s.str.charAt(1),f;g in s.node.edges?f=s.node.edges[g]:(f=new t.TokenSet,s.node.edges[g]=f),s.str.length==1&&(f.final=!0),i.push({node:f,editsRemaining:s.editsRemaining-1,str:l+s.str.slice(2)})}}}return n},t.TokenSet.fromString=function(e){for(var r=new t.TokenSet,n=r,i=0,s=e.length;i<s;i++){var o=e[i],a=i==s-1;if(o=="*")r.edges[o]=r,r.final=a;else{var c=new t.TokenSet;c.final=a,r.edges[o]=c,r=c}}return n},t.TokenSet.prototype.toArray=function(){for(var e=[],r=[{prefix:"",node:this}];r.length;){var n=r.pop(),i=Object.keys(n.node.edges),s=i.length;n.node.final&&(n.prefix.charAt(0),e.push(n.prefix));for(var o=0;o<s;o++){var a=i[o];r.push({prefix:n.prefix.concat(a),node:n.node.edges[a]})}}return e},t.TokenSet.prototype.toString=function(){if(this._str)return this._str;for(var e=this.final?"1":"0",r=Object.keys(this.edges).sort(),n=r.length,i=0;i<n;i++){var s=r[i],o=this.edges[s];e=e+s+o.id}return e},t.TokenSet.prototype.intersect=function(e){for(var r=new t.TokenSet,n=void 0,i=[{qNode:e,output:r,node:this}];i.length;){n=i.pop();for(var s=Object.keys(n.qNode.edges),o=s.length,a=Object.keys(n.node.edges),c=a.length,u=0;u<o;u++)for(var l=s[u],g=0;g<c;g++){var f=a[g];if(f==l||l=="*"){var v=n.node.edges[f],m=n.qNode.edges[l],x=v.final&&m.final,d=void 0;f in n.output.edges?(d=n.output.edges[f],d.final=d.final||x):(d=new t.TokenSet,d.final=x,n.output.edges[f]=d),i.push({qNode:m,output:d,node:v})}}}return r},t.TokenSet.Builder=function(){this.previousWord="",this.root=new t.TokenSet,this.uncheckedNodes=[],this.minimizedNodes={}},t.TokenSet.Builder.prototype.insert=function(e){var r,n=0;if(e<this.previousWord)throw new Error("Out of order word insertion");for(var i=0;i<e.length&&i<this.previousWord.length&&e[i]==this.previousWord[i];i++)n++;this.minimize(n),this.uncheckedNodes.length==0?r=this.root:r=this.uncheckedNodes[this.uncheckedNodes.length-1].child;for(var i=n;i<e.length;i++){var s=new t.TokenSet,o=e[i];r.edges[o]=s,this.uncheckedNodes.push({parent:r,char:o,child:s}),r=s}r.final=!0,this.previousWord=e},t.TokenSet.Builder.prototype.finish=function(){this.minimize(0)},t.TokenSet.Builder.prototype.minimize=function(e){for(var r=this.uncheckedNodes.length-1;r>=e;r--){var n=this.uncheckedNodes[r],i=n.child.toString();i in this.minimizedNodes?n.parent.edges[n.char]=this.minimizedNodes[i]:(n.child._str=i,this.minimizedNodes[i]=n.child),this.uncheckedNodes.pop()}};/*!
+ */t.TokenSet=function(){this.final=!1,this.edges={},this.id=t.TokenSet._nextId,t.TokenSet._nextId+=1},t.TokenSet._nextId=1,t.TokenSet.fromArray=function(e){for(var r=new t.TokenSet.Builder,n=0,i=e.length;n<i;n++)r.insert(e[n]);return r.finish(),r.root},t.TokenSet.fromClause=function(e){return"editDistance"in e?t.TokenSet.fromFuzzyString(e.term,e.editDistance):t.TokenSet.fromString(e.term)},t.TokenSet.fromFuzzyString=function(e,r){for(var n=new t.TokenSet,i=[{node:n,editsRemaining:r,str:e}];i.length;){var s=i.pop();if(s.str.length>0){var o=s.str.charAt(0),a;o in s.node.edges?a=s.node.edges[o]:(a=new t.TokenSet,s.node.edges[o]=a),s.str.length==1&&(a.final=!0),i.push({node:a,editsRemaining:s.editsRemaining,str:s.str.slice(1)})}if(s.editsRemaining!=0){if("*"in s.node.edges)var u=s.node.edges["*"];else{var u=new t.TokenSet;s.node.edges["*"]=u}if(s.str.length==0&&(u.final=!0),i.push({node:u,editsRemaining:s.editsRemaining-1,str:s.str}),s.str.length>1&&i.push({node:s.node,editsRemaining:s.editsRemaining-1,str:s.str.slice(1)}),s.str.length==1&&(s.node.final=!0),s.str.length>=1){if("*"in s.node.edges)var c=s.node.edges["*"];else{var c=new t.TokenSet;s.node.edges["*"]=c}s.str.length==1&&(c.final=!0),i.push({node:c,editsRemaining:s.editsRemaining-1,str:s.str.slice(1)})}if(s.str.length>1){var h=s.str.charAt(0),y=s.str.charAt(1),g;y in s.node.edges?g=s.node.edges[y]:(g=new t.TokenSet,s.node.edges[y]=g),s.str.length==1&&(g.final=!0),i.push({node:g,editsRemaining:s.editsRemaining-1,str:h+s.str.slice(2)})}}}return n},t.TokenSet.fromString=function(e){for(var r=new t.TokenSet,n=r,i=0,s=e.length;i<s;i++){var o=e[i],a=i==s-1;if(o=="*")r.edges[o]=r,r.final=a;else{var u=new t.TokenSet;u.final=a,r.edges[o]=u,r=u}}return n},t.TokenSet.prototype.toArray=function(){for(var e=[],r=[{prefix:"",node:this}];r.length;){var n=r.pop(),i=Object.keys(n.node.edges),s=i.length;n.node.final&&(n.prefix.charAt(0),e.push(n.prefix));for(var o=0;o<s;o++){var a=i[o];r.push({prefix:n.prefix.concat(a),node:n.node.edges[a]})}}return e},t.TokenSet.prototype.toString=function(){if(this._str)return this._str;for(var e=this.final?"1":"0",r=Object.keys(this.edges).sort(),n=r.length,i=0;i<n;i++){var s=r[i],o=this.edges[s];e=e+s+o.id}return e},t.TokenSet.prototype.intersect=function(e){for(var r=new t.TokenSet,n=void 0,i=[{qNode:e,output:r,node:this}];i.length;){n=i.pop();for(var s=Object.keys(n.qNode.edges),o=s.length,a=Object.keys(n.node.edges),u=a.length,c=0;c<o;c++)for(var h=s[c],y=0;y<u;y++){var g=a[y];if(g==h||h=="*"){var b=n.node.edges[g],m=n.qNode.edges[h],Q=b.final&&m.final,p=void 0;g in n.output.edges?(p=n.output.edges[g],p.final=p.final||Q):(p=new t.TokenSet,p.final=Q,n.output.edges[g]=p),i.push({qNode:m,output:p,node:b})}}}return r},t.TokenSet.Builder=function(){this.previousWord="",this.root=new t.TokenSet,this.uncheckedNodes=[],this.minimizedNodes={}},t.TokenSet.Builder.prototype.insert=function(e){var r,n=0;if(e<this.previousWord)throw new Error("Out of order word insertion");for(var i=0;i<e.length&&i<this.previousWord.length&&e[i]==this.previousWord[i];i++)n++;this.minimize(n),this.uncheckedNodes.length==0?r=this.root:r=this.uncheckedNodes[this.uncheckedNodes.length-1].child;for(var i=n;i<e.length;i++){var s=new t.TokenSet,o=e[i];r.edges[o]=s,this.uncheckedNodes.push({parent:r,char:o,child:s}),r=s}r.final=!0,this.previousWord=e},t.TokenSet.Builder.prototype.finish=function(){this.minimize(0)},t.TokenSet.Builder.prototype.minimize=function(e){for(var r=this.uncheckedNodes.length-1;r>=e;r--){var n=this.uncheckedNodes[r],i=n.child.toString();i in this.minimizedNodes?n.parent.edges[n.char]=this.minimizedNodes[i]:(n.child._str=i,this.minimizedNodes[i]=n.child),this.uncheckedNodes.pop()}};/*!
  * lunr.Index
  * Copyright (C) 2020 Oliver Nightingale
- */t.Index=function(e){this.invertedIndex=e.invertedIndex,this.fieldVectors=e.fieldVectors,this.tokenSet=e.tokenSet,this.fields=e.fields,this.pipeline=e.pipeline},t.Index.prototype.search=function(e){return this.query(function(r){var n=new t.QueryParser(e,r);n.parse()})},t.Index.prototype.query=function(e){for(var r=new t.Query(this.fields),n=Object.create(null),i=Object.create(null),s=Object.create(null),o=Object.create(null),a=Object.create(null),c=0;c<this.fields.length;c++)i[this.fields[c]]=new t.Vector;e.call(r,r);for(var c=0;c<r.clauses.length;c++){var u=r.clauses[c],l=null,g=t.Set.empty;u.usePipeline?l=this.pipeline.runString(u.term,{fields:u.fields}):l=[u.term];for(var f=0;f<l.length;f++){var v=l[f];u.term=v;var m=t.TokenSet.fromClause(u),x=this.tokenSet.intersect(m).toArray();if(x.length===0&&u.presence===t.Query.presence.REQUIRED){for(var d=0;d<u.fields.length;d++){var y=u.fields[d];o[y]=t.Set.empty}break}for(var b=0;b<x.length;b++)for(var E=x[b],w=this.invertedIndex[E],R=w._index,d=0;d<u.fields.length;d++){var y=u.fields[d],j=w[y],_=Object.keys(j),D=E+"/"+y,N=new t.Set(_);if(u.presence==t.Query.presence.REQUIRED&&(g=g.union(N),o[y]===void 0&&(o[y]=t.Set.complete)),u.presence==t.Query.presence.PROHIBITED){a[y]===void 0&&(a[y]=t.Set.empty),a[y]=a[y].union(N);continue}if(i[y].upsert(R,u.boost,function(ge,ye){return ge+ye}),!s[D]){for(var C=0;C<_.length;C++){var V=_[C],P=new t.FieldRef(V,y),z=j[V],$;($=n[P])===void 0?n[P]=new t.MatchData(E,y,z):$.add(E,y,z)}s[D]=!0}}}if(u.presence===t.Query.presence.REQUIRED)for(var d=0;d<u.fields.length;d++){var y=u.fields[d];o[y]=o[y].intersect(g)}}for(var M=t.Set.complete,I=t.Set.empty,c=0;c<this.fields.length;c++){var y=this.fields[c];o[y]&&(M=M.intersect(o[y])),a[y]&&(I=I.union(a[y]))}var h=Object.keys(n),S=[],k=Object.create(null);if(r.isNegated()){h=Object.keys(this.fieldVectors);for(var c=0;c<h.length;c++){var P=h[c],L=t.FieldRef.fromString(P);n[P]=new t.MatchData}}for(var c=0;c<h.length;c++){var L=t.FieldRef.fromString(h[c]),p=L.docRef;if(M.contains(p)&&!I.contains(p)){var T=this.fieldVectors[L],O=i[L.fieldName].similarity(T),F;if((F=k[p])!==void 0)F.score+=O,F.matchData.combine(n[L]);else{var Q={ref:p,score:O,matchData:n[L]};k[p]=Q,S.push(Q)}}}return S.sort(function(de,pe){return pe.score-de.score})},t.Index.prototype.toJSON=function(){var e=Object.keys(this.invertedIndex).sort().map(function(n){return[n,this.invertedIndex[n]]},this),r=Object.keys(this.fieldVectors).map(function(n){return[n,this.fieldVectors[n].toJSON()]},this);return{version:t.version,fields:this.fields,fieldVectors:r,invertedIndex:e,pipeline:this.pipeline.toJSON()}},t.Index.load=function(e){var r={},n={},i=e.fieldVectors,s=Object.create(null),o=e.invertedIndex,a=new t.TokenSet.Builder,c=t.Pipeline.load(e.pipeline);e.version!=t.version&&t.utils.warn("Version mismatch when loading serialised index. Current version of lunr '"+t.version+"' does not match serialized index '"+e.version+"'");for(var u=0;u<i.length;u++){var l=i[u],g=l[0],f=l[1];n[g]=new t.Vector(f)}for(var u=0;u<o.length;u++){var l=o[u],v=l[0],m=l[1];a.insert(v),s[v]=m}return a.finish(),r.fields=e.fields,r.fieldVectors=n,r.invertedIndex=s,r.tokenSet=a.root,r.pipeline=c,new t.Index(r)};/*!
+ */t.Index=function(e){this.invertedIndex=e.invertedIndex,this.fieldVectors=e.fieldVectors,this.tokenSet=e.tokenSet,this.fields=e.fields,this.pipeline=e.pipeline},t.Index.prototype.search=function(e){return this.query(function(r){var n=new t.QueryParser(e,r);n.parse()})},t.Index.prototype.query=function(e){for(var r=new t.Query(this.fields),n=Object.create(null),i=Object.create(null),s=Object.create(null),o=Object.create(null),a=Object.create(null),u=0;u<this.fields.length;u++)i[this.fields[u]]=new t.Vector;e.call(r,r);for(var u=0;u<r.clauses.length;u++){var c=r.clauses[u],h=null,y=t.Set.empty;c.usePipeline?h=this.pipeline.runString(c.term,{fields:c.fields}):h=[c.term];for(var g=0;g<h.length;g++){var b=h[g];c.term=b;var m=t.TokenSet.fromClause(c),Q=this.tokenSet.intersect(m).toArray();if(Q.length===0&&c.presence===t.Query.presence.REQUIRED){for(var p=0;p<c.fields.length;p++){var d=c.fields[p];o[d]=t.Set.empty}break}for(var w=0;w<Q.length;w++)for(var L=Q[w],k=this.invertedIndex[L],I=k._index,p=0;p<c.fields.length;p++){var d=c.fields[p],j=k[d],F=Object.keys(j),A=L+"/"+d,N=new t.Set(F);if(c.presence==t.Query.presence.REQUIRED&&(y=y.union(N),o[d]===void 0&&(o[d]=t.Set.complete)),c.presence==t.Query.presence.PROHIBITED){a[d]===void 0&&(a[d]=t.Set.empty),a[d]=a[d].union(N);continue}if(i[d].upsert(I,c.boost,function(pe,de){return pe+de}),!s[A]){for(var C=0;C<F.length;C++){var $=F[C],T=new t.FieldRef($,d),V=j[$],B;(B=n[T])===void 0?n[T]=new t.MatchData(L,d,V):B.add(L,d,V)}s[A]=!0}}}if(c.presence===t.Query.presence.REQUIRED)for(var p=0;p<c.fields.length;p++){var d=c.fields[p];o[d]=o[d].intersect(y)}}for(var D=t.Set.complete,O=t.Set.empty,u=0;u<this.fields.length;u++){var d=this.fields[u];o[d]&&(D=D.intersect(o[d])),a[d]&&(O=O.union(a[d]))}var l=Object.keys(n),v=[],P=Object.create(null);if(r.isNegated()){l=Object.keys(this.fieldVectors);for(var u=0;u<l.length;u++){var T=l[u],E=t.FieldRef.fromString(T);n[T]=new t.MatchData}}for(var u=0;u<l.length;u++){var E=t.FieldRef.fromString(l[u]),f=E.docRef;if(!!D.contains(f)&&!O.contains(f)){var x=this.fieldVectors[E],R=i[E.fieldName].similarity(x),_;if((_=P[f])!==void 0)_.score+=R,_.matchData.combine(n[E]);else{var S={ref:f,score:R,matchData:n[E]};P[f]=S,v.push(S)}}}return v.sort(function(he,fe){return fe.score-he.score})},t.Index.prototype.toJSON=function(){var e=Object.keys(this.invertedIndex).sort().map(function(n){return[n,this.invertedIndex[n]]},this),r=Object.keys(this.fieldVectors).map(function(n){return[n,this.fieldVectors[n].toJSON()]},this);return{version:t.version,fields:this.fields,fieldVectors:r,invertedIndex:e,pipeline:this.pipeline.toJSON()}},t.Index.load=function(e){var r={},n={},i=e.fieldVectors,s=Object.create(null),o=e.invertedIndex,a=new t.TokenSet.Builder,u=t.Pipeline.load(e.pipeline);e.version!=t.version&&t.utils.warn("Version mismatch when loading serialised index. Current version of lunr '"+t.version+"' does not match serialized index '"+e.version+"'");for(var c=0;c<i.length;c++){var h=i[c],y=h[0],g=h[1];n[y]=new t.Vector(g)}for(var c=0;c<o.length;c++){var h=o[c],b=h[0],m=h[1];a.insert(b),s[b]=m}return a.finish(),r.fields=e.fields,r.fieldVectors=n,r.invertedIndex=s,r.tokenSet=a.root,r.pipeline=u,new t.Index(r)};/*!
  * lunr.Builder
  * Copyright (C) 2020 Oliver Nightingale
- */t.Builder=function(){this._ref="id",this._fields=Object.create(null),this._documents=Object.create(null),this.invertedIndex=Object.create(null),this.fieldTermFrequencies={},this.fieldLengths={},this.tokenizer=t.tokenizer,this.pipeline=new t.Pipeline,this.searchPipeline=new t.Pipeline,this.documentCount=0,this._b=.75,this._k1=1.2,this.termIndex=0,this.metadataWhitelist=[]},t.Builder.prototype.ref=function(e){this._ref=e},t.Builder.prototype.field=function(e,r){if(/\//.test(e))throw new RangeError("Field '"+e+"' contains illegal character '/'");this._fields[e]=r||{}},t.Builder.prototype.b=function(e){e<0?this._b=0:e>1?this._b=1:this._b=e},t.Builder.prototype.k1=function(e){this._k1=e},t.Builder.prototype.add=function(e,r){var n=e[this._ref],i=Object.keys(this._fields);this._documents[n]=r||{},this.documentCount+=1;for(var s=0;s<i.length;s++){var o=i[s],a=this._fields[o].extractor,c=a?a(e):e[o],u=this.tokenizer(c,{fields:[o]}),l=this.pipeline.run(u),g=new t.FieldRef(n,o),f=Object.create(null);this.fieldTermFrequencies[g]=f,this.fieldLengths[g]=0,this.fieldLengths[g]+=l.length;for(var v=0;v<l.length;v++){var m=l[v];if(f[m]==null&&(f[m]=0),f[m]+=1,this.invertedIndex[m]==null){var x=Object.create(null);x._index=this.termIndex,this.termIndex+=1;for(var d=0;d<i.length;d++)x[i[d]]=Object.create(null);this.invertedIndex[m]=x}this.invertedIndex[m][o][n]==null&&(this.invertedIndex[m][o][n]=Object.create(null));for(var y=0;y<this.metadataWhitelist.length;y++){var b=this.metadataWhitelist[y],E=m.metadata[b];this.invertedIndex[m][o][n][b]==null&&(this.invertedIndex[m][o][n][b]=[]),this.invertedIndex[m][o][n][b].push(E)}}}},t.Builder.prototype.calculateAverageFieldLengths=function(){for(var e=Object.keys(this.fieldLengths),r=e.length,n={},i={},s=0;s<r;s++){var o=t.FieldRef.fromString(e[s]),a=o.fieldName;i[a]||(i[a]=0),i[a]+=1,n[a]||(n[a]=0),n[a]+=this.fieldLengths[o]}for(var c=Object.keys(this._fields),s=0;s<c.length;s++){var u=c[s];n[u]=n[u]/i[u]}this.averageFieldLength=n},t.Builder.prototype.createFieldVectors=function(){for(var e={},r=Object.keys(this.fieldTermFrequencies),n=r.length,i=Object.create(null),s=0;s<n;s++){for(var o=t.FieldRef.fromString(r[s]),a=o.fieldName,c=this.fieldLengths[o],u=new t.Vector,l=this.fieldTermFrequencies[o],g=Object.keys(l),f=g.length,v=this._fields[a].boost||1,m=this._documents[o.docRef].boost||1,x=0;x<f;x++){var d=g[x],y=l[d],b=this.invertedIndex[d]._index,E,w,R;i[d]===void 0?(E=t.idf(this.invertedIndex[d],this.documentCount),i[d]=E):E=i[d],w=E*((this._k1+1)*y)/(this._k1*(1-this._b+this._b*(c/this.averageFieldLength[a]))+y),w*=v,w*=m,R=Math.round(w*1e3)/1e3,u.insert(b,R)}e[o]=u}this.fieldVectors=e},t.Builder.prototype.createTokenSet=function(){this.tokenSet=t.TokenSet.fromArray(Object.keys(this.invertedIndex).sort())},t.Builder.prototype.build=function(){return this.calculateAverageFieldLengths(),this.createFieldVectors(),this.createTokenSet(),new t.Index({invertedIndex:this.invertedIndex,fieldVectors:this.fieldVectors,tokenSet:this.tokenSet,fields:Object.keys(this._fields),pipeline:this.searchPipeline})},t.Builder.prototype.use=function(e){var r=Array.prototype.slice.call(arguments,1);r.unshift(this),e.apply(this,r)},t.MatchData=function(e,r,n){for(var i=Object.create(null),s=Object.keys(n||{}),o=0;o<s.length;o++){var a=s[o];i[a]=n[a].slice()}this.metadata=Object.create(null),e!==void 0&&(this.metadata[e]=Object.create(null),this.metadata[e][r]=i)},t.MatchData.prototype.combine=function(e){for(var r=Object.keys(e.metadata),n=0;n<r.length;n++){var i=r[n],s=Object.keys(e.metadata[i]);this.metadata[i]==null&&(this.metadata[i]=Object.create(null));for(var o=0;o<s.length;o++){var a=s[o],c=Object.keys(e.metadata[i][a]);this.metadata[i][a]==null&&(this.metadata[i][a]=Object.create(null));for(var u=0;u<c.length;u++){var 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-//# sourceMappingURL=search.208ed371.min.js.map
+ */t.Builder=function(){this._ref="id",this._fields=Object.create(null),this._documents=Object.create(null),this.invertedIndex=Object.create(null),this.fieldTermFrequencies={},this.fieldLengths={},this.tokenizer=t.tokenizer,this.pipeline=new t.Pipeline,this.searchPipeline=new t.Pipeline,this.documentCount=0,this._b=.75,this._k1=1.2,this.termIndex=0,this.metadataWhitelist=[]},t.Builder.prototype.ref=function(e){this._ref=e},t.Builder.prototype.field=function(e,r){if(/\//.test(e))throw new RangeError("Field '"+e+"' contains illegal character '/'");this._fields[e]=r||{}},t.Builder.prototype.b=function(e){e<0?this._b=0:e>1?this._b=1:this._b=e},t.Builder.prototype.k1=function(e){this._k1=e},t.Builder.prototype.add=function(e,r){var n=e[this._ref],i=Object.keys(this._fields);this._documents[n]=r||{},this.documentCount+=1;for(var s=0;s<i.length;s++){var o=i[s],a=this._fields[o].extractor,u=a?a(e):e[o],c=this.tokenizer(u,{fields:[o]}),h=this.pipeline.run(c),y=new t.FieldRef(n,o),g=Object.create(null);this.fieldTermFrequencies[y]=g,this.fieldLengths[y]=0,this.fieldLengths[y]+=h.length;for(var b=0;b<h.length;b++){var m=h[b];if(g[m]==null&&(g[m]=0),g[m]+=1,this.invertedIndex[m]==null){var Q=Object.create(null);Q._index=this.termIndex,this.termIndex+=1;for(var p=0;p<i.length;p++)Q[i[p]]=Object.create(null);this.invertedIndex[m]=Q}this.invertedIndex[m][o][n]==null&&(this.invertedIndex[m][o][n]=Object.create(null));for(var d=0;d<this.metadataWhitelist.length;d++){var w=this.metadataWhitelist[d],L=m.metadata[w];this.invertedIndex[m][o][n][w]==null&&(this.invertedIndex[m][o][n][w]=[]),this.invertedIndex[m][o][n][w].push(L)}}}},t.Builder.prototype.calculateAverageFieldLengths=function(){for(var e=Object.keys(this.fieldLengths),r=e.length,n={},i={},s=0;s<r;s++){var o=t.FieldRef.fromString(e[s]),a=o.fieldName;i[a]||(i[a]=0),i[a]+=1,n[a]||(n[a]=0),n[a]+=this.fieldLengths[o]}for(var u=Object.keys(this._fields),s=0;s<u.length;s++){var c=u[s];n[c]=n[c]/i[c]}this.averageFieldLength=n},t.Builder.prototype.createFieldVectors=function(){for(var e={},r=Object.keys(this.fieldTermFrequencies),n=r.length,i=Object.create(null),s=0;s<n;s++){for(var o=t.FieldRef.fromString(r[s]),a=o.fieldName,u=this.fieldLengths[o],c=new t.Vector,h=this.fieldTermFrequencies[o],y=Object.keys(h),g=y.length,b=this._fields[a].boost||1,m=this._documents[o.docRef].boost||1,Q=0;Q<g;Q++){var p=y[Q],d=h[p],w=this.invertedIndex[p]._index,L,k,I;i[p]===void 0?(L=t.idf(this.invertedIndex[p],this.documentCount),i[p]=L):L=i[p],k=L*((this._k1+1)*d)/(this._k1*(1-this._b+this._b*(u/this.averageFieldLength[a]))+d),k*=b,k*=m,I=Math.round(k*1e3)/1e3,c.insert(w,I)}e[o]=c}this.fieldVectors=e},t.Builder.prototype.createTokenSet=function(){this.tokenSet=t.TokenSet.fromArray(Object.keys(this.invertedIndex).sort())},t.Builder.prototype.build=function(){return this.calculateAverageFieldLengths(),this.createFieldVectors(),this.createTokenSet(),new t.Index({invertedIndex:this.invertedIndex,fieldVectors:this.fieldVectors,tokenSet:this.tokenSet,fields:Object.keys(this._fields),pipeline:this.searchPipeline})},t.Builder.prototype.use=function(e){var r=Array.prototype.slice.call(arguments,1);r.unshift(this),e.apply(this,r)},t.MatchData=function(e,r,n){for(var i=Object.create(null),s=Object.keys(n||{}),o=0;o<s.length;o++){var a=s[o];i[a]=n[a].slice()}this.metadata=Object.create(null),e!==void 0&&(this.metadata[e]=Object.create(null),this.metadata[e][r]=i)},t.MatchData.prototype.combine=function(e){for(var r=Object.keys(e.metadata),n=0;n<r.length;n++){var i=r[n],s=Object.keys(e.metadata[i]);this.metadata[i]==null&&(this.metadata[i]=Object.create(null));for(var o=0;o<s.length;o++){var a=s[o],u=Object.keys(e.metadata[i][a]);this.metadata[i][a]==null&&(this.metadata[i][a]=Object.create(null));for(var c=0;c<u.length;c++){var h=u[c];this.metadata[i][a][h]==null?this.metadata[i][a][h]=e.metadata[i][a][h]:this.metadata[i][a][h]=this.metadata[i][a][h].concat(e.metadata[i][a][h])}}}},t.MatchData.prototype.add=function(e,r,n){if(!(e in this.metadata)){this.metadata[e]=Object.create(null),this.metadata[e][r]=n;return}if(!(r in this.metadata[e])){this.metadata[e][r]=n;return}for(var i=Object.keys(n),s=0;s<i.length;s++){var o=i[s];o in this.metadata[e][r]?this.metadata[e][r][o]=this.metadata[e][r][o].concat(n[o]):this.metadata[e][r][o]=n[o]}},t.Query=function(e){this.clauses=[],this.allFields=e},t.Query.wildcard=new String("*"),t.Query.wildcard.NONE=0,t.Query.wildcard.LEADING=1,t.Query.wildcard.TRAILING=2,t.Query.presence={OPTIONAL:1,REQUIRED:2,PROHIBITED:3},t.Query.prototype.clause=function(e){return"fields"in e||(e.fields=this.allFields),"boost"in e||(e.boost=1),"usePipeline"in e||(e.usePipeline=!0),"wildcard"in e||(e.wildcard=t.Query.wildcard.NONE),e.wildcard&t.Query.wildcard.LEADING&&e.term.charAt(0)!=t.Query.wildcard&&(e.term="*"+e.term),e.wildcard&t.Query.wildcard.TRAILING&&e.term.slice(-1)!=t.Query.wildcard&&(e.term=""+e.term+"*"),"presence"in e||(e.presence=t.Query.presence.OPTIONAL),this.clauses.push(e),this},t.Query.prototype.isNegated=function(){for(var e=0;e<this.clauses.length;e++)if(this.clauses[e].presence!=t.Query.presence.PROHIBITED)return!1;return!0},t.Query.prototype.term=function(e,r){if(Array.isArray(e))return e.forEach(function(i){this.term(i,t.utils.clone(r))},this),this;var n=r||{};return n.term=e.toString(),this.clause(n),this},t.QueryParseError=function(e,r,n){this.name="QueryParseError",this.message=e,this.start=r,this.end=n},t.QueryParseError.prototype=new Error,t.QueryLexer=function(e){this.lexemes=[],this.str=e,this.length=e.length,this.pos=0,this.start=0,this.escapeCharPositions=[]},t.QueryLexer.prototype.run=function(){for(var e=t.QueryLexer.lexText;e;)e=e(this)},t.QueryLexer.prototype.sliceString=function(){for(var e=[],r=this.start,n=this.pos,i=0;i<this.escapeCharPositions.length;i++)n=this.escapeCharPositions[i],e.push(this.str.slice(r,n)),r=n+1;return e.push(this.str.slice(r,this.pos)),this.escapeCharPositions.length=0,e.join("")},t.QueryLexer.prototype.emit=function(e){this.lexemes.push({type:e,str:this.sliceString(),start:this.start,end:this.pos}),this.start=this.pos},t.QueryLexer.prototype.escapeCharacter=function(){this.escapeCharPositions.push(this.pos-1),this.pos+=1},t.QueryLexer.prototype.next=function(){if(this.pos>=this.length)return t.QueryLexer.EOS;var e=this.str.charAt(this.pos);return this.pos+=1,e},t.QueryLexer.prototype.width=function(){return this.pos-this.start},t.QueryLexer.prototype.ignore=function(){this.start==this.pos&&(this.pos+=1),this.start=this.pos},t.QueryLexer.prototype.backup=function(){this.pos-=1},t.QueryLexer.prototype.acceptDigitRun=function(){var e,r;do e=this.next(),r=e.charCodeAt(0);while(r>47&&r<58);e!=t.QueryLexer.EOS&&this.backup()},t.QueryLexer.prototype.more=function(){return this.pos<this.length},t.QueryLexer.EOS="EOS",t.QueryLexer.FIELD="FIELD",t.QueryLexer.TERM="TERM",t.QueryLexer.EDIT_DISTANCE="EDIT_DISTANCE",t.QueryLexer.BOOST="BOOST",t.QueryLexer.PRESENCE="PRESENCE",t.QueryLexer.lexField=function(e){return e.backup(),e.emit(t.QueryLexer.FIELD),e.ignore(),t.QueryLexer.lexText},t.QueryLexer.lexTerm=function(e){if(e.width()>1&&(e.backup(),e.emit(t.QueryLexer.TERM)),e.ignore(),e.more())return t.QueryLexer.lexText},t.QueryLexer.lexEditDistance=function(e){return e.ignore(),e.acceptDigitRun(),e.emit(t.QueryLexer.EDIT_DISTANCE),t.QueryLexer.lexText},t.QueryLexer.lexBoost=function(e){return e.ignore(),e.acceptDigitRun(),e.emit(t.QueryLexer.BOOST),t.QueryLexer.lexText},t.QueryLexer.lexEOS=function(e){e.width()>0&&e.emit(t.QueryLexer.TERM)},t.QueryLexer.termSeparator=t.tokenizer.separator,t.QueryLexer.lexText=function(e){for(;;){var r=e.next();if(r==t.QueryLexer.EOS)return t.QueryLexer.lexEOS;if(r.charCodeAt(0)==92){e.escapeCharacter();continue}if(r==":")return t.QueryLexer.lexField;if(r=="~")return e.backup(),e.width()>0&&e.emit(t.QueryLexer.TERM),t.QueryLexer.lexEditDistance;if(r=="^")return e.backup(),e.width()>0&&e.emit(t.QueryLexer.TERM),t.QueryLexer.lexBoost;if(r=="+"&&e.width()===1||r=="-"&&e.width()===1)return e.emit(t.QueryLexer.PRESENCE),t.QueryLexer.lexText;if(r.match(t.QueryLexer.termSeparator))return t.QueryLexer.lexTerm}},t.QueryParser=function(e,r){this.lexer=new t.QueryLexer(e),this.query=r,this.currentClause={},this.lexemeIdx=0},t.QueryParser.prototype.parse=function(){this.lexer.run(),this.lexemes=this.lexer.lexemes;for(var e=t.QueryParser.parseClause;e;)e=e(this);return this.query},t.QueryParser.prototype.peekLexeme=function(){return this.lexemes[this.lexemeIdx]},t.QueryParser.prototype.consumeLexeme=function(){var e=this.peekLexeme();return this.lexemeIdx+=1,e},t.QueryParser.prototype.nextClause=function(){var e=this.currentClause;this.query.clause(e),this.currentClause={}},t.QueryParser.parseClause=function(e){var r=e.peekLexeme();if(r!=null)switch(r.type){case t.QueryLexer.PRESENCE:return t.QueryParser.parsePresence;case t.QueryLexer.FIELD:return t.QueryParser.parseField;case t.QueryLexer.TERM:return t.QueryParser.parseTerm;default:var n="expected either a field or a term, found "+r.type;throw r.str.length>=1&&(n+=" with value '"+r.str+"'"),new t.QueryParseError(n,r.start,r.end)}},t.QueryParser.parsePresence=function(e){var r=e.consumeLexeme();if(r!=null){switch(r.str){case"-":e.currentClause.presence=t.Query.presence.PROHIBITED;break;case"+":e.currentClause.presence=t.Query.presence.REQUIRED;break;default:var n="unrecognised presence operator'"+r.str+"'";throw new t.QueryParseError(n,r.start,r.end)}var i=e.peekLexeme();if(i==null){var n="expecting term or field, found nothing";throw new t.QueryParseError(n,r.start,r.end)}switch(i.type){case t.QueryLexer.FIELD:return t.QueryParser.parseField;case t.QueryLexer.TERM:return t.QueryParser.parseTerm;default:var n="expecting term or field, found '"+i.type+"'";throw new t.QueryParseError(n,i.start,i.end)}}},t.QueryParser.parseField=function(e){var r=e.consumeLexeme();if(r!=null){if(e.query.allFields.indexOf(r.str)==-1){var n=e.query.allFields.map(function(o){return"'"+o+"'"}).join(", "),i="unrecognised field '"+r.str+"', possible fields: "+n;throw new t.QueryParseError(i,r.start,r.end)}e.currentClause.fields=[r.str];var s=e.peekLexeme();if(s==null){var i="expecting term, found nothing";throw new t.QueryParseError(i,r.start,r.end)}switch(s.type){case t.QueryLexer.TERM:return t.QueryParser.parseTerm;default:var i="expecting term, found '"+s.type+"'";throw new t.QueryParseError(i,s.start,s.end)}}},t.QueryParser.parseTerm=function(e){var r=e.consumeLexeme();if(r!=null){e.currentClause.term=r.str.toLowerCase(),r.str.indexOf("*")!=-1&&(e.currentClause.usePipeline=!1);var n=e.peekLexeme();if(n==null){e.nextClause();return}switch(n.type){case t.QueryLexer.TERM:return e.nextClause(),t.QueryParser.parseTerm;case t.QueryLexer.FIELD:return e.nextClause(),t.QueryParser.parseField;case t.QueryLexer.EDIT_DISTANCE:return t.QueryParser.parseEditDistance;case t.QueryLexer.BOOST:return t.QueryParser.parseBoost;case t.QueryLexer.PRESENCE:return e.nextClause(),t.QueryParser.parsePresence;default:var i="Unexpected lexeme type '"+n.type+"'";throw new t.QueryParseError(i,n.start,n.end)}}},t.QueryParser.parseEditDistance=function(e){var r=e.consumeLexeme();if(r!=null){var n=parseInt(r.str,10);if(isNaN(n)){var i="edit distance must be numeric";throw new t.QueryParseError(i,r.start,r.end)}e.currentClause.editDistance=n;var s=e.peekLexeme();if(s==null){e.nextClause();return}switch(s.type){case t.QueryLexer.TERM:return e.nextClause(),t.QueryParser.parseTerm;case t.QueryLexer.FIELD:return e.nextClause(),t.QueryParser.parseField;case t.QueryLexer.EDIT_DISTANCE:return t.QueryParser.parseEditDistance;case t.QueryLexer.BOOST:return t.QueryParser.parseBoost;case t.QueryLexer.PRESENCE:return e.nextClause(),t.QueryParser.parsePresence;default:var i="Unexpected lexeme type '"+s.type+"'";throw new t.QueryParseError(i,s.start,s.end)}}},t.QueryParser.parseBoost=function(e){var r=e.consumeLexeme();if(r!=null){var n=parseInt(r.str,10);if(isNaN(n)){var i="boost must be numeric";throw new t.QueryParseError(i,r.start,r.end)}e.currentClause.boost=n;var s=e.peekLexeme();if(s==null){e.nextClause();return}switch(s.type){case t.QueryLexer.TERM:return e.nextClause(),t.QueryParser.parseTerm;case t.QueryLexer.FIELD:return e.nextClause(),t.QueryParser.parseField;case t.QueryLexer.EDIT_DISTANCE:return t.QueryParser.parseEditDistance;case t.QueryLexer.BOOST:return t.QueryParser.parseBoost;case t.QueryLexer.PRESENCE:return e.nextClause(),t.QueryParser.parsePresence;default:var i="Unexpected lexeme type '"+s.type+"'";throw new t.QueryParseError(i,s.start,s.end)}}},function(e,r){typeof define=="function"&&define.amd?define(r):typeof ee=="object"?te.exports=r():e.lunr=r()}(this,function(){return t})})()});var q=K((Re,ne)=>{"use strict";/*!
+ * escape-html
+ * Copyright(c) 2012-2013 TJ Holowaychuk
+ * Copyright(c) 2015 Andreas Lubbe
+ * Copyright(c) 2015 Tiancheng "Timothy" Gu
+ * MIT Licensed
+ */var Le=/["'&<>]/;ne.exports=we;function we(t){var e=""+t,r=Le.exec(e);if(!r)return e;var n,i="",s=0,o=0;for(s=r.index;s<e.length;s++){switch(e.charCodeAt(s)){case 34:n="&quot;";break;case 38:n="&amp;";break;case 39:n="&#39;";break;case 60:n="&lt;";break;case 62:n="&gt;";break;default:continue}o!==s&&(i+=e.substring(o,s)),o=s+1,i+=n}return o!==s?i+e.substring(o,s):i}});var le=H(re());Object.entries||(Object.entries=function(t){let e=[];for(let r of Object.keys(t))e.push([r,t[r]]);return e});Object.values||(Object.values=function(t){let e=[];for(let r of Object.keys(t))e.push(t[r]);return e});typeof Element!="undefined"&&(Element.prototype.scrollTo||(Element.prototype.scrollTo=function(t,e){typeof t=="object"?(this.scrollLeft=t.left,this.scrollTop=t.top):(this.scrollLeft=t,this.scrollTop=e)}),Element.prototype.replaceWith||(Element.prototype.replaceWith=function(...t){let e=this.parentNode;if(e){t.length===0&&e.removeChild(this);for(let r=t.length-1;r>=0;r--){let n=t[r];typeof n=="string"?n=document.createTextNode(n):n.parentNode&&n.parentNode.removeChild(n),r?e.insertBefore(this.previousSibling,n):e.replaceChild(n,this)}}}));var ie=H(q());function se(t){let e=new Map,r=new Set;for(let n of t){let[i,s]=n.location.split("#"),o=n.location,a=n.title,u=n.tags,c=(0,ie.default)(n.text).replace(/\s+(?=[,.:;!?])/g,"").replace(/\s+/g," ");if(s){let h=e.get(i);r.has(h)?e.set(o,{location:o,title:a,text:c,parent:h}):(h.title=n.title,h.text=c,r.add(h))}else e.set(o,M({location:o,title:a,text:c},u&&{tags:u}))}return e}var oe=H(q());function ae(t,e){let r=new RegExp(t.separator,"img"),n=(i,s,o)=>`${s}<mark data-md-highlight>${o}</mark>`;return i=>{i=i.replace(/[\s*+\-:~^]+/g," ").trim();let s=new RegExp(`(^|${t.separator})(${i.replace(/[|\\{}()[\]^$+*?.-]/g,"\\$&").replace(r,"|")})`,"img");return o=>(e?(0,oe.default)(o):o).replace(s,n).replace(/<\/mark>(\s+)<mark[^>]*>/img,"$1")}}function ue(t){let e=new lunr.Query(["title","text"]);return new lunr.QueryParser(t,e).parse(),e.clauses}function ce(t,e){var i;let r=new Set(t),n={};for(let s=0;s<e.length;s++)for(let o of r)e[s].startsWith(o.term)&&(n[o.term]=!0,r.delete(o));for(let s of r)(i=lunr.stopWordFilter)!=null&&i.call(lunr,s.term)&&(n[s.term]=!1);return n}function Ee(t,e){let[r,n]=[new Set(t),new Set(e)];return[...new Set([...r].filter(i=>!n.has(i)))]}var U=class{constructor({config:e,docs:r,options:n}){this.options=n,this.documents=se(r),this.highlight=ae(e,!1),lunr.tokenizer.separator=new RegExp(e.separator),this.index=lunr(function(){e.lang.length===1&&e.lang[0]!=="en"?this.use(lunr[e.lang[0]]):e.lang.length>1&&this.use(lunr.multiLanguage(...e.lang));let i=Ee(["trimmer","stopWordFilter","stemmer"],n.pipeline);for(let s of e.lang.map(o=>o==="en"?lunr:lunr[o]))for(let o of i)this.pipeline.remove(s[o]),this.searchPipeline.remove(s[o]);this.ref("location"),this.field("title",{boost:1e3}),this.field("text"),this.field("tags",{boost:1e6,extractor:s=>{let{tags:o=[]}=s;return o.reduce((a,u)=>[...a,...lunr.tokenizer(u)],[])}});for(let s of r)this.add(s,{boost:s.boost})})}search(e){if(e)try{let r=this.highlight(e),n=ue(e).filter(o=>o.presence!==lunr.Query.presence.PROHIBITED),i=this.index.search(`${e}*`).reduce((o,{ref:a,score:u,matchData:c})=>{let h=this.documents.get(a);if(typeof h!="undefined"){let{location:y,title:g,text:b,tags:m,parent:Q}=h,p=ce(n,Object.keys(c.metadata)),d=+!Q+ +Object.values(p).every(w=>w);o.push(Z(M({location:y,title:r(g),text:r(b)},m&&{tags:m.map(r)}),{score:u*(1+d),terms:p}))}return o},[]).sort((o,a)=>a.score-o.score).reduce((o,a)=>{let u=this.documents.get(a.location);if(typeof u!="undefined"){let c="parent"in u?u.parent.location:u.location;o.set(c,[...o.get(c)||[],a])}return o},new Map),s;if(this.options.suggestions){let o=this.index.query(a=>{for(let u of n)a.term(u.term,{fields:["title"],presence:lunr.Query.presence.REQUIRED,wildcard:lunr.Query.wildcard.TRAILING})});s=o.length?Object.keys(o[0].matchData.metadata):[]}return M({items:[...i.values()]},typeof s!="undefined"&&{suggestions:s})}catch(r){console.warn(`Invalid query: ${e} \u2013 see https://bit.ly/2s3ChXG`)}return{items:[]}}};var Y;function ke(t){return z(this,null,function*(){let e="../lunr";if(typeof parent!="undefined"&&"IFrameWorker"in parent){let n=document.querySelector("script[src]"),[i]=n.src.split("/worker");e=e.replace("..",i)}let r=[];for(let n of t.lang){switch(n){case"ja":r.push(`${e}/tinyseg.js`);break;case"hi":case"th":r.push(`${e}/wordcut.js`);break}n!=="en"&&r.push(`${e}/min/lunr.${n}.min.js`)}t.lang.length>1&&r.push(`${e}/min/lunr.multi.min.js`),r.length&&(yield importScripts(`${e}/min/lunr.stemmer.support.min.js`,...r))})}function Te(t){return z(this,null,function*(){switch(t.type){case 0:return yield ke(t.data.config),Y=new U(t.data),{type:1};case 2:return{type:3,data:Y?Y.search(t.data):{items:[]}};default:throw new TypeError("Invalid message type")}})}self.lunr=le.default;addEventListener("message",t=>z(void 0,null,function*(){postMessage(yield Te(t.data))}));})();
+//# sourceMappingURL=search.16e2a7d4.min.js.map
 
diff --git a/assets/javascripts/workers/search.208ed371.min.js.map b/assets/javascripts/workers/search.16e2a7d4.min.js.map
similarity index 53%
rename from assets/javascripts/workers/search.208ed371.min.js.map
rename to assets/javascripts/workers/search.16e2a7d4.min.js.map
index 75dc4f987f..fa01f37427 100644
--- a/assets/javascripts/workers/search.208ed371.min.js.map
+++ b/assets/javascripts/workers/search.16e2a7d4.min.js.map
@@ -1,8 +1,8 @@
 {
   "version": 3,
-  "sources": ["node_modules/lunr/lunr.js", "src/assets/javascripts/integrations/search/worker/main/index.ts", "src/assets/javascripts/browser/element/_/index.ts", "src/assets/javascripts/polyfills/index.ts", "src/assets/javascripts/integrations/search/config/index.ts", "src/assets/javascripts/integrations/search/internal/_/index.ts", "src/assets/javascripts/integrations/search/internal/extract/index.ts", "src/assets/javascripts/integrations/search/internal/highlight/index.ts", "src/assets/javascripts/integrations/search/internal/tokenize/index.ts", "src/assets/javascripts/integrations/search/query/transform/index.ts", "src/assets/javascripts/integrations/search/query/_/index.ts", "src/assets/javascripts/integrations/search/_/index.ts"],
+  "sources": ["node_modules/lunr/lunr.js", "node_modules/escape-html/index.js", "src/assets/javascripts/integrations/search/worker/main/index.ts", "src/assets/javascripts/polyfills/index.ts", "src/assets/javascripts/integrations/search/document/index.ts", "src/assets/javascripts/integrations/search/highlighter/index.ts", "src/assets/javascripts/integrations/search/query/_/index.ts", "src/assets/javascripts/integrations/search/_/index.ts"],
   "sourceRoot": "../../../..",
-  "sourcesContent": ["/**\n * lunr - http://lunrjs.com - A bit like Solr, but much smaller and not as bright - 2.3.9\n * Copyright (C) 2020 Oliver Nightingale\n * @license MIT\n */\n\n;(function(){\n\n/**\n * A convenience function for configuring and constructing\n * a new lunr Index.\n *\n * A lunr.Builder instance is created and the pipeline setup\n * with a trimmer, stop word filter and stemmer.\n *\n * This builder object is yielded to the configuration function\n * that is passed as a parameter, allowing the list of fields\n * and other builder parameters to be customised.\n *\n * All documents _must_ be added within the passed config function.\n *\n * @example\n * var idx = lunr(function () {\n *   this.field('title')\n *   this.field('body')\n *   this.ref('id')\n *\n *   documents.forEach(function (doc) {\n *     this.add(doc)\n *   }, this)\n * })\n *\n * @see {@link lunr.Builder}\n * @see {@link lunr.Pipeline}\n * @see {@link lunr.trimmer}\n * @see {@link lunr.stopWordFilter}\n * @see {@link lunr.stemmer}\n * @namespace {function} lunr\n */\nvar lunr = function (config) {\n  var builder = new lunr.Builder\n\n  builder.pipeline.add(\n    lunr.trimmer,\n    lunr.stopWordFilter,\n    lunr.stemmer\n  )\n\n  builder.searchPipeline.add(\n    lunr.stemmer\n  )\n\n  config.call(builder, builder)\n  return builder.build()\n}\n\nlunr.version = \"2.3.9\"\n/*!\n * lunr.utils\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A namespace containing utils for the rest of the lunr library\n * @namespace lunr.utils\n */\nlunr.utils = {}\n\n/**\n * Print a warning message to the console.\n *\n * @param {String} message The message to be printed.\n * @memberOf lunr.utils\n * @function\n */\nlunr.utils.warn = (function (global) {\n  /* eslint-disable no-console */\n  return function (message) {\n    if (global.console && console.warn) {\n      console.warn(message)\n    }\n  }\n  /* eslint-enable no-console */\n})(this)\n\n/**\n * Convert an object to a string.\n *\n * In the case of `null` and `undefined` the function returns\n * the empty string, in all other cases the result of calling\n * `toString` on the passed object is returned.\n *\n * @param {Any} obj The object to convert to a string.\n * @return {String} string representation of the passed object.\n * @memberOf lunr.utils\n */\nlunr.utils.asString = function (obj) {\n  if (obj === void 0 || obj === null) {\n    return \"\"\n  } else {\n    return obj.toString()\n  }\n}\n\n/**\n * Clones an object.\n *\n * Will create a copy of an existing object such that any mutations\n * on the copy cannot affect the original.\n *\n * Only shallow objects are supported, passing a nested object to this\n * function will cause a TypeError.\n *\n * Objects with primitives, and arrays of primitives are supported.\n *\n * @param {Object} obj The object to clone.\n * @return {Object} a clone of the passed object.\n * @throws {TypeError} when a nested object is passed.\n * @memberOf Utils\n */\nlunr.utils.clone = function (obj) {\n  if (obj === null || obj === undefined) {\n    return obj\n  }\n\n  var clone = Object.create(null),\n      keys = Object.keys(obj)\n\n  for (var i = 0; i < keys.length; i++) {\n    var key = keys[i],\n        val = obj[key]\n\n    if (Array.isArray(val)) {\n      clone[key] = val.slice()\n      continue\n    }\n\n    if (typeof val === 'string' ||\n        typeof val === 'number' ||\n        typeof val === 'boolean') {\n      clone[key] = val\n      continue\n    }\n\n    throw new TypeError(\"clone is not deep and does not support nested objects\")\n  }\n\n  return clone\n}\nlunr.FieldRef = function (docRef, fieldName, stringValue) {\n  this.docRef = docRef\n  this.fieldName = fieldName\n  this._stringValue = stringValue\n}\n\nlunr.FieldRef.joiner = \"/\"\n\nlunr.FieldRef.fromString = function (s) {\n  var n = s.indexOf(lunr.FieldRef.joiner)\n\n  if (n === -1) {\n    throw \"malformed field ref string\"\n  }\n\n  var fieldRef = s.slice(0, n),\n      docRef = s.slice(n + 1)\n\n  return new lunr.FieldRef (docRef, fieldRef, s)\n}\n\nlunr.FieldRef.prototype.toString = function () {\n  if (this._stringValue == undefined) {\n    this._stringValue = this.fieldName + lunr.FieldRef.joiner + this.docRef\n  }\n\n  return this._stringValue\n}\n/*!\n * lunr.Set\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A lunr set.\n *\n * @constructor\n */\nlunr.Set = function (elements) {\n  this.elements = Object.create(null)\n\n  if (elements) {\n    this.length = elements.length\n\n    for (var i = 0; i < this.length; i++) {\n      this.elements[elements[i]] = true\n    }\n  } else {\n    this.length = 0\n  }\n}\n\n/**\n * A complete set that contains all elements.\n *\n * @static\n * @readonly\n * @type {lunr.Set}\n */\nlunr.Set.complete = {\n  intersect: function (other) {\n    return other\n  },\n\n  union: function () {\n    return this\n  },\n\n  contains: function () {\n    return true\n  }\n}\n\n/**\n * An empty set that contains no elements.\n *\n * @static\n * @readonly\n * @type {lunr.Set}\n */\nlunr.Set.empty = {\n  intersect: function () {\n    return this\n  },\n\n  union: function (other) {\n    return other\n  },\n\n  contains: function () {\n    return false\n  }\n}\n\n/**\n * Returns true if this set contains the specified object.\n *\n * @param {object} object - Object whose presence in this set is to be tested.\n * @returns {boolean} - True if this set contains the specified object.\n */\nlunr.Set.prototype.contains = function (object) {\n  return !!this.elements[object]\n}\n\n/**\n * Returns a new set containing only the elements that are present in both\n * this set and the specified set.\n *\n * @param {lunr.Set} other - set to intersect with this set.\n * @returns {lunr.Set} a new set that is the intersection of this and the specified set.\n */\n\nlunr.Set.prototype.intersect = function (other) {\n  var a, b, elements, intersection = []\n\n  if (other === lunr.Set.complete) {\n    return this\n  }\n\n  if (other === lunr.Set.empty) {\n    return other\n  }\n\n  if (this.length < other.length) {\n    a = this\n    b = other\n  } else {\n    a = other\n    b = this\n  }\n\n  elements = Object.keys(a.elements)\n\n  for (var i = 0; i < elements.length; i++) {\n    var element = elements[i]\n    if (element in b.elements) {\n      intersection.push(element)\n    }\n  }\n\n  return new lunr.Set (intersection)\n}\n\n/**\n * Returns a new set combining the elements of this and the specified set.\n *\n * @param {lunr.Set} other - set to union with this set.\n * @return {lunr.Set} a new set that is the union of this and the specified set.\n */\n\nlunr.Set.prototype.union = function (other) {\n  if (other === lunr.Set.complete) {\n    return lunr.Set.complete\n  }\n\n  if (other === lunr.Set.empty) {\n    return this\n  }\n\n  return new lunr.Set(Object.keys(this.elements).concat(Object.keys(other.elements)))\n}\n/**\n * A function to calculate the inverse document frequency for\n * a posting. This is shared between the builder and the index\n *\n * @private\n * @param {object} posting - The posting for a given term\n * @param {number} documentCount - The total number of documents.\n */\nlunr.idf = function (posting, documentCount) {\n  var documentsWithTerm = 0\n\n  for (var fieldName in posting) {\n    if (fieldName == '_index') continue // Ignore the term index, its not a field\n    documentsWithTerm += Object.keys(posting[fieldName]).length\n  }\n\n  var x = (documentCount - documentsWithTerm + 0.5) / (documentsWithTerm + 0.5)\n\n  return Math.log(1 + Math.abs(x))\n}\n\n/**\n * A token wraps a string representation of a token\n * as it is passed through the text processing pipeline.\n *\n * @constructor\n * @param {string} [str=''] - The string token being wrapped.\n * @param {object} [metadata={}] - Metadata associated with this token.\n */\nlunr.Token = function (str, metadata) {\n  this.str = str || \"\"\n  this.metadata = metadata || {}\n}\n\n/**\n * Returns the token string that is being wrapped by this object.\n *\n * @returns {string}\n */\nlunr.Token.prototype.toString = function () {\n  return this.str\n}\n\n/**\n * A token update function is used when updating or optionally\n * when cloning a token.\n *\n * @callback lunr.Token~updateFunction\n * @param {string} str - The string representation of the token.\n * @param {Object} metadata - All metadata associated with this token.\n */\n\n/**\n * Applies the given function to the wrapped string token.\n *\n * @example\n * token.update(function (str, metadata) {\n *   return str.toUpperCase()\n * })\n *\n * @param {lunr.Token~updateFunction} fn - A function to apply to the token string.\n * @returns {lunr.Token}\n */\nlunr.Token.prototype.update = function (fn) {\n  this.str = fn(this.str, this.metadata)\n  return this\n}\n\n/**\n * Creates a clone of this token. Optionally a function can be\n * applied to the cloned token.\n *\n * @param {lunr.Token~updateFunction} [fn] - An optional function to apply to the cloned token.\n * @returns {lunr.Token}\n */\nlunr.Token.prototype.clone = function (fn) {\n  fn = fn || function (s) { return s }\n  return new lunr.Token (fn(this.str, this.metadata), this.metadata)\n}\n/*!\n * lunr.tokenizer\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A function for splitting a string into tokens ready to be inserted into\n * the search index. Uses `lunr.tokenizer.separator` to split strings, change\n * the value of this property to change how strings are split into tokens.\n *\n * This tokenizer will convert its parameter to a string by calling `toString` and\n * then will split this string on the character in `lunr.tokenizer.separator`.\n * Arrays will have their elements converted to strings and wrapped in a lunr.Token.\n *\n * Optional metadata can be passed to the tokenizer, this metadata will be cloned and\n * added as metadata to every token that is created from the object to be tokenized.\n *\n * @static\n * @param {?(string|object|object[])} obj - The object to convert into tokens\n * @param {?object} metadata - Optional metadata to associate with every token\n * @returns {lunr.Token[]}\n * @see {@link lunr.Pipeline}\n */\nlunr.tokenizer = function (obj, metadata) {\n  if (obj == null || obj == undefined) {\n    return []\n  }\n\n  if (Array.isArray(obj)) {\n    return obj.map(function (t) {\n      return new lunr.Token(\n        lunr.utils.asString(t).toLowerCase(),\n        lunr.utils.clone(metadata)\n      )\n    })\n  }\n\n  var str = obj.toString().toLowerCase(),\n      len = str.length,\n      tokens = []\n\n  for (var sliceEnd = 0, sliceStart = 0; sliceEnd <= len; sliceEnd++) {\n    var char = str.charAt(sliceEnd),\n        sliceLength = sliceEnd - sliceStart\n\n    if ((char.match(lunr.tokenizer.separator) || sliceEnd == len)) {\n\n      if (sliceLength > 0) {\n        var tokenMetadata = lunr.utils.clone(metadata) || {}\n        tokenMetadata[\"position\"] = [sliceStart, sliceLength]\n        tokenMetadata[\"index\"] = tokens.length\n\n        tokens.push(\n          new lunr.Token (\n            str.slice(sliceStart, sliceEnd),\n            tokenMetadata\n          )\n        )\n      }\n\n      sliceStart = sliceEnd + 1\n    }\n\n  }\n\n  return tokens\n}\n\n/**\n * The separator used to split a string into tokens. Override this property to change the behaviour of\n * `lunr.tokenizer` behaviour when tokenizing strings. By default this splits on whitespace and hyphens.\n *\n * @static\n * @see lunr.tokenizer\n */\nlunr.tokenizer.separator = /[\\s\\-]+/\n/*!\n * lunr.Pipeline\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.Pipelines maintain an ordered list of functions to be applied to all\n * tokens in documents entering the search index and queries being ran against\n * the index.\n *\n * An instance of lunr.Index created with the lunr shortcut will contain a\n * pipeline with a stop word filter and an English language stemmer. Extra\n * functions can be added before or after either of these functions or these\n * default functions can be removed.\n *\n * When run the pipeline will call each function in turn, passing a token, the\n * index of that token in the original list of all tokens and finally a list of\n * all the original tokens.\n *\n * The output of functions in the pipeline will be passed to the next function\n * in the pipeline. To exclude a token from entering the index the function\n * should return undefined, the rest of the pipeline will not be called with\n * this token.\n *\n * For serialisation of pipelines to work, all functions used in an instance of\n * a pipeline should be registered with lunr.Pipeline. Registered functions can\n * then be loaded. If trying to load a serialised pipeline that uses functions\n * that are not registered an error will be thrown.\n *\n * If not planning on serialising the pipeline then registering pipeline functions\n * is not necessary.\n *\n * @constructor\n */\nlunr.Pipeline = function () {\n  this._stack = []\n}\n\nlunr.Pipeline.registeredFunctions = Object.create(null)\n\n/**\n * A pipeline function maps lunr.Token to lunr.Token. A lunr.Token contains the token\n * string as well as all known metadata. A pipeline function can mutate the token string\n * or mutate (or add) metadata for a given token.\n *\n * A pipeline function can indicate that the passed token should be discarded by returning\n * null, undefined or an empty string. This token will not be passed to any downstream pipeline\n * functions and will not be added to the index.\n *\n * Multiple tokens can be returned by returning an array of tokens. Each token will be passed\n * to any downstream pipeline functions and all will returned tokens will be added to the index.\n *\n * Any number of pipeline functions may be chained together using a lunr.Pipeline.\n *\n * @interface lunr.PipelineFunction\n * @param {lunr.Token} token - A token from the document being processed.\n * @param {number} i - The index of this token in the complete list of tokens for this document/field.\n * @param {lunr.Token[]} tokens - All tokens for this document/field.\n * @returns {(?lunr.Token|lunr.Token[])}\n */\n\n/**\n * Register a function with the pipeline.\n *\n * Functions that are used in the pipeline should be registered if the pipeline\n * needs to be serialised, or a serialised pipeline needs to be loaded.\n *\n * Registering a function does not add it to a pipeline, functions must still be\n * added to instances of the pipeline for them to be used when running a pipeline.\n *\n * @param {lunr.PipelineFunction} fn - The function to check for.\n * @param {String} label - The label to register this function with\n */\nlunr.Pipeline.registerFunction = function (fn, label) {\n  if (label in this.registeredFunctions) {\n    lunr.utils.warn('Overwriting existing registered function: ' + label)\n  }\n\n  fn.label = label\n  lunr.Pipeline.registeredFunctions[fn.label] = fn\n}\n\n/**\n * Warns if the function is not registered as a Pipeline function.\n *\n * @param {lunr.PipelineFunction} fn - The function to check for.\n * @private\n */\nlunr.Pipeline.warnIfFunctionNotRegistered = function (fn) {\n  var isRegistered = fn.label && (fn.label in this.registeredFunctions)\n\n  if (!isRegistered) {\n    lunr.utils.warn('Function is not registered with pipeline. This may cause problems when serialising the index.\\n', fn)\n  }\n}\n\n/**\n * Loads a previously serialised pipeline.\n *\n * All functions to be loaded must already be registered with lunr.Pipeline.\n * If any function from the serialised data has not been registered then an\n * error will be thrown.\n *\n * @param {Object} serialised - The serialised pipeline to load.\n * @returns {lunr.Pipeline}\n */\nlunr.Pipeline.load = function (serialised) {\n  var pipeline = new lunr.Pipeline\n\n  serialised.forEach(function (fnName) {\n    var fn = lunr.Pipeline.registeredFunctions[fnName]\n\n    if (fn) {\n      pipeline.add(fn)\n    } else {\n      throw new Error('Cannot load unregistered function: ' + fnName)\n    }\n  })\n\n  return pipeline\n}\n\n/**\n * Adds new functions to the end of the pipeline.\n *\n * Logs a warning if the function has not been registered.\n *\n * @param {lunr.PipelineFunction[]} functions - Any number of functions to add to the pipeline.\n */\nlunr.Pipeline.prototype.add = function () {\n  var fns = Array.prototype.slice.call(arguments)\n\n  fns.forEach(function (fn) {\n    lunr.Pipeline.warnIfFunctionNotRegistered(fn)\n    this._stack.push(fn)\n  }, this)\n}\n\n/**\n * Adds a single function after a function that already exists in the\n * pipeline.\n *\n * Logs a warning if the function has not been registered.\n *\n * @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline.\n * @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline.\n */\nlunr.Pipeline.prototype.after = function (existingFn, newFn) {\n  lunr.Pipeline.warnIfFunctionNotRegistered(newFn)\n\n  var pos = this._stack.indexOf(existingFn)\n  if (pos == -1) {\n    throw new Error('Cannot find existingFn')\n  }\n\n  pos = pos + 1\n  this._stack.splice(pos, 0, newFn)\n}\n\n/**\n * Adds a single function before a function that already exists in the\n * pipeline.\n *\n * Logs a warning if the function has not been registered.\n *\n * @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline.\n * @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline.\n */\nlunr.Pipeline.prototype.before = function (existingFn, newFn) {\n  lunr.Pipeline.warnIfFunctionNotRegistered(newFn)\n\n  var pos = this._stack.indexOf(existingFn)\n  if (pos == -1) {\n    throw new Error('Cannot find existingFn')\n  }\n\n  this._stack.splice(pos, 0, newFn)\n}\n\n/**\n * Removes a function from the pipeline.\n *\n * @param {lunr.PipelineFunction} fn The function to remove from the pipeline.\n */\nlunr.Pipeline.prototype.remove = function (fn) {\n  var pos = this._stack.indexOf(fn)\n  if (pos == -1) {\n    return\n  }\n\n  this._stack.splice(pos, 1)\n}\n\n/**\n * Runs the current list of functions that make up the pipeline against the\n * passed tokens.\n *\n * @param {Array} tokens The tokens to run through the pipeline.\n * @returns {Array}\n */\nlunr.Pipeline.prototype.run = function (tokens) {\n  var stackLength = this._stack.length\n\n  for (var i = 0; i < stackLength; i++) {\n    var fn = this._stack[i]\n    var memo = []\n\n    for (var j = 0; j < tokens.length; j++) {\n      var result = fn(tokens[j], j, tokens)\n\n      if (result === null || result === void 0 || result === '') continue\n\n      if (Array.isArray(result)) {\n        for (var k = 0; k < result.length; k++) {\n          memo.push(result[k])\n        }\n      } else {\n        memo.push(result)\n      }\n    }\n\n    tokens = memo\n  }\n\n  return tokens\n}\n\n/**\n * Convenience method for passing a string through a pipeline and getting\n * strings out. This method takes care of wrapping the passed string in a\n * token and mapping the resulting tokens back to strings.\n *\n * @param {string} str - The string to pass through the pipeline.\n * @param {?object} metadata - Optional metadata to associate with the token\n * passed to the pipeline.\n * @returns {string[]}\n */\nlunr.Pipeline.prototype.runString = function (str, metadata) {\n  var token = new lunr.Token (str, metadata)\n\n  return this.run([token]).map(function (t) {\n    return t.toString()\n  })\n}\n\n/**\n * Resets the pipeline by removing any existing processors.\n *\n */\nlunr.Pipeline.prototype.reset = function () {\n  this._stack = []\n}\n\n/**\n * Returns a representation of the pipeline ready for serialisation.\n *\n * Logs a warning if the function has not been registered.\n *\n * @returns {Array}\n */\nlunr.Pipeline.prototype.toJSON = function () {\n  return this._stack.map(function (fn) {\n    lunr.Pipeline.warnIfFunctionNotRegistered(fn)\n\n    return fn.label\n  })\n}\n/*!\n * lunr.Vector\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A vector is used to construct the vector space of documents and queries. These\n * vectors support operations to determine the similarity between two documents or\n * a document and a query.\n *\n * Normally no parameters are required for initializing a vector, but in the case of\n * loading a previously dumped vector the raw elements can be provided to the constructor.\n *\n * For performance reasons vectors are implemented with a flat array, where an elements\n * index is immediately followed by its value. E.g. [index, value, index, value]. This\n * allows the underlying array to be as sparse as possible and still offer decent\n * performance when being used for vector calculations.\n *\n * @constructor\n * @param {Number[]} [elements] - The flat list of element index and element value pairs.\n */\nlunr.Vector = function (elements) {\n  this._magnitude = 0\n  this.elements = elements || []\n}\n\n\n/**\n * Calculates the position within the vector to insert a given index.\n *\n * This is used internally by insert and upsert. If there are duplicate indexes then\n * the position is returned as if the value for that index were to be updated, but it\n * is the callers responsibility to check whether there is a duplicate at that index\n *\n * @param {Number} insertIdx - The index at which the element should be inserted.\n * @returns {Number}\n */\nlunr.Vector.prototype.positionForIndex = function (index) {\n  // For an empty vector the tuple can be inserted at the beginning\n  if (this.elements.length == 0) {\n    return 0\n  }\n\n  var start = 0,\n      end = this.elements.length / 2,\n      sliceLength = end - start,\n      pivotPoint = Math.floor(sliceLength / 2),\n      pivotIndex = this.elements[pivotPoint * 2]\n\n  while (sliceLength > 1) {\n    if (pivotIndex < index) {\n      start = pivotPoint\n    }\n\n    if (pivotIndex > index) {\n      end = pivotPoint\n    }\n\n    if (pivotIndex == index) {\n      break\n    }\n\n    sliceLength = end - start\n    pivotPoint = start + Math.floor(sliceLength / 2)\n    pivotIndex = this.elements[pivotPoint * 2]\n  }\n\n  if (pivotIndex == index) {\n    return pivotPoint * 2\n  }\n\n  if (pivotIndex > index) {\n    return pivotPoint * 2\n  }\n\n  if (pivotIndex < index) {\n    return (pivotPoint + 1) * 2\n  }\n}\n\n/**\n * Inserts an element at an index within the vector.\n *\n * Does not allow duplicates, will throw an error if there is already an entry\n * for this index.\n *\n * @param {Number} insertIdx - The index at which the element should be inserted.\n * @param {Number} val - The value to be inserted into the vector.\n */\nlunr.Vector.prototype.insert = function (insertIdx, val) {\n  this.upsert(insertIdx, val, function () {\n    throw \"duplicate index\"\n  })\n}\n\n/**\n * Inserts or updates an existing index within the vector.\n *\n * @param {Number} insertIdx - The index at which the element should be inserted.\n * @param {Number} val - The value to be inserted into the vector.\n * @param {function} fn - A function that is called for updates, the existing value and the\n * requested value are passed as arguments\n */\nlunr.Vector.prototype.upsert = function (insertIdx, val, fn) {\n  this._magnitude = 0\n  var position = this.positionForIndex(insertIdx)\n\n  if (this.elements[position] == insertIdx) {\n    this.elements[position + 1] = fn(this.elements[position + 1], val)\n  } else {\n    this.elements.splice(position, 0, insertIdx, val)\n  }\n}\n\n/**\n * Calculates the magnitude of this vector.\n *\n * @returns {Number}\n */\nlunr.Vector.prototype.magnitude = function () {\n  if (this._magnitude) return this._magnitude\n\n  var sumOfSquares = 0,\n      elementsLength = this.elements.length\n\n  for (var i = 1; i < elementsLength; i += 2) {\n    var val = this.elements[i]\n    sumOfSquares += val * val\n  }\n\n  return this._magnitude = Math.sqrt(sumOfSquares)\n}\n\n/**\n * Calculates the dot product of this vector and another vector.\n *\n * @param {lunr.Vector} otherVector - The vector to compute the dot product with.\n * @returns {Number}\n */\nlunr.Vector.prototype.dot = function (otherVector) {\n  var dotProduct = 0,\n      a = this.elements, b = otherVector.elements,\n      aLen = a.length, bLen = b.length,\n      aVal = 0, bVal = 0,\n      i = 0, j = 0\n\n  while (i < aLen && j < bLen) {\n    aVal = a[i], bVal = b[j]\n    if (aVal < bVal) {\n      i += 2\n    } else if (aVal > bVal) {\n      j += 2\n    } else if (aVal == bVal) {\n      dotProduct += a[i + 1] * b[j + 1]\n      i += 2\n      j += 2\n    }\n  }\n\n  return dotProduct\n}\n\n/**\n * Calculates the similarity between this vector and another vector.\n *\n * @param {lunr.Vector} otherVector - The other vector to calculate the\n * similarity with.\n * @returns {Number}\n */\nlunr.Vector.prototype.similarity = function (otherVector) {\n  return this.dot(otherVector) / this.magnitude() || 0\n}\n\n/**\n * Converts the vector to an array of the elements within the vector.\n *\n * @returns {Number[]}\n */\nlunr.Vector.prototype.toArray = function () {\n  var output = new Array (this.elements.length / 2)\n\n  for (var i = 1, j = 0; i < this.elements.length; i += 2, j++) {\n    output[j] = this.elements[i]\n  }\n\n  return output\n}\n\n/**\n * A JSON serializable representation of the vector.\n *\n * @returns {Number[]}\n */\nlunr.Vector.prototype.toJSON = function () {\n  return this.elements\n}\n/* eslint-disable */\n/*!\n * lunr.stemmer\n * Copyright (C) 2020 Oliver Nightingale\n * Includes code from - http://tartarus.org/~martin/PorterStemmer/js.txt\n */\n\n/**\n * lunr.stemmer is an english language stemmer, this is a JavaScript\n * implementation of the PorterStemmer taken from http://tartarus.org/~martin\n *\n * @static\n * @implements {lunr.PipelineFunction}\n * @param {lunr.Token} token - The string to stem\n * @returns {lunr.Token}\n * @see {@link lunr.Pipeline}\n * @function\n */\nlunr.stemmer = (function(){\n  var step2list = {\n      \"ational\" : \"ate\",\n      \"tional\" : \"tion\",\n      \"enci\" : \"ence\",\n      \"anci\" : \"ance\",\n      \"izer\" : \"ize\",\n      \"bli\" : \"ble\",\n      \"alli\" : \"al\",\n      \"entli\" : \"ent\",\n      \"eli\" : \"e\",\n      \"ousli\" : \"ous\",\n      \"ization\" : \"ize\",\n      \"ation\" : \"ate\",\n      \"ator\" : \"ate\",\n      \"alism\" : \"al\",\n      \"iveness\" : \"ive\",\n      \"fulness\" : \"ful\",\n      \"ousness\" : \"ous\",\n      \"aliti\" : \"al\",\n      \"iviti\" : \"ive\",\n      \"biliti\" : \"ble\",\n      \"logi\" : \"log\"\n    },\n\n    step3list = {\n      \"icate\" : \"ic\",\n      \"ative\" : \"\",\n      \"alize\" : \"al\",\n      \"iciti\" : \"ic\",\n      \"ical\" : \"ic\",\n      \"ful\" : \"\",\n      \"ness\" : \"\"\n    },\n\n    c = \"[^aeiou]\",          // consonant\n    v = \"[aeiouy]\",          // vowel\n    C = c + \"[^aeiouy]*\",    // consonant sequence\n    V = v + \"[aeiou]*\",      // vowel sequence\n\n    mgr0 = \"^(\" + C + \")?\" + V + C,               // [C]VC... is m>0\n    meq1 = \"^(\" + C + \")?\" + V + C + \"(\" + V + \")?$\",  // [C]VC[V] is m=1\n    mgr1 = \"^(\" + C + \")?\" + V + C + V + C,       // [C]VCVC... is m>1\n    s_v = \"^(\" + C + \")?\" + v;                   // vowel in stem\n\n  var re_mgr0 = new RegExp(mgr0);\n  var re_mgr1 = new RegExp(mgr1);\n  var re_meq1 = new RegExp(meq1);\n  var re_s_v = new RegExp(s_v);\n\n  var re_1a = /^(.+?)(ss|i)es$/;\n  var re2_1a = /^(.+?)([^s])s$/;\n  var re_1b = /^(.+?)eed$/;\n  var re2_1b = /^(.+?)(ed|ing)$/;\n  var re_1b_2 = /.$/;\n  var re2_1b_2 = /(at|bl|iz)$/;\n  var re3_1b_2 = new RegExp(\"([^aeiouylsz])\\\\1$\");\n  var re4_1b_2 = new RegExp(\"^\" + C + v + \"[^aeiouwxy]$\");\n\n  var re_1c = /^(.+?[^aeiou])y$/;\n  var re_2 = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;\n\n  var re_3 = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;\n\n  var re_4 = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;\n  var re2_4 = /^(.+?)(s|t)(ion)$/;\n\n  var re_5 = /^(.+?)e$/;\n  var re_5_1 = /ll$/;\n  var re3_5 = new RegExp(\"^\" + C + v + \"[^aeiouwxy]$\");\n\n  var porterStemmer = function porterStemmer(w) {\n    var stem,\n      suffix,\n      firstch,\n      re,\n      re2,\n      re3,\n      re4;\n\n    if (w.length < 3) { return w; }\n\n    firstch = w.substr(0,1);\n    if (firstch == \"y\") {\n      w = firstch.toUpperCase() + w.substr(1);\n    }\n\n    // Step 1a\n    re = re_1a\n    re2 = re2_1a;\n\n    if (re.test(w)) { w = w.replace(re,\"$1$2\"); }\n    else if (re2.test(w)) { w = w.replace(re2,\"$1$2\"); }\n\n    // Step 1b\n    re = re_1b;\n    re2 = re2_1b;\n    if (re.test(w)) {\n      var fp = re.exec(w);\n      re = re_mgr0;\n      if (re.test(fp[1])) {\n        re = re_1b_2;\n        w = w.replace(re,\"\");\n      }\n    } else if (re2.test(w)) {\n      var fp = re2.exec(w);\n      stem = fp[1];\n      re2 = re_s_v;\n      if (re2.test(stem)) {\n        w = stem;\n        re2 = re2_1b_2;\n        re3 = re3_1b_2;\n        re4 = re4_1b_2;\n        if (re2.test(w)) { w = w + \"e\"; }\n        else if (re3.test(w)) { re = re_1b_2; w = w.replace(re,\"\"); }\n        else if (re4.test(w)) { w = w + \"e\"; }\n      }\n    }\n\n    // Step 1c - replace suffix y or Y by i if preceded by a non-vowel which is not the first letter of the word (so cry -> cri, by -> by, say -> say)\n    re = re_1c;\n    if (re.test(w)) {\n      var fp = re.exec(w);\n      stem = fp[1];\n      w = stem + \"i\";\n    }\n\n    // Step 2\n    re = re_2;\n    if (re.test(w)) {\n      var fp = re.exec(w);\n      stem = fp[1];\n      suffix = fp[2];\n      re = re_mgr0;\n      if (re.test(stem)) {\n        w = stem + step2list[suffix];\n      }\n    }\n\n    // Step 3\n    re = re_3;\n    if (re.test(w)) {\n      var fp = re.exec(w);\n      stem = fp[1];\n      suffix = fp[2];\n      re = re_mgr0;\n      if (re.test(stem)) {\n        w = stem + step3list[suffix];\n      }\n    }\n\n    // Step 4\n    re = re_4;\n    re2 = re2_4;\n    if (re.test(w)) {\n      var fp = re.exec(w);\n      stem = fp[1];\n      re = re_mgr1;\n      if (re.test(stem)) {\n        w = stem;\n      }\n    } else if (re2.test(w)) {\n      var fp = re2.exec(w);\n      stem = fp[1] + fp[2];\n      re2 = re_mgr1;\n      if (re2.test(stem)) {\n        w = stem;\n      }\n    }\n\n    // Step 5\n    re = re_5;\n    if (re.test(w)) {\n      var fp = re.exec(w);\n      stem = fp[1];\n      re = re_mgr1;\n      re2 = re_meq1;\n      re3 = re3_5;\n      if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) {\n        w = stem;\n      }\n    }\n\n    re = re_5_1;\n    re2 = re_mgr1;\n    if (re.test(w) && re2.test(w)) {\n      re = re_1b_2;\n      w = w.replace(re,\"\");\n    }\n\n    // and turn initial Y back to y\n\n    if (firstch == \"y\") {\n      w = firstch.toLowerCase() + w.substr(1);\n    }\n\n    return w;\n  };\n\n  return function (token) {\n    return token.update(porterStemmer);\n  }\n})();\n\nlunr.Pipeline.registerFunction(lunr.stemmer, 'stemmer')\n/*!\n * lunr.stopWordFilter\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.generateStopWordFilter builds a stopWordFilter function from the provided\n * list of stop words.\n *\n * The built in lunr.stopWordFilter is built using this generator and can be used\n * to generate custom stopWordFilters for applications or non English languages.\n *\n * @function\n * @param {Array} token The token to pass through the filter\n * @returns {lunr.PipelineFunction}\n * @see lunr.Pipeline\n * @see lunr.stopWordFilter\n */\nlunr.generateStopWordFilter = function (stopWords) {\n  var words = stopWords.reduce(function (memo, stopWord) {\n    memo[stopWord] = stopWord\n    return memo\n  }, {})\n\n  return function (token) {\n    if (token && words[token.toString()] !== token.toString()) return token\n  }\n}\n\n/**\n * lunr.stopWordFilter is an English language stop word list filter, any words\n * contained in the list will not be passed through the filter.\n *\n * This is intended to be used in the Pipeline. If the token does not pass the\n * filter then undefined will be returned.\n *\n * @function\n * @implements {lunr.PipelineFunction}\n * @params {lunr.Token} token - A token to check for being a stop word.\n * @returns {lunr.Token}\n * @see {@link lunr.Pipeline}\n */\nlunr.stopWordFilter = lunr.generateStopWordFilter([\n  'a',\n  'able',\n  'about',\n  'across',\n  'after',\n  'all',\n  'almost',\n  'also',\n  'am',\n  'among',\n  'an',\n  'and',\n  'any',\n  'are',\n  'as',\n  'at',\n  'be',\n  'because',\n  'been',\n  'but',\n  'by',\n  'can',\n  'cannot',\n  'could',\n  'dear',\n  'did',\n  'do',\n  'does',\n  'either',\n  'else',\n  'ever',\n  'every',\n  'for',\n  'from',\n  'get',\n  'got',\n  'had',\n  'has',\n  'have',\n  'he',\n  'her',\n  'hers',\n  'him',\n  'his',\n  'how',\n  'however',\n  'i',\n  'if',\n  'in',\n  'into',\n  'is',\n  'it',\n  'its',\n  'just',\n  'least',\n  'let',\n  'like',\n  'likely',\n  'may',\n  'me',\n  'might',\n  'most',\n  'must',\n  'my',\n  'neither',\n  'no',\n  'nor',\n  'not',\n  'of',\n  'off',\n  'often',\n  'on',\n  'only',\n  'or',\n  'other',\n  'our',\n  'own',\n  'rather',\n  'said',\n  'say',\n  'says',\n  'she',\n  'should',\n  'since',\n  'so',\n  'some',\n  'than',\n  'that',\n  'the',\n  'their',\n  'them',\n  'then',\n  'there',\n  'these',\n  'they',\n  'this',\n  'tis',\n  'to',\n  'too',\n  'twas',\n  'us',\n  'wants',\n  'was',\n  'we',\n  'were',\n  'what',\n  'when',\n  'where',\n  'which',\n  'while',\n  'who',\n  'whom',\n  'why',\n  'will',\n  'with',\n  'would',\n  'yet',\n  'you',\n  'your'\n])\n\nlunr.Pipeline.registerFunction(lunr.stopWordFilter, 'stopWordFilter')\n/*!\n * lunr.trimmer\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.trimmer is a pipeline function for trimming non word\n * characters from the beginning and end of tokens before they\n * enter the index.\n *\n * This implementation may not work correctly for non latin\n * characters and should either be removed or adapted for use\n * with languages with non-latin characters.\n *\n * @static\n * @implements {lunr.PipelineFunction}\n * @param {lunr.Token} token The token to pass through the filter\n * @returns {lunr.Token}\n * @see lunr.Pipeline\n */\nlunr.trimmer = function (token) {\n  return token.update(function (s) {\n    return s.replace(/^\\W+/, '').replace(/\\W+$/, '')\n  })\n}\n\nlunr.Pipeline.registerFunction(lunr.trimmer, 'trimmer')\n/*!\n * lunr.TokenSet\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A token set is used to store the unique list of all tokens\n * within an index. Token sets are also used to represent an\n * incoming query to the index, this query token set and index\n * token set are then intersected to find which tokens to look\n * up in the inverted index.\n *\n * A token set can hold multiple tokens, as in the case of the\n * index token set, or it can hold a single token as in the\n * case of a simple query token set.\n *\n * Additionally token sets are used to perform wildcard matching.\n * Leading, contained and trailing wildcards are supported, and\n * from this edit distance matching can also be provided.\n *\n * Token sets are implemented as a minimal finite state automata,\n * where both common prefixes and suffixes are shared between tokens.\n * This helps to reduce the space used for storing the token set.\n *\n * @constructor\n */\nlunr.TokenSet = function () {\n  this.final = false\n  this.edges = {}\n  this.id = lunr.TokenSet._nextId\n  lunr.TokenSet._nextId += 1\n}\n\n/**\n * Keeps track of the next, auto increment, identifier to assign\n * to a new tokenSet.\n *\n * TokenSets require a unique identifier to be correctly minimised.\n *\n * @private\n */\nlunr.TokenSet._nextId = 1\n\n/**\n * Creates a TokenSet instance from the given sorted array of words.\n *\n * @param {String[]} arr - A sorted array of strings to create the set from.\n * @returns {lunr.TokenSet}\n * @throws Will throw an error if the input array is not sorted.\n */\nlunr.TokenSet.fromArray = function (arr) {\n  var builder = new lunr.TokenSet.Builder\n\n  for (var i = 0, len = arr.length; i < len; i++) {\n    builder.insert(arr[i])\n  }\n\n  builder.finish()\n  return builder.root\n}\n\n/**\n * Creates a token set from a query clause.\n *\n * @private\n * @param {Object} clause - A single clause from lunr.Query.\n * @param {string} clause.term - The query clause term.\n * @param {number} [clause.editDistance] - The optional edit distance for the term.\n * @returns {lunr.TokenSet}\n */\nlunr.TokenSet.fromClause = function (clause) {\n  if ('editDistance' in clause) {\n    return lunr.TokenSet.fromFuzzyString(clause.term, clause.editDistance)\n  } else {\n    return lunr.TokenSet.fromString(clause.term)\n  }\n}\n\n/**\n * Creates a token set representing a single string with a specified\n * edit distance.\n *\n * Insertions, deletions, substitutions and transpositions are each\n * treated as an edit distance of 1.\n *\n * Increasing the allowed edit distance will have a dramatic impact\n * on the performance of both creating and intersecting these TokenSets.\n * It is advised to keep the edit distance less than 3.\n *\n * @param {string} str - The string to create the token set from.\n * @param {number} editDistance - The allowed edit distance to match.\n * @returns {lunr.Vector}\n */\nlunr.TokenSet.fromFuzzyString = function (str, editDistance) {\n  var root = new lunr.TokenSet\n\n  var stack = [{\n    node: root,\n    editsRemaining: editDistance,\n    str: str\n  }]\n\n  while (stack.length) {\n    var frame = stack.pop()\n\n    // no edit\n    if (frame.str.length > 0) {\n      var char = frame.str.charAt(0),\n          noEditNode\n\n      if (char in frame.node.edges) {\n        noEditNode = frame.node.edges[char]\n      } else {\n        noEditNode = new lunr.TokenSet\n        frame.node.edges[char] = noEditNode\n      }\n\n      if (frame.str.length == 1) {\n        noEditNode.final = true\n      }\n\n      stack.push({\n        node: noEditNode,\n        editsRemaining: frame.editsRemaining,\n        str: frame.str.slice(1)\n      })\n    }\n\n    if (frame.editsRemaining == 0) {\n      continue\n    }\n\n    // insertion\n    if (\"*\" in frame.node.edges) {\n      var insertionNode = frame.node.edges[\"*\"]\n    } else {\n      var insertionNode = new lunr.TokenSet\n      frame.node.edges[\"*\"] = insertionNode\n    }\n\n    if (frame.str.length == 0) {\n      insertionNode.final = true\n    }\n\n    stack.push({\n      node: insertionNode,\n      editsRemaining: frame.editsRemaining - 1,\n      str: frame.str\n    })\n\n    // deletion\n    // can only do a deletion if we have enough edits remaining\n    // and if there are characters left to delete in the string\n    if (frame.str.length > 1) {\n      stack.push({\n        node: frame.node,\n        editsRemaining: frame.editsRemaining - 1,\n        str: frame.str.slice(1)\n      })\n    }\n\n    // deletion\n    // just removing the last character from the str\n    if (frame.str.length == 1) {\n      frame.node.final = true\n    }\n\n    // substitution\n    // can only do a substitution if we have enough edits remaining\n    // and if there are characters left to substitute\n    if (frame.str.length >= 1) {\n      if (\"*\" in frame.node.edges) {\n        var substitutionNode = frame.node.edges[\"*\"]\n      } else {\n        var substitutionNode = new lunr.TokenSet\n        frame.node.edges[\"*\"] = substitutionNode\n      }\n\n      if (frame.str.length == 1) {\n        substitutionNode.final = true\n      }\n\n      stack.push({\n        node: substitutionNode,\n        editsRemaining: frame.editsRemaining - 1,\n        str: frame.str.slice(1)\n      })\n    }\n\n    // transposition\n    // can only do a transposition if there are edits remaining\n    // and there are enough characters to transpose\n    if (frame.str.length > 1) {\n      var charA = frame.str.charAt(0),\n          charB = frame.str.charAt(1),\n          transposeNode\n\n      if (charB in frame.node.edges) {\n        transposeNode = frame.node.edges[charB]\n      } else {\n        transposeNode = new lunr.TokenSet\n        frame.node.edges[charB] = transposeNode\n      }\n\n      if (frame.str.length == 1) {\n        transposeNode.final = true\n      }\n\n      stack.push({\n        node: transposeNode,\n        editsRemaining: frame.editsRemaining - 1,\n        str: charA + frame.str.slice(2)\n      })\n    }\n  }\n\n  return root\n}\n\n/**\n * Creates a TokenSet from a string.\n *\n * The string may contain one or more wildcard characters (*)\n * that will allow wildcard matching when intersecting with\n * another TokenSet.\n *\n * @param {string} str - The string to create a TokenSet from.\n * @returns {lunr.TokenSet}\n */\nlunr.TokenSet.fromString = function (str) {\n  var node = new lunr.TokenSet,\n      root = node\n\n  /*\n   * Iterates through all characters within the passed string\n   * appending a node for each character.\n   *\n   * When a wildcard character is found then a self\n   * referencing edge is introduced to continually match\n   * any number of any characters.\n   */\n  for (var i = 0, len = str.length; i < len; i++) {\n    var char = str[i],\n        final = (i == len - 1)\n\n    if (char == \"*\") {\n      node.edges[char] = node\n      node.final = final\n\n    } else {\n      var next = new lunr.TokenSet\n      next.final = final\n\n      node.edges[char] = next\n      node = next\n    }\n  }\n\n  return root\n}\n\n/**\n * Converts this TokenSet into an array of strings\n * contained within the TokenSet.\n *\n * This is not intended to be used on a TokenSet that\n * contains wildcards, in these cases the results are\n * undefined and are likely to cause an infinite loop.\n *\n * @returns {string[]}\n */\nlunr.TokenSet.prototype.toArray = function () {\n  var words = []\n\n  var stack = [{\n    prefix: \"\",\n    node: this\n  }]\n\n  while (stack.length) {\n    var frame = stack.pop(),\n        edges = Object.keys(frame.node.edges),\n        len = edges.length\n\n    if (frame.node.final) {\n      /* In Safari, at this point the prefix is sometimes corrupted, see:\n       * https://github.com/olivernn/lunr.js/issues/279 Calling any\n       * String.prototype method forces Safari to \"cast\" this string to what\n       * it's supposed to be, fixing the bug. */\n      frame.prefix.charAt(0)\n      words.push(frame.prefix)\n    }\n\n    for (var i = 0; i < len; i++) {\n      var edge = edges[i]\n\n      stack.push({\n        prefix: frame.prefix.concat(edge),\n        node: frame.node.edges[edge]\n      })\n    }\n  }\n\n  return words\n}\n\n/**\n * Generates a string representation of a TokenSet.\n *\n * This is intended to allow TokenSets to be used as keys\n * in objects, largely to aid the construction and minimisation\n * of a TokenSet. As such it is not designed to be a human\n * friendly representation of the TokenSet.\n *\n * @returns {string}\n */\nlunr.TokenSet.prototype.toString = function () {\n  // NOTE: Using Object.keys here as this.edges is very likely\n  // to enter 'hash-mode' with many keys being added\n  //\n  // avoiding a for-in loop here as it leads to the function\n  // being de-optimised (at least in V8). From some simple\n  // benchmarks the performance is comparable, but allowing\n  // V8 to optimize may mean easy performance wins in the future.\n\n  if (this._str) {\n    return this._str\n  }\n\n  var str = this.final ? '1' : '0',\n      labels = Object.keys(this.edges).sort(),\n      len = labels.length\n\n  for (var i = 0; i < len; i++) {\n    var label = labels[i],\n        node = this.edges[label]\n\n    str = str + label + node.id\n  }\n\n  return str\n}\n\n/**\n * Returns a new TokenSet that is the intersection of\n * this TokenSet and the passed TokenSet.\n *\n * This intersection will take into account any wildcards\n * contained within the TokenSet.\n *\n * @param {lunr.TokenSet} b - An other TokenSet to intersect with.\n * @returns {lunr.TokenSet}\n */\nlunr.TokenSet.prototype.intersect = function (b) {\n  var output = new lunr.TokenSet,\n      frame = undefined\n\n  var stack = [{\n    qNode: b,\n    output: output,\n    node: this\n  }]\n\n  while (stack.length) {\n    frame = stack.pop()\n\n    // NOTE: As with the #toString method, we are using\n    // Object.keys and a for loop instead of a for-in loop\n    // as both of these objects enter 'hash' mode, causing\n    // the function to be de-optimised in V8\n    var qEdges = Object.keys(frame.qNode.edges),\n        qLen = qEdges.length,\n        nEdges = Object.keys(frame.node.edges),\n        nLen = nEdges.length\n\n    for (var q = 0; q < qLen; q++) {\n      var qEdge = qEdges[q]\n\n      for (var n = 0; n < nLen; n++) {\n        var nEdge = nEdges[n]\n\n        if (nEdge == qEdge || qEdge == '*') {\n          var node = frame.node.edges[nEdge],\n              qNode = frame.qNode.edges[qEdge],\n              final = node.final && qNode.final,\n              next = undefined\n\n          if (nEdge in frame.output.edges) {\n            // an edge already exists for this character\n            // no need to create a new node, just set the finality\n            // bit unless this node is already final\n            next = frame.output.edges[nEdge]\n            next.final = next.final || final\n\n          } else {\n            // no edge exists yet, must create one\n            // set the finality bit and insert it\n            // into the output\n            next = new lunr.TokenSet\n            next.final = final\n            frame.output.edges[nEdge] = next\n          }\n\n          stack.push({\n            qNode: qNode,\n            output: next,\n            node: node\n          })\n        }\n      }\n    }\n  }\n\n  return output\n}\nlunr.TokenSet.Builder = function () {\n  this.previousWord = \"\"\n  this.root = new lunr.TokenSet\n  this.uncheckedNodes = []\n  this.minimizedNodes = {}\n}\n\nlunr.TokenSet.Builder.prototype.insert = function (word) {\n  var node,\n      commonPrefix = 0\n\n  if (word < this.previousWord) {\n    throw new Error (\"Out of order word insertion\")\n  }\n\n  for (var i = 0; i < word.length && i < this.previousWord.length; i++) {\n    if (word[i] != this.previousWord[i]) break\n    commonPrefix++\n  }\n\n  this.minimize(commonPrefix)\n\n  if (this.uncheckedNodes.length == 0) {\n    node = this.root\n  } else {\n    node = this.uncheckedNodes[this.uncheckedNodes.length - 1].child\n  }\n\n  for (var i = commonPrefix; i < word.length; i++) {\n    var nextNode = new lunr.TokenSet,\n        char = word[i]\n\n    node.edges[char] = nextNode\n\n    this.uncheckedNodes.push({\n      parent: node,\n      char: char,\n      child: nextNode\n    })\n\n    node = nextNode\n  }\n\n  node.final = true\n  this.previousWord = word\n}\n\nlunr.TokenSet.Builder.prototype.finish = function () {\n  this.minimize(0)\n}\n\nlunr.TokenSet.Builder.prototype.minimize = function (downTo) {\n  for (var i = this.uncheckedNodes.length - 1; i >= downTo; i--) {\n    var node = this.uncheckedNodes[i],\n        childKey = node.child.toString()\n\n    if (childKey in this.minimizedNodes) {\n      node.parent.edges[node.char] = this.minimizedNodes[childKey]\n    } else {\n      // Cache the key for this node since\n      // we know it can't change anymore\n      node.child._str = childKey\n\n      this.minimizedNodes[childKey] = node.child\n    }\n\n    this.uncheckedNodes.pop()\n  }\n}\n/*!\n * lunr.Index\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * An index contains the built index of all documents and provides a query interface\n * to the index.\n *\n * Usually instances of lunr.Index will not be created using this constructor, instead\n * lunr.Builder should be used to construct new indexes, or lunr.Index.load should be\n * used to load previously built and serialized indexes.\n *\n * @constructor\n * @param {Object} attrs - The attributes of the built search index.\n * @param {Object} attrs.invertedIndex - An index of term/field to document reference.\n * @param {Object<string, lunr.Vector>} attrs.fieldVectors - Field vectors\n * @param {lunr.TokenSet} attrs.tokenSet - An set of all corpus tokens.\n * @param {string[]} attrs.fields - The names of indexed document fields.\n * @param {lunr.Pipeline} attrs.pipeline - The pipeline to use for search terms.\n */\nlunr.Index = function (attrs) {\n  this.invertedIndex = attrs.invertedIndex\n  this.fieldVectors = attrs.fieldVectors\n  this.tokenSet = attrs.tokenSet\n  this.fields = attrs.fields\n  this.pipeline = attrs.pipeline\n}\n\n/**\n * A result contains details of a document matching a search query.\n * @typedef {Object} lunr.Index~Result\n * @property {string} ref - The reference of the document this result represents.\n * @property {number} score - A number between 0 and 1 representing how similar this document is to the query.\n * @property {lunr.MatchData} matchData - Contains metadata about this match including which term(s) caused the match.\n */\n\n/**\n * Although lunr provides the ability to create queries using lunr.Query, it also provides a simple\n * query language which itself is parsed into an instance of lunr.Query.\n *\n * For programmatically building queries it is advised to directly use lunr.Query, the query language\n * is best used for human entered text rather than program generated text.\n *\n * At its simplest queries can just be a single term, e.g. `hello`, multiple terms are also supported\n * and will be combined with OR, e.g `hello world` will match documents that contain either 'hello'\n * or 'world', though those that contain both will rank higher in the results.\n *\n * Wildcards can be included in terms to match one or more unspecified characters, these wildcards can\n * be inserted anywhere within the term, and more than one wildcard can exist in a single term. Adding\n * wildcards will increase the number of documents that will be found but can also have a negative\n * impact on query performance, especially with wildcards at the beginning of a term.\n *\n * Terms can be restricted to specific fields, e.g. `title:hello`, only documents with the term\n * hello in the title field will match this query. Using a field not present in the index will lead\n * to an error being thrown.\n *\n * Modifiers can also be added to terms, lunr supports edit distance and boost modifiers on terms. A term\n * boost will make documents matching that term score higher, e.g. `foo^5`. Edit distance is also supported\n * to provide fuzzy matching, e.g. 'hello~2' will match documents with hello with an edit distance of 2.\n * Avoid large values for edit distance to improve query performance.\n *\n * Each term also supports a presence modifier. By default a term's presence in document is optional, however\n * this can be changed to either required or prohibited. For a term's presence to be required in a document the\n * term should be prefixed with a '+', e.g. `+foo bar` is a search for documents that must contain 'foo' and\n * optionally contain 'bar'. Conversely a leading '-' sets the terms presence to prohibited, i.e. it must not\n * appear in a document, e.g. `-foo bar` is a search for documents that do not contain 'foo' but may contain 'bar'.\n *\n * To escape special characters the backslash character '\\' can be used, this allows searches to include\n * characters that would normally be considered modifiers, e.g. `foo\\~2` will search for a term \"foo~2\" instead\n * of attempting to apply a boost of 2 to the search term \"foo\".\n *\n * @typedef {string} lunr.Index~QueryString\n * @example <caption>Simple single term query</caption>\n * hello\n * @example <caption>Multiple term query</caption>\n * hello world\n * @example <caption>term scoped to a field</caption>\n * title:hello\n * @example <caption>term with a boost of 10</caption>\n * hello^10\n * @example <caption>term with an edit distance of 2</caption>\n * hello~2\n * @example <caption>terms with presence modifiers</caption>\n * -foo +bar baz\n */\n\n/**\n * Performs a search against the index using lunr query syntax.\n *\n * Results will be returned sorted by their score, the most relevant results\n * will be returned first.  For details on how the score is calculated, please see\n * the {@link https://lunrjs.com/guides/searching.html#scoring|guide}.\n *\n * For more programmatic querying use lunr.Index#query.\n *\n * @param {lunr.Index~QueryString} queryString - A string containing a lunr query.\n * @throws {lunr.QueryParseError} If the passed query string cannot be parsed.\n * @returns {lunr.Index~Result[]}\n */\nlunr.Index.prototype.search = function (queryString) {\n  return this.query(function (query) {\n    var parser = new lunr.QueryParser(queryString, query)\n    parser.parse()\n  })\n}\n\n/**\n * A query builder callback provides a query object to be used to express\n * the query to perform on the index.\n *\n * @callback lunr.Index~queryBuilder\n * @param {lunr.Query} query - The query object to build up.\n * @this lunr.Query\n */\n\n/**\n * Performs a query against the index using the yielded lunr.Query object.\n *\n * If performing programmatic queries against the index, this method is preferred\n * over lunr.Index#search so as to avoid the additional query parsing overhead.\n *\n * A query object is yielded to the supplied function which should be used to\n * express the query to be run against the index.\n *\n * Note that although this function takes a callback parameter it is _not_ an\n * asynchronous operation, the callback is just yielded a query object to be\n * customized.\n *\n * @param {lunr.Index~queryBuilder} fn - A function that is used to build the query.\n * @returns {lunr.Index~Result[]}\n */\nlunr.Index.prototype.query = function (fn) {\n  // for each query clause\n  // * process terms\n  // * expand terms from token set\n  // * find matching documents and metadata\n  // * get document vectors\n  // * score documents\n\n  var query = new lunr.Query(this.fields),\n      matchingFields = Object.create(null),\n      queryVectors = Object.create(null),\n      termFieldCache = Object.create(null),\n      requiredMatches = Object.create(null),\n      prohibitedMatches = Object.create(null)\n\n  /*\n   * To support field level boosts a query vector is created per\n   * field. An empty vector is eagerly created to support negated\n   * queries.\n   */\n  for (var i = 0; i < this.fields.length; i++) {\n    queryVectors[this.fields[i]] = new lunr.Vector\n  }\n\n  fn.call(query, query)\n\n  for (var i = 0; i < query.clauses.length; i++) {\n    /*\n     * Unless the pipeline has been disabled for this term, which is\n     * the case for terms with wildcards, we need to pass the clause\n     * term through the search pipeline. A pipeline returns an array\n     * of processed terms. Pipeline functions may expand the passed\n     * term, which means we may end up performing multiple index lookups\n     * for a single query term.\n     */\n    var clause = query.clauses[i],\n        terms = null,\n        clauseMatches = lunr.Set.empty\n\n    if (clause.usePipeline) {\n      terms = this.pipeline.runString(clause.term, {\n        fields: clause.fields\n      })\n    } else {\n      terms = [clause.term]\n    }\n\n    for (var m = 0; m < terms.length; m++) {\n      var term = terms[m]\n\n      /*\n       * Each term returned from the pipeline needs to use the same query\n       * clause object, e.g. the same boost and or edit distance. The\n       * simplest way to do this is to re-use the clause object but mutate\n       * its term property.\n       */\n      clause.term = term\n\n      /*\n       * From the term in the clause we create a token set which will then\n       * be used to intersect the indexes token set to get a list of terms\n       * to lookup in the inverted index\n       */\n      var termTokenSet = lunr.TokenSet.fromClause(clause),\n          expandedTerms = this.tokenSet.intersect(termTokenSet).toArray()\n\n      /*\n       * If a term marked as required does not exist in the tokenSet it is\n       * impossible for the search to return any matches. We set all the field\n       * scoped required matches set to empty and stop examining any further\n       * clauses.\n       */\n      if (expandedTerms.length === 0 && clause.presence === lunr.Query.presence.REQUIRED) {\n        for (var k = 0; k < clause.fields.length; k++) {\n          var field = clause.fields[k]\n          requiredMatches[field] = lunr.Set.empty\n        }\n\n        break\n      }\n\n      for (var j = 0; j < expandedTerms.length; j++) {\n        /*\n         * For each term get the posting and termIndex, this is required for\n         * building the query vector.\n         */\n        var expandedTerm = expandedTerms[j],\n            posting = this.invertedIndex[expandedTerm],\n            termIndex = posting._index\n\n        for (var k = 0; k < clause.fields.length; k++) {\n          /*\n           * For each field that this query term is scoped by (by default\n           * all fields are in scope) we need to get all the document refs\n           * that have this term in that field.\n           *\n           * The posting is the entry in the invertedIndex for the matching\n           * term from above.\n           */\n          var field = clause.fields[k],\n              fieldPosting = posting[field],\n              matchingDocumentRefs = Object.keys(fieldPosting),\n              termField = expandedTerm + \"/\" + field,\n              matchingDocumentsSet = new lunr.Set(matchingDocumentRefs)\n\n          /*\n           * if the presence of this term is required ensure that the matching\n           * documents are added to the set of required matches for this clause.\n           *\n           */\n          if (clause.presence == lunr.Query.presence.REQUIRED) {\n            clauseMatches = clauseMatches.union(matchingDocumentsSet)\n\n            if (requiredMatches[field] === undefined) {\n              requiredMatches[field] = lunr.Set.complete\n            }\n          }\n\n          /*\n           * if the presence of this term is prohibited ensure that the matching\n           * documents are added to the set of prohibited matches for this field,\n           * creating that set if it does not yet exist.\n           */\n          if (clause.presence == lunr.Query.presence.PROHIBITED) {\n            if (prohibitedMatches[field] === undefined) {\n              prohibitedMatches[field] = lunr.Set.empty\n            }\n\n            prohibitedMatches[field] = prohibitedMatches[field].union(matchingDocumentsSet)\n\n            /*\n             * Prohibited matches should not be part of the query vector used for\n             * similarity scoring and no metadata should be extracted so we continue\n             * to the next field\n             */\n            continue\n          }\n\n          /*\n           * The query field vector is populated using the termIndex found for\n           * the term and a unit value with the appropriate boost applied.\n           * Using upsert because there could already be an entry in the vector\n           * for the term we are working with. In that case we just add the scores\n           * together.\n           */\n          queryVectors[field].upsert(termIndex, clause.boost, function (a, b) { return a + b })\n\n          /**\n           * If we've already seen this term, field combo then we've already collected\n           * the matching documents and metadata, no need to go through all that again\n           */\n          if (termFieldCache[termField]) {\n            continue\n          }\n\n          for (var l = 0; l < matchingDocumentRefs.length; l++) {\n            /*\n             * All metadata for this term/field/document triple\n             * are then extracted and collected into an instance\n             * of lunr.MatchData ready to be returned in the query\n             * results\n             */\n            var matchingDocumentRef = matchingDocumentRefs[l],\n                matchingFieldRef = new lunr.FieldRef (matchingDocumentRef, field),\n                metadata = fieldPosting[matchingDocumentRef],\n                fieldMatch\n\n            if ((fieldMatch = matchingFields[matchingFieldRef]) === undefined) {\n              matchingFields[matchingFieldRef] = new lunr.MatchData (expandedTerm, field, metadata)\n            } else {\n              fieldMatch.add(expandedTerm, field, metadata)\n            }\n\n          }\n\n          termFieldCache[termField] = true\n        }\n      }\n    }\n\n    /**\n     * If the presence was required we need to update the requiredMatches field sets.\n     * We do this after all fields for the term have collected their matches because\n     * the clause terms presence is required in _any_ of the fields not _all_ of the\n     * fields.\n     */\n    if (clause.presence === lunr.Query.presence.REQUIRED) {\n      for (var k = 0; k < clause.fields.length; k++) {\n        var field = clause.fields[k]\n        requiredMatches[field] = requiredMatches[field].intersect(clauseMatches)\n      }\n    }\n  }\n\n  /**\n   * Need to combine the field scoped required and prohibited\n   * matching documents into a global set of required and prohibited\n   * matches\n   */\n  var allRequiredMatches = lunr.Set.complete,\n      allProhibitedMatches = lunr.Set.empty\n\n  for (var i = 0; i < this.fields.length; i++) {\n    var field = this.fields[i]\n\n    if (requiredMatches[field]) {\n      allRequiredMatches = allRequiredMatches.intersect(requiredMatches[field])\n    }\n\n    if (prohibitedMatches[field]) {\n      allProhibitedMatches = allProhibitedMatches.union(prohibitedMatches[field])\n    }\n  }\n\n  var matchingFieldRefs = Object.keys(matchingFields),\n      results = [],\n      matches = Object.create(null)\n\n  /*\n   * If the query is negated (contains only prohibited terms)\n   * we need to get _all_ fieldRefs currently existing in the\n   * index. This is only done when we know that the query is\n   * entirely prohibited terms to avoid any cost of getting all\n   * fieldRefs unnecessarily.\n   *\n   * Additionally, blank MatchData must be created to correctly\n   * populate the results.\n   */\n  if (query.isNegated()) {\n    matchingFieldRefs = Object.keys(this.fieldVectors)\n\n    for (var i = 0; i < matchingFieldRefs.length; i++) {\n      var matchingFieldRef = matchingFieldRefs[i]\n      var fieldRef = lunr.FieldRef.fromString(matchingFieldRef)\n      matchingFields[matchingFieldRef] = new lunr.MatchData\n    }\n  }\n\n  for (var i = 0; i < matchingFieldRefs.length; i++) {\n    /*\n     * Currently we have document fields that match the query, but we\n     * need to return documents. The matchData and scores are combined\n     * from multiple fields belonging to the same document.\n     *\n     * Scores are calculated by field, using the query vectors created\n     * above, and combined into a final document score using addition.\n     */\n    var fieldRef = lunr.FieldRef.fromString(matchingFieldRefs[i]),\n        docRef = fieldRef.docRef\n\n    if (!allRequiredMatches.contains(docRef)) {\n      continue\n    }\n\n    if (allProhibitedMatches.contains(docRef)) {\n      continue\n    }\n\n    var fieldVector = this.fieldVectors[fieldRef],\n        score = queryVectors[fieldRef.fieldName].similarity(fieldVector),\n        docMatch\n\n    if ((docMatch = matches[docRef]) !== undefined) {\n      docMatch.score += score\n      docMatch.matchData.combine(matchingFields[fieldRef])\n    } else {\n      var match = {\n        ref: docRef,\n        score: score,\n        matchData: matchingFields[fieldRef]\n      }\n      matches[docRef] = match\n      results.push(match)\n    }\n  }\n\n  /*\n   * Sort the results objects by score, highest first.\n   */\n  return results.sort(function (a, b) {\n    return b.score - a.score\n  })\n}\n\n/**\n * Prepares the index for JSON serialization.\n *\n * The schema for this JSON blob will be described in a\n * separate JSON schema file.\n *\n * @returns {Object}\n */\nlunr.Index.prototype.toJSON = function () {\n  var invertedIndex = Object.keys(this.invertedIndex)\n    .sort()\n    .map(function (term) {\n      return [term, this.invertedIndex[term]]\n    }, this)\n\n  var fieldVectors = Object.keys(this.fieldVectors)\n    .map(function (ref) {\n      return [ref, this.fieldVectors[ref].toJSON()]\n    }, this)\n\n  return {\n    version: lunr.version,\n    fields: this.fields,\n    fieldVectors: fieldVectors,\n    invertedIndex: invertedIndex,\n    pipeline: this.pipeline.toJSON()\n  }\n}\n\n/**\n * Loads a previously serialized lunr.Index\n *\n * @param {Object} serializedIndex - A previously serialized lunr.Index\n * @returns {lunr.Index}\n */\nlunr.Index.load = function (serializedIndex) {\n  var attrs = {},\n      fieldVectors = {},\n      serializedVectors = serializedIndex.fieldVectors,\n      invertedIndex = Object.create(null),\n      serializedInvertedIndex = serializedIndex.invertedIndex,\n      tokenSetBuilder = new lunr.TokenSet.Builder,\n      pipeline = lunr.Pipeline.load(serializedIndex.pipeline)\n\n  if (serializedIndex.version != lunr.version) {\n    lunr.utils.warn(\"Version mismatch when loading serialised index. Current version of lunr '\" + lunr.version + \"' does not match serialized index '\" + serializedIndex.version + \"'\")\n  }\n\n  for (var i = 0; i < serializedVectors.length; i++) {\n    var tuple = serializedVectors[i],\n        ref = tuple[0],\n        elements = tuple[1]\n\n    fieldVectors[ref] = new lunr.Vector(elements)\n  }\n\n  for (var i = 0; i < serializedInvertedIndex.length; i++) {\n    var tuple = serializedInvertedIndex[i],\n        term = tuple[0],\n        posting = tuple[1]\n\n    tokenSetBuilder.insert(term)\n    invertedIndex[term] = posting\n  }\n\n  tokenSetBuilder.finish()\n\n  attrs.fields = serializedIndex.fields\n\n  attrs.fieldVectors = fieldVectors\n  attrs.invertedIndex = invertedIndex\n  attrs.tokenSet = tokenSetBuilder.root\n  attrs.pipeline = pipeline\n\n  return new lunr.Index(attrs)\n}\n/*!\n * lunr.Builder\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.Builder performs indexing on a set of documents and\n * returns instances of lunr.Index ready for querying.\n *\n * All configuration of the index is done via the builder, the\n * fields to index, the document reference, the text processing\n * pipeline and document scoring parameters are all set on the\n * builder before indexing.\n *\n * @constructor\n * @property {string} _ref - Internal reference to the document reference field.\n * @property {string[]} _fields - Internal reference to the document fields to index.\n * @property {object} invertedIndex - The inverted index maps terms to document fields.\n * @property {object} documentTermFrequencies - Keeps track of document term frequencies.\n * @property {object} documentLengths - Keeps track of the length of documents added to the index.\n * @property {lunr.tokenizer} tokenizer - Function for splitting strings into tokens for indexing.\n * @property {lunr.Pipeline} pipeline - The pipeline performs text processing on tokens before indexing.\n * @property {lunr.Pipeline} searchPipeline - A pipeline for processing search terms before querying the index.\n * @property {number} documentCount - Keeps track of the total number of documents indexed.\n * @property {number} _b - A parameter to control field length normalization, setting this to 0 disabled normalization, 1 fully normalizes field lengths, the default value is 0.75.\n * @property {number} _k1 - A parameter to control how quickly an increase in term frequency results in term frequency saturation, the default value is 1.2.\n * @property {number} termIndex - A counter incremented for each unique term, used to identify a terms position in the vector space.\n * @property {array} metadataWhitelist - A list of metadata keys that have been whitelisted for entry in the index.\n */\nlunr.Builder = function () {\n  this._ref = \"id\"\n  this._fields = Object.create(null)\n  this._documents = Object.create(null)\n  this.invertedIndex = Object.create(null)\n  this.fieldTermFrequencies = {}\n  this.fieldLengths = {}\n  this.tokenizer = lunr.tokenizer\n  this.pipeline = new lunr.Pipeline\n  this.searchPipeline = new lunr.Pipeline\n  this.documentCount = 0\n  this._b = 0.75\n  this._k1 = 1.2\n  this.termIndex = 0\n  this.metadataWhitelist = []\n}\n\n/**\n * Sets the document field used as the document reference. Every document must have this field.\n * The type of this field in the document should be a string, if it is not a string it will be\n * coerced into a string by calling toString.\n *\n * The default ref is 'id'.\n *\n * The ref should _not_ be changed during indexing, it should be set before any documents are\n * added to the index. Changing it during indexing can lead to inconsistent results.\n *\n * @param {string} ref - The name of the reference field in the document.\n */\nlunr.Builder.prototype.ref = function (ref) {\n  this._ref = ref\n}\n\n/**\n * A function that is used to extract a field from a document.\n *\n * Lunr expects a field to be at the top level of a document, if however the field\n * is deeply nested within a document an extractor function can be used to extract\n * the right field for indexing.\n *\n * @callback fieldExtractor\n * @param {object} doc - The document being added to the index.\n * @returns {?(string|object|object[])} obj - The object that will be indexed for this field.\n * @example <caption>Extracting a nested field</caption>\n * function (doc) { return doc.nested.field }\n */\n\n/**\n * Adds a field to the list of document fields that will be indexed. Every document being\n * indexed should have this field. Null values for this field in indexed documents will\n * not cause errors but will limit the chance of that document being retrieved by searches.\n *\n * All fields should be added before adding documents to the index. Adding fields after\n * a document has been indexed will have no effect on already indexed documents.\n *\n * Fields can be boosted at build time. This allows terms within that field to have more\n * importance when ranking search results. Use a field boost to specify that matches within\n * one field are more important than other fields.\n *\n * @param {string} fieldName - The name of a field to index in all documents.\n * @param {object} attributes - Optional attributes associated with this field.\n * @param {number} [attributes.boost=1] - Boost applied to all terms within this field.\n * @param {fieldExtractor} [attributes.extractor] - Function to extract a field from a document.\n * @throws {RangeError} fieldName cannot contain unsupported characters '/'\n */\nlunr.Builder.prototype.field = function (fieldName, attributes) {\n  if (/\\//.test(fieldName)) {\n    throw new RangeError (\"Field '\" + fieldName + \"' contains illegal character '/'\")\n  }\n\n  this._fields[fieldName] = attributes || {}\n}\n\n/**\n * A parameter to tune the amount of field length normalisation that is applied when\n * calculating relevance scores. A value of 0 will completely disable any normalisation\n * and a value of 1 will fully normalise field lengths. The default is 0.75. Values of b\n * will be clamped to the range 0 - 1.\n *\n * @param {number} number - The value to set for this tuning parameter.\n */\nlunr.Builder.prototype.b = function (number) {\n  if (number < 0) {\n    this._b = 0\n  } else if (number > 1) {\n    this._b = 1\n  } else {\n    this._b = number\n  }\n}\n\n/**\n * A parameter that controls the speed at which a rise in term frequency results in term\n * frequency saturation. The default value is 1.2. Setting this to a higher value will give\n * slower saturation levels, a lower value will result in quicker saturation.\n *\n * @param {number} number - The value to set for this tuning parameter.\n */\nlunr.Builder.prototype.k1 = function (number) {\n  this._k1 = number\n}\n\n/**\n * Adds a document to the index.\n *\n * Before adding fields to the index the index should have been fully setup, with the document\n * ref and all fields to index already having been specified.\n *\n * The document must have a field name as specified by the ref (by default this is 'id') and\n * it should have all fields defined for indexing, though null or undefined values will not\n * cause errors.\n *\n * Entire documents can be boosted at build time. Applying a boost to a document indicates that\n * this document should rank higher in search results than other documents.\n *\n * @param {object} doc - The document to add to the index.\n * @param {object} attributes - Optional attributes associated with this document.\n * @param {number} [attributes.boost=1] - Boost applied to all terms within this document.\n */\nlunr.Builder.prototype.add = function (doc, attributes) {\n  var docRef = doc[this._ref],\n      fields = Object.keys(this._fields)\n\n  this._documents[docRef] = attributes || {}\n  this.documentCount += 1\n\n  for (var i = 0; i < fields.length; i++) {\n    var fieldName = fields[i],\n        extractor = this._fields[fieldName].extractor,\n        field = extractor ? extractor(doc) : doc[fieldName],\n        tokens = this.tokenizer(field, {\n          fields: [fieldName]\n        }),\n        terms = this.pipeline.run(tokens),\n        fieldRef = new lunr.FieldRef (docRef, fieldName),\n        fieldTerms = Object.create(null)\n\n    this.fieldTermFrequencies[fieldRef] = fieldTerms\n    this.fieldLengths[fieldRef] = 0\n\n    // store the length of this field for this document\n    this.fieldLengths[fieldRef] += terms.length\n\n    // calculate term frequencies for this field\n    for (var j = 0; j < terms.length; j++) {\n      var term = terms[j]\n\n      if (fieldTerms[term] == undefined) {\n        fieldTerms[term] = 0\n      }\n\n      fieldTerms[term] += 1\n\n      // add to inverted index\n      // create an initial posting if one doesn't exist\n      if (this.invertedIndex[term] == undefined) {\n        var posting = Object.create(null)\n        posting[\"_index\"] = this.termIndex\n        this.termIndex += 1\n\n        for (var k = 0; k < fields.length; k++) {\n          posting[fields[k]] = Object.create(null)\n        }\n\n        this.invertedIndex[term] = posting\n      }\n\n      // add an entry for this term/fieldName/docRef to the invertedIndex\n      if (this.invertedIndex[term][fieldName][docRef] == undefined) {\n        this.invertedIndex[term][fieldName][docRef] = Object.create(null)\n      }\n\n      // store all whitelisted metadata about this token in the\n      // inverted index\n      for (var l = 0; l < this.metadataWhitelist.length; l++) {\n        var metadataKey = this.metadataWhitelist[l],\n            metadata = term.metadata[metadataKey]\n\n        if (this.invertedIndex[term][fieldName][docRef][metadataKey] == undefined) {\n          this.invertedIndex[term][fieldName][docRef][metadataKey] = []\n        }\n\n        this.invertedIndex[term][fieldName][docRef][metadataKey].push(metadata)\n      }\n    }\n\n  }\n}\n\n/**\n * Calculates the average document length for this index\n *\n * @private\n */\nlunr.Builder.prototype.calculateAverageFieldLengths = function () {\n\n  var fieldRefs = Object.keys(this.fieldLengths),\n      numberOfFields = fieldRefs.length,\n      accumulator = {},\n      documentsWithField = {}\n\n  for (var i = 0; i < numberOfFields; i++) {\n    var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]),\n        field = fieldRef.fieldName\n\n    documentsWithField[field] || (documentsWithField[field] = 0)\n    documentsWithField[field] += 1\n\n    accumulator[field] || (accumulator[field] = 0)\n    accumulator[field] += this.fieldLengths[fieldRef]\n  }\n\n  var fields = Object.keys(this._fields)\n\n  for (var i = 0; i < fields.length; i++) {\n    var fieldName = fields[i]\n    accumulator[fieldName] = accumulator[fieldName] / documentsWithField[fieldName]\n  }\n\n  this.averageFieldLength = accumulator\n}\n\n/**\n * Builds a vector space model of every document using lunr.Vector\n *\n * @private\n */\nlunr.Builder.prototype.createFieldVectors = function () {\n  var fieldVectors = {},\n      fieldRefs = Object.keys(this.fieldTermFrequencies),\n      fieldRefsLength = fieldRefs.length,\n      termIdfCache = Object.create(null)\n\n  for (var i = 0; i < fieldRefsLength; i++) {\n    var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]),\n        fieldName = fieldRef.fieldName,\n        fieldLength = this.fieldLengths[fieldRef],\n        fieldVector = new lunr.Vector,\n        termFrequencies = this.fieldTermFrequencies[fieldRef],\n        terms = Object.keys(termFrequencies),\n        termsLength = terms.length\n\n\n    var fieldBoost = this._fields[fieldName].boost || 1,\n        docBoost = this._documents[fieldRef.docRef].boost || 1\n\n    for (var j = 0; j < termsLength; j++) {\n      var term = terms[j],\n          tf = termFrequencies[term],\n          termIndex = this.invertedIndex[term]._index,\n          idf, score, scoreWithPrecision\n\n      if (termIdfCache[term] === undefined) {\n        idf = lunr.idf(this.invertedIndex[term], this.documentCount)\n        termIdfCache[term] = idf\n      } else {\n        idf = termIdfCache[term]\n      }\n\n      score = idf * ((this._k1 + 1) * tf) / (this._k1 * (1 - this._b + this._b * (fieldLength / this.averageFieldLength[fieldName])) + tf)\n      score *= fieldBoost\n      score *= docBoost\n      scoreWithPrecision = Math.round(score * 1000) / 1000\n      // Converts 1.23456789 to 1.234.\n      // Reducing the precision so that the vectors take up less\n      // space when serialised. Doing it now so that they behave\n      // the same before and after serialisation. Also, this is\n      // the fastest approach to reducing a number's precision in\n      // JavaScript.\n\n      fieldVector.insert(termIndex, scoreWithPrecision)\n    }\n\n    fieldVectors[fieldRef] = fieldVector\n  }\n\n  this.fieldVectors = fieldVectors\n}\n\n/**\n * Creates a token set of all tokens in the index using lunr.TokenSet\n *\n * @private\n */\nlunr.Builder.prototype.createTokenSet = function () {\n  this.tokenSet = lunr.TokenSet.fromArray(\n    Object.keys(this.invertedIndex).sort()\n  )\n}\n\n/**\n * Builds the index, creating an instance of lunr.Index.\n *\n * This completes the indexing process and should only be called\n * once all documents have been added to the index.\n *\n * @returns {lunr.Index}\n */\nlunr.Builder.prototype.build = function () {\n  this.calculateAverageFieldLengths()\n  this.createFieldVectors()\n  this.createTokenSet()\n\n  return new lunr.Index({\n    invertedIndex: this.invertedIndex,\n    fieldVectors: this.fieldVectors,\n    tokenSet: this.tokenSet,\n    fields: Object.keys(this._fields),\n    pipeline: this.searchPipeline\n  })\n}\n\n/**\n * Applies a plugin to the index builder.\n *\n * A plugin is a function that is called with the index builder as its context.\n * Plugins can be used to customise or extend the behaviour of the index\n * in some way. A plugin is just a function, that encapsulated the custom\n * behaviour that should be applied when building the index.\n *\n * The plugin function will be called with the index builder as its argument, additional\n * arguments can also be passed when calling use. The function will be called\n * with the index builder as its context.\n *\n * @param {Function} plugin The plugin to apply.\n */\nlunr.Builder.prototype.use = function (fn) {\n  var args = Array.prototype.slice.call(arguments, 1)\n  args.unshift(this)\n  fn.apply(this, args)\n}\n/**\n * Contains and collects metadata about a matching document.\n * A single instance of lunr.MatchData is returned as part of every\n * lunr.Index~Result.\n *\n * @constructor\n * @param {string} term - The term this match data is associated with\n * @param {string} field - The field in which the term was found\n * @param {object} metadata - The metadata recorded about this term in this field\n * @property {object} metadata - A cloned collection of metadata associated with this document.\n * @see {@link lunr.Index~Result}\n */\nlunr.MatchData = function (term, field, metadata) {\n  var clonedMetadata = Object.create(null),\n      metadataKeys = Object.keys(metadata || {})\n\n  // Cloning the metadata to prevent the original\n  // being mutated during match data combination.\n  // Metadata is kept in an array within the inverted\n  // index so cloning the data can be done with\n  // Array#slice\n  for (var i = 0; i < metadataKeys.length; i++) {\n    var key = metadataKeys[i]\n    clonedMetadata[key] = metadata[key].slice()\n  }\n\n  this.metadata = Object.create(null)\n\n  if (term !== undefined) {\n    this.metadata[term] = Object.create(null)\n    this.metadata[term][field] = clonedMetadata\n  }\n}\n\n/**\n * An instance of lunr.MatchData will be created for every term that matches a\n * document. However only one instance is required in a lunr.Index~Result. This\n * method combines metadata from another instance of lunr.MatchData with this\n * objects metadata.\n *\n * @param {lunr.MatchData} otherMatchData - Another instance of match data to merge with this one.\n * @see {@link lunr.Index~Result}\n */\nlunr.MatchData.prototype.combine = function (otherMatchData) {\n  var terms = Object.keys(otherMatchData.metadata)\n\n  for (var i = 0; i < terms.length; i++) {\n    var term = terms[i],\n        fields = Object.keys(otherMatchData.metadata[term])\n\n    if (this.metadata[term] == undefined) {\n      this.metadata[term] = Object.create(null)\n    }\n\n    for (var j = 0; j < fields.length; j++) {\n      var field = fields[j],\n          keys = Object.keys(otherMatchData.metadata[term][field])\n\n      if (this.metadata[term][field] == undefined) {\n        this.metadata[term][field] = Object.create(null)\n      }\n\n      for (var k = 0; k < keys.length; k++) {\n        var key = keys[k]\n\n        if (this.metadata[term][field][key] == undefined) {\n          this.metadata[term][field][key] = otherMatchData.metadata[term][field][key]\n        } else {\n          this.metadata[term][field][key] = this.metadata[term][field][key].concat(otherMatchData.metadata[term][field][key])\n        }\n\n      }\n    }\n  }\n}\n\n/**\n * Add metadata for a term/field pair to this instance of match data.\n *\n * @param {string} term - The term this match data is associated with\n * @param {string} field - The field in which the term was found\n * @param {object} metadata - The metadata recorded about this term in this field\n */\nlunr.MatchData.prototype.add = function (term, field, metadata) {\n  if (!(term in this.metadata)) {\n    this.metadata[term] = Object.create(null)\n    this.metadata[term][field] = metadata\n    return\n  }\n\n  if (!(field in this.metadata[term])) {\n    this.metadata[term][field] = metadata\n    return\n  }\n\n  var metadataKeys = Object.keys(metadata)\n\n  for (var i = 0; i < metadataKeys.length; i++) {\n    var key = metadataKeys[i]\n\n    if (key in this.metadata[term][field]) {\n      this.metadata[term][field][key] = this.metadata[term][field][key].concat(metadata[key])\n    } else {\n      this.metadata[term][field][key] = metadata[key]\n    }\n  }\n}\n/**\n * A lunr.Query provides a programmatic way of defining queries to be performed\n * against a {@link lunr.Index}.\n *\n * Prefer constructing a lunr.Query using the {@link lunr.Index#query} method\n * so the query object is pre-initialized with the right index fields.\n *\n * @constructor\n * @property {lunr.Query~Clause[]} clauses - An array of query clauses.\n * @property {string[]} allFields - An array of all available fields in a lunr.Index.\n */\nlunr.Query = function (allFields) {\n  this.clauses = []\n  this.allFields = allFields\n}\n\n/**\n * Constants for indicating what kind of automatic wildcard insertion will be used when constructing a query clause.\n *\n * This allows wildcards to be added to the beginning and end of a term without having to manually do any string\n * concatenation.\n *\n * The wildcard constants can be bitwise combined to select both leading and trailing wildcards.\n *\n * @constant\n * @default\n * @property {number} wildcard.NONE - The term will have no wildcards inserted, this is the default behaviour\n * @property {number} wildcard.LEADING - Prepend the term with a wildcard, unless a leading wildcard already exists\n * @property {number} wildcard.TRAILING - Append a wildcard to the term, unless a trailing wildcard already exists\n * @see lunr.Query~Clause\n * @see lunr.Query#clause\n * @see lunr.Query#term\n * @example <caption>query term with trailing wildcard</caption>\n * query.term('foo', { wildcard: lunr.Query.wildcard.TRAILING })\n * @example <caption>query term with leading and trailing wildcard</caption>\n * query.term('foo', {\n *   wildcard: lunr.Query.wildcard.LEADING | lunr.Query.wildcard.TRAILING\n * })\n */\n\nlunr.Query.wildcard = new String (\"*\")\nlunr.Query.wildcard.NONE = 0\nlunr.Query.wildcard.LEADING = 1\nlunr.Query.wildcard.TRAILING = 2\n\n/**\n * Constants for indicating what kind of presence a term must have in matching documents.\n *\n * @constant\n * @enum {number}\n * @see lunr.Query~Clause\n * @see lunr.Query#clause\n * @see lunr.Query#term\n * @example <caption>query term with required presence</caption>\n * query.term('foo', { presence: lunr.Query.presence.REQUIRED })\n */\nlunr.Query.presence = {\n  /**\n   * Term's presence in a document is optional, this is the default value.\n   */\n  OPTIONAL: 1,\n\n  /**\n   * Term's presence in a document is required, documents that do not contain\n   * this term will not be returned.\n   */\n  REQUIRED: 2,\n\n  /**\n   * Term's presence in a document is prohibited, documents that do contain\n   * this term will not be returned.\n   */\n  PROHIBITED: 3\n}\n\n/**\n * A single clause in a {@link lunr.Query} contains a term and details on how to\n * match that term against a {@link lunr.Index}.\n *\n * @typedef {Object} lunr.Query~Clause\n * @property {string[]} fields - The fields in an index this clause should be matched against.\n * @property {number} [boost=1] - Any boost that should be applied when matching this clause.\n * @property {number} [editDistance] - Whether the term should have fuzzy matching applied, and how fuzzy the match should be.\n * @property {boolean} [usePipeline] - Whether the term should be passed through the search pipeline.\n * @property {number} [wildcard=lunr.Query.wildcard.NONE] - Whether the term should have wildcards appended or prepended.\n * @property {number} [presence=lunr.Query.presence.OPTIONAL] - The terms presence in any matching documents.\n */\n\n/**\n * Adds a {@link lunr.Query~Clause} to this query.\n *\n * Unless the clause contains the fields to be matched all fields will be matched. In addition\n * a default boost of 1 is applied to the clause.\n *\n * @param {lunr.Query~Clause} clause - The clause to add to this query.\n * @see lunr.Query~Clause\n * @returns {lunr.Query}\n */\nlunr.Query.prototype.clause = function (clause) {\n  if (!('fields' in clause)) {\n    clause.fields = this.allFields\n  }\n\n  if (!('boost' in clause)) {\n    clause.boost = 1\n  }\n\n  if (!('usePipeline' in clause)) {\n    clause.usePipeline = true\n  }\n\n  if (!('wildcard' in clause)) {\n    clause.wildcard = lunr.Query.wildcard.NONE\n  }\n\n  if ((clause.wildcard & lunr.Query.wildcard.LEADING) && (clause.term.charAt(0) != lunr.Query.wildcard)) {\n    clause.term = \"*\" + clause.term\n  }\n\n  if ((clause.wildcard & lunr.Query.wildcard.TRAILING) && (clause.term.slice(-1) != lunr.Query.wildcard)) {\n    clause.term = \"\" + clause.term + \"*\"\n  }\n\n  if (!('presence' in clause)) {\n    clause.presence = lunr.Query.presence.OPTIONAL\n  }\n\n  this.clauses.push(clause)\n\n  return this\n}\n\n/**\n * A negated query is one in which every clause has a presence of\n * prohibited. These queries require some special processing to return\n * the expected results.\n *\n * @returns boolean\n */\nlunr.Query.prototype.isNegated = function () {\n  for (var i = 0; i < this.clauses.length; i++) {\n    if (this.clauses[i].presence != lunr.Query.presence.PROHIBITED) {\n      return false\n    }\n  }\n\n  return true\n}\n\n/**\n * Adds a term to the current query, under the covers this will create a {@link lunr.Query~Clause}\n * to the list of clauses that make up this query.\n *\n * The term is used as is, i.e. no tokenization will be performed by this method. Instead conversion\n * to a token or token-like string should be done before calling this method.\n *\n * The term will be converted to a string by calling `toString`. Multiple terms can be passed as an\n * array, each term in the array will share the same options.\n *\n * @param {object|object[]} term - The term(s) to add to the query.\n * @param {object} [options] - Any additional properties to add to the query clause.\n * @returns {lunr.Query}\n * @see lunr.Query#clause\n * @see lunr.Query~Clause\n * @example <caption>adding a single term to a query</caption>\n * query.term(\"foo\")\n * @example <caption>adding a single term to a query and specifying search fields, term boost and automatic trailing wildcard</caption>\n * query.term(\"foo\", {\n *   fields: [\"title\"],\n *   boost: 10,\n *   wildcard: lunr.Query.wildcard.TRAILING\n * })\n * @example <caption>using lunr.tokenizer to convert a string to tokens before using them as terms</caption>\n * query.term(lunr.tokenizer(\"foo bar\"))\n */\nlunr.Query.prototype.term = function (term, options) {\n  if (Array.isArray(term)) {\n    term.forEach(function (t) { this.term(t, lunr.utils.clone(options)) }, this)\n    return this\n  }\n\n  var clause = options || {}\n  clause.term = term.toString()\n\n  this.clause(clause)\n\n  return this\n}\nlunr.QueryParseError = function (message, start, end) {\n  this.name = \"QueryParseError\"\n  this.message = message\n  this.start = start\n  this.end = end\n}\n\nlunr.QueryParseError.prototype = new Error\nlunr.QueryLexer = function (str) {\n  this.lexemes = []\n  this.str = str\n  this.length = str.length\n  this.pos = 0\n  this.start = 0\n  this.escapeCharPositions = []\n}\n\nlunr.QueryLexer.prototype.run = function () {\n  var state = lunr.QueryLexer.lexText\n\n  while (state) {\n    state = state(this)\n  }\n}\n\nlunr.QueryLexer.prototype.sliceString = function () {\n  var subSlices = [],\n      sliceStart = this.start,\n      sliceEnd = this.pos\n\n  for (var i = 0; i < this.escapeCharPositions.length; i++) {\n    sliceEnd = this.escapeCharPositions[i]\n    subSlices.push(this.str.slice(sliceStart, sliceEnd))\n    sliceStart = sliceEnd + 1\n  }\n\n  subSlices.push(this.str.slice(sliceStart, this.pos))\n  this.escapeCharPositions.length = 0\n\n  return subSlices.join('')\n}\n\nlunr.QueryLexer.prototype.emit = function (type) {\n  this.lexemes.push({\n    type: type,\n    str: this.sliceString(),\n    start: this.start,\n    end: this.pos\n  })\n\n  this.start = this.pos\n}\n\nlunr.QueryLexer.prototype.escapeCharacter = function () {\n  this.escapeCharPositions.push(this.pos - 1)\n  this.pos += 1\n}\n\nlunr.QueryLexer.prototype.next = function () {\n  if (this.pos >= this.length) {\n    return lunr.QueryLexer.EOS\n  }\n\n  var char = this.str.charAt(this.pos)\n  this.pos += 1\n  return char\n}\n\nlunr.QueryLexer.prototype.width = function () {\n  return this.pos - this.start\n}\n\nlunr.QueryLexer.prototype.ignore = function () {\n  if (this.start == this.pos) {\n    this.pos += 1\n  }\n\n  this.start = this.pos\n}\n\nlunr.QueryLexer.prototype.backup = function () {\n  this.pos -= 1\n}\n\nlunr.QueryLexer.prototype.acceptDigitRun = function () {\n  var char, charCode\n\n  do {\n    char = this.next()\n    charCode = char.charCodeAt(0)\n  } while (charCode > 47 && charCode < 58)\n\n  if (char != lunr.QueryLexer.EOS) {\n    this.backup()\n  }\n}\n\nlunr.QueryLexer.prototype.more = function () {\n  return this.pos < this.length\n}\n\nlunr.QueryLexer.EOS = 'EOS'\nlunr.QueryLexer.FIELD = 'FIELD'\nlunr.QueryLexer.TERM = 'TERM'\nlunr.QueryLexer.EDIT_DISTANCE = 'EDIT_DISTANCE'\nlunr.QueryLexer.BOOST = 'BOOST'\nlunr.QueryLexer.PRESENCE = 'PRESENCE'\n\nlunr.QueryLexer.lexField = function (lexer) {\n  lexer.backup()\n  lexer.emit(lunr.QueryLexer.FIELD)\n  lexer.ignore()\n  return lunr.QueryLexer.lexText\n}\n\nlunr.QueryLexer.lexTerm = function (lexer) {\n  if (lexer.width() > 1) {\n    lexer.backup()\n    lexer.emit(lunr.QueryLexer.TERM)\n  }\n\n  lexer.ignore()\n\n  if (lexer.more()) {\n    return lunr.QueryLexer.lexText\n  }\n}\n\nlunr.QueryLexer.lexEditDistance = function (lexer) {\n  lexer.ignore()\n  lexer.acceptDigitRun()\n  lexer.emit(lunr.QueryLexer.EDIT_DISTANCE)\n  return lunr.QueryLexer.lexText\n}\n\nlunr.QueryLexer.lexBoost = function (lexer) {\n  lexer.ignore()\n  lexer.acceptDigitRun()\n  lexer.emit(lunr.QueryLexer.BOOST)\n  return lunr.QueryLexer.lexText\n}\n\nlunr.QueryLexer.lexEOS = function (lexer) {\n  if (lexer.width() > 0) {\n    lexer.emit(lunr.QueryLexer.TERM)\n  }\n}\n\n// This matches the separator used when tokenising fields\n// within a document. These should match otherwise it is\n// not possible to search for some tokens within a document.\n//\n// It is possible for the user to change the separator on the\n// tokenizer so it _might_ clash with any other of the special\n// characters already used within the search string, e.g. :.\n//\n// This means that it is possible to change the separator in\n// such a way that makes some words unsearchable using a search\n// string.\nlunr.QueryLexer.termSeparator = lunr.tokenizer.separator\n\nlunr.QueryLexer.lexText = function (lexer) {\n  while (true) {\n    var char = lexer.next()\n\n    if (char == lunr.QueryLexer.EOS) {\n      return lunr.QueryLexer.lexEOS\n    }\n\n    // Escape character is '\\'\n    if (char.charCodeAt(0) == 92) {\n      lexer.escapeCharacter()\n      continue\n    }\n\n    if (char == \":\") {\n      return lunr.QueryLexer.lexField\n    }\n\n    if (char == \"~\") {\n      lexer.backup()\n      if (lexer.width() > 0) {\n        lexer.emit(lunr.QueryLexer.TERM)\n      }\n      return lunr.QueryLexer.lexEditDistance\n    }\n\n    if (char == \"^\") {\n      lexer.backup()\n      if (lexer.width() > 0) {\n        lexer.emit(lunr.QueryLexer.TERM)\n      }\n      return lunr.QueryLexer.lexBoost\n    }\n\n    // \"+\" indicates term presence is required\n    // checking for length to ensure that only\n    // leading \"+\" are considered\n    if (char == \"+\" && lexer.width() === 1) {\n      lexer.emit(lunr.QueryLexer.PRESENCE)\n      return lunr.QueryLexer.lexText\n    }\n\n    // \"-\" indicates term presence is prohibited\n    // checking for length to ensure that only\n    // leading \"-\" are considered\n    if (char == \"-\" && lexer.width() === 1) {\n      lexer.emit(lunr.QueryLexer.PRESENCE)\n      return lunr.QueryLexer.lexText\n    }\n\n    if (char.match(lunr.QueryLexer.termSeparator)) {\n      return lunr.QueryLexer.lexTerm\n    }\n  }\n}\n\nlunr.QueryParser = function (str, query) {\n  this.lexer = new lunr.QueryLexer (str)\n  this.query = query\n  this.currentClause = {}\n  this.lexemeIdx = 0\n}\n\nlunr.QueryParser.prototype.parse = function () {\n  this.lexer.run()\n  this.lexemes = this.lexer.lexemes\n\n  var state = lunr.QueryParser.parseClause\n\n  while (state) {\n    state = state(this)\n  }\n\n  return this.query\n}\n\nlunr.QueryParser.prototype.peekLexeme = function () {\n  return this.lexemes[this.lexemeIdx]\n}\n\nlunr.QueryParser.prototype.consumeLexeme = function () {\n  var lexeme = this.peekLexeme()\n  this.lexemeIdx += 1\n  return lexeme\n}\n\nlunr.QueryParser.prototype.nextClause = function () {\n  var completedClause = this.currentClause\n  this.query.clause(completedClause)\n  this.currentClause = {}\n}\n\nlunr.QueryParser.parseClause = function (parser) {\n  var lexeme = parser.peekLexeme()\n\n  if (lexeme == undefined) {\n    return\n  }\n\n  switch (lexeme.type) {\n    case lunr.QueryLexer.PRESENCE:\n      return lunr.QueryParser.parsePresence\n    case lunr.QueryLexer.FIELD:\n      return lunr.QueryParser.parseField\n    case lunr.QueryLexer.TERM:\n      return lunr.QueryParser.parseTerm\n    default:\n      var errorMessage = \"expected either a field or a term, found \" + lexeme.type\n\n      if (lexeme.str.length >= 1) {\n        errorMessage += \" with value '\" + lexeme.str + \"'\"\n      }\n\n      throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n  }\n}\n\nlunr.QueryParser.parsePresence = function (parser) {\n  var lexeme = parser.consumeLexeme()\n\n  if (lexeme == undefined) {\n    return\n  }\n\n  switch (lexeme.str) {\n    case \"-\":\n      parser.currentClause.presence = lunr.Query.presence.PROHIBITED\n      break\n    case \"+\":\n      parser.currentClause.presence = lunr.Query.presence.REQUIRED\n      break\n    default:\n      var errorMessage = \"unrecognised presence operator'\" + lexeme.str + \"'\"\n      throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n  }\n\n  var nextLexeme = parser.peekLexeme()\n\n  if (nextLexeme == undefined) {\n    var errorMessage = \"expecting term or field, found nothing\"\n    throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n  }\n\n  switch (nextLexeme.type) {\n    case lunr.QueryLexer.FIELD:\n      return lunr.QueryParser.parseField\n    case lunr.QueryLexer.TERM:\n      return lunr.QueryParser.parseTerm\n    default:\n      var errorMessage = \"expecting term or field, found '\" + nextLexeme.type + \"'\"\n      throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n  }\n}\n\nlunr.QueryParser.parseField = function (parser) {\n  var lexeme = parser.consumeLexeme()\n\n  if (lexeme == undefined) {\n    return\n  }\n\n  if (parser.query.allFields.indexOf(lexeme.str) == -1) {\n    var possibleFields = parser.query.allFields.map(function (f) { return \"'\" + f + \"'\" }).join(', '),\n        errorMessage = \"unrecognised field '\" + lexeme.str + \"', possible fields: \" + possibleFields\n\n    throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n  }\n\n  parser.currentClause.fields = [lexeme.str]\n\n  var nextLexeme = parser.peekLexeme()\n\n  if (nextLexeme == undefined) {\n    var errorMessage = \"expecting term, found nothing\"\n    throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n  }\n\n  switch (nextLexeme.type) {\n    case lunr.QueryLexer.TERM:\n      return lunr.QueryParser.parseTerm\n    default:\n      var errorMessage = \"expecting term, found '\" + nextLexeme.type + \"'\"\n      throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n  }\n}\n\nlunr.QueryParser.parseTerm = function (parser) {\n  var lexeme = parser.consumeLexeme()\n\n  if (lexeme == undefined) {\n    return\n  }\n\n  parser.currentClause.term = lexeme.str.toLowerCase()\n\n  if (lexeme.str.indexOf(\"*\") != -1) {\n    parser.currentClause.usePipeline = false\n  }\n\n  var nextLexeme = parser.peekLexeme()\n\n  if (nextLexeme == undefined) {\n    parser.nextClause()\n    return\n  }\n\n  switch (nextLexeme.type) {\n    case lunr.QueryLexer.TERM:\n      parser.nextClause()\n      return lunr.QueryParser.parseTerm\n    case lunr.QueryLexer.FIELD:\n      parser.nextClause()\n      return lunr.QueryParser.parseField\n    case lunr.QueryLexer.EDIT_DISTANCE:\n      return lunr.QueryParser.parseEditDistance\n    case lunr.QueryLexer.BOOST:\n      return lunr.QueryParser.parseBoost\n    case lunr.QueryLexer.PRESENCE:\n      parser.nextClause()\n      return lunr.QueryParser.parsePresence\n    default:\n      var errorMessage = \"Unexpected lexeme type '\" + nextLexeme.type + \"'\"\n      throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n  }\n}\n\nlunr.QueryParser.parseEditDistance = function (parser) {\n  var lexeme = parser.consumeLexeme()\n\n  if (lexeme == undefined) {\n    return\n  }\n\n  var editDistance = parseInt(lexeme.str, 10)\n\n  if (isNaN(editDistance)) {\n    var errorMessage = \"edit distance must be numeric\"\n    throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n  }\n\n  parser.currentClause.editDistance = editDistance\n\n  var nextLexeme = parser.peekLexeme()\n\n  if (nextLexeme == undefined) {\n    parser.nextClause()\n    return\n  }\n\n  switch (nextLexeme.type) {\n    case lunr.QueryLexer.TERM:\n      parser.nextClause()\n      return lunr.QueryParser.parseTerm\n    case lunr.QueryLexer.FIELD:\n      parser.nextClause()\n      return lunr.QueryParser.parseField\n    case lunr.QueryLexer.EDIT_DISTANCE:\n      return lunr.QueryParser.parseEditDistance\n    case lunr.QueryLexer.BOOST:\n      return lunr.QueryParser.parseBoost\n    case lunr.QueryLexer.PRESENCE:\n      parser.nextClause()\n      return lunr.QueryParser.parsePresence\n    default:\n      var errorMessage = \"Unexpected lexeme type '\" + nextLexeme.type + \"'\"\n      throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n  }\n}\n\nlunr.QueryParser.parseBoost = function (parser) {\n  var lexeme = parser.consumeLexeme()\n\n  if (lexeme == undefined) {\n    return\n  }\n\n  var boost = parseInt(lexeme.str, 10)\n\n  if (isNaN(boost)) {\n    var errorMessage = \"boost must be numeric\"\n    throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n  }\n\n  parser.currentClause.boost = boost\n\n  var nextLexeme = parser.peekLexeme()\n\n  if (nextLexeme == undefined) {\n    parser.nextClause()\n    return\n  }\n\n  switch (nextLexeme.type) {\n    case lunr.QueryLexer.TERM:\n      parser.nextClause()\n      return lunr.QueryParser.parseTerm\n    case lunr.QueryLexer.FIELD:\n      parser.nextClause()\n      return lunr.QueryParser.parseField\n    case lunr.QueryLexer.EDIT_DISTANCE:\n      return lunr.QueryParser.parseEditDistance\n    case lunr.QueryLexer.BOOST:\n      return lunr.QueryParser.parseBoost\n    case lunr.QueryLexer.PRESENCE:\n      parser.nextClause()\n      return lunr.QueryParser.parsePresence\n    default:\n      var errorMessage = \"Unexpected lexeme type '\" + nextLexeme.type + \"'\"\n      throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n  }\n}\n\n  /**\n   * export the module via AMD, CommonJS or as a browser global\n   * Export code from https://github.com/umdjs/umd/blob/master/returnExports.js\n   */\n  ;(function (root, factory) {\n    if (typeof define === 'function' && define.amd) {\n      // AMD. Register as an anonymous module.\n      define(factory)\n    } else if (typeof exports === 'object') {\n      /**\n       * Node. Does not work with strict CommonJS, but\n       * only CommonJS-like enviroments that support module.exports,\n       * like Node.\n       */\n      module.exports = factory()\n    } else {\n      // Browser globals (root is window)\n      root.lunr = factory()\n    }\n  }(this, function () {\n    /**\n     * Just return a value to define the module export.\n     * This example returns an object, but the module\n     * can return a function as the exported value.\n     */\n    return lunr\n  }))\n})();\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A RTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport lunr from \"lunr\"\n\nimport { getElement } from \"~/browser/element/_\"\nimport \"~/polyfills\"\n\nimport { Search } from \"../../_\"\nimport { SearchConfig } from \"../../config\"\nimport {\n  SearchMessage,\n  SearchMessageType\n} from \"../message\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Add support for `iframe-worker` shim\n *\n * While `importScripts` is synchronous when executed inside of a web worker,\n * it's not possible to provide a synchronous shim implementation. The cool\n * thing is that awaiting a non-Promise will convert it into a Promise, so\n * extending the type definition to return a `Promise` shouldn't break anything.\n *\n * @see https://bit.ly/2PjDnXi - GitHub comment\n *\n * @param urls - Scripts to load\n *\n * @returns Promise resolving with no result\n */\ndeclare global {\n  function importScripts(...urls: string[]): Promise<void> | void\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Search index\n */\nlet index: Search\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch (= import) multi-language support through `lunr-languages`\n *\n * This function automatically imports the stemmers necessary to process the\n * languages which are defined as part of the search configuration.\n *\n * If the worker runs inside of an `iframe` (when using `iframe-worker` as\n * a shim), the base URL for the stemmers to be loaded must be determined by\n * searching for the first `script` element with a `src` attribute, which will\n * contain the contents of this script.\n *\n * @param config - Search configuration\n *\n * @returns Promise resolving with no result\n */\nasync function setupSearchLanguages(\n  config: SearchConfig\n): Promise<void> {\n  let base = \"../lunr\"\n\n  /* Detect `iframe-worker` and fix base URL */\n  if (typeof parent !== \"undefined\" && \"IFrameWorker\" in parent) {\n    const worker = getElement<HTMLScriptElement>(\"script[src]\")!\n    const [path] = worker.src.split(\"/worker\")\n\n    /* Prefix base with path */\n    base = base.replace(\"..\", path)\n  }\n\n  /* Add scripts for languages */\n  const scripts = []\n  for (const lang of config.lang) {\n    switch (lang) {\n\n      /* Add segmenter for Japanese */\n      case \"ja\":\n        scripts.push(`${base}/tinyseg.js`)\n        break\n\n      /* Add segmenter for Hindi and Thai */\n      case \"hi\":\n      case \"th\":\n        scripts.push(`${base}/wordcut.js`)\n        break\n    }\n\n    /* Add language support */\n    if (lang !== \"en\")\n      scripts.push(`${base}/min/lunr.${lang}.min.js`)\n  }\n\n  /* Add multi-language support */\n  if (config.lang.length > 1)\n    scripts.push(`${base}/min/lunr.multi.min.js`)\n\n  /* Load scripts synchronously */\n  if (scripts.length)\n    await importScripts(\n      `${base}/min/lunr.stemmer.support.min.js`,\n      ...scripts\n    )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Message handler\n *\n * @param message - Source message\n *\n * @returns Target message\n */\nexport async function handler(\n  message: SearchMessage\n): Promise<SearchMessage> {\n  switch (message.type) {\n\n    /* Search setup message */\n    case SearchMessageType.SETUP:\n      await setupSearchLanguages(message.data.config)\n      index = new Search(message.data)\n      return {\n        type: SearchMessageType.READY\n      }\n\n    /* Search query message */\n    case SearchMessageType.QUERY:\n      const query = message.data\n      try {\n        return {\n          type: SearchMessageType.RESULT,\n          data: index.search(query)\n        }\n\n      /* Return empty result in case of error */\n      } catch (err) {\n        console.warn(`Invalid query: ${query} \u2013 see https://bit.ly/2s3ChXG`)\n        console.warn(err)\n        return {\n          type: SearchMessageType.RESULT,\n          data: { items: [] }\n        }\n      }\n\n    /* All other messages */\n    default:\n      throw new TypeError(\"Invalid message type\")\n  }\n}\n\n/* ----------------------------------------------------------------------------\n * Worker\n * ------------------------------------------------------------------------- */\n\n/* Expose Lunr.js in global scope, or stemmers won't work */\nself.lunr = lunr\n\n/* Handle messages */\naddEventListener(\"message\", async ev => {\n  postMessage(await handler(ev.data))\n})\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve all elements matching the query selector\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Elements\n */\nexport function getElements<T extends keyof HTMLElementTagNameMap>(\n  selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T][]\n\nexport function getElements<T extends HTMLElement>(\n  selector: string, node?: ParentNode\n): T[]\n\nexport function getElements<T extends HTMLElement>(\n  selector: string, node: ParentNode = document\n): T[] {\n  return Array.from(node.querySelectorAll<T>(selector))\n}\n\n/**\n * Retrieve an element matching a query selector or throw a reference error\n *\n * Note that this function assumes that the element is present. If unsure if an\n * element is existent, use the `getOptionalElement` function instead.\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Element\n */\nexport function getElement<T extends keyof HTMLElementTagNameMap>(\n  selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T]\n\nexport function getElement<T extends HTMLElement>(\n  selector: string, node?: ParentNode\n): T\n\nexport function getElement<T extends HTMLElement>(\n  selector: string, node: ParentNode = document\n): T {\n  const el = getOptionalElement<T>(selector, node)\n  if (typeof el === \"undefined\")\n    throw new ReferenceError(\n      `Missing element: expected \"${selector}\" to be present`\n    )\n\n  /* Return element */\n  return el\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Retrieve an optional element matching the query selector\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Element or nothing\n */\nexport function getOptionalElement<T extends keyof HTMLElementTagNameMap>(\n  selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T] | undefined\n\nexport function getOptionalElement<T extends HTMLElement>(\n  selector: string, node?: ParentNode\n): T | undefined\n\nexport function getOptionalElement<T extends HTMLElement>(\n  selector: string, node: ParentNode = document\n): T | undefined {\n  return node.querySelector<T>(selector) || undefined\n}\n\n/**\n * Retrieve the currently active element\n *\n * @returns Element or nothing\n */\nexport function getActiveElement(): HTMLElement | undefined {\n  return document.activeElement instanceof HTMLElement\n    ? document.activeElement || undefined\n    : undefined\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Polyfills\n * ------------------------------------------------------------------------- */\n\n/* Polyfill `Object.entries` */\nif (!Object.entries)\n  Object.entries = function (obj: object) {\n    const data: [string, string][] = []\n    for (const key of Object.keys(obj))\n      // @ts-expect-error - ignore property access warning\n      data.push([key, obj[key]])\n\n    /* Return entries */\n    return data\n  }\n\n/* Polyfill `Object.values` */\nif (!Object.values)\n  Object.values = function (obj: object) {\n    const data: string[] = []\n    for (const key of Object.keys(obj))\n      // @ts-expect-error - ignore property access warning\n      data.push(obj[key])\n\n    /* Return values */\n    return data\n  }\n\n/* ------------------------------------------------------------------------- */\n\n/* Polyfills for `Element` */\nif (typeof Element !== \"undefined\") {\n\n  /* Polyfill `Element.scrollTo` */\n  if (!Element.prototype.scrollTo)\n    Element.prototype.scrollTo = function (\n      x?: ScrollToOptions | number, y?: number\n    ): void {\n      if (typeof x === \"object\") {\n        this.scrollLeft = x.left!\n        this.scrollTop = x.top!\n      } else {\n        this.scrollLeft = x!\n        this.scrollTop = y!\n      }\n    }\n\n  /* Polyfill `Element.replaceWith` */\n  if (!Element.prototype.replaceWith)\n    Element.prototype.replaceWith = function (\n      ...nodes: Array<string | Node>\n    ): void {\n      const parent = this.parentNode\n      if (parent) {\n        if (nodes.length === 0)\n          parent.removeChild(this)\n\n        /* Replace children and create text nodes */\n        for (let i = nodes.length - 1; i >= 0; i--) {\n          let node = nodes[i]\n          if (typeof node === \"string\")\n            node = document.createTextNode(node)\n          else if (node.parentNode)\n            node.parentNode.removeChild(node)\n\n          /* Replace child or insert before previous sibling */\n          if (!i)\n            parent.replaceChild(node, this)\n          else\n            parent.insertBefore(this.previousSibling!, node)\n        }\n      }\n    }\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search configuration\n */\nexport interface SearchConfig {\n  lang: string[]                       /* Search languages */\n  separator: string                    /* Search separator */\n  pipeline: SearchPipelineFn[]         /* Search pipeline */\n}\n\n/**\n * Search document\n */\nexport interface SearchDocument {\n  location: string                     /* Document location */\n  title: string                        /* Document title */\n  text: string                         /* Document text */\n  tags?: string[]                      /* Document tags */\n  boost?: number                       /* Document boost */\n  parent?: SearchDocument              /* Document parent */\n}\n\n/**\n * Search options\n */\nexport interface SearchOptions {\n  suggest: boolean                     /* Search suggestions */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search index\n */\nexport interface SearchIndex {\n  config: SearchConfig                 /* Search configuration */\n  docs: SearchDocument[]               /* Search documents */\n  options: SearchOptions               /* Search options */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search pipeline function\n */\ntype SearchPipelineFn =\n  | \"trimmer\"                          /* Trimmer */\n  | \"stopWordFilter\"                   /* Stop word filter */\n  | \"stemmer\"                          /* Stemmer */\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create a search document map\n *\n * This function creates a mapping of URLs (including anchors) to the actual\n * articles and sections. It relies on the invariant that the search index is\n * ordered with the main article appearing before all sections with anchors.\n * If this is not the case, the logic music be changed.\n *\n * @param docs - Search documents\n *\n * @returns Search document map\n */\nexport function setupSearchDocumentMap(\n  docs: SearchDocument[]\n): Map<string, SearchDocument> {\n  const map = new Map<string, SearchDocument>()\n  for (const doc of docs) {\n    const [path] = doc.location.split(\"#\")\n\n    /* Add document article */\n    const article = map.get(path)\n    if (typeof article === \"undefined\") {\n      map.set(path, doc)\n\n      /* Add document section */\n    } else {\n      map.set(doc.location, doc)\n      doc.parent = article\n    }\n  }\n\n  /* Return search document map */\n  return map\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Visitor function\n *\n * @param start - Start offset\n * @param end - End offset\n */\ntype VisitorFn = (\n  start: number, end: number\n) => void\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Split a string using the given separator\n *\n * @param input - Input value\n * @param separator - Separator\n * @param fn - Visitor function\n */\nexport function split(\n  input: string, separator: RegExp, fn: VisitorFn\n): void {\n  separator = new RegExp(separator, \"g\")\n\n  /* Split string using separator */\n  let match: RegExpExecArray | null\n  let index = 0\n  do {\n    match = separator.exec(input)\n\n    /* Emit non-empty range */\n    const until = match?.index ?? input.length\n    if (index < until)\n      fn(index, until)\n\n    /* Update last index */\n    if (match) {\n      const [term] = match\n      index = match.index + term.length\n\n      /* Support zero-length lookaheads */\n      if (term.length === 0)\n        separator.lastIndex = match.index + 1\n    }\n  } while (match)\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Extraction type\n *\n * This type defines the possible values that are encoded into the first two\n * bits of a section that is part of the blocks of a tokenization table. There\n * are three types of interest: HTML opening and closing tags, as well as the\n * actual text content we need to extract for indexing.\n */\nexport const enum Extract {\n  TAG_OPEN  = 0,                       /* HTML opening tag */\n  TEXT      = 1,                       /* Text content */\n  TAG_CLOSE = 2                        /* HTML closing tag */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Visitor function\n *\n * @param block - Block index\n * @param type - Extraction type\n * @param start - Start offset\n * @param end - End offset\n */\ntype VisitorFn = (\n  block: number, type: Extract, start: number, end: number\n) => void\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Split a string into markup and text sections\n *\n * This function scans a string and divides it up into sections of markup and\n * text. For each section, it invokes the given visitor function with the block\n * index, extraction type, as well as start and end offsets. Using a visitor\n * function (= streaming data) is ideal for minimizing pressure on the GC.\n *\n * @param input - Input value\n * @param fn - Visitor function\n */\nexport function extract(\n  input: string, fn: VisitorFn\n): void {\n\n  let block = 0                        /* Current block */\n  let start = 0                        /* Current start offset */\n  let end = 0                          /* Current end offset */\n\n  /* Split string into sections */\n  for (let stack = 0; end < input.length; end++) {\n\n    /* Opening tag after non-empty section */\n    if (input.charAt(end) === \"<\" && end > start) {\n      fn(block, Extract.TEXT, start, start = end)\n\n    /* Closing tag */\n    } else if (input.charAt(end) === \">\") {\n      if (input.charAt(start + 1) === \"/\") {\n        if (--stack === 0)\n          fn(block++, Extract.TAG_CLOSE, start, end + 1)\n\n      /* Tag is not self-closing */\n      } else if (input.charAt(end - 1) !== \"/\") {\n        if (stack++ === 0)\n          fn(block, Extract.TAG_OPEN, start, end + 1)\n      }\n\n      /* New section */\n      start = end + 1\n    }\n  }\n\n  /* Add trailing section */\n  if (end > start)\n    fn(block, Extract.TEXT, start, end)\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Position table\n */\nexport type PositionTable = number[][]\n\n/**\n * Position\n */\nexport type Position = number\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Highlight all occurrences in a string\n *\n * This function receives a field's value (e.g. like `title` or `text`), it's\n * position table that was generated during indexing, and the positions found\n * when executing the query. It then highlights all occurrences, and returns\n * their concatenation. In case of multiple blocks, two are returned.\n *\n * @param input - Input value\n * @param table - Table for indexing\n * @param positions - Occurrences\n *\n * @returns Highlighted string value\n */\nexport function highlight(\n  input: string, table: PositionTable, positions: Position[]\n): string {\n  return highlightAll([input], table, positions).pop()!\n}\n\n/**\n * Highlight all occurrences in a set of strings\n *\n * @param inputs - Input values\n * @param table - Table for indexing\n * @param positions - Occurrences\n *\n * @returns Highlighted string values\n */\nexport function highlightAll(\n  inputs: string[], table: PositionTable, positions: Position[]\n): string[] {\n\n  /* Map blocks to input values */\n  const mapping = [0]\n  for (let t = 1; t < table.length; t++) {\n    const prev = table[t - 1]\n    const next = table[t]\n\n    /* Check if table points to new block */\n    const p = prev[prev.length - 1] >>> 2 & 0x3FF\n    const q = next[0]               >>> 12\n\n    /* Add block to mapping */\n    mapping.push(+(p > q) + mapping[mapping.length - 1])\n  }\n\n  /* Highlight strings one after another */\n  return inputs.map((input, i) => {\n\n    /* Map occurrences to blocks */\n    const blocks = new Map<number, number[]>()\n    for (const p of positions.sort((a, b) => a - b)) {\n      const index = p & 0xFFFFF\n      const block = p >>> 20\n      if (mapping[block] !== i)\n        continue\n\n      /* Ensure presence of block group */\n      let group = blocks.get(block)\n      if (typeof group === \"undefined\")\n        blocks.set(block, group = [])\n\n      /* Add index to group */\n      group.push(index)\n    }\n\n    /* Just return string, if no occurrences */\n    if (blocks.size === 0)\n      return input\n\n    /* Compute slices */\n    const slices: string[] = []\n    for (const [block, indexes] of blocks) {\n      const t = table[block]\n\n      /* Extract positions and length */\n      const start  = t[0]            >>> 12\n      const end    = t[t.length - 1] >>> 12\n      const length = t[t.length - 1] >>> 2 & 0x3FF\n\n      /* Extract and highlight slice */\n      let slice = input.slice(start, end + length)\n      for (const j of indexes.sort((a, b) => b - a)) {\n\n        /* Retrieve offset and length of match */\n        const p = (t[j] >>> 12) - start\n        const q = (t[j] >>> 2 & 0x3FF) + p\n\n        /* Wrap occurrence */\n        slice = [\n          slice.slice(0, p),\n          \"<mark>\",\n          slice.slice(p, q),\n          \"</mark>\",\n          slice.slice(q)\n        ].join(\"\")\n      }\n\n      /* Append slice and abort if we have two */\n      if (slices.push(slice) === 2)\n        break\n    }\n\n    /* Return highlighted slices */\n    return slices.join(\"\")\n  })\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { split } from \"../_\"\nimport {\n  Extract,\n  extract\n} from \"../extract\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Split a string or set of strings into tokens\n *\n * This tokenizer supersedes the default tokenizer that is provided by Lunr.js,\n * as it is aware of HTML tags and allows for multi-character splitting.\n *\n * It takes the given inputs, splits each of them into markup and text sections,\n * tokenizes and segments (if necessary) each of them, and then indexes them in\n * a table by using a compact bit representation. Bitwise techniques are used\n * to write and read from the table during indexing and querying.\n *\n * @see https://bit.ly/3W3Xw4J - Search: better, faster, smaller\n *\n * @param input - Input value(s)\n *\n * @returns Tokens\n */\nexport function tokenize(\n  input?: string | string[]\n): lunr.Token[] {\n  const tokens: lunr.Token[] = []\n  if (typeof input === \"undefined\")\n    return tokens\n\n  /* Tokenize strings one after another */\n  const inputs = Array.isArray(input) ? input : [input]\n  for (let i = 0; i < inputs.length; i++) {\n    const table = lunr.tokenizer.table\n    const total = table.length\n\n    /* Split string into sections and tokenize content blocks */\n    extract(inputs[i], (block, type, start, end) => {\n      table[block += total] ||= []\n      switch (type) {\n\n        /* Handle markup */\n        case Extract.TAG_OPEN:\n        case Extract.TAG_CLOSE:\n          table[block].push(\n            start       << 12 |\n            end - start <<  2 |\n            type\n          )\n          break\n\n        /* Handle text content */\n        case Extract.TEXT:\n          const section = inputs[i].slice(start, end)\n          split(section, lunr.tokenizer.separator, (index, until) => {\n\n            /**\n             * Apply segmenter after tokenization. Note that the segmenter will\n             * also split words at word boundaries, which is not what we want,\n             * so we need to check if we can somehow mitigate this behavior.\n             */\n            if (typeof lunr.segmenter !== \"undefined\") {\n              const subsection = section.slice(index, until)\n              if (/^[MHIK]$/.test(lunr.segmenter.ctype_(subsection))) {\n                const segments = lunr.segmenter.segment(subsection)\n                for (let s = 0, l = 0; s < segments.length; s++) {\n\n                  /* Add block to section */\n                  table[block] ||= []\n                  table[block].push(\n                    start + index + l  << 12 |\n                    segments[s].length <<  2 |\n                    type\n                  )\n\n                  /* Add token with position */\n                  tokens.push(new lunr.Token(\n                    segments[s].toLowerCase(), {\n                      position: block << 20 | table[block].length - 1\n                    }\n                  ))\n\n                  /* Keep track of length */\n                  l += segments[s].length\n                }\n                return\n              }\n            }\n\n            /* Add block to section */\n            table[block].push(\n              start + index << 12 |\n              until - index <<  2 |\n              type\n            )\n\n            /* Add token with position */\n            tokens.push(new lunr.Token(\n              section.slice(index, until).toLowerCase(), {\n                position: block << 20 | table[block].length - 1\n              }\n            ))\n          })\n      }\n    })\n  }\n\n  /* Return tokens */\n  return tokens\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Visitor function\n *\n * @param value - String value\n *\n * @returns String term(s)\n */\ntype VisitorFn = (\n  value: string\n) => string | string[]\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Default transformation function\n *\n * 1. Trim excess whitespace from left and right.\n *\n * 2. Search for parts in quotation marks and prepend a `+` modifier to denote\n *    that the resulting document must contain all parts, converting the query\n *    to an `AND` query (as opposed to the default `OR` behavior). While users\n *    may expect parts enclosed in quotation marks to map to span queries, i.e.\n *    for which order is important, Lunr.js doesn't support them, so the best\n *    we can do is to convert the parts to an `AND` query.\n *\n * 3. Replace control characters which are not located at the beginning of the\n *    query or preceded by white space, or are not followed by a non-whitespace\n *    character or are at the end of the query string. Furthermore, filter\n *    unmatched quotation marks.\n *\n * 4. Split the query string at whitespace, then pass each part to the visitor\n *    function for tokenization, and append a wildcard to every resulting term\n *    that is not explicitly marked with a `+`, `-`, `~` or `^` modifier, since\n *    it ensures consistent and stable ranking when multiple terms are entered.\n *    Also, if a fuzzy or boost modifier are given, but no numeric value has\n *    been entered, default to 1 to not induce a query error.\n *\n * @param query - Query value\n * @param fn - Visitor function\n *\n * @returns Transformed query value\n */\nexport function transform(\n  query: string, fn: VisitorFn = term => term\n): string {\n  return query\n\n    /* => 1 */\n    .trim()\n\n    /* => 2 */\n    .split(/\"([^\"]+)\"/g)\n      .map((parts, index) => index & 1\n        ? parts.replace(/^\\b|^(?![^\\x00-\\x7F]|$)|\\s+/g, \" +\")\n        : parts\n      )\n      .join(\"\")\n\n    /* => 3 */\n    .replace(/\"|(?:^|\\s+)[*+\\-:^~]+(?=\\s+|$)/g, \"\")\n\n    /* => 4 */\n    .split(/\\s+/g)\n      .flatMap(fn)\n      .map(term => /([~^]$)/.test(term) ? `${term}1` : term)\n      .map(term => /(^[+-]|[~^]\\d+$)/.test(term) ? term : `${term}*`)\n      .join(\" \")\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { split } from \"../../internal\"\nimport { transform } from \"../transform\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search query clause\n */\nexport interface SearchQueryClause {\n  presence: lunr.Query.presence        /* Clause presence */\n  term: string                         /* Clause term */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search query terms\n */\nexport type SearchQueryTerms = Record<string, boolean>\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Transform search query\n *\n * This function lexes the given search query and applies the transformation\n * function to each term, preserving markup like `+` and `-` modifiers.\n *\n * @param query - Search query\n *\n * @returns Search query\n */\nexport function transformSearchQuery(\n  query: string\n): string {\n\n  /* Split query terms with tokenizer */\n  return transform(query, part => {\n    const terms: string[] = []\n\n    /* Initialize lexer and analyze part */\n    const lexer = new lunr.QueryLexer(part)\n    lexer.run()\n\n    /* Extract and tokenize term from lexeme */\n    for (const { type, str: term, start, end } of lexer.lexemes)\n      switch (type) {\n\n        /* Hack: remove colon - see https://bit.ly/3wD3T3I */\n        case \"FIELD\":\n          if (![\"title\", \"text\", \"tags\"].includes(term))\n            part = [\n              part.slice(0, end),\n              \" \",\n              part.slice(end + 1)\n            ].join(\"\")\n          break\n\n        /* Tokenize term */\n        case \"TERM\":\n          split(term, lunr.tokenizer.separator, (...range) => {\n            terms.push([\n              part.slice(0, start),\n              term.slice(...range),\n              part.slice(end)\n            ].join(\"\"))\n          })\n      }\n\n    /* Return terms */\n    return terms\n  })\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Parse a search query for analysis\n *\n * Lunr.js itself has a bug where it doesn't detect or remove wildcards for\n * query clauses, so we must do this here.\n *\n * @see https://bit.ly/3DpTGtz - GitHub issue\n *\n * @param value - Query value\n *\n * @returns Search query clauses\n */\nexport function parseSearchQuery(\n  value: string\n): SearchQueryClause[] {\n  const query  = new lunr.Query([\"title\", \"text\", \"tags\"])\n  const parser = new lunr.QueryParser(value, query)\n\n  /* Parse Search query */\n  parser.parse()\n  for (const clause of query.clauses) {\n    clause.usePipeline = true\n\n    /* Handle leading wildcard */\n    if (clause.term.startsWith(\"*\")) {\n      clause.wildcard = lunr.Query.wildcard.LEADING\n      clause.term = clause.term.slice(1)\n    }\n\n    /* Handle trailing wildcard */\n    if (clause.term.endsWith(\"*\")) {\n      clause.wildcard = lunr.Query.wildcard.TRAILING\n      clause.term = clause.term.slice(0, -1)\n    }\n  }\n\n  /* Return query clauses */\n  return query.clauses\n}\n\n/**\n * Analyze the search query clauses in regard to the search terms found\n *\n * @param query - Search query clauses\n * @param terms - Search terms\n *\n * @returns Search query terms\n */\nexport function getSearchQueryTerms(\n  query: SearchQueryClause[], terms: string[]\n): SearchQueryTerms {\n  const clauses = new Set<SearchQueryClause>(query)\n\n  /* Match query clauses against terms */\n  const result: SearchQueryTerms = {}\n  for (let t = 0; t < terms.length; t++)\n    for (const clause of clauses)\n      if (terms[t].startsWith(clause.term)) {\n        result[clause.term] = true\n        clauses.delete(clause)\n      }\n\n  /* Annotate unmatched non-stopword query clauses */\n  for (const clause of clauses)\n    if (lunr.stopWordFilter?.(clause.term))\n      result[clause.term] = false\n\n  /* Return query terms */\n  return result\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  SearchDocument,\n  SearchIndex,\n  SearchOptions,\n  setupSearchDocumentMap\n} from \"../config\"\nimport {\n  Position,\n  PositionTable,\n  highlight,\n  highlightAll,\n  tokenize\n} from \"../internal\"\nimport {\n  SearchQueryTerms,\n  getSearchQueryTerms,\n  parseSearchQuery,\n  transformSearchQuery\n} from \"../query\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search item\n */\nexport interface SearchItem\n  extends SearchDocument\n{\n  score: number                        /* Score (relevance) */\n  terms: SearchQueryTerms              /* Search query terms */\n}\n\n/**\n * Search result\n */\nexport interface SearchResult {\n  items: SearchItem[][]                /* Search items */\n  suggest?: string[]                   /* Search suggestions */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create field extractor factory\n *\n * @param table - Position table map\n *\n * @returns Extractor factory\n */\nfunction extractor(table: Map<string, PositionTable>) {\n  return (name: keyof SearchDocument) => {\n    return (doc: SearchDocument) => {\n      if (typeof doc[name] === \"undefined\")\n        return undefined\n\n      /* Compute identifier and initialize table */\n      const id = [doc.location, name].join(\":\")\n      table.set(id, lunr.tokenizer.table = [])\n\n      /* Return field value */\n      return doc[name]\n    }\n  }\n}\n\n/**\n * Compute the difference of two lists of strings\n *\n * @param a - 1st list of strings\n * @param b - 2nd list of strings\n *\n * @returns Difference\n */\nfunction difference(a: string[], b: string[]): string[] {\n  const [x, y] = [new Set(a), new Set(b)]\n  return [\n    ...new Set([...x].filter(value => !y.has(value)))\n  ]\n}\n\n/* ----------------------------------------------------------------------------\n * Class\n * ------------------------------------------------------------------------- */\n\n/**\n * Search index\n */\nexport class Search {\n\n  /**\n   * Search document map\n   */\n  protected map: Map<string, SearchDocument>\n\n  /**\n   * Search options\n   */\n  protected options: SearchOptions\n\n  /**\n   * The underlying Lunr.js search index\n   */\n  protected index: lunr.Index\n\n  /**\n   * Internal position table map\n   */\n  protected table: Map<string, PositionTable>\n\n  /**\n   * Create the search integration\n   *\n   * @param data - Search index\n   */\n  public constructor({ config, docs, options }: SearchIndex) {\n    const field = extractor(this.table = new Map())\n\n    /* Set up document map and options */\n    this.map = setupSearchDocumentMap(docs)\n    this.options = options\n\n    /* Set up document index */\n    this.index = lunr(function () {\n      this.metadataWhitelist = [\"position\"]\n      this.b(0)\n\n      /* Set up (multi-)language support */\n      if (config.lang.length === 1 && config.lang[0] !== \"en\") {\n        // @ts-expect-error - namespace indexing not supported\n        this.use(lunr[config.lang[0]])\n      } else if (config.lang.length > 1) {\n        this.use(lunr.multiLanguage(...config.lang))\n      }\n\n      /* Set up custom tokenizer (must be after language setup) */\n      this.tokenizer = tokenize as typeof lunr.tokenizer\n      lunr.tokenizer.separator = new RegExp(config.separator)\n\n      /* Set up custom segmenter, if loaded */\n      lunr.segmenter = \"TinySegmenter\" in lunr\n        ? new lunr.TinySegmenter()\n        : undefined\n\n      /* Compute functions to be removed from the pipeline */\n      const fns = difference([\n        \"trimmer\", \"stopWordFilter\", \"stemmer\"\n      ], config.pipeline)\n\n      /* Remove functions from the pipeline for registered languages */\n      for (const lang of config.lang.map(language => (\n        // @ts-expect-error - namespace indexing not supported\n        language === \"en\" ? lunr : lunr[language]\n      )))\n        for (const fn of fns) {\n          this.pipeline.remove(lang[fn])\n          this.searchPipeline.remove(lang[fn])\n        }\n\n      /* Set up index reference */\n      this.ref(\"location\")\n\n      /* Set up index fields */\n      this.field(\"title\", { boost: 1e3, extractor: field(\"title\") })\n      this.field(\"text\",  { boost: 1e0, extractor: field(\"text\") })\n      this.field(\"tags\",  { boost: 1e6, extractor: field(\"tags\") })\n\n      /* Add documents to index */\n      for (const doc of docs)\n        this.add(doc, { boost: doc.boost })\n    })\n  }\n\n  /**\n   * Search for matching documents\n   *\n   * @param query - Search query\n   *\n   * @returns Search result\n   */\n  public search(query: string): SearchResult {\n    query = transformSearchQuery(query)\n    if (!query)\n      return { items: [] }\n\n    /* Parse query to extract clauses for analysis */\n    const clauses = parseSearchQuery(query)\n      .filter(clause => (\n        clause.presence !== lunr.Query.presence.PROHIBITED\n      ))\n\n    /* Perform search and post-process results */\n    const groups = this.index.search(query)\n\n      /* Apply post-query boosts based on title and search query terms */\n      .reduce<SearchItem[]>((item, { ref, score, matchData }) => {\n        let doc = this.map.get(ref)\n        if (typeof doc !== \"undefined\") {\n\n          /* Shallow copy document */\n          doc = { ...doc }\n          if (doc.tags)\n            doc.tags = [...doc.tags]\n\n          /* Compute and analyze search query terms */\n          const terms = getSearchQueryTerms(\n            clauses,\n            Object.keys(matchData.metadata)\n          )\n\n          /* Highlight matches in fields */\n          for (const field of this.index.fields) {\n            if (typeof doc[field] === \"undefined\")\n              continue\n\n            /* Collect positions from matches */\n            const positions: Position[] = []\n            for (const match of Object.values(matchData.metadata))\n              if (typeof match[field] !== \"undefined\")\n                positions.push(...match[field].position)\n\n            /* Skip highlighting, if no positions were collected */\n            if (!positions.length)\n              continue\n\n            /* Load table and determine highlighting method */\n            const table = this.table.get([doc.location, field].join(\":\"))!\n            const fn = Array.isArray(doc[field])\n              ? highlightAll\n              : highlight\n\n            // @ts-expect-error - stop moaning, TypeScript!\n            doc[field] = fn(doc[field], table, positions)\n          }\n\n          /* Highlight title and text and apply post-query boosts */\n          const boost = +!doc.parent +\n            Object.values(terms)\n              .filter(t => t).length /\n            Object.keys(terms).length\n\n          /* Append item */\n          item.push({\n            ...doc,\n            score: score * (1 + boost ** 2),\n            terms\n          })\n        }\n        return item\n      }, [])\n\n      /* Sort search results again after applying boosts */\n      .sort((a, b) => b.score - a.score)\n\n      /* Group search results by article */\n      .reduce((items, result) => {\n        const doc = this.map.get(result.location)\n        if (typeof doc !== \"undefined\") {\n          const ref = doc.parent\n            ? doc.parent.location\n            : doc.location\n          items.set(ref, [...items.get(ref) || [], result])\n        }\n        return items\n      }, new Map<string, SearchItem[]>())\n\n    /* Ensure that every item set has an article */\n    for (const [ref, items] of groups)\n      if (!items.find(item => item.location === ref)) {\n        const doc = this.map.get(ref)!\n        items.push({ ...doc, score: 0, terms: {} })\n      }\n\n    /* Generate search suggestions, if desired */\n    let suggest: string[] | undefined\n    if (this.options.suggest) {\n      const titles = this.index.query(builder => {\n        for (const clause of clauses)\n          builder.term(clause.term, {\n            fields: [\"title\"],\n            presence: lunr.Query.presence.REQUIRED,\n            wildcard: lunr.Query.wildcard.TRAILING\n          })\n      })\n\n      /* Retrieve suggestions for best match */\n      suggest = titles.length\n        ? Object.keys(titles[0].matchData.metadata)\n        : []\n    }\n\n    /* Return search result */\n    return {\n      items: [...groups.values()],\n      ...typeof suggest !== \"undefined\" && { suggest }\n    }\n  }\n}\n"],
-  "mappings": 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+  "sourcesContent": ["/**\n * lunr - http://lunrjs.com - A bit like Solr, but much smaller and not as bright - 2.3.9\n * Copyright (C) 2020 Oliver Nightingale\n * @license MIT\n */\n\n;(function(){\n\n/**\n * A convenience function for configuring and constructing\n * a new lunr Index.\n *\n * A lunr.Builder instance is created and the pipeline setup\n * with a trimmer, stop word filter and stemmer.\n *\n * This builder object is yielded to the configuration function\n * that is passed as a parameter, allowing the list of fields\n * and other builder parameters to be customised.\n *\n * All documents _must_ be added within the passed config function.\n *\n * @example\n * var idx = lunr(function () {\n *   this.field('title')\n *   this.field('body')\n *   this.ref('id')\n *\n *   documents.forEach(function (doc) {\n *     this.add(doc)\n *   }, this)\n * })\n *\n * @see {@link lunr.Builder}\n * @see {@link lunr.Pipeline}\n * @see {@link lunr.trimmer}\n * @see {@link lunr.stopWordFilter}\n * @see {@link lunr.stemmer}\n * @namespace {function} lunr\n */\nvar lunr = function (config) {\n  var builder = new lunr.Builder\n\n  builder.pipeline.add(\n    lunr.trimmer,\n    lunr.stopWordFilter,\n    lunr.stemmer\n  )\n\n  builder.searchPipeline.add(\n    lunr.stemmer\n  )\n\n  config.call(builder, builder)\n  return builder.build()\n}\n\nlunr.version = \"2.3.9\"\n/*!\n * lunr.utils\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A namespace containing utils for the rest of the lunr library\n * @namespace lunr.utils\n */\nlunr.utils = {}\n\n/**\n * Print a warning message to the console.\n *\n * @param {String} message The message to be printed.\n * @memberOf lunr.utils\n * @function\n */\nlunr.utils.warn = (function (global) {\n  /* eslint-disable no-console */\n  return function (message) {\n    if (global.console && console.warn) {\n      console.warn(message)\n    }\n  }\n  /* eslint-enable no-console */\n})(this)\n\n/**\n * Convert an object to a string.\n *\n * In the case of `null` and `undefined` the function returns\n * the empty string, in all other cases the result of calling\n * `toString` on the passed object is returned.\n *\n * @param {Any} obj The object to convert to a string.\n * @return {String} string representation of the passed object.\n * @memberOf lunr.utils\n */\nlunr.utils.asString = function (obj) {\n  if (obj === void 0 || obj === null) {\n    return \"\"\n  } else {\n    return obj.toString()\n  }\n}\n\n/**\n * Clones an object.\n *\n * Will create a copy of an existing object such that any mutations\n * on the copy cannot affect the original.\n *\n * Only shallow objects are supported, passing a nested object to this\n * function will cause a TypeError.\n *\n * Objects with primitives, and arrays of primitives are supported.\n *\n * @param {Object} obj The object to clone.\n * @return {Object} a clone of the passed object.\n * @throws {TypeError} when a nested object is passed.\n * @memberOf Utils\n */\nlunr.utils.clone = function (obj) {\n  if (obj === null || obj === undefined) {\n    return obj\n  }\n\n  var clone = Object.create(null),\n      keys = Object.keys(obj)\n\n  for (var i = 0; i < keys.length; i++) {\n    var key = keys[i],\n        val = obj[key]\n\n    if (Array.isArray(val)) {\n      clone[key] = val.slice()\n      continue\n    }\n\n    if (typeof val === 'string' ||\n        typeof val === 'number' ||\n        typeof val === 'boolean') {\n      clone[key] = val\n      continue\n    }\n\n    throw new TypeError(\"clone is not deep and does not support nested objects\")\n  }\n\n  return clone\n}\nlunr.FieldRef = function (docRef, fieldName, stringValue) {\n  this.docRef = docRef\n  this.fieldName = fieldName\n  this._stringValue = stringValue\n}\n\nlunr.FieldRef.joiner = \"/\"\n\nlunr.FieldRef.fromString = function (s) {\n  var n = s.indexOf(lunr.FieldRef.joiner)\n\n  if (n === -1) {\n    throw \"malformed field ref string\"\n  }\n\n  var fieldRef = s.slice(0, n),\n      docRef = s.slice(n + 1)\n\n  return new lunr.FieldRef (docRef, fieldRef, s)\n}\n\nlunr.FieldRef.prototype.toString = function () {\n  if (this._stringValue == undefined) {\n    this._stringValue = this.fieldName + lunr.FieldRef.joiner + this.docRef\n  }\n\n  return this._stringValue\n}\n/*!\n * lunr.Set\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A lunr set.\n *\n * @constructor\n */\nlunr.Set = function (elements) {\n  this.elements = Object.create(null)\n\n  if (elements) {\n    this.length = elements.length\n\n    for (var i = 0; i < this.length; i++) {\n      this.elements[elements[i]] = true\n    }\n  } else {\n    this.length = 0\n  }\n}\n\n/**\n * A complete set that contains all elements.\n *\n * @static\n * @readonly\n * @type {lunr.Set}\n */\nlunr.Set.complete = {\n  intersect: function (other) {\n    return other\n  },\n\n  union: function () {\n    return this\n  },\n\n  contains: function () {\n    return true\n  }\n}\n\n/**\n * An empty set that contains no elements.\n *\n * @static\n * @readonly\n * @type {lunr.Set}\n */\nlunr.Set.empty = {\n  intersect: function () {\n    return this\n  },\n\n  union: function (other) {\n    return other\n  },\n\n  contains: function () {\n    return false\n  }\n}\n\n/**\n * Returns true if this set contains the specified object.\n *\n * @param {object} object - Object whose presence in this set is to be tested.\n * @returns {boolean} - True if this set contains the specified object.\n */\nlunr.Set.prototype.contains = function (object) {\n  return !!this.elements[object]\n}\n\n/**\n * Returns a new set containing only the elements that are present in both\n * this set and the specified set.\n *\n * @param {lunr.Set} other - set to intersect with this set.\n * @returns {lunr.Set} a new set that is the intersection of this and the specified set.\n */\n\nlunr.Set.prototype.intersect = function (other) {\n  var a, b, elements, intersection = []\n\n  if (other === lunr.Set.complete) {\n    return this\n  }\n\n  if (other === lunr.Set.empty) {\n    return other\n  }\n\n  if (this.length < other.length) {\n    a = this\n    b = other\n  } else {\n    a = other\n    b = this\n  }\n\n  elements = Object.keys(a.elements)\n\n  for (var i = 0; i < elements.length; i++) {\n    var element = elements[i]\n    if (element in b.elements) {\n      intersection.push(element)\n    }\n  }\n\n  return new lunr.Set (intersection)\n}\n\n/**\n * Returns a new set combining the elements of this and the specified set.\n *\n * @param {lunr.Set} other - set to union with this set.\n * @return {lunr.Set} a new set that is the union of this and the specified set.\n */\n\nlunr.Set.prototype.union = function (other) {\n  if (other === lunr.Set.complete) {\n    return lunr.Set.complete\n  }\n\n  if (other === lunr.Set.empty) {\n    return this\n  }\n\n  return new lunr.Set(Object.keys(this.elements).concat(Object.keys(other.elements)))\n}\n/**\n * A function to calculate the inverse document frequency for\n * a posting. This is shared between the builder and the index\n *\n * @private\n * @param {object} posting - The posting for a given term\n * @param {number} documentCount - The total number of documents.\n */\nlunr.idf = function (posting, documentCount) {\n  var documentsWithTerm = 0\n\n  for (var fieldName in posting) {\n    if (fieldName == '_index') continue // Ignore the term index, its not a field\n    documentsWithTerm += Object.keys(posting[fieldName]).length\n  }\n\n  var x = (documentCount - documentsWithTerm + 0.5) / (documentsWithTerm + 0.5)\n\n  return Math.log(1 + Math.abs(x))\n}\n\n/**\n * A token wraps a string representation of a token\n * as it is passed through the text processing pipeline.\n *\n * @constructor\n * @param {string} [str=''] - The string token being wrapped.\n * @param {object} [metadata={}] - Metadata associated with this token.\n */\nlunr.Token = function (str, metadata) {\n  this.str = str || \"\"\n  this.metadata = metadata || {}\n}\n\n/**\n * Returns the token string that is being wrapped by this object.\n *\n * @returns {string}\n */\nlunr.Token.prototype.toString = function () {\n  return this.str\n}\n\n/**\n * A token update function is used when updating or optionally\n * when cloning a token.\n *\n * @callback lunr.Token~updateFunction\n * @param {string} str - The string representation of the token.\n * @param {Object} metadata - All metadata associated with this token.\n */\n\n/**\n * Applies the given function to the wrapped string token.\n *\n * @example\n * token.update(function (str, metadata) {\n *   return str.toUpperCase()\n * })\n *\n * @param {lunr.Token~updateFunction} fn - A function to apply to the token string.\n * @returns {lunr.Token}\n */\nlunr.Token.prototype.update = function (fn) {\n  this.str = fn(this.str, this.metadata)\n  return this\n}\n\n/**\n * Creates a clone of this token. Optionally a function can be\n * applied to the cloned token.\n *\n * @param {lunr.Token~updateFunction} [fn] - An optional function to apply to the cloned token.\n * @returns {lunr.Token}\n */\nlunr.Token.prototype.clone = function (fn) {\n  fn = fn || function (s) { return s }\n  return new lunr.Token (fn(this.str, this.metadata), this.metadata)\n}\n/*!\n * lunr.tokenizer\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A function for splitting a string into tokens ready to be inserted into\n * the search index. Uses `lunr.tokenizer.separator` to split strings, change\n * the value of this property to change how strings are split into tokens.\n *\n * This tokenizer will convert its parameter to a string by calling `toString` and\n * then will split this string on the character in `lunr.tokenizer.separator`.\n * Arrays will have their elements converted to strings and wrapped in a lunr.Token.\n *\n * Optional metadata can be passed to the tokenizer, this metadata will be cloned and\n * added as metadata to every token that is created from the object to be tokenized.\n *\n * @static\n * @param {?(string|object|object[])} obj - The object to convert into tokens\n * @param {?object} metadata - Optional metadata to associate with every token\n * @returns {lunr.Token[]}\n * @see {@link lunr.Pipeline}\n */\nlunr.tokenizer = function (obj, metadata) {\n  if (obj == null || obj == undefined) {\n    return []\n  }\n\n  if (Array.isArray(obj)) {\n    return obj.map(function (t) {\n      return new lunr.Token(\n        lunr.utils.asString(t).toLowerCase(),\n        lunr.utils.clone(metadata)\n      )\n    })\n  }\n\n  var str = obj.toString().toLowerCase(),\n      len = str.length,\n      tokens = []\n\n  for (var sliceEnd = 0, sliceStart = 0; sliceEnd <= len; sliceEnd++) {\n    var char = str.charAt(sliceEnd),\n        sliceLength = sliceEnd - sliceStart\n\n    if ((char.match(lunr.tokenizer.separator) || sliceEnd == len)) {\n\n      if (sliceLength > 0) {\n        var tokenMetadata = lunr.utils.clone(metadata) || {}\n        tokenMetadata[\"position\"] = [sliceStart, sliceLength]\n        tokenMetadata[\"index\"] = tokens.length\n\n        tokens.push(\n          new lunr.Token (\n            str.slice(sliceStart, sliceEnd),\n            tokenMetadata\n          )\n        )\n      }\n\n      sliceStart = sliceEnd + 1\n    }\n\n  }\n\n  return tokens\n}\n\n/**\n * The separator used to split a string into tokens. Override this property to change the behaviour of\n * `lunr.tokenizer` behaviour when tokenizing strings. By default this splits on whitespace and hyphens.\n *\n * @static\n * @see lunr.tokenizer\n */\nlunr.tokenizer.separator = /[\\s\\-]+/\n/*!\n * lunr.Pipeline\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.Pipelines maintain an ordered list of functions to be applied to all\n * tokens in documents entering the search index and queries being ran against\n * the index.\n *\n * An instance of lunr.Index created with the lunr shortcut will contain a\n * pipeline with a stop word filter and an English language stemmer. Extra\n * functions can be added before or after either of these functions or these\n * default functions can be removed.\n *\n * When run the pipeline will call each function in turn, passing a token, the\n * index of that token in the original list of all tokens and finally a list of\n * all the original tokens.\n *\n * The output of functions in the pipeline will be passed to the next function\n * in the pipeline. To exclude a token from entering the index the function\n * should return undefined, the rest of the pipeline will not be called with\n * this token.\n *\n * For serialisation of pipelines to work, all functions used in an instance of\n * a pipeline should be registered with lunr.Pipeline. Registered functions can\n * then be loaded. If trying to load a serialised pipeline that uses functions\n * that are not registered an error will be thrown.\n *\n * If not planning on serialising the pipeline then registering pipeline functions\n * is not necessary.\n *\n * @constructor\n */\nlunr.Pipeline = function () {\n  this._stack = []\n}\n\nlunr.Pipeline.registeredFunctions = Object.create(null)\n\n/**\n * A pipeline function maps lunr.Token to lunr.Token. A lunr.Token contains the token\n * string as well as all known metadata. A pipeline function can mutate the token string\n * or mutate (or add) metadata for a given token.\n *\n * A pipeline function can indicate that the passed token should be discarded by returning\n * null, undefined or an empty string. This token will not be passed to any downstream pipeline\n * functions and will not be added to the index.\n *\n * Multiple tokens can be returned by returning an array of tokens. Each token will be passed\n * to any downstream pipeline functions and all will returned tokens will be added to the index.\n *\n * Any number of pipeline functions may be chained together using a lunr.Pipeline.\n *\n * @interface lunr.PipelineFunction\n * @param {lunr.Token} token - A token from the document being processed.\n * @param {number} i - The index of this token in the complete list of tokens for this document/field.\n * @param {lunr.Token[]} tokens - All tokens for this document/field.\n * @returns {(?lunr.Token|lunr.Token[])}\n */\n\n/**\n * Register a function with the pipeline.\n *\n * Functions that are used in the pipeline should be registered if the pipeline\n * needs to be serialised, or a serialised pipeline needs to be loaded.\n *\n * Registering a function does not add it to a pipeline, functions must still be\n * added to instances of the pipeline for them to be used when running a pipeline.\n *\n * @param {lunr.PipelineFunction} fn - The function to check for.\n * @param {String} label - The label to register this function with\n */\nlunr.Pipeline.registerFunction = function (fn, label) {\n  if (label in this.registeredFunctions) {\n    lunr.utils.warn('Overwriting existing registered function: ' + label)\n  }\n\n  fn.label = label\n  lunr.Pipeline.registeredFunctions[fn.label] = fn\n}\n\n/**\n * Warns if the function is not registered as a Pipeline function.\n *\n * @param {lunr.PipelineFunction} fn - The function to check for.\n * @private\n */\nlunr.Pipeline.warnIfFunctionNotRegistered = function (fn) {\n  var isRegistered = fn.label && (fn.label in this.registeredFunctions)\n\n  if (!isRegistered) {\n    lunr.utils.warn('Function is not registered with pipeline. This may cause problems when serialising the index.\\n', fn)\n  }\n}\n\n/**\n * Loads a previously serialised pipeline.\n *\n * All functions to be loaded must already be registered with lunr.Pipeline.\n * If any function from the serialised data has not been registered then an\n * error will be thrown.\n *\n * @param {Object} serialised - The serialised pipeline to load.\n * @returns {lunr.Pipeline}\n */\nlunr.Pipeline.load = function (serialised) {\n  var pipeline = new lunr.Pipeline\n\n  serialised.forEach(function (fnName) {\n    var fn = lunr.Pipeline.registeredFunctions[fnName]\n\n    if (fn) {\n      pipeline.add(fn)\n    } else {\n      throw new Error('Cannot load unregistered function: ' + fnName)\n    }\n  })\n\n  return pipeline\n}\n\n/**\n * Adds new functions to the end of the pipeline.\n *\n * Logs a warning if the function has not been registered.\n *\n * @param {lunr.PipelineFunction[]} functions - Any number of functions to add to the pipeline.\n */\nlunr.Pipeline.prototype.add = function () {\n  var fns = Array.prototype.slice.call(arguments)\n\n  fns.forEach(function (fn) {\n    lunr.Pipeline.warnIfFunctionNotRegistered(fn)\n    this._stack.push(fn)\n  }, this)\n}\n\n/**\n * Adds a single function after a function that already exists in the\n * pipeline.\n *\n * Logs a warning if the function has not been registered.\n *\n * @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline.\n * @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline.\n */\nlunr.Pipeline.prototype.after = function (existingFn, newFn) {\n  lunr.Pipeline.warnIfFunctionNotRegistered(newFn)\n\n  var pos = this._stack.indexOf(existingFn)\n  if (pos == -1) {\n    throw new Error('Cannot find existingFn')\n  }\n\n  pos = pos + 1\n  this._stack.splice(pos, 0, newFn)\n}\n\n/**\n * Adds a single function before a function that already exists in the\n * pipeline.\n *\n * Logs a warning if the function has not been registered.\n *\n * @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline.\n * @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline.\n */\nlunr.Pipeline.prototype.before = function (existingFn, newFn) {\n  lunr.Pipeline.warnIfFunctionNotRegistered(newFn)\n\n  var pos = this._stack.indexOf(existingFn)\n  if (pos == -1) {\n    throw new Error('Cannot find existingFn')\n  }\n\n  this._stack.splice(pos, 0, newFn)\n}\n\n/**\n * Removes a function from the pipeline.\n *\n * @param {lunr.PipelineFunction} fn The function to remove from the pipeline.\n */\nlunr.Pipeline.prototype.remove = function (fn) {\n  var pos = this._stack.indexOf(fn)\n  if (pos == -1) {\n    return\n  }\n\n  this._stack.splice(pos, 1)\n}\n\n/**\n * Runs the current list of functions that make up the pipeline against the\n * passed tokens.\n *\n * @param {Array} tokens The tokens to run through the pipeline.\n * @returns {Array}\n */\nlunr.Pipeline.prototype.run = function (tokens) {\n  var stackLength = this._stack.length\n\n  for (var i = 0; i < stackLength; i++) {\n    var fn = this._stack[i]\n    var memo = []\n\n    for (var j = 0; j < tokens.length; j++) {\n      var result = fn(tokens[j], j, tokens)\n\n      if (result === null || result === void 0 || result === '') continue\n\n      if (Array.isArray(result)) {\n        for (var k = 0; k < result.length; k++) {\n          memo.push(result[k])\n        }\n      } else {\n        memo.push(result)\n      }\n    }\n\n    tokens = memo\n  }\n\n  return tokens\n}\n\n/**\n * Convenience method for passing a string through a pipeline and getting\n * strings out. This method takes care of wrapping the passed string in a\n * token and mapping the resulting tokens back to strings.\n *\n * @param {string} str - The string to pass through the pipeline.\n * @param {?object} metadata - Optional metadata to associate with the token\n * passed to the pipeline.\n * @returns {string[]}\n */\nlunr.Pipeline.prototype.runString = function (str, metadata) {\n  var token = new lunr.Token (str, metadata)\n\n  return this.run([token]).map(function (t) {\n    return t.toString()\n  })\n}\n\n/**\n * Resets the pipeline by removing any existing processors.\n *\n */\nlunr.Pipeline.prototype.reset = function () {\n  this._stack = []\n}\n\n/**\n * Returns a representation of the pipeline ready for serialisation.\n *\n * Logs a warning if the function has not been registered.\n *\n * @returns {Array}\n */\nlunr.Pipeline.prototype.toJSON = function () {\n  return this._stack.map(function (fn) {\n    lunr.Pipeline.warnIfFunctionNotRegistered(fn)\n\n    return fn.label\n  })\n}\n/*!\n * lunr.Vector\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A vector is used to construct the vector space of documents and queries. These\n * vectors support operations to determine the similarity between two documents or\n * a document and a query.\n *\n * Normally no parameters are required for initializing a vector, but in the case of\n * loading a previously dumped vector the raw elements can be provided to the constructor.\n *\n * For performance reasons vectors are implemented with a flat array, where an elements\n * index is immediately followed by its value. E.g. [index, value, index, value]. This\n * allows the underlying array to be as sparse as possible and still offer decent\n * performance when being used for vector calculations.\n *\n * @constructor\n * @param {Number[]} [elements] - The flat list of element index and element value pairs.\n */\nlunr.Vector = function (elements) {\n  this._magnitude = 0\n  this.elements = elements || []\n}\n\n\n/**\n * Calculates the position within the vector to insert a given index.\n *\n * This is used internally by insert and upsert. If there are duplicate indexes then\n * the position is returned as if the value for that index were to be updated, but it\n * is the callers responsibility to check whether there is a duplicate at that index\n *\n * @param {Number} insertIdx - The index at which the element should be inserted.\n * @returns {Number}\n */\nlunr.Vector.prototype.positionForIndex = function (index) {\n  // For an empty vector the tuple can be inserted at the beginning\n  if (this.elements.length == 0) {\n    return 0\n  }\n\n  var start = 0,\n      end = this.elements.length / 2,\n      sliceLength = end - start,\n      pivotPoint = Math.floor(sliceLength / 2),\n      pivotIndex = this.elements[pivotPoint * 2]\n\n  while (sliceLength > 1) {\n    if (pivotIndex < index) {\n      start = pivotPoint\n    }\n\n    if (pivotIndex > index) {\n      end = pivotPoint\n    }\n\n    if (pivotIndex == index) {\n      break\n    }\n\n    sliceLength = end - start\n    pivotPoint = start + Math.floor(sliceLength / 2)\n    pivotIndex = this.elements[pivotPoint * 2]\n  }\n\n  if (pivotIndex == index) {\n    return pivotPoint * 2\n  }\n\n  if (pivotIndex > index) {\n    return pivotPoint * 2\n  }\n\n  if (pivotIndex < index) {\n    return (pivotPoint + 1) * 2\n  }\n}\n\n/**\n * Inserts an element at an index within the vector.\n *\n * Does not allow duplicates, will throw an error if there is already an entry\n * for this index.\n *\n * @param {Number} insertIdx - The index at which the element should be inserted.\n * @param {Number} val - The value to be inserted into the vector.\n */\nlunr.Vector.prototype.insert = function (insertIdx, val) {\n  this.upsert(insertIdx, val, function () {\n    throw \"duplicate index\"\n  })\n}\n\n/**\n * Inserts or updates an existing index within the vector.\n *\n * @param {Number} insertIdx - The index at which the element should be inserted.\n * @param {Number} val - The value to be inserted into the vector.\n * @param {function} fn - A function that is called for updates, the existing value and the\n * requested value are passed as arguments\n */\nlunr.Vector.prototype.upsert = function (insertIdx, val, fn) {\n  this._magnitude = 0\n  var position = this.positionForIndex(insertIdx)\n\n  if (this.elements[position] == insertIdx) {\n    this.elements[position + 1] = fn(this.elements[position + 1], val)\n  } else {\n    this.elements.splice(position, 0, insertIdx, val)\n  }\n}\n\n/**\n * Calculates the magnitude of this vector.\n *\n * @returns {Number}\n */\nlunr.Vector.prototype.magnitude = function () {\n  if (this._magnitude) return this._magnitude\n\n  var sumOfSquares = 0,\n      elementsLength = this.elements.length\n\n  for (var i = 1; i < elementsLength; i += 2) {\n    var val = this.elements[i]\n    sumOfSquares += val * val\n  }\n\n  return this._magnitude = Math.sqrt(sumOfSquares)\n}\n\n/**\n * Calculates the dot product of this vector and another vector.\n *\n * @param {lunr.Vector} otherVector - The vector to compute the dot product with.\n * @returns {Number}\n */\nlunr.Vector.prototype.dot = function (otherVector) {\n  var dotProduct = 0,\n      a = this.elements, b = otherVector.elements,\n      aLen = a.length, bLen = b.length,\n      aVal = 0, bVal = 0,\n      i = 0, j = 0\n\n  while (i < aLen && j < bLen) {\n    aVal = a[i], bVal = b[j]\n    if (aVal < bVal) {\n      i += 2\n    } else if (aVal > bVal) {\n      j += 2\n    } else if (aVal == bVal) {\n      dotProduct += a[i + 1] * b[j + 1]\n      i += 2\n      j += 2\n    }\n  }\n\n  return dotProduct\n}\n\n/**\n * Calculates the similarity between this vector and another vector.\n *\n * @param {lunr.Vector} otherVector - The other vector to calculate the\n * similarity with.\n * @returns {Number}\n */\nlunr.Vector.prototype.similarity = function (otherVector) {\n  return this.dot(otherVector) / this.magnitude() || 0\n}\n\n/**\n * Converts the vector to an array of the elements within the vector.\n *\n * @returns {Number[]}\n */\nlunr.Vector.prototype.toArray = function () {\n  var output = new Array (this.elements.length / 2)\n\n  for (var i = 1, j = 0; i < this.elements.length; i += 2, j++) {\n    output[j] = this.elements[i]\n  }\n\n  return output\n}\n\n/**\n * A JSON serializable representation of the vector.\n *\n * @returns {Number[]}\n */\nlunr.Vector.prototype.toJSON = function () {\n  return this.elements\n}\n/* eslint-disable */\n/*!\n * lunr.stemmer\n * Copyright (C) 2020 Oliver Nightingale\n * Includes code from - http://tartarus.org/~martin/PorterStemmer/js.txt\n */\n\n/**\n * lunr.stemmer is an english language stemmer, this is a JavaScript\n * implementation of the PorterStemmer taken from http://tartarus.org/~martin\n *\n * @static\n * @implements {lunr.PipelineFunction}\n * @param {lunr.Token} token - The string to stem\n * @returns {lunr.Token}\n * @see {@link lunr.Pipeline}\n * @function\n */\nlunr.stemmer = (function(){\n  var step2list = {\n      \"ational\" : \"ate\",\n      \"tional\" : \"tion\",\n      \"enci\" : \"ence\",\n      \"anci\" : \"ance\",\n      \"izer\" : \"ize\",\n      \"bli\" : \"ble\",\n      \"alli\" : \"al\",\n      \"entli\" : \"ent\",\n      \"eli\" : \"e\",\n      \"ousli\" : \"ous\",\n      \"ization\" : \"ize\",\n      \"ation\" : \"ate\",\n      \"ator\" : \"ate\",\n      \"alism\" : \"al\",\n      \"iveness\" : \"ive\",\n      \"fulness\" : \"ful\",\n      \"ousness\" : \"ous\",\n      \"aliti\" : \"al\",\n      \"iviti\" : \"ive\",\n      \"biliti\" : \"ble\",\n      \"logi\" : \"log\"\n    },\n\n    step3list = {\n      \"icate\" : \"ic\",\n      \"ative\" : \"\",\n      \"alize\" : \"al\",\n      \"iciti\" : \"ic\",\n      \"ical\" : \"ic\",\n      \"ful\" : \"\",\n      \"ness\" : \"\"\n    },\n\n    c = \"[^aeiou]\",          // consonant\n    v = \"[aeiouy]\",          // vowel\n    C = c + \"[^aeiouy]*\",    // consonant sequence\n    V = v + \"[aeiou]*\",      // vowel sequence\n\n    mgr0 = \"^(\" + C + \")?\" + V + C,               // [C]VC... is m>0\n    meq1 = \"^(\" + C + \")?\" + V + C + \"(\" + V + \")?$\",  // [C]VC[V] is m=1\n    mgr1 = \"^(\" + C + \")?\" + V + C + V + C,       // [C]VCVC... is m>1\n    s_v = \"^(\" + C + \")?\" + v;                   // vowel in stem\n\n  var re_mgr0 = new RegExp(mgr0);\n  var re_mgr1 = new RegExp(mgr1);\n  var re_meq1 = new RegExp(meq1);\n  var re_s_v = new RegExp(s_v);\n\n  var re_1a = /^(.+?)(ss|i)es$/;\n  var re2_1a = /^(.+?)([^s])s$/;\n  var re_1b = /^(.+?)eed$/;\n  var re2_1b = /^(.+?)(ed|ing)$/;\n  var re_1b_2 = /.$/;\n  var re2_1b_2 = /(at|bl|iz)$/;\n  var re3_1b_2 = new RegExp(\"([^aeiouylsz])\\\\1$\");\n  var re4_1b_2 = new RegExp(\"^\" + C + v + \"[^aeiouwxy]$\");\n\n  var re_1c = /^(.+?[^aeiou])y$/;\n  var re_2 = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;\n\n  var re_3 = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;\n\n  var re_4 = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;\n  var re2_4 = /^(.+?)(s|t)(ion)$/;\n\n  var re_5 = /^(.+?)e$/;\n  var re_5_1 = /ll$/;\n  var re3_5 = new RegExp(\"^\" + C + v + \"[^aeiouwxy]$\");\n\n  var porterStemmer = function porterStemmer(w) {\n    var stem,\n      suffix,\n      firstch,\n      re,\n      re2,\n      re3,\n      re4;\n\n    if (w.length < 3) { return w; }\n\n    firstch = w.substr(0,1);\n    if (firstch == \"y\") {\n      w = firstch.toUpperCase() + w.substr(1);\n    }\n\n    // Step 1a\n    re = re_1a\n    re2 = re2_1a;\n\n    if (re.test(w)) { w = w.replace(re,\"$1$2\"); }\n    else if (re2.test(w)) { w = w.replace(re2,\"$1$2\"); }\n\n    // Step 1b\n    re = re_1b;\n    re2 = re2_1b;\n    if (re.test(w)) {\n      var fp = re.exec(w);\n      re = re_mgr0;\n      if (re.test(fp[1])) {\n        re = re_1b_2;\n        w = w.replace(re,\"\");\n      }\n    } else if (re2.test(w)) {\n      var fp = re2.exec(w);\n      stem = fp[1];\n      re2 = re_s_v;\n      if (re2.test(stem)) {\n        w = stem;\n        re2 = re2_1b_2;\n        re3 = re3_1b_2;\n        re4 = re4_1b_2;\n        if (re2.test(w)) { w = w + \"e\"; }\n        else if (re3.test(w)) { re = re_1b_2; w = w.replace(re,\"\"); }\n        else if (re4.test(w)) { w = w + \"e\"; }\n      }\n    }\n\n    // Step 1c - replace suffix y or Y by i if preceded by a non-vowel which is not the first letter of the word (so cry -> cri, by -> by, say -> say)\n    re = re_1c;\n    if (re.test(w)) {\n      var fp = re.exec(w);\n      stem = fp[1];\n      w = stem + \"i\";\n    }\n\n    // Step 2\n    re = re_2;\n    if (re.test(w)) {\n      var fp = re.exec(w);\n      stem = fp[1];\n      suffix = fp[2];\n      re = re_mgr0;\n      if (re.test(stem)) {\n        w = stem + step2list[suffix];\n      }\n    }\n\n    // Step 3\n    re = re_3;\n    if (re.test(w)) {\n      var fp = re.exec(w);\n      stem = fp[1];\n      suffix = fp[2];\n      re = re_mgr0;\n      if (re.test(stem)) {\n        w = stem + step3list[suffix];\n      }\n    }\n\n    // Step 4\n    re = re_4;\n    re2 = re2_4;\n    if (re.test(w)) {\n      var fp = re.exec(w);\n      stem = fp[1];\n      re = re_mgr1;\n      if (re.test(stem)) {\n        w = stem;\n      }\n    } else if (re2.test(w)) {\n      var fp = re2.exec(w);\n      stem = fp[1] + fp[2];\n      re2 = re_mgr1;\n      if (re2.test(stem)) {\n        w = stem;\n      }\n    }\n\n    // Step 5\n    re = re_5;\n    if (re.test(w)) {\n      var fp = re.exec(w);\n      stem = fp[1];\n      re = re_mgr1;\n      re2 = re_meq1;\n      re3 = re3_5;\n      if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) {\n        w = stem;\n      }\n    }\n\n    re = re_5_1;\n    re2 = re_mgr1;\n    if (re.test(w) && re2.test(w)) {\n      re = re_1b_2;\n      w = w.replace(re,\"\");\n    }\n\n    // and turn initial Y back to y\n\n    if (firstch == \"y\") {\n      w = firstch.toLowerCase() + w.substr(1);\n    }\n\n    return w;\n  };\n\n  return function (token) {\n    return token.update(porterStemmer);\n  }\n})();\n\nlunr.Pipeline.registerFunction(lunr.stemmer, 'stemmer')\n/*!\n * lunr.stopWordFilter\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.generateStopWordFilter builds a stopWordFilter function from the provided\n * list of stop words.\n *\n * The built in lunr.stopWordFilter is built using this generator and can be used\n * to generate custom stopWordFilters for applications or non English languages.\n *\n * @function\n * @param {Array} token The token to pass through the filter\n * @returns {lunr.PipelineFunction}\n * @see lunr.Pipeline\n * @see lunr.stopWordFilter\n */\nlunr.generateStopWordFilter = function (stopWords) {\n  var words = stopWords.reduce(function (memo, stopWord) {\n    memo[stopWord] = stopWord\n    return memo\n  }, {})\n\n  return function (token) {\n    if (token && words[token.toString()] !== token.toString()) return token\n  }\n}\n\n/**\n * lunr.stopWordFilter is an English language stop word list filter, any words\n * contained in the list will not be passed through the filter.\n *\n * This is intended to be used in the Pipeline. If the token does not pass the\n * filter then undefined will be returned.\n *\n * @function\n * @implements {lunr.PipelineFunction}\n * @params {lunr.Token} token - A token to check for being a stop word.\n * @returns {lunr.Token}\n * @see {@link lunr.Pipeline}\n */\nlunr.stopWordFilter = lunr.generateStopWordFilter([\n  'a',\n  'able',\n  'about',\n  'across',\n  'after',\n  'all',\n  'almost',\n  'also',\n  'am',\n  'among',\n  'an',\n  'and',\n  'any',\n  'are',\n  'as',\n  'at',\n  'be',\n  'because',\n  'been',\n  'but',\n  'by',\n  'can',\n  'cannot',\n  'could',\n  'dear',\n  'did',\n  'do',\n  'does',\n  'either',\n  'else',\n  'ever',\n  'every',\n  'for',\n  'from',\n  'get',\n  'got',\n  'had',\n  'has',\n  'have',\n  'he',\n  'her',\n  'hers',\n  'him',\n  'his',\n  'how',\n  'however',\n  'i',\n  'if',\n  'in',\n  'into',\n  'is',\n  'it',\n  'its',\n  'just',\n  'least',\n  'let',\n  'like',\n  'likely',\n  'may',\n  'me',\n  'might',\n  'most',\n  'must',\n  'my',\n  'neither',\n  'no',\n  'nor',\n  'not',\n  'of',\n  'off',\n  'often',\n  'on',\n  'only',\n  'or',\n  'other',\n  'our',\n  'own',\n  'rather',\n  'said',\n  'say',\n  'says',\n  'she',\n  'should',\n  'since',\n  'so',\n  'some',\n  'than',\n  'that',\n  'the',\n  'their',\n  'them',\n  'then',\n  'there',\n  'these',\n  'they',\n  'this',\n  'tis',\n  'to',\n  'too',\n  'twas',\n  'us',\n  'wants',\n  'was',\n  'we',\n  'were',\n  'what',\n  'when',\n  'where',\n  'which',\n  'while',\n  'who',\n  'whom',\n  'why',\n  'will',\n  'with',\n  'would',\n  'yet',\n  'you',\n  'your'\n])\n\nlunr.Pipeline.registerFunction(lunr.stopWordFilter, 'stopWordFilter')\n/*!\n * lunr.trimmer\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.trimmer is a pipeline function for trimming non word\n * characters from the beginning and end of tokens before they\n * enter the index.\n *\n * This implementation may not work correctly for non latin\n * characters and should either be removed or adapted for use\n * with languages with non-latin characters.\n *\n * @static\n * @implements {lunr.PipelineFunction}\n * @param {lunr.Token} token The token to pass through the filter\n * @returns {lunr.Token}\n * @see lunr.Pipeline\n */\nlunr.trimmer = function (token) {\n  return token.update(function (s) {\n    return s.replace(/^\\W+/, '').replace(/\\W+$/, '')\n  })\n}\n\nlunr.Pipeline.registerFunction(lunr.trimmer, 'trimmer')\n/*!\n * lunr.TokenSet\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A token set is used to store the unique list of all tokens\n * within an index. Token sets are also used to represent an\n * incoming query to the index, this query token set and index\n * token set are then intersected to find which tokens to look\n * up in the inverted index.\n *\n * A token set can hold multiple tokens, as in the case of the\n * index token set, or it can hold a single token as in the\n * case of a simple query token set.\n *\n * Additionally token sets are used to perform wildcard matching.\n * Leading, contained and trailing wildcards are supported, and\n * from this edit distance matching can also be provided.\n *\n * Token sets are implemented as a minimal finite state automata,\n * where both common prefixes and suffixes are shared between tokens.\n * This helps to reduce the space used for storing the token set.\n *\n * @constructor\n */\nlunr.TokenSet = function () {\n  this.final = false\n  this.edges = {}\n  this.id = lunr.TokenSet._nextId\n  lunr.TokenSet._nextId += 1\n}\n\n/**\n * Keeps track of the next, auto increment, identifier to assign\n * to a new tokenSet.\n *\n * TokenSets require a unique identifier to be correctly minimised.\n *\n * @private\n */\nlunr.TokenSet._nextId = 1\n\n/**\n * Creates a TokenSet instance from the given sorted array of words.\n *\n * @param {String[]} arr - A sorted array of strings to create the set from.\n * @returns {lunr.TokenSet}\n * @throws Will throw an error if the input array is not sorted.\n */\nlunr.TokenSet.fromArray = function (arr) {\n  var builder = new lunr.TokenSet.Builder\n\n  for (var i = 0, len = arr.length; i < len; i++) {\n    builder.insert(arr[i])\n  }\n\n  builder.finish()\n  return builder.root\n}\n\n/**\n * Creates a token set from a query clause.\n *\n * @private\n * @param {Object} clause - A single clause from lunr.Query.\n * @param {string} clause.term - The query clause term.\n * @param {number} [clause.editDistance] - The optional edit distance for the term.\n * @returns {lunr.TokenSet}\n */\nlunr.TokenSet.fromClause = function (clause) {\n  if ('editDistance' in clause) {\n    return lunr.TokenSet.fromFuzzyString(clause.term, clause.editDistance)\n  } else {\n    return lunr.TokenSet.fromString(clause.term)\n  }\n}\n\n/**\n * Creates a token set representing a single string with a specified\n * edit distance.\n *\n * Insertions, deletions, substitutions and transpositions are each\n * treated as an edit distance of 1.\n *\n * Increasing the allowed edit distance will have a dramatic impact\n * on the performance of both creating and intersecting these TokenSets.\n * It is advised to keep the edit distance less than 3.\n *\n * @param {string} str - The string to create the token set from.\n * @param {number} editDistance - The allowed edit distance to match.\n * @returns {lunr.Vector}\n */\nlunr.TokenSet.fromFuzzyString = function (str, editDistance) {\n  var root = new lunr.TokenSet\n\n  var stack = [{\n    node: root,\n    editsRemaining: editDistance,\n    str: str\n  }]\n\n  while (stack.length) {\n    var frame = stack.pop()\n\n    // no edit\n    if (frame.str.length > 0) {\n      var char = frame.str.charAt(0),\n          noEditNode\n\n      if (char in frame.node.edges) {\n        noEditNode = frame.node.edges[char]\n      } else {\n        noEditNode = new lunr.TokenSet\n        frame.node.edges[char] = noEditNode\n      }\n\n      if (frame.str.length == 1) {\n        noEditNode.final = true\n      }\n\n      stack.push({\n        node: noEditNode,\n        editsRemaining: frame.editsRemaining,\n        str: frame.str.slice(1)\n      })\n    }\n\n    if (frame.editsRemaining == 0) {\n      continue\n    }\n\n    // insertion\n    if (\"*\" in frame.node.edges) {\n      var insertionNode = frame.node.edges[\"*\"]\n    } else {\n      var insertionNode = new lunr.TokenSet\n      frame.node.edges[\"*\"] = insertionNode\n    }\n\n    if (frame.str.length == 0) {\n      insertionNode.final = true\n    }\n\n    stack.push({\n      node: insertionNode,\n      editsRemaining: frame.editsRemaining - 1,\n      str: frame.str\n    })\n\n    // deletion\n    // can only do a deletion if we have enough edits remaining\n    // and if there are characters left to delete in the string\n    if (frame.str.length > 1) {\n      stack.push({\n        node: frame.node,\n        editsRemaining: frame.editsRemaining - 1,\n        str: frame.str.slice(1)\n      })\n    }\n\n    // deletion\n    // just removing the last character from the str\n    if (frame.str.length == 1) {\n      frame.node.final = true\n    }\n\n    // substitution\n    // can only do a substitution if we have enough edits remaining\n    // and if there are characters left to substitute\n    if (frame.str.length >= 1) {\n      if (\"*\" in frame.node.edges) {\n        var substitutionNode = frame.node.edges[\"*\"]\n      } else {\n        var substitutionNode = new lunr.TokenSet\n        frame.node.edges[\"*\"] = substitutionNode\n      }\n\n      if (frame.str.length == 1) {\n        substitutionNode.final = true\n      }\n\n      stack.push({\n        node: substitutionNode,\n        editsRemaining: frame.editsRemaining - 1,\n        str: frame.str.slice(1)\n      })\n    }\n\n    // transposition\n    // can only do a transposition if there are edits remaining\n    // and there are enough characters to transpose\n    if (frame.str.length > 1) {\n      var charA = frame.str.charAt(0),\n          charB = frame.str.charAt(1),\n          transposeNode\n\n      if (charB in frame.node.edges) {\n        transposeNode = frame.node.edges[charB]\n      } else {\n        transposeNode = new lunr.TokenSet\n        frame.node.edges[charB] = transposeNode\n      }\n\n      if (frame.str.length == 1) {\n        transposeNode.final = true\n      }\n\n      stack.push({\n        node: transposeNode,\n        editsRemaining: frame.editsRemaining - 1,\n        str: charA + frame.str.slice(2)\n      })\n    }\n  }\n\n  return root\n}\n\n/**\n * Creates a TokenSet from a string.\n *\n * The string may contain one or more wildcard characters (*)\n * that will allow wildcard matching when intersecting with\n * another TokenSet.\n *\n * @param {string} str - The string to create a TokenSet from.\n * @returns {lunr.TokenSet}\n */\nlunr.TokenSet.fromString = function (str) {\n  var node = new lunr.TokenSet,\n      root = node\n\n  /*\n   * Iterates through all characters within the passed string\n   * appending a node for each character.\n   *\n   * When a wildcard character is found then a self\n   * referencing edge is introduced to continually match\n   * any number of any characters.\n   */\n  for (var i = 0, len = str.length; i < len; i++) {\n    var char = str[i],\n        final = (i == len - 1)\n\n    if (char == \"*\") {\n      node.edges[char] = node\n      node.final = final\n\n    } else {\n      var next = new lunr.TokenSet\n      next.final = final\n\n      node.edges[char] = next\n      node = next\n    }\n  }\n\n  return root\n}\n\n/**\n * Converts this TokenSet into an array of strings\n * contained within the TokenSet.\n *\n * This is not intended to be used on a TokenSet that\n * contains wildcards, in these cases the results are\n * undefined and are likely to cause an infinite loop.\n *\n * @returns {string[]}\n */\nlunr.TokenSet.prototype.toArray = function () {\n  var words = []\n\n  var stack = [{\n    prefix: \"\",\n    node: this\n  }]\n\n  while (stack.length) {\n    var frame = stack.pop(),\n        edges = Object.keys(frame.node.edges),\n        len = edges.length\n\n    if (frame.node.final) {\n      /* In Safari, at this point the prefix is sometimes corrupted, see:\n       * https://github.com/olivernn/lunr.js/issues/279 Calling any\n       * String.prototype method forces Safari to \"cast\" this string to what\n       * it's supposed to be, fixing the bug. */\n      frame.prefix.charAt(0)\n      words.push(frame.prefix)\n    }\n\n    for (var i = 0; i < len; i++) {\n      var edge = edges[i]\n\n      stack.push({\n        prefix: frame.prefix.concat(edge),\n        node: frame.node.edges[edge]\n      })\n    }\n  }\n\n  return words\n}\n\n/**\n * Generates a string representation of a TokenSet.\n *\n * This is intended to allow TokenSets to be used as keys\n * in objects, largely to aid the construction and minimisation\n * of a TokenSet. As such it is not designed to be a human\n * friendly representation of the TokenSet.\n *\n * @returns {string}\n */\nlunr.TokenSet.prototype.toString = function () {\n  // NOTE: Using Object.keys here as this.edges is very likely\n  // to enter 'hash-mode' with many keys being added\n  //\n  // avoiding a for-in loop here as it leads to the function\n  // being de-optimised (at least in V8). From some simple\n  // benchmarks the performance is comparable, but allowing\n  // V8 to optimize may mean easy performance wins in the future.\n\n  if (this._str) {\n    return this._str\n  }\n\n  var str = this.final ? '1' : '0',\n      labels = Object.keys(this.edges).sort(),\n      len = labels.length\n\n  for (var i = 0; i < len; i++) {\n    var label = labels[i],\n        node = this.edges[label]\n\n    str = str + label + node.id\n  }\n\n  return str\n}\n\n/**\n * Returns a new TokenSet that is the intersection of\n * this TokenSet and the passed TokenSet.\n *\n * This intersection will take into account any wildcards\n * contained within the TokenSet.\n *\n * @param {lunr.TokenSet} b - An other TokenSet to intersect with.\n * @returns {lunr.TokenSet}\n */\nlunr.TokenSet.prototype.intersect = function (b) {\n  var output = new lunr.TokenSet,\n      frame = undefined\n\n  var stack = [{\n    qNode: b,\n    output: output,\n    node: this\n  }]\n\n  while (stack.length) {\n    frame = stack.pop()\n\n    // NOTE: As with the #toString method, we are using\n    // Object.keys and a for loop instead of a for-in loop\n    // as both of these objects enter 'hash' mode, causing\n    // the function to be de-optimised in V8\n    var qEdges = Object.keys(frame.qNode.edges),\n        qLen = qEdges.length,\n        nEdges = Object.keys(frame.node.edges),\n        nLen = nEdges.length\n\n    for (var q = 0; q < qLen; q++) {\n      var qEdge = qEdges[q]\n\n      for (var n = 0; n < nLen; n++) {\n        var nEdge = nEdges[n]\n\n        if (nEdge == qEdge || qEdge == '*') {\n          var node = frame.node.edges[nEdge],\n              qNode = frame.qNode.edges[qEdge],\n              final = node.final && qNode.final,\n              next = undefined\n\n          if (nEdge in frame.output.edges) {\n            // an edge already exists for this character\n            // no need to create a new node, just set the finality\n            // bit unless this node is already final\n            next = frame.output.edges[nEdge]\n            next.final = next.final || final\n\n          } else {\n            // no edge exists yet, must create one\n            // set the finality bit and insert it\n            // into the output\n            next = new lunr.TokenSet\n            next.final = final\n            frame.output.edges[nEdge] = next\n          }\n\n          stack.push({\n            qNode: qNode,\n            output: next,\n            node: node\n          })\n        }\n      }\n    }\n  }\n\n  return output\n}\nlunr.TokenSet.Builder = function () {\n  this.previousWord = \"\"\n  this.root = new lunr.TokenSet\n  this.uncheckedNodes = []\n  this.minimizedNodes = {}\n}\n\nlunr.TokenSet.Builder.prototype.insert = function (word) {\n  var node,\n      commonPrefix = 0\n\n  if (word < this.previousWord) {\n    throw new Error (\"Out of order word insertion\")\n  }\n\n  for (var i = 0; i < word.length && i < this.previousWord.length; i++) {\n    if (word[i] != this.previousWord[i]) break\n    commonPrefix++\n  }\n\n  this.minimize(commonPrefix)\n\n  if (this.uncheckedNodes.length == 0) {\n    node = this.root\n  } else {\n    node = this.uncheckedNodes[this.uncheckedNodes.length - 1].child\n  }\n\n  for (var i = commonPrefix; i < word.length; i++) {\n    var nextNode = new lunr.TokenSet,\n        char = word[i]\n\n    node.edges[char] = nextNode\n\n    this.uncheckedNodes.push({\n      parent: node,\n      char: char,\n      child: nextNode\n    })\n\n    node = nextNode\n  }\n\n  node.final = true\n  this.previousWord = word\n}\n\nlunr.TokenSet.Builder.prototype.finish = function () {\n  this.minimize(0)\n}\n\nlunr.TokenSet.Builder.prototype.minimize = function (downTo) {\n  for (var i = this.uncheckedNodes.length - 1; i >= downTo; i--) {\n    var node = this.uncheckedNodes[i],\n        childKey = node.child.toString()\n\n    if (childKey in this.minimizedNodes) {\n      node.parent.edges[node.char] = this.minimizedNodes[childKey]\n    } else {\n      // Cache the key for this node since\n      // we know it can't change anymore\n      node.child._str = childKey\n\n      this.minimizedNodes[childKey] = node.child\n    }\n\n    this.uncheckedNodes.pop()\n  }\n}\n/*!\n * lunr.Index\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * An index contains the built index of all documents and provides a query interface\n * to the index.\n *\n * Usually instances of lunr.Index will not be created using this constructor, instead\n * lunr.Builder should be used to construct new indexes, or lunr.Index.load should be\n * used to load previously built and serialized indexes.\n *\n * @constructor\n * @param {Object} attrs - The attributes of the built search index.\n * @param {Object} attrs.invertedIndex - An index of term/field to document reference.\n * @param {Object<string, lunr.Vector>} attrs.fieldVectors - Field vectors\n * @param {lunr.TokenSet} attrs.tokenSet - An set of all corpus tokens.\n * @param {string[]} attrs.fields - The names of indexed document fields.\n * @param {lunr.Pipeline} attrs.pipeline - The pipeline to use for search terms.\n */\nlunr.Index = function (attrs) {\n  this.invertedIndex = attrs.invertedIndex\n  this.fieldVectors = attrs.fieldVectors\n  this.tokenSet = attrs.tokenSet\n  this.fields = attrs.fields\n  this.pipeline = attrs.pipeline\n}\n\n/**\n * A result contains details of a document matching a search query.\n * @typedef {Object} lunr.Index~Result\n * @property {string} ref - The reference of the document this result represents.\n * @property {number} score - A number between 0 and 1 representing how similar this document is to the query.\n * @property {lunr.MatchData} matchData - Contains metadata about this match including which term(s) caused the match.\n */\n\n/**\n * Although lunr provides the ability to create queries using lunr.Query, it also provides a simple\n * query language which itself is parsed into an instance of lunr.Query.\n *\n * For programmatically building queries it is advised to directly use lunr.Query, the query language\n * is best used for human entered text rather than program generated text.\n *\n * At its simplest queries can just be a single term, e.g. `hello`, multiple terms are also supported\n * and will be combined with OR, e.g `hello world` will match documents that contain either 'hello'\n * or 'world', though those that contain both will rank higher in the results.\n *\n * Wildcards can be included in terms to match one or more unspecified characters, these wildcards can\n * be inserted anywhere within the term, and more than one wildcard can exist in a single term. Adding\n * wildcards will increase the number of documents that will be found but can also have a negative\n * impact on query performance, especially with wildcards at the beginning of a term.\n *\n * Terms can be restricted to specific fields, e.g. `title:hello`, only documents with the term\n * hello in the title field will match this query. Using a field not present in the index will lead\n * to an error being thrown.\n *\n * Modifiers can also be added to terms, lunr supports edit distance and boost modifiers on terms. A term\n * boost will make documents matching that term score higher, e.g. `foo^5`. Edit distance is also supported\n * to provide fuzzy matching, e.g. 'hello~2' will match documents with hello with an edit distance of 2.\n * Avoid large values for edit distance to improve query performance.\n *\n * Each term also supports a presence modifier. By default a term's presence in document is optional, however\n * this can be changed to either required or prohibited. For a term's presence to be required in a document the\n * term should be prefixed with a '+', e.g. `+foo bar` is a search for documents that must contain 'foo' and\n * optionally contain 'bar'. Conversely a leading '-' sets the terms presence to prohibited, i.e. it must not\n * appear in a document, e.g. `-foo bar` is a search for documents that do not contain 'foo' but may contain 'bar'.\n *\n * To escape special characters the backslash character '\\' can be used, this allows searches to include\n * characters that would normally be considered modifiers, e.g. `foo\\~2` will search for a term \"foo~2\" instead\n * of attempting to apply a boost of 2 to the search term \"foo\".\n *\n * @typedef {string} lunr.Index~QueryString\n * @example <caption>Simple single term query</caption>\n * hello\n * @example <caption>Multiple term query</caption>\n * hello world\n * @example <caption>term scoped to a field</caption>\n * title:hello\n * @example <caption>term with a boost of 10</caption>\n * hello^10\n * @example <caption>term with an edit distance of 2</caption>\n * hello~2\n * @example <caption>terms with presence modifiers</caption>\n * -foo +bar baz\n */\n\n/**\n * Performs a search against the index using lunr query syntax.\n *\n * Results will be returned sorted by their score, the most relevant results\n * will be returned first.  For details on how the score is calculated, please see\n * the {@link https://lunrjs.com/guides/searching.html#scoring|guide}.\n *\n * For more programmatic querying use lunr.Index#query.\n *\n * @param {lunr.Index~QueryString} queryString - A string containing a lunr query.\n * @throws {lunr.QueryParseError} If the passed query string cannot be parsed.\n * @returns {lunr.Index~Result[]}\n */\nlunr.Index.prototype.search = function (queryString) {\n  return this.query(function (query) {\n    var parser = new lunr.QueryParser(queryString, query)\n    parser.parse()\n  })\n}\n\n/**\n * A query builder callback provides a query object to be used to express\n * the query to perform on the index.\n *\n * @callback lunr.Index~queryBuilder\n * @param {lunr.Query} query - The query object to build up.\n * @this lunr.Query\n */\n\n/**\n * Performs a query against the index using the yielded lunr.Query object.\n *\n * If performing programmatic queries against the index, this method is preferred\n * over lunr.Index#search so as to avoid the additional query parsing overhead.\n *\n * A query object is yielded to the supplied function which should be used to\n * express the query to be run against the index.\n *\n * Note that although this function takes a callback parameter it is _not_ an\n * asynchronous operation, the callback is just yielded a query object to be\n * customized.\n *\n * @param {lunr.Index~queryBuilder} fn - A function that is used to build the query.\n * @returns {lunr.Index~Result[]}\n */\nlunr.Index.prototype.query = function (fn) {\n  // for each query clause\n  // * process terms\n  // * expand terms from token set\n  // * find matching documents and metadata\n  // * get document vectors\n  // * score documents\n\n  var query = new lunr.Query(this.fields),\n      matchingFields = Object.create(null),\n      queryVectors = Object.create(null),\n      termFieldCache = Object.create(null),\n      requiredMatches = Object.create(null),\n      prohibitedMatches = Object.create(null)\n\n  /*\n   * To support field level boosts a query vector is created per\n   * field. An empty vector is eagerly created to support negated\n   * queries.\n   */\n  for (var i = 0; i < this.fields.length; i++) {\n    queryVectors[this.fields[i]] = new lunr.Vector\n  }\n\n  fn.call(query, query)\n\n  for (var i = 0; i < query.clauses.length; i++) {\n    /*\n     * Unless the pipeline has been disabled for this term, which is\n     * the case for terms with wildcards, we need to pass the clause\n     * term through the search pipeline. A pipeline returns an array\n     * of processed terms. Pipeline functions may expand the passed\n     * term, which means we may end up performing multiple index lookups\n     * for a single query term.\n     */\n    var clause = query.clauses[i],\n        terms = null,\n        clauseMatches = lunr.Set.empty\n\n    if (clause.usePipeline) {\n      terms = this.pipeline.runString(clause.term, {\n        fields: clause.fields\n      })\n    } else {\n      terms = [clause.term]\n    }\n\n    for (var m = 0; m < terms.length; m++) {\n      var term = terms[m]\n\n      /*\n       * Each term returned from the pipeline needs to use the same query\n       * clause object, e.g. the same boost and or edit distance. The\n       * simplest way to do this is to re-use the clause object but mutate\n       * its term property.\n       */\n      clause.term = term\n\n      /*\n       * From the term in the clause we create a token set which will then\n       * be used to intersect the indexes token set to get a list of terms\n       * to lookup in the inverted index\n       */\n      var termTokenSet = lunr.TokenSet.fromClause(clause),\n          expandedTerms = this.tokenSet.intersect(termTokenSet).toArray()\n\n      /*\n       * If a term marked as required does not exist in the tokenSet it is\n       * impossible for the search to return any matches. We set all the field\n       * scoped required matches set to empty and stop examining any further\n       * clauses.\n       */\n      if (expandedTerms.length === 0 && clause.presence === lunr.Query.presence.REQUIRED) {\n        for (var k = 0; k < clause.fields.length; k++) {\n          var field = clause.fields[k]\n          requiredMatches[field] = lunr.Set.empty\n        }\n\n        break\n      }\n\n      for (var j = 0; j < expandedTerms.length; j++) {\n        /*\n         * For each term get the posting and termIndex, this is required for\n         * building the query vector.\n         */\n        var expandedTerm = expandedTerms[j],\n            posting = this.invertedIndex[expandedTerm],\n            termIndex = posting._index\n\n        for (var k = 0; k < clause.fields.length; k++) {\n          /*\n           * For each field that this query term is scoped by (by default\n           * all fields are in scope) we need to get all the document refs\n           * that have this term in that field.\n           *\n           * The posting is the entry in the invertedIndex for the matching\n           * term from above.\n           */\n          var field = clause.fields[k],\n              fieldPosting = posting[field],\n              matchingDocumentRefs = Object.keys(fieldPosting),\n              termField = expandedTerm + \"/\" + field,\n              matchingDocumentsSet = new lunr.Set(matchingDocumentRefs)\n\n          /*\n           * if the presence of this term is required ensure that the matching\n           * documents are added to the set of required matches for this clause.\n           *\n           */\n          if (clause.presence == lunr.Query.presence.REQUIRED) {\n            clauseMatches = clauseMatches.union(matchingDocumentsSet)\n\n            if (requiredMatches[field] === undefined) {\n              requiredMatches[field] = lunr.Set.complete\n            }\n          }\n\n          /*\n           * if the presence of this term is prohibited ensure that the matching\n           * documents are added to the set of prohibited matches for this field,\n           * creating that set if it does not yet exist.\n           */\n          if (clause.presence == lunr.Query.presence.PROHIBITED) {\n            if (prohibitedMatches[field] === undefined) {\n              prohibitedMatches[field] = lunr.Set.empty\n            }\n\n            prohibitedMatches[field] = prohibitedMatches[field].union(matchingDocumentsSet)\n\n            /*\n             * Prohibited matches should not be part of the query vector used for\n             * similarity scoring and no metadata should be extracted so we continue\n             * to the next field\n             */\n            continue\n          }\n\n          /*\n           * The query field vector is populated using the termIndex found for\n           * the term and a unit value with the appropriate boost applied.\n           * Using upsert because there could already be an entry in the vector\n           * for the term we are working with. In that case we just add the scores\n           * together.\n           */\n          queryVectors[field].upsert(termIndex, clause.boost, function (a, b) { return a + b })\n\n          /**\n           * If we've already seen this term, field combo then we've already collected\n           * the matching documents and metadata, no need to go through all that again\n           */\n          if (termFieldCache[termField]) {\n            continue\n          }\n\n          for (var l = 0; l < matchingDocumentRefs.length; l++) {\n            /*\n             * All metadata for this term/field/document triple\n             * are then extracted and collected into an instance\n             * of lunr.MatchData ready to be returned in the query\n             * results\n             */\n            var matchingDocumentRef = matchingDocumentRefs[l],\n                matchingFieldRef = new lunr.FieldRef (matchingDocumentRef, field),\n                metadata = fieldPosting[matchingDocumentRef],\n                fieldMatch\n\n            if ((fieldMatch = matchingFields[matchingFieldRef]) === undefined) {\n              matchingFields[matchingFieldRef] = new lunr.MatchData (expandedTerm, field, metadata)\n            } else {\n              fieldMatch.add(expandedTerm, field, metadata)\n            }\n\n          }\n\n          termFieldCache[termField] = true\n        }\n      }\n    }\n\n    /**\n     * If the presence was required we need to update the requiredMatches field sets.\n     * We do this after all fields for the term have collected their matches because\n     * the clause terms presence is required in _any_ of the fields not _all_ of the\n     * fields.\n     */\n    if (clause.presence === lunr.Query.presence.REQUIRED) {\n      for (var k = 0; k < clause.fields.length; k++) {\n        var field = clause.fields[k]\n        requiredMatches[field] = requiredMatches[field].intersect(clauseMatches)\n      }\n    }\n  }\n\n  /**\n   * Need to combine the field scoped required and prohibited\n   * matching documents into a global set of required and prohibited\n   * matches\n   */\n  var allRequiredMatches = lunr.Set.complete,\n      allProhibitedMatches = lunr.Set.empty\n\n  for (var i = 0; i < this.fields.length; i++) {\n    var field = this.fields[i]\n\n    if (requiredMatches[field]) {\n      allRequiredMatches = allRequiredMatches.intersect(requiredMatches[field])\n    }\n\n    if (prohibitedMatches[field]) {\n      allProhibitedMatches = allProhibitedMatches.union(prohibitedMatches[field])\n    }\n  }\n\n  var matchingFieldRefs = Object.keys(matchingFields),\n      results = [],\n      matches = Object.create(null)\n\n  /*\n   * If the query is negated (contains only prohibited terms)\n   * we need to get _all_ fieldRefs currently existing in the\n   * index. This is only done when we know that the query is\n   * entirely prohibited terms to avoid any cost of getting all\n   * fieldRefs unnecessarily.\n   *\n   * Additionally, blank MatchData must be created to correctly\n   * populate the results.\n   */\n  if (query.isNegated()) {\n    matchingFieldRefs = Object.keys(this.fieldVectors)\n\n    for (var i = 0; i < matchingFieldRefs.length; i++) {\n      var matchingFieldRef = matchingFieldRefs[i]\n      var fieldRef = lunr.FieldRef.fromString(matchingFieldRef)\n      matchingFields[matchingFieldRef] = new lunr.MatchData\n    }\n  }\n\n  for (var i = 0; i < matchingFieldRefs.length; i++) {\n    /*\n     * Currently we have document fields that match the query, but we\n     * need to return documents. The matchData and scores are combined\n     * from multiple fields belonging to the same document.\n     *\n     * Scores are calculated by field, using the query vectors created\n     * above, and combined into a final document score using addition.\n     */\n    var fieldRef = lunr.FieldRef.fromString(matchingFieldRefs[i]),\n        docRef = fieldRef.docRef\n\n    if (!allRequiredMatches.contains(docRef)) {\n      continue\n    }\n\n    if (allProhibitedMatches.contains(docRef)) {\n      continue\n    }\n\n    var fieldVector = this.fieldVectors[fieldRef],\n        score = queryVectors[fieldRef.fieldName].similarity(fieldVector),\n        docMatch\n\n    if ((docMatch = matches[docRef]) !== undefined) {\n      docMatch.score += score\n      docMatch.matchData.combine(matchingFields[fieldRef])\n    } else {\n      var match = {\n        ref: docRef,\n        score: score,\n        matchData: matchingFields[fieldRef]\n      }\n      matches[docRef] = match\n      results.push(match)\n    }\n  }\n\n  /*\n   * Sort the results objects by score, highest first.\n   */\n  return results.sort(function (a, b) {\n    return b.score - a.score\n  })\n}\n\n/**\n * Prepares the index for JSON serialization.\n *\n * The schema for this JSON blob will be described in a\n * separate JSON schema file.\n *\n * @returns {Object}\n */\nlunr.Index.prototype.toJSON = function () {\n  var invertedIndex = Object.keys(this.invertedIndex)\n    .sort()\n    .map(function (term) {\n      return [term, this.invertedIndex[term]]\n    }, this)\n\n  var fieldVectors = Object.keys(this.fieldVectors)\n    .map(function (ref) {\n      return [ref, this.fieldVectors[ref].toJSON()]\n    }, this)\n\n  return {\n    version: lunr.version,\n    fields: this.fields,\n    fieldVectors: fieldVectors,\n    invertedIndex: invertedIndex,\n    pipeline: this.pipeline.toJSON()\n  }\n}\n\n/**\n * Loads a previously serialized lunr.Index\n *\n * @param {Object} serializedIndex - A previously serialized lunr.Index\n * @returns {lunr.Index}\n */\nlunr.Index.load = function (serializedIndex) {\n  var attrs = {},\n      fieldVectors = {},\n      serializedVectors = serializedIndex.fieldVectors,\n      invertedIndex = Object.create(null),\n      serializedInvertedIndex = serializedIndex.invertedIndex,\n      tokenSetBuilder = new lunr.TokenSet.Builder,\n      pipeline = lunr.Pipeline.load(serializedIndex.pipeline)\n\n  if (serializedIndex.version != lunr.version) {\n    lunr.utils.warn(\"Version mismatch when loading serialised index. Current version of lunr '\" + lunr.version + \"' does not match serialized index '\" + serializedIndex.version + \"'\")\n  }\n\n  for (var i = 0; i < serializedVectors.length; i++) {\n    var tuple = serializedVectors[i],\n        ref = tuple[0],\n        elements = tuple[1]\n\n    fieldVectors[ref] = new lunr.Vector(elements)\n  }\n\n  for (var i = 0; i < serializedInvertedIndex.length; i++) {\n    var tuple = serializedInvertedIndex[i],\n        term = tuple[0],\n        posting = tuple[1]\n\n    tokenSetBuilder.insert(term)\n    invertedIndex[term] = posting\n  }\n\n  tokenSetBuilder.finish()\n\n  attrs.fields = serializedIndex.fields\n\n  attrs.fieldVectors = fieldVectors\n  attrs.invertedIndex = invertedIndex\n  attrs.tokenSet = tokenSetBuilder.root\n  attrs.pipeline = pipeline\n\n  return new lunr.Index(attrs)\n}\n/*!\n * lunr.Builder\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.Builder performs indexing on a set of documents and\n * returns instances of lunr.Index ready for querying.\n *\n * All configuration of the index is done via the builder, the\n * fields to index, the document reference, the text processing\n * pipeline and document scoring parameters are all set on the\n * builder before indexing.\n *\n * @constructor\n * @property {string} _ref - Internal reference to the document reference field.\n * @property {string[]} _fields - Internal reference to the document fields to index.\n * @property {object} invertedIndex - The inverted index maps terms to document fields.\n * @property {object} documentTermFrequencies - Keeps track of document term frequencies.\n * @property {object} documentLengths - Keeps track of the length of documents added to the index.\n * @property {lunr.tokenizer} tokenizer - Function for splitting strings into tokens for indexing.\n * @property {lunr.Pipeline} pipeline - The pipeline performs text processing on tokens before indexing.\n * @property {lunr.Pipeline} searchPipeline - A pipeline for processing search terms before querying the index.\n * @property {number} documentCount - Keeps track of the total number of documents indexed.\n * @property {number} _b - A parameter to control field length normalization, setting this to 0 disabled normalization, 1 fully normalizes field lengths, the default value is 0.75.\n * @property {number} _k1 - A parameter to control how quickly an increase in term frequency results in term frequency saturation, the default value is 1.2.\n * @property {number} termIndex - A counter incremented for each unique term, used to identify a terms position in the vector space.\n * @property {array} metadataWhitelist - A list of metadata keys that have been whitelisted for entry in the index.\n */\nlunr.Builder = function () {\n  this._ref = \"id\"\n  this._fields = Object.create(null)\n  this._documents = Object.create(null)\n  this.invertedIndex = Object.create(null)\n  this.fieldTermFrequencies = {}\n  this.fieldLengths = {}\n  this.tokenizer = lunr.tokenizer\n  this.pipeline = new lunr.Pipeline\n  this.searchPipeline = new lunr.Pipeline\n  this.documentCount = 0\n  this._b = 0.75\n  this._k1 = 1.2\n  this.termIndex = 0\n  this.metadataWhitelist = []\n}\n\n/**\n * Sets the document field used as the document reference. Every document must have this field.\n * The type of this field in the document should be a string, if it is not a string it will be\n * coerced into a string by calling toString.\n *\n * The default ref is 'id'.\n *\n * The ref should _not_ be changed during indexing, it should be set before any documents are\n * added to the index. Changing it during indexing can lead to inconsistent results.\n *\n * @param {string} ref - The name of the reference field in the document.\n */\nlunr.Builder.prototype.ref = function (ref) {\n  this._ref = ref\n}\n\n/**\n * A function that is used to extract a field from a document.\n *\n * Lunr expects a field to be at the top level of a document, if however the field\n * is deeply nested within a document an extractor function can be used to extract\n * the right field for indexing.\n *\n * @callback fieldExtractor\n * @param {object} doc - The document being added to the index.\n * @returns {?(string|object|object[])} obj - The object that will be indexed for this field.\n * @example <caption>Extracting a nested field</caption>\n * function (doc) { return doc.nested.field }\n */\n\n/**\n * Adds a field to the list of document fields that will be indexed. Every document being\n * indexed should have this field. Null values for this field in indexed documents will\n * not cause errors but will limit the chance of that document being retrieved by searches.\n *\n * All fields should be added before adding documents to the index. Adding fields after\n * a document has been indexed will have no effect on already indexed documents.\n *\n * Fields can be boosted at build time. This allows terms within that field to have more\n * importance when ranking search results. Use a field boost to specify that matches within\n * one field are more important than other fields.\n *\n * @param {string} fieldName - The name of a field to index in all documents.\n * @param {object} attributes - Optional attributes associated with this field.\n * @param {number} [attributes.boost=1] - Boost applied to all terms within this field.\n * @param {fieldExtractor} [attributes.extractor] - Function to extract a field from a document.\n * @throws {RangeError} fieldName cannot contain unsupported characters '/'\n */\nlunr.Builder.prototype.field = function (fieldName, attributes) {\n  if (/\\//.test(fieldName)) {\n    throw new RangeError (\"Field '\" + fieldName + \"' contains illegal character '/'\")\n  }\n\n  this._fields[fieldName] = attributes || {}\n}\n\n/**\n * A parameter to tune the amount of field length normalisation that is applied when\n * calculating relevance scores. A value of 0 will completely disable any normalisation\n * and a value of 1 will fully normalise field lengths. The default is 0.75. Values of b\n * will be clamped to the range 0 - 1.\n *\n * @param {number} number - The value to set for this tuning parameter.\n */\nlunr.Builder.prototype.b = function (number) {\n  if (number < 0) {\n    this._b = 0\n  } else if (number > 1) {\n    this._b = 1\n  } else {\n    this._b = number\n  }\n}\n\n/**\n * A parameter that controls the speed at which a rise in term frequency results in term\n * frequency saturation. The default value is 1.2. Setting this to a higher value will give\n * slower saturation levels, a lower value will result in quicker saturation.\n *\n * @param {number} number - The value to set for this tuning parameter.\n */\nlunr.Builder.prototype.k1 = function (number) {\n  this._k1 = number\n}\n\n/**\n * Adds a document to the index.\n *\n * Before adding fields to the index the index should have been fully setup, with the document\n * ref and all fields to index already having been specified.\n *\n * The document must have a field name as specified by the ref (by default this is 'id') and\n * it should have all fields defined for indexing, though null or undefined values will not\n * cause errors.\n *\n * Entire documents can be boosted at build time. Applying a boost to a document indicates that\n * this document should rank higher in search results than other documents.\n *\n * @param {object} doc - The document to add to the index.\n * @param {object} attributes - Optional attributes associated with this document.\n * @param {number} [attributes.boost=1] - Boost applied to all terms within this document.\n */\nlunr.Builder.prototype.add = function (doc, attributes) {\n  var docRef = doc[this._ref],\n      fields = Object.keys(this._fields)\n\n  this._documents[docRef] = attributes || {}\n  this.documentCount += 1\n\n  for (var i = 0; i < fields.length; i++) {\n    var fieldName = fields[i],\n        extractor = this._fields[fieldName].extractor,\n        field = extractor ? extractor(doc) : doc[fieldName],\n        tokens = this.tokenizer(field, {\n          fields: [fieldName]\n        }),\n        terms = this.pipeline.run(tokens),\n        fieldRef = new lunr.FieldRef (docRef, fieldName),\n        fieldTerms = Object.create(null)\n\n    this.fieldTermFrequencies[fieldRef] = fieldTerms\n    this.fieldLengths[fieldRef] = 0\n\n    // store the length of this field for this document\n    this.fieldLengths[fieldRef] += terms.length\n\n    // calculate term frequencies for this field\n    for (var j = 0; j < terms.length; j++) {\n      var term = terms[j]\n\n      if (fieldTerms[term] == undefined) {\n        fieldTerms[term] = 0\n      }\n\n      fieldTerms[term] += 1\n\n      // add to inverted index\n      // create an initial posting if one doesn't exist\n      if (this.invertedIndex[term] == undefined) {\n        var posting = Object.create(null)\n        posting[\"_index\"] = this.termIndex\n        this.termIndex += 1\n\n        for (var k = 0; k < fields.length; k++) {\n          posting[fields[k]] = Object.create(null)\n        }\n\n        this.invertedIndex[term] = posting\n      }\n\n      // add an entry for this term/fieldName/docRef to the invertedIndex\n      if (this.invertedIndex[term][fieldName][docRef] == undefined) {\n        this.invertedIndex[term][fieldName][docRef] = Object.create(null)\n      }\n\n      // store all whitelisted metadata about this token in the\n      // inverted index\n      for (var l = 0; l < this.metadataWhitelist.length; l++) {\n        var metadataKey = this.metadataWhitelist[l],\n            metadata = term.metadata[metadataKey]\n\n        if (this.invertedIndex[term][fieldName][docRef][metadataKey] == undefined) {\n          this.invertedIndex[term][fieldName][docRef][metadataKey] = []\n        }\n\n        this.invertedIndex[term][fieldName][docRef][metadataKey].push(metadata)\n      }\n    }\n\n  }\n}\n\n/**\n * Calculates the average document length for this index\n *\n * @private\n */\nlunr.Builder.prototype.calculateAverageFieldLengths = function () {\n\n  var fieldRefs = Object.keys(this.fieldLengths),\n      numberOfFields = fieldRefs.length,\n      accumulator = {},\n      documentsWithField = {}\n\n  for (var i = 0; i < numberOfFields; i++) {\n    var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]),\n        field = fieldRef.fieldName\n\n    documentsWithField[field] || (documentsWithField[field] = 0)\n    documentsWithField[field] += 1\n\n    accumulator[field] || (accumulator[field] = 0)\n    accumulator[field] += this.fieldLengths[fieldRef]\n  }\n\n  var fields = Object.keys(this._fields)\n\n  for (var i = 0; i < fields.length; i++) {\n    var fieldName = fields[i]\n    accumulator[fieldName] = accumulator[fieldName] / documentsWithField[fieldName]\n  }\n\n  this.averageFieldLength = accumulator\n}\n\n/**\n * Builds a vector space model of every document using lunr.Vector\n *\n * @private\n */\nlunr.Builder.prototype.createFieldVectors = function () {\n  var fieldVectors = {},\n      fieldRefs = Object.keys(this.fieldTermFrequencies),\n      fieldRefsLength = fieldRefs.length,\n      termIdfCache = Object.create(null)\n\n  for (var i = 0; i < fieldRefsLength; i++) {\n    var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]),\n        fieldName = fieldRef.fieldName,\n        fieldLength = this.fieldLengths[fieldRef],\n        fieldVector = new lunr.Vector,\n        termFrequencies = this.fieldTermFrequencies[fieldRef],\n        terms = Object.keys(termFrequencies),\n        termsLength = terms.length\n\n\n    var fieldBoost = this._fields[fieldName].boost || 1,\n        docBoost = this._documents[fieldRef.docRef].boost || 1\n\n    for (var j = 0; j < termsLength; j++) {\n      var term = terms[j],\n          tf = termFrequencies[term],\n          termIndex = this.invertedIndex[term]._index,\n          idf, score, scoreWithPrecision\n\n      if (termIdfCache[term] === undefined) {\n        idf = lunr.idf(this.invertedIndex[term], this.documentCount)\n        termIdfCache[term] = idf\n      } else {\n        idf = termIdfCache[term]\n      }\n\n      score = idf * ((this._k1 + 1) * tf) / (this._k1 * (1 - this._b + this._b * (fieldLength / this.averageFieldLength[fieldName])) + tf)\n      score *= fieldBoost\n      score *= docBoost\n      scoreWithPrecision = Math.round(score * 1000) / 1000\n      // Converts 1.23456789 to 1.234.\n      // Reducing the precision so that the vectors take up less\n      // space when serialised. Doing it now so that they behave\n      // the same before and after serialisation. Also, this is\n      // the fastest approach to reducing a number's precision in\n      // JavaScript.\n\n      fieldVector.insert(termIndex, scoreWithPrecision)\n    }\n\n    fieldVectors[fieldRef] = fieldVector\n  }\n\n  this.fieldVectors = fieldVectors\n}\n\n/**\n * Creates a token set of all tokens in the index using lunr.TokenSet\n *\n * @private\n */\nlunr.Builder.prototype.createTokenSet = function () {\n  this.tokenSet = lunr.TokenSet.fromArray(\n    Object.keys(this.invertedIndex).sort()\n  )\n}\n\n/**\n * Builds the index, creating an instance of lunr.Index.\n *\n * This completes the indexing process and should only be called\n * once all documents have been added to the index.\n *\n * @returns {lunr.Index}\n */\nlunr.Builder.prototype.build = function () {\n  this.calculateAverageFieldLengths()\n  this.createFieldVectors()\n  this.createTokenSet()\n\n  return new lunr.Index({\n    invertedIndex: this.invertedIndex,\n    fieldVectors: this.fieldVectors,\n    tokenSet: this.tokenSet,\n    fields: Object.keys(this._fields),\n    pipeline: this.searchPipeline\n  })\n}\n\n/**\n * Applies a plugin to the index builder.\n *\n * A plugin is a function that is called with the index builder as its context.\n * Plugins can be used to customise or extend the behaviour of the index\n * in some way. A plugin is just a function, that encapsulated the custom\n * behaviour that should be applied when building the index.\n *\n * The plugin function will be called with the index builder as its argument, additional\n * arguments can also be passed when calling use. The function will be called\n * with the index builder as its context.\n *\n * @param {Function} plugin The plugin to apply.\n */\nlunr.Builder.prototype.use = function (fn) {\n  var args = Array.prototype.slice.call(arguments, 1)\n  args.unshift(this)\n  fn.apply(this, args)\n}\n/**\n * Contains and collects metadata about a matching document.\n * A single instance of lunr.MatchData is returned as part of every\n * lunr.Index~Result.\n *\n * @constructor\n * @param {string} term - The term this match data is associated with\n * @param {string} field - The field in which the term was found\n * @param {object} metadata - The metadata recorded about this term in this field\n * @property {object} metadata - A cloned collection of metadata associated with this document.\n * @see {@link lunr.Index~Result}\n */\nlunr.MatchData = function (term, field, metadata) {\n  var clonedMetadata = Object.create(null),\n      metadataKeys = Object.keys(metadata || {})\n\n  // Cloning the metadata to prevent the original\n  // being mutated during match data combination.\n  // Metadata is kept in an array within the inverted\n  // index so cloning the data can be done with\n  // Array#slice\n  for (var i = 0; i < metadataKeys.length; i++) {\n    var key = metadataKeys[i]\n    clonedMetadata[key] = metadata[key].slice()\n  }\n\n  this.metadata = Object.create(null)\n\n  if (term !== undefined) {\n    this.metadata[term] = Object.create(null)\n    this.metadata[term][field] = clonedMetadata\n  }\n}\n\n/**\n * An instance of lunr.MatchData will be created for every term that matches a\n * document. However only one instance is required in a lunr.Index~Result. This\n * method combines metadata from another instance of lunr.MatchData with this\n * objects metadata.\n *\n * @param {lunr.MatchData} otherMatchData - Another instance of match data to merge with this one.\n * @see {@link lunr.Index~Result}\n */\nlunr.MatchData.prototype.combine = function (otherMatchData) {\n  var terms = Object.keys(otherMatchData.metadata)\n\n  for (var i = 0; i < terms.length; i++) {\n    var term = terms[i],\n        fields = Object.keys(otherMatchData.metadata[term])\n\n    if (this.metadata[term] == undefined) {\n      this.metadata[term] = Object.create(null)\n    }\n\n    for (var j = 0; j < fields.length; j++) {\n      var field = fields[j],\n          keys = Object.keys(otherMatchData.metadata[term][field])\n\n      if (this.metadata[term][field] == undefined) {\n        this.metadata[term][field] = Object.create(null)\n      }\n\n      for (var k = 0; k < keys.length; k++) {\n        var key = keys[k]\n\n        if (this.metadata[term][field][key] == undefined) {\n          this.metadata[term][field][key] = otherMatchData.metadata[term][field][key]\n        } else {\n          this.metadata[term][field][key] = this.metadata[term][field][key].concat(otherMatchData.metadata[term][field][key])\n        }\n\n      }\n    }\n  }\n}\n\n/**\n * Add metadata for a term/field pair to this instance of match data.\n *\n * @param {string} term - The term this match data is associated with\n * @param {string} field - The field in which the term was found\n * @param {object} metadata - The metadata recorded about this term in this field\n */\nlunr.MatchData.prototype.add = function (term, field, metadata) {\n  if (!(term in this.metadata)) {\n    this.metadata[term] = Object.create(null)\n    this.metadata[term][field] = metadata\n    return\n  }\n\n  if (!(field in this.metadata[term])) {\n    this.metadata[term][field] = metadata\n    return\n  }\n\n  var metadataKeys = Object.keys(metadata)\n\n  for (var i = 0; i < metadataKeys.length; i++) {\n    var key = metadataKeys[i]\n\n    if (key in this.metadata[term][field]) {\n      this.metadata[term][field][key] = this.metadata[term][field][key].concat(metadata[key])\n    } else {\n      this.metadata[term][field][key] = metadata[key]\n    }\n  }\n}\n/**\n * A lunr.Query provides a programmatic way of defining queries to be performed\n * against a {@link lunr.Index}.\n *\n * Prefer constructing a lunr.Query using the {@link lunr.Index#query} method\n * so the query object is pre-initialized with the right index fields.\n *\n * @constructor\n * @property {lunr.Query~Clause[]} clauses - An array of query clauses.\n * @property {string[]} allFields - An array of all available fields in a lunr.Index.\n */\nlunr.Query = function (allFields) {\n  this.clauses = []\n  this.allFields = allFields\n}\n\n/**\n * Constants for indicating what kind of automatic wildcard insertion will be used when constructing a query clause.\n *\n * This allows wildcards to be added to the beginning and end of a term without having to manually do any string\n * concatenation.\n *\n * The wildcard constants can be bitwise combined to select both leading and trailing wildcards.\n *\n * @constant\n * @default\n * @property {number} wildcard.NONE - The term will have no wildcards inserted, this is the default behaviour\n * @property {number} wildcard.LEADING - Prepend the term with a wildcard, unless a leading wildcard already exists\n * @property {number} wildcard.TRAILING - Append a wildcard to the term, unless a trailing wildcard already exists\n * @see lunr.Query~Clause\n * @see lunr.Query#clause\n * @see lunr.Query#term\n * @example <caption>query term with trailing wildcard</caption>\n * query.term('foo', { wildcard: lunr.Query.wildcard.TRAILING })\n * @example <caption>query term with leading and trailing wildcard</caption>\n * query.term('foo', {\n *   wildcard: lunr.Query.wildcard.LEADING | lunr.Query.wildcard.TRAILING\n * })\n */\n\nlunr.Query.wildcard = new String (\"*\")\nlunr.Query.wildcard.NONE = 0\nlunr.Query.wildcard.LEADING = 1\nlunr.Query.wildcard.TRAILING = 2\n\n/**\n * Constants for indicating what kind of presence a term must have in matching documents.\n *\n * @constant\n * @enum {number}\n * @see lunr.Query~Clause\n * @see lunr.Query#clause\n * @see lunr.Query#term\n * @example <caption>query term with required presence</caption>\n * query.term('foo', { presence: lunr.Query.presence.REQUIRED })\n */\nlunr.Query.presence = {\n  /**\n   * Term's presence in a document is optional, this is the default value.\n   */\n  OPTIONAL: 1,\n\n  /**\n   * Term's presence in a document is required, documents that do not contain\n   * this term will not be returned.\n   */\n  REQUIRED: 2,\n\n  /**\n   * Term's presence in a document is prohibited, documents that do contain\n   * this term will not be returned.\n   */\n  PROHIBITED: 3\n}\n\n/**\n * A single clause in a {@link lunr.Query} contains a term and details on how to\n * match that term against a {@link lunr.Index}.\n *\n * @typedef {Object} lunr.Query~Clause\n * @property {string[]} fields - The fields in an index this clause should be matched against.\n * @property {number} [boost=1] - Any boost that should be applied when matching this clause.\n * @property {number} [editDistance] - Whether the term should have fuzzy matching applied, and how fuzzy the match should be.\n * @property {boolean} [usePipeline] - Whether the term should be passed through the search pipeline.\n * @property {number} [wildcard=lunr.Query.wildcard.NONE] - Whether the term should have wildcards appended or prepended.\n * @property {number} [presence=lunr.Query.presence.OPTIONAL] - The terms presence in any matching documents.\n */\n\n/**\n * Adds a {@link lunr.Query~Clause} to this query.\n *\n * Unless the clause contains the fields to be matched all fields will be matched. In addition\n * a default boost of 1 is applied to the clause.\n *\n * @param {lunr.Query~Clause} clause - The clause to add to this query.\n * @see lunr.Query~Clause\n * @returns {lunr.Query}\n */\nlunr.Query.prototype.clause = function (clause) {\n  if (!('fields' in clause)) {\n    clause.fields = this.allFields\n  }\n\n  if (!('boost' in clause)) {\n    clause.boost = 1\n  }\n\n  if (!('usePipeline' in clause)) {\n    clause.usePipeline = true\n  }\n\n  if (!('wildcard' in clause)) {\n    clause.wildcard = lunr.Query.wildcard.NONE\n  }\n\n  if ((clause.wildcard & lunr.Query.wildcard.LEADING) && (clause.term.charAt(0) != lunr.Query.wildcard)) {\n    clause.term = \"*\" + clause.term\n  }\n\n  if ((clause.wildcard & lunr.Query.wildcard.TRAILING) && (clause.term.slice(-1) != lunr.Query.wildcard)) {\n    clause.term = \"\" + clause.term + \"*\"\n  }\n\n  if (!('presence' in clause)) {\n    clause.presence = lunr.Query.presence.OPTIONAL\n  }\n\n  this.clauses.push(clause)\n\n  return this\n}\n\n/**\n * A negated query is one in which every clause has a presence of\n * prohibited. These queries require some special processing to return\n * the expected results.\n *\n * @returns boolean\n */\nlunr.Query.prototype.isNegated = function () {\n  for (var i = 0; i < this.clauses.length; i++) {\n    if (this.clauses[i].presence != lunr.Query.presence.PROHIBITED) {\n      return false\n    }\n  }\n\n  return true\n}\n\n/**\n * Adds a term to the current query, under the covers this will create a {@link lunr.Query~Clause}\n * to the list of clauses that make up this query.\n *\n * The term is used as is, i.e. no tokenization will be performed by this method. Instead conversion\n * to a token or token-like string should be done before calling this method.\n *\n * The term will be converted to a string by calling `toString`. Multiple terms can be passed as an\n * array, each term in the array will share the same options.\n *\n * @param {object|object[]} term - The term(s) to add to the query.\n * @param {object} [options] - Any additional properties to add to the query clause.\n * @returns {lunr.Query}\n * @see lunr.Query#clause\n * @see lunr.Query~Clause\n * @example <caption>adding a single term to a query</caption>\n * query.term(\"foo\")\n * @example <caption>adding a single term to a query and specifying search fields, term boost and automatic trailing wildcard</caption>\n * query.term(\"foo\", {\n *   fields: [\"title\"],\n *   boost: 10,\n *   wildcard: lunr.Query.wildcard.TRAILING\n * })\n * @example <caption>using lunr.tokenizer to convert a string to tokens before using them as terms</caption>\n * query.term(lunr.tokenizer(\"foo bar\"))\n */\nlunr.Query.prototype.term = function (term, options) {\n  if (Array.isArray(term)) {\n    term.forEach(function (t) { this.term(t, lunr.utils.clone(options)) }, this)\n    return this\n  }\n\n  var clause = options || {}\n  clause.term = term.toString()\n\n  this.clause(clause)\n\n  return this\n}\nlunr.QueryParseError = function (message, start, end) {\n  this.name = \"QueryParseError\"\n  this.message = message\n  this.start = start\n  this.end = end\n}\n\nlunr.QueryParseError.prototype = new Error\nlunr.QueryLexer = function (str) {\n  this.lexemes = []\n  this.str = str\n  this.length = str.length\n  this.pos = 0\n  this.start = 0\n  this.escapeCharPositions = []\n}\n\nlunr.QueryLexer.prototype.run = function () {\n  var state = lunr.QueryLexer.lexText\n\n  while (state) {\n    state = state(this)\n  }\n}\n\nlunr.QueryLexer.prototype.sliceString = function () {\n  var subSlices = [],\n      sliceStart = this.start,\n      sliceEnd = this.pos\n\n  for (var i = 0; i < this.escapeCharPositions.length; i++) {\n    sliceEnd = this.escapeCharPositions[i]\n    subSlices.push(this.str.slice(sliceStart, sliceEnd))\n    sliceStart = sliceEnd + 1\n  }\n\n  subSlices.push(this.str.slice(sliceStart, this.pos))\n  this.escapeCharPositions.length = 0\n\n  return subSlices.join('')\n}\n\nlunr.QueryLexer.prototype.emit = function (type) {\n  this.lexemes.push({\n    type: type,\n    str: this.sliceString(),\n    start: this.start,\n    end: this.pos\n  })\n\n  this.start = this.pos\n}\n\nlunr.QueryLexer.prototype.escapeCharacter = function () {\n  this.escapeCharPositions.push(this.pos - 1)\n  this.pos += 1\n}\n\nlunr.QueryLexer.prototype.next = function () {\n  if (this.pos >= this.length) {\n    return lunr.QueryLexer.EOS\n  }\n\n  var char = this.str.charAt(this.pos)\n  this.pos += 1\n  return char\n}\n\nlunr.QueryLexer.prototype.width = function () {\n  return this.pos - this.start\n}\n\nlunr.QueryLexer.prototype.ignore = function () {\n  if (this.start == this.pos) {\n    this.pos += 1\n  }\n\n  this.start = this.pos\n}\n\nlunr.QueryLexer.prototype.backup = function () {\n  this.pos -= 1\n}\n\nlunr.QueryLexer.prototype.acceptDigitRun = function () {\n  var char, charCode\n\n  do {\n    char = this.next()\n    charCode = char.charCodeAt(0)\n  } while (charCode > 47 && charCode < 58)\n\n  if (char != lunr.QueryLexer.EOS) {\n    this.backup()\n  }\n}\n\nlunr.QueryLexer.prototype.more = function () {\n  return this.pos < this.length\n}\n\nlunr.QueryLexer.EOS = 'EOS'\nlunr.QueryLexer.FIELD = 'FIELD'\nlunr.QueryLexer.TERM = 'TERM'\nlunr.QueryLexer.EDIT_DISTANCE = 'EDIT_DISTANCE'\nlunr.QueryLexer.BOOST = 'BOOST'\nlunr.QueryLexer.PRESENCE = 'PRESENCE'\n\nlunr.QueryLexer.lexField = function (lexer) {\n  lexer.backup()\n  lexer.emit(lunr.QueryLexer.FIELD)\n  lexer.ignore()\n  return lunr.QueryLexer.lexText\n}\n\nlunr.QueryLexer.lexTerm = function (lexer) {\n  if (lexer.width() > 1) {\n    lexer.backup()\n    lexer.emit(lunr.QueryLexer.TERM)\n  }\n\n  lexer.ignore()\n\n  if (lexer.more()) {\n    return lunr.QueryLexer.lexText\n  }\n}\n\nlunr.QueryLexer.lexEditDistance = function (lexer) {\n  lexer.ignore()\n  lexer.acceptDigitRun()\n  lexer.emit(lunr.QueryLexer.EDIT_DISTANCE)\n  return lunr.QueryLexer.lexText\n}\n\nlunr.QueryLexer.lexBoost = function (lexer) {\n  lexer.ignore()\n  lexer.acceptDigitRun()\n  lexer.emit(lunr.QueryLexer.BOOST)\n  return lunr.QueryLexer.lexText\n}\n\nlunr.QueryLexer.lexEOS = function (lexer) {\n  if (lexer.width() > 0) {\n    lexer.emit(lunr.QueryLexer.TERM)\n  }\n}\n\n// This matches the separator used when tokenising fields\n// within a document. These should match otherwise it is\n// not possible to search for some tokens within a document.\n//\n// It is possible for the user to change the separator on the\n// tokenizer so it _might_ clash with any other of the special\n// characters already used within the search string, e.g. :.\n//\n// This means that it is possible to change the separator in\n// such a way that makes some words unsearchable using a search\n// string.\nlunr.QueryLexer.termSeparator = lunr.tokenizer.separator\n\nlunr.QueryLexer.lexText = function (lexer) {\n  while (true) {\n    var char = lexer.next()\n\n    if (char == lunr.QueryLexer.EOS) {\n      return lunr.QueryLexer.lexEOS\n    }\n\n    // Escape character is '\\'\n    if (char.charCodeAt(0) == 92) {\n      lexer.escapeCharacter()\n      continue\n    }\n\n    if (char == \":\") {\n      return lunr.QueryLexer.lexField\n    }\n\n    if (char == \"~\") {\n      lexer.backup()\n      if (lexer.width() > 0) {\n        lexer.emit(lunr.QueryLexer.TERM)\n      }\n      return lunr.QueryLexer.lexEditDistance\n    }\n\n    if (char == \"^\") {\n      lexer.backup()\n      if (lexer.width() > 0) {\n        lexer.emit(lunr.QueryLexer.TERM)\n      }\n      return lunr.QueryLexer.lexBoost\n    }\n\n    // \"+\" indicates term presence is required\n    // checking for length to ensure that only\n    // leading \"+\" are considered\n    if (char == \"+\" && lexer.width() === 1) {\n      lexer.emit(lunr.QueryLexer.PRESENCE)\n      return lunr.QueryLexer.lexText\n    }\n\n    // \"-\" indicates term presence is prohibited\n    // checking for length to ensure that only\n    // leading \"-\" are considered\n    if (char == \"-\" && lexer.width() === 1) {\n      lexer.emit(lunr.QueryLexer.PRESENCE)\n      return lunr.QueryLexer.lexText\n    }\n\n    if (char.match(lunr.QueryLexer.termSeparator)) {\n      return lunr.QueryLexer.lexTerm\n    }\n  }\n}\n\nlunr.QueryParser = function (str, query) {\n  this.lexer = new lunr.QueryLexer (str)\n  this.query = query\n  this.currentClause = {}\n  this.lexemeIdx = 0\n}\n\nlunr.QueryParser.prototype.parse = function () {\n  this.lexer.run()\n  this.lexemes = this.lexer.lexemes\n\n  var state = lunr.QueryParser.parseClause\n\n  while (state) {\n    state = state(this)\n  }\n\n  return this.query\n}\n\nlunr.QueryParser.prototype.peekLexeme = function () {\n  return this.lexemes[this.lexemeIdx]\n}\n\nlunr.QueryParser.prototype.consumeLexeme = function () {\n  var lexeme = this.peekLexeme()\n  this.lexemeIdx += 1\n  return lexeme\n}\n\nlunr.QueryParser.prototype.nextClause = function () {\n  var completedClause = this.currentClause\n  this.query.clause(completedClause)\n  this.currentClause = {}\n}\n\nlunr.QueryParser.parseClause = function (parser) {\n  var lexeme = parser.peekLexeme()\n\n  if (lexeme == undefined) {\n    return\n  }\n\n  switch (lexeme.type) {\n    case lunr.QueryLexer.PRESENCE:\n      return lunr.QueryParser.parsePresence\n    case lunr.QueryLexer.FIELD:\n      return lunr.QueryParser.parseField\n    case lunr.QueryLexer.TERM:\n      return lunr.QueryParser.parseTerm\n    default:\n      var errorMessage = \"expected either a field or a term, found \" + lexeme.type\n\n      if (lexeme.str.length >= 1) {\n        errorMessage += \" with value '\" + lexeme.str + \"'\"\n      }\n\n      throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n  }\n}\n\nlunr.QueryParser.parsePresence = function (parser) {\n  var lexeme = parser.consumeLexeme()\n\n  if (lexeme == undefined) {\n    return\n  }\n\n  switch (lexeme.str) {\n    case \"-\":\n      parser.currentClause.presence = lunr.Query.presence.PROHIBITED\n      break\n    case \"+\":\n      parser.currentClause.presence = lunr.Query.presence.REQUIRED\n      break\n    default:\n      var errorMessage = \"unrecognised presence operator'\" + lexeme.str + \"'\"\n      throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n  }\n\n  var nextLexeme = parser.peekLexeme()\n\n  if (nextLexeme == undefined) {\n    var errorMessage = \"expecting term or field, found nothing\"\n    throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n  }\n\n  switch (nextLexeme.type) {\n    case lunr.QueryLexer.FIELD:\n      return lunr.QueryParser.parseField\n    case lunr.QueryLexer.TERM:\n      return lunr.QueryParser.parseTerm\n    default:\n      var errorMessage = \"expecting term or field, found '\" + nextLexeme.type + \"'\"\n      throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n  }\n}\n\nlunr.QueryParser.parseField = function (parser) {\n  var lexeme = parser.consumeLexeme()\n\n  if (lexeme == undefined) {\n    return\n  }\n\n  if (parser.query.allFields.indexOf(lexeme.str) == -1) {\n    var possibleFields = parser.query.allFields.map(function (f) { return \"'\" + f + \"'\" }).join(', '),\n        errorMessage = \"unrecognised field '\" + lexeme.str + \"', possible fields: \" + possibleFields\n\n    throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n  }\n\n  parser.currentClause.fields = [lexeme.str]\n\n  var nextLexeme = parser.peekLexeme()\n\n  if (nextLexeme == undefined) {\n    var errorMessage = \"expecting term, found nothing\"\n    throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n  }\n\n  switch (nextLexeme.type) {\n    case lunr.QueryLexer.TERM:\n      return lunr.QueryParser.parseTerm\n    default:\n      var errorMessage = \"expecting term, found '\" + nextLexeme.type + \"'\"\n      throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n  }\n}\n\nlunr.QueryParser.parseTerm = function (parser) {\n  var lexeme = parser.consumeLexeme()\n\n  if (lexeme == undefined) {\n    return\n  }\n\n  parser.currentClause.term = lexeme.str.toLowerCase()\n\n  if (lexeme.str.indexOf(\"*\") != -1) {\n    parser.currentClause.usePipeline = false\n  }\n\n  var nextLexeme = parser.peekLexeme()\n\n  if (nextLexeme == undefined) {\n    parser.nextClause()\n    return\n  }\n\n  switch (nextLexeme.type) {\n    case lunr.QueryLexer.TERM:\n      parser.nextClause()\n      return lunr.QueryParser.parseTerm\n    case lunr.QueryLexer.FIELD:\n      parser.nextClause()\n      return lunr.QueryParser.parseField\n    case lunr.QueryLexer.EDIT_DISTANCE:\n      return lunr.QueryParser.parseEditDistance\n    case lunr.QueryLexer.BOOST:\n      return lunr.QueryParser.parseBoost\n    case lunr.QueryLexer.PRESENCE:\n      parser.nextClause()\n      return lunr.QueryParser.parsePresence\n    default:\n      var errorMessage = \"Unexpected lexeme type '\" + nextLexeme.type + \"'\"\n      throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n  }\n}\n\nlunr.QueryParser.parseEditDistance = function (parser) {\n  var lexeme = parser.consumeLexeme()\n\n  if (lexeme == undefined) {\n    return\n  }\n\n  var editDistance = parseInt(lexeme.str, 10)\n\n  if (isNaN(editDistance)) {\n    var errorMessage = \"edit distance must be numeric\"\n    throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n  }\n\n  parser.currentClause.editDistance = editDistance\n\n  var nextLexeme = parser.peekLexeme()\n\n  if (nextLexeme == undefined) {\n    parser.nextClause()\n    return\n  }\n\n  switch (nextLexeme.type) {\n    case lunr.QueryLexer.TERM:\n      parser.nextClause()\n      return lunr.QueryParser.parseTerm\n    case lunr.QueryLexer.FIELD:\n      parser.nextClause()\n      return lunr.QueryParser.parseField\n    case lunr.QueryLexer.EDIT_DISTANCE:\n      return lunr.QueryParser.parseEditDistance\n    case lunr.QueryLexer.BOOST:\n      return lunr.QueryParser.parseBoost\n    case lunr.QueryLexer.PRESENCE:\n      parser.nextClause()\n      return lunr.QueryParser.parsePresence\n    default:\n      var errorMessage = \"Unexpected lexeme type '\" + nextLexeme.type + \"'\"\n      throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n  }\n}\n\nlunr.QueryParser.parseBoost = function (parser) {\n  var lexeme = parser.consumeLexeme()\n\n  if (lexeme == undefined) {\n    return\n  }\n\n  var boost = parseInt(lexeme.str, 10)\n\n  if (isNaN(boost)) {\n    var errorMessage = \"boost must be numeric\"\n    throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n  }\n\n  parser.currentClause.boost = boost\n\n  var nextLexeme = parser.peekLexeme()\n\n  if (nextLexeme == undefined) {\n    parser.nextClause()\n    return\n  }\n\n  switch (nextLexeme.type) {\n    case lunr.QueryLexer.TERM:\n      parser.nextClause()\n      return lunr.QueryParser.parseTerm\n    case lunr.QueryLexer.FIELD:\n      parser.nextClause()\n      return lunr.QueryParser.parseField\n    case lunr.QueryLexer.EDIT_DISTANCE:\n      return lunr.QueryParser.parseEditDistance\n    case lunr.QueryLexer.BOOST:\n      return lunr.QueryParser.parseBoost\n    case lunr.QueryLexer.PRESENCE:\n      parser.nextClause()\n      return lunr.QueryParser.parsePresence\n    default:\n      var errorMessage = \"Unexpected lexeme type '\" + nextLexeme.type + \"'\"\n      throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n  }\n}\n\n  /**\n   * export the module via AMD, CommonJS or as a browser global\n   * Export code from https://github.com/umdjs/umd/blob/master/returnExports.js\n   */\n  ;(function (root, factory) {\n    if (typeof define === 'function' && define.amd) {\n      // AMD. Register as an anonymous module.\n      define(factory)\n    } else if (typeof exports === 'object') {\n      /**\n       * Node. Does not work with strict CommonJS, but\n       * only CommonJS-like enviroments that support module.exports,\n       * like Node.\n       */\n      module.exports = factory()\n    } else {\n      // Browser globals (root is window)\n      root.lunr = factory()\n    }\n  }(this, function () {\n    /**\n     * Just return a value to define the module export.\n     * This example returns an object, but the module\n     * can return a function as the exported value.\n     */\n    return lunr\n  }))\n})();\n", "/*!\n * escape-html\n * Copyright(c) 2012-2013 TJ Holowaychuk\n * Copyright(c) 2015 Andreas Lubbe\n * Copyright(c) 2015 Tiancheng \"Timothy\" Gu\n * MIT Licensed\n */\n\n'use strict';\n\n/**\n * Module variables.\n * @private\n */\n\nvar matchHtmlRegExp = /[\"'&<>]/;\n\n/**\n * Module exports.\n * @public\n */\n\nmodule.exports = escapeHtml;\n\n/**\n * Escape special characters in the given string of html.\n *\n * @param  {string} string The string to escape for inserting into HTML\n * @return {string}\n * @public\n */\n\nfunction escapeHtml(string) {\n  var str = '' + string;\n  var match = matchHtmlRegExp.exec(str);\n\n  if (!match) {\n    return str;\n  }\n\n  var escape;\n  var html = '';\n  var index = 0;\n  var lastIndex = 0;\n\n  for (index = match.index; index < str.length; index++) {\n    switch (str.charCodeAt(index)) {\n      case 34: // \"\n        escape = '&quot;';\n        break;\n      case 38: // &\n        escape = '&amp;';\n        break;\n      case 39: // '\n        escape = '&#39;';\n        break;\n      case 60: // <\n        escape = '&lt;';\n        break;\n      case 62: // >\n        escape = '&gt;';\n        break;\n      default:\n        continue;\n    }\n\n    if (lastIndex !== index) {\n      html += str.substring(lastIndex, index);\n    }\n\n    lastIndex = index + 1;\n    html += escape;\n  }\n\n  return lastIndex !== index\n    ? html + str.substring(lastIndex, index)\n    : html;\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A RTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport lunr from \"lunr\"\n\nimport \"~/polyfills\"\n\nimport { Search, SearchIndexConfig } from \"../../_\"\nimport {\n  SearchMessage,\n  SearchMessageType\n} from \"../message\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Add support for usage with `iframe-worker` polyfill\n *\n * While `importScripts` is synchronous when executed inside of a web worker,\n * it's not possible to provide a synchronous polyfilled implementation. The\n * cool thing is that awaiting a non-Promise is a noop, so extending the type\n * definition to return a `Promise` shouldn't break anything.\n *\n * @see https://bit.ly/2PjDnXi - GitHub comment\n */\ndeclare global {\n  function importScripts(...urls: string[]): Promise<void> | void\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Search index\n */\nlet index: Search\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch (= import) multi-language support through `lunr-languages`\n *\n * This function automatically imports the stemmers necessary to process the\n * languages, which are defined through the search index configuration.\n *\n * If the worker runs inside of an `iframe` (when using `iframe-worker` as\n * a shim), the base URL for the stemmers to be loaded must be determined by\n * searching for the first `script` element with a `src` attribute, which will\n * contain the contents of this script.\n *\n * @param config - Search index configuration\n *\n * @returns Promise resolving with no result\n */\nasync function setupSearchLanguages(\n  config: SearchIndexConfig\n): Promise<void> {\n  let base = \"../lunr\"\n\n  /* Detect `iframe-worker` and fix base URL */\n  if (typeof parent !== \"undefined\" && \"IFrameWorker\" in parent) {\n    const worker = document.querySelector<HTMLScriptElement>(\"script[src]\")!\n    const [path] = worker.src.split(\"/worker\")\n\n    /* Prefix base with path */\n    base = base.replace(\"..\", path)\n  }\n\n  /* Add scripts for languages */\n  const scripts = []\n  for (const lang of config.lang) {\n    switch (lang) {\n\n      /* Add segmenter for Japanese */\n      case \"ja\":\n        scripts.push(`${base}/tinyseg.js`)\n        break\n\n      /* Add segmenter for Hindi and Thai */\n      case \"hi\":\n      case \"th\":\n        scripts.push(`${base}/wordcut.js`)\n        break\n    }\n\n    /* Add language support */\n    if (lang !== \"en\")\n      scripts.push(`${base}/min/lunr.${lang}.min.js`)\n  }\n\n  /* Add multi-language support */\n  if (config.lang.length > 1)\n    scripts.push(`${base}/min/lunr.multi.min.js`)\n\n  /* Load scripts synchronously */\n  if (scripts.length)\n    await importScripts(\n      `${base}/min/lunr.stemmer.support.min.js`,\n      ...scripts\n    )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Message handler\n *\n * @param message - Source message\n *\n * @returns Target message\n */\nexport async function handler(\n  message: SearchMessage\n): Promise<SearchMessage> {\n  switch (message.type) {\n\n    /* Search setup message */\n    case SearchMessageType.SETUP:\n      await setupSearchLanguages(message.data.config)\n      index = new Search(message.data)\n      return {\n        type: SearchMessageType.READY\n      }\n\n    /* Search query message */\n    case SearchMessageType.QUERY:\n      return {\n        type: SearchMessageType.RESULT,\n        data: index ? index.search(message.data) : { items: [] }\n      }\n\n    /* All other messages */\n    default:\n      throw new TypeError(\"Invalid message type\")\n  }\n}\n\n/* ----------------------------------------------------------------------------\n * Worker\n * ------------------------------------------------------------------------- */\n\n/* @ts-expect-error - expose Lunr.js in global scope, or stemmers won't work */\nself.lunr = lunr\n\n/* Handle messages */\naddEventListener(\"message\", async ev => {\n  postMessage(await handler(ev.data))\n})\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Polyfills\n * ------------------------------------------------------------------------- */\n\n/* Polyfill `Object.entries` */\nif (!Object.entries)\n  Object.entries = function (obj: object) {\n    const data: [string, string][] = []\n    for (const key of Object.keys(obj))\n      // @ts-expect-error - ignore property access warning\n      data.push([key, obj[key]])\n\n    /* Return entries */\n    return data\n  }\n\n/* Polyfill `Object.values` */\nif (!Object.values)\n  Object.values = function (obj: object) {\n    const data: string[] = []\n    for (const key of Object.keys(obj))\n      // @ts-expect-error - ignore property access warning\n      data.push(obj[key])\n\n    /* Return values */\n    return data\n  }\n\n/* ------------------------------------------------------------------------- */\n\n/* Polyfills for `Element` */\nif (typeof Element !== \"undefined\") {\n\n  /* Polyfill `Element.scrollTo` */\n  if (!Element.prototype.scrollTo)\n    Element.prototype.scrollTo = function (\n      x?: ScrollToOptions | number, y?: number\n    ): void {\n      if (typeof x === \"object\") {\n        this.scrollLeft = x.left!\n        this.scrollTop = x.top!\n      } else {\n        this.scrollLeft = x!\n        this.scrollTop = y!\n      }\n    }\n\n  /* Polyfill `Element.replaceWith` */\n  if (!Element.prototype.replaceWith)\n    Element.prototype.replaceWith = function (\n      ...nodes: Array<string | Node>\n    ): void {\n      const parent = this.parentNode\n      if (parent) {\n        if (nodes.length === 0)\n          parent.removeChild(this)\n\n        /* Replace children and create text nodes */\n        for (let i = nodes.length - 1; i >= 0; i--) {\n          let node = nodes[i]\n          if (typeof node === \"string\")\n            node = document.createTextNode(node)\n          else if (node.parentNode)\n            node.parentNode.removeChild(node)\n\n          /* Replace child or insert before previous sibling */\n          if (!i)\n            parent.replaceChild(node, this)\n          else\n            parent.insertBefore(this.previousSibling!, node)\n        }\n      }\n    }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport escapeHTML from \"escape-html\"\n\nimport { SearchIndexDocument } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search document\n */\nexport interface SearchDocument extends SearchIndexDocument {\n  parent?: SearchIndexDocument         /* Parent article */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search document mapping\n */\nexport type SearchDocumentMap = Map<string, SearchDocument>\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create a search document mapping\n *\n * @param docs - Search index documents\n *\n * @returns Search document map\n */\nexport function setupSearchDocumentMap(\n  docs: SearchIndexDocument[]\n): SearchDocumentMap {\n  const documents = new Map<string, SearchDocument>()\n  const parents   = new Set<SearchDocument>()\n  for (const doc of docs) {\n    const [path, hash] = doc.location.split(\"#\")\n\n    /* Extract location, title and tags */\n    const location = doc.location\n    const title    = doc.title\n    const tags     = doc.tags\n\n    /* Escape and cleanup text */\n    const text = escapeHTML(doc.text)\n      .replace(/\\s+(?=[,.:;!?])/g, \"\")\n      .replace(/\\s+/g, \" \")\n\n    /* Handle section */\n    if (hash) {\n      const parent = documents.get(path)!\n\n      /* Ignore first section, override article */\n      if (!parents.has(parent)) {\n        parent.title = doc.title\n        parent.text  = text\n\n        /* Remember that we processed the article */\n        parents.add(parent)\n\n      /* Add subsequent section */\n      } else {\n        documents.set(location, {\n          location,\n          title,\n          text,\n          parent\n        })\n      }\n\n    /* Add article */\n    } else {\n      documents.set(location, {\n        location,\n        title,\n        text,\n        ...tags && { tags }\n      })\n    }\n  }\n  return documents\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport escapeHTML from \"escape-html\"\n\nimport { SearchIndexConfig } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search highlight function\n *\n * @param value - Value\n *\n * @returns Highlighted value\n */\nexport type SearchHighlightFn = (value: string) => string\n\n/**\n * Search highlight factory function\n *\n * @param query - Query value\n *\n * @returns Search highlight function\n */\nexport type SearchHighlightFactoryFn = (query: string) => SearchHighlightFn\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create a search highlighter\n *\n * @param config - Search index configuration\n * @param escape - Whether to escape HTML\n *\n * @returns Search highlight factory function\n */\nexport function setupSearchHighlighter(\n  config: SearchIndexConfig, escape: boolean\n): SearchHighlightFactoryFn {\n  const separator = new RegExp(config.separator, \"img\")\n  const highlight = (_: unknown, data: string, term: string) => {\n    return `${data}<mark data-md-highlight>${term}</mark>`\n  }\n\n  /* Return factory function */\n  return (query: string) => {\n    query = query\n      .replace(/[\\s*+\\-:~^]+/g, \" \")\n      .trim()\n\n    /* Create search term match expression */\n    const match = new RegExp(`(^|${config.separator})(${\n      query\n        .replace(/[|\\\\{}()[\\]^$+*?.-]/g, \"\\\\$&\")\n        .replace(separator, \"|\")\n    })`, \"img\")\n\n    /* Highlight string value */\n    return value => (\n      escape\n        ? escapeHTML(value)\n        : value\n      )\n        .replace(match, highlight)\n        .replace(/<\\/mark>(\\s+)<mark[^>]*>/img, \"$1\")\n  }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search query clause\n */\nexport interface SearchQueryClause {\n  presence: lunr.Query.presence        /* Clause presence */\n  term: string                         /* Clause term */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search query terms\n */\nexport type SearchQueryTerms = Record<string, boolean>\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Parse a search query for analysis\n *\n * @param value - Query value\n *\n * @returns Search query clauses\n */\nexport function parseSearchQuery(\n  value: string\n): SearchQueryClause[] {\n  const query  = new (lunr as any).Query([\"title\", \"text\"])\n  const parser = new (lunr as any).QueryParser(value, query)\n\n  /* Parse and return query clauses */\n  parser.parse()\n  return query.clauses\n}\n\n/**\n * Analyze the search query clauses in regard to the search terms found\n *\n * @param query - Search query clauses\n * @param terms - Search terms\n *\n * @returns Search query terms\n */\nexport function getSearchQueryTerms(\n  query: SearchQueryClause[], terms: string[]\n): SearchQueryTerms {\n  const clauses = new Set<SearchQueryClause>(query)\n\n  /* Match query clauses against terms */\n  const result: SearchQueryTerms = {}\n  for (let t = 0; t < terms.length; t++)\n    for (const clause of clauses)\n      if (terms[t].startsWith(clause.term)) {\n        result[clause.term] = true\n        clauses.delete(clause)\n      }\n\n  /* Annotate unmatched non-stopword query clauses */\n  for (const clause of clauses)\n    if (lunr.stopWordFilter?.(clause.term as any))\n      result[clause.term] = false\n\n  /* Return query terms */\n  return result\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath <martin.donath@squidfunk.com>\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n  SearchDocument,\n  SearchDocumentMap,\n  setupSearchDocumentMap\n} from \"../document\"\nimport {\n  SearchHighlightFactoryFn,\n  setupSearchHighlighter\n} from \"../highlighter\"\nimport { SearchOptions } from \"../options\"\nimport {\n  SearchQueryTerms,\n  getSearchQueryTerms,\n  parseSearchQuery\n} from \"../query\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search index configuration\n */\nexport interface SearchIndexConfig {\n  lang: string[]                       /* Search languages */\n  separator: string                    /* Search separator */\n}\n\n/**\n * Search index document\n */\nexport interface SearchIndexDocument {\n  location: string                     /* Document location */\n  title: string                        /* Document title */\n  text: string                         /* Document text */\n  tags?: string[]                      /* Document tags */\n  boost?: number                       /* Document boost */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search index\n *\n * This interfaces describes the format of the `search_index.json` file which\n * is automatically built by the MkDocs search plugin.\n */\nexport interface SearchIndex {\n  config: SearchIndexConfig            /* Search index configuration */\n  docs: SearchIndexDocument[]          /* Search index documents */\n  options: SearchOptions               /* Search options */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search metadata\n */\nexport interface SearchMetadata {\n  score: number                        /* Score (relevance) */\n  terms: SearchQueryTerms              /* Search query terms */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search result document\n */\nexport type SearchResultDocument = SearchDocument & SearchMetadata\n\n/**\n * Search result item\n */\nexport type SearchResultItem = SearchResultDocument[]\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search result\n */\nexport interface SearchResult {\n  items: SearchResultItem[]            /* Search result items */\n  suggestions?: string[]               /* Search suggestions */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Compute the difference of two lists of strings\n *\n * @param a - 1st list of strings\n * @param b - 2nd list of strings\n *\n * @returns Difference\n */\nfunction difference(a: string[], b: string[]): string[] {\n  const [x, y] = [new Set(a), new Set(b)]\n  return [\n    ...new Set([...x].filter(value => !y.has(value)))\n  ]\n}\n\n/* ----------------------------------------------------------------------------\n * Class\n * ------------------------------------------------------------------------- */\n\n/**\n * Search index\n */\nexport class Search {\n\n  /**\n   * Search document mapping\n   *\n   * A mapping of URLs (including hash fragments) to the actual articles and\n   * sections of the documentation. The search document mapping must be created\n   * regardless of whether the index was prebuilt or not, as Lunr.js itself\n   * only stores the actual index.\n   */\n  protected documents: SearchDocumentMap\n\n  /**\n   * Search highlight factory function\n   */\n  protected highlight: SearchHighlightFactoryFn\n\n  /**\n   * The underlying Lunr.js search index\n   */\n  protected index: lunr.Index\n\n  /**\n   * Search options\n   */\n  protected options: SearchOptions\n\n  /**\n   * Create the search integration\n   *\n   * @param data - Search index\n   */\n  public constructor({ config, docs, options }: SearchIndex) {\n    this.options = options\n\n    /* Set up document map and highlighter factory */\n    this.documents = setupSearchDocumentMap(docs)\n    this.highlight = setupSearchHighlighter(config, false)\n\n    /* Set separator for tokenizer */\n    lunr.tokenizer.separator = new RegExp(config.separator)\n\n    /* Create search index */\n    this.index = lunr(function () {\n\n      /* Set up multi-language support */\n      if (config.lang.length === 1 && config.lang[0] !== \"en\") {\n        this.use((lunr as any)[config.lang[0]])\n      } else if (config.lang.length > 1) {\n        this.use((lunr as any).multiLanguage(...config.lang))\n      }\n\n      /* Compute functions to be removed from the pipeline */\n      const fns = difference([\n        \"trimmer\", \"stopWordFilter\", \"stemmer\"\n      ], options.pipeline)\n\n      /* Remove functions from the pipeline for registered languages */\n      for (const lang of config.lang.map(language => (\n        language === \"en\" ? lunr : (lunr as any)[language]\n      ))) {\n        for (const fn of fns) {\n          this.pipeline.remove(lang[fn])\n          this.searchPipeline.remove(lang[fn])\n        }\n      }\n\n      /* Set up reference */\n      this.ref(\"location\")\n\n      /* Set up fields */\n      this.field(\"title\", { boost: 1e3 })\n      this.field(\"text\")\n      this.field(\"tags\", { boost: 1e6, extractor: doc => {\n        const { tags = [] } = doc as SearchDocument\n        return tags.reduce((list, tag) => [\n          ...list,\n          ...lunr.tokenizer(tag)\n        ], [] as lunr.Token[])\n      } })\n\n      /* Index documents */\n      for (const doc of docs)\n        this.add(doc, { boost: doc.boost })\n    })\n  }\n\n  /**\n   * Search for matching documents\n   *\n   * The search index which MkDocs provides is divided up into articles, which\n   * contain the whole content of the individual pages, and sections, which only\n   * contain the contents of the subsections obtained by breaking the individual\n   * pages up at `h1` ... `h6`. As there may be many sections on different pages\n   * with identical titles (for example within this very project, e.g. \"Usage\"\n   * or \"Installation\"), they need to be put into the context of the containing\n   * page. For this reason, section results are grouped within their respective\n   * articles which are the top-level results that are returned.\n   *\n   * @param query - Query value\n   *\n   * @returns Search results\n   */\n  public search(query: string): SearchResult {\n    if (query) {\n      try {\n        const highlight = this.highlight(query)\n\n        /* Parse query to extract clauses for analysis */\n        const clauses = parseSearchQuery(query)\n          .filter(clause => (\n            clause.presence !== lunr.Query.presence.PROHIBITED\n          ))\n\n        /* Perform search and post-process results */\n        const groups = this.index.search(`${query}*`)\n\n          /* Apply post-query boosts based on title and search query terms */\n          .reduce<SearchResultItem>((item, { ref, score, matchData }) => {\n            const document = this.documents.get(ref)\n            if (typeof document !== \"undefined\") {\n              const { location, title, text, tags, parent } = document\n\n              /* Compute and analyze search query terms */\n              const terms = getSearchQueryTerms(\n                clauses,\n                Object.keys(matchData.metadata)\n              )\n\n              /* Highlight title and text and apply post-query boosts */\n              const boost = +!parent + +Object.values(terms).every(t => t)\n              item.push({\n                location,\n                title: highlight(title),\n                text:  highlight(text),\n                ...tags && { tags: tags.map(highlight) },\n                score: score * (1 + boost),\n                terms\n              })\n            }\n            return item\n          }, [])\n\n          /* Sort search results again after applying boosts */\n          .sort((a, b) => b.score - a.score)\n\n          /* Group search results by page */\n          .reduce((items, result) => {\n            const document = this.documents.get(result.location)\n            if (typeof document !== \"undefined\") {\n              const ref = \"parent\" in document\n                ? document.parent!.location\n                : document.location\n              items.set(ref, [...items.get(ref) || [], result])\n            }\n            return items\n          }, new Map<string, SearchResultItem>())\n\n        /* Generate search suggestions, if desired */\n        let suggestions: string[] | undefined\n        if (this.options.suggestions) {\n          const titles = this.index.query(builder => {\n            for (const clause of clauses)\n              builder.term(clause.term, {\n                fields: [\"title\"],\n                presence: lunr.Query.presence.REQUIRED,\n                wildcard: lunr.Query.wildcard.TRAILING\n              })\n          })\n\n          /* Retrieve suggestions for best match */\n          suggestions = titles.length\n            ? Object.keys(titles[0].matchData.metadata)\n            : []\n        }\n\n        /* Return items and suggestions */\n        return {\n          items: [...groups.values()],\n          ...typeof suggestions !== \"undefined\" && { suggestions }\n        }\n\n      /* Log errors to console (for now) */\n      } catch {\n        console.warn(`Invalid query: ${query} \u2013 see https://bit.ly/2s3ChXG`)\n      }\n    }\n\n    /* Return nothing in case of error or empty query */\n    return { items: [] }\n  }\n}\n"],
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deleted file mode 100644
index d6684d58a1..0000000000
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diff --git a/assets/stylesheets/main.472b142f.min.css b/assets/stylesheets/main.472b142f.min.css
new file mode 100644
index 0000000000..ef52c08527
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+++ b/assets/stylesheets/main.472b142f.min.css
@@ -0,0 +1 @@
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diff --git a/assets/stylesheets/palette.08040f6c.min.css b/assets/stylesheets/palette.08040f6c.min.css
new file mode 100644
index 0000000000..9ba9032fd8
--- /dev/null
+++ b/assets/stylesheets/palette.08040f6c.min.css
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+@media screen{[data-md-color-scheme=slate]{--md-hue:232;--md-default-fg-color:hsla(var(--md-hue),75%,95%,1);--md-default-fg-color--light:hsla(var(--md-hue),75%,90%,0.62);--md-default-fg-color--lighter:hsla(var(--md-hue),75%,90%,0.32);--md-default-fg-color--lightest:hsla(var(--md-hue),75%,90%,0.12);--md-default-bg-color:hsla(var(--md-hue),15%,21%,1);--md-default-bg-color--light:hsla(var(--md-hue),15%,21%,0.54);--md-default-bg-color--lighter:hsla(var(--md-hue),15%,21%,0.26);--md-default-bg-color--lightest:hsla(var(--md-hue),15%,21%,0.07);--md-code-fg-color:hsla(var(--md-hue),18%,86%,1);--md-code-bg-color:hsla(var(--md-hue),15%,15%,1);--md-code-hl-color:rgba(66,135,255,.15);--md-code-hl-number-color:#e6695b;--md-code-hl-special-color:#f06090;--md-code-hl-function-color:#c973d9;--md-code-hl-constant-color:#9383e2;--md-code-hl-keyword-color:#6791e0;--md-code-hl-string-color:#2fb170;--md-code-hl-name-color:var(--md-code-fg-color);--md-code-hl-operator-color:var(--md-default-fg-color--light);--md-code-hl-punctuation-color:var(--md-default-fg-color--light);--md-code-hl-comment-color:var(--md-default-fg-color--light);--md-code-hl-generic-color:var(--md-default-fg-color--light);--md-code-hl-variable-color:var(--md-default-fg-color--light);--md-typeset-color:var(--md-default-fg-color);--md-typeset-a-color:var(--md-primary-fg-color);--md-typeset-mark-color:rgba(66,135,255,.3);--md-typeset-kbd-color:hsla(var(--md-hue),15%,94%,0.12);--md-typeset-kbd-accent-color:hsla(var(--md-hue),15%,94%,0.2);--md-typeset-kbd-border-color:hsla(var(--md-hue),15%,14%,1);--md-typeset-table-color:hsla(var(--md-hue),75%,95%,0.12);--md-admonition-fg-color:var(--md-default-fg-color);--md-admonition-bg-color:var(--md-default-bg-color);--md-footer-bg-color:hsla(var(--md-hue),15%,12%,0.87);--md-footer-bg-color--dark:hsla(var(--md-hue),15%,10%,1);--md-shadow-z1:0 0.2rem 0.5rem rgba(0,0,0,.2),0 0 0.05rem rgba(0,0,0,.1);--md-shadow-z2:0 0.2rem 0.5rem rgba(0,0,0,.3),0 0 0.05rem rgba(0,0,0,.25);--md-shadow-z3:0 0.2rem 0.5rem rgba(0,0,0,.4),0 0 0.05rem rgba(0,0,0,.35)}[data-md-color-scheme=slate] img[src$="#gh-light-mode-only"],[data-md-color-scheme=slate] img[src$="#only-light"]{display:none}[data-md-color-scheme=slate] img[src$="#gh-dark-mode-only"],[data-md-color-scheme=slate] img[src$="#only-dark"]{display:initial}[data-md-color-scheme=slate][data-md-color-primary=pink]{--md-typeset-a-color:#ed5487}[data-md-color-scheme=slate][data-md-color-primary=purple]{--md-typeset-a-color:#bd78c9}[data-md-color-scheme=slate][data-md-color-primary=deep-purple]{--md-typeset-a-color:#a682e3}[data-md-color-scheme=slate][data-md-color-primary=indigo]{--md-typeset-a-color:#6c91d5}[data-md-color-scheme=slate][data-md-color-primary=teal]{--md-typeset-a-color:#00ccb8}[data-md-color-scheme=slate][data-md-color-primary=green]{--md-typeset-a-color:#71c174}[data-md-color-scheme=slate][data-md-color-primary=deep-orange]{--md-typeset-a-color:#ff9575}[data-md-color-scheme=slate][data-md-color-primary=brown]{--md-typeset-a-color:#c7846b}[data-md-color-scheme=slate][data-md-color-primary=black],[data-md-color-scheme=slate][data-md-color-primary=blue-grey],[data-md-color-scheme=slate][data-md-color-primary=grey],[data-md-color-scheme=slate][data-md-color-primary=white]{--md-typeset-a-color:#6c91d5}[data-md-color-switching] *,[data-md-color-switching] :after,[data-md-color-switching] :before{transition-duration:0ms!important}}[data-md-color-accent=red]{--md-accent-fg-color:#ff1947;--md-accent-fg-color--transparent:rgba(255,25,71,.1);--md-accent-bg-color:#fff;--md-accent-bg-color--light:hsla(0,0%,100%,.7)}[data-md-color-accent=pink]{--md-accent-fg-color:#f50056;--md-accent-fg-color--transparent:rgba(245,0,86,.1);--md-accent-bg-color:#fff;--md-accent-bg-color--light:hsla(0,0%,100%,.7)}[data-md-color-accent=purple]{--md-accent-fg-color:#df41fb;--md-accent-fg-color--transparent:rgba(223,65,251,.1);--md-accent-bg-color:#fff;--md-accent-bg-color--light:hsla(0,0%,100%,.7)}[data-md-color-accent=deep-purple]{--md-accent-fg-color:#7c4dff;--md-accent-fg-color--transparent:rgba(124,77,255,.1);--md-accent-bg-color:#fff;--md-accent-bg-color--light:hsla(0,0%,100%,.7)}[data-md-color-accent=indigo]{--md-accent-fg-color:#526cfe;--md-accent-fg-color--transparent:rgba(82,108,254,.1);--md-accent-bg-color:#fff;--md-accent-bg-color--light:hsla(0,0%,100%,.7)}[data-md-color-accent=blue]{--md-accent-fg-color:#4287ff;--md-accent-fg-color--transparent:rgba(66,135,255,.1);--md-accent-bg-color:#fff;--md-accent-bg-color--light:hsla(0,0%,100%,.7)}[data-md-color-accent=light-blue]{--md-accent-fg-color:#0091eb;--md-accent-fg-color--transparent:rgba(0,145,235,.1);--md-accent-bg-color:#fff;--md-accent-bg-color--light:hsla(0,0%,100%,.7)}[data-md-color-accent=cyan]{--md-accent-fg-color:#00bad6;--md-accent-fg-color--transparent:rgba(0,186,214,.1);--md-accent-bg-color:#fff;--md-accent-bg-color--light:hsla(0,0%,100%,.7)}[data-md-color-accent=teal]{--md-accent-fg-color:#00bda4;--md-accent-fg-color--transparent:rgba(0,189,164,.1);--md-accent-bg-color:#fff;--md-accent-bg-color--light:hsla(0,0%,100%,.7)}[data-md-color-accent=green]{--md-accent-fg-color:#00c753;--md-accent-fg-color--transparent:rgba(0,199,83,.1);--md-accent-bg-color:#fff;--md-accent-bg-color--light:hsla(0,0%,100%,.7)}[data-md-color-accent=light-green]{--md-accent-fg-color:#63de17;--md-accent-fg-color--transparent:rgba(99,222,23,.1);--md-accent-bg-color:#fff;--md-accent-bg-color--light:hsla(0,0%,100%,.7)}[data-md-color-accent=lime]{--md-accent-fg-color:#b0eb00;--md-accent-fg-color--transparent:rgba(176,235,0,.1);--md-accent-bg-color:rgba(0,0,0,.87);--md-accent-bg-color--light:rgba(0,0,0,.54)}[data-md-color-accent=yellow]{--md-accent-fg-color:#ffd500;--md-accent-fg-color--transparent:rgba(255,213,0,.1);--md-accent-bg-color:rgba(0,0,0,.87);--md-accent-bg-color--light:rgba(0,0,0,.54)}[data-md-color-accent=amber]{--md-accent-fg-color:#fa0;--md-accent-fg-color--transparent:rgba(255,170,0,.1);--md-accent-bg-color:rgba(0,0,0,.87);--md-accent-bg-color--light:rgba(0,0,0,.54)}[data-md-color-accent=orange]{--md-accent-fg-color:#ff9100;--md-accent-fg-color--transparent:rgba(255,145,0,.1);--md-accent-bg-color:rgba(0,0,0,.87);--md-accent-bg-color--light:rgba(0,0,0,.54)}[data-md-color-accent=deep-orange]{--md-accent-fg-color:#ff6e42;--md-accent-fg-color--transparent:rgba(255,110,66,.1);--md-accent-bg-color:#fff;--md-accent-bg-color--light:hsla(0,0%,100%,.7)}[data-md-color-primary=red]{--md-primary-fg-color:#ef5552;--md-primary-fg-color--light:#e57171;--md-primary-fg-color--dark:#e53734;--md-primary-bg-color:#fff;--md-primary-bg-color--light:hsla(0,0%,100%,.7)}[data-md-color-primary=pink]{--md-primary-fg-color:#e92063;--md-primary-fg-color--light:#ec417a;--md-primary-fg-color--dark:#c3185d;--md-primary-bg-color:#fff;--md-primary-bg-color--light:hsla(0,0%,100%,.7)}[data-md-color-primary=purple]{--md-primary-fg-color:#ab47bd;--md-primary-fg-color--light:#bb69c9;--md-primary-fg-color--dark:#8c24a8;--md-primary-bg-color:#fff;--md-primary-bg-color--light:hsla(0,0%,100%,.7)}[data-md-color-primary=deep-purple]{--md-primary-fg-color:#7e56c2;--md-primary-fg-color--light:#9574cd;--md-primary-fg-color--dark:#673ab6;--md-primary-bg-color:#fff;--md-primary-bg-color--light:hsla(0,0%,100%,.7)}[data-md-color-primary=indigo]{--md-primary-fg-color:#4051b5;--md-primary-fg-color--light:#5d6cc0;--md-primary-fg-color--dark:#303fa1;--md-primary-bg-color:#fff;--md-primary-bg-color--light:hsla(0,0%,100%,.7)}[data-md-color-primary=blue]{--md-primary-fg-color:#2094f3;--md-primary-fg-color--light:#42a5f5;--md-primary-fg-color--dark:#1975d2;--md-primary-bg-color:#fff;--md-primary-bg-color--light:hsla(0,0%,100%,.7)}[data-md-color-primary=light-blue]{--md-primary-fg-color:#02a6f2;--md-primary-fg-color--light:#28b5f6;--md-primary-fg-color--dark:#0287cf;--md-primary-bg-color:#fff;--md-primary-bg-color--light:hsla(0,0%,100%,.7)}[data-md-color-primary=cyan]{--md-primary-fg-color:#00bdd6;--md-primary-fg-color--light:#25c5da;--md-primary-fg-color--dark:#0097a8;--md-primary-bg-color:#fff;--md-primary-bg-color--light:hsla(0,0%,100%,.7)}[data-md-color-primary=teal]{--md-primary-fg-color:#009485;--md-primary-fg-color--light:#26a699;--md-primary-fg-color--dark:#007a6c;--md-primary-bg-color:#fff;--md-primary-bg-color--light:hsla(0,0%,100%,.7)}[data-md-color-primary=green]{--md-primary-fg-color:#4cae4f;--md-primary-fg-color--light:#68bb6c;--md-primary-fg-color--dark:#398e3d;--md-primary-bg-color:#fff;--md-primary-bg-color--light:hsla(0,0%,100%,.7)}[data-md-color-primary=light-green]{--md-primary-fg-color:#8bc34b;--md-primary-fg-color--light:#9ccc66;--md-primary-fg-color--dark:#689f38;--md-primary-bg-color:#fff;--md-primary-bg-color--light:hsla(0,0%,100%,.7)}[data-md-color-primary=lime]{--md-primary-fg-color:#cbdc38;--md-primary-fg-color--light:#d3e156;--md-primary-fg-color--dark:#b0b52c;--md-primary-bg-color:rgba(0,0,0,.87);--md-primary-bg-color--light:rgba(0,0,0,.54)}[data-md-color-primary=yellow]{--md-primary-fg-color:#ffec3d;--md-primary-fg-color--light:#ffee57;--md-primary-fg-color--dark:#fbc02d;--md-primary-bg-color:rgba(0,0,0,.87);--md-primary-bg-color--light:rgba(0,0,0,.54)}[data-md-color-primary=amber]{--md-primary-fg-color:#ffc105;--md-primary-fg-color--light:#ffc929;--md-primary-fg-color--dark:#ffa200;--md-primary-bg-color:rgba(0,0,0,.87);--md-primary-bg-color--light:rgba(0,0,0,.54)}[data-md-color-primary=orange]{--md-primary-fg-color:#ffa724;--md-primary-fg-color--light:#ffa724;--md-primary-fg-color--dark:#fa8900;--md-primary-bg-color:rgba(0,0,0,.87);--md-primary-bg-color--light:rgba(0,0,0,.54)}[data-md-color-primary=deep-orange]{--md-primary-fg-color:#ff6e42;--md-primary-fg-color--light:#ff8a66;--md-primary-fg-color--dark:#f4511f;--md-primary-bg-color:#fff;--md-primary-bg-color--light:hsla(0,0%,100%,.7)}[data-md-color-primary=brown]{--md-primary-fg-color:#795649;--md-primary-fg-color--light:#8d6e62;--md-primary-fg-color--dark:#5d4037;--md-primary-bg-color:#fff;--md-primary-bg-color--light:hsla(0,0%,100%,.7)}[data-md-color-primary=grey]{--md-primary-fg-color:#757575;--md-primary-fg-color--light:#9e9e9e;--md-primary-fg-color--dark:#616161;--md-primary-bg-color:#fff;--md-primary-bg-color--light:hsla(0,0%,100%,.7);--md-typeset-a-color:#4051b5}[data-md-color-primary=blue-grey]{--md-primary-fg-color:#546d78;--md-primary-fg-color--light:#607c8a;--md-primary-fg-color--dark:#455a63;--md-primary-bg-color:#fff;--md-primary-bg-color--light:hsla(0,0%,100%,.7);--md-typeset-a-color:#4051b5}[data-md-color-primary=light-green]:not([data-md-color-scheme=slate]){--md-typeset-a-color:#72ad2e}[data-md-color-primary=lime]:not([data-md-color-scheme=slate]){--md-typeset-a-color:#8b990a}[data-md-color-primary=yellow]:not([data-md-color-scheme=slate]){--md-typeset-a-color:#b8a500}[data-md-color-primary=amber]:not([data-md-color-scheme=slate]){--md-typeset-a-color:#d19d00}[data-md-color-primary=orange]:not([data-md-color-scheme=slate]){--md-typeset-a-color:#e68a00}[data-md-color-primary=white]{--md-primary-fg-color:#fff;--md-primary-fg-color--light:hsla(0,0%,100%,.7);--md-primary-fg-color--dark:rgba(0,0,0,.07);--md-primary-bg-color:rgba(0,0,0,.87);--md-primary-bg-color--light:rgba(0,0,0,.54);--md-typeset-a-color:#4051b5}[data-md-color-primary=white] .md-button{color:var(--md-typeset-a-color)}[data-md-color-primary=white] .md-button--primary{background-color:var(--md-typeset-a-color);border-color:var(--md-typeset-a-color);color:#fff}@media screen and (min-width:60em){[data-md-color-primary=white] .md-search__form{background-color:rgba(0,0,0,.07)}[data-md-color-primary=white] .md-search__form:hover{background-color:rgba(0,0,0,.32)}[data-md-color-primary=white] .md-search__input+.md-search__icon{color:rgba(0,0,0,.87)}}@media screen and (min-width:76.25em){[data-md-color-primary=white] .md-tabs{border-bottom:.05rem solid rgba(0,0,0,.07)}}[data-md-color-primary=black]{--md-primary-fg-color:#000;--md-primary-fg-color--light:rgba(0,0,0,.54);--md-primary-fg-color--dark:#000;--md-primary-bg-color:#fff;--md-primary-bg-color--light:hsla(0,0%,100%,.7);--md-typeset-a-color:#4051b5}[data-md-color-primary=black] .md-button{color:var(--md-typeset-a-color)}[data-md-color-primary=black] .md-button--primary{background-color:var(--md-typeset-a-color);border-color:var(--md-typeset-a-color);color:#fff}[data-md-color-primary=black] .md-header{background-color:#000}@media screen and (max-width:59.9375em){[data-md-color-primary=black] .md-nav__source{background-color:rgba(0,0,0,.87)}}@media screen and (min-width:60em){[data-md-color-primary=black] .md-search__form{background-color:hsla(0,0%,100%,.12)}[data-md-color-primary=black] .md-search__form:hover{background-color:hsla(0,0%,100%,.3)}}@media screen and (max-width:76.1875em){html [data-md-color-primary=black] .md-nav--primary .md-nav__title[for=__drawer]{background-color:#000}}@media screen and (min-width:76.25em){[data-md-color-primary=black] .md-tabs{background-color:#000}}
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diff --git a/assets/stylesheets/palette.08040f6c.min.css.map b/assets/stylesheets/palette.08040f6c.min.css.map
new file mode 100644
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+++ b/assets/stylesheets/palette.08040f6c.min.css.map
@@ -0,0 +1 @@
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deleted file mode 100644
index 35420eb6f8..0000000000
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diff --git a/assets/stylesheets/palette.ecc896b0.min.css.map b/assets/stylesheets/palette.ecc896b0.min.css.map
deleted file mode 100644
index d616c2a6d5..0000000000
--- a/assets/stylesheets/palette.ecc896b0.min.css.map
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diff --git a/com.unity.ml-agents.extensions/index.html b/com.unity.ml-agents.extensions/index.html
index 2a028ec5f0..e1199a3f31 100644
--- a/com.unity.ml-agents.extensions/index.html
+++ b/com.unity.ml-agents.extensions/index.html
@@ -14,10 +14,8 @@
       
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-      
-      
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-      <meta name="generator" content="mkdocs-1.4.3, mkdocs-material-9.1.15">
+      <meta name="generator" content="mkdocs-1.4.2, mkdocs-material-8.5.10">
     
     
       
@@ -25,10 +23,15 @@
       
     
     
-      <link rel="stylesheet" href="../assets/stylesheets/main.26e3688c.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.472b142f.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.ecc896b0.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.08040f6c.min.css">
+        
+          
+          
+          <meta name="theme-color" content="#2094f3">
+        
       
       
 
@@ -83,9 +86,7 @@
     
       
 
-  
-
-<header class="md-header md-header--shadow" data-md-component="header">
+<header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
     <a href=".." title="Unity ML-Agents Toolkit" class="md-header__button md-logo" aria-label="Unity ML-Agents Toolkit" data-md-component="logo">
       
@@ -112,8 +113,6 @@
       </div>
     </div>
     
-      
-    
     
     
       <label class="md-header__button md-icon" for="__search">
@@ -142,7 +141,7 @@
           <div class="md-search-result__meta">
             Initializing search
           </div>
-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
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@@ -154,7 +153,7 @@
         <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
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+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -198,7 +197,7 @@
       <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -264,18 +263,17 @@
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+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_4" type="checkbox" id="__nav_4" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
       
       
-      
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_4">
           Background
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         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Background" data-md-level="1">
         <label class="md-nav__title" for="__nav_4">
           <span class="md-nav__icon md-icon"></span>
           Background
@@ -341,18 +339,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_5" type="checkbox" id="__nav_5" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
-      
       
       
-        <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_5">
           Interfacing with Unity Builds
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Interfacing with Unity Builds" data-md-level="1">
         <label class="md-nav__title" for="__nav_5">
           <span class="md-nav__icon md-icon"></span>
           Interfacing with Unity Builds
@@ -418,18 +415,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_6" type="checkbox" id="__nav_6" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" >
-      
       
       
-        <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_6">
           Python API Docs
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Python API Docs" data-md-level="1">
         <label class="md-nav__title" for="__nav_6">
           <span class="md-nav__icon md-icon"></span>
           Python API Docs
@@ -509,18 +505,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_7" type="checkbox" id="__nav_7" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_7" >
       
       
-      
-        <label class="md-nav__link" for="__nav_7" id="__nav_7_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_7">
           Tutorials
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_7_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Tutorials" data-md-level="1">
         <label class="md-nav__title" for="__nav_7">
           <span class="md-nav__icon md-icon"></span>
           Tutorials
@@ -558,18 +553,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_8" type="checkbox" id="__nav_8" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_8" >
-      
       
       
-        <label class="md-nav__link" for="__nav_8" id="__nav_8_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_8">
           About
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="About" data-md-level="1">
         <label class="md-nav__title" for="__nav_8">
           <span class="md-nav__icon md-icon"></span>
           About
@@ -733,8 +727,10 @@
                 
                   
 
-  
-  
+  <a href="https://github.com/Unity-Technologies/ml-agents/edit/main/docs/com.unity.ml-agents.extensions.md" title="Edit this page" class="md-content__button md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
 
 <h1 id="about-ml-agents-extensions-package-comunityml-agentsextensions">About ML-Agents Extensions package (<code>com.unity.ml-agents.extensions</code>)</h1>
@@ -854,10 +850,10 @@ <h2 id="need-help">Need Help?</h2>
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
-    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.208ed371.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.16e2a7d4.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.b4d07000.min.js"></script>
+      <script src="../assets/javascripts/bundle.d6c3db9e.min.js"></script>
       
     
   </body>
diff --git a/com.unity.ml-agents/index.html b/com.unity.ml-agents/index.html
index 39e45ae523..2966d8a959 100644
--- a/com.unity.ml-agents/index.html
+++ b/com.unity.ml-agents/index.html
@@ -14,10 +14,8 @@
       
         <link rel="canonical" href="https://unity-technologies.github.io/ml-agents/com.unity.ml-agents/">
       
-      
-      
       <link rel="icon" href="../images/unity-logo-black.png">
-      <meta name="generator" content="mkdocs-1.4.3, mkdocs-material-9.1.15">
+      <meta name="generator" content="mkdocs-1.4.2, mkdocs-material-8.5.10">
     
     
       
@@ -25,10 +23,15 @@
       
     
     
-      <link rel="stylesheet" href="../assets/stylesheets/main.26e3688c.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.472b142f.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.ecc896b0.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.08040f6c.min.css">
+        
+          
+          
+          <meta name="theme-color" content="#2094f3">
+        
       
       
 
@@ -83,9 +86,7 @@
     
       
 
-  
-
-<header class="md-header md-header--shadow" data-md-component="header">
+<header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
     <a href=".." title="Unity ML-Agents Toolkit" class="md-header__button md-logo" aria-label="Unity ML-Agents Toolkit" data-md-component="logo">
       
@@ -112,8 +113,6 @@
       </div>
     </div>
     
-      
-    
     
     
       <label class="md-header__button md-icon" for="__search">
@@ -142,7 +141,7 @@
           <div class="md-search-result__meta">
             Initializing search
           </div>
-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
         </div>
       </div>
     </div>
@@ -154,7 +153,7 @@
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="false">
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-  
-  
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+  </a>
 
 
 <h1 id="about-ml-agents-package-comunityml-agents">About ML-Agents package (<code>com.unity.ml-agents</code>)</h1>
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+      <script src="../assets/javascripts/bundle.d6c3db9e.min.js"></script>
       
     
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diff --git a/doxygen/Readme/index.html b/doxygen/Readme/index.html
index ec2ed560c5..45a14fde1e 100644
--- a/doxygen/Readme/index.html
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+        <link rel="stylesheet" href="../../assets/stylesheets/palette.08040f6c.min.css">
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@@ -198,7 +197,7 @@
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-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_7_label" aria-expanded="false">
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 <h1 id="doxygen-files">Doxygen files</h1>
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diff --git a/images/elo_expected_score_formula.png b/images/elo_expected_score_formula.png
new file mode 100644
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diff --git a/index.html b/index.html
index 2fc73e717e..18c59df0e0 100644
--- a/index.html
+++ b/index.html
@@ -14,12 +14,8 @@
       
         <link rel="canonical" href="https://unity-technologies.github.io/ml-agents/">
       
-      
-      
-        <link rel="next" href="ML-Agents-Overview/">
-      
       <link rel="icon" href="images/unity-logo-black.png">
-      <meta name="generator" content="mkdocs-1.4.3, mkdocs-material-9.1.15">
+      <meta name="generator" content="mkdocs-1.4.2, mkdocs-material-8.5.10">
     
     
       
@@ -27,10 +23,15 @@
       
     
     
-      <link rel="stylesheet" href="assets/stylesheets/main.26e3688c.min.css">
+      <link rel="stylesheet" href="assets/stylesheets/main.472b142f.min.css">
       
         
-        <link rel="stylesheet" href="assets/stylesheets/palette.ecc896b0.min.css">
+        <link rel="stylesheet" href="assets/stylesheets/palette.08040f6c.min.css">
+        
+          
+          
+          <meta name="theme-color" content="#2094f3">
+        
       
       
 
@@ -72,6 +73,11 @@
     <label class="md-overlay" for="__drawer"></label>
     <div data-md-component="skip">
       
+        
+        <a href="#unity-ml-agents-toolkit" class="md-skip">
+          Skip to content
+        </a>
+      
     </div>
     <div data-md-component="announce">
       
@@ -80,9 +86,7 @@
     
       
 
-  
-
-<header class="md-header md-header--shadow" data-md-component="header">
+<header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
     <a href="." title="Unity ML-Agents Toolkit" class="md-header__button md-logo" aria-label="Unity ML-Agents Toolkit" data-md-component="logo">
       
@@ -109,8 +113,6 @@
       </div>
     </div>
     
-      
-    
     
     
       <label class="md-header__button md-icon" for="__search">
@@ -139,7 +141,7 @@
           <div class="md-search-result__meta">
             Initializing search
           </div>
-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
         </div>
       </div>
     </div>
@@ -151,7 +153,7 @@
         <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -195,7 +197,7 @@
       <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -216,14 +218,88 @@
   
     <li class="md-nav__item md-nav__item--active">
       
-      <input class="md-nav__toggle md-toggle" type="checkbox" id="__toc">
+      <input class="md-nav__toggle md-toggle" data-md-toggle="toc" type="checkbox" id="__toc">
+      
+      
+        
       
       
+        <label class="md-nav__link md-nav__link--active" for="__toc">
+          Home
+          <span class="md-nav__icon md-icon"></span>
+        </label>
       
       <a href="." class="md-nav__link md-nav__link--active">
         Home
       </a>
       
+        
+
+<nav class="md-nav md-nav--secondary" aria-label="Table of contents">
+  
+  
+  
+    
+  
+  
+    <label class="md-nav__title" for="__toc">
+      <span class="md-nav__icon md-icon"></span>
+      Table of contents
+    </label>
+    <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
+      
+        <li class="md-nav__item">
+  <a href="#features" class="md-nav__link">
+    Features
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#releases-documentation" class="md-nav__link">
+    Releases &amp; Documentation
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#additional-resources" class="md-nav__link">
+    Additional Resources
+  </a>
+  
+    <nav class="md-nav" aria-label="Additional Resources">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#more-from-unity" class="md-nav__link">
+    More from Unity
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#community-and-feedback" class="md-nav__link">
+    Community and Feedback
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#privacy" class="md-nav__link">
+    Privacy
+  </a>
+  
+</li>
+      
+    </ul>
+  
+</nav>
+      
     </li>
   
 
@@ -269,18 +345,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_4" type="checkbox" id="__nav_4" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
-      
       
       
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_4">
           Background
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Background" data-md-level="1">
         <label class="md-nav__title" for="__nav_4">
           <span class="md-nav__icon md-icon"></span>
           Background
@@ -346,18 +421,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_5" type="checkbox" id="__nav_5" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
-      
       
       
-        <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_5">
           Interfacing with Unity Builds
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Interfacing with Unity Builds" data-md-level="1">
         <label class="md-nav__title" for="__nav_5">
           <span class="md-nav__icon md-icon"></span>
           Interfacing with Unity Builds
@@ -423,18 +497,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_6" type="checkbox" id="__nav_6" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" >
       
       
-      
-        <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_6">
           Python API Docs
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Python API Docs" data-md-level="1">
         <label class="md-nav__title" for="__nav_6">
           <span class="md-nav__icon md-icon"></span>
           Python API Docs
@@ -514,18 +587,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_7" type="checkbox" id="__nav_7" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_7" >
-      
       
       
-        <label class="md-nav__link" for="__nav_7" id="__nav_7_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_7">
           Tutorials
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_7_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Tutorials" data-md-level="1">
         <label class="md-nav__title" for="__nav_7">
           <span class="md-nav__icon md-icon"></span>
           Tutorials
@@ -563,18 +635,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_8" type="checkbox" id="__nav_8" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_8" >
-      
       
       
-        <label class="md-nav__link" for="__nav_8" id="__nav_8_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_8">
           About
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         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="About" data-md-level="1">
         <label class="md-nav__title" for="__nav_8">
           <span class="md-nav__icon md-icon"></span>
           About
@@ -660,6 +731,64 @@
   
   
   
+    
+  
+  
+    <label class="md-nav__title" for="__toc">
+      <span class="md-nav__icon md-icon"></span>
+      Table of contents
+    </label>
+    <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
+      
+        <li class="md-nav__item">
+  <a href="#features" class="md-nav__link">
+    Features
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#releases-documentation" class="md-nav__link">
+    Releases &amp; Documentation
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#additional-resources" class="md-nav__link">
+    Additional Resources
+  </a>
+  
+    <nav class="md-nav" aria-label="Additional Resources">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#more-from-unity" class="md-nav__link">
+    More from Unity
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#community-and-feedback" class="md-nav__link">
+    Community and Feedback
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+  
+</li>
+      
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+  <a href="#privacy" class="md-nav__link">
+    Privacy
+  </a>
+  
+</li>
+      
+    </ul>
   
 </nav>
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@@ -673,13 +802,223 @@
                 
                   
 
-  
-  
-
+  <a href="https://github.com/Unity-Technologies/ml-agents/edit/main/docs/index.md" title="Edit this page" class="md-content__button md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
-  <h1>Home</h1>
 
 <p><img src="images/image-banner.png" align="middle" width="3000"/></p>
+<h1 id="unity-ml-agents-toolkit">Unity ML-Agents Toolkit</h1>
+<p><a href="https://github.com/Unity-Technologies/ml-agents/tree/release_20_docs/docs/"><img alt="docs badge" src="https://img.shields.io/badge/docs-reference-blue.svg" /></a></p>
+<p><a href="../LICENSE.md"><img alt="license badge" src="https://img.shields.io/badge/license-Apache--2.0-green.svg" /></a></p>
+<p>(<a href="https://github.com/Unity-Technologies/ml-agents/releases/tag/latest_release">latest release</a>)
+(<a href="https://github.com/Unity-Technologies/ml-agents/releases">all releases</a>)</p>
+<p><strong>The Unity Machine Learning Agents Toolkit</strong> (ML-Agents) is an open-source
+project that enables games and simulations to serve as environments for
+training intelligent agents. We provide implementations (based on PyTorch)
+of state-of-the-art algorithms to enable game developers and hobbyists to easily
+train intelligent agents for 2D, 3D and VR/AR games. Researchers can also use the
+provided simple-to-use Python API to train Agents using reinforcement learning,
+imitation learning, neuroevolution, or any other methods. These trained agents can be
+used for multiple purposes, including controlling NPC behavior (in a variety of
+settings such as multi-agent and adversarial), automated testing of game builds
+and evaluating different game design decisions pre-release. The ML-Agents
+Toolkit is mutually beneficial for both game developers and AI researchers as it
+provides a central platform where advances in AI can be evaluated on Unity’s
+rich environments and then made accessible to the wider research and game
+developer communities.</p>
+<h2 id="features">Features</h2>
+<ul>
+<li>17+ <a href="Learning-Environment-Examples/">example Unity environments</a></li>
+<li>Support for multiple environment configurations and training scenarios</li>
+<li>Flexible Unity SDK that can be integrated into your game or custom Unity scene</li>
+<li>Support for training single-agent, multi-agent cooperative, and multi-agent
+  competitive scenarios via several Deep Reinforcement Learning algorithms (PPO, SAC, MA-POCA, self-play).</li>
+<li>Support for learning from demonstrations through two Imitation Learning algorithms (BC and GAIL).</li>
+<li>Quickly and easily add your own <a href="Python-Custom-Trainer-Plugin/">custom training algorithm</a> and/or components.</li>
+<li>Easily definable Curriculum Learning scenarios for complex tasks</li>
+<li>Train robust agents using environment randomization</li>
+<li>Flexible agent control with On Demand Decision Making</li>
+<li>Train using multiple concurrent Unity environment instances</li>
+<li>Utilizes the <a href="Unity-Inference-Engine/">Unity Inference Engine</a> to
+  provide native cross-platform support</li>
+<li>Unity environment <a href="Python-LLAPI/">control from Python</a></li>
+<li>Wrap Unity learning environments as a <a href="Python-Gym-API/">gym</a> environment</li>
+<li>Wrap Unity learning environments as a <a href="Python-PettingZoo-API/">PettingZoo</a> environment</li>
+</ul>
+<p>See our <a href="ML-Agents-Overview/">ML-Agents Overview</a> page for detailed
+descriptions of all these features. Or go straight to our <a href="https://unity-technologies.github.io/ml-agents/">web docs</a>.</p>
+<h2 id="releases-documentation">Releases &amp; Documentation</h2>
+<p><strong>Our latest, stable release is <code>Release 20</code>. Click
+<a href="Getting-Started/">here</a>
+to get started with the latest release of ML-Agents.</strong></p>
+<p><strong>You can also check out our new <a href="https://unity-technologies.github.io/ml-agents/">web docs</a>!</strong></p>
+<p>The table below lists all our releases, including our <code>main</code> branch which is
+under active development and may be unstable. A few helpful guidelines:
+- The <a href="Versioning/">Versioning page</a> overviews how we manage our GitHub
+  releases and the versioning process for each of the ML-Agents components.
+- The <a href="https://github.com/Unity-Technologies/ml-agents/releases">Releases page</a>
+  contains details of the changes between releases.
+- The <a href="Migrating/">Migration page</a> contains details on how to upgrade
+  from earlier releases of the ML-Agents Toolkit.
+- The <strong>Documentation</strong> links in the table below include installation and usage
+  instructions specific to each release. Remember to always use the
+  documentation that corresponds to the release version you're using.
+- The <code>com.unity.ml-agents</code> package is <a href="https://docs.unity3d.com/2020.1/Documentation/Manual/pack-safe.html">verified</a>
+  for Unity 2020.1 and later. Verified packages releases are numbered 1.0.x.</p>
+<table>
+<thead>
+<tr>
+<th align="center"><strong>Version</strong></th>
+<th align="center"><strong>Release Date</strong></th>
+<th align="center"><strong>Source</strong></th>
+<th align="center"><strong>Documentation</strong></th>
+<th align="center"><strong>Download</strong></th>
+<th align="center"><strong>Python Package</strong></th>
+<th align="center"><strong>Unity Package</strong></th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td align="center"><strong>Release 20</strong></td>
+<td align="center"><strong>November 21, 2022</strong></td>
+<td align="center"><strong><a href="https://github.com/Unity-Technologies/ml-agents/tree/release_20">source</a></strong></td>
+<td align="center"><strong><a href="https://github.com/Unity-Technologies/ml-agents/tree/release_20_docs/docs/Readme.md">docs</a></strong></td>
+<td align="center"><strong><a href="https://github.com/Unity-Technologies/ml-agents/archive/release_20.zip">download</a></strong></td>
+<td align="center"><strong><a href="https://pypi.org/project/mlagents/0.30.0/">0.30.0</a></strong></td>
+<td align="center"><strong><a href="https://docs.unity3d.com/Packages/com.unity.ml-agents@2.3/manual/index.html">2.3.0</a></strong></td>
+</tr>
+<tr>
+<td align="center"><strong>main (unstable)</strong></td>
+<td align="center">--</td>
+<td align="center"><a href="https://github.com/Unity-Technologies/ml-agents/tree/main">source</a></td>
+<td align="center"><a href="https://github.com/Unity-Technologies/ml-agents/tree/main/docs/Readme.md">docs</a></td>
+<td align="center"><a href="https://github.com/Unity-Technologies/ml-agents/archive/main.zip">download</a></td>
+<td align="center">--</td>
+<td align="center">--</td>
+</tr>
+<tr>
+<td align="center"><strong>Verified Package 1.0.8</strong></td>
+<td align="center"><strong>May 26, 2021</strong></td>
+<td align="center"><strong><a href="https://github.com/Unity-Technologies/ml-agents/tree/com.unity.ml-agents_1.0.8">source</a></strong></td>
+<td align="center"><strong><a href="https://github.com/Unity-Technologies/ml-agents/blob/release_20_verified_docs/docs/Readme.md">docs</a></strong></td>
+<td align="center"><strong><a href="https://github.com/Unity-Technologies/ml-agents/archive/com.unity.ml-agents_1.0.8.zip">download</a></strong></td>
+<td align="center"><strong><a href="https://pypi.org/project/mlagents/0.16.1/">0.16.1</a></strong></td>
+<td align="center"><strong><a href="https://docs.unity3d.com/Packages/com.unity.ml-agents@1.0/manual/index.html">1.0.8</a></strong></td>
+</tr>
+</tbody>
+</table>
+<p>If you are a researcher interested in a discussion of Unity as an AI platform,
+see a pre-print of our
+<a href="https://arxiv.org/abs/1809.02627">reference paper on Unity and the ML-Agents Toolkit</a>.</p>
+<p>If you use Unity or the ML-Agents Toolkit to conduct research, we ask that you
+cite the following paper as a reference:</p>
+<pre><code>@article{juliani2020,
+  title={Unity: A general platform for intelligent agents},
+  author={Juliani, Arthur and Berges, Vincent-Pierre and Teng, Ervin and Cohen, Andrew and Harper, Jonathan and Elion, Chris and Goy, Chris and Gao, Yuan and Henry, Hunter and Mattar, Marwan and Lange, Danny},
+  journal={arXiv preprint arXiv:1809.02627},
+  url={https://arxiv.org/pdf/1809.02627.pdf},
+  year={2020}
+}
+</code></pre>
+<p>Additionally, if you use the MA-POCA trainer in your research, we ask that you
+cite the following paper as a reference:</p>
+<pre><code>@article{cohen2022,
+  title={On the Use and Misuse of Absorbing States in Multi-agent Reinforcement Learning},
+  author={Cohen, Andrew and Teng, Ervin and Berges, Vincent-Pierre and Dong, Ruo-Ping and Henry, Hunter and Mattar, Marwan and Zook, Alexander and Ganguly, Sujoy},
+  journal={RL in Games Workshop AAAI 2022},
+  url={http://aaai-rlg.mlanctot.info/papers/AAAI22-RLG_paper_32.pdf},
+  year={2022}
+}
+</code></pre>
+<h2 id="additional-resources">Additional Resources</h2>
+<p>We have a Unity Learn course,
+<a href="https://learn.unity.com/course/ml-agents-hummingbirds">ML-Agents: Hummingbirds</a>,
+that provides a gentle introduction to Unity and the ML-Agents Toolkit.</p>
+<p>We've also partnered with
+<a href="https://www.youtube.com/c/CodeMonkeyUnity">CodeMonkeyUnity</a> to create a
+<a href="https://www.youtube.com/playlist?list=PLzDRvYVwl53vehwiN_odYJkPBzcqFw110">series of tutorial videos</a>
+on how to implement and use the ML-Agents Toolkit.</p>
+<p>We have also published a series of blog posts that are relevant for ML-Agents:</p>
+<ul>
+<li>(July 12, 2021)
+  <a href="https://blog.unity.com/technology/ml-agents-plays-dodgeball">ML-Agents plays Dodgeball</a></li>
+<li>(May 5, 2021)
+  <a href="https://blogs.unity3d.com/2021/05/05/ml-agents-v2-0-release-now-supports-training-complex-cooperative-behaviors/">ML-Agents v2.0 release: Now supports training complex cooperative behaviors</a></li>
+<li>(December 28, 2020)
+  <a href="https://blogs.unity3d.com/2020/12/28/happy-holidays-from-the-unity-ml-agents-team/">Happy holidays from the Unity ML-Agents team!</a></li>
+<li>(November 20, 2020)
+  <a href="https://blogs.unity3d.com/2020/11/20/how-eidos-montreal-created-grid-sensors-to-improve-observations-for-training-agents/">How Eidos-Montréal created Grid Sensors to improve observations for training agents</a></li>
+<li>(November 11, 2020)
+  <a href="https://blogs.unity3d.com/2020/11/11/2020-aiunity-interns-shoutout/">2020 AI@Unity interns shoutout</a></li>
+<li>(May 12, 2020)
+  <a href="https://blogs.unity3d.com/2020/05/12/announcing-ml-agents-unity-package-v1-0/">Announcing ML-Agents Unity Package v1.0!</a></li>
+<li>(February 28, 2020)
+  <a href="https://blogs.unity3d.com/2020/02/28/training-intelligent-adversaries-using-self-play-with-ml-agents/">Training intelligent adversaries using self-play with ML-Agents</a></li>
+<li>(November 11, 2019)
+  <a href="https://blogs.unity3d.com/2019/11/11/training-your-agents-7-times-faster-with-ml-agents/">Training your agents 7 times faster with ML-Agents</a></li>
+<li>(October 21, 2019)
+  <a href="https://blogs.unity3d.com/2019/10/21/the-aiunity-interns-help-shape-the-world/">The AI@Unity interns help shape the world</a></li>
+<li>(April 15, 2019)
+  <a href="https://blogs.unity3d.com/2019/04/15/unity-ml-agents-toolkit-v0-8-faster-training-on-real-games/">Unity ML-Agents Toolkit v0.8: Faster training on real games</a></li>
+<li>(March 1, 2019)
+  <a href="https://blogs.unity3d.com/2019/03/01/unity-ml-agents-toolkit-v0-7-a-leap-towards-cross-platform-inference/">Unity ML-Agents Toolkit v0.7: A leap towards cross-platform inference</a></li>
+<li>(December 17, 2018)
+  <a href="https://blogs.unity3d.com/2018/12/17/ml-agents-toolkit-v0-6-improved-usability-of-brains-and-imitation-learning/">ML-Agents Toolkit v0.6: Improved usability of Brains and Imitation Learning</a></li>
+<li>(October 2, 2018)
+  <a href="https://blogs.unity3d.com/2018/10/02/puppo-the-corgi-cuteness-overload-with-the-unity-ml-agents-toolkit/">Puppo, The Corgi: Cuteness Overload with the Unity ML-Agents Toolkit</a></li>
+<li>(September 11, 2018)
+  <a href="https://blogs.unity3d.com/2018/09/11/ml-agents-toolkit-v0-5-new-resources-for-ai-researchers-available-now/">ML-Agents Toolkit v0.5, new resources for AI researchers available now</a></li>
+<li>(June 26, 2018)
+  <a href="https://blogs.unity3d.com/2018/06/26/solving-sparse-reward-tasks-with-curiosity/">Solving sparse-reward tasks with Curiosity</a></li>
+<li>(June 19, 2018)
+  <a href="https://blogs.unity3d.com/2018/06/19/unity-ml-agents-toolkit-v0-4-and-udacity-deep-reinforcement-learning-nanodegree/">Unity ML-Agents Toolkit v0.4 and Udacity Deep Reinforcement Learning Nanodegree</a></li>
+<li>(May 24, 2018)
+  <a href="https://blogs.unity3d.com/2018/05/24/imitation-learning-in-unity-the-workflow/">Imitation Learning in Unity: The Workflow</a></li>
+<li>(March 15, 2018)
+  <a href="https://blogs.unity3d.com/2018/03/15/ml-agents-v0-3-beta-released-imitation-learning-feedback-driven-features-and-more/">ML-Agents Toolkit v0.3 Beta released: Imitation Learning, feedback-driven features, and more</a></li>
+<li>(December 11, 2017)
+  <a href="https://blogs.unity3d.com/2017/12/11/using-machine-learning-agents-in-a-real-game-a-beginners-guide/">Using Machine Learning Agents in a real game: a beginner’s guide</a></li>
+<li>(December 8, 2017)
+  <a href="https://blogs.unity3d.com/2017/12/08/introducing-ml-agents-v0-2-curriculum-learning-new-environments-and-more/">Introducing ML-Agents Toolkit v0.2: Curriculum Learning, new environments, and more</a></li>
+<li>(September 19, 2017)
+  <a href="https://blogs.unity3d.com/2017/09/19/introducing-unity-machine-learning-agents/">Introducing: Unity Machine Learning Agents Toolkit</a></li>
+<li>Overviewing reinforcement learning concepts
+  (<a href="https://blogs.unity3d.com/2017/06/26/unity-ai-themed-blog-entries/">multi-armed bandit</a>
+  and
+  <a href="https://blogs.unity3d.com/2017/08/22/unity-ai-reinforcement-learning-with-q-learning/">Q-learning</a>)</li>
+</ul>
+<h3 id="more-from-unity">More from Unity</h3>
+<ul>
+<li><a href="https://github.com/Unity-Technologies/Unity-Robotics-Hub">Unity Robotics</a></li>
+<li><a href="https://github.com/Unity-Technologies/com.unity.perception">Unity Computer Vision</a></li>
+</ul>
+<h2 id="community-and-feedback">Community and Feedback</h2>
+<p>The ML-Agents Toolkit is an open-source project and we encourage and welcome
+contributions. If you wish to contribute, be sure to review our
+<a href="CONTRIBUTING/">contribution guidelines</a> and
+<a href="../CODE_OF_CONDUCT.md">code of conduct</a>.</p>
+<p>For problems with the installation and setup of the ML-Agents Toolkit, or
+discussions about how to best setup or train your agents, please create a new
+thread on the
+<a href="https://forum.unity.com/forums/ml-agents.453/">Unity ML-Agents forum</a> and make
+sure to include as much detail as possible. If you run into any other problems
+using the ML-Agents Toolkit or have a specific feature request, please
+<a href="https://github.com/Unity-Technologies/ml-agents/issues">submit a GitHub issue</a>.</p>
+<p>Please tell us which samples you would like to see shipped with the ML-Agents Unity
+package by replying to
+<a href="https://forum.unity.com/threads/feedback-wanted-shipping-sample-s-with-the-ml-agents-package.1073468/">this forum thread</a>.</p>
+<p>Your opinion matters a great deal to us. Only by hearing your thoughts on the
+Unity ML-Agents Toolkit can we continue to improve and grow. Please take a few
+minutes to
+<a href="https://unitysoftware.co1.qualtrics.com/jfe/form/SV_55pQKCZ578t0kbc">let us know about it</a>.</p>
+<p>For any other questions or feedback, connect directly with the ML-Agents team at
+ml-agents@unity3d.com.</p>
+<h2 id="privacy">Privacy</h2>
+<p>In order to improve the developer experience for Unity ML-Agents Toolkit, we have added in-editor analytics.
+Please refer to "Information that is passively collected by Unity" in the
+<a href="https://unity3d.com/legal/privacy-policy">Unity Privacy Policy</a>.</p>
 
 
 
@@ -696,6 +1035,27 @@ <h1>Home</h1>
       
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diff --git a/ml-agents-envs/index.html b/ml-agents-envs/index.html
index 2fa1e2bedc..e6c55b34ed 100644
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+++ b/ml-agents-envs/index.html
@@ -14,10 +14,8 @@
       
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         <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -198,7 +197,7 @@
       <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -264,18 +263,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_4" type="checkbox" id="__nav_4" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
       
       
-      
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_4">
           Background
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Background" data-md-level="1">
         <label class="md-nav__title" for="__nav_4">
           <span class="md-nav__icon md-icon"></span>
           Background
@@ -341,18 +339,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_5" type="checkbox" id="__nav_5" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
-      
       
       
-        <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_5">
           Interfacing with Unity Builds
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Interfacing with Unity Builds" data-md-level="1">
         <label class="md-nav__title" for="__nav_5">
           <span class="md-nav__icon md-icon"></span>
           Interfacing with Unity Builds
@@ -418,18 +415,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_6" type="checkbox" id="__nav_6" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" >
-      
       
       
-        <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_6">
           Python API Docs
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Python API Docs" data-md-level="1">
         <label class="md-nav__title" for="__nav_6">
           <span class="md-nav__icon md-icon"></span>
           Python API Docs
@@ -509,18 +505,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_7" type="checkbox" id="__nav_7" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_7" >
       
       
-      
-        <label class="md-nav__link" for="__nav_7" id="__nav_7_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_7">
           Tutorials
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_7_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Tutorials" data-md-level="1">
         <label class="md-nav__title" for="__nav_7">
           <span class="md-nav__icon md-icon"></span>
           Tutorials
@@ -558,18 +553,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_8" type="checkbox" id="__nav_8" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_8" >
-      
       
       
-        <label class="md-nav__link" for="__nav_8" id="__nav_8_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_8">
           About
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="About" data-md-level="1">
         <label class="md-nav__title" for="__nav_8">
           <span class="md-nav__icon md-icon"></span>
           About
@@ -699,8 +693,10 @@
                 
                   
 
-  
-  
+  <a href="https://github.com/Unity-Technologies/ml-agents/edit/main/docs/ml-agents-envs.md" title="Edit this page" class="md-content__button md-icon">
+    
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25Z"/></svg>
+  </a>
 
 
 <h1 id="unity-ml-agents-python-interface">Unity ML-Agents Python Interface</h1>
@@ -778,10 +774,10 @@ <h2 id="limitations">Limitations</h2>
       <div class="md-dialog__inner md-typeset"></div>
     </div>
     
-    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.208ed371.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version": "Select version"}}</script>
+    <script id="__config" type="application/json">{"base": "..", "features": [], "search": "../assets/javascripts/workers/search.16e2a7d4.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
     
     
-      <script src="../assets/javascripts/bundle.b4d07000.min.js"></script>
+      <script src="../assets/javascripts/bundle.d6c3db9e.min.js"></script>
       
     
   </body>
diff --git a/ml-agents/index.html b/ml-agents/index.html
index 6aa843e7ed..5319089273 100644
--- a/ml-agents/index.html
+++ b/ml-agents/index.html
@@ -14,10 +14,8 @@
       
         <link rel="canonical" href="https://unity-technologies.github.io/ml-agents/ml-agents/">
       
-      
-      
       <link rel="icon" href="../images/unity-logo-black.png">
-      <meta name="generator" content="mkdocs-1.4.3, mkdocs-material-9.1.15">
+      <meta name="generator" content="mkdocs-1.4.2, mkdocs-material-8.5.10">
     
     
       
@@ -25,10 +23,15 @@
       
     
     
-      <link rel="stylesheet" href="../assets/stylesheets/main.26e3688c.min.css">
+      <link rel="stylesheet" href="../assets/stylesheets/main.472b142f.min.css">
       
         
-        <link rel="stylesheet" href="../assets/stylesheets/palette.ecc896b0.min.css">
+        <link rel="stylesheet" href="../assets/stylesheets/palette.08040f6c.min.css">
+        
+          
+          
+          <meta name="theme-color" content="#2094f3">
+        
       
       
 
@@ -83,9 +86,7 @@
     
       
 
-  
-
-<header class="md-header md-header--shadow" data-md-component="header">
+<header class="md-header" data-md-component="header">
   <nav class="md-header__inner md-grid" aria-label="Header">
     <a href=".." title="Unity ML-Agents Toolkit" class="md-header__button md-logo" aria-label="Unity ML-Agents Toolkit" data-md-component="logo">
       
@@ -112,8 +113,6 @@
       </div>
     </div>
     
-      
-    
     
     
       <label class="md-header__button md-icon" for="__search">
@@ -142,7 +141,7 @@
           <div class="md-search-result__meta">
             Initializing search
           </div>
-          <ol class="md-search-result__list" role="presentation"></ol>
+          <ol class="md-search-result__list"></ol>
         </div>
       </div>
     </div>
@@ -154,7 +153,7 @@
         <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -198,7 +197,7 @@
       <a href="https://github.com/Unity-Technologies/ml-agents" title="Go to repository" class="md-source" data-md-component="source">
   <div class="md-source__icon md-icon">
     
-    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.4.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2023 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 448 512"><!--! Font Awesome Free 6.2.0 by @fontawesome - https://fontawesome.com License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) Copyright 2022 Fonticons, Inc.--><path d="M439.55 236.05 244 40.45a28.87 28.87 0 0 0-40.81 0l-40.66 40.63 51.52 51.52c27.06-9.14 52.68 16.77 43.39 43.68l49.66 49.66c34.23-11.8 61.18 31 35.47 56.69-26.49 26.49-70.21-2.87-56-37.34L240.22 199v121.85c25.3 12.54 22.26 41.85 9.08 55a34.34 34.34 0 0 1-48.55 0c-17.57-17.6-11.07-46.91 11.25-56v-123c-20.8-8.51-24.6-30.74-18.64-45L142.57 101 8.45 235.14a28.86 28.86 0 0 0 0 40.81l195.61 195.6a28.86 28.86 0 0 0 40.8 0l194.69-194.69a28.86 28.86 0 0 0 0-40.81z"/></svg>
   </div>
   <div class="md-source__repository">
     GitHub
@@ -264,18 +263,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_4" type="checkbox" id="__nav_4" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_4" >
       
       
-      
-        <label class="md-nav__link" for="__nav_4" id="__nav_4_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_4">
           Background
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_4_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Background" data-md-level="1">
         <label class="md-nav__title" for="__nav_4">
           <span class="md-nav__icon md-icon"></span>
           Background
@@ -341,18 +339,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_5" type="checkbox" id="__nav_5" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_5" >
-      
       
       
-        <label class="md-nav__link" for="__nav_5" id="__nav_5_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_5">
           Interfacing with Unity Builds
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_5_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Interfacing with Unity Builds" data-md-level="1">
         <label class="md-nav__title" for="__nav_5">
           <span class="md-nav__icon md-icon"></span>
           Interfacing with Unity Builds
@@ -418,18 +415,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_6" type="checkbox" id="__nav_6" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_6" >
-      
       
       
-        <label class="md-nav__link" for="__nav_6" id="__nav_6_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_6">
           Python API Docs
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_6_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Python API Docs" data-md-level="1">
         <label class="md-nav__title" for="__nav_6">
           <span class="md-nav__icon md-icon"></span>
           Python API Docs
@@ -509,18 +505,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_7" type="checkbox" id="__nav_7" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_7" >
       
       
-      
-        <label class="md-nav__link" for="__nav_7" id="__nav_7_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_7">
           Tutorials
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_7_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="Tutorials" data-md-level="1">
         <label class="md-nav__title" for="__nav_7">
           <span class="md-nav__icon md-icon"></span>
           Tutorials
@@ -558,18 +553,17 @@
     <li class="md-nav__item md-nav__item--nested">
       
       
+        <input class="md-nav__toggle md-toggle" data-md-toggle="__nav_8" type="checkbox" id="__nav_8" >
       
       
-      <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_8" >
-      
       
       
-        <label class="md-nav__link" for="__nav_8" id="__nav_8_label" tabindex="0">
+        <label class="md-nav__link" for="__nav_8">
           About
           <span class="md-nav__icon md-icon"></span>
         </label>
       
-      <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_8_label" aria-expanded="false">
+      <nav class="md-nav" aria-label="About" data-md-level="1">
         <label class="md-nav__title" for="__nav_8">
           <span class="md-nav__icon md-icon"></span>
           About
@@ -699,8 +693,10 @@
                 
                   
 
-  
-  
+  <a href="https://github.com/Unity-Technologies/ml-agents/edit/main/docs/ml-agents.md" title="Edit this page" class="md-content__button md-icon">
+    
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-{"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"]},"docs":[{"location":"","title":"Home","text":""},{"location":"API-Reference/","title":"API Reference","text":"<p>Our developer-facing C# classes have been documented to be compatible with Doxygen for auto-generating HTML documentation.</p> <p>To generate the API reference, download Doxygen and run the following command within the <code>docs/</code> directory:</p> <pre><code>doxygen dox-ml-agents.conf\n</code></pre> <p><code>dox-ml-agents.conf</code> is a Doxygen configuration file for the ML-Agents Toolkit that includes the classes that have been properly formatted. The generated HTML files will be placed in the <code>html/</code> subdirectory. Open <code>index.html</code> within that subdirectory to navigate to the API reference home. Note that <code>html/</code> is already included in the repository's <code>.gitignore</code> file.</p> <p>In the near future, we aim to expand our documentation to include the Python classes.</p>"},{"location":"Background-Machine-Learning/","title":"Background: Machine Learning","text":"<p>Given that a number of users of the ML-Agents Toolkit might not have a formal machine learning background, this page provides an overview to facilitate the understanding of the ML-Agents Toolkit. However, we will not attempt to provide a thorough treatment of machine learning as there are fantastic resources online.</p> <p>Machine learning, a branch of artificial intelligence, focuses on learning patterns from data. The three main classes of machine learning algorithms include: unsupervised learning, supervised learning and reinforcement learning. Each class of algorithm learns from a different type of data. The following paragraphs provide an overview for each of these classes of machine learning, as well as introductory examples.</p>"},{"location":"Background-Machine-Learning/#unsupervised-learning","title":"Unsupervised Learning","text":"<p>The goal of unsupervised learning is to group or cluster similar items in a data set. For example, consider the players of a game. We may want to group the players depending on how engaged they are with the game. This would enable us to target different groups (e.g. for highly-engaged players we might invite them to be beta testers for new features, while for unengaged players we might email them helpful tutorials). Say that we wish to split our players into two groups. We would first define basic attributes of the players, such as the number of hours played, total money spent on in-app purchases and number of levels completed. We can then feed this data set (three attributes for every player) to an unsupervised learning algorithm where we specify the number of groups to be two. The algorithm would then split the data set of players into two groups where the players within each group would be similar to each other. Given the attributes we used to describe each player, in this case, the output would be a split of all the players into two groups, where one group would semantically represent the engaged players and the second group would semantically represent the unengaged players.</p> <p>With unsupervised learning, we did not provide specific examples of which players are considered engaged and which are considered unengaged. We just defined the appropriate attributes and relied on the algorithm to uncover the two groups on its own. This type of data set is typically called an unlabeled data set as it is lacking these direct labels. Consequently, unsupervised learning can be helpful in situations where these labels can be expensive or hard to produce. In the next paragraph, we overview supervised learning algorithms which accept input labels in addition to attributes.</p>"},{"location":"Background-Machine-Learning/#supervised-learning","title":"Supervised Learning","text":"<p>In supervised learning, we do not want to just group similar items but directly learn a mapping from each item to the group (or class) that it belongs to. Returning to our earlier example of clustering players, let's say we now wish to predict which of our players are about to churn (that is stop playing the game for the next 30 days). We can look into our historical records and create a data set that contains attributes of our players in addition to a label indicating whether they have churned or not. Note that the player attributes we use for this churn prediction task may be different from the ones we used for our earlier clustering task. We can then feed this data set (attributes and label for each player) into a supervised learning algorithm which would learn a mapping from the player attributes to a label indicating whether that player will churn or not. The intuition is that the supervised learning algorithm will learn which values of these attributes typically correspond to players who have churned and not churned (for example, it may learn that players who spend very little and play for very short periods will most likely churn). Now given this learned model, we can provide it the attributes of a new player (one that recently started playing the game) and it would output a predicted label for that player. This prediction is the algorithms expectation of whether the player will churn or not. We can now use these predictions to target the players who are expected to churn and entice them to continue playing the game.</p> <p>As you may have noticed, for both supervised and unsupervised learning, there are two tasks that need to be performed: attribute selection and model selection. Attribute selection (also called feature selection) pertains to selecting how we wish to represent the entity of interest, in this case, the player. Model selection, on the other hand, pertains to selecting the algorithm (and its parameters) that perform the task well. Both of these tasks are active areas of machine learning research and, in practice, require several iterations to achieve good performance.</p> <p>We now switch to reinforcement learning, the third class of machine learning algorithms, and arguably the one most relevant for the ML-Agents Toolkit.</p>"},{"location":"Background-Machine-Learning/#reinforcement-learning","title":"Reinforcement Learning","text":"<p>Reinforcement learning can be viewed as a form of learning for sequential decision making that is commonly associated with controlling robots (but is, in fact, much more general). Consider an autonomous firefighting robot that is tasked with navigating into an area, finding the fire and neutralizing it. At any given moment, the robot perceives the environment through its sensors (e.g. camera, heat, touch), processes this information and produces an action (e.g. move to the left, rotate the water hose, turn on the water). In other words, it is continuously making decisions about how to interact in this environment given its view of the world (i.e. sensors input) and objective (i.e. neutralizing the fire). Teaching a robot to be a successful firefighting machine is precisely what reinforcement learning is designed to do.</p> <p>More specifically, the goal of reinforcement learning is to learn a policy, which is essentially a mapping from observations to actions. An observation is what the robot can measure from its environment (in this case, all its sensory inputs) and an action, in its most raw form, is a change to the configuration of the robot (e.g. position of its base, position of its water hose and whether the hose is on or off).</p> <p>The last remaining piece of the reinforcement learning task is the reward signal. The robot is trained to learn a policy that maximizes its overall rewards. When training a robot to be a mean firefighting machine, we provide it with rewards (positive and negative) indicating how well it is doing on completing the task. Note that the robot does not know how to put out fires before it is trained. It learns the objective because it receives a large positive reward when it puts out the fire and a small negative reward for every passing second. The fact that rewards are sparse (i.e. may not be provided at every step, but only when a robot arrives at a success or failure situation), is a defining characteristic of reinforcement learning and precisely why learning good policies can be difficult (and/or time-consuming) for complex environments.</p> <p>Learning a policy usually requires many trials and iterative policy updates. More specifically, the robot is placed in several fire situations and over time learns an optimal policy which allows it to put out fires more effectively. Obviously, we cannot expect to train a robot repeatedly in the real world, particularly when fires are involved. This is precisely why the use of Unity as a simulator serves as the perfect training grounds for learning such behaviors. While our discussion of reinforcement learning has centered around robots, there are strong parallels between robots and characters in a game. In fact, in many ways, one can view a non-playable character (NPC) as a virtual robot, with its own observations about the environment, its own set of actions and a specific objective. Thus it is natural to explore how we can train behaviors within Unity using reinforcement learning. This is precisely what the ML-Agents Toolkit offers. The video linked below includes a reinforcement learning demo showcasing training character behaviors using the ML-Agents Toolkit.</p> <p> </p> <p>Similar to both unsupervised and supervised learning, reinforcement learning also involves two tasks: attribute selection and model selection. Attribute selection is defining the set of observations for the robot that best help it complete its objective, while model selection is defining the form of the policy (mapping from observations to actions) and its parameters. In practice, training behaviors is an iterative process that may require changing the attribute and model choices.</p>"},{"location":"Background-Machine-Learning/#training-and-inference","title":"Training and Inference","text":"<p>One common aspect of all three branches of machine learning is that they all involve a training phase and an inference phase. While the details of the training and inference phases are different for each of the three, at a high-level, the training phase involves building a model using the provided data, while the inference phase involves applying this model to new, previously unseen, data. More specifically:</p> <ul> <li>For our unsupervised learning example, the training phase learns the optimal   two clusters based on the data describing existing players, while the   inference phase assigns a new player to one of these two clusters.</li> <li>For our supervised learning example, the training phase learns the mapping   from player attributes to player label (whether they churned or not), and the   inference phase predicts whether a new player will churn or not based on that   learned mapping.</li> <li>For our reinforcement learning example, the training phase learns the optimal   policy through guided trials, and in the inference phase, the agent observes   and takes actions in the wild using its learned policy.</li> </ul> <p>To briefly summarize: all three classes of algorithms involve training and inference phases in addition to attribute and model selections. What ultimately separates them is the type of data available to learn from. In unsupervised learning our data set was a collection of attributes, in supervised learning our data set was a collection of attribute-label pairs, and, lastly, in reinforcement learning our data set was a collection of observation-action-reward tuples.</p>"},{"location":"Background-Machine-Learning/#deep-learning","title":"Deep Learning","text":"<p>Deep learning is a family of algorithms that can be used to address any of the problems introduced above. More specifically, they can be used to solve both attribute and model selection tasks. Deep learning has gained popularity in recent years due to its outstanding performance on several challenging machine learning tasks. One example is AlphaGo, a computer Go program, that leverages deep learning, that was able to beat Lee Sedol (a Go world champion).</p> <p>A key characteristic of deep learning algorithms is their ability to learn very complex functions from large amounts of training data. This makes them a natural choice for reinforcement learning tasks when a large amount of data can be generated, say through the use of a simulator or engine such as Unity. By generating hundreds of thousands of simulations of the environment within Unity, we can learn policies for very complex environments (a complex environment is one where the number of observations an agent perceives and the number of actions they can take are large). Many of the algorithms we provide in ML-Agents use some form of deep learning, built on top of the open-source library, PyTorch.</p>"},{"location":"Background-PyTorch/","title":"Background: PyTorch","text":"<p>As discussed in our machine learning background page, many of the algorithms we provide in the ML-Agents Toolkit leverage some form of deep learning. More specifically, our implementations are built on top of the open-source library PyTorch. In this page we provide a brief overview of PyTorch and TensorBoard that we leverage within the ML-Agents Toolkit.</p>"},{"location":"Background-PyTorch/#pytorch","title":"PyTorch","text":"<p>PyTorch is an open source library for performing computations using data flow graphs, the underlying representation of deep learning models. It facilitates training and inference on CPUs and GPUs in a desktop, server, or mobile device. Within the ML-Agents Toolkit, when you train the behavior of an agent, the output is a model (.onnx) file that you can then associate with an Agent. Unless you implement a new algorithm, the use of PyTorch is mostly abstracted away and behind the scenes.</p>"},{"location":"Background-PyTorch/#tensorboard","title":"TensorBoard","text":"<p>One component of training models with PyTorch is setting the values of certain model attributes (called hyperparameters). Finding the right values of these hyperparameters can require a few iterations. Consequently, we leverage a visualization tool called TensorBoard. It allows the visualization of certain agent attributes (e.g. reward) throughout training which can be helpful in both building intuitions for the different hyperparameters and setting the optimal values for your Unity environment. We provide more details on setting the hyperparameters in the Training ML-Agents page. If you are unfamiliar with TensorBoard we recommend our guide on using TensorBoard with ML-Agents or this tutorial.</p>"},{"location":"Background-Unity/","title":"Background: Unity","text":"<p>If you are not familiar with the Unity Engine, we highly recommend the Unity Manual and Tutorials page. The Roll-a-ball tutorial is a fantastic resource to learn all the basic concepts of Unity to get started with the ML-Agents Toolkit:</p> <ul> <li>Editor</li> <li>Scene</li> <li>GameObject</li> <li>Rigidbody</li> <li>Camera</li> <li>Scripting</li> <li>Physics</li> <li>Ordering of event functions   (e.g. FixedUpdate, Update)</li> <li>Prefabs</li> </ul>"},{"location":"CODE_OF_CONDUCT/","title":"Contributor Covenant Code of Conduct","text":""},{"location":"CODE_OF_CONDUCT/#our-pledge","title":"Our Pledge","text":"<p>In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to making participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, gender identity and expression, level of experience, nationality, personal appearance, race, religion, or sexual identity and orientation.</p>"},{"location":"CODE_OF_CONDUCT/#our-standards","title":"Our Standards","text":"<p>Examples of behavior that contributes to creating a positive environment include:</p> <ul> <li>Using welcoming and inclusive language</li> <li>Being respectful of differing viewpoints and experiences</li> <li>Gracefully accepting constructive criticism</li> <li>Focusing on what is best for the community</li> <li>Showing empathy towards other community members</li> </ul> <p>Examples of unacceptable behavior by participants include:</p> <ul> <li>The use of sexualized language or imagery and unwelcome sexual attention or   advances</li> <li>Trolling, insulting/derogatory comments, and personal or political attacks</li> <li>Public or private harassment</li> <li>Publishing others' private information, such as a physical or electronic   address, without explicit permission</li> <li>Other conduct which could reasonably be considered inappropriate in a   professional setting</li> </ul>"},{"location":"CODE_OF_CONDUCT/#our-responsibilities","title":"Our Responsibilities","text":"<p>Project maintainers are responsible for clarifying the standards of acceptable behavior and are expected to take appropriate and fair corrective action in response to any instances of unacceptable behavior.</p> <p>Project maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful.</p>"},{"location":"CODE_OF_CONDUCT/#scope","title":"Scope","text":"<p>This Code of Conduct applies both within project spaces and in public spaces when an individual is representing the project or its community. Examples of representing a project or community include using an official project e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. Representation of a project may be further defined and clarified by project maintainers.</p>"},{"location":"CODE_OF_CONDUCT/#enforcement","title":"Enforcement","text":"<p>Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project team at ml-agents@unity3d.com. All complaints will be reviewed and investigated and will result in a response that is deemed necessary and appropriate to the circumstances. The project team is obligated to maintain confidentiality with regard to the reporter of an incident. Further details of specific enforcement policies may be posted separately.</p> <p>Project maintainers who do not follow or enforce the Code of Conduct in good faith may face temporary or permanent repercussions as determined by other members of the project's leadership.</p>"},{"location":"CODE_OF_CONDUCT/#attribution","title":"Attribution","text":"<p>This Code of Conduct is adapted from the Contributor Covenant, version 1.4, available at https://www.contributor-covenant.org/version/1/4/code-of-conduct/</p>"},{"location":"CONTRIBUTING/","title":"Contribution Guidelines","text":"<p>Thank you for your interest in contributing to the ML-Agents Toolkit! We are incredibly excited to see how members of our community will use and extend the ML-Agents Toolkit. To facilitate your contributions, we've outlined a brief set of guidelines to ensure that your extensions can be easily integrated.</p>"},{"location":"CONTRIBUTING/#communication","title":"Communication","text":"<p>First, please read through our code of conduct, as we expect all our contributors to follow it.</p> <p>Second, before starting on a project that you intend to contribute to the ML-Agents Toolkit (whether environments or modifications to the codebase), we strongly recommend posting on our Issues page and briefly outlining the changes you plan to make. This will enable us to provide some context that may be helpful for you. This could range from advice and feedback on how to optimally perform your changes or reasons for not doing it.</p> <p>Lastly, if you're looking for input on what to contribute, feel free to reach out to us directly at ml-agents@unity3d.com and/or browse the GitHub issues with the <code>Requests</code> or <code>Bug</code> label.</p>"},{"location":"CONTRIBUTING/#git-branches","title":"Git Branches","text":"<p>The main branch corresponds to the most recent version of the project. Note that this may be newer that the latest release.</p> <p>When contributing to the project, please make sure that your Pull Request (PR) contains the following:</p> <ul> <li>Detailed description of the changes performed</li> <li>Corresponding changes to documentation, unit tests and sample environments (if   applicable)</li> <li>Summary of the tests performed to validate your changes</li> <li>Issue numbers that the PR resolves (if any)</li> </ul>"},{"location":"CONTRIBUTING/#environments","title":"Environments","text":"<p>We are currently not accepting environment contributions directly into ML-Agents. However, we believe community created enviornments have a lot of value to the community. If you have an interesting enviornment and are willing to share, feel free to showcase it and share any relevant files in the ML-Agents forum.</p>"},{"location":"CONTRIBUTING/#continuous-integration-ci","title":"Continuous Integration (CI)","text":"<p>We run continuous integration on all PRs; all tests must be passing before the PR is merged.</p> <p>Several static checks are run on the codebase using the pre-commit framework during CI. To execute the same checks locally, run:</p> <pre><code>pip install pre-commit&gt;=2.8.0\npip install identify&gt;==2.1.3\npre-commit run --all-files\n</code></pre> <p>Some hooks (for example, <code>black</code>) will output the corrected version of the code; others (like <code>mypy</code>) may require more effort to fix. You can optionally run <code>pre-commit install</code> to install it as a git hook; after this it will run on all commits that you make.</p>"},{"location":"CONTRIBUTING/#code-style","title":"Code style","text":"<p>All python code should be formatted with <code>black</code>.</p> <p>C# code is formatted using <code>dotnet-format</code>. You must have dotnet installed first (but don't need to install <code>dotnet-format</code> - <code>pre-commit</code> will do that for you).</p>"},{"location":"CONTRIBUTING/#python-type-annotations","title":"Python type annotations","text":"<p>We use <code>mypy</code> to perform static type checking on python code. Currently not all code is annotated but we will increase coverage over time. If you are adding or refactoring code, please</p> <ol> <li>Add type annotations to the new or refactored code.</li> <li>Make sure that code calling or called by the modified code also has type    annotations.</li> </ol> <p>The type hint cheat sheet provides a good introduction to adding type hints.</p>"},{"location":"CONTRIBUTING/#contributor-license-agreements","title":"Contributor License Agreements","text":"<p>When you open a pull request, you will be asked to acknolwedge our Contributor License Agreement. We allow both individual contributions and contributions made on behalf of companies. We use an open source tool called CLA assistant. If you have any questions on our CLA, please submit an issue or email us at ml-agents@unity3d.com.</p>"},{"location":"Custom-SideChannels/","title":"Custom Side Channels","text":"<p>You can create your own side channel in C# and Python and use it to communicate custom data structures between the two. This can be useful for situations in which the data to be sent is too complex or structured for the built-in <code>EnvironmentParameters</code>, or is not related to any specific agent, and therefore inappropriate as an agent observation.</p>"},{"location":"Custom-SideChannels/#overview","title":"Overview","text":"<p>In order to use a side channel, it must be implemented as both Unity and Python classes.</p>"},{"location":"Custom-SideChannels/#unity-side","title":"Unity side","text":"<p>The side channel will have to implement the <code>SideChannel</code> abstract class and the following method.</p> <ul> <li><code>OnMessageReceived(IncomingMessage msg)</code> : You must implement this method and   read the data from IncomingMessage. The data must be read in the order that it   was written.</li> </ul> <p>The side channel must also assign a <code>ChannelId</code> property in the constructor. The <code>ChannelId</code> is a Guid (or UUID in Python) used to uniquely identify a side channel. This Guid must be the same on C# and Python. There can only be one side channel of a certain id during communication.</p> <p>To send data from C# to Python, create an <code>OutgoingMessage</code> instance, add data to it, call the <code>base.QueueMessageToSend(msg)</code> method inside the side channel, and call the <code>OutgoingMessage.Dispose()</code> method.</p> <p>To register a side channel on the Unity side, call <code>SideChannelManager.RegisterSideChannel</code> with the side channel as only argument.</p>"},{"location":"Custom-SideChannels/#python-side","title":"Python side","text":"<p>The side channel will have to implement the <code>SideChannel</code> abstract class. You must implement :</p> <ul> <li><code>on_message_received(self, msg: \"IncomingMessage\") -&gt; None</code> : You must   implement this method and read the data from IncomingMessage. The data must be   read in the order that it was written.</li> </ul> <p>The side channel must also assign a <code>channel_id</code> property in the constructor. The <code>channel_id</code> is a UUID (referred in C# as Guid) used to uniquely identify a side channel. This number must be the same on C# and Python. There can only be one side channel of a certain id during communication.</p> <p>To assign the <code>channel_id</code> call the abstract class constructor with the appropriate <code>channel_id</code> as follows:</p> <pre><code>super().__init__(my_channel_id)\n</code></pre> <p>To send a byte array from Python to C#, create an <code>OutgoingMessage</code> instance, add data to it, and call the <code>super().queue_message_to_send(msg)</code> method inside the side channel.</p> <p>To register a side channel on the Python side, pass the side channel as argument when creating the <code>UnityEnvironment</code> object. One of the arguments of the constructor (<code>side_channels</code>) is a list of side channels.</p>"},{"location":"Custom-SideChannels/#example-implementation","title":"Example implementation","text":"<p>Below is a simple implementation of a side channel that will exchange ASCII encoded strings between a Unity environment and Python.</p>"},{"location":"Custom-SideChannels/#example-unity-c-code","title":"Example Unity C# code","text":"<p>The first step is to create the <code>StringLogSideChannel</code> class within the Unity project. Here is an implementation of a <code>StringLogSideChannel</code> that will listen for messages from python and print them to the Unity debug log, as well as send error messages from Unity to python.</p> <pre><code>using UnityEngine;\nusing Unity.MLAgents;\nusing Unity.MLAgents.SideChannels;\nusing System.Text;\nusing System;\n\npublic class StringLogSideChannel : SideChannel\n{\n    public StringLogSideChannel()\n    {\n        ChannelId = new Guid(\"621f0a70-4f87-11ea-a6bf-784f4387d1f7\");\n    }\n\n    protected override void OnMessageReceived(IncomingMessage msg)\n    {\n        var receivedString = msg.ReadString();\n        Debug.Log(\"From Python : \" + receivedString);\n    }\n\n    public void SendDebugStatementToPython(string logString, string stackTrace, LogType type)\n    {\n        if (type == LogType.Error)\n        {\n            var stringToSend = type.ToString() + \": \" + logString + \"\\n\" + stackTrace;\n            using (var msgOut = new OutgoingMessage())\n            {\n                msgOut.WriteString(stringToSend);\n                QueueMessageToSend(msgOut);\n            }\n        }\n    }\n}\n</code></pre> <p>Once we have defined our custom side channel class, we need to ensure that it is instantiated and registered. This can typically be done wherever the logic of the side channel makes sense to be associated, for example on a MonoBehaviour object that might need to access data from the side channel. Here we show a simple MonoBehaviour object which instantiates and registers the new side channel. If you have not done it already, make sure that the MonoBehaviour which registers the side channel is attached to a GameObject which will be live in your Unity scene.</p> <pre><code>using UnityEngine;\nusing Unity.MLAgents;\n\n\npublic class RegisterStringLogSideChannel : MonoBehaviour\n{\n\n    StringLogSideChannel stringChannel;\n    public void Awake()\n    {\n        // We create the Side Channel\n        stringChannel = new StringLogSideChannel();\n\n        // When a Debug.Log message is created, we send it to the stringChannel\n        Application.logMessageReceived += stringChannel.SendDebugStatementToPython;\n\n        // The channel must be registered with the SideChannelManager class\n        SideChannelManager.RegisterSideChannel(stringChannel);\n    }\n\n    public void OnDestroy()\n    {\n        // De-register the Debug.Log callback\n        Application.logMessageReceived -= stringChannel.SendDebugStatementToPython;\n        if (Academy.IsInitialized){\n            SideChannelManager.UnregisterSideChannel(stringChannel);\n        }\n    }\n\n    public void Update()\n    {\n        // Optional : If the space bar is pressed, raise an error !\n        if (Input.GetKeyDown(KeyCode.Space))\n        {\n            Debug.LogError(\"This is a fake error. Space bar was pressed in Unity.\");\n        }\n    }\n}\n</code></pre>"},{"location":"Custom-SideChannels/#example-python-code","title":"Example Python code","text":"<p>Now that we have created the necessary Unity C# classes, we can create their Python counterparts.</p> <pre><code>from mlagents_envs.environment import UnityEnvironment\nfrom mlagents_envs.side_channel.side_channel import (\n    SideChannel,\n    IncomingMessage,\n    OutgoingMessage,\n)\nimport numpy as np\nimport uuid\n\n\n# Create the StringLogChannel class\nclass StringLogChannel(SideChannel):\n\n    def __init__(self) -&gt; None:\n        super().__init__(uuid.UUID(\"621f0a70-4f87-11ea-a6bf-784f4387d1f7\"))\n\n    def on_message_received(self, msg: IncomingMessage) -&gt; None:\n        \"\"\"\n        Note: We must implement this method of the SideChannel interface to\n        receive messages from Unity\n        \"\"\"\n        # We simply read a string from the message and print it.\n        print(msg.read_string())\n\n    def send_string(self, data: str) -&gt; None:\n        # Add the string to an OutgoingMessage\n        msg = OutgoingMessage()\n        msg.write_string(data)\n        # We call this method to queue the data we want to send\n        super().queue_message_to_send(msg)\n</code></pre> <p>We can then instantiate the new side channel, launch a <code>UnityEnvironment</code> with that side channel active, and send a series of messages to the Unity environment from Python using it.</p> <pre><code># Create the channel\nstring_log = StringLogChannel()\n\n# We start the communication with the Unity Editor and pass the string_log side channel as input\nenv = UnityEnvironment(side_channels=[string_log])\nenv.reset()\nstring_log.send_string(\"The environment was reset\")\n\ngroup_name = list(env.behavior_specs.keys())[0]  # Get the first group_name\ngroup_spec = env.behavior_specs[group_name]\nfor i in range(1000):\n    decision_steps, terminal_steps = env.get_steps(group_name)\n    # We send data to Unity : A string with the number of Agent at each\n    string_log.send_string(\n        f\"Step {i} occurred with {len(decision_steps)} deciding agents and \"\n        f\"{len(terminal_steps)} terminal agents\"\n    )\n    env.step()  # Move the simulation forward\n\nenv.close()\n</code></pre> <p>Now, if you run this script and press <code>Play</code> the Unity Editor when prompted, the console in the Unity Editor will display a message at every Python step. Additionally, if you press the Space Bar in the Unity Engine, a message will appear in the terminal.</p>"},{"location":"FAQ/","title":"Frequently Asked Questions","text":""},{"location":"FAQ/#installation-problems","title":"Installation problems","text":""},{"location":"FAQ/#environment-permission-error","title":"Environment Permission Error","text":"<p>If you directly import your Unity environment without building it in the editor, you might need to give it additional permissions to execute it.</p> <p>If you receive such a permission error on macOS, run:</p> <pre><code>chmod -R 755 *.app\n</code></pre> <p>or on Linux:</p> <pre><code>chmod -R 755 *.x86_64\n</code></pre> <p>On Windows, you can find instructions.</p>"},{"location":"FAQ/#environment-connection-timeout","title":"Environment Connection Timeout","text":"<p>If you are able to launch the environment from <code>UnityEnvironment</code> but then receive a timeout error like this:</p> <pre><code>UnityAgentsException: The Communicator was unable to connect. Please make sure the External process is ready to accept communication with Unity.\n</code></pre> <p>There may be a number of possible causes:</p> <ul> <li>Cause: There may be no agent in the scene</li> <li>Cause: On OSX, the firewall may be preventing communication with the   environment. Solution: Add the built environment binary to the list of   exceptions on the firewall by following   instructions.</li> <li>Cause: An error happened in the Unity Environment preventing communication.   Solution: Look into the   log files generated by the   Unity Environment to figure what error happened.</li> <li>Cause: You have assigned <code>HTTP_PROXY</code> and <code>HTTPS_PROXY</code> values in your   environment variables. Solution: Remove these values and try again.</li> <li>Cause: You are running in a headless environment (e.g. remotely connected   to a server). Solution: Pass <code>--no-graphics</code> to <code>mlagents-learn</code>, or   <code>no_graphics=True</code> to <code>RemoteRegistryEntry.make()</code> or the <code>UnityEnvironment</code>   initializer. If you need graphics for visual observations, you will need to   set up <code>xvfb</code> (or equivalent).</li> </ul>"},{"location":"FAQ/#communication-port-still-in-use","title":"Communication port {} still in use","text":"<p>If you receive an exception <code>\"Couldn't launch new environment because communication port {} is still in use. \"</code>, you can change the worker number in the Python script when calling</p> <pre><code>UnityEnvironment(file_name=filename, worker_id=X)\n</code></pre>"},{"location":"FAQ/#mean-reward-nan","title":"Mean reward : nan","text":"<p>If you receive a message <code>Mean reward : nan</code> when attempting to train a model using PPO, this is due to the episodes of the Learning Environment not terminating. In order to address this, set <code>Max Steps</code> for the Agents within the Scene Inspector to a value greater than 0. Alternatively, it is possible to manually set <code>done</code> conditions for episodes from within scripts for custom episode-terminating events.</p>"},{"location":"FAQ/#file-name-cannot-be-opened-because-the-developer-cannot-be-verified","title":"\"File name\" cannot be opened because the developer cannot be verified.","text":"<p>If you have downloaded the repository using the github website on macOS 10.15 (Catalina) or later, you may see this error when attempting to play scenes in the Unity project. Workarounds include installing the package using the Unity Package Manager (this is the officially supported approach - see here), or following the instructions here to verify the relevant files on your machine on a file-by-file basis.</p>"},{"location":"Getting-Started/","title":"Getting Started Guide","text":"<p>This guide walks through the end-to-end process of opening one of our example environments in Unity, training an Agent in it, and embedding the trained model into the Unity environment. After reading this tutorial, you should be able to train any of the example environments. If you are not familiar with the Unity Engine, view our Background: Unity page for helpful pointers. Additionally, if you're not familiar with machine learning, view our Background: Machine Learning page for a brief overview and helpful pointers.</p> <p></p> <p>For this guide, we'll use the 3D Balance Ball environment which contains a number of agent cubes and balls (which are all copies of each other). Each agent cube tries to keep its ball from falling by rotating either horizontally or vertically. In this environment, an agent cube is an Agent that receives a reward for every step that it balances the ball. An agent is also penalized with a negative reward for dropping the ball. The goal of the training process is to have the agents learn to balance the ball on their head.</p> <p>Let's get started!</p>"},{"location":"Getting-Started/#installation","title":"Installation","text":"<p>If you haven't already, follow the installation instructions. Afterwards, open the Unity Project that contains all the example environments:</p> <ol> <li>Open the Package Manager Window by navigating to <code>Window -&gt; Package Manager</code>    in the menu.</li> <li>Navigate to the ML-Agents Package and click on it.</li> <li>Find the <code>3D Ball</code> sample and click <code>Import</code>.</li> <li>In the Project window, go to the    <code>Assets/ML-Agents/Examples/3DBall/Scenes</code> folder and open the <code>3DBall</code> scene    file.</li> </ol>"},{"location":"Getting-Started/#understanding-a-unity-environment","title":"Understanding a Unity Environment","text":"<p>An agent is an autonomous actor that observes and interacts with an environment. In the context of Unity, an environment is a scene containing one or more Agent objects, and, of course, the other entities that an agent interacts with.</p> <p></p> <p>Note: In Unity, the base object of everything in a scene is the GameObject. The GameObject is essentially a container for everything else, including behaviors, graphics, physics, etc. To see the components that make up a GameObject, select the GameObject in the Scene window, and open the Inspector window. The Inspector shows every component on a GameObject.</p> <p>The first thing you may notice after opening the 3D Balance Ball scene is that it contains not one, but several agent cubes. Each agent cube in the scene is an independent agent, but they all share the same Behavior. 3D Balance Ball does this to speed up training since all twelve agents contribute to training in parallel.</p>"},{"location":"Getting-Started/#agent","title":"Agent","text":"<p>The Agent is the actor that observes and takes actions in the environment. In the 3D Balance Ball environment, the Agent components are placed on the twelve \"Agent\" GameObjects. The base Agent object has a few properties that affect its behavior:</p> <ul> <li>Behavior Parameters \u2014 Every Agent must have a Behavior. The Behavior   determines how an Agent makes decisions.</li> <li>Max Step \u2014 Defines how many simulation steps can occur before the Agent's   episode ends. In 3D Balance Ball, an Agent restarts after 5000 steps.</li> </ul>"},{"location":"Getting-Started/#behavior-parameters-vector-observation-space","title":"Behavior Parameters : Vector Observation Space","text":"<p>Before making a decision, an agent collects its observation about its state in the world. The vector observation is a vector of floating point numbers which contain relevant information for the agent to make decisions.</p> <p>The Behavior Parameters of the 3D Balance Ball example uses a <code>Space Size</code> of 8. This means that the feature vector containing the Agent's observations contains eight elements: the <code>x</code> and <code>z</code> components of the agent cube's rotation and the <code>x</code>, <code>y</code>, and <code>z</code> components of the ball's relative position and velocity.</p>"},{"location":"Getting-Started/#behavior-parameters-actions","title":"Behavior Parameters : Actions","text":"<p>An Agent is given instructions in the form of actions. ML-Agents Toolkit classifies actions into two types: continuous and discrete. The 3D Balance Ball example is programmed to use continuous actions, which are a vector of floating-point numbers that can vary continuously. More specifically, it uses a <code>Space Size</code> of 2 to control the amount of <code>x</code> and <code>z</code> rotations to apply to itself to keep the ball balanced on its head.</p>"},{"location":"Getting-Started/#running-a-pre-trained-model","title":"Running a pre-trained model","text":"<p>We include pre-trained models for our agents (<code>.onnx</code> files) and we use the Unity Inference Engine to run these models inside Unity. In this section, we will use the pre-trained model for the 3D Ball example.</p> <ol> <li>In the Project window, go to the    <code>Assets/ML-Agents/Examples/3DBall/Prefabs</code> folder. Expand <code>3DBall</code> and click    on the <code>Agent</code> prefab. You should see the <code>Agent</code> prefab in the Inspector    window.</li> </ol> <p>Note: The platforms in the <code>3DBall</code> scene were created using the <code>3DBall</code>    prefab. Instead of updating all 12 platforms individually, you can update the    <code>3DBall</code> prefab instead.</p> <p></p> <ol> <li>In the Project window, drag the 3DBall Model located in    <code>Assets/ML-Agents/Examples/3DBall/TFModels</code> into the <code>Model</code> property under    <code>Behavior Parameters (Script)</code> component in the Agent GameObject    Inspector window.</li> </ol> <p></p> <ol> <li>You should notice that each <code>Agent</code> under each <code>3DBall</code> in the Hierarchy    windows now contains 3DBall as <code>Model</code> on the <code>Behavior Parameters</code>.    Note : You can modify multiple game objects in a scene by selecting them    all at once using the search bar in the Scene Hierarchy.</li> <li>Set the Inference Device to use for this model as <code>CPU</code>.</li> <li>Click the Play button in the Unity Editor and you will see the platforms    balance the balls using the pre-trained model.</li> </ol>"},{"location":"Getting-Started/#training-a-new-model-with-reinforcement-learning","title":"Training a new model with Reinforcement Learning","text":"<p>While we provide pre-trained models for the agents in this environment, any environment you make yourself will require training agents from scratch to generate a new model file. In this section we will demonstrate how to use the reinforcement learning algorithms that are part of the ML-Agents Python package to accomplish this. We have provided a convenient command <code>mlagents-learn</code> which accepts arguments used to configure both training and inference phases.</p>"},{"location":"Getting-Started/#training-the-environment","title":"Training the environment","text":"<ol> <li>Open a command or terminal window.</li> <li>Navigate to the folder where you cloned the <code>ml-agents</code> repository. Note:    If you followed the default installation, then you should    be able to run <code>mlagents-learn</code> from any directory.</li> <li>Run <code>mlagents-learn config/ppo/3DBall.yaml --run-id=first3DBallRun</code>.</li> <li><code>config/ppo/3DBall.yaml</code> is the path to a default training      configuration file that we provide. The <code>config/ppo</code> folder includes training configuration      files for all our example environments, including 3DBall.</li> <li><code>run-id</code> is a unique name for this training session.</li> <li>When the message \"Start training by pressing the Play button in the Unity    Editor\" is displayed on the screen, you can press the Play button in    Unity to start training in the Editor.</li> </ol> <p>If <code>mlagents-learn</code> runs correctly and starts training, you should see something like this:</p> <pre><code>INFO:mlagents_envs:\n'Ball3DAcademy' started successfully!\nUnity Academy name: Ball3DAcademy\n\nINFO:mlagents_envs:Connected new brain:\nUnity brain name: 3DBallLearning\n        Number of Visual Observations (per agent): 0\n        Vector Observation space size (per agent): 8\n        Number of stacked Vector Observation: 1\nINFO:mlagents_envs:Hyperparameters for the PPO Trainer of brain 3DBallLearning:\n        batch_size:          64\n        beta:                0.001\n        buffer_size:         12000\n        epsilon:             0.2\n        gamma:               0.995\n        hidden_units:        128\n        lambd:               0.99\n        learning_rate:       0.0003\n        max_steps:           5.0e4\n        normalize:           True\n        num_epoch:           3\n        num_layers:          2\n        time_horizon:        1000\n        sequence_length:     64\n        summary_freq:        1000\n        use_recurrent:       False\n        memory_size:         256\n        use_curiosity:       False\n        curiosity_strength:  0.01\n        curiosity_enc_size:  128\n        output_path: ./results/first3DBallRun/3DBallLearning\nINFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 1000. Mean Reward: 1.242. Std of Reward: 0.746. Training.\nINFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 2000. Mean Reward: 1.319. Std of Reward: 0.693. Training.\nINFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 3000. Mean Reward: 1.804. Std of Reward: 1.056. Training.\nINFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 4000. Mean Reward: 2.151. Std of Reward: 1.432. Training.\nINFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 5000. Mean Reward: 3.175. Std of Reward: 2.250. Training.\nINFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 6000. Mean Reward: 4.898. Std of Reward: 4.019. Training.\nINFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 7000. Mean Reward: 6.716. Std of Reward: 5.125. Training.\nINFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 8000. Mean Reward: 12.124. Std of Reward: 11.929. Training.\nINFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 9000. Mean Reward: 18.151. Std of Reward: 16.871. Training.\nINFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 10000. Mean Reward: 27.284. Std of Reward: 28.667. Training.\n</code></pre> <p>Note how the <code>Mean Reward</code> value printed to the screen increases as training progresses. This is a positive sign that training is succeeding.</p> <p>Note: You can train using an executable rather than the Editor. To do so, follow the instructions in Using an Executable.</p>"},{"location":"Getting-Started/#observing-training-progress","title":"Observing Training Progress","text":"<p>Once you start training using <code>mlagents-learn</code> in the way described in the previous section, the <code>ml-agents</code> directory will contain a <code>results</code> directory. In order to observe the training process in more detail, you can use TensorBoard. From the command line run:</p> <pre><code>tensorboard --logdir results\n</code></pre> <p>Then navigate to <code>localhost:6006</code> in your browser to view the TensorBoard summary statistics as shown below. For the purposes of this section, the most important statistic is <code>Environment/Cumulative Reward</code> which should increase throughout training, eventually converging close to <code>100</code> which is the maximum reward the agent can accumulate.</p> <p></p>"},{"location":"Getting-Started/#embedding-the-model-into-the-unity-environment","title":"Embedding the model into the Unity Environment","text":"<p>Once the training process completes, and the training process saves the model (denoted by the <code>Saved Model</code> message) you can add it to the Unity project and use it with compatible Agents (the Agents that generated the model). Note: Do not just close the Unity Window once the <code>Saved Model</code> message appears. Either wait for the training process to close the window or press <code>Ctrl+C</code> at the command-line prompt. If you close the window manually, the <code>.onnx</code> file containing the trained model is not exported into the ml-agents folder.</p> <p>If you've quit the training early using <code>Ctrl+C</code> and want to resume training, run the same command again, appending the <code>--resume</code> flag:</p> <pre><code>mlagents-learn config/ppo/3DBall.yaml --run-id=first3DBallRun --resume\n</code></pre> <p>Your trained model will be at <code>results/&lt;run-identifier&gt;/&lt;behavior_name&gt;.onnx</code> where <code>&lt;behavior_name&gt;</code> is the name of the <code>Behavior Name</code> of the agents corresponding to the model. This file corresponds to your model's latest checkpoint. You can now embed this trained model into your Agents by following the steps below, which is similar to the steps described above.</p> <ol> <li>Move your model file into    <code>Project/Assets/ML-Agents/Examples/3DBall/TFModels/</code>.</li> <li>Open the Unity Editor, and select the 3DBall scene as described above.</li> <li>Select the 3DBall prefab Agent object.</li> <li>Drag the <code>&lt;behavior_name&gt;.onnx</code> file from the Project window of the Editor to    the Model placeholder in the Ball3DAgent inspector window.</li> <li>Press the Play button at the top of the Editor.</li> </ol>"},{"location":"Getting-Started/#next-steps","title":"Next Steps","text":"<ul> <li>For more information on the ML-Agents Toolkit, in addition to helpful   background, check out the ML-Agents Toolkit Overview   page.</li> <li>For a \"Hello World\" introduction to creating your own Learning Environment,   check out the   Making a New Learning Environment page.</li> <li>For an overview on the more complex example environments that are provided in   this toolkit, check out the   Example Environments page.</li> <li>For more information on the various training options available, check out the   Training ML-Agents page.</li> </ul>"},{"location":"Glossary/","title":"ML-Agents Toolkit Glossary","text":"<ul> <li>Academy - Singleton object which controls timing, reset, and   training/inference settings of the environment.</li> <li>Action - The carrying-out of a decision on the part of an agent within the   environment.</li> <li>Agent - Unity Component which produces observations and takes actions in   the environment. Agents actions are determined by decisions produced by a   Policy.</li> <li>Decision - The specification produced by a Policy for an action to be   carried out given an observation.</li> <li>Editor - The Unity Editor, which may include any pane (e.g. Hierarchy,   Scene, Inspector).</li> <li>Environment - The Unity scene which contains Agents.</li> <li>Experience - Corresponds to a tuple of [Agent observations, actions,   rewards] of a single Agent obtained after a Step.</li> <li>External Coordinator - ML-Agents class responsible for communication with   outside processes (in this case, the Python API).</li> <li>FixedUpdate - Unity method called each time the game engine is stepped.   ML-Agents logic should be placed here.</li> <li>Frame - An instance of rendering the main camera for the display.   Corresponds to each <code>Update</code> call of the game engine.</li> <li>Observation - Partial information describing the state of the environment   available to a given agent. (e.g. Vector, Visual)</li> <li>Policy - The decision making mechanism for producing decisions from   observations, typically a neural network model.</li> <li>Reward - Signal provided at every step used to indicate desirability of an   agent\u2019s action within the current state of the environment.</li> <li>State - The underlying properties of the environment (including all agents   within it) at a given time.</li> <li>Step - Corresponds to an atomic change of the engine that happens between   Agent decisions.</li> <li>Trainer - Python class which is responsible for training a given group of   Agents.</li> <li>Update - Unity function called each time a frame is rendered. ML-Agents   logic should not be placed here.</li> </ul>"},{"location":"Installation-Anaconda-Windows/","title":"Installing ML-Agents Toolkit for Windows (Deprecated)","text":"<p>:warning: Note: We no longer use this guide ourselves and so it may not work correctly. We've decided to keep it up just in case it is helpful to you.</p> <p>The ML-Agents Toolkit supports Windows 10. While it might be possible to run the ML-Agents Toolkit using other versions of Windows, it has not been tested on other versions. Furthermore, the ML-Agents Toolkit has not been tested on a Windows VM such as Bootcamp or Parallels.</p> <p>To use the ML-Agents Toolkit, you install Python and the required Python packages as outlined below. This guide also covers how set up GPU-based training (for advanced users). GPU-based training is not currently required for the ML-Agents Toolkit. However, training on a GPU might be required by future versions and features.</p>"},{"location":"Installation-Anaconda-Windows/#step-1-install-python-via-anaconda","title":"Step 1: Install Python via Anaconda","text":"<p>Download and install Anaconda for Windows. By using Anaconda, you can manage separate environments for different distributions of Python. Python 3.7.2 or higher is required as we no longer support Python 2. In this guide, we are using Python version 3.7 and Anaconda version 5.1 (64-bit or 32-bit direct links).</p> <p> </p> <p>We recommend the default advanced installation options. However, select the options appropriate for your specific situation.</p> <p> </p> <p>After installation, you must open Anaconda Navigator to finish the setup. From the Windows search bar, type anaconda navigator. You can close Anaconda Navigator after it opens.</p> <p>If environment variables were not created, you will see error \"conda is not recognized as internal or external command\" when you type <code>conda</code> into the command line. To solve this you will need to set the environment variable correctly.</p> <p>Type <code>environment variables</code> in the search bar (this can be reached by hitting the Windows key or the bottom left Windows button). You should see an option called Edit the system environment variables.</p> <p> </p> <p>From here, click the Environment Variables button. Double click \"Path\" under System variable to edit the \"Path\" variable, click New to add the following new paths.</p> <pre><code>%UserProfile%\\Anaconda3\\Scripts\n%UserProfile%\\Anaconda3\\Scripts\\conda.exe\n%UserProfile%\\Anaconda3\n%UserProfile%\\Anaconda3\\python.exe\n</code></pre>"},{"location":"Installation-Anaconda-Windows/#step-2-setup-and-activate-a-new-conda-environment","title":"Step 2: Setup and Activate a New Conda Environment","text":"<p>You will create a new Conda environment to be used with the ML-Agents Toolkit. This means that all the packages that you install are localized to just this environment. It will not affect any other installation of Python or other environments. Whenever you want to run ML-Agents, you will need activate this Conda environment.</p> <p>To create a new Conda environment, open a new Anaconda Prompt (Anaconda Prompt in the search bar) and type in the following command:</p> <pre><code>conda create -n ml-agents python=3.7\n</code></pre> <p>You may be asked to install new packages. Type <code>y</code> and press enter (make sure you are connected to the Internet). You must install these required packages. The new Conda environment is called ml-agents and uses Python version 3.7.</p> <p> </p> <p>To use this environment, you must activate it. (To use this environment In the future, you can run the same command). In the same Anaconda Prompt, type in the following command:</p> <pre><code>activate ml-agents\n</code></pre> <p>You should see <code>(ml-agents)</code> prepended on the last line.</p> <p>Next, install <code>tensorflow</code>. Install this package using <code>pip</code> - which is a package management system used to install Python packages. Latest versions of TensorFlow won't work, so you will need to make sure that you install version 1.7.1. In the same Anaconda Prompt, type in the following command (make sure you are connected to the Internet):</p> <pre><code>pip install tensorflow==1.7.1\n</code></pre>"},{"location":"Installation-Anaconda-Windows/#step-3-install-required-python-packages","title":"Step 3: Install Required Python Packages","text":"<p>The ML-Agents Toolkit depends on a number of Python packages. Use <code>pip</code> to install these Python dependencies.</p> <p>If you haven't already, clone the ML-Agents Toolkit Github repository to your local computer. You can do this using Git (download here) and running the following commands in an Anaconda Prompt (if you open a new prompt, be sure to activate the ml-agents Conda environment by typing <code>activate ml-agents</code>):</p> <pre><code>git clone --branch release_19 https://github.com/Unity-Technologies/ml-agents.git\n</code></pre> <p>The <code>--branch release_19</code> option will switch to the tag of the latest stable release. Omitting that will get the <code>main</code> branch which is potentially unstable.</p> <p>If you don't want to use Git, you can find download links on the releases page.</p> <p>The <code>com.unity.ml-agents</code> subdirectory contains the core code to add to your projects. The <code>Project</code> subdirectory contains many example environments to help you get started.</p> <p>The <code>ml-agents</code> subdirectory contains a Python package which provides deep reinforcement learning trainers to use with Unity environments.</p> <p>The <code>ml-agents-envs</code> subdirectory contains a Python API to interface with Unity, which the <code>ml-agents</code> package depends on.</p> <p>The <code>gym-unity</code> subdirectory contains a package to interface with OpenAI Gym.</p> <p>Keep in mind where the files were downloaded, as you will need the trainer config files in this directory when running <code>mlagents-learn</code>. Make sure you are connected to the Internet and then type in the Anaconda Prompt:</p> <pre><code>python -m pip install mlagents==0.30.0\n</code></pre> <p>This will complete the installation of all the required Python packages to run the ML-Agents Toolkit.</p> <p>Sometimes on Windows, when you use pip to install certain Python packages, the pip will get stuck when trying to read the cache of the package. If you see this, you can try:</p> <pre><code>python -m pip install mlagents==0.30.0 --no-cache-dir\n</code></pre> <p>This <code>--no-cache-dir</code> tells the pip to disable the cache.</p>"},{"location":"Installation-Anaconda-Windows/#installing-for-development","title":"Installing for Development","text":"<p>If you intend to make modifications to <code>ml-agents</code> or <code>ml-agents-envs</code>, you should install the packages from the cloned repo rather than from PyPi. To do this, you will need to install <code>ml-agents</code> and <code>ml-agents-envs</code> separately.</p> <p>In our example, the files are located in <code>C:\\Downloads</code>. After you have either cloned or downloaded the files, from the Anaconda Prompt, change to the ml-agents subdirectory inside the ml-agents directory:</p> <pre><code>cd C:\\Downloads\\ml-agents\n</code></pre> <p>From the repo's main directory, now run:</p> <pre><code>cd ml-agents-envs\npip install -e .\ncd ..\ncd ml-agents\npip install -e .\n</code></pre> <p>Running pip with the <code>-e</code> flag will let you make changes to the Python files directly and have those reflected when you run <code>mlagents-learn</code>. It is important to install these packages in this order as the <code>mlagents</code> package depends on <code>mlagents_envs</code>, and installing it in the other order will download <code>mlagents_envs</code> from PyPi.</p>"},{"location":"Installation-Anaconda-Windows/#optional-step-4-gpu-training-using-the-ml-agents-toolkit","title":"(Optional) Step 4: GPU Training using The ML-Agents Toolkit","text":"<p>GPU is not required for the ML-Agents Toolkit and won't speed up the PPO algorithm a lot during training(but something in the future will benefit from GPU). This is a guide for advanced users who want to train using GPUs. Additionally, you will need to check if your GPU is CUDA compatible. Please check Nvidia's page here.</p> <p>Currently for the ML-Agents Toolkit, only CUDA v9.0 and cuDNN v7.0.5 is supported.</p>"},{"location":"Installation-Anaconda-Windows/#install-nvidia-cuda-toolkit","title":"Install Nvidia CUDA toolkit","text":"<p>Download and install the CUDA toolkit 9.0 from Nvidia's archive. The toolkit includes GPU-accelerated libraries, debugging and optimization tools, a C/C++ (Step Visual Studio 2017) compiler and a runtime library and is needed to run the ML-Agents Toolkit. In this guide, we are using version 9.0.176).</p> <p>Before installing, please make sure you close any running instances of Unity or Visual Studio.</p> <p>Run the installer and select the Express option. Note the directory where you installed the CUDA toolkit. In this guide, we installed in the directory <code>C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v9.0</code></p>"},{"location":"Installation-Anaconda-Windows/#install-nvidia-cudnn-library","title":"Install Nvidia cuDNN library","text":"<p>Download and install the cuDNN library from Nvidia. cuDNN is a GPU-accelerated library of primitives for deep neural networks. Before you can download, you will need to sign up for free to the Nvidia Developer Program.</p> <p> </p> <p>Once you've signed up, go back to the cuDNN downloads page. You may or may not be asked to fill out a short survey. When you get to the list cuDNN releases, make sure you are downloading the right version for the CUDA toolkit you installed in Step 1. In this guide, we are using version 7.0.5 for CUDA toolkit version 9.0 (direct link).</p> <p>After you have downloaded the cuDNN files, you will need to extract the files into the CUDA toolkit directory. In the cuDNN zip file, there are three folders called <code>bin</code>, <code>include</code>, and <code>lib</code>.</p> <p> </p> <p>Copy these three folders into the CUDA toolkit directory. The CUDA toolkit directory is located at <code>C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v9.0</code></p> <p> </p>"},{"location":"Installation-Anaconda-Windows/#set-environment-variables","title":"Set Environment Variables","text":"<p>You will need to add one environment variable and two path variables.</p> <p>To set the environment variable, type <code>environment variables</code> in the search bar (this can be reached by hitting the Windows key or the bottom left Windows button). You should see an option called Edit the system environment variables.</p> <p> </p> <p>From here, click the Environment Variables button. Click New to add a new system variable (make sure you do this under System variables and not User variables.</p> <p> </p> <p>For Variable Name, enter <code>CUDA_HOME</code>. For the variable value, put the directory location for the CUDA toolkit. In this guide, the directory location is <code>C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v9.0</code>. Press OK once.</p> <p> </p> <p>To set the two path variables, inside the same Environment Variables window and under the second box called System Variables, find a variable called <code>Path</code> and click Edit. You will add two directories to the list. For this guide, the two entries would look like:</p> <pre><code>C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v9.0\\lib\\x64\nC:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v9.0\\extras\\CUPTI\\libx64\n</code></pre> <p>Make sure to replace the relevant directory location with the one you have installed. Please note that case sensitivity matters.</p> <p> </p>"},{"location":"Installation-Anaconda-Windows/#install-tensorflow-gpu","title":"Install TensorFlow GPU","text":"<p>Next, install <code>tensorflow-gpu</code> using <code>pip</code>. You'll need version 1.7.1. In an Anaconda Prompt with the Conda environment ml-agents activated, type in the following command to uninstall TensorFlow for cpu and install TensorFlow for gpu (make sure you are connected to the Internet):</p> <pre><code>pip uninstall tensorflow\npip install tensorflow-gpu==1.7.1\n</code></pre> <p>Lastly, you should test to see if everything installed properly and that TensorFlow can identify your GPU. In the same Anaconda Prompt, open Python in the Prompt by calling:</p> <pre><code>python\n</code></pre> <p>And then type the following commands:</p> <pre><code>import tensorflow as tf\n\nsess = tf.Session(config=tf.ConfigProto(log_device_placement=True))\n</code></pre> <p>You should see something similar to:</p> <pre><code>Found device 0 with properties ...\n</code></pre>"},{"location":"Installation-Anaconda-Windows/#acknowledgments","title":"Acknowledgments","text":"<p>We would like to thank Jason Weimann and Nitish S. Mutha for writing the original articles which were used to create this guide.</p>"},{"location":"Installation/","title":"Installation","text":"<p>The ML-Agents Toolkit contains several components:</p> <ul> <li>Unity package (<code>com.unity.ml-agents</code>) contains the   Unity C# SDK that will be integrated into your Unity project.  This package contains   a sample to help you get started with ML-Agents.</li> <li>Unity package   (<code>com.unity.ml-agents.extensions</code>)   contains experimental C#/Unity components that are not yet ready to be part   of the base <code>com.unity.ml-agents</code> package. <code>com.unity.ml-agents.extensions</code>   has a direct dependency on <code>com.unity.ml-agents</code>.</li> <li>Two Python packages:</li> <li><code>mlagents</code> contains the machine learning algorithms that     enables you to train behaviors in your Unity scene. Most users of ML-Agents     will only need to directly install <code>mlagents</code>.</li> <li><code>mlagents_envs</code> contains a set of Python APIs to interact with     a Unity scene. It is a foundational layer that facilitates data messaging     between Unity scene and the Python machine learning algorithms.     Consequently, <code>mlagents</code> depends on <code>mlagents_envs</code>.</li> <li>Unity Project that contains several   example environments that highlight the   various features of the toolkit to help you get started.</li> </ul> <p>Consequently, to install and use the ML-Agents Toolkit you will need to:</p> <ul> <li>Install Unity (2021.3 or later)</li> <li>Install Python (3.8.13 or higher)</li> <li>Clone this repository (Optional)</li> <li>Note: If you do not clone the repository, then you will not be   able to access the example environments and training configurations or the   <code>com.unity.ml-agents.extensions</code> package. Additionally, the   Getting Started Guide assumes that you have cloned the   repository.</li> <li>Install the <code>com.unity.ml-agents</code> Unity package</li> <li>Install the <code>com.unity.ml-agents.extensions</code> Unity package (Optional)</li> <li>Install the <code>mlagents</code> Python package</li> </ul>"},{"location":"Installation/#install-unity-20213-or-later","title":"Install Unity 2021.3 or Later","text":"<p>Download and install Unity. We strongly recommend that you install Unity through the Unity Hub as it will enable you to manage multiple Unity versions.</p>"},{"location":"Installation/#install-python-372-or-higher","title":"Install Python 3.7.2 or Higher","text":"<p>We recommend installing Python 3.7. If you are using Windows, please install the x86-64 version and not x86. If your Python environment doesn't include <code>pip3</code>, see these instructions on installing it.</p>"},{"location":"Installation/#clone-the-ml-agents-toolkit-repository-optional","title":"Clone the ML-Agents Toolkit Repository (Optional)","text":"<p>Now that you have installed Unity and Python, you can now install the Unity and Python packages. You do not need to clone the repository to install those packages, but you may choose to clone the repository if you'd like download our example environments and training configurations to experiment with them (some of our tutorials / guides assume you have access to our example environments).</p> <p>NOTE: There are samples shipped with the Unity Package.  You only need to clone the repository if you would like to explore more examples.</p> <pre><code>git clone --branch release_19 https://github.com/Unity-Technologies/ml-agents.git\n</code></pre> <p>The <code>--branch release_19</code> option will switch to the tag of the latest stable release. Omitting that will get the <code>main</code> branch which is potentially unstable.</p>"},{"location":"Installation/#advanced-local-installation-for-development","title":"Advanced: Local Installation for Development","text":"<p>You will need to clone the repository if you plan to modify or extend the ML-Agents Toolkit for your purposes. If you plan to contribute those changes back, make sure to clone the <code>main</code> branch (by omitting <code>--branch release_19</code> from the command above). See our Contributions Guidelines for more information on contributing to the ML-Agents Toolkit.</p>"},{"location":"Installation/#install-the-comunityml-agents-unity-package","title":"Install the <code>com.unity.ml-agents</code> Unity package","text":"<p>The Unity ML-Agents C# SDK is a Unity Package. You can install the <code>com.unity.ml-agents</code> package directly from the Package Manager registry. Please make sure you enable 'Preview Packages' in the 'Advanced' dropdown in order to find the latest Preview release of the package.</p> <p>NOTE: If you do not see the ML-Agents package listed in the Package Manager please follow the advanced installation instructions below.</p>"},{"location":"Installation/#advanced-local-installation-for-development_1","title":"Advanced: Local Installation for Development","text":"<p>You can add the local <code>com.unity.ml-agents</code> package (from the repository that you just cloned) to your project by:</p> <ol> <li>navigating to the menu <code>Window</code> -&gt; <code>Package Manager</code>.</li> <li>In the package manager window click on the <code>+</code> button on the top left of the packages list).</li> <li>Select <code>Add package from disk...</code></li> <li>Navigate into the <code>com.unity.ml-agents</code> folder.</li> <li>Select the <code>package.json</code> file.</li> </ol> <p> </p> <p>If you are going to follow the examples from our documentation, you can open the <code>Project</code> folder in Unity and start tinkering immediately.</p>"},{"location":"Installation/#install-the-comunityml-agentsextensions-unity-package-optional","title":"Install the <code>com.unity.ml-agents.extensions</code> Unity package (Optional)","text":"<p>To install the <code>com.unity.ml-agents.extensions</code> package, you need to first clone the repo and then complete a local installation similar to what was outlined in the previous Advanced: Local Installation for Development section. Complete installation steps can be found in the package documentation.</p>"},{"location":"Installation/#install-the-mlagents-python-package","title":"Install the <code>mlagents</code> Python package","text":"<p>Installing the <code>mlagents</code> Python package involves installing other Python packages that <code>mlagents</code> depends on. So you may run into installation issues if your machine has older versions of any of those dependencies already installed. Consequently, our supported path for installing <code>mlagents</code> is to leverage Python Virtual Environments. Virtual Environments provide a mechanism for isolating the dependencies for each project and are supported on Mac / Windows / Linux. We offer a dedicated guide on Virtual Environments.</p>"},{"location":"Installation/#windows-installing-pytorch","title":"(Windows) Installing PyTorch","text":"<p>On Windows, you'll have to install the PyTorch package separately prior to installing ML-Agents. Activate your virtual environment and run from the command line:</p> <pre><code>pip3 install torch~=1.7.1 -f https://download.pytorch.org/whl/torch_stable.html\n</code></pre> <p>Note that on Windows, you may also need Microsoft's Visual C++ Redistributable if you don't have it already. See the PyTorch installation guide for more installation options and versions.</p>"},{"location":"Installation/#installing-mlagents","title":"Installing <code>mlagents</code>","text":"<p>To install the <code>mlagents</code> Python package, activate your virtual environment and run from the command line:</p> <pre><code>python -m pip install mlagents==0.30.0\n</code></pre> <p>Note that this will install <code>mlagents</code> from PyPi, not from the cloned repository. If you installed this correctly, you should be able to run <code>mlagents-learn --help</code>, after which you will see the command line parameters you can use with <code>mlagents-learn</code>.</p> <p>By installing the <code>mlagents</code> package, the dependencies listed in the setup.py file are also installed. These include PyTorch (Requires a CPU w/ AVX support).</p>"},{"location":"Installation/#advanced-local-installation-for-development_2","title":"Advanced: Local Installation for Development","text":"<p>If you intend to make modifications to <code>mlagents</code> or <code>mlagents_envs</code>, you should install the packages from the cloned repository rather than from PyPi. To do this, you will need to install <code>mlagents</code> and <code>mlagents_envs</code> separately. From the repository's root directory, run:</p> <pre><code>pip3 install torch -f https://download.pytorch.org/whl/torch_stable.html\npip3 install -e ./ml-agents-envs\npip3 install -e ./ml-agents\n</code></pre> <p>Running pip with the <code>-e</code> flag will let you make changes to the Python files directly and have those reflected when you run <code>mlagents-learn</code>. It is important to install these packages in this order as the <code>mlagents</code> package depends on <code>mlagents_envs</code>, and installing it in the other order will download <code>mlagents_envs</code> from PyPi.</p>"},{"location":"Installation/#next-steps","title":"Next Steps","text":"<p>The Getting Started guide contains several short tutorials on setting up the ML-Agents Toolkit within Unity, running a pre-trained model, in addition to building and training environments.</p>"},{"location":"Installation/#help","title":"Help","text":"<p>If you run into any problems regarding ML-Agents, refer to our FAQ and our Limitations pages. If you can't find anything please submit an issue and make sure to cite relevant information on OS, Python version, and exact error message (whenever possible).</p>"},{"location":"Integrations-Match3/","title":"Match-3 with ML-Agents","text":""},{"location":"Integrations-Match3/#getting-started","title":"Getting started","text":"<p>The C# code for Match-3 exists inside of the Unity package (<code>com.unity.ml-agents</code>). The good first step would be to take a look at how we have implemented the C# code in the example Match-3 scene (located under /Project/Assets/ML-Agents/Examples/match3). Once you have some familiarity, then the next step would be to implement the C# code for Match-3 from the extensions package.</p> <p>Additionally, see below for additional technical specifications on the C# code for Match-3. Please note the Match-3 game isn't human playable as implemented and can be only played via training.</p>"},{"location":"Integrations-Match3/#technical-specifications-for-match-3-with-ml-agents","title":"Technical specifications for Match-3 with ML-Agents","text":""},{"location":"Integrations-Match3/#abstractboard-class","title":"AbstractBoard class","text":"<p>The <code>AbstractBoard</code> is the bridge between ML-Agents and your game. It allows ML-Agents to * ask your game what the current and maximum sizes (rows, columns, and potential piece types) of the board are * ask your game what the \"color\" of a cell is * ask whether the cell is a \"special\" piece type or not * ask your game whether a move is allowed * request that your game make a move</p> <p>These are handled by implementing the abstract methods of <code>AbstractBoard</code>.</p>"},{"location":"Integrations-Match3/#public-abstract-boardsize-getmaxboardsize","title":"<code>public abstract BoardSize GetMaxBoardSize()</code>","text":"<p>Returns the largest <code>BoardSize</code> that the game can use. This is used to determine the sizes of observations and sensors, so don't make it larger than necessary.</p>"},{"location":"Integrations-Match3/#public-virtual-boardsize-getcurrentboardsize","title":"<code>public virtual BoardSize GetCurrentBoardSize()</code>","text":"<p>Returns the current size of the board. Each field on this BoardSize must be less than or equal to the corresponding field returned by <code>GetMaxBoardSize()</code>. This method is optional; if your always use the same size board, you don't need to override it.</p> <p>If the current board size is smaller than the maximum board size, <code>GetCellType()</code> and <code>GetSpecialType()</code> will not be called for cells outside the current board size, and <code>IsValidMove</code> won't be called for moves that would go outside of the current board size.</p>"},{"location":"Integrations-Match3/#public-abstract-int-getcelltypeint-row-int-col","title":"<code>public abstract int GetCellType(int row, int col)</code>","text":"<p>Returns the \"color\" of piece at the given row and column. This should be between 0 and BoardSize.NumCellTypes-1 (inclusive). The actual order of the values doesn't matter.</p>"},{"location":"Integrations-Match3/#public-abstract-int-getspecialtypeint-row-int-col","title":"<code>public abstract int GetSpecialType(int row, int col)</code>","text":"<p>Returns the special type of the piece at the given row and column. This should be between 0 and BoardSize.NumSpecialTypes (inclusive). The actual order of the values doesn't matter.</p>"},{"location":"Integrations-Match3/#public-abstract-bool-ismovevalidmove-m","title":"<code>public abstract bool IsMoveValid(Move m)</code>","text":"<p>Check whether the particular <code>Move</code> is valid for the game. The actual results will depend on the rules of the game, but we provide the <code>SimpleIsMoveValid()</code> method that handles basic match3 rules with no special or immovable pieces.</p>"},{"location":"Integrations-Match3/#public-abstract-bool-makemovemove-m","title":"<code>public abstract bool MakeMove(Move m)</code>","text":"<p>Instruct the game to make the given move. Returns true if the move was made. Note that during training, a move that was marked as invalid may occasionally still be requested. If this happens, it is safe to do nothing and request another move.</p>"},{"location":"Integrations-Match3/#move-struct","title":"<code>Move</code> struct","text":"<p>The Move struct encapsulates a swap of two adjacent cells. You can get the number of potential moves for a board of a given size with. <code>Move.NumPotentialMoves(maxBoardSize)</code>. There are two helper functions to create a new <code>Move</code>: * <code>public static Move FromMoveIndex(int moveIndex, BoardSize maxBoardSize)</code> can be used to iterate over all potential moves for the board by looping from 0 to <code>Move.NumPotentialMoves()</code> * <code>public static Move FromPositionAndDirection(int row, int col, Direction dir, BoardSize maxBoardSize)</code> creates a <code>Move</code> from a row, column, and direction (and board size).</p>"},{"location":"Integrations-Match3/#boardsize-struct","title":"<code>BoardSize</code> struct","text":"<p>Describes the \"size\" of the board, including the number of potential piece types that the board can have. This is returned by the AbstractBoard.GetMaxBoardSize() and GetCurrentBoardSize() methods.</p>"},{"location":"Integrations-Match3/#match3sensor-and-match3sensorcomponent-classes","title":"<code>Match3Sensor</code> and <code>Match3SensorComponent</code> classes","text":"<p>The <code>Match3Sensor</code> generates observations about the state using the <code>AbstractBoard</code> interface. You can choose whether to use vector or \"visual\" observations; in theory, visual observations should perform better because they are 2-dimensional like the board, but we need to experiment more on this.</p> <p>A <code>Match3SensorComponent</code> generates <code>Match3Sensor</code>s (the exact number of sensors depends on your configuration) at runtime, and should be added to the same GameObject as your <code>Agent</code> implementation. You do not need to write any additional code to use them.</p>"},{"location":"Integrations-Match3/#match3actuator-and-match3actuatorcomponent-classes","title":"<code>Match3Actuator</code> and <code>Match3ActuatorComponent</code> classes","text":"<p>The <code>Match3Actuator</code> converts actions from training or inference into a <code>Move</code> that is sent to<code>AbstractBoard.MakeMove()</code> It also checks <code>AbstractBoard.IsMoveValid</code> for each potential move and uses this to set the action mask for Agent.</p> <p>A <code>Match3ActuatorComponent</code> generates a <code>Match3Actuator</code> at runtime, and should be added to the same GameObject as your <code>Agent</code> implementation.  You do not need to write any additional code to use them.</p>"},{"location":"Integrations-Match3/#setting-up-match-3-simulation","title":"Setting up Match-3 simulation","text":"<ul> <li>Implement the <code>AbstractBoard</code> methods to integrate with your game.</li> <li>Give the <code>Agent</code> rewards when it does what you want it to (match multiple pieces in a row, clears pieces of a certain type, etc).</li> <li>Add the <code>Agent</code>, <code>AbstractBoard</code> implementation, <code>Match3SensorComponent</code>, and <code>Match3ActuatorComponent</code> to the same <code>GameObject</code>.</li> <li>Call <code>Agent.RequestDecision()</code> when you're ready for the <code>Agent</code> to make a move on the next <code>Academy</code> step. During the next <code>Academy</code> step, the <code>MakeMove()</code> method on the board will be called.</li> </ul>"},{"location":"Integrations-Match3/#implementation-details","title":"Implementation Details","text":""},{"location":"Integrations-Match3/#action-space","title":"Action Space","text":"<p>The indexing for actions is the same as described in Human Like Playtesting with Deep Learning (for example, Figure 2b). The horizontal moves are enumerated first, then the vertical ones. </p>"},{"location":"Integrations-Match3/#feedback","title":"Feedback","text":"<p>If you are a Match-3 developer and are trying to leverage ML-Agents for this scenario, we want to hear from you. Additionally, we are also looking for interested Match-3 teams to speak with us for 45 minutes. If you are interested, please indicate that in the form. If selected, we will provide gift cards as a token of appreciation.</p>"},{"location":"Integrations/","title":"Game Integrations","text":"<p>ML-Agents provides some utilities to make it easier to integrate with some common genres of games.</p>"},{"location":"Integrations/#match-3","title":"Match-3","text":"<p>The Match-3 integration provides an abstraction of a match-3 game board and moves, along with a sensor to observe the game state, and an actuator to translate the ML-Agent actions into game moves.</p>"},{"location":"Integrations/#interested-in-more-game-templates","title":"Interested in more game templates?","text":"<p>Do you have a type of game you are interested for ML-Agents? If so, please post a forum issue with <code>[GAME TEMPLATE]</code> in the title.</p>"},{"location":"LICENSE/","title":"LICENSE","text":"<p>Copyright 2017-2021 Unity Technologies</p> <p>Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at</p> <pre><code>http://www.apache.org/licenses/LICENSE-2.0\n</code></pre> <p>Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.</p> <pre><code>                             Apache License\n                       Version 2.0, January 2004\n                    http://www.apache.org/licenses/\n</code></pre> <p>TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION</p> <ol> <li> <p>Definitions.</p> <p>\"License\" shall mean the terms and conditions for use, reproduction,   and distribution as defined by Sections 1 through 9 of this document.</p> <p>\"Licensor\" shall mean the copyright owner or entity authorized by   the copyright owner that is granting the License.</p> <p>\"Legal Entity\" shall mean the union of the acting entity and all   other entities that control, are controlled by, or are under common   control with that entity. 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While redistributing       the Work or Derivative Works thereof, You may choose to offer,       and charge a fee for, acceptance of support, warranty, indemnity,       or other liability obligations and/or rights consistent with this       License. However, in accepting such obligations, You may act only       on Your own behalf and on Your sole responsibility, not on behalf       of any other Contributor, and only if You agree to indemnify,       defend, and hold each Contributor harmless for any liability       incurred by, or claims asserted against, such Contributor by reason       of your accepting any such warranty or additional liability.</p> </li> </ol> <p>END OF TERMS AND CONDITIONS</p> <p>APPENDIX: How to apply the Apache License to your work.</p> <pre><code>  To apply the Apache License to your work, attach the following\n  boilerplate notice, with the fields enclosed by brackets \"{}\"\n  replaced with your own identifying information. (Don't include\n  the brackets!)  The text should be enclosed in the appropriate\n  comment syntax for the file format. We also recommend that a\n  file or class name and description of purpose be included on the\n  same \"printed page\" as the copyright notice for easier\n  identification within third-party archives.\n</code></pre>"},{"location":"Learning-Environment-Create-New/","title":"Making a New Learning Environment","text":"<p>This tutorial walks through the process of creating a Unity Environment from scratch. We recommend first reading the Getting Started guide to understand the concepts presented here first in an already-built environment.</p> <p></p> <p>In this example, we will create an agent capable of controlling a ball on a platform. We will then train the agent to roll the ball toward the cube while avoiding falling off the platform.</p>"},{"location":"Learning-Environment-Create-New/#overview","title":"Overview","text":"<p>Using the ML-Agents Toolkit in a Unity project involves the following basic steps:</p> <ol> <li>Create an environment for your agents to live in. An environment can range    from a simple physical simulation containing a few objects to an entire game    or ecosystem.</li> <li>Implement your Agent subclasses. An Agent subclass defines the code an Agent    uses to observe its environment, to carry out assigned actions, and to    calculate the rewards used for reinforcement training. You can also implement    optional methods to reset the Agent when it has finished or failed its task.</li> <li>Add your Agent subclasses to appropriate GameObjects, typically, the object    in the scene that represents the Agent in the simulation.</li> </ol> <p>Note: If you are unfamiliar with Unity, refer to the Unity manual if an Editor task isn't explained sufficiently in this tutorial.</p> <p>If you haven't already, follow the installation instructions.</p>"},{"location":"Learning-Environment-Create-New/#set-up-the-unity-project","title":"Set Up the Unity Project","text":"<p>The first task to accomplish is simply creating a new Unity project and importing the ML-Agents assets into it:</p> <ol> <li>Launch Unity Hub and create a new 3D project named \"RollerBall\".</li> <li>Add the ML-Agents Unity package    to your project.</li> </ol> <p>Your Unity Project window should contain the following assets:</p> <p> </p>"},{"location":"Learning-Environment-Create-New/#create-the-environment","title":"Create the Environment","text":"<p>Next, we will create a very simple scene to act as our learning environment. The \"physical\" components of the environment include a Plane to act as the floor for the Agent to move around on, a Cube to act as the goal or target for the agent to seek, and a Sphere to represent the Agent itself.</p>"},{"location":"Learning-Environment-Create-New/#create-the-floor-plane","title":"Create the Floor Plane","text":"<ol> <li>Right click in Hierarchy window, select 3D Object &gt; Plane.</li> <li>Name the GameObject \"Floor\".</li> <li>Select the Floor Plane to view its properties in the Inspector window.</li> <li>Set Transform to Position = <code>(0, 0, 0)</code>, Rotation = <code>(0, 0, 0)</code>, Scale =    <code>(1, 1, 1)</code>.</li> </ol>"},{"location":"Learning-Environment-Create-New/#add-the-target-cube","title":"Add the Target Cube","text":"<ol> <li>Right click in Hierarchy window, select 3D Object &gt; Cube.</li> <li>Name the GameObject \"Target\".</li> <li>Select the Target Cube to view its properties in the Inspector window.</li> <li>Set Transform to Position = <code>(3, 0.5, 3)</code>, Rotation = <code>(0, 0, 0)</code>, Scale =    <code>(1, 1, 1)</code>.</li> </ol>"},{"location":"Learning-Environment-Create-New/#add-the-agent-sphere","title":"Add the Agent Sphere","text":"<ol> <li>Right click in Hierarchy window, select 3D Object &gt; Sphere.</li> <li>Name the GameObject \"RollerAgent\".</li> <li>Select the RollerAgent Sphere to view its properties in the Inspector window.</li> <li>Set Transform to Position = <code>(0, 0.5, 0)</code>, Rotation = <code>(0, 0, 0)</code>, Scale =    <code>(1, 1, 1)</code>.</li> <li>Click Add Component.</li> <li>Add the <code>Rigidbody</code> component to the Sphere.</li> </ol>"},{"location":"Learning-Environment-Create-New/#group-into-training-area","title":"Group into Training Area","text":"<p>Group the floor, target and agent under a single, empty, GameObject. This will simplify some of our subsequent steps.</p> <p>To do so:</p> <ol> <li>Right-click on your Project Hierarchy and create a new empty GameObject. Name    it TrainingArea.</li> <li>Reset the TrainingArea\u2019s Transform so that it is at <code>(0,0,0)</code> with Rotation    <code>(0,0,0)</code> and Scale <code>(1,1,1)</code>.</li> <li>Drag the Floor, Target, and RollerAgent GameObjects in the Hierarchy into the    TrainingArea GameObject.</li> </ol> <p> </p>"},{"location":"Learning-Environment-Create-New/#implement-an-agent","title":"Implement an Agent","text":"<p>To create the Agent Script:</p> <ol> <li>Select the RollerAgent GameObject to view it in the Inspector window.</li> <li>Click Add Component.</li> <li>Click New Script in the list of components (at the bottom).</li> <li>Name the script \"RollerAgent\".</li> <li>Click Create and Add.</li> </ol> <p>Then, edit the new <code>RollerAgent</code> script:</p> <ol> <li>In the Unity Project window, double-click the <code>RollerAgent</code> script to open it    in your code editor.</li> <li>Import ML-Agent package by adding</li> </ol> <p><code>csharp    using Unity.MLAgents;    using Unity.MLAgents.Sensors;    using Unity.MLAgents.Actuators;</code>    then change the base class from <code>MonoBehaviour</code> to <code>Agent</code>. 1. Delete <code>Update()</code> since we are not using it, but keep <code>Start()</code>.</p> <p>So far, these are the basic steps that you would use to add ML-Agents to any Unity project. Next, we will add the logic that will let our Agent learn to roll to the cube using reinforcement learning. More specifically, we will need to extend three methods from the <code>Agent</code> base class:</p> <ul> <li><code>OnEpisodeBegin()</code></li> <li><code>CollectObservations(VectorSensor sensor)</code></li> <li><code>OnActionReceived(ActionBuffers actionBuffers)</code></li> </ul> <p>We overview each of these in more detail in the dedicated subsections below.</p>"},{"location":"Learning-Environment-Create-New/#initialization-and-resetting-the-agent","title":"Initialization and Resetting the Agent","text":"<p>The process of training in the ML-Agents Toolkit involves running episodes where the Agent (Sphere) attempts to solve the task. Each episode lasts until the Agents solves the task (i.e. reaches the cube), fails (rolls off the platform) or times out (takes too long to solve or fail at the task). At the start of each episode, <code>OnEpisodeBegin()</code> is called to set-up the environment for a new episode. Typically the scene is initialized in a random manner to enable the agent to learn to solve the task under a variety of conditions.</p> <p>In this example, each time the Agent (Sphere) reaches its target (Cube), the episode ends and the target (Cube) is moved to a new random location; and if the Agent rolls off the platform, it will be put back onto the floor. These are all handled in <code>OnEpisodeBegin()</code>.</p> <p>To move the target (Cube), we need a reference to its Transform (which stores a GameObject's position, orientation and scale in the 3D world). To get this reference, add a public field of type <code>Transform</code> to the RollerAgent class. Public fields of a component in Unity get displayed in the Inspector window, allowing you to choose which GameObject to use as the target in the Unity Editor.</p> <p>To reset the Agent's velocity (and later to apply force to move the agent) we need a reference to the Rigidbody component. A Rigidbody is Unity's primary element for physics simulation. (See Physics for full documentation of Unity physics.) Since the Rigidbody component is on the same GameObject as our Agent script, the best way to get this reference is using <code>GameObject.GetComponent&lt;T&gt;()</code>, which we can call in our script's <code>Start()</code> method.</p> <p>So far, our RollerAgent script looks like:</p> <pre><code>using System.Collections.Generic;\nusing UnityEngine;\nusing Unity.MLAgents;\nusing Unity.MLAgents.Sensors;\n\npublic class RollerAgent : Agent\n{\n    Rigidbody rBody;\n    void Start () {\n        rBody = GetComponent&lt;Rigidbody&gt;();\n    }\n\n    public Transform Target;\n    public override void OnEpisodeBegin()\n    {\n       // If the Agent fell, zero its momentum\n        if (this.transform.localPosition.y &lt; 0)\n        {\n            this.rBody.angularVelocity = Vector3.zero;\n            this.rBody.velocity = Vector3.zero;\n            this.transform.localPosition = new Vector3( 0, 0.5f, 0);\n        }\n\n        // Move the target to a new spot\n        Target.localPosition = new Vector3(Random.value * 8 - 4,\n                                           0.5f,\n                                           Random.value * 8 - 4);\n    }\n}\n</code></pre> <p>Next, let's implement the <code>Agent.CollectObservations(VectorSensor sensor)</code> method.</p>"},{"location":"Learning-Environment-Create-New/#observing-the-environment","title":"Observing the Environment","text":"<p>The Agent sends the information we collect to the Brain, which uses it to make a decision. When you train the Agent (or use a trained model), the data is fed into a neural network as a feature vector. For an Agent to successfully learn a task, we need to provide the correct information. A good rule of thumb for deciding what information to collect is to consider what you would need to calculate an analytical solution to the problem.</p> <p>In our case, the information our Agent collects includes the position of the target, the position of the agent itself, and the velocity of the agent. This helps the Agent learn to control its speed so it doesn't overshoot the target and roll off the platform. In total, the agent observation contains 8 values as implemented below:</p> <pre><code>public override void CollectObservations(VectorSensor sensor)\n{\n    // Target and Agent positions\n    sensor.AddObservation(Target.localPosition);\n    sensor.AddObservation(this.transform.localPosition);\n\n    // Agent velocity\n    sensor.AddObservation(rBody.velocity.x);\n    sensor.AddObservation(rBody.velocity.z);\n}\n</code></pre>"},{"location":"Learning-Environment-Create-New/#taking-actions-and-assigning-rewards","title":"Taking Actions and Assigning Rewards","text":"<p>The final part of the Agent code is the <code>Agent.OnActionReceived()</code> method, which receives actions and assigns the reward.</p>"},{"location":"Learning-Environment-Create-New/#actions","title":"Actions","text":"<p>To solve the task of moving towards the target, the Agent (Sphere) needs to be able to move in the <code>x</code> and <code>z</code> directions. As such, the agent needs 2 actions: the first determines the force applied along the x-axis; and the second determines the force applied along the z-axis. (If we allowed the Agent to move in three dimensions, then we would need a third action.)</p> <p>The RollerAgent applies the values from the <code>action[]</code> array to its Rigidbody component <code>rBody</code>, using <code>Rigidbody.AddForce()</code>:</p> <pre><code>Vector3 controlSignal = Vector3.zero;\ncontrolSignal.x = action[0];\ncontrolSignal.z = action[1];\nrBody.AddForce(controlSignal * forceMultiplier);\n</code></pre>"},{"location":"Learning-Environment-Create-New/#rewards","title":"Rewards","text":"<p>Reinforcement learning requires rewards to signal which decisions are good and which are bad. The learning algorithm uses the rewards to determine whether it is giving the Agent the optimal actions. You want to reward an Agent for completing the assigned task. In this case, the Agent is given a reward of 1.0 for reaching the Target cube.</p> <p>Rewards are assigned in <code>OnActionReceived()</code>. The RollerAgent calculates the distance to detect when it reaches the target. When it does, the code calls <code>Agent.SetReward()</code> to assign a reward of 1.0 and marks the agent as finished by calling <code>EndEpisode()</code> on the Agent.</p> <pre><code>float distanceToTarget = Vector3.Distance(this.transform.localPosition, Target.localPosition);\n// Reached target\nif (distanceToTarget &lt; 1.42f)\n{\n    SetReward(1.0f);\n    EndEpisode();\n}\n</code></pre> <p>Finally, if the Agent falls off the platform, end the episode so that it can reset itself:</p> <pre><code>// Fell off platform\nif (this.transform.localPosition.y &lt; 0)\n{\n    EndEpisode();\n}\n</code></pre>"},{"location":"Learning-Environment-Create-New/#onactionreceived","title":"OnActionReceived()","text":"<p>With the action and reward logic outlined above, the final version of <code>OnActionReceived()</code> looks like:</p> <pre><code>public float forceMultiplier = 10;\npublic override void OnActionReceived(ActionBuffers actionBuffers)\n{\n    // Actions, size = 2\n    Vector3 controlSignal = Vector3.zero;\n    controlSignal.x = actionBuffers.ContinuousActions[0];\n    controlSignal.z = actionBuffers.ContinuousActions[1];\n    rBody.AddForce(controlSignal * forceMultiplier);\n\n    // Rewards\n    float distanceToTarget = Vector3.Distance(this.transform.localPosition, Target.localPosition);\n\n    // Reached target\n    if (distanceToTarget &lt; 1.42f)\n    {\n        SetReward(1.0f);\n        EndEpisode();\n    }\n\n    // Fell off platform\n    else if (this.transform.localPosition.y &lt; 0)\n    {\n        EndEpisode();\n    }\n}\n</code></pre> <p>Note the <code>forceMultiplier</code> class variable is defined before the method definition. Since <code>forceMultiplier</code> is public, you can set the value from the Inspector window.</p>"},{"location":"Learning-Environment-Create-New/#final-agent-setup-in-editor","title":"Final Agent Setup in Editor","text":"<p>Now that all the GameObjects and ML-Agent components are in place, it is time to connect everything together in the Unity Editor. This involves adding and setting some of the Agent Component's properties so that they are compatible with our Agent script.</p> <ol> <li>Select the RollerAgent GameObject to show its properties in the Inspector    window.</li> <li>Drag the Target GameObject in the Hierarchy into the <code>Target</code> field in RollerAgent Script.</li> <li>Add a <code>Decision Requester</code> script with the Add Component button.    Set the Decision Period to <code>10</code>. For more information on decisions,    see the Agent documentation</li> <li>Add a <code>Behavior Parameters</code> script with the Add Component button.    Set the Behavior Parameters of the Agent to the following:</li> <li><code>Behavior Name</code>: RollerBall</li> <li><code>Vector Observation</code> &gt; <code>Space Size</code> = 8</li> <li><code>Actions</code> &gt; <code>Continuous Actions</code> = 2</li> </ol> <p>In the inspector, the <code>RollerAgent</code> should look like this now:</p> <p> </p> <p>Now you are ready to test the environment before training.</p>"},{"location":"Learning-Environment-Create-New/#testing-the-environment","title":"Testing the Environment","text":"<p>It is always a good idea to first test your environment by controlling the Agent using the keyboard. To do so, you will need to extend the <code>Heuristic()</code> method in the <code>RollerAgent</code> class. For our example, the heuristic will generate an action corresponding to the values of the \"Horizontal\" and \"Vertical\" input axis (which correspond to the keyboard arrow keys):</p> <pre><code>public override void Heuristic(in ActionBuffers actionsOut)\n{\n    var continuousActionsOut = actionsOut.ContinuousActions;\n    continuousActionsOut[0] = Input.GetAxis(\"Horizontal\");\n    continuousActionsOut[1] = Input.GetAxis(\"Vertical\");\n}\n</code></pre> <p>In order for the Agent to use the Heuristic, You will need to set the <code>Behavior Type</code> to <code>Heuristic Only</code> in the <code>Behavior Parameters</code> of the RollerAgent.</p> <p>Press Play to run the scene and use the arrows keys to move the Agent around the platform. Make sure that there are no errors displayed in the Unity Editor Console window and that the Agent resets when it reaches its target or falls from the platform.</p>"},{"location":"Learning-Environment-Create-New/#training-the-environment","title":"Training the Environment","text":"<p>The process is the same as described in the Getting Started Guide.</p> <p>The hyperparameters for training are specified in a configuration file that you pass to the <code>mlagents-learn</code> program. Create a new <code>rollerball_config.yaml</code> file under <code>config/</code> and include the following hyperparameter values:</p> <pre><code>behaviors:\n  RollerBall:\n    trainer_type: ppo\n    hyperparameters:\n      batch_size: 10\n      buffer_size: 100\n      learning_rate: 3.0e-4\n      beta: 5.0e-4\n      epsilon: 0.2\n      lambd: 0.99\n      num_epoch: 3\n      learning_rate_schedule: linear\n      beta_schedule: constant\n      epsilon_schedule: linear\n    network_settings:\n      normalize: false\n      hidden_units: 128\n      num_layers: 2\n    reward_signals:\n      extrinsic:\n        gamma: 0.99\n        strength: 1.0\n    max_steps: 500000\n    time_horizon: 64\n    summary_freq: 10000\n</code></pre> <p>Hyperparameters are explained in the training configuration file documentation</p> <p>Since this example creates a very simple training environment with only a few inputs and outputs, using small batch and buffer sizes speeds up the training considerably. However, if you add more complexity to the environment or change the reward or observation functions, you might also find that training performs better with different hyperparameter values. In addition to setting these hyperparameter values, the Agent DecisionFrequency parameter has a large effect on training time and success. A larger value reduces the number of decisions the training algorithm has to consider and, in this simple environment, speeds up training.</p> <p>To train your agent, run the following command before pressing Play in the Editor:</p> <pre><code>mlagents-learn config/rollerball_config.yaml --run-id=RollerBall\n</code></pre> <p>To monitor the statistics of Agent performance during training, use TensorBoard.</p> <p></p> <p>In particular, the cumulative_reward and value_estimate statistics show how well the Agent is achieving the task. In this example, the maximum reward an Agent can earn is 1.0, so these statistics approach that value when the Agent has successfully solved the problem.</p>"},{"location":"Learning-Environment-Create-New/#optional-multiple-training-areas-within-the-same-scene","title":"Optional: Multiple Training Areas within the Same Scene","text":"<p>In many of the example environments, many copies of the training area are instantiated in the scene. This generally speeds up training, allowing the environment to gather many experiences in parallel. This can be achieved simply by instantiating many Agents with the same <code>Behavior Name</code>. Note that we've already simplified our transition to using multiple areas by creating the <code>TrainingArea</code> GameObject and relying on local positions in <code>RollerAgent.cs</code>. Use the following steps to parallelize your RollerBall environment:</p> <ol> <li>Drag the TrainingArea GameObject, along with its attached GameObjects, into    your Assets browser, turning it into a prefab.</li> <li>You can now instantiate copies of the TrainingArea prefab. Drag them into    your scene, positioning them so that they do not overlap.</li> </ol> <p>Alternatively, you can use the <code>TrainingAreaReplicator</code> to replicate training areas. Use the following steps:</p> <ol> <li>Create a new empty Game Object in the scene.</li> <li>Click on the new object and add a TrainingAreaReplicator component to the empty Game Object through the inspector.</li> <li>Drag the training area to <code>Base Area</code> in the Training Area Replicator.</li> <li>Specify the number of areas to replicate and the separation between areas.</li> <li>Hit play and the areas will be replicated automatically!</li> </ol>"},{"location":"Learning-Environment-Create-New/#optional-training-using-concurrent-unity-instances","title":"Optional: Training Using Concurrent Unity Instances","text":"<p>Another level of parallelization comes by training using concurrent Unity instances. For example,</p> <pre><code>mlagents-learn config/rollerball_config.yaml --run-id=RollerBall --num-envs=2\n</code></pre> <p>will start ML Agents training with two environment instances. Combining multiple training areas within the same scene, with concurrent Unity instances, effectively gives you two levels of parallelism to speed up training. The command line option <code>--num-envs=&lt;n&gt;</code> controls the number of concurrent Unity instances that are executed in parallel during training.</p>"},{"location":"Learning-Environment-Design-Agents/","title":"Agents","text":"<p>Table of Contents:</p> <ul> <li>Decisions</li> <li>Observations and Sensors</li> <li>Generating Observations<ul> <li>Agent.CollectObservations()</li> <li>Observable Fields and Properties</li> <li>ISensor interface and SensorComponents</li> </ul> </li> <li>Vector Observations<ul> <li>One-hot encoding categorical information</li> <li>Normalization</li> <li>Stacking</li> <li>Vector Observation Summary &amp; Best Practices</li> </ul> </li> <li>Visual Observations<ul> <li>Visual Observation Summary &amp; Best Practices</li> </ul> </li> <li>Raycast Observations<ul> <li>RayCast Observation Summary &amp; Best Practices</li> </ul> </li> <li>Variable Length Observations<ul> <li>Variable Length Observation Summary &amp; Best Practices</li> </ul> </li> <li>Goal Signal<ul> <li>Goal Signal Summary &amp; Best Practices</li> </ul> </li> <li>Actions and Actuators</li> <li>Continuous Actions</li> <li>Discrete Actions<ul> <li>Masking Discrete Actions</li> </ul> </li> <li>Actions Summary &amp; Best Practices</li> <li>Rewards</li> <li>Examples</li> <li>Rewards Summary &amp; Best Practices</li> <li>Agent Properties</li> <li>Destroying an Agent</li> <li>Defining Multi-agent Scenarios</li> <li>Teams for Adversarial Scenarios</li> <li>Groups for Cooperative Scenarios</li> <li>Recording Demonstrations</li> </ul> <p>An agent is an entity that can observe its environment, decide on the best course of action using those observations, and execute those actions within its environment. Agents can be created in Unity by extending the <code>Agent</code> class. The most important aspects of creating agents that can successfully learn are the observations the agent collects, and the reward you assign to estimate the value of the agent's current state toward accomplishing its tasks.</p> <p>An Agent passes its observations to its Policy. The Policy then makes a decision and passes the chosen action back to the agent. Your agent code must execute the action, for example, move the agent in one direction or another. In order to train an agent using reinforcement learning, your agent must calculate a reward value at each action. The reward is used to discover the optimal decision-making policy.</p> <p>The <code>Policy</code> class abstracts out the decision making logic from the Agent itself so that you can use the same Policy in multiple Agents. How a Policy makes its decisions depends on the <code>Behavior Parameters</code> associated with the agent. If you set <code>Behavior Type</code> to <code>Heuristic Only</code>, the Agent will use its <code>Heuristic()</code> method to make decisions which can allow you to control the Agent manually or write your own Policy. If the Agent has a <code>Model</code> file, its Policy will use the neural network <code>Model</code> to take decisions.</p> <p>When you create an Agent, you should usually extend the base Agent class. This includes implementing the following methods:</p> <ul> <li><code>Agent.OnEpisodeBegin()</code> \u2014 Called at the beginning of an Agent's episode,   including at the beginning of the simulation.</li> <li><code>Agent.CollectObservations(VectorSensor sensor)</code> \u2014 Called every step that the Agent   requests a decision. This is one possible way for collecting the Agent's   observations of the environment; see Generating Observations   below for more options.</li> <li><code>Agent.OnActionReceived()</code> \u2014 Called every time the Agent receives an action to   take. Receives the action chosen by the Agent. It is also common to assign a   reward in this method.</li> <li><code>Agent.Heuristic()</code> - When the <code>Behavior Type</code> is set to <code>Heuristic Only</code> in   the Behavior Parameters of the Agent, the Agent will use the <code>Heuristic()</code>   method to generate the actions of the Agent. As such, the <code>Heuristic()</code> method   writes to the array of floats provided to the Heuristic method as argument.   Note: Do not create a new float array of action in the <code>Heuristic()</code> method,   as this will prevent writing floats to the original action array.</li> </ul> <p>As a concrete example, here is how the Ball3DAgent class implements these methods:</p> <ul> <li><code>Agent.OnEpisodeBegin()</code> \u2014 Resets the agent cube and ball to their starting   positions. The function randomizes the reset values so that the training   generalizes to more than a specific starting position and agent cube   orientation.</li> <li><code>Agent.CollectObservations(VectorSensor sensor)</code> \u2014 Adds information about the   orientation of the agent cube, the ball velocity, and the relative position   between the ball and the cube. Since the  <code>CollectObservations()</code>   method calls <code>VectorSensor.AddObservation()</code> such that vector size adds up to 8,   the Behavior Parameters of the Agent are set with vector observation space   with a state size of 8.</li> <li><code>Agent.OnActionReceived()</code> \u2014 The action results   in a small change in the agent cube's rotation at each step. In this example,   an Agent receives a small positive reward for each step it keeps the ball on the   agent cube's head and a larger, negative reward for dropping the ball. An   Agent's episode is also ended when it drops the ball so that it will reset   with a new ball for the next simulation step.</li> <li><code>Agent.Heuristic()</code> - Converts the keyboard inputs into actions.</li> </ul>"},{"location":"Learning-Environment-Design-Agents/#decisions","title":"Decisions","text":"<p>The observation-decision-action-reward cycle repeats each time the Agent request a decision. Agents will request a decision when <code>Agent.RequestDecision()</code> is called. If you need the Agent to request decisions on its own at regular intervals, add a <code>Decision Requester</code> component to the Agent's GameObject. Making decisions at regular step intervals is generally most appropriate for physics-based simulations. For example, an agent in a robotic simulator that must provide fine-control of joint torques should make its decisions every step of the simulation. On the other hand, an agent that only needs to make decisions when certain game or simulation events occur, such as in a turn-based game, should call <code>Agent.RequestDecision()</code> manually.</p>"},{"location":"Learning-Environment-Design-Agents/#observations-and-sensors","title":"Observations and Sensors","text":"<p>In order for an agent to learn, the observations should include all the information an agent needs to accomplish its task. Without sufficient and relevant information, an agent may learn poorly or may not learn at all. A reasonable approach for determining what information should be included is to consider what you would need to calculate an analytical solution to the problem, or what you would expect a human to be able to use to solve the problem.</p>"},{"location":"Learning-Environment-Design-Agents/#generating-observations","title":"Generating Observations","text":"<p>ML-Agents provides multiple ways for an Agent to make observations:   1. Overriding the <code>Agent.CollectObservations()</code> method and passing the     observations to the provided <code>VectorSensor</code>.   1. Adding the <code>[Observable]</code> attribute to fields and properties on the Agent.   1. Implementing the <code>ISensor</code> interface, using a <code>SensorComponent</code> attached to   the Agent to create the <code>ISensor</code>.</p>"},{"location":"Learning-Environment-Design-Agents/#agentcollectobservations","title":"Agent.CollectObservations()","text":"<p>Agent.CollectObservations() is best used for aspects of the environment which are numerical and non-visual. The Policy class calls the <code>CollectObservations(VectorSensor sensor)</code> method of each Agent. Your implementation of this function must call <code>VectorSensor.AddObservation</code> to add vector observations.</p> <p>The <code>VectorSensor.AddObservation</code> method provides a number of overloads for adding common types of data to your observation vector. You can add Integers and booleans directly to the observation vector, as well as some common Unity data types such as <code>Vector2</code>, <code>Vector3</code>, and <code>Quaternion</code>.</p> <p>For examples of various state observation functions, you can look at the example environments included in the ML-Agents SDK. For instance, the 3DBall example uses the rotation of the platform, the relative position of the ball, and the velocity of the ball as its state observation.</p> <pre><code>public GameObject ball;\n\npublic override void CollectObservations(VectorSensor sensor)\n{\n    // Orientation of the cube (2 floats)\n    sensor.AddObservation(gameObject.transform.rotation.z);\n    sensor.AddObservation(gameObject.transform.rotation.x);\n    // Relative position of the ball to the cube (3 floats)\n    sensor.AddObservation(ball.transform.position - gameObject.transform.position);\n    // Velocity of the ball (3 floats)\n    sensor.AddObservation(m_BallRb.velocity);\n    // 8 floats total\n}\n</code></pre> <p>As an experiment, you can remove the velocity components from the observation and retrain the 3DBall agent. While it will learn to balance the ball reasonably well, the performance of the agent without using velocity is noticeably worse.</p> <p>The observations passed to <code>VectorSensor.AddObservation()</code> must always contain the same number of elements must always be in the same order. If the number of observed entities in an environment can vary, you can pad the calls with zeros for any missing entities in a specific observation, or you can limit an agent's observations to a fixed subset. For example, instead of observing every enemy in an environment, you could only observe the closest five.</p> <p>Additionally, when you set up an Agent's <code>Behavior Parameters</code> in the Unity Editor, you must set the Vector Observations &gt; Space Size to equal the number of floats that are written by <code>CollectObservations()</code>.</p>"},{"location":"Learning-Environment-Design-Agents/#observable-fields-and-properties","title":"Observable Fields and Properties","text":"<p>Another approach is to define the relevant observations as fields or properties on your Agent class, and annotate them with an <code>ObservableAttribute</code>. For example, in the Ball3DHardAgent, the difference between positions could be observed by adding a property to the Agent:</p> <pre><code>using Unity.MLAgents.Sensors.Reflection;\n\npublic class Ball3DHardAgent : Agent {\n\n    [Observable(numStackedObservations: 9)]\n    Vector3 PositionDelta\n    {\n        get\n        {\n            return ball.transform.position - gameObject.transform.position;\n        }\n    }\n}\n</code></pre> <p><code>ObservableAttribute</code> currently supports most basic types (e.g. floats, ints, bools), as well as <code>Vector2</code>, <code>Vector3</code>, <code>Vector4</code>, <code>Quaternion</code>, and enums.</p> <p>The behavior of <code>ObservableAttribute</code>s are controlled by the \"Observable Attribute Handling\" in the Agent's <code>Behavior Parameters</code>. The possible values for this are:  * Ignore (default) - All ObservableAttributes on the Agent will be ignored.   If there are no ObservableAttributes on the Agent, this will result in the   fastest  initialization time.  * Exclude Inherited - Only members on the declared class will be examined;   members that are inherited are ignored. This is a reasonable tradeoff between   performance and flexibility.  * Examine All All members on the class will be examined. This can lead to   slower startup times.</p> <p>\"Exclude Inherited\" is generally sufficient, but if your Agent inherits from another Agent implementation that has Observable members, you will need to use \"Examine All\".</p> <p>Internally, ObservableAttribute uses reflection to determine which members of the Agent have ObservableAttributes, and also uses reflection to access the fields or invoke the properties at runtime. This may be slower than using CollectObservations or an ISensor, although this might not be enough to noticeably affect performance.</p> <p>NOTE: you do not need to adjust the Space Size in the Agent's <code>Behavior Parameters</code> when you add <code>[Observable]</code> fields or properties to an Agent, since their size can be computed before they are used.</p>"},{"location":"Learning-Environment-Design-Agents/#isensor-interface-and-sensorcomponents","title":"ISensor interface and SensorComponents","text":"<p>The <code>ISensor</code> interface is generally intended for advanced users. The <code>Write()</code> method is used to actually generate the observation, but some other methods such as returning the shape of the observations must also be implemented.</p> <p>The <code>SensorComponent</code> abstract class is used to create the actual <code>ISensor</code> at runtime. It must be attached to the same <code>GameObject</code> as the <code>Agent</code>, or to a child <code>GameObject</code>.</p> <p>There are several SensorComponents provided in the API, including: - <code>CameraSensorComponent</code> - Uses images from a <code>Camera</code> as observations. - <code>RenderTextureSensorComponent</code> - Uses the content of a <code>RenderTexture</code> as observations. - <code>RayPerceptionSensorComponent</code> - Uses the information from set of ray casts as observations. - <code>Match3SensorComponent</code> - Uses the board of a Match-3 game as observations. - <code>GridSensorComponent</code> - Uses a set of box queries in a grid shape as observations.</p> <p>NOTE: you do not need to adjust the Space Size in the Agent's <code>Behavior Parameters</code> when using <code>SensorComponents</code>s.</p> <p>Internally, both <code>Agent.CollectObservations</code> and <code>[Observable]</code> attribute use an ISensors to write observations, although this is mostly abstracted from the user.</p>"},{"location":"Learning-Environment-Design-Agents/#vector-observations","title":"Vector Observations","text":"<p>Both <code>Agent.CollectObservations()</code> and <code>ObservableAttribute</code>s produce vector observations, which are represented at lists of <code>float</code>s. <code>ISensor</code>s can produce both vector observations and visual observations, which are multi-dimensional arrays of floats.</p> <p>Below are some additional considerations when dealing with vector observations:</p>"},{"location":"Learning-Environment-Design-Agents/#one-hot-encoding-categorical-information","title":"One-hot encoding categorical information","text":"<p>Type enumerations should be encoded in the one-hot style. That is, add an element to the feature vector for each element of enumeration, setting the element representing the observed member to one and set the rest to zero. For example, if your enumeration contains [Sword, Shield, Bow] and the agent observes that the current item is a Bow, you would add the elements: 0, 0, 1 to the feature vector. The following code example illustrates how to add.</p> <pre><code>enum ItemType { Sword, Shield, Bow, LastItem }\npublic override void CollectObservations(VectorSensor sensor)\n{\n    for (int ci = 0; ci &lt; (int)ItemType.LastItem; ci++)\n    {\n        sensor.AddObservation((int)currentItem == ci ? 1.0f : 0.0f);\n    }\n}\n</code></pre> <p><code>VectorSensor</code> also provides a two-argument function <code>AddOneHotObservation()</code> as a shortcut for one-hot style observations. The following example is identical to the previous one.</p> <pre><code>enum ItemType { Sword, Shield, Bow, LastItem }\nconst int NUM_ITEM_TYPES = (int)ItemType.LastItem + 1;\n\npublic override void CollectObservations(VectorSensor sensor)\n{\n    // The first argument is the selection index; the second is the\n    // number of possibilities\n    sensor.AddOneHotObservation((int)currentItem, NUM_ITEM_TYPES);\n}\n</code></pre> <p><code>ObservableAttribute</code> has built-in support for enums. Note that you don't need the <code>LastItem</code> placeholder in this case:</p> <pre><code>enum ItemType { Sword, Shield, Bow }\n\npublic class HeroAgent : Agent\n{\n    [Observable]\n    ItemType m_CurrentItem;\n}\n</code></pre>"},{"location":"Learning-Environment-Design-Agents/#normalization","title":"Normalization","text":"<p>For the best results when training, you should normalize the components of your feature vector to the range [-1, +1] or [0, 1]. When you normalize the values, the PPO neural network can often converge to a solution faster. Note that it isn't always necessary to normalize to these recommended ranges, but it is considered a best practice when using neural networks. The greater the variation in ranges between the components of your observation, the more likely that training will be affected.</p> <p>To normalize a value to [0, 1], you can use the following formula:</p> <pre><code>normalizedValue = (currentValue - minValue)/(maxValue - minValue)\n</code></pre> <p>:warning: For vectors, you should apply the above formula to each component (x, y, and z). Note that this is not the same as using the <code>Vector3.normalized</code> property or <code>Vector3.Normalize()</code> method in Unity (and similar for <code>Vector2</code>).</p> <p>Rotations and angles should also be normalized. For angles between 0 and 360 degrees, you can use the following formulas:</p> <pre><code>Quaternion rotation = transform.rotation;\nVector3 normalized = rotation.eulerAngles / 180.0f - Vector3.one;  // [-1,1]\nVector3 normalized = rotation.eulerAngles / 360.0f;  // [0,1]\n</code></pre> <p>For angles that can be outside the range [0,360], you can either reduce the angle, or, if the number of turns is significant, increase the maximum value used in your normalization formula.</p>"},{"location":"Learning-Environment-Design-Agents/#stacking","title":"Stacking","text":"<p>Stacking refers to repeating observations from previous steps as part of a larger observation. For example, consider an Agent that generates these observations in four steps</p> <pre><code>step 1: [0.1]\nstep 2: [0.2]\nstep 3: [0.3]\nstep 4: [0.4]\n</code></pre> <p>If we use a stack size of 3, the observations would instead be:</p> <pre><code>step 1: [0.1, 0.0, 0.0]\nstep 2: [0.2, 0.1, 0.0]\nstep 3: [0.3, 0.2, 0.1]\nstep 4: [0.4, 0.3, 0.2]\n</code></pre> <p>(The observations are padded with zeroes for the first <code>stackSize-1</code> steps). This is a simple way to give an Agent limited \"memory\" without the complexity of adding a recurrent neural network (RNN).</p> <p>The steps for enabling stacking depends on how you generate observations: * For Agent.CollectObservations(), set \"Stacked Vectors\" on the Agent's   <code>Behavior Parameters</code> to a value greater than 1. * For ObservableAttribute, set the <code>numStackedObservations</code> parameter in the   constructor, e.g. <code>[Observable(numStackedObservations: 2)]</code>. * For <code>ISensor</code>s, wrap them in a <code>StackingSensor</code> (which is also an <code>ISensor</code>).   Generally, this should happen in the <code>CreateSensor()</code> method of your   <code>SensorComponent</code>.</p>"},{"location":"Learning-Environment-Design-Agents/#vector-observation-summary-best-practices","title":"Vector Observation Summary &amp; Best Practices","text":"<ul> <li>Vector Observations should include all variables relevant for allowing the   agent to take the optimally informed decision, and ideally no extraneous   information.</li> <li>In cases where Vector Observations need to be remembered or compared over   time, either an RNN should be used in the model, or the <code>Stacked Vectors</code>   value in the agent GameObject's <code>Behavior Parameters</code> should be changed.</li> <li>Categorical variables such as type of object (Sword, Shield, Bow) should be   encoded in one-hot fashion (i.e. <code>3</code> -&gt; <code>0, 0, 1</code>). This can be done   automatically using the <code>AddOneHotObservation()</code> method of the <code>VectorSensor</code>,   or using <code>[Observable]</code> on an enum field or property of the Agent.</li> <li>In general, all inputs should be normalized to be in the range 0 to +1 (or -1   to 1). For example, the <code>x</code> position information of an agent where the maximum   possible value is <code>maxValue</code> should be recorded as   <code>VectorSensor.AddObservation(transform.position.x / maxValue);</code> rather than   <code>VectorSensor.AddObservation(transform.position.x);</code>.</li> <li>Positional information of relevant GameObjects should be encoded in relative   coordinates wherever possible. This is often relative to the agent position.</li> </ul>"},{"location":"Learning-Environment-Design-Agents/#visual-observations","title":"Visual Observations","text":"<p>Visual observations are generally provided to agent via either a <code>CameraSensor</code> or <code>RenderTextureSensor</code>. These collect image information and transforms it into a 3D Tensor which can be fed into the convolutional neural network (CNN) of the agent policy. For more information on CNNs, see this guide. This allows agents to learn from spatial regularities in the observation images. It is possible to use visual and vector observations with the same agent.</p> <p>Agents using visual observations can capture state of arbitrary complexity and are useful when the state is difficult to describe numerically. However, they are also typically less efficient and slower to train, and sometimes don't succeed at all as compared to vector observations. As such, they should only be used when it is not possible to properly define the problem using vector or ray-cast observations.</p> <p>Visual observations can be derived from Cameras or RenderTextures within your scene. To add a visual observation to an Agent, add either a Camera Sensor Component or RenderTextures Sensor Component to the Agent. Then drag the camera or render texture you want to add to the <code>Camera</code> or <code>RenderTexture</code> field. You can have more than one camera or render texture and even use a combination of both attached to an Agent. For each visual observation, set the width and height of the image (in pixels) and whether or not the observation is color or grayscale.</p> <p></p> <p>or</p> <p></p> <p>Each Agent that uses the same Policy must have the same number of visual observations, and they must all have the same resolutions (including whether or not they are grayscale). Additionally, each Sensor Component on an Agent must have a unique name so that they can be sorted deterministically (the name must be unique for that Agent, but multiple Agents can have a Sensor Component with the same name).</p> <p>Visual observations also support stacking, by specifying <code>Observation Stacks</code> to a value greater than 1. The visual observations from the last <code>stackSize</code> steps will be stacked on the last dimension (channel dimension).</p> <p>When using <code>RenderTexture</code> visual observations, a handy feature for debugging is adding a <code>Canvas</code>, then adding a <code>Raw Image</code> with it's texture set to the Agent's <code>RenderTexture</code>. This will render the agent observation on the game screen.</p> <p></p> <p>The GridWorld environment is an example on how to use a RenderTexture for both debugging and observation. Note that in this example, a Camera is rendered to a RenderTexture, which is then used for observations and debugging. To update the RenderTexture, the Camera must be asked to render every time a decision is requested within the game code. When using Cameras as observations directly, this is done automatically by the Agent.</p> <p></p>"},{"location":"Learning-Environment-Design-Agents/#visual-observation-summary-best-practices","title":"Visual Observation Summary &amp; Best Practices","text":"<ul> <li>To collect visual observations, attach <code>CameraSensor</code> or <code>RenderTextureSensor</code>   components to the agent GameObject.</li> <li>Visual observations should generally only be used when vector observations are   not sufficient.</li> <li>Image size should be kept as small as possible, without the loss of needed   details for decision making.</li> <li>Images should be made grayscale in situations where color information is not   needed for making informed decisions.</li> </ul>"},{"location":"Learning-Environment-Design-Agents/#raycast-observations","title":"Raycast Observations","text":"<p>Raycasts are another possible method for providing observations to an agent. This can be easily implemented by adding a <code>RayPerceptionSensorComponent3D</code> (or <code>RayPerceptionSensorComponent2D</code>) to the Agent GameObject.</p> <p>During observations, several rays (or spheres, depending on settings) are cast into the physics world, and the objects that are hit determine the observation vector that is produced.</p> <p></p> <p>Both sensor components have several settings:</p> <ul> <li>Detectable Tags A list of strings corresponding to the types of objects that   the Agent should be able to distinguish between. For example, in the WallJump   example, we use \"wall\", \"goal\", and \"block\" as the list of objects to detect.</li> <li>Rays Per Direction Determines the number of rays that are cast. One ray is   always cast forward, and this many rays are cast to the left and right.</li> <li>Max Ray Degrees The angle (in degrees) for the outermost rays. 90 degrees   corresponds to the left and right of the agent.</li> <li>Sphere Cast Radius The size of the sphere used for sphere casting. If set to   0, rays will be used instead of spheres. Rays may be more efficient,   especially in complex scenes.</li> <li>Ray Length The length of the casts</li> <li>Ray Layer Mask The LayerMask   passed to the raycast or spherecast. This can be used to ignore certain types   of objects when casting.</li> <li>Observation Stacks The number of previous results to \"stack\" with the cast   results. Note that this can be independent of the \"Stacked Vectors\" setting in   <code>Behavior Parameters</code>.</li> <li>Start Vertical Offset (3D only) The vertical offset of the ray start point.</li> <li>End Vertical Offset (3D only) The vertical offset of the ray end point.</li> <li>Alternating Ray Order Alternating is the default, it gives an order of (0,   -delta, delta, -2delta, 2delta, ..., -ndelta, ndelta). If alternating is   disabled the order is left to right (-ndelta, -(n-1)delta, ..., -delta, 0,   delta, ..., (n-1)delta, ndelta). For general usage there is no difference   but if using custom models the left-to-right layout that matches the spatial   structuring can be preferred (e.g. for processing with conv nets).</li> </ul> <p>In the example image above, the Agent has two <code>RayPerceptionSensorComponent3D</code>s. Both use 3 Rays Per Direction and 90 Max Ray Degrees. One of the components had a vertical offset, so the Agent can tell whether it's clear to jump over the wall.</p> <p>The total size of the created observations is</p> <pre><code>(Observation Stacks) * (1 + 2 * Rays Per Direction) * (Num Detectable Tags + 2)\n</code></pre> <p>so the number of rays and tags should be kept as small as possible to reduce the amount of data used. Note that this is separate from the State Size defined in <code>Behavior Parameters</code>, so you don't need to worry about the formula above when setting the State Size.</p>"},{"location":"Learning-Environment-Design-Agents/#raycast-observation-summary-best-practices","title":"RayCast Observation Summary &amp; Best Practices","text":"<ul> <li>Attach <code>RayPerceptionSensorComponent3D</code> or <code>RayPerceptionSensorComponent2D</code> to   use.</li> <li>This observation type is best used when there is relevant spatial information   for the agent that doesn't require a fully rendered image to convey.</li> <li>Use as few rays and tags as necessary to solve the problem in order to improve   learning stability and agent performance.</li> </ul>"},{"location":"Learning-Environment-Design-Agents/#grid-observations","title":"Grid Observations","text":"<p>Grid-base observations combine the advantages of 2D spatial representation in visual observations, and the flexibility of defining detectable objects in RayCast observations. The sensor uses a set of box queries in a grid shape and gives a top-down 2D view around the agent. This can be implemented by adding a <code>GridSensorComponent</code> to the Agent GameObject.</p> <p>During observations, the sensor detects the presence of detectable objects in each cell and encode that into one-hot representation. The collected information from each cell forms a 3D tensor observation and will be fed into the convolutional neural network (CNN) of the agent policy just like visual observations.</p> <p></p> <p>The sensor component has the following settings: - Cell Scale The scale of each cell in the grid. - Grid Size Number of cells on each side of the grid. - Agent Game Object The Agent that holds the grid sensor. This is used to   disambiguate objects with the same tag as the agent so that the agent doesn't   detect itself. - Rotate With Agent Whether the grid rotates with the Agent. - Detectable Tags A list of strings corresponding to the types of objects that   the Agent should be able to distinguish between. - Collider Mask The LayerMask   passed to the collider detection. This can be used to ignore certain types   of objects. - Initial Collider Buffer Size The initial size of the Collider buffer used   in the non-allocating Physics calls for each cell. - Max Collider Buffer Size The max size of the Collider buffer used in the   non-allocating Physics calls for each cell.</p> <p>The observation for each grid cell is a one-hot encoding of the detected object. The total size of the created observations is</p> <pre><code>GridSize.x * GridSize.z * Num Detectable Tags\n</code></pre> <p>so the number of detectable tags and size of the grid should be kept as small as possible to reduce the amount of data used. This makes a trade-off between the granularity of the observation and training speed.</p> <p>To allow more variety of observations that grid sensor can capture, the <code>GridSensorComponent</code> and the underlying <code>GridSensorBase</code> also provides interfaces that can be overridden to collect customized observation from detected objects. See the doc on extending grid Sensors for more details on custom grid sensors.</p> <p>Note: The <code>GridSensor</code> only works in 3D environments and will not behave properly in 2D environments.</p>"},{"location":"Learning-Environment-Design-Agents/#grid-observation-summary-best-practices","title":"Grid Observation Summary &amp; Best Practices","text":"<ul> <li>Attach <code>GridSensorComponent</code> to use.</li> <li>This observation type is best used when there is relevant non-visual spatial information that   can be best captured in 2D representations.</li> <li>Use as small grid size and as few tags as necessary to solve the problem in order to improve   learning stability and agent performance.</li> <li>Do not use <code>GridSensor</code> in a 2D game.</li> </ul>"},{"location":"Learning-Environment-Design-Agents/#variable-length-observations","title":"Variable Length Observations","text":"<p>It is possible for agents to collect observations from a varying number of GameObjects by using a <code>BufferSensor</code>. You can add a <code>BufferSensor</code> to your Agent by adding a <code>BufferSensorComponent</code> to its GameObject. The <code>BufferSensor</code> can be useful in situations in which the Agent must pay attention to a varying number of entities (for example, a varying number of enemies or projectiles). On the trainer side, the <code>BufferSensor</code> is processed using an attention module. More information about attention mechanisms can be found here. Training or doing inference with variable length observations can be slower than using a flat vector observation. However, attention mechanisms enable solving problems that require comparative reasoning between entities in a scene such as our Sorter environment. Note that even though the <code>BufferSensor</code> can process a variable number of entities, you still need to define a maximum number of entities. This is because our network architecture requires to know what the shape of the observations will be. If fewer entities are observed than the maximum, the observation will be padded with zeros and the trainer will ignore the padded observations. Note that attention layers are invariant to the order of the entities, so there is no need to properly \"order\" the entities before feeding them into the <code>BufferSensor</code>.</p> <p>The <code>BufferSensorComponent</code> Editor inspector has two arguments:  - <code>Observation Size</code> : This is how many floats each entities will be  represented with. This number is fixed and all entities must  have the same representation. For example, if the entities you want to  put into the <code>BufferSensor</code> have for relevant information position and  speed, then the <code>Observation Size</code> should be 6 floats.  - <code>Maximum Number of Entities</code> : This is the maximum number of entities  the <code>BufferSensor</code> will be able to collect.</p> <p>To add an entity's observations to a <code>BufferSensorComponent</code>, you need to call <code>BufferSensorComponent.AppendObservation()</code> in the Agent.CollectObservations() method with a float array of size <code>Observation Size</code> as argument.</p> <p>Note: Currently, the observations put into the <code>BufferSensor</code> are not normalized, you will need to normalize your observations manually between -1 and 1.</p>"},{"location":"Learning-Environment-Design-Agents/#variable-length-observation-summary-best-practices","title":"Variable Length Observation Summary &amp; Best Practices","text":"<ul> <li>Attach <code>BufferSensorComponent</code> to use.</li> <li>Call <code>BufferSensorComponent.AppendObservation()</code> in the  Agent.CollectObservations() methodto add the observations  of an entity to the <code>BufferSensor</code>.</li> <li>Normalize the entities observations before feeding them into the <code>BufferSensor</code>.</li> </ul>"},{"location":"Learning-Environment-Design-Agents/#goal-signal","title":"Goal Signal","text":"<p>It is possible for agents to collect observations that will be treated as \"goal signal\". A goal signal is used to condition the policy of the agent, meaning that if the goal changes, the policy (i.e. the mapping from observations to actions) will change as well. Note that this is true for any observation since all observations influence the policy of the Agent to some degree. But by specifying a goal signal explicitly, we can make this conditioning more important to the agent. This feature can be used in settings where an agent must learn to solve different tasks that are similar by some aspects because the agent will learn to reuse learnings from different tasks to generalize better. In Unity, you can specify that a <code>VectorSensor</code> or a <code>CameraSensor</code> is a goal by attaching a <code>VectorSensorComponent</code> or a <code>CameraSensorComponent</code> to the Agent and selecting <code>Goal Signal</code> as <code>Observation Type</code>. On the trainer side, there are two different ways to condition the policy. This setting is determined by the conditioning_type parameter. If set to <code>hyper</code> (default) a HyperNetwork will be used to generate some of the weights of the policy using the goal observations as input. Note that using a HyperNetwork requires a lot of computations, it is recommended to use a smaller number of hidden units in the policy to alleviate this. If set to <code>none</code> the goal signal will be considered as regular observations. For an example on how to use a goal signal, see the GridWorld example.</p>"},{"location":"Learning-Environment-Design-Agents/#goal-signal-summary-best-practices","title":"Goal Signal Summary &amp; Best Practices","text":"<ul> <li>Attach a <code>VectorSensorComponent</code> or <code>CameraSensorComponent</code> to an agent and  set the observation type to goal to use the feature.</li> <li>Set the conditioning_type parameter in the training configuration.</li> <li>Reduce the number of hidden units in the network when using the HyperNetwork  conditioning type.</li> </ul>"},{"location":"Learning-Environment-Design-Agents/#actions-and-actuators","title":"Actions and Actuators","text":"<p>An action is an instruction from the Policy that the agent carries out. The action is passed to the an <code>IActionReceiver</code> (either an <code>Agent</code> or an <code>IActuator</code>) as the <code>ActionBuffers</code> parameter when the Academy invokes the <code>IActionReciever.OnActionReceived()</code> function. There are two types of actions supported: Continuous and Discrete.</p> <p>Neither the Policy nor the training algorithm know anything about what the action values themselves mean. The training algorithm simply tries different values for the action list and observes the affect on the accumulated rewards over time and many training episodes. Thus, the only place actions are defined for an Agent is in the <code>OnActionReceived()</code> function.</p> <p>For example, if you designed an agent to move in two dimensions, you could use either continuous or the discrete actions. In the continuous case, you would set the action size to two (one for each dimension), and the agent's Policy would output an action with two floating point values. In the discrete case, you would use one Branch with a size of four (one for each direction), and the Policy would create an action array containing a single element with a value ranging from zero to three. Alternatively, you could create two branches of size two (one for horizontal movement and one for vertical movement), and the Policy would output an action array containing two elements with values ranging from zero to one. You could alternatively use a combination of continuous and discrete actions e.g., using one continuous action for horizontal movement and a discrete branch of size two for the vertical movement.</p> <p>Note that when you are programming actions for an agent, it is often helpful to test your action logic using the <code>Heuristic()</code> method of the Agent, which lets you map keyboard commands to actions.</p>"},{"location":"Learning-Environment-Design-Agents/#continuous-actions","title":"Continuous Actions","text":"<p>When an Agent's Policy has Continuous actions, the <code>ActionBuffers.ContinuousActions</code> passed to the Agent's <code>OnActionReceived()</code> function is an array with length equal to the <code>Continuous Action Size</code> property value. The individual values in the array have whatever meanings that you ascribe to them. If you assign an element in the array as the speed of an Agent, for example, the training process learns to control the speed of the Agent through this parameter.</p> <p>The 3DBall example uses continuous actions with two control values.</p> <p></p> <p>These control values are applied as rotation to the cube:</p> <pre><code>    public override void OnActionReceived(ActionBuffers actionBuffers)\n    {\n        var actionZ = 2f * Mathf.Clamp(actionBuffers.ContinuousActions[0], -1f, 1f);\n        var actionX = 2f * Mathf.Clamp(actionBuffers.ContinuousActions[1], -1f, 1f);\n\n        gameObject.transform.Rotate(new Vector3(0, 0, 1), actionZ);\n        gameObject.transform.Rotate(new Vector3(1, 0, 0), actionX);\n    }\n</code></pre> <p>By default the output from our provided PPO algorithm pre-clamps the values of <code>ActionBuffers.ContinuousActions</code> into the [-1, 1] range. It is a best practice to manually clip these as well, if you plan to use a 3rd party algorithm with your environment. As shown above, you can scale the control values as needed after clamping them.</p>"},{"location":"Learning-Environment-Design-Agents/#discrete-actions","title":"Discrete Actions","text":"<p>When an Agent's Policy uses discrete actions, the <code>ActionBuffers.DiscreteActions</code> passed to the Agent's <code>OnActionReceived()</code> function is an array of integers with length equal to <code>Discrete Branch Size</code>. When defining the discrete actions, <code>Branches</code> is an array of integers, each value corresponds to the number of possibilities for each branch.</p> <p>For example, if we wanted an Agent that can move in a plane and jump, we could define two branches (one for motion and one for jumping) because we want our agent be able to move and jump concurrently. We define the first branch to have 5 possible actions (don't move, go left, go right, go backward, go forward) and the second one to have 2 possible actions (don't jump, jump). The <code>OnActionReceived()</code> method would look something like:</p> <pre><code>// Get the action index for movement\nint movement = actionBuffers.DiscreteActions[0];\n// Get the action index for jumping\nint jump = actionBuffers.DiscreteActions[1];\n\n// Look up the index in the movement action list:\nif (movement == 1) { directionX = -1; }\nif (movement == 2) { directionX = 1; }\nif (movement == 3) { directionZ = -1; }\nif (movement == 4) { directionZ = 1; }\n// Look up the index in the jump action list:\nif (jump == 1 &amp;&amp; IsGrounded()) { directionY = 1; }\n\n// Apply the action results to move the Agent\ngameObject.GetComponent&lt;Rigidbody&gt;().AddForce(\n    new Vector3(\n        directionX * 40f, directionY * 300f, directionZ * 40f));\n</code></pre>"},{"location":"Learning-Environment-Design-Agents/#masking-discrete-actions","title":"Masking Discrete Actions","text":"<p>When using Discrete Actions, it is possible to specify that some actions are impossible for the next decision. When the Agent is controlled by a neural network, the Agent will be unable to perform the specified action. Note that when the Agent is controlled by its Heuristic, the Agent will still be able to decide to perform the masked action. In order to disallow an action, override the <code>Agent.WriteDiscreteActionMask()</code> virtual method, and call <code>SetActionEnabled()</code> on the provided <code>IDiscreteActionMask</code>:</p> <pre><code>public override void WriteDiscreteActionMask(IDiscreteActionMask actionMask)\n{\n    actionMask.SetActionEnabled(branch, actionIndex, isEnabled);\n}\n</code></pre> <p>Where:</p> <ul> <li><code>branch</code> is the index (starting at 0) of the branch on which you want to allow or disallow the action</li> <li><code>actionIndex</code> is the index of the action that you want to allow or disallow.</li> <li><code>isEnabled</code> is a bool indicating whether the action should be allowed or now.</li> </ul> <p>For example, if you have an Agent with 2 branches and on the first branch (branch 0) there are 4 possible actions : \"do nothing\", \"jump\", \"shoot\" and \"change weapon\". Then with the code bellow, the Agent will either \"do nothing\" or \"change weapon\" for their next decision (since action index 1 and 2 are masked)</p> <pre><code>actionMask.SetActionEnabled(0, 1, false);\nactionMask.SetActionEnabled(0, 2, false);\n</code></pre> <p>Notes:</p> <ul> <li>You can call <code>SetActionEnabled</code> multiple times if you want to put masks on multiple   branches.</li> <li>At each step, the state of an action is reset and enabled by default.</li> <li>You cannot mask all the actions of a branch.</li> <li>You cannot mask actions in continuous control.</li> </ul>"},{"location":"Learning-Environment-Design-Agents/#iactuator-interface-and-actuatorcomponents","title":"IActuator interface and ActuatorComponents","text":"<p>The Actuator API allows users to abstract behavior out of Agents and in to components (similar to the ISensor API).  The <code>IActuator</code> interface and <code>Agent</code> class both implement the <code>IActionReceiver</code> interface to allow for backward compatibility with the current <code>Agent.OnActionReceived</code>. This means you will not have to change your code until you decide to use the <code>IActuator</code> API.</p> <p>Like the <code>ISensor</code> interface, the <code>IActuator</code> interface is intended for advanced users.</p> <p>The <code>ActuatorComponent</code> abstract class is used to create the actual <code>IActuator</code> at runtime. It must be attached to the same <code>GameObject</code> as the <code>Agent</code>, or to a child <code>GameObject</code>.  Actuators and all of their data structures are initialized during <code>Agent.Initialize</code>.  This was done to prevent an unexpected allocations at runtime.</p> <p>You can find an example of an <code>IActuator</code> implementation in the <code>Basic</code> example scene. NOTE: you do not need to adjust the Actions in the Agent's <code>Behavior Parameters</code> when using an <code>IActuator</code> and <code>ActuatorComponents</code>.</p> <p>Internally, <code>Agent.OnActionReceived</code> uses an <code>IActuator</code> to send actions to the Agent, although this is mostly abstracted from the user.</p>"},{"location":"Learning-Environment-Design-Agents/#actions-summary-best-practices","title":"Actions Summary &amp; Best Practices","text":"<ul> <li>Agents can use <code>Discrete</code> and/or <code>Continuous</code> actions.</li> <li>Discrete actions can have multiple action branches, and it's possible to mask   certain actions so that they won't be taken.</li> <li>In general, fewer actions will make for easier learning.</li> <li>Be sure to set the Continuous Action Size and Discrete Branch Size to the desired   number for each type of action, and not greater, as doing the latter can interfere with the   efficiency of the training process.</li> <li>Continuous action values should be clipped to an   appropriate range. The provided PPO model automatically clips these values   between -1 and 1, but third party training systems may not do so.</li> </ul>"},{"location":"Learning-Environment-Design-Agents/#rewards","title":"Rewards","text":"<p>In reinforcement learning, the reward is a signal that the agent has done something right. The PPO reinforcement learning algorithm works by optimizing the choices an agent makes such that the agent earns the highest cumulative reward over time. The better your reward mechanism, the better your agent will learn.</p> <p>Note: Rewards are not used during inference by an Agent using a trained model and is also not used during imitation learning.</p> <p>Perhaps the best advice is to start simple and only add complexity as needed. In general, you should reward results rather than actions you think will lead to the desired results. You can even use the Agent's Heuristic to control the Agent while watching how it accumulates rewards.</p> <p>Allocate rewards to an Agent by calling the <code>AddReward()</code> or <code>SetReward()</code> methods on the agent. The reward assigned between each decision should be in the range [-1,1]. Values outside this range can lead to unstable training. The <code>reward</code> value is reset to zero when the agent receives a new decision. If there are multiple calls to <code>AddReward()</code> for a single agent decision, the rewards will be summed together to evaluate how good the previous decision was. The <code>SetReward()</code> will override all previous rewards given to an agent since the previous decision.</p>"},{"location":"Learning-Environment-Design-Agents/#examples","title":"Examples","text":"<p>You can examine the <code>OnActionReceived()</code> functions defined in the example environments to see how those projects allocate rewards.</p> <p>The <code>GridAgent</code> class in the GridWorld example uses a very simple reward system:</p> <pre><code>Collider[] hitObjects = Physics.OverlapBox(trueAgent.transform.position,\n                                           new Vector3(0.3f, 0.3f, 0.3f));\nif (hitObjects.Where(col =&gt; col.gameObject.tag == \"goal\").ToArray().Length == 1)\n{\n    AddReward(1.0f);\n    EndEpisode();\n}\nelse if (hitObjects.Where(col =&gt; col.gameObject.tag == \"pit\").ToArray().Length == 1)\n{\n    AddReward(-1f);\n    EndEpisode();\n}\n</code></pre> <p>The agent receives a positive reward when it reaches the goal and a negative reward when it falls into the pit. Otherwise, it gets no rewards. This is an example of a sparse reward system. The agent must explore a lot to find the infrequent reward.</p> <p>In contrast, the <code>AreaAgent</code> in the Area example gets a small negative reward every step. In order to get the maximum reward, the agent must finish its task of reaching the goal square as quickly as possible:</p> <pre><code>AddReward( -0.005f);\nMoveAgent(act);\n\nif (gameObject.transform.position.y &lt; 0.0f ||\n    Mathf.Abs(gameObject.transform.position.x - area.transform.position.x) &gt; 8f ||\n    Mathf.Abs(gameObject.transform.position.z + 5 - area.transform.position.z) &gt; 8)\n{\n    AddReward(-1f);\n    EndEpisode();\n}\n</code></pre> <p>The agent also gets a larger negative penalty if it falls off the playing surface.</p> <p>The <code>Ball3DAgent</code> in the 3DBall takes a similar approach, but allocates a small positive reward as long as the agent balances the ball. The agent can maximize its rewards by keeping the ball on the platform:</p> <pre><code>\nSetReward(0.1f);\n\n// When ball falls mark Agent as finished and give a negative penalty\nif ((ball.transform.position.y - gameObject.transform.position.y) &lt; -2f ||\n    Mathf.Abs(ball.transform.position.x - gameObject.transform.position.x) &gt; 3f ||\n    Mathf.Abs(ball.transform.position.z - gameObject.transform.position.z) &gt; 3f)\n{\n    SetReward(-1f);\n    EndEpisode();\n\n}\n</code></pre> <p>The <code>Ball3DAgent</code> also assigns a negative penalty when the ball falls off the platform.</p> <p>Note that all of these environments make use of the <code>EndEpisode()</code> method, which manually terminates an episode when a termination condition is reached. This can be called independently of the <code>Max Step</code> property.</p>"},{"location":"Learning-Environment-Design-Agents/#rewards-summary-best-practices","title":"Rewards Summary &amp; Best Practices","text":"<ul> <li>Use <code>AddReward()</code> to accumulate rewards between decisions. Use <code>SetReward()</code>   to overwrite any previous rewards accumulate between decisions.</li> <li>The magnitude of any given reward should typically not be greater than 1.0 in   order to ensure a more stable learning process.</li> <li>Positive rewards are often more helpful to shaping the desired behavior of an   agent than negative rewards. Excessive negative rewards can result in the   agent failing to learn any meaningful behavior.</li> <li>For locomotion tasks, a small positive reward (+0.1) for forward velocity is   typically used.</li> <li>If you want the agent to finish a task quickly, it is often helpful to provide   a small penalty every step (-0.05) that the agent does not complete the task.   In this case completion of the task should also coincide with the end of the   episode by calling <code>EndEpisode()</code> on the agent when it has accomplished its   goal.</li> </ul>"},{"location":"Learning-Environment-Design-Agents/#agent-properties","title":"Agent Properties","text":"<ul> <li><code>Behavior Parameters</code> - The parameters dictating what Policy the Agent will   receive.</li> <li><code>Behavior Name</code> - The identifier for the behavior. Agents with the same     behavior name will learn the same policy.</li> <li><code>Vector Observation</code><ul> <li><code>Space Size</code> - Length of vector observation for the Agent.</li> <li><code>Stacked Vectors</code> - The number of previous vector observations that will   be stacked and used collectively for decision making. This results in the   effective size of the vector observation being passed to the Policy being:   Space Size x Stacked Vectors.</li> </ul> </li> <li><code>Actions</code><ul> <li><code>Continuous Actions</code> - The number of concurrent continuous actions that  the Agent can take.</li> <li><code>Discrete Branches</code> - An array of integers, defines multiple concurrent   discrete actions. The values in the <code>Discrete Branches</code> array correspond   to the number of possible discrete values for each action branch.</li> </ul> </li> <li><code>Model</code> - The neural network model used for inference (obtained after     training)</li> <li><code>Inference Device</code> - Whether to use CPU or GPU to run the model during     inference</li> <li><code>Behavior Type</code> - Determines whether the Agent will do training, inference,     or use its Heuristic() method:<ul> <li><code>Default</code> - the Agent will train if they connect to a python trainer,   otherwise they will perform inference.</li> <li><code>Heuristic Only</code> - the Agent will always use the <code>Heuristic()</code> method.</li> <li><code>Inference Only</code> - the Agent will always perform inference.</li> </ul> </li> <li><code>Team ID</code> - Used to define the team for self-play</li> <li><code>Use Child Sensors</code> - Whether to use all Sensor components attached to child     GameObjects of this Agent.</li> <li><code>Max Step</code> - The per-agent maximum number of steps. Once this number is   reached, the Agent will be reset.</li> </ul>"},{"location":"Learning-Environment-Design-Agents/#destroying-an-agent","title":"Destroying an Agent","text":"<p>You can destroy an Agent GameObject during the simulation. Make sure that there is always at least one Agent training at all times by either spawning a new Agent every time one is destroyed or by re-spawning new Agents when the whole environment resets.</p>"},{"location":"Learning-Environment-Design-Agents/#defining-multi-agent-scenarios","title":"Defining Multi-agent Scenarios","text":""},{"location":"Learning-Environment-Design-Agents/#teams-for-adversarial-scenarios","title":"Teams for Adversarial Scenarios","text":"<p>Self-play is triggered by including the self-play hyperparameter hierarchy in the trainer configuration. To distinguish opposing agents, set the team ID to different integer values in the behavior parameters script on the agent prefab.</p> <p> </p> <p>Team ID must be 0 or an integer greater than 0.</p> <p>In symmetric games, since all agents (even on opposing teams) will share the same policy, they should have the same 'Behavior Name' in their Behavior Parameters Script. In asymmetric games, they should have a different Behavior Name in their Behavior Parameters script. Note, in asymmetric games, the agents must have both different Behavior Names and different team IDs!</p> <p>For examples of how to use this feature, you can see the trainer configurations and agent prefabs for our Tennis and Soccer environments. Tennis and Soccer provide examples of symmetric games. To train an asymmetric game, specify trainer configurations for each of your behavior names and include the self-play hyperparameter hierarchy in both.</p>"},{"location":"Learning-Environment-Design-Agents/#groups-for-cooperative-scenarios","title":"Groups for Cooperative Scenarios","text":"<p>Cooperative behavior in ML-Agents can be enabled by instantiating a <code>SimpleMultiAgentGroup</code>, typically in an environment controller or similar script, and adding agents to it using the <code>RegisterAgent(Agent agent)</code> method. Note that all agents added to the same <code>SimpleMultiAgentGroup</code> must have the same behavior name and Behavior Parameters. Using <code>SimpleMultiAgentGroup</code> enables the agents within a group to learn how to work together to achieve a common goal (i.e., maximize a group-given reward), even if one or more of the group members are removed before the episode ends. You can then use this group to add/set rewards, end or interrupt episodes at a group level using the <code>AddGroupReward()</code>, <code>SetGroupReward()</code>, <code>EndGroupEpisode()</code>, and <code>GroupEpisodeInterrupted()</code> methods. For example:</p> <pre><code>// Create a Multi Agent Group in Start() or Initialize()\nm_AgentGroup = new SimpleMultiAgentGroup();\n\n// Register agents in group at the beginning of an episode\nfor (var agent in AgentList)\n{\n  m_AgentGroup.RegisterAgent(agent);\n}\n\n// if the team scores a goal\nm_AgentGroup.AddGroupReward(rewardForGoal);\n\n// If the goal is reached and the episode is over\nm_AgentGroup.EndGroupEpisode();\nResetScene();\n\n// If time ran out and we need to interrupt the episode\nm_AgentGroup.GroupEpisodeInterrupted();\nResetScene();\n</code></pre> <p>Multi Agent Groups should be used with the MA-POCA trainer, which is explicitly designed to train cooperative environments. This can be enabled by using the <code>poca</code> trainer - see the training configurations doc for more information on configuring MA-POCA. When using MA-POCA, agents which are deactivated or removed from the Scene during the episode will still learn to contribute to the group's long term rewards, even if they are not active in the scene to experience them.</p> <p>See the Cooperative Push Block environment for an example of how to use Multi Agent Groups, and the Dungeon Escape environment for an example of how the Multi Agent Group can be used with agents that are removed from the scene mid-episode.</p> <p>NOTE: Groups differ from Teams (for competitive settings) in the following way - Agents working together should be added to the same Group, while agents playing against each other should be given different Team Ids. If in the Scene there is one playing field and two teams, there should be two Groups, one for each team, and each team should be assigned a different Team Id. If this playing field is duplicated many times in the Scene (e.g. for training speedup), there should be two Groups per playing field, and two unique Team Ids for the entire Scene. In environments with both Groups and Team Ids configured, MA-POCA and self-play can be used together for training. In the diagram below, there are two agents on each team, and two playing fields where teams are pitted against each other. All the blue agents should share a Team Id (and the orange ones a different ID), and there should be four group managers, one per pair of agents.</p> <p> </p> <p>Please see the SoccerTwos environment for an example.</p>"},{"location":"Learning-Environment-Design-Agents/#cooperative-behaviors-notes-and-best-practices","title":"Cooperative Behaviors Notes and Best Practices","text":"<ul> <li> <p>An agent can only be registered to one MultiAgentGroup at a time. If you want to re-assign an agent from one group to another, you have to unregister it from the current group first.</p> </li> <li> <p>Agents with different behavior names in the same group are not supported.</p> </li> <li> <p>Agents within groups should always set the <code>Max Steps</code> parameter in the Agent script to 0. Instead, handle Max Steps using the MultiAgentGroup by ending the episode for the entire Group using <code>GroupEpisodeInterrupted()</code>.</p> </li> <li> <p><code>EndGroupEpisode</code> and <code>GroupEpisodeInterrupted</code> do the same job in the game, but has slightly different effect on the training. If the episode is completed, you would want to call <code>EndGroupEpisode</code>. But if the episode is not over but it has been running for enough steps, i.e. reaching max step, you would call <code>GroupEpisodeInterrupted</code>.</p> </li> <li> <p>If an agent finished earlier, e.g. completed tasks/be removed/be killed in the game, do not call <code>EndEpisode()</code> on the Agent. Instead, disable the agent and re-enable it when the next episode starts, or destroy the agent entirely. This is because calling <code>EndEpisode()</code> will call <code>OnEpisodeBegin()</code>, which will reset the agent immediately. While it is possible to call <code>EndEpisode()</code> in this way, it is usually not the desired behavior when training groups of agents.</p> </li> <li> <p>If an agent that was disabled in a scene needs to be re-enabled, it must be re-registered to the MultiAgentGroup.</p> </li> <li> <p>Group rewards are meant to reinforce agents to act in the group's best interest instead of individual ones, and are treated differently than individual agent rewards during training. So calling <code>AddGroupReward()</code> is not equivalent to calling agent.AddReward() on each agent in the group.</p> </li> <li> <p>You can still add incremental rewards to agents using <code>Agent.AddReward()</code> if they are in a Group. These rewards will only be given to those agents and are received when the Agent is active.</p> </li> <li> <p>Environments which use Multi Agent Groups can be trained using PPO or SAC, but agents will not be able to learn from group rewards after deactivation/removal, nor will they behave as cooperatively.</p> </li> </ul>"},{"location":"Learning-Environment-Design-Agents/#recording-demonstrations","title":"Recording Demonstrations","text":"<p>In order to record demonstrations from an agent, add the <code>Demonstration Recorder</code> component to a GameObject in the scene which contains an <code>Agent</code> component. Once added, it is possible to name the demonstration that will be recorded from the agent.</p> <p> </p> <p>When <code>Record</code> is checked, a demonstration will be created whenever the scene is played from the Editor. Depending on the complexity of the task, anywhere from a few minutes or a few hours of demonstration data may be necessary to be useful for imitation learning. To specify an exact number of steps you want to record use the <code>Num Steps To Record</code> field and the editor will end your play session automatically once that many steps are recorded. If you set <code>Num Steps To Record</code> to <code>0</code> then recording will continue until you manually end the play session. Once the play session ends a <code>.demo</code> file will be created in the <code>Assets/Demonstrations</code> folder (by default). This file contains the demonstrations. Clicking on the file will provide metadata about the demonstration in the inspector.</p> <p> </p> <p>You can then specify the path to this file in your training configurations.</p>"},{"location":"Learning-Environment-Design/","title":"Designing a Learning Environment","text":"<p>This page contains general advice on how to design your learning environment, in addition to overviewing aspects of the ML-Agents Unity SDK that pertain to setting up your scene and simulation as opposed to designing your agents within the scene. We have a dedicated page on Designing Agents which includes how to instrument observations, actions and rewards, define teams for multi-agent scenarios and record agent demonstrations for imitation learning.</p> <p>To help on-board to the entire set of functionality provided by the ML-Agents Toolkit, we recommend exploring our API documentation. Additionally, our example environments are a great resource as they provide sample usage of almost all of our features.</p>"},{"location":"Learning-Environment-Design/#the-simulation-and-training-process","title":"The Simulation and Training Process","text":"<p>Training and simulation proceed in steps orchestrated by the ML-Agents Academy class. The Academy works with Agent objects in the scene to step through the simulation.</p> <p>During training, the external Python training process communicates with the Academy to run a series of episodes while it collects data and optimizes its neural network model. When training is completed successfully, you can add the trained model file to your Unity project for later use.</p> <p>The ML-Agents Academy class orchestrates the agent simulation loop as follows:</p> <ol> <li>Calls your Academy's <code>OnEnvironmentReset</code> delegate.</li> <li>Calls the <code>OnEpisodeBegin()</code> function for each Agent in the scene.</li> <li>Gathers information about the scene. This is done by calling the   <code>CollectObservations(VectorSensor sensor)</code> function for each Agent in the   scene, as well as updating their sensor and collecting the resulting   observations.</li> <li>Uses each Agent's Policy to decide on the Agent's next action.</li> <li>Calls the <code>OnActionReceived()</code> function for each Agent in the scene, passing    in the action chosen by the Agent's Policy.</li> <li>Calls the Agent's <code>OnEpisodeBegin()</code> function if the Agent has reached its    <code>Max Step</code> count or has otherwise marked itself as <code>EndEpisode()</code>.</li> </ol> <p>To create a training environment, extend the Agent class to implement the above methods whether you need to implement them or not depends on your specific scenario.</p>"},{"location":"Learning-Environment-Design/#organizing-the-unity-scene","title":"Organizing the Unity Scene","text":"<p>To train and use the ML-Agents Toolkit in a Unity scene, the scene as many Agent subclasses as you need. Agent instances should be attached to the GameObject representing that Agent.</p>"},{"location":"Learning-Environment-Design/#academy","title":"Academy","text":"<p>The Academy is a singleton which orchestrates Agents and their decision making processes. Only a single Academy exists at a time.</p>"},{"location":"Learning-Environment-Design/#academy-resetting","title":"Academy resetting","text":"<p>To alter the environment at the start of each episode, add your method to the Academy's OnEnvironmentReset action.</p> <pre><code>public class MySceneBehavior : MonoBehaviour\n{\n    public void Awake()\n    {\n        Academy.Instance.OnEnvironmentReset += EnvironmentReset;\n    }\n\n    void EnvironmentReset()\n    {\n        // Reset the scene here\n    }\n}\n</code></pre> <p>For example, you might want to reset an Agent to its starting position or move a goal to a random position. An environment resets when the <code>reset()</code> method is called on the Python <code>UnityEnvironment</code>.</p> <p>When you reset an environment, consider the factors that should change so that training is generalizable to different conditions. For example, if you were training a maze-solving agent, you would probably want to change the maze itself for each training episode. Otherwise, the agent would probably on learn to solve one, particular maze, not mazes in general.</p>"},{"location":"Learning-Environment-Design/#multiple-areas","title":"Multiple Areas","text":"<p>In many of the example environments, many copies of the training area are instantiated in the scene. This generally speeds up training, allowing the environment to gather many experiences in parallel. This can be achieved simply by instantiating many Agents with the same Behavior Name. If possible, consider designing your scene to support multiple areas.</p> <p>Check out our example environments to see examples of multiple areas. Additionally, the Making a New Learning Environment guide demonstrates this option.</p>"},{"location":"Learning-Environment-Design/#environments","title":"Environments","text":"<p>When you create a training environment in Unity, you must set up the scene so that it can be controlled by the external training process. Considerations include:</p> <ul> <li>The training scene must start automatically when your Unity application is   launched by the training process.</li> <li>The Academy must reset the scene to a valid starting point for each episode of   training.</li> <li>A training episode must have a definite end \u2014 either using <code>Max Steps</code> or by   each Agent ending its episode manually with <code>EndEpisode()</code>.</li> </ul>"},{"location":"Learning-Environment-Design/#environment-parameters","title":"Environment Parameters","text":"<p>Curriculum learning and environment parameter randomization are two training methods that control specific parameters in your environment. As such, it is important to ensure that your environment parameters are updated at each step to the correct values. To enable this, we expose a <code>EnvironmentParameters</code> C# class that you can use to retrieve the values of the parameters defined in the training configurations for both of those features. Please see our documentation for curriculum learning and environment parameter randomization for details.</p> <p>We recommend modifying the environment from the Agent's <code>OnEpisodeBegin()</code> function by leveraging <code>Academy.Instance.EnvironmentParameters</code>. See the WallJump example environment for a sample usage (specifically, WallJumpAgent.cs ).</p>"},{"location":"Learning-Environment-Design/#agent","title":"Agent","text":"<p>The Agent class represents an actor in the scene that collects observations and carries out actions. The Agent class is typically attached to the GameObject in the scene that otherwise represents the actor \u2014 for example, to a player object in a football game or a car object in a vehicle simulation. Every Agent must have appropriate <code>Behavior Parameters</code>.</p> <p>Generally, when creating an Agent, you should extend the Agent class and implement the <code>CollectObservations(VectorSensor sensor)</code> and <code>OnActionReceived()</code> methods:</p> <ul> <li><code>CollectObservations(VectorSensor sensor)</code> \u2014 Collects the Agent's observation   of its environment.</li> <li><code>OnActionReceived()</code> \u2014 Carries out the action chosen by the Agent's Policy and   assigns a reward to the current state.</li> </ul> <p>Your implementations of these functions determine how the Behavior Parameters assigned to this Agent must be set.</p> <p>You must also determine how an Agent finishes its task or times out. You can manually terminate an Agent episode in your <code>OnActionReceived()</code> function when the Agent has finished (or irrevocably failed) its task by calling the <code>EndEpisode()</code> function. You can also set the Agent's <code>Max Steps</code> property to a positive value and the Agent will consider the episode over after it has taken that many steps. You can use the <code>Agent.OnEpisodeBegin()</code> function to prepare the Agent to start again.</p> <p>See Agents for detailed information about programming your own Agents.</p>"},{"location":"Learning-Environment-Design/#recording-statistics","title":"Recording Statistics","text":"<p>We offer developers a mechanism to record statistics from within their Unity environments. These statistics are aggregated and generated during the training process. To record statistics, see the <code>StatsRecorder</code> C# class.</p> <p>See the FoodCollector example environment for a sample usage (specifically, FoodCollectorSettings.cs ).</p>"},{"location":"Learning-Environment-Examples/","title":"Example Learning Environments","text":"<p>The Unity ML-Agents Toolkit includes an expanding set of example environments that highlight the various features of the toolkit. These environments can also serve as templates for new environments or as ways to test new ML algorithms. Environments are located in <code>Project/Assets/ML-Agents/Examples</code> and summarized below.</p> <p>For the environments that highlight specific features of the toolkit, we provide the pre-trained model files and the training config file that enables you to train the scene yourself. The environments that are designed to serve as challenges for researchers do not have accompanying pre-trained model files or training configs and are marked as Optional below.</p> <p>This page only overviews the example environments we provide. To learn more on how to design and build your own environments see our Making a New Learning Environment page. If you would like to contribute environments, please see our contribution guidelines page.</p>"},{"location":"Learning-Environment-Examples/#basic","title":"Basic","text":"<ul> <li>Set-up: A linear movement task where the agent must move left or right to   rewarding states.</li> <li>Goal: Move to the most reward state.</li> <li>Agents: The environment contains one agent.</li> <li>Agent Reward Function:</li> <li>-0.01 at each step</li> <li>+0.1 for arriving at suboptimal state.</li> <li>+1.0 for arriving at optimal state.</li> <li>Behavior Parameters:</li> <li>Vector Observation space: One variable corresponding to current state.</li> <li>Actions: 1 discrete action branch with 3 actions (Move left, do nothing, move     right).</li> <li>Visual Observations: None</li> <li>Float Properties: None</li> <li>Benchmark Mean Reward: 0.93</li> </ul>"},{"location":"Learning-Environment-Examples/#3dball-3d-balance-ball","title":"3DBall: 3D Balance Ball","text":"<ul> <li>Set-up: A balance-ball task, where the agent balances the ball on it's head.</li> <li>Goal: The agent must balance the ball on it's head for as long as possible.</li> <li>Agents: The environment contains 12 agents of the same kind, all using the   same Behavior Parameters.</li> <li>Agent Reward Function:</li> <li>+0.1 for every step the ball remains on it's head.</li> <li>-1.0 if the ball falls off.</li> <li>Behavior Parameters:</li> <li>Vector Observation space: 8 variables corresponding to rotation of the agent     cube, and position and velocity of ball.</li> <li>Vector Observation space (Hard Version): 5 variables corresponding to     rotation of the agent cube and position of ball.</li> <li>Actions: 2 continuous actions, with one value corresponding to     X-rotation, and the other to Z-rotation.</li> <li>Visual Observations: Third-person view from the upper-front of the agent. Use     <code>Visual3DBall</code> scene.</li> <li>Float Properties: Three</li> <li>scale: Specifies the scale of the ball in the 3 dimensions (equal across the     three dimensions)<ul> <li>Default: 1</li> <li>Recommended Minimum: 0.2</li> <li>Recommended Maximum: 5</li> </ul> </li> <li>gravity: Magnitude of gravity<ul> <li>Default: 9.81</li> <li>Recommended Minimum: 4</li> <li>Recommended Maximum: 105</li> </ul> </li> <li>mass: Specifies mass of the ball<ul> <li>Default: 1</li> <li>Recommended Minimum: 0.1</li> <li>Recommended Maximum: 20</li> </ul> </li> <li>Benchmark Mean Reward: 100</li> </ul>"},{"location":"Learning-Environment-Examples/#gridworld","title":"GridWorld","text":"<ul> <li>Set-up: A multi-goal version of the grid-world task. Scene contains agent, goal,   and obstacles.</li> <li>Goal: The agent must navigate the grid to the appropriate goal while   avoiding the obstacles.</li> <li>Agents: The environment contains nine agents with the same Behavior   Parameters.</li> <li>Agent Reward Function:</li> <li>-0.01 for every step.</li> <li>+1.0 if the agent navigates to the correct goal (episode ends).</li> <li>-1.0 if the agent navigates to an incorrect goal (episode ends).</li> <li>Behavior Parameters:</li> <li>Vector Observation space: None</li> <li>Actions: 1 discrete action branch with 5 actions, corresponding to movement in     cardinal directions or not moving. Note that for this environment,     action masking     is turned on by default (this option can be toggled using the <code>Mask Actions</code>     checkbox within the <code>trueAgent</code> GameObject). The trained model file provided     was generated with action masking turned on.</li> <li>Visual Observations: One corresponding to top-down view of GridWorld.</li> <li>Goal Signal : A one hot vector corresponding to which color is the correct goal   for the Agent</li> <li>Float Properties: Three, corresponding to grid size, number of green goals, and   number of red goals.</li> <li>Benchmark Mean Reward: 0.8</li> </ul>"},{"location":"Learning-Environment-Examples/#push-block","title":"Push Block","text":"<ul> <li>Set-up: A platforming environment where the agent can push a block around.</li> <li>Goal: The agent must push the block to the goal.</li> <li>Agents: The environment contains one agent.</li> <li>Agent Reward Function:</li> <li>-0.0025 for every step.</li> <li>+1.0 if the block touches the goal.</li> <li>Behavior Parameters:</li> <li>Vector Observation space: (Continuous) 70 variables corresponding to 14     ray-casts each detecting one of three possible objects (wall, goal, or     block).</li> <li>Actions: 1 discrete action branch with 7 actions, corresponding to turn clockwise     and counterclockwise, move along four different face directions, or do nothing.</li> <li>Float Properties: Four</li> <li>block_scale: Scale of the block along the x and z dimensions<ul> <li>Default: 2</li> <li>Recommended Minimum: 0.5</li> <li>Recommended Maximum: 4</li> </ul> </li> <li>dynamic_friction: Coefficient of friction for the ground material acting on     moving objects<ul> <li>Default: 0</li> <li>Recommended Minimum: 0</li> <li>Recommended Maximum: 1</li> </ul> </li> <li>static_friction: Coefficient of friction for the ground material acting on     stationary objects<ul> <li>Default: 0</li> <li>Recommended Minimum: 0</li> <li>Recommended Maximum: 1</li> </ul> </li> <li>block_drag: Effect of air resistance on block<ul> <li>Default: 0.5</li> <li>Recommended Minimum: 0</li> <li>Recommended Maximum: 2000</li> </ul> </li> <li>Benchmark Mean Reward: 4.5</li> </ul>"},{"location":"Learning-Environment-Examples/#wall-jump","title":"Wall Jump","text":"<ul> <li>Set-up: A platforming environment where the agent can jump over a wall.</li> <li>Goal: The agent must use the block to scale the wall and reach the goal.</li> <li>Agents: The environment contains one agent linked to two different Models. The   Policy the agent is linked to changes depending on the height of the wall. The   change of Policy is done in the WallJumpAgent class.</li> <li>Agent Reward Function:</li> <li>-0.0005 for every step.</li> <li>+1.0 if the agent touches the goal.</li> <li>-1.0 if the agent falls off the platform.</li> <li>Behavior Parameters:</li> <li>Vector Observation space: Size of 74, corresponding to 14 ray casts each     detecting 4 possible objects. plus the global position of the agent and     whether or not the agent is grounded.</li> <li>Actions: 4 discrete action branches:<ul> <li>Forward Motion (3 possible actions: Forward, Backwards, No Action)</li> <li>Rotation (3 possible actions: Rotate Left, Rotate Right, No Action)</li> <li>Side Motion (3 possible actions: Left, Right, No Action)</li> <li>Jump (2 possible actions: Jump, No Action)</li> </ul> </li> <li>Visual Observations: None</li> <li>Float Properties: Four</li> <li>Benchmark Mean Reward (Big &amp; Small Wall): 0.8</li> </ul>"},{"location":"Learning-Environment-Examples/#crawler","title":"Crawler","text":"<ul> <li>Set-up: A creature with 4 arms and 4 forearms.</li> <li>Goal: The agents must move its body toward the goal direction without falling.</li> <li>Agents: The environment contains 10 agents with same Behavior Parameters.</li> <li>Agent Reward Function (independent):   The reward function is now geometric meaning the reward each step is a product   of all the rewards instead of a sum, this helps the agent try to maximize all   rewards instead of the easiest rewards.</li> <li>Body velocity matches goal velocity. (normalized between (0,1))</li> <li>Head direction alignment with goal direction. (normalized between (0,1))</li> <li>Behavior Parameters:</li> <li>Vector Observation space: 172 variables corresponding to position, rotation,     velocity, and angular velocities of each limb plus the acceleration and     angular acceleration of the body.</li> <li>Actions: 20 continuous actions, corresponding to target     rotations for joints.</li> <li>Visual Observations: None</li> <li>Float Properties: None</li> <li>Benchmark Mean Reward: 3000</li> </ul>"},{"location":"Learning-Environment-Examples/#worm","title":"Worm","text":"<ul> <li>Set-up: A worm with a head and 3 body segments.</li> <li>Goal: The agents must move its body toward the goal direction.</li> <li>Agents: The environment contains 10 agents with same Behavior Parameters.</li> <li>Agent Reward Function (independent):   The reward function is now geometric meaning the reward each step is a product   of all the rewards instead of a sum, this helps the agent try to maximize all   rewards instead of the easiest rewards.</li> <li>Body velocity matches goal velocity. (normalized between (0,1))</li> <li>Body direction alignment with goal direction. (normalized between (0,1))</li> <li>Behavior Parameters:</li> <li>Vector Observation space: 64 variables corresponding to position, rotation,     velocity, and angular velocities of each limb plus the acceleration and     angular acceleration of the body.</li> <li>Actions: 9 continuous actions, corresponding to target     rotations for joints.</li> <li>Visual Observations: None</li> <li>Float Properties: None</li> <li>Benchmark Mean Reward: 800</li> </ul>"},{"location":"Learning-Environment-Examples/#food-collector","title":"Food Collector","text":"<ul> <li>Set-up: A multi-agent environment where agents compete to collect food.</li> <li>Goal: The agents must learn to collect as many green food spheres as possible   while avoiding red spheres.</li> <li>Agents: The environment contains 5 agents with same Behavior Parameters.</li> <li>Agent Reward Function (independent):</li> <li>+1 for interaction with green spheres</li> <li>-1 for interaction with red spheres</li> <li>Behavior Parameters:</li> <li>Vector Observation space: 53 corresponding to velocity of agent (2), whether     agent is frozen and/or shot its laser (2), plus grid based perception of     objects around agent's forward direction (40 by 40 with 6 different categories).</li> <li>Actions:<ul> <li>3 continuous actions correspond to Forward Motion, Side Motion and Rotation</li> <li>1 discrete acion branch for Laser with 2 possible actions corresponding to   Shoot Laser or No Action</li> </ul> </li> <li>Visual Observations (Optional): First-person camera per-agent, plus one vector     flag representing the frozen state of the agent. This scene uses a combination     of vector and visual observations and the training will not succeed without     the frozen vector flag. Use <code>VisualFoodCollector</code> scene.</li> <li>Float Properties: Two</li> <li>laser_length: Length of the laser used by the agent<ul> <li>Default: 1</li> <li>Recommended Minimum: 0.2</li> <li>Recommended Maximum: 7</li> </ul> </li> <li>agent_scale: Specifies the scale of the agent in the 3 dimensions (equal     across the three dimensions)<ul> <li>Default: 1</li> <li>Recommended Minimum: 0.5</li> <li>Recommended Maximum: 5</li> </ul> </li> <li>Benchmark Mean Reward: 10</li> </ul>"},{"location":"Learning-Environment-Examples/#hallway","title":"Hallway","text":"<ul> <li>Set-up: Environment where the agent needs to find information in a room,   remember it, and use it to move to the correct goal.</li> <li>Goal: Move to the goal which corresponds to the color of the block in the   room.</li> <li>Agents: The environment contains one agent.</li> <li>Agent Reward Function (independent):</li> <li>+1 For moving to correct goal.</li> <li>-0.1 For moving to incorrect goal.</li> <li>-0.0003 Existential penalty.</li> <li>Behavior Parameters:</li> <li>Vector Observation space: 30 corresponding to local ray-casts detecting     objects, goals, and walls.</li> <li>Actions: 1 discrete action Branch, with 4 actions corresponding to agent     rotation and forward/backward movement.</li> <li>Float Properties: None</li> <li>Benchmark Mean Reward: 0.7</li> <li>To train this environment, you can enable curiosity by adding the <code>curiosity</code> reward signal     in <code>config/ppo/Hallway.yaml</code></li> </ul>"},{"location":"Learning-Environment-Examples/#soccer-twos","title":"Soccer Twos","text":"<ul> <li>Set-up: Environment where four agents compete in a 2 vs 2 toy soccer game.</li> <li>Goal:</li> <li>Get the ball into the opponent's goal while preventing the ball from     entering own goal.</li> <li>Agents: The environment contains two different Multi Agent Groups with two agents in each.   Parameters : SoccerTwos.</li> <li>Agent Reward Function (dependent):</li> <li>(1 - <code>accumulated time penalty</code>) When ball enters opponent's goal     <code>accumulated time penalty</code> is incremented by (1 / <code>MaxStep</code>) every fixed     update and is reset to 0 at the beginning of an episode.</li> <li>-1 When ball enters team's goal.</li> <li>Behavior Parameters:</li> <li>Vector Observation space: 336 corresponding to 11 ray-casts forward     distributed over 120 degrees and 3 ray-casts backward distributed over 90     degrees each detecting 6 possible object types, along with the object's     distance. The forward ray-casts contribute 264 state dimensions and backward     72 state dimensions over three observation stacks.</li> <li>Actions: 3 discrete branched actions corresponding to     forward, backward, sideways movement, as well as rotation.</li> <li>Visual Observations: None</li> <li>Float Properties: Two</li> <li>ball_scale: Specifies the scale of the ball in the 3 dimensions (equal     across the three dimensions)<ul> <li>Default: 7.5</li> <li>Recommended minimum: 4</li> <li>Recommended maximum: 10</li> </ul> </li> <li>gravity: Magnitude of the gravity<ul> <li>Default: 9.81</li> <li>Recommended minimum: 6</li> <li>Recommended maximum: 20</li> </ul> </li> </ul>"},{"location":"Learning-Environment-Examples/#strikers-vs-goalie","title":"Strikers Vs. Goalie","text":"<ul> <li>Set-up: Environment where two agents compete in a 2 vs 1 soccer variant.</li> <li>Goal:</li> <li>Striker: Get the ball into the opponent's goal.</li> <li>Goalie: Keep the ball out of the goal.</li> <li>Agents: The environment contains two different Multi Agent Groups. One with two Strikers and the other one Goalie.   Behavior Parameters : Striker, Goalie.</li> <li>Striker Agent Reward Function (dependent):</li> <li>+1 When ball enters opponent's goal.</li> <li>-0.001 Existential penalty.</li> <li>Goalie Agent Reward Function (dependent):</li> <li>-1 When ball enters goal.</li> <li>0.001 Existential bonus.</li> <li>Behavior Parameters:</li> <li>Striker Vector Observation space: 294 corresponding to 11 ray-casts forward     distributed over 120 degrees and 3 ray-casts backward distributed over 90     degrees each detecting 5 possible object types, along with the object's     distance. The forward ray-casts contribute 231 state dimensions and backward     63 state dimensions over three observation stacks.</li> <li>Striker Actions: 3 discrete branched actions corresponding     to forward, backward, sideways movement, as well as rotation.</li> <li>Goalie Vector Observation space: 738 corresponding to 41 ray-casts     distributed over 360 degrees each detecting 4 possible object types, along     with the object's distance and 3 observation stacks.</li> <li>Goalie Actions: 3 discrete branched actions corresponding     to forward, backward, sideways movement, as well as rotation.</li> <li>Visual Observations: None</li> <li>Float Properties: Two</li> <li>ball_scale: Specifies the scale of the ball in the 3 dimensions (equal     across the three dimensions)<ul> <li>Default: 7.5</li> <li>Recommended minimum: 4</li> <li>Recommended maximum: 10</li> </ul> </li> <li>gravity: Magnitude of the gravity<ul> <li>Default: 9.81</li> <li>Recommended minimum: 6</li> <li>Recommended maximum: 20</li> </ul> </li> </ul>"},{"location":"Learning-Environment-Examples/#walker","title":"Walker","text":"<ul> <li>Set-up: Physics-based Humanoid agents with 26 degrees of freedom. These DOFs   correspond to articulation of the following body-parts: hips, chest, spine,   head, thighs, shins, feet, arms, forearms and hands.</li> <li>Goal: The agents must move its body toward the goal direction without falling.</li> <li>Agents: The environment contains 10 independent agents with same Behavior   Parameters.</li> <li>Agent Reward Function (independent):   The reward function is now geometric meaning the reward each step is a product   of all the rewards instead of a sum, this helps the agent try to maximize all   rewards instead of the easiest rewards.</li> <li>Body velocity matches goal velocity. (normalized between (0,1))</li> <li>Head direction alignment with goal direction. (normalized between (0,1))</li> <li>Behavior Parameters:</li> <li>Vector Observation space: 243 variables corresponding to position, rotation,     velocity, and angular velocities of each limb, along with goal direction.</li> <li>Actions: 39 continuous actions, corresponding to target     rotations and strength applicable to the joints.</li> <li>Visual Observations: None</li> <li>Float Properties: Four</li> <li>gravity: Magnitude of gravity<ul> <li>Default: 9.81</li> <li>Recommended Minimum:</li> <li>Recommended Maximum:</li> </ul> </li> <li>hip_mass: Mass of the hip component of the walker<ul> <li>Default: 8</li> <li>Recommended Minimum: 7</li> <li>Recommended Maximum: 28</li> </ul> </li> <li>chest_mass: Mass of the chest component of the walker<ul> <li>Default: 8</li> <li>Recommended Minimum: 3</li> <li>Recommended Maximum: 20</li> </ul> </li> <li>spine_mass: Mass of the spine component of the walker<ul> <li>Default: 8</li> <li>Recommended Minimum: 3</li> <li>Recommended Maximum: 20</li> </ul> </li> <li>Benchmark Mean Reward : 2500</li> </ul>"},{"location":"Learning-Environment-Examples/#pyramids","title":"Pyramids","text":"<ul> <li>Set-up: Environment where the agent needs to press a button to spawn a   pyramid, then navigate to the pyramid, knock it over, and move to the gold   brick at the top.</li> <li>Goal: Move to the golden brick on top of the spawned pyramid.</li> <li>Agents: The environment contains one agent.</li> <li>Agent Reward Function (independent):</li> <li>+2 For moving to golden brick (minus 0.001 per step).</li> <li>Behavior Parameters:</li> <li>Vector Observation space: 148 corresponding to local ray-casts detecting     switch, bricks, golden brick, and walls, plus variable indicating switch     state.</li> <li>Actions: 1 discrete action branch, with 4 actions corresponding to agent rotation and     forward/backward movement.</li> <li>Float Properties: None</li> <li>Benchmark Mean Reward: 1.75</li> </ul>"},{"location":"Learning-Environment-Examples/#match-3","title":"Match 3","text":"<ul> <li>Set-up: Simple match-3 game. Matched pieces are removed, and remaining pieces drop down. New pieces are spawned randomly at the top, with a chance of being \"special\".</li> <li>Goal: Maximize score from matching pieces.</li> <li>Agents: The environment contains several independent Agents.</li> <li>Agent Reward Function (independent):</li> <li>.01 for each normal piece cleared. Special pieces are worth 2x or 3x.</li> <li>Behavior Parameters:</li> <li>None</li> <li>Observations and actions are defined with a sensor and actuator respectively.</li> <li>Float Properties: None</li> <li>Benchmark Mean Reward:</li> <li>39.5 for visual observations</li> <li>38.5 for vector observations</li> <li>34.2 for simple heuristic (pick a random valid move)</li> <li>37.0 for greedy heuristic (pick the highest-scoring valid move)</li> </ul>"},{"location":"Learning-Environment-Examples/#sorter","title":"Sorter","text":"<ul> <li>Set-up: The Agent is in a circular room with numbered tiles. The values of the  tiles are random between 1 and 20. The tiles present in the room are randomized  at each episode. When the Agent visits a tile, it turns green.</li> <li>Goal: Visit all the tiles in ascending order.</li> <li>Agents: The environment contains a single Agent</li> <li>Agent Reward Function:</li> <li>-.0002 Existential penalty.</li> <li>+1 For visiting the right tile</li> <li>-1 For visiting the wrong tile</li> <li>BehaviorParameters:</li> <li>Vector Observations : 4 : 2 floats for Position and 2 floats for orientation</li> <li>Variable Length Observations : Between 1 and 20 entities (one for each tile)   each with 22 observations, the first 20 are one hot encoding of the value of the tile,   the 21st and 22nd represent the position of the tile relative to the Agent and the 23rd   is <code>1</code> if the tile was visited and <code>0</code> otherwise.</li> <li>Actions: 3 discrete branched actions corresponding to forward, backward,   sideways movement, as well as rotation.</li> <li>Float Properties: One<ul> <li>num_tiles: The maximum number of tiles to sample.</li> <li>Default: 2</li> <li>Recommended Minimum: 1</li> <li>Recommended Maximum: 20</li> </ul> </li> <li>Benchmark Mean Reward: Depends on the number of tiles.</li> </ul>"},{"location":"Learning-Environment-Examples/#cooperative-push-block","title":"Cooperative Push Block","text":"<ul> <li>Set-up: Similar to Push Block, the agents are in an area with blocks that need to be pushed into a goal. Small blocks can be pushed by one agents and are worth +1 value, medium blocks require two agents to push in and are worth +2, and large blocks require all 3 agents to push and are worth +3.</li> <li>Goal: Push all blocks into the goal.</li> <li>Agents: The environment contains three Agents in a Multi Agent Group.</li> <li>Agent Reward Function:</li> <li>-0.0001 Existential penalty, as a group reward.</li> <li>+1, +2, or +3 for pushing in a block, added as a group reward.</li> <li>Behavior Parameters:</li> <li>Observation space: A single Grid Sensor with separate tags for each block size,     the goal, the walls, and other agents.</li> <li>Actions: 1 discrete action branch with 7 actions, corresponding to turn clockwise     and counterclockwise, move along four different face directions, or do nothing.</li> <li>Float Properties: None</li> <li>Benchmark Mean Reward: 11 (Group Reward)</li> </ul>"},{"location":"Learning-Environment-Examples/#dungeon-escape","title":"Dungeon Escape","text":"<ul> <li>Set-up: Agents are trapped in a dungeon with a dragon, and must work together to escape.   To retrieve the key, one of the agents must find and slay the dragon, sacrificing itself   to do so. The dragon will drop a key for the others to use. The other agents can then pick   up this key and unlock the dungeon door. If the agents take too long, the dragon will escape   through a portal and the environment resets.</li> <li>Goal: Unlock the dungeon door and leave.</li> <li>Agents: The environment contains three Agents in a Multi Agent Group and one Dragon, which   moves in a predetermined pattern.</li> <li>Agent Reward Function:</li> <li>+1 group reward if any agent successfully unlocks the door and leaves the dungeon.</li> <li>Behavior Parameters:</li> <li>Observation space: A Ray Perception Sensor with separate tags for the walls, other agents,     the door, key, the dragon, and the dragon's portal. A single Vector Observation which indicates     whether the agent is holding a key.</li> <li>Actions: 1 discrete action branch with 7 actions, corresponding to turn clockwise     and counterclockwise, move along four different face directions, or do nothing.</li> <li>Float Properties: None</li> <li>Benchmark Mean Reward: 1.0 (Group Reward)</li> </ul>"},{"location":"Learning-Environment-Executable/","title":"Using an Environment Executable","text":"<p>This section will help you create and use built environments rather than the Editor to interact with an environment. Using an executable has some advantages over using the Editor:</p> <ul> <li>You can exchange executable with other people without having to share your   entire repository.</li> <li>You can put your executable on a remote machine for faster training.</li> <li>You can use <code>Server Build</code> (<code>Headless</code>) mode for faster training (as long as the executable does not need rendering).</li> <li>You can keep using the Unity Editor for other tasks while the agents are   training.</li> </ul>"},{"location":"Learning-Environment-Executable/#building-the-3dball-environment","title":"Building the 3DBall environment","text":"<p>The first step is to open the Unity scene containing the 3D Balance Ball environment:</p> <ol> <li>Launch Unity.</li> <li>On the Projects dialog, choose the Open option at the top of the window.</li> <li>Using the file dialog that opens, locate the <code>Project</code> folder within the    ML-Agents project and click Open.</li> <li>In the Project window, navigate to the folder    <code>Assets/ML-Agents/Examples/3DBall/Scenes/</code>.</li> <li>Double-click the <code>3DBall</code> file to load the scene containing the Balance Ball    environment.</li> </ol> <p></p> <p>Next, we want the set up scene to play correctly when the training process launches our environment executable. This means:</p> <ul> <li>The environment application runs in the background.</li> <li>No dialogs require interaction.</li> <li> <p>The correct scene loads automatically.</p> </li> <li> <p>Open Player Settings (menu: Edit &gt; Project Settings &gt; Player).</p> </li> <li>Under Resolution and Presentation:</li> <li>Ensure that Run in Background is Checked.</li> <li>Ensure that Display Resolution Dialog is set to Disabled. (Note: this     setting may not be available in newer versions of the editor.)</li> <li>Open the Build Settings window (menu:File &gt; Build Settings).</li> <li>Choose your target platform.</li> <li>(optional) Select \u201cDevelopment Build\u201d to      log debug messages.</li> <li>If any scenes are shown in the Scenes in Build list, make sure that the    3DBall Scene is the only one checked. (If the list is empty, then only the    current scene is included in the build).</li> <li>Click Build:</li> <li>In the File dialog, navigate to your ML-Agents directory.</li> <li>Assign a file name and click Save.</li> <li>(For Windows\uff09With Unity 2018.1, it will ask you to select a folder instead      of a file name. Create a subfolder within the root directory and select      that folder to build. In the following steps you will refer to this      subfolder's name as <code>env_name</code>. You cannot create builds in the Assets      folder</li> </ul> <p></p> <p>Now that we have a Unity executable containing the simulation environment, we can interact with it.</p>"},{"location":"Learning-Environment-Executable/#interacting-with-the-environment","title":"Interacting with the Environment","text":"<p>If you want to use the Python API to interact with your executable, you can pass the name of the executable with the argument 'file_name' of the <code>UnityEnvironment</code>. For instance:</p> <pre><code>from mlagents_envs.environment import UnityEnvironment\nenv = UnityEnvironment(file_name=&lt;env_name&gt;)\n</code></pre>"},{"location":"Learning-Environment-Executable/#training-the-environment","title":"Training the Environment","text":"<ol> <li>Open a command or terminal window.</li> <li>Navigate to the folder where you installed the ML-Agents Toolkit. If you    followed the default installation, then navigate to the    <code>ml-agents/</code> folder.</li> <li>Run    <code>mlagents-learn &lt;trainer-config-file&gt; --env=&lt;env_name&gt; --run-id=&lt;run-identifier&gt;</code>    Where:</li> <li><code>&lt;trainer-config-file&gt;</code> is the file path of the trainer configuration yaml</li> <li><code>&lt;env_name&gt;</code> is the name and path to the executable you exported from Unity      (without extension)</li> <li><code>&lt;run-identifier&gt;</code> is a string used to separate the results of different      training runs</li> </ol> <p>For example, if you are training with a 3DBall executable, and you saved it to the directory where you installed the ML-Agents Toolkit, run:</p> <pre><code>mlagents-learn config/ppo/3DBall.yaml --env=3DBall --run-id=firstRun\n</code></pre> <p>And you should see something like</p> <pre><code>ml-agents$ mlagents-learn config/ppo/3DBall.yaml --env=3DBall --run-id=first-run\n\n\n                        \u2584\u2584\u2584\u2593\u2593\u2593\u2593\n                   \u2553\u2593\u2593\u2593\u2593\u2593\u2593\u2588\u2593\u2593\u2593\u2593\u2593\n              ,\u2584\u2584\u2584m\u2580\u2580\u2580'  ,\u2593\u2593\u2593\u2580\u2593\u2593\u2584                           \u2593\u2593\u2593  \u2593\u2593\u258c\n            \u2584\u2593\u2593\u2593\u2580'      \u2584\u2593\u2593\u2580  \u2593\u2593\u2593      \u2584\u2584     \u2584\u2584 ,\u2584\u2584 \u2584\u2584\u2584\u2584   ,\u2584\u2584 \u2584\u2593\u2593\u258c\u2584 \u2584\u2584\u2584    ,\u2584\u2584\n          \u2584\u2593\u2593\u2593\u2580        \u2584\u2593\u2593\u2580   \u2590\u2593\u2593\u258c     \u2593\u2593\u258c   \u2590\u2593\u2593 \u2590\u2593\u2593\u2593\u2580\u2580\u2580\u2593\u2593\u258c \u2593\u2593\u2593 \u2580\u2593\u2593\u258c\u2580 ^\u2593\u2593\u258c  \u2552\u2593\u2593\u258c\n        \u2584\u2593\u2593\u2593\u2593\u2593\u2584\u2584\u2584\u2584\u2584\u2584\u2584\u2584\u2593\u2593\u2593      \u2593\u2580      \u2593\u2593\u258c   \u2590\u2593\u2593 \u2590\u2593\u2593    \u2593\u2593\u2593 \u2593\u2593\u2593  \u2593\u2593\u258c   \u2590\u2593\u2593\u2584 \u2593\u2593\u258c\n        \u2580\u2593\u2593\u2593\u2593\u2580\u2580\u2580\u2580\u2580\u2580\u2580\u2580\u2580\u2580\u2593\u2593\u2584     \u2593\u2593      \u2593\u2593\u258c   \u2590\u2593\u2593 \u2590\u2593\u2593    \u2593\u2593\u2593 \u2593\u2593\u2593  \u2593\u2593\u258c    \u2590\u2593\u2593\u2590\u2593\u2593\n          ^\u2588\u2593\u2593\u2593        \u2580\u2593\u2593\u2584   \u2590\u2593\u2593\u258c     \u2593\u2593\u2593\u2593\u2584\u2593\u2593\u2593\u2593 \u2590\u2593\u2593    \u2593\u2593\u2593 \u2593\u2593\u2593  \u2593\u2593\u2593\u2584    \u2593\u2593\u2593\u2593`\n            '\u2580\u2593\u2593\u2593\u2584      ^\u2593\u2593\u2593  \u2593\u2593\u2593       \u2514\u2580\u2580\u2580\u2580 \u2580\u2580 ^\u2580\u2580    `\u2580\u2580 `\u2580\u2580   '\u2580\u2580    \u2590\u2593\u2593\u258c\n               \u2580\u2580\u2580\u2580\u2593\u2584\u2584\u2584   \u2593\u2593\u2593\u2593\u2593\u2593,                                      \u2593\u2593\u2593\u2593\u2580\n                   `\u2580\u2588\u2593\u2593\u2593\u2593\u2593\u2593\u2593\u2593\u2593\u258c\n                        \u00ac`\u2580\u2580\u2580\u2588\u2593\n\n</code></pre> <p>Note: If you're using Anaconda, don't forget to activate the ml-agents environment first.</p> <p>If <code>mlagents-learn</code> runs correctly and starts training, you should see something like this:</p> <pre><code>CrashReporter: initialized\nMono path[0] = '/Users/dericp/workspace/ml-agents/3DBall.app/Contents/Resources/Data/Managed'\nMono config path = '/Users/dericp/workspace/ml-agents/3DBall.app/Contents/MonoBleedingEdge/etc'\nINFO:mlagents_envs:\n'Ball3DAcademy' started successfully!\nUnity Academy name: Ball3DAcademy\n\nINFO:mlagents_envs:Connected new brain:\nUnity brain name: Ball3DLearning\n        Number of Visual Observations (per agent): 0\n        Vector Observation space size (per agent): 8\n        Number of stacked Vector Observation: 1\nINFO:mlagents_envs:Hyperparameters for the PPO Trainer of brain Ball3DLearning:\n        batch_size:          64\n        beta:                0.001\n        buffer_size:         12000\n        epsilon:             0.2\n        gamma:               0.995\n        hidden_units:        128\n        lambd:               0.99\n        learning_rate:       0.0003\n        max_steps:           5.0e4\n        normalize:           True\n        num_epoch:           3\n        num_layers:          2\n        time_horizon:        1000\n        sequence_length:     64\n        summary_freq:        1000\n        use_recurrent:       False\n        memory_size:         256\n        use_curiosity:       False\n        curiosity_strength:  0.01\n        curiosity_enc_size:  128\n        output_path: ./results/first-run-0/Ball3DLearning\nINFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 1000. Mean Reward: 1.242. Std of Reward: 0.746. Training.\nINFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 2000. Mean Reward: 1.319. Std of Reward: 0.693. Training.\nINFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 3000. Mean Reward: 1.804. Std of Reward: 1.056. Training.\nINFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 4000. Mean Reward: 2.151. Std of Reward: 1.432. Training.\nINFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 5000. Mean Reward: 3.175. Std of Reward: 2.250. Training.\nINFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 6000. Mean Reward: 4.898. Std of Reward: 4.019. Training.\nINFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 7000. Mean Reward: 6.716. Std of Reward: 5.125. Training.\nINFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 8000. Mean Reward: 12.124. Std of Reward: 11.929. Training.\nINFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 9000. Mean Reward: 18.151. Std of Reward: 16.871. Training.\nINFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 10000. Mean Reward: 27.284. Std of Reward: 28.667. Training.\n</code></pre> <p>You can press Ctrl+C to stop the training, and your trained model will be at <code>results/&lt;run-identifier&gt;/&lt;behavior_name&gt;.onnx</code>, which corresponds to your model's latest checkpoint. (Note: There is a known bug on Windows that causes the saving of the model to fail when you early terminate the training, it's recommended to wait until Step has reached the max_steps parameter you set in your config YAML.) You can now embed this trained model into your Agent by following the steps below:</p> <ol> <li>Move your model file into    <code>Project/Assets/ML-Agents/Examples/3DBall/TFModels/</code>.</li> <li>Open the Unity Editor, and select the 3DBall scene as described above.</li> <li>Select the 3DBall prefab from the Project window and select Agent.</li> <li>Drag the <code>&lt;behavior_name&gt;.onnx</code> file from the Project window of the Editor to    the Model placeholder in the Ball3DAgent inspector window.</li> <li>Press the Play button at the top of the Editor.</li> </ol>"},{"location":"Learning-Environment-Executable/#training-on-headless-server","title":"Training on Headless Server","text":"<p>To run training on headless server with no graphics rendering support, you need to turn off graphics display in the Unity executable. There are two ways to achieve this: 1. Pass <code>--no-graphics</code> option to mlagents-learn training command. This is equivalent to    adding <code>-nographics -batchmode</code> to the Unity executable's commandline. 2. Build your Unity executable with Server Build. You can find this setting in Build Settings    in the Unity Editor.</p> <p>If you want to train with graphics (for example, using camera and visual observations), you'll need to set up display rendering support (e.g. xvfb) on you server machine. In our Colab Notebook Tutorials, the Setup section has examples of setting up xvfb on servers.</p>"},{"location":"Limitations/","title":"Limitations","text":"<p>See the package-specific Limitations pages:</p> <ul> <li><code>com.unity.mlagents</code> Unity package</li> <li><code>mlagents</code> Python package</li> <li><code>mlagents_envs</code> Python package</li> </ul>"},{"location":"ML-Agents-Envs-README/","title":"Unity ML-Agents Python Interface","text":"<p>The <code>mlagents_envs</code> Python package is part of the ML-Agents Toolkit. <code>mlagents_envs</code> provides three Python APIs that allows direct interaction with the Unity game engine: - A single agent API (Gym API) - A gym-like multi-agent API (PettingZoo API) - A low-level API (LLAPI)</p> <p>The LLAPI is used by the trainer implementation in <code>mlagents</code>. <code>mlagents_envs</code> can be used independently of <code>mlagents</code> for Python communication.</p>"},{"location":"ML-Agents-Envs-README/#installation","title":"Installation","text":"<p>Install the <code>mlagents_envs</code> package with:</p> <pre><code>python -m pip install mlagents_envs==0.30.0\n</code></pre>"},{"location":"ML-Agents-Envs-README/#usage-more-information","title":"Usage &amp; More Information","text":"<p>See - Gym API Guide - PettingZoo API Guide - Python API Guide</p> <p>for more information on how to use the API to interact with a Unity environment.</p> <p>For more information on the ML-Agents Toolkit and how to instrument a Unity scene with the ML-Agents SDK, check out the main ML-Agents Toolkit documentation.</p>"},{"location":"ML-Agents-Envs-README/#limitations","title":"Limitations","text":"<ul> <li><code>mlagents_envs</code> uses localhost ports to exchange data between Unity and   Python. As such, multiple instances can have their ports collide, leading to   errors. Make sure to use a different port if you are using multiple instances   of <code>UnityEnvironment</code>.</li> <li>Communication between Unity and the Python <code>UnityEnvironment</code> is not secure.</li> <li>On Linux, ports are not released immediately after the communication closes.   As such, you cannot reuse ports right after closing a <code>UnityEnvironment</code>.</li> </ul>"},{"location":"ML-Agents-Overview/","title":"ML-Agents Toolkit Overview","text":"<p>Table of Contents</p> <ul> <li>Running Example: Training NPC Behaviors</li> <li>Key Components</li> <li>Training Modes</li> <li>Built-in Training and Inference<ul> <li>Cross-Platform Inference</li> </ul> </li> <li>Custom Training and Inference</li> <li>Flexible Training Scenarios</li> <li>Training Methods: Environment-agnostic</li> <li>A Quick Note on Reward Signals</li> <li>Deep Reinforcement Learning<ul> <li>Curiosity for Sparse-reward Environments</li> <li>RND for Sparse-reward Environments</li> </ul> </li> <li>Imitation Learning<ul> <li>GAIL (Generative Adversarial Imitation Learning)</li> <li>Behavioral Cloning (BC)</li> <li>Recording Demonstrations</li> </ul> </li> <li>Summary</li> <li>Training Methods: Environment-specific</li> <li>Training in Competitive Multi-Agent Environments with Self-Play</li> <li>Training in Cooperative Multi-Agent Environments with MA-POCA</li> <li>Solving Complex Tasks using Curriculum Learning</li> <li>Training Robust Agents using Environment Parameter Randomization</li> <li>Model Types</li> <li>Learning from Vector Observations</li> <li>Learning from Cameras using Convolutional Neural Networks</li> <li>Learning from Variable Length Observations using Attention</li> <li>Memory-enhanced Agents using Recurrent Neural Networks</li> <li>Additional Features</li> <li>Summary and Next Steps</li> </ul> <p>The Unity Machine Learning Agents Toolkit (ML-Agents Toolkit) is an open-source project that enables games and simulations to serve as environments for training intelligent agents. Agents can be trained using reinforcement learning, imitation learning, neuroevolution, or other machine learning methods through a simple-to-use Python API. We also provide implementations (based on PyTorch) of state-of-the-art algorithms to enable game developers and hobbyists to easily train intelligent agents for 2D, 3D and VR/AR games. These trained agents can be used for multiple purposes, including controlling NPC behavior (in a variety of settings such as multi-agent and adversarial), automated testing of game builds and evaluating different game design decisions pre-release. The ML-Agents Toolkit is mutually beneficial for both game developers and AI researchers as it provides a central platform where advances in AI can be evaluated on Unity\u2019s rich environments and then made accessible to the wider research and game developer communities.</p> <p>Depending on your background (i.e. researcher, game developer, hobbyist), you may have very different questions on your mind at the moment. To make your transition to the ML-Agents Toolkit easier, we provide several background pages that include overviews and helpful resources on the Unity Engine, machine learning and PyTorch. We strongly recommend browsing the relevant background pages if you're not familiar with a Unity scene, basic machine learning concepts or have not previously heard of PyTorch.</p> <p>The remainder of this page contains a deep dive into ML-Agents, its key components, different training modes and scenarios. By the end of it, you should have a good sense of what the ML-Agents Toolkit allows you to do. The subsequent documentation pages provide examples of how to use ML-Agents. To get started, watch this demo video of ML-Agents in action.</p>"},{"location":"ML-Agents-Overview/#running-example-training-npc-behaviors","title":"Running Example: Training NPC Behaviors","text":"<p>To help explain the material and terminology in this page, we'll use a hypothetical, running example throughout. We will explore the problem of training the behavior of a non-playable character (NPC) in a game. (An NPC is a game character that is never controlled by a human player and its behavior is pre-defined by the game developer.) More specifically, let's assume we're building a multi-player, war-themed game in which players control the soldiers. In this game, we have a single NPC who serves as a medic, finding and reviving wounded players. Lastly, let us assume that there are two teams, each with five players and one NPC medic.</p> <p>The behavior of a medic is quite complex. It first needs to avoid getting injured, which requires detecting when it is in danger and moving to a safe location. Second, it needs to be aware of which of its team members are injured and require assistance. In the case of multiple injuries, it needs to assess the degree of injury and decide who to help first. Lastly, a good medic will always place itself in a position where it can quickly help its team members. Factoring in all of these traits means that at every instance, the medic needs to measure several attributes of the environment (e.g. position of team members, position of enemies, which of its team members are injured and to what degree) and then decide on an action (e.g. hide from enemy fire, move to help one of its members). Given the large number of settings of the environment and the large number of actions that the medic can take, defining and implementing such complex behaviors by hand is challenging and prone to errors.</p> <p>With ML-Agents, it is possible to train the behaviors of such NPCs (called Agents) using a variety of methods. The basic idea is quite simple. We need to define three entities at every moment of the game (called environment):</p> <ul> <li>Observations - what the medic perceives about the environment.   Observations can be numeric and/or visual. Numeric observations measure   attributes of the environment from the point of view of the agent. For our   medic this would be attributes of the battlefield that are visible to it. For   most interesting environments, an agent will require several continuous   numeric observations. Visual observations, on the other hand, are images   generated from the cameras attached to the agent and represent what the agent   is seeing at that point in time. It is common to confuse an agent's   observation with the environment (or game) state. The environment state   represents information about the entire scene containing all the game   characters. The agents observation, however, only contains information that   the agent is aware of and is typically a subset of the environment state. For   example, the medic observation cannot include information about an enemy in   hiding that the medic is unaware of.</li> <li>Actions - what actions the medic can take. Similar to observations,   actions can either be continuous or discrete depending on the complexity of   the environment and agent. In the case of the medic, if the environment is a   simple grid world where only their location matters, then a discrete action   taking on one of four values (north, south, east, west) suffices. However, if   the environment is more complex and the medic can move freely then using two   continuous actions (one for direction and another for speed) is more   appropriate.</li> <li>Reward signals - a scalar value indicating how well the medic is doing.   Note that the reward signal need not be provided at every moment, but only   when the medic performs an action that is good or bad. For example, it can   receive a large negative reward if it dies, a modest positive reward whenever   it revives a wounded team member, and a modest negative reward when a wounded   team member dies due to lack of assistance. Note that the reward signal is how   the objectives of the task are communicated to the agent, so they need to be   set up in a manner where maximizing reward generates the desired optimal   behavior.</li> </ul> <p>After defining these three entities (the building blocks of a reinforcement learning task), we can now train the medic's behavior. This is achieved by simulating the environment for many trials where the medic, over time, learns what is the optimal action to take for every observation it measures by maximizing its future reward. The key is that by learning the actions that maximize its reward, the medic is learning the behaviors that make it a good medic (i.e. one who saves the most number of lives). In reinforcement learning terminology, the behavior that is learned is called a policy, which is essentially a (optimal) mapping from observations to actions. Note that the process of learning a policy through running simulations is called the training phase, while playing the game with an NPC that is using its learned policy is called the inference phase.</p> <p>The ML-Agents Toolkit provides all the necessary tools for using Unity as the simulation engine for learning the policies of different objects in a Unity environment. In the next few sections, we discuss how the ML-Agents Toolkit achieves this and what features it provides.</p>"},{"location":"ML-Agents-Overview/#key-components","title":"Key Components","text":"<p>The ML-Agents Toolkit contains five high-level components:</p> <ul> <li>Learning Environment - which contains the Unity scene and all the game   characters. The Unity scene provides the environment in which agents observe,   act, and learn. How you set up the Unity scene to serve as a learning   environment really depends on your goal. You may be trying to solve a specific   reinforcement learning problem of limited scope, in which case you can use the   same scene for both training and for testing trained agents. Or, you may be   training agents to operate in a complex game or simulation. In this case, it   might be more efficient and practical to create a purpose-built training   scene. The ML-Agents Toolkit includes an ML-Agents Unity SDK   (<code>com.unity.ml-agents</code> package) that enables you to transform any Unity scene   into a learning environment by defining the agents and their behaviors.</li> <li>Python Low-Level API - which contains a low-level Python interface for   interacting and manipulating a learning environment. Note that, unlike the   Learning Environment, the Python API is not part of Unity, but lives outside   and communicates with Unity through the Communicator. This API is contained in   a dedicated <code>mlagents_envs</code> Python package and is used by the Python training   process to communicate with and control the Academy during training. However,   it can be used for other purposes as well. For example, you could use the API   to use Unity as the simulation engine for your own machine learning   algorithms. See Python API for more information.</li> <li>External Communicator - which connects the Learning Environment with the   Python Low-Level API. It lives within the Learning Environment.</li> <li>Python Trainers which contains all the machine learning algorithms that   enable training agents. The algorithms are implemented in Python and are part   of their own <code>mlagents</code> Python package. The package exposes a single   command-line utility <code>mlagents-learn</code> that supports all the training methods   and options outlined in this document. The Python Trainers interface solely   with the Python Low-Level API.</li> <li>Gym Wrapper (not pictured). A common way in which machine learning   researchers interact with simulation environments is via a wrapper provided by   OpenAI called gym. We provide a gym wrapper   in the <code>ml-agents-envs</code> package and instructions for using   it with existing machine learning algorithms which utilize gym.</li> <li>PettingZoo Wrapper (not pictured) PettingZoo is python API for   interacting with multi-agent simulation environments that provides a   gym-like interface. We provide a PettingZoo wrapper for Unity ML-Agents   environments in the <code>ml-agents-envs</code> package and   instructions for using it with machine learning   algorithms.</li> </ul> <p> </p> <p>Simplified block diagram of ML-Agents.</p> <p>The Learning Environment contains two Unity Components that help organize the Unity scene:</p> <ul> <li>Agents - which is attached to a Unity GameObject (any character within a   scene) and handles generating its observations, performing the actions it   receives and assigning a reward (positive / negative) when appropriate. Each   Agent is linked to a Behavior.</li> <li>Behavior - defines specific attributes of the agent such as the number of   actions that agent can take. Each Behavior is uniquely identified by a   <code>Behavior Name</code> field. A Behavior can be thought as a function that receives   observations and rewards from the Agent and returns actions. A Behavior can be   of one of three types: Learning, Heuristic or Inference. A Learning Behavior   is one that is not, yet, defined but about to be trained. A Heuristic Behavior   is one that is defined by a hard-coded set of rules implemented in code. An   Inference Behavior is one that includes a trained Neural Network file. In   essence, after a Learning Behavior is trained, it becomes an Inference   Behavior.</li> </ul> <p>Every Learning Environment will always have one Agent for every character in the scene. While each Agent must be linked to a Behavior, it is possible for Agents that have similar observations and actions to have the same Behavior. In our sample game, we have two teams each with their own medic. Thus we will have two Agents in our Learning Environment, one for each medic, but both of these medics can have the same Behavior. This does not mean that at each instance they will have identical observation and action values.</p> <p> </p> <p>Example block diagram of ML-Agents Toolkit for our sample game.</p> <p>Note that in a single environment, there can be multiple Agents and multiple Behaviors at the same time. For example, if we expanded our game to include tank driver NPCs, then the Agent attached to those characters cannot share its Behavior with the Agent linked to the medics (medics and drivers have different actions). The Learning Environment through the Academy (not represented in the diagram) ensures that all the Agents are in sync in addition to controlling environment-wide settings.</p> <p>Lastly, it is possible to exchange data between Unity and Python outside of the machine learning loop through Side Channels. One example of using Side Channels is to exchange data with Python about Environment Parameters. The following diagram illustrates the above.</p> <p> </p>"},{"location":"ML-Agents-Overview/#training-modes","title":"Training Modes","text":"<p>Given the flexibility of ML-Agents, there are a few ways in which training and inference can proceed.</p>"},{"location":"ML-Agents-Overview/#built-in-training-and-inference","title":"Built-in Training and Inference","text":"<p>As mentioned previously, the ML-Agents Toolkit ships with several implementations of state-of-the-art algorithms for training intelligent agents. More specifically, during training, all the medics in the scene send their observations to the Python API through the External Communicator. The Python API processes these observations and sends back actions for each medic to take. During training these actions are mostly exploratory to help the Python API learn the best policy for each medic. Once training concludes, the learned policy for each medic can be exported as a model file. Then during the inference phase, the medics still continue to generate their observations, but instead of being sent to the Python API, they will be fed into their (internal, embedded) model to generate the optimal action for each medic to take at every point in time.</p> <p>The Getting Started Guide tutorial covers this training mode with the 3D Balance Ball sample environment.</p>"},{"location":"ML-Agents-Overview/#cross-platform-inference","title":"Cross-Platform Inference","text":"<p>It is important to note that the ML-Agents Toolkit leverages the Unity Inference Engine to run the models within a Unity scene such that an agent can take the optimal action at each step. Given that the Unity Inference Engine support most platforms that Unity does, this means that any model you train with the ML-Agents Toolkit can be embedded into your Unity application that runs on any platform. See our dedicated blog post for additional information.</p>"},{"location":"ML-Agents-Overview/#custom-training-and-inference","title":"Custom Training and Inference","text":"<p>In the previous mode, the Agents were used for training to generate a PyTorch model that the Agents can later use. However, any user of the ML-Agents Toolkit can leverage their own algorithms for training. In this case, the behaviors of all the Agents in the scene will be controlled within Python. You can even turn your environment into a gym.</p> <p>We do not currently have a tutorial highlighting this mode, but you can learn more about the Python API here.</p>"},{"location":"ML-Agents-Overview/#flexible-training-scenarios","title":"Flexible Training Scenarios","text":"<p>While the discussion so-far has mostly focused on training a single agent, with ML-Agents, several training scenarios are possible. We are excited to see what kinds of novel and fun environments the community creates. For those new to training intelligent agents, below are a few examples that can serve as inspiration:</p> <ul> <li>Single-Agent. A single agent, with its own reward signal. The traditional way   of training an agent. An example is any single-player game, such as Chicken.</li> <li>Simultaneous Single-Agent. Multiple independent agents with independent reward   signals with same <code>Behavior Parameters</code>. A parallelized version of the   traditional training scenario, which can speed-up and stabilize the training   process. Helpful when you have multiple versions of the same character in an   environment who should learn similar behaviors. An example might be training a   dozen robot-arms to each open a door simultaneously.</li> <li>Adversarial Self-Play. Two interacting agents with inverse reward signals. In   two-player games, adversarial self-play can allow an agent to become   increasingly more skilled, while always having the perfectly matched opponent:   itself. This was the strategy employed when training AlphaGo, and more   recently used by OpenAI to train a human-beating 1-vs-1 Dota 2 agent.</li> <li>Cooperative Multi-Agent. Multiple interacting agents with a shared reward   signal with same or different <code>Behavior Parameters</code>. In this scenario, all   agents must work together to accomplish a task that cannot be done alone.   Examples include environments where each agent only has access to partial   information, which needs to be shared in order to accomplish the task or   collaboratively solve a puzzle.</li> <li>Competitive Multi-Agent. Multiple interacting agents with inverse reward   signals with same or different <code>Behavior Parameters</code>. In this scenario, agents   must compete with one another to either win a competition, or obtain some   limited set of resources. All team sports fall into this scenario.</li> <li>Ecosystem. Multiple interacting agents with independent reward signals with   same or different <code>Behavior Parameters</code>. This scenario can be thought of as   creating a small world in which animals with different goals all interact,   such as a savanna in which there might be zebras, elephants and giraffes, or   an autonomous driving simulation within an urban environment.</li> </ul>"},{"location":"ML-Agents-Overview/#training-methods-environment-agnostic","title":"Training Methods: Environment-agnostic","text":"<p>The remaining sections overview the various state-of-the-art machine learning algorithms that are part of the ML-Agents Toolkit. If you aren't studying machine and reinforcement learning as a subject and just want to train agents to accomplish tasks, you can treat these algorithms as black boxes. There are a few training-related parameters to adjust inside Unity as well as on the Python training side, but you do not need in-depth knowledge of the algorithms themselves to successfully create and train agents. Step-by-step procedures for running the training process are provided in the Training ML-Agents page.</p> <p>This section specifically focuses on the training methods that are available regardless of the specifics of your learning environment.</p>"},{"location":"ML-Agents-Overview/#a-quick-note-on-reward-signals","title":"A Quick Note on Reward Signals","text":"<p>In this section we introduce the concepts of intrinsic and extrinsic rewards, which helps explain some of the training methods.</p> <p>In reinforcement learning, the end goal for the Agent is to discover a behavior (a Policy) that maximizes a reward. You will need to provide the agent one or more reward signals to use during training. Typically, a reward is defined by your environment, and corresponds to reaching some goal. These are what we refer to as extrinsic rewards, as they are defined external of the learning algorithm.</p> <p>Rewards, however, can be defined outside of the environment as well, to encourage the agent to behave in certain ways, or to aid the learning of the true extrinsic reward. We refer to these rewards as intrinsic reward signals. The total reward that the agent will learn to maximize can be a mix of extrinsic and intrinsic reward signals.</p> <p>The ML-Agents Toolkit allows reward signals to be defined in a modular way, and we provide four reward signals that can the mixed and matched to help shape your agent's behavior:</p> <ul> <li><code>extrinsic</code>: represents the rewards defined in your environment, and is   enabled by default</li> <li><code>gail</code>: represents an intrinsic reward signal that is defined by GAIL (see   below)</li> <li><code>curiosity</code>: represents an intrinsic reward signal that encourages exploration   in sparse-reward environments that is defined by the Curiosity module (see   below).</li> <li><code>rnd</code>: represents an intrinsic reward signal that encourages exploration   in sparse-reward environments that is defined by the Curiosity module (see   below).</li> </ul>"},{"location":"ML-Agents-Overview/#deep-reinforcement-learning","title":"Deep Reinforcement Learning","text":"<p>ML-Agents provide an implementation of two reinforcement learning algorithms:</p> <ul> <li>Proximal Policy Optimization (PPO)</li> <li>Soft Actor-Critic (SAC)</li> </ul> <p>The default algorithm is PPO. This is a method that has been shown to be more general purpose and stable than many other RL algorithms.</p> <p>In contrast with PPO, SAC is off-policy, which means it can learn from experiences collected at any time during the past. As experiences are collected, they are placed in an experience replay buffer and randomly drawn during training. This makes SAC significantly more sample-efficient, often requiring 5-10 times less samples to learn the same task as PPO. However, SAC tends to require more model updates. SAC is a good choice for heavier or slower environments (about 0.1 seconds per step or more). SAC is also a \"maximum entropy\" algorithm, and enables exploration in an intrinsic way. Read more about maximum entropy RL here.</p>"},{"location":"ML-Agents-Overview/#curiosity-for-sparse-reward-environments","title":"Curiosity for Sparse-reward Environments","text":"<p>In environments where the agent receives rare or infrequent rewards (i.e. sparse-reward), an agent may never receive a reward signal on which to bootstrap its training process. This is a scenario where the use of an intrinsic reward signals can be valuable. Curiosity is one such signal which can help the agent explore when extrinsic rewards are sparse.</p> <p>The <code>curiosity</code> Reward Signal enables the Intrinsic Curiosity Module. This is an implementation of the approach described in Curiosity-driven Exploration by Self-supervised Prediction by Pathak, et al. It trains two networks:</p> <ul> <li>an inverse model, which takes the current and next observation of the agent,   encodes them, and uses the encoding to predict the action that was taken   between the observations</li> <li>a forward model, which takes the encoded current observation and action, and   predicts the next encoded observation.</li> </ul> <p>The loss of the forward model (the difference between the predicted and actual encoded observations) is used as the intrinsic reward, so the more surprised the model is, the larger the reward will be.</p> <p>For more information, see our dedicated blog post on the Curiosity module.</p>"},{"location":"ML-Agents-Overview/#rnd-for-sparse-reward-environments","title":"RND for Sparse-reward Environments","text":"<p>Similarly to Curiosity, Random Network Distillation (RND) is useful in sparse or rare reward environments as it helps the Agent explore. The RND Module is implemented following the paper Exploration by Random Network Distillation. RND uses two networks:  - The first is a network with fixed random weights that takes observations as inputs and  generates an encoding  - The second is a network with similar architecture that is trained to predict the  outputs of the first network and uses the observations the Agent collects as training data.</p> <p>The loss (the squared difference between the predicted and actual encoded observations) of the trained model is used as intrinsic reward. The more an Agent visits a state, the more accurate the predictions and the lower the rewards which encourages the Agent to explore new states with higher prediction errors.</p>"},{"location":"ML-Agents-Overview/#imitation-learning","title":"Imitation Learning","text":"<p>It is often more intuitive to simply demonstrate the behavior we want an agent to perform, rather than attempting to have it learn via trial-and-error methods. For example, instead of indirectly training a medic with the help of a reward function, we can give the medic real world examples of observations from the game and actions from a game controller to guide the medic's behavior. Imitation Learning uses pairs of observations and actions from a demonstration to learn a policy. See this video demo of imitation learning .</p> <p>Imitation learning can either be used alone or in conjunction with reinforcement learning. If used alone it can provide a mechanism for learning a specific type of behavior (i.e. a specific style of solving the task). If used in conjunction with reinforcement learning it can dramatically reduce the time the agent takes to solve the environment. This can be especially pronounced in sparse-reward environments. For instance, on the Pyramids environment, using 6 episodes of demonstrations can reduce training steps by more than 4 times. See Behavioral Cloning + GAIL + Curiosity + RL below.</p> <p> </p> <p>The ML-Agents Toolkit provides a way to learn directly from demonstrations, as well as use them to help speed up reward-based training (RL). We include two algorithms called Behavioral Cloning (BC) and Generative Adversarial Imitation Learning (GAIL). In most scenarios, you can combine these two features:</p> <ul> <li>If you want to help your agents learn (especially with environments that have   sparse rewards) using pre-recorded demonstrations, you can generally enable   both GAIL and Behavioral Cloning at low strengths in addition to having an   extrinsic reward. An example of this is provided for the PushBlock example   environment in <code>config/imitation/PushBlock.yaml</code>.</li> <li>If you want to train purely from demonstrations with GAIL and BC without an   extrinsic reward signal, please see the CrawlerStatic example environment under   in <code>config/imitation/CrawlerStatic.yaml</code>.</li> </ul> <p>Note: GAIL introduces a survivor bias to the learning process. That is, by giving positive rewards based on similarity to the expert, the agent is incentivized to remain alive for as long as possible. This can directly conflict with goal-oriented tasks like our PushBlock or Pyramids example environments where an agent must reach a goal state thus ending the episode as quickly as possible. In these cases, we strongly recommend that you use a low strength GAIL reward signal and a sparse extrinisic signal when the agent achieves the task. This way, the GAIL reward signal will guide the agent until it discovers the extrnisic signal and will not overpower it. If the agent appears to be ignoring the extrinsic reward signal, you should reduce the strength of GAIL.</p>"},{"location":"ML-Agents-Overview/#gail-generative-adversarial-imitation-learning","title":"GAIL (Generative Adversarial Imitation Learning)","text":"<p>GAIL, or Generative Adversarial Imitation Learning, uses an adversarial approach to reward your Agent for behaving similar to a set of demonstrations. GAIL can be used with or without environment rewards, and works well when there are a limited number of demonstrations. In this framework, a second neural network, the discriminator, is taught to distinguish whether an observation/action is from a demonstration or produced by the agent. This discriminator can then examine a new observation/action and provide it a reward based on how close it believes this new observation/action is to the provided demonstrations.</p> <p>At each training step, the agent tries to learn how to maximize this reward. Then, the discriminator is trained to better distinguish between demonstrations and agent state/actions. In this way, while the agent gets better and better at mimicking the demonstrations, the discriminator keeps getting stricter and stricter and the agent must try harder to \"fool\" it.</p> <p>This approach learns a policy that produces states and actions similar to the demonstrations, requiring fewer demonstrations than direct cloning of the actions. In addition to learning purely from demonstrations, the GAIL reward signal can be mixed with an extrinsic reward signal to guide the learning process.</p>"},{"location":"ML-Agents-Overview/#behavioral-cloning-bc","title":"Behavioral Cloning (BC)","text":"<p>BC trains the Agent's policy to exactly mimic the actions shown in a set of demonstrations. The BC feature can be enabled on the PPO or SAC trainers. As BC cannot generalize past the examples shown in the demonstrations, BC tends to work best when there exists demonstrations for nearly all of the states that the agent can experience, or in conjunction with GAIL and/or an extrinsic reward.</p>"},{"location":"ML-Agents-Overview/#recording-demonstrations","title":"Recording Demonstrations","text":"<p>Demonstrations of agent behavior can be recorded from the Unity Editor or build, and saved as assets. These demonstrations contain information on the observations, actions, and rewards for a given agent during the recording session. They can be managed in the Editor, as well as used for training with BC and GAIL. See the Designing Agents page for more information on how to record demonstrations for your agent.</p>"},{"location":"ML-Agents-Overview/#summary","title":"Summary","text":"<p>To summarize, we provide 3 training methods: BC, GAIL and RL (PPO or SAC) that can be used independently or together:</p> <ul> <li>BC can be used on its own or as a pre-training step before GAIL and/or RL</li> <li>GAIL can be used with or without extrinsic rewards</li> <li>RL can be used on its own (either PPO or SAC) or in conjunction with BC and/or   GAIL.</li> </ul> <p>Leveraging either BC or GAIL requires recording demonstrations to be provided as input to the training algorithms.</p>"},{"location":"ML-Agents-Overview/#training-methods-environment-specific","title":"Training Methods: Environment-specific","text":"<p>In addition to the three environment-agnostic training methods introduced in the previous section, the ML-Agents Toolkit provides additional methods that can aid in training behaviors for specific types of environments.</p>"},{"location":"ML-Agents-Overview/#training-in-competitive-multi-agent-environments-with-self-play","title":"Training in Competitive Multi-Agent Environments with Self-Play","text":"<p>ML-Agents provides the functionality to train both symmetric and asymmetric adversarial games with Self-Play. A symmetric game is one in which opposing agents are equal in form, function and objective. Examples of symmetric games are our Tennis and Soccer example environments. In reinforcement learning, this means both agents have the same observation and actions and learn from the same reward function and so they can share the same policy. In asymmetric games, this is not the case. An example of an asymmetric games are Hide and Seek. Agents in these types of games do not always have the same observation or actions and so sharing policy networks is not necessarily ideal.</p> <p>With self-play, an agent learns in adversarial games by competing against fixed, past versions of its opponent (which could be itself as in symmetric games) to provide a more stable, stationary learning environment. This is compared to competing against the current, best opponent in every episode, which is constantly changing (because it's learning).</p> <p>Self-play can be used with our implementations of both Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC). However, from the perspective of an individual agent, these scenarios appear to have non-stationary dynamics because the opponent is often changing. This can cause significant issues in the experience replay mechanism used by SAC. Thus, we recommend that users use PPO. For further reading on this issue in particular, see the paper Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning.</p> <p>See our Designing Agents page for more information on setting up teams in your Unity scene. Also, read our blog post on self-play for additional information.</p>"},{"location":"ML-Agents-Overview/#training-in-cooperative-multi-agent-environments-with-ma-poca","title":"Training In Cooperative Multi-Agent Environments with MA-POCA","text":"<p>ML-Agents provides the functionality for training cooperative behaviors - i.e., groups of agents working towards a common goal, where the success of the individual is linked to the success of the whole group. In such a scenario, agents typically receive rewards as a group. For instance, if a team of agents wins a game against an opposing team, everyone is rewarded - even agents who did not directly contribute to the win. This makes learning what to do as an individual difficult - you may get a win for doing nothing, and a loss for doing your best.</p> <p>In ML-Agents, we provide MA-POCA (MultiAgent POsthumous Credit Assignment), which is a novel multi-agent trainer that trains a centralized critic, a neural network that acts as a \"coach\" for a whole group of agents. You can then give rewards to the team as a whole, and the agents will learn how best to contribute to achieving that reward. Agents can also be given rewards individually, and the team will work together to help the individual achieve those goals. During an episode, agents can be added or removed from the group, such as when agents spawn or die in a game. If agents are removed mid-episode (e.g., if teammates die or are removed from the game), they will still learn whether their actions contributed to the team winning later, enabling agents to take group-beneficial actions even if they result in the individual being removed from the game (i.e., self-sacrifice). MA-POCA can also be combined with self-play to train teams of agents to play against each other.</p> <p>To learn more about enabling cooperative behaviors for agents in an ML-Agents environment, check out this page.</p> <p>To learn more about MA-POCA, please see our paper On the Use and Misuse of Absorbing States in Multi-Agent Reinforcement Learning. For further reading, MA-POCA builds on previous work in multi-agent cooperative learning (Lowe et al., Foerster et al., among others) to enable the above use-cases.</p>"},{"location":"ML-Agents-Overview/#solving-complex-tasks-using-curriculum-learning","title":"Solving Complex Tasks using Curriculum Learning","text":"<p>Curriculum learning is a way of training a machine learning model where more difficult aspects of a problem are gradually introduced in such a way that the model is always optimally challenged. This idea has been around for a long time, and it is how we humans typically learn. If you imagine any childhood primary school education, there is an ordering of classes and topics. Arithmetic is taught before algebra, for example. Likewise, algebra is taught before calculus. The skills and knowledge learned in the earlier subjects provide a scaffolding for later lessons. The same principle can be applied to machine learning, where training on easier tasks can provide a scaffolding for harder tasks in the future.</p> <p>Imagine training the medic to scale a wall to arrive at a wounded team member. The starting point when training a medic to accomplish this task will be a random policy. That starting policy will have the medic running in circles, and will likely never, or very rarely scale the wall properly to revive their team member (and achieve the reward). If we start with a simpler task, such as moving toward an unobstructed team member, then the medic can easily learn to accomplish the task. From there, we can slowly add to the difficulty of the task by increasing the size of the wall until the medic can complete the initially near-impossible task of scaling the wall. We have included an environment to demonstrate this with ML-Agents, called Wall Jump.</p> <p></p> <p>Demonstration of a hypothetical curriculum training scenario in which a progressively taller wall obstructs the path to the goal.</p> <p>[Note: The example provided above is for instructional purposes, and was based on an early version of the Wall Jump example environment. As such, it is not possible to directly replicate the results here using that environment.]</p> <p>The ML-Agents Toolkit supports modifying custom environment parameters during the training process to aid in learning. This allows elements of the environment related to difficulty or complexity to be dynamically adjusted based on training progress. The Training ML-Agents page has more information on defining training curriculums.</p>"},{"location":"ML-Agents-Overview/#training-robust-agents-using-environment-parameter-randomization","title":"Training Robust Agents using Environment Parameter Randomization","text":"<p>An agent trained on a specific environment, may be unable to generalize to any tweaks or variations in the environment (in machine learning this is referred to as overfitting). This becomes problematic in cases where environments are instantiated with varying objects or properties. One mechanism to alleviate this and train more robust agents that can generalize to unseen variations of the environment is to expose them to these variations during training. Similar to Curriculum Learning, where environments become more difficult as the agent learns, the ML-Agents Toolkit provides a way to randomly sample parameters of the environment during training. We refer to this approach as Environment Parameter Randomization. For those familiar with Reinforcement Learning research, this approach is based on the concept of Domain Randomization. By using parameter randomization during training, the agent can be better suited to adapt (with higher performance) to future unseen variations of the environment.</p> Ball scale of 0.5 Ball scale of 4 <p>Example of variations of the 3D Ball environment. The environment parameters are <code>gravity</code>, <code>ball_mass</code> and <code>ball_scale</code>.</p>"},{"location":"ML-Agents-Overview/#model-types","title":"Model Types","text":"<p>Regardless of the training method deployed, there are a few model types that users can train using the ML-Agents Toolkit. This is due to the flexibility in defining agent observations, which include vector, ray cast and visual observations. You can learn more about how to instrument an agent's observation in the Designing Agents guide.</p>"},{"location":"ML-Agents-Overview/#learning-from-vector-observations","title":"Learning from Vector Observations","text":"<p>Whether an agent's observations are ray cast or vector, the ML-Agents Toolkit provides a fully connected neural network model to learn from those observations. At training time you can configure different aspects of this model such as the number of hidden units and number of layers.</p>"},{"location":"ML-Agents-Overview/#learning-from-cameras-using-convolutional-neural-networks","title":"Learning from Cameras using Convolutional Neural Networks","text":"<p>Unlike other platforms, where the agent\u2019s observation might be limited to a single vector or image, the ML-Agents Toolkit allows multiple cameras to be used for observations per agent. This enables agents to learn to integrate information from multiple visual streams. This can be helpful in several scenarios such as training a self-driving car which requires multiple cameras with different viewpoints, or a navigational agent which might need to integrate aerial and first-person visuals. You can learn more about adding visual observations to an agent here.</p> <p>When visual observations are utilized, the ML-Agents Toolkit leverages convolutional neural networks (CNN) to learn from the input images. We offer three network architectures:</p> <ul> <li>a simple encoder which consists of two convolutional layers</li> <li>the implementation proposed by   Mnih et al., consisting of   three convolutional layers,</li> <li>the IMPALA Resnet consisting of three   stacked layers, each with two residual blocks, making a much larger network   than the other two.</li> </ul> <p>The choice of the architecture depends on the visual complexity of the scene and the available computational resources.</p>"},{"location":"ML-Agents-Overview/#learning-from-variable-length-observations-using-attention","title":"Learning from Variable Length Observations using Attention","text":"<p>Using the ML-Agents Toolkit, it is possible to have agents learn from a varying number of inputs. To do so, each agent can keep track of a buffer of vector observations. At each step, the agent will go through all the elements in the buffer and extract information but the elements in the buffer can change at every step. This can be useful in scenarios in which the agents must keep track of a varying number of elements throughout the episode. For example in a game where an agent must learn to avoid projectiles, but the projectiles can vary in numbers.</p> <p></p> <p>You can learn more about variable length observations here. When variable length observations are utilized, the ML-Agents Toolkit leverages attention networks to learn from a varying number of entities. Agents using attention will ignore entities that are deemed not relevant and pay special attention to entities relevant to the current situation based on context.</p>"},{"location":"ML-Agents-Overview/#memory-enhanced-agents-using-recurrent-neural-networks","title":"Memory-enhanced Agents using Recurrent Neural Networks","text":"<p>Have you ever entered a room to get something and immediately forgot what you were looking for? Don't let that happen to your agents.</p> <p></p> <p>In some scenarios, agents must learn to remember the past in order to take the best decision. When an agent only has partial observability of the environment, keeping track of past observations can help the agent learn. Deciding what the agents should remember in order to solve a task is not easy to do by hand, but our training algorithms can learn to keep track of what is important to remember with LSTM.</p>"},{"location":"ML-Agents-Overview/#additional-features","title":"Additional Features","text":"<p>Beyond the flexible training scenarios available, the ML-Agents Toolkit includes additional features which improve the flexibility and interpretability of the training process.</p> <ul> <li>Concurrent Unity Instances - We enable developers to run concurrent,   parallel instances of the Unity executable during training. For certain   scenarios, this should speed up training. Check out our dedicated page on   creating a Unity executable and the   Training ML-Agents   page for instructions on how to set the number of concurrent instances.</li> <li>Recording Statistics from Unity - We enable developers to   record statistics from   within their Unity environments. These statistics are aggregated and generated   during the training process.</li> <li>Custom Side Channels - We enable developers to   create custom side channels to manage data transfer   between Unity and Python that is unique to their training workflow and/or   environment.</li> <li>Custom Samplers - We enable developers to   create custom sampling methods   for Environment Parameter Randomization. This enables users to customize this   training method for their particular environment.</li> </ul>"},{"location":"ML-Agents-Overview/#summary-and-next-steps","title":"Summary and Next Steps","text":"<p>To briefly summarize: The ML-Agents Toolkit enables games and simulations built in Unity to serve as the platform for training intelligent agents. It is designed to enable a large variety of training modes and scenarios and comes packed with several features to enable researchers and developers to leverage (and enhance) machine learning within Unity.</p> <p>In terms of next steps:</p> <ul> <li>For a walkthrough of running ML-Agents with a simple scene, check out the   Getting Started guide.</li> <li>For a \"Hello World\" introduction to creating your own Learning Environment,   check out the   Making a New Learning Environment page.</li> <li>For an overview on the more complex example environments that are provided in   this toolkit, check out the   Example Environments page.</li> <li>For more information on the various training options available, check out the   Training ML-Agents page.</li> </ul>"},{"location":"ML-Agents-README/","title":"Unity ML-Agents Trainers","text":"<p>The <code>mlagents</code> Python package is part of the ML-Agents Toolkit. <code>mlagents</code> provides a set of reinforcement and imitation learning algorithms designed to be used with Unity environments. The algorithms interface with the Python API provided by the <code>mlagents_envs</code> package. See here for more information on <code>mlagents_envs</code>.</p> <p>The algorithms can be accessed using the: <code>mlagents-learn</code> access point. See here for more information on using this package.</p>"},{"location":"ML-Agents-README/#installation","title":"Installation","text":"<p>Install the <code>mlagents</code> package with:</p> <pre><code>python -m pip install mlagents==0.30.0\n</code></pre>"},{"location":"ML-Agents-README/#usage-more-information","title":"Usage &amp; More Information","text":"<p>For more information on the ML-Agents Toolkit and how to instrument a Unity scene with the ML-Agents SDK, check out the main ML-Agents Toolkit documentation.</p>"},{"location":"ML-Agents-README/#limitations","title":"Limitations","text":"<ul> <li>Resuming self-play from a checkpoint resets the reported ELO to the default   value.</li> </ul>"},{"location":"ML-Agents-Toolkit-Documentation/","title":"Unity ML-Agents Toolkit Documentation","text":""},{"location":"ML-Agents-Toolkit-Documentation/#installation-set-up","title":"Installation &amp; Set-up","text":"<ul> <li>Installation</li> <li>Using Virtual Environment</li> </ul>"},{"location":"ML-Agents-Toolkit-Documentation/#getting-started","title":"Getting Started","text":"<ul> <li>Getting Started Guide</li> <li>ML-Agents Toolkit Overview</li> <li>Background: Unity</li> <li>Background: Machine Learning</li> <li>Background: PyTorch</li> <li>Example Environments</li> </ul>"},{"location":"ML-Agents-Toolkit-Documentation/#creating-learning-environments","title":"Creating Learning Environments","text":"<ul> <li>Making a New Learning Environment</li> <li>Designing a Learning Environment</li> <li>Designing Agents</li> <li>Using an Executable Environment</li> <li>ML-Agents Package Settings</li> </ul>"},{"location":"ML-Agents-Toolkit-Documentation/#training-inference","title":"Training &amp; Inference","text":"<ul> <li>Training ML-Agents</li> <li>Training Configuration File</li> <li>Using TensorBoard to Observe Training</li> <li>Profiling Trainers</li> <li>Unity Inference Engine</li> </ul>"},{"location":"ML-Agents-Toolkit-Documentation/#extending-ml-agents","title":"Extending ML-Agents","text":"<ul> <li>Creating Custom Side Channels</li> <li>Creating Custom Samplers for Environment Parameter Randomization</li> </ul>"},{"location":"ML-Agents-Toolkit-Documentation/#python-tutorial-with-google-colab","title":"Python Tutorial with Google Colab","text":"<ul> <li>Using a UnityEnvironment</li> <li>Q-Learning with a UnityEnvironment</li> <li>Using Side Channels on a UnityEnvironment</li> </ul>"},{"location":"ML-Agents-Toolkit-Documentation/#help","title":"Help","text":"<ul> <li>Migrating from earlier versions of ML-Agents</li> <li>Frequently Asked Questions</li> <li>ML-Agents Glossary</li> <li>Limitations</li> </ul>"},{"location":"ML-Agents-Toolkit-Documentation/#api-docs","title":"API Docs","text":"<ul> <li>API Reference</li> <li>Python API Documentation</li> <li>How to use the Python API</li> <li>How to use the Unity Environment Registry</li> <li>Wrapping Learning Environment as a Gym (+Baselines/Dopamine Integration)</li> </ul>"},{"location":"ML-Agents-Toolkit-Documentation/#translations","title":"Translations","text":"<p>To make the Unity ML-Agents Toolkit accessible to the global research and Unity developer communities, we're attempting to create and maintain translations of our documentation. We've started with translating a subset of the documentation to one language (Chinese), but we hope to continue translating more pages and to other languages. Consequently, we welcome any enhancements and improvements from the community.</p> <ul> <li>Chinese</li> <li>Korean</li> </ul>"},{"location":"ML-Agents-Toolkit-Documentation/#deprecated-docs","title":"Deprecated Docs","text":"<p>We no longer use them ourselves and so they may not be up-to-date. We've decided to keep them up just in case they are helpful to you.</p> <ul> <li>Windows Anaconda Installation</li> <li>Using Docker</li> <li>Training on the Cloud with Amazon Web Services</li> <li>Training on the Cloud with Microsoft Azure</li> <li>Using the Video Recorder</li> </ul>"},{"location":"Migrating/","title":"Upgrading","text":""},{"location":"Migrating/#migrating","title":"Migrating","text":""},{"location":"Migrating/#migrating-to-the-ml-agents-envs-0290-package","title":"Migrating to the ml-agents-envs 0.29.0 package","text":"<ul> <li>Python 3.8 is now the minimum version of python supported due to python3.6 EOL.   Please update your python installation to 3.8.13 or higher.</li> <li>The <code>gym-unity</code> package has been refactored into the <code>ml-agents-envs</code> package. Please update your imports accordingly.</li> <li>Example:</li> <li>Before</li> </ul> <pre><code>from gym_unity.unity_gym_env import UnityToGymWrapper\n</code></pre> <ul> <li>After:</li> </ul> <pre><code>from mlagents_envs.envs.unity_gym_env import UnityToGymWrapper\n</code></pre>"},{"location":"Migrating/#migrating-the-package-to-version-20","title":"Migrating the package to version 2.0","text":"<ul> <li>The official version of Unity ML-Agents supports is now 2021.3 LTS. If you run   into issues, please consider deleting your project's Library folder and reponening your   project.</li> <li>If you used any of the APIs that were deprecated before version 2.0, you need to use their replacement. These deprecated APIs have been removed. See the migration steps bellow for specific API replacements.</li> </ul>"},{"location":"Migrating/#deprecated-methods-removed","title":"Deprecated methods removed","text":"Deprecated API Suggested Replacement <code>IActuator ActuatorComponent.CreateActuator()</code> <code>IActuator[] ActuatorComponent.CreateActuators()</code> <code>IActionReceiver.PackActions(in float[] destination)</code> none <code>Agent.CollectDiscreteActionMasks(DiscreteActionMasker actionMasker)</code> <code>Agent.WriteDiscreteActionMask(IDiscreteActionMask actionMask)</code> <code>Agent.Heuristic(float[] actionsOut)</code> <code>Agent.Heuristic(in ActionBuffers actionsOut)</code> <code>Agent.OnActionReceived(float[] vectorAction)</code> <code>Agent.OnActionReceived(ActionBuffers actions)</code> <code>Agent.GetAction()</code> <code>Agent.GetStoredActionBuffers()</code> <code>BrainParameters.SpaceType</code>, <code>VectorActionSize</code>, <code>VectorActionSpaceType</code>, and <code>NumActions</code> <code>BrainParameters.ActionSpec</code> <code>ObservationWriter.AddRange(IEnumerable&lt;float&gt; data, int writeOffset = 0)</code> <code>ObservationWriter. AddList(IList&lt;float&gt; data, int writeOffset = 0</code> <code>SensorComponent.IsVisual()</code> and <code>IsVector()</code> none <code>VectorSensor.AddObservation(IEnumerable&lt;float&gt; observation)</code> <code>VectorSensor.AddObservation(IList&lt;float&gt; observation)</code> <code>SideChannelsManager</code> <code>SideChannelManager</code>"},{"location":"Migrating/#idiscreteactionmask-changes","title":"IDiscreteActionMask changes","text":"<ul> <li>The interface for disabling specific discrete actions has changed. <code>IDiscreteActionMask.WriteMask()</code> was removed, and replaced with <code>SetActionEnabled()</code>. Instead of returning an IEnumerable with indices to disable, you can now call <code>SetActionEnabled</code> for each index to disable (or enable). As an example, if you overrode <code>Agent.WriteDiscreteActionMask()</code> with something that looked like:</li> </ul> <pre><code>public override void WriteDiscreteActionMask(IDiscreteActionMask actionMask)\n{\n    var branch = 2;\n    var actionsToDisable = new[] {1, 3};\n    actionMask.WriteMask(branch, actionsToDisable);\n}\n</code></pre> <p>the equivalent code would now be</p> <pre><code>public override void WriteDiscreteActionMask(IDiscreteActionMask actionMask)\n{\n    var branch = 2;\n    actionMask.SetActionEnabled(branch, 1, false);\n    actionMask.SetActionEnabled(branch, 3, false);\n}\n</code></pre>"},{"location":"Migrating/#iactuator-changes","title":"IActuator changes","text":"<ul> <li>The <code>IActuator</code> interface now implements <code>IHeuristicProvider</code>.  Please add the corresponding <code>Heuristic(in ActionBuffers)</code> method to your custom Actuator classes.</li> </ul>"},{"location":"Migrating/#isensor-and-sensorcomponent-changes","title":"ISensor and SensorComponent changes","text":"<ul> <li>The <code>ISensor.GetObservationShape()</code> method and <code>ITypedSensor</code> and <code>IDimensionPropertiesSensor</code> interfaces were removed, and <code>GetObservationSpec()</code> was added. You can use <code>ObservationSpec.Vector()</code> or <code>ObservationSpec.Visual()</code> to generate <code>ObservationSpec</code>s that are equivalent to the previous shape. For example, if your old ISensor looked like:</li> </ul> <pre><code>public override int[] GetObservationShape()\n{\n    return new[] { m_Height, m_Width, m_NumChannels };\n}\n</code></pre> <p>the equivalent code would now be</p> <pre><code>public override ObservationSpec GetObservationSpec()\n{\n    return ObservationSpec.Visual(m_Height, m_Width, m_NumChannels);\n}\n</code></pre> <ul> <li>The <code>ISensor.GetCompressionType()</code> method and <code>ISparseChannelSensor</code> interface was removed, and <code>GetCompressionSpec()</code> was added. You can use <code>CompressionSpec.Default()</code> or <code>CompressionSpec.Compressed()</code> to generate <code>CompressionSpec</code>s that are  equivalent to  the previous values. For example, if your old ISensor looked like:  ```csharp public virtual SensorCompressionType GetCompressionType() {     return SensorCompressionType.None; }</li> </ul> <pre><code>\nthe equivalent code would now be\n\n```csharp\npublic CompressionSpec GetCompressionSpec()\n{\n    return CompressionSpec.Default();\n}\n</code></pre> <ul> <li>The abstract method <code>SensorComponent.GetObservationShape()</code> was removed.</li> <li>The abstract method <code>SensorComponent.CreateSensor()</code> was replaced with <code>CreateSensors()</code>, which returns an <code>ISensor[]</code>.</li> </ul>"},{"location":"Migrating/#match3-integration-changes","title":"Match3 integration changes","text":"<p>The Match-3 integration utilities were moved from <code>com.unity.ml-agents.extensions</code> to <code>com.unity.ml-agents</code>.</p> <p>The <code>AbstractBoard</code> interface was changed: * <code>AbstractBoard</code> no longer contains <code>Rows</code>, <code>Columns</code>, <code>NumCellTypes</code>, and <code>NumSpecialTypes</code> fields. * <code>public abstract BoardSize GetMaxBoardSize()</code> was added as an abstract method. <code>BoardSize</code> is a new struct that contains <code>Rows</code>, <code>Columns</code>, <code>NumCellTypes</code>, and <code>NumSpecialTypes</code> fields, with the same meanings as the old <code>AbstractBoard</code> fields. * <code>public virtual BoardSize GetCurrentBoardSize()</code> is an optional method; by default it returns <code>GetMaxBoardSize()</code>. If you wish to use a single behavior to work with multiple board sizes, override <code>GetCurrentBoardSize()</code> to return the current <code>BoardSize</code>. The values returned by <code>GetCurrentBoardSize()</code> must be less than or equal to the corresponding values from <code>GetMaxBoardSize()</code>.</p>"},{"location":"Migrating/#gridsensor-changes","title":"GridSensor changes","text":"<p>The sensor configuration has changed: * The sensor implementation has been refactored and exsisting GridSensor created from extension package will not work in newer version. Some errors might show up when loading the old sensor in the scene. You'll need to remove the old sensor and create a new GridSensor. * These parameters names have changed but still refer to the same concept in the sensor: <code>GridNumSide</code> -&gt; <code>GridSize</code>, <code>RotateToAgent</code> -&gt; <code>RotateWithAgent</code>, <code>ObserveMask</code> -&gt; <code>ColliderMask</code>, <code>DetectableObjects</code> -&gt; <code>DetectableTags</code> * <code>DepthType</code> (<code>ChanelBase</code>/<code>ChannelHot</code>) option and <code>ChannelDepth</code> are removed. Now the default is one-hot encoding for detected tag. If you were using original GridSensor without overriding any method, switching to new GridSensor will produce similar effect for training although the actual observations will be slightly different.</p> <p>For creating your GridSensor implementation with custom data: * To create custom GridSensor, derive from <code>GridSensorBase</code> instead of <code>GridSensor</code>. Besides overriding <code>GetObjectData()</code>, you will also need to consider override <code>GetCellObservationSize()</code>, <code>IsDataNormalized()</code> and <code>GetProcessCollidersMethod()</code> according to the data you collect. Also you'll need to override <code>GridSensorComponent.GetGridSensors()</code> and return your custom GridSensor. * The input argument <code>tagIndex</code> in <code>GetObjectData()</code> has changed from 1-indexed to 0-indexed and the data type changed from <code>float</code> to <code>int</code>. The index of first detectable tag will be 0 instead of 1. <code>normalizedDistance</code> was removed from input. * The observation data should be written to the input <code>dataBuffer</code> instead of creating and returning a new array. * Removed the constraint of all data required to be normalized. You should specify it in <code>IsDataNormalized()</code>. Sensors with non-normalized data cannot use PNG compression type. * The sensor will not further encode the data recieved from <code>GetObjectData()</code> anymore. The values recieved from <code>GetObjectData()</code> will be the observation sent to the trainer.</p>"},{"location":"Migrating/#lstm-models-from-previous-releases-no-longer-supported","title":"LSTM models from previous releases no longer supported","text":"<p>The way the Unity Inference Engine processes LSTM (recurrent neural networks) has changed. As a result, models trained with previous versions of ML-Agents will not be usable at inference if they were trained with a <code>memory</code> setting in the <code>.yaml</code> config file. If you want to use a model that has a recurrent neural network in this release of ML-Agents, you need to train the model using the python trainer from this release.</p>"},{"location":"Migrating/#migrating-to-release-13","title":"Migrating to Release 13","text":""},{"location":"Migrating/#implementing-iheuristic-in-your-iactuator-implementations","title":"Implementing IHeuristic in your IActuator implementations","text":"<ul> <li>If you have any custom actuators, you can now implement the <code>IHeuristicProvider</code> interface to have your actuator   handle the generation of actions when an Agent is running in heuristic mode.</li> <li><code>VectorSensor.AddObservation(IEnumerable&lt;float&gt;)</code> is deprecated. Use <code>VectorSensor.AddObservation(IList&lt;float&gt;)</code>   instead.</li> <li><code>ObservationWriter.AddRange()</code> is deprecated. Use <code>ObservationWriter.AddList()</code> instead.</li> <li><code>ActuatorComponent.CreateAcuator()</code> is deprecated.  Please use override <code>ActuatorComponent.CreateActuators</code>   instead.  Since <code>ActuatorComponent.CreateActuator()</code> is abstract, you will still need to override it in your   class until it is removed.  It is only ever called if you don't override <code>ActuatorComponent.CreateActuators</code>.   You can suppress the warnings by surrounding the method with the following pragma:     <code>c#     #pragma warning disable 672     public IActuator CreateActuator() { ... }     #pragma warning restore 672</code></li> </ul>"},{"location":"Migrating/#migrating_1","title":"Migrating","text":""},{"location":"Migrating/#migrating-to-release-11","title":"Migrating to Release 11","text":""},{"location":"Migrating/#agent-virtual-method-deprecation","title":"Agent virtual method deprecation","text":"<ul> <li><code>Agent.CollectDiscreteActionMasks()</code> was deprecated and should be replaced with <code>Agent.WriteDiscreteActionMask()</code></li> <li><code>Agent.Heuristic(float[])</code> was deprecated and should be replaced with <code>Agent.Heuristic(ActionBuffers)</code>.</li> <li><code>Agent.OnActionReceived(float[])</code> was deprecated and should be replaced with <code>Agent.OnActionReceived(ActionBuffers)</code>.</li> <li><code>Agent.GetAction()</code> was deprecated and should be replaced with <code>Agent.GetStoredActionBuffers()</code>.</li> </ul> <p>The default implementation of these will continue to call the deprecated versions where appropriate. However, the deprecated versions may not be compatible with continuous and discrete actions on the same Agent.</p>"},{"location":"Migrating/#brainparameters-field-and-method-deprecation","title":"BrainParameters field and method deprecation","text":"<ul> <li><code>BrainParameters.VectorActionSize</code> was deprecated; you can now set <code>BrainParameters.ActionSpec.NumContinuousActions</code>  or <code>BrainParameters.ActionSpec.BranchSizes</code> instead.</li> <li><code>BrainParameters.VectorActionSpaceType</code> was deprecated, since both continuous and discrete actions can now be used.</li> <li><code>BrainParameters.NumActions()</code> was deprecated. Use  <code>BrainParameters.ActionSpec.NumContinuousActions</code> and  <code>BrainParameters.ActionSpec.NumDiscreteActions</code> instead.</li> </ul>"},{"location":"Migrating/#migrating-from-release-7-to-latest","title":"Migrating from Release 7 to latest","text":""},{"location":"Migrating/#important-changes","title":"Important changes","text":"<ul> <li>Some trainer files were moved. If you were using the <code>TrainerFactory</code> class, it was moved to the <code>trainers/trainer</code> folder.</li> <li>The <code>components</code> folder containing <code>bc</code> and <code>reward_signals</code> code was moved to the <code>trainers/tf</code> folder</li> </ul>"},{"location":"Migrating/#steps-to-migrate","title":"Steps to Migrate","text":"<ul> <li>Replace calls to <code>from mlagents.trainers.trainer_util import TrainerFactory</code> to <code>from mlagents.trainers.trainer import TrainerFactory</code></li> <li>Replace calls to <code>from mlagents.trainers.trainer_util import handle_existing_directories</code> to <code>from mlagents.trainers.directory_utils import validate_existing_directories</code></li> <li>Replace <code>mlagents.trainers.components</code> with <code>mlagents.trainers.tf.components</code> in your import statements.</li> </ul>"},{"location":"Migrating/#migrating-from-release-3-to-release-7","title":"Migrating from Release 3 to Release 7","text":""},{"location":"Migrating/#important-changes_1","title":"Important changes","text":"<ul> <li>The Parameter Randomization feature has been merged with the Curriculum feature. It is now possible to specify a sampler in the lesson of a Curriculum. Curriculum has been refactored and is now specified at the level of the parameter, not the behavior. More information here.(#4160)</li> </ul>"},{"location":"Migrating/#steps-to-migrate_1","title":"Steps to Migrate","text":"<ul> <li>The configuration format for curriculum and parameter randomization has changed. To upgrade your configuration files, an upgrade script has been provided. Run <code>python -m mlagents.trainers.upgrade_config -h</code> to see the script usage. Note that you will have had to upgrade to/install the current version of ML-Agents before running the script. To update manually:</li> <li>If your config file used a <code>parameter_randomization</code> section, rename that section to <code>environment_parameters</code></li> <li>If your config file used a <code>curriculum</code> section, you will need to rewrite your curriculum with this format.</li> </ul>"},{"location":"Migrating/#migrating-from-release-1-to-release-3","title":"Migrating from Release 1 to Release 3","text":""},{"location":"Migrating/#important-changes_2","title":"Important changes","text":"<ul> <li>Training artifacts (trained models, summaries) are now found under <code>results/</code>   instead of <code>summaries/</code> and <code>models/</code>.</li> <li>Trainer configuration, curriculum configuration, and parameter randomization   configuration have all been moved to a single YAML file. (#3791)</li> <li>Trainer configuration format has changed, and using a \"default\" behavior name has   been deprecated. (#3936)</li> <li><code>max_step</code> in the <code>TerminalStep</code> and <code>TerminalSteps</code> objects was renamed <code>interrupted</code>.</li> <li>On the UnityEnvironment API, <code>get_behavior_names()</code> and <code>get_behavior_specs()</code> methods were combined into the property <code>behavior_specs</code> that contains a mapping from behavior names to behavior spec.</li> <li><code>use_visual</code> and <code>allow_multiple_visual_obs</code> in the <code>UnityToGymWrapper</code> constructor were replaced by <code>allow_multiple_obs</code> which allows one or more visual observations and vector observations to be used simultaneously.</li> <li><code>--save-freq</code> has been removed from the CLI and is now configurable in the trainer configuration   file.</li> <li><code>--lesson</code> has been removed from the CLI. Lessons will resume when using <code>--resume</code>.   To start at a different lesson, modify your Curriculum configuration.</li> </ul>"},{"location":"Migrating/#steps-to-migrate_2","title":"Steps to Migrate","text":"<ul> <li>To upgrade your configuration files, an upgrade script has been provided. Run   <code>python -m mlagents.trainers.upgrade_config -h</code> to see the script usage. Note that you will have   had to upgrade to/install the current version of ML-Agents before running the script.</li> </ul> <p>To do it manually, copy your <code>&lt;BehaviorName&gt;</code> sections from <code>trainer_config.yaml</code> into a separate trainer configuration file, under a <code>behaviors</code> section.   The <code>default</code> section is no longer needed. This new file should be specific to your environment, and not contain   configurations for multiple environments (unless they have the same Behavior Names).   - You will need to reformat your trainer settings as per the example.   - If your training uses curriculum, move those configurations under a <code>curriculum</code> section.   - If your training uses parameter randomization, move   the contents of the sampler config to <code>parameter_randomization</code> in the main trainer configuration. - If you are using <code>UnityEnvironment</code> directly, replace <code>max_step</code> with <code>interrupted</code>  in the <code>TerminalStep</code> and <code>TerminalSteps</code> objects.  - Replace usage of <code>get_behavior_names()</code> and <code>get_behavior_specs()</code> in UnityEnvironment with <code>behavior_specs</code>.  - If you use the <code>UnityToGymWrapper</code>, remove <code>use_visual</code> and <code>allow_multiple_visual_obs</code>  from the constructor and add <code>allow_multiple_obs = True</code> if the environment contains either  both visual and vector observations or multiple visual observations.  - If you were setting <code>--save-freq</code> in the CLI, add a <code>checkpoint_interval</code> value in your   trainer configuration, and set it equal to <code>save-freq * n_agents_in_scene</code>.</p>"},{"location":"Migrating/#migrating-from-015-to-release-1","title":"Migrating from 0.15 to Release 1","text":""},{"location":"Migrating/#important-changes_3","title":"Important changes","text":"<ul> <li>The <code>MLAgents</code> C# namespace was renamed to <code>Unity.MLAgents</code>, and other nested   namespaces were similarly renamed (#3843).</li> <li>The <code>--load</code> and <code>--train</code> command-line flags have been deprecated and   replaced with <code>--resume</code> and <code>--inference</code>.</li> <li>Running with the same <code>--run-id</code> twice will now throw an error.</li> <li>The <code>play_against_current_self_ratio</code> self-play trainer hyperparameter has   been renamed to <code>play_against_latest_model_ratio</code></li> <li>Removed the multi-agent gym option from the gym wrapper. For multi-agent   scenarios, use the Low Level Python API.</li> <li>The low level Python API has changed. You can look at the document   Low Level Python API documentation for more information. If   you use <code>mlagents-learn</code> for training, this should be a transparent change.</li> <li>The obsolete <code>Agent</code> methods <code>GiveModel</code>, <code>Done</code>, <code>InitializeAgent</code>,   <code>AgentAction</code> and <code>AgentReset</code> have been removed.</li> <li>The signature of <code>Agent.Heuristic()</code> was changed to take a <code>float[]</code> as a   parameter, instead of returning the array. This was done to prevent a common   source of error where users would return arrays of the wrong size.</li> <li>The SideChannel API has changed (#3833, #3660) :</li> <li>Introduced the <code>SideChannelManager</code> to register, unregister and access side     channels.</li> <li><code>EnvironmentParameters</code> replaces the default <code>FloatProperties</code>. You can     access the <code>EnvironmentParameters</code> with     <code>Academy.Instance.EnvironmentParameters</code> on C#. If you were previously     creating a <code>UnityEnvironment</code> in python and passing it a     <code>FloatPropertiesChannel</code>, create an <code>EnvironmentParametersChannel</code> instead.</li> <li><code>SideChannel.OnMessageReceived</code> is now a protected method (was public)</li> <li>SideChannel IncomingMessages methods now take an optional default argument,     which is used when trying to read more data than the message contains.</li> <li>Added a feature to allow sending stats from C# environments to TensorBoard     (and other python StatsWriters). To do this from your code, use     <code>Academy.Instance.StatsRecorder.Add(key, value)</code>(#3660)</li> <li><code>num_updates</code> and <code>train_interval</code> for SAC have been replaced with   <code>steps_per_update</code>.</li> <li>The <code>UnityEnv</code> class from the <code>gym-unity</code> package was renamed   <code>UnityToGymWrapper</code> and no longer creates the <code>UnityEnvironment</code>. Instead, the   <code>UnityEnvironment</code> must be passed as input to the constructor of   <code>UnityToGymWrapper</code></li> <li>Public fields and properties on several classes were renamed to follow Unity's   C# style conventions. All public fields and properties now use \"PascalCase\"   instead of \"camelCase\"; for example, <code>Agent.maxStep</code> was renamed to   <code>Agent.MaxStep</code>. For a full list of changes, see the pull request. (#3828)</li> <li><code>WriteAdapter</code> was renamed to <code>ObservationWriter</code>. (#3834)</li> </ul>"},{"location":"Migrating/#steps-to-migrate_3","title":"Steps to Migrate","text":"<ul> <li>In C# code, replace <code>using MLAgents</code> with <code>using Unity.MLAgents</code>. Replace   other nested namespaces such as <code>using MLAgents.Sensors</code> with   <code>using Unity.MLAgents.Sensors</code></li> <li>Replace the <code>--load</code> flag with <code>--resume</code> when calling <code>mlagents-learn</code>, and   don't use the <code>--train</code> flag as training will happen by default. To run   without training, use <code>--inference</code>.</li> <li>To force-overwrite files from a pre-existing run, add the <code>--force</code>   command-line flag.</li> <li>The Jupyter notebooks have been removed from the repository.</li> <li>If your Agent class overrides <code>Heuristic()</code>, change the signature to   <code>public override void Heuristic(float[] actionsOut)</code> and assign values to   <code>actionsOut</code> instead of returning an array.</li> <li>If you used <code>SideChannels</code> you must:</li> <li>Replace <code>Academy.FloatProperties</code> with     <code>Academy.Instance.EnvironmentParameters</code>.</li> <li><code>Academy.RegisterSideChannel</code> and <code>Academy.UnregisterSideChannel</code> were     removed. Use <code>SideChannelManager.RegisterSideChannel</code> and     <code>SideChannelManager.UnregisterSideChannel</code> instead.</li> <li>Set <code>steps_per_update</code> to be around equal to the number of agents in your   environment, times <code>num_updates</code> and divided by <code>train_interval</code>.</li> <li>Replace <code>UnityEnv</code> with <code>UnityToGymWrapper</code> in your code. The constructor no   longer takes a file name as input but a fully constructed <code>UnityEnvironment</code>   instead.</li> <li>Update uses of \"camelCase\" fields and properties to \"PascalCase\".</li> </ul>"},{"location":"Migrating/#migrating-from-014-to-015","title":"Migrating from 0.14 to 0.15","text":""},{"location":"Migrating/#important-changes_4","title":"Important changes","text":"<ul> <li>The <code>Agent.CollectObservations()</code> virtual method now takes as input a   <code>VectorSensor</code> sensor as argument. The <code>Agent.AddVectorObs()</code> methods were   removed.</li> <li>The <code>SetMask</code> was renamed to <code>SetMask</code> method must now be called on the   <code>DiscreteActionMasker</code> argument of the <code>CollectDiscreteActionMasks</code> virtual   method.</li> <li>We consolidated our API for <code>DiscreteActionMasker</code>. <code>SetMask</code> takes two   arguments : the branch index and the list of masked actions for that branch.</li> <li>The <code>Monitor</code> class has been moved to the Examples Project. (It was prone to   errors during testing)</li> <li>The <code>MLAgents.Sensors</code> namespace has been introduced. All sensors classes are   part of the <code>MLAgents.Sensors</code> namespace.</li> <li>The <code>MLAgents.SideChannels</code> namespace has been introduced. All side channel   classes are part of the <code>MLAgents.SideChannels</code> namespace.</li> <li>The interface for <code>RayPerceptionSensor.PerceiveStatic()</code> was changed to take   an input class and write to an output class, and the method was renamed to   <code>Perceive()</code>.</li> <li>The <code>SetMask</code> method must now be called on the <code>DiscreteActionMasker</code> argument   of the <code>CollectDiscreteActionMasks</code> method.</li> <li>The method <code>GetStepCount()</code> on the Agent class has been replaced with the   property getter <code>StepCount</code></li> <li>The <code>--multi-gpu</code> option has been removed temporarily.</li> <li><code>AgentInfo.actionMasks</code> has been renamed to <code>AgentInfo.discreteActionMasks</code>.</li> <li><code>BrainParameters</code> and <code>SpaceType</code> have been removed from the public API</li> <li><code>BehaviorParameters</code> have been removed from the public API.</li> <li><code>DecisionRequester</code> has been made internal (you can still use the   DecisionRequesterComponent from the inspector). <code>RepeatAction</code> was renamed   <code>TakeActionsBetweenDecisions</code> for clarity.</li> <li>The following methods in the <code>Agent</code> class have been renamed. The original   method names will be removed in a later release:</li> <li><code>InitializeAgent()</code> was renamed to <code>Initialize()</code></li> <li><code>AgentAction()</code> was renamed to <code>OnActionReceived()</code></li> <li><code>AgentReset()</code> was renamed to <code>OnEpsiodeBegin()</code></li> <li><code>Done()</code> was renamed to <code>EndEpisode()</code></li> <li><code>GiveModel()</code> was renamed to <code>SetModel()</code></li> <li>The <code>IFloatProperties</code> interface has been removed.</li> <li>The interface for SideChannels was changed:</li> <li>In C#, <code>OnMessageReceived</code> now takes a <code>IncomingMessage</code> argument, and     <code>QueueMessageToSend</code> takes an <code>OutgoingMessage</code> argument.</li> <li>In python, <code>on_message_received</code> now takes a <code>IncomingMessage</code> argument, and     <code>queue_message_to_send</code> takes an <code>OutgoingMessage</code> argument.</li> <li>Automatic stepping for Academy is now controlled from the     AutomaticSteppingEnabled property.</li> </ul>"},{"location":"Migrating/#steps-to-migrate_4","title":"Steps to Migrate","text":"<ul> <li>Add the <code>using MLAgents.Sensors;</code> in addition to <code>using MLAgents;</code> on top of   your Agent's script.</li> <li>Replace your Agent's implementation of <code>CollectObservations()</code> with   <code>CollectObservations(VectorSensor sensor)</code>. In addition, replace all calls to   <code>AddVectorObs()</code> with <code>sensor.AddObservation()</code> or   <code>sensor.AddOneHotObservation()</code> on the <code>VectorSensor</code> passed as argument.</li> <li>Replace your calls to <code>SetActionMask</code> on your Agent to   <code>DiscreteActionMasker.SetActionMask</code> in <code>CollectDiscreteActionMasks</code>.</li> <li>If you call <code>RayPerceptionSensor.PerceiveStatic()</code> manually, add your inputs   to a <code>RayPerceptionInput</code>. To get the previous float array output, iterate   through <code>RayPerceptionOutput.rayOutputs</code> and call   <code>RayPerceptionOutput.RayOutput.ToFloatArray()</code>.</li> <li>Replace all calls to <code>Agent.GetStepCount()</code> with <code>Agent.StepCount</code></li> <li>We strongly recommend replacing the following methods with their new   equivalent as they will be removed in a later release:</li> <li><code>InitializeAgent()</code> to <code>Initialize()</code></li> <li><code>AgentAction()</code> to <code>OnActionReceived()</code></li> <li><code>AgentReset()</code> to <code>OnEpisodeBegin()</code></li> <li><code>Done()</code> to <code>EndEpisode()</code></li> <li><code>GiveModel()</code> to <code>SetModel()</code></li> <li>Replace <code>IFloatProperties</code> variables with <code>FloatPropertiesChannel</code> variables.</li> <li>If you implemented custom <code>SideChannels</code>, update the signatures of your   methods, and add your data to the <code>OutgoingMessage</code> or read it from the   <code>IncomingMessage</code>.</li> <li>Replace calls to Academy.EnableAutomaticStepping()/DisableAutomaticStepping()   with Academy.AutomaticSteppingEnabled = true/false.</li> </ul>"},{"location":"Migrating/#migrating-from-013-to-014","title":"Migrating from 0.13 to 0.14","text":""},{"location":"Migrating/#important-changes_5","title":"Important changes","text":"<ul> <li>The <code>UnitySDK</code> folder has been split into a Unity Package   (<code>com.unity.ml-agents</code>) and an examples project (<code>Project</code>). Please follow the   Installation Guide to get up and running with this new repo   structure.</li> <li>Several changes were made to how agents are reset and marked as done:</li> <li>Calling <code>Done()</code> on the Agent will now reset it immediately and call the     <code>AgentReset</code> virtual method. (This is to simplify the previous logic in     which the Agent had to wait for the next <code>EnvironmentStep</code> to reset)</li> <li>The \"Reset on Done\" setting in AgentParameters was removed; this is now     effectively always true. <code>AgentOnDone</code> virtual method on the Agent has been     removed.</li> <li>The <code>Decision Period</code> and <code>On Demand decision</code> checkbox have been removed from   the Agent. On demand decision is now the default (calling <code>RequestDecision</code> on   the Agent manually.)</li> <li>The Academy class was changed to a singleton, and its virtual methods were   removed.</li> <li>Trainer steps are now counted per-Agent, not per-environment as in previous   versions. For instance, if you have 10 Agents in the scene, 20 environment   steps now corresponds to 200 steps as printed in the terminal and in   Tensorboard.</li> <li>Curriculum config files are now YAML formatted and all curricula for a   training run are combined into a single file.</li> <li>The <code>--num-runs</code> command-line option has been removed from <code>mlagents-learn</code>.</li> <li>Several fields on the Agent were removed or made private in order to simplify   the interface.</li> <li>The <code>agentParameters</code> field of the Agent has been removed. (Contained only     <code>maxStep</code> information)</li> <li><code>maxStep</code> is now a public field on the Agent. (Was moved from     <code>agentParameters</code>)</li> <li>The <code>Info</code> field of the Agent has been made private. (Was only used     internally and not meant to be modified outside of the Agent)</li> <li>The <code>GetReward()</code> method on the Agent has been removed. (It was being     confused with <code>GetCumulativeReward()</code>)</li> <li>The <code>AgentAction</code> struct no longer contains a <code>value</code> field. (Value     estimates were not set during inference)</li> <li>The <code>GetValueEstimate()</code> method on the Agent has been removed.</li> <li>The <code>UpdateValueAction()</code> method on the Agent has been removed.</li> <li>The deprecated <code>RayPerception3D</code> and <code>RayPerception2D</code> classes were removed,   and the <code>legacyHitFractionBehavior</code> argument was removed from   <code>RayPerceptionSensor.PerceiveStatic()</code>.</li> <li>RayPerceptionSensor was inconsistent in how it handle scale on the Agent's   transform. It now scales the ray length and sphere size for casting as the   transform's scale changes.</li> </ul>"},{"location":"Migrating/#steps-to-migrate_5","title":"Steps to Migrate","text":"<ul> <li>Follow the instructions on how to install the <code>com.unity.ml-agents</code> package   into your project in the Installation Guide.</li> <li>If your Agent implemented <code>AgentOnDone</code> and did not have the checkbox   <code>Reset On Done</code> checked in the inspector, you must call the code that was in   <code>AgentOnDone</code> manually.</li> <li>If you give your Agent a reward or penalty at the end of an episode (e.g. for   reaching a goal or falling off of a platform), make sure you call   <code>AddReward()</code> or <code>SetReward()</code> before calling <code>Done()</code>. Previously, the   order didn't matter.</li> <li>If you were not using <code>On Demand Decision</code> for your Agent, you must add a   <code>DecisionRequester</code> component to your Agent GameObject and set its   <code>Decision Period</code> field to the old <code>Decision Period</code> of the Agent.</li> <li>If you have a class that inherits from Academy:</li> <li>If the class didn't override any of the virtual methods and didn't store any     additional data, you can just remove the old script from the scene.</li> <li>If the class had additional data, create a new MonoBehaviour and store the     data in the new MonoBehaviour instead.</li> <li>If the class overrode the virtual methods, create a new MonoBehaviour and     move the logic to it:<ul> <li>Move the InitializeAcademy code to MonoBehaviour.Awake</li> <li>Move the AcademyStep code to MonoBehaviour.FixedUpdate</li> <li>Move the OnDestroy code to MonoBehaviour.OnDestroy.</li> <li>Move the AcademyReset code to a new method and add it to the   Academy.OnEnvironmentReset action.</li> </ul> </li> <li>Multiply <code>max_steps</code> and <code>summary_freq</code> in your <code>trainer_config.yaml</code> by the   number of Agents in the scene.</li> <li>Combine curriculum configs into a single file. See   the WallJump curricula for an example of   the new curriculum config format. A tool like https://www.json2yaml.com may be   useful to help with the conversion.</li> <li>If you have a model trained which uses RayPerceptionSensor and has non-1.0   scale in the Agent's transform, it must be retrained.</li> </ul>"},{"location":"Migrating/#migrating-from-ml-agents-toolkit-v0120-to-v0130","title":"Migrating from ML-Agents Toolkit v0.12.0 to v0.13.0","text":""},{"location":"Migrating/#important-changes_6","title":"Important changes","text":"<ul> <li>The low level Python API has changed. You can look at the document   Low Level Python API documentation for more information. This   should only affect you if you're writing a custom trainer; if you use   <code>mlagents-learn</code> for training, this should be a transparent change.</li> <li><code>reset()</code> on the Low-Level Python API no longer takes a <code>train_mode</code>     argument. To modify the performance/speed of the engine, you must use an     <code>EngineConfigurationChannel</code></li> <li><code>reset()</code> on the Low-Level Python API no longer takes a <code>config</code> argument.     <code>UnityEnvironment</code> no longer has a <code>reset_parameters</code> field. To modify float     properties in the environment, you must use a <code>FloatPropertiesChannel</code>. For     more information, refer to the     Low Level Python API documentation</li> <li><code>CustomResetParameters</code> are now removed.</li> <li>The Academy no longer has a <code>Training Configuration</code> nor   <code>Inference Configuration</code> field in the inspector. To modify the configuration   from the Low-Level Python API, use an <code>EngineConfigurationChannel</code>. To modify   it during training, use the new command line arguments <code>--width</code>, <code>--height</code>,   <code>--quality-level</code>, <code>--time-scale</code> and <code>--target-frame-rate</code> in   <code>mlagents-learn</code>.</li> <li>The Academy no longer has a <code>Default Reset Parameters</code> field in the inspector.   The Academy class no longer has a <code>ResetParameters</code>. To access shared float   properties with Python, use the new <code>FloatProperties</code> field on the Academy.</li> <li>Offline Behavioral Cloning has been removed. To learn from demonstrations, use   the GAIL and Behavioral Cloning features with either PPO or SAC.</li> <li><code>mlagents.envs</code> was renamed to <code>mlagents_envs</code>. The previous repo layout   depended on PEP420, which caused   problems with some of our tooling such as mypy and pylint.</li> <li>The official version of Unity ML-Agents supports is now 2018.4 LTS. If you run   into issues, please consider deleting your library folder and reponening your   projects. You will need to install the Barracuda package into your project in   order to ML-Agents to compile correctly.</li> </ul>"},{"location":"Migrating/#steps-to-migrate_6","title":"Steps to Migrate","text":"<ul> <li>If you had a custom <code>Training Configuration</code> in the Academy inspector, you   will need to pass your custom configuration at every training run using the   new command line arguments <code>--width</code>, <code>--height</code>, <code>--quality-level</code>,   <code>--time-scale</code> and <code>--target-frame-rate</code>.</li> <li>If you were using <code>--slow</code> in <code>mlagents-learn</code>, you will need to pass your old   <code>Inference Configuration</code> of the Academy inspector with the new command line   arguments <code>--width</code>, <code>--height</code>, <code>--quality-level</code>, <code>--time-scale</code> and   <code>--target-frame-rate</code> instead.</li> <li>Any imports from <code>mlagents.envs</code> should be replaced with <code>mlagents_envs</code>.</li> </ul>"},{"location":"Migrating/#migrating-from-ml-agents-toolkit-v0110-to-v0120","title":"Migrating from ML-Agents Toolkit v0.11.0 to v0.12.0","text":""},{"location":"Migrating/#important-changes_7","title":"Important Changes","text":"<ul> <li>Text actions and observations, and custom action and observation protos have   been removed.</li> <li>RayPerception3D and RayPerception2D are marked deprecated, and will be removed   in a future release. They can be replaced by RayPerceptionSensorComponent3D   and RayPerceptionSensorComponent2D.</li> <li>The <code>Use Heuristic</code> checkbox in Behavior Parameters has been replaced with a   <code>Behavior Type</code> dropdown menu. This has the following options:</li> <li><code>Default</code> corresponds to the previous unchecked behavior, meaning that     Agents will train if they connect to a python trainer, otherwise they will     perform inference.</li> <li><code>Heuristic Only</code> means the Agent will always use the <code>Heuristic()</code> method.     This corresponds to having \"Use Heuristic\" selected in 0.11.0.</li> <li><code>Inference Only</code> means the Agent will always perform inference.</li> <li>Barracuda was upgraded to 0.3.2, and it is now installed via the Unity Package   Manager.</li> </ul>"},{"location":"Migrating/#steps-to-migrate_7","title":"Steps to Migrate","text":"<ul> <li>We fixed a bug in   <code>RayPerception3d.Perceive()</code> that was causing the <code>endOffset</code> to be used   incorrectly. However this may produce different behavior from previous   versions if you use a non-zero <code>startOffset</code>. To reproduce the old behavior,   you should increase the value of <code>endOffset</code> by <code>startOffset</code>. You can   verify your raycasts are performing as expected in scene view using the debug   rays.</li> <li>If you use RayPerception3D, replace it with RayPerceptionSensorComponent3D   (and similarly for 2D). The settings, such as ray angles and detectable tags,   are configured on the component now. RayPerception3D would contribute   <code>(# of rays) * (# of tags + 2)</code> to the State Size in Behavior Parameters, but   this is no longer necessary, so you should reduce the State Size by this   amount. Making this change will require retraining your model, since the   observations that RayPerceptionSensorComponent3D produces are different from   the old behavior.</li> <li>If you see messages such as   <code>The type or namespace 'Barracuda' could not be found</code> or   <code>The type or namespace 'Google' could not be found</code>, you will need to   install the Barracuda preview package.</li> </ul>"},{"location":"Migrating/#migrating-from-ml-agents-toolkit-v010-to-v0110","title":"Migrating from ML-Agents Toolkit v0.10 to v0.11.0","text":""},{"location":"Migrating/#important-changes_8","title":"Important Changes","text":"<ul> <li>The definition of the gRPC service has changed.</li> <li>The online BC training feature has been removed.</li> <li>The BroadcastHub has been deprecated. If there is a training Python process,   all LearningBrains in the scene will automatically be trained. If there is no   Python process, inference will be used.</li> <li>The Brain ScriptableObjects have been deprecated. The Brain Parameters are now   on the Agent and are referred to as Behavior Parameters. Make sure the   Behavior Parameters is attached to the Agent GameObject.</li> <li>To use a heuristic behavior, implement the <code>Heuristic()</code> method in the Agent   class and check the <code>use heuristic</code> checkbox in the Behavior Parameters.</li> <li>Several changes were made to the setup for visual observations (i.e. using   Cameras or RenderTextures):</li> <li>Camera resolutions are no longer stored in the Brain Parameters.</li> <li>AgentParameters no longer stores lists of Cameras and RenderTextures</li> <li>To add visual observations to an Agent, you must now attach a     CameraSensorComponent or RenderTextureComponent to the agent. The     corresponding Camera or RenderTexture can be added to these in the editor,     and the resolution and color/grayscale is configured on the component     itself.</li> </ul>"},{"location":"Migrating/#steps-to-migrate_8","title":"Steps to Migrate","text":"<ul> <li>In order to be able to train, make sure both your ML-Agents Python package and   UnitySDK code come from the v0.11 release. Training will not work, for   example, if you update the ML-Agents Python package, and only update the API   Version in UnitySDK.</li> <li>If your Agents used visual observations, you must add a CameraSensorComponent   corresponding to each old Camera in the Agent's camera list (and similarly for   RenderTextures).</li> <li>Since Brain ScriptableObjects have been removed, you will need to delete all   the Brain ScriptableObjects from your <code>Assets</code> folder. Then, add a   <code>Behavior Parameters</code> component to each <code>Agent</code> GameObject. You will then need   to complete the fields on the new <code>Behavior Parameters</code> component with the   BrainParameters of the old Brain.</li> </ul>"},{"location":"Migrating/#migrating-from-ml-agents-toolkit-v09-to-v010","title":"Migrating from ML-Agents Toolkit v0.9 to v0.10","text":""},{"location":"Migrating/#important-changes_9","title":"Important Changes","text":"<ul> <li>We have updated the C# code in our repository to be in line with Unity Coding   Conventions. This has changed the name of some public facing classes and   enums.</li> <li>The example environments have been updated. If you were using these   environments to benchmark your training, please note that the resulting   rewards may be slightly different in v0.10.</li> </ul>"},{"location":"Migrating/#steps-to-migrate_9","title":"Steps to Migrate","text":"<ul> <li><code>UnitySDK/Assets/ML-Agents/Scripts/Communicator.cs</code> and its class   <code>Communicator</code> have been renamed to   <code>UnitySDK/Assets/ML-Agents/Scripts/ICommunicator.cs</code> and <code>ICommunicator</code>   respectively.</li> <li>The <code>SpaceType</code> Enums <code>discrete</code>, and <code>continuous</code> have been renamed to   <code>Discrete</code> and <code>Continuous</code>.</li> <li>We have removed the <code>Done</code> call as well as the capacity to set <code>Max Steps</code> on   the Academy. Therefore an AcademyReset will never be triggered from C# (only   from Python). If you want to reset the simulation after a fixed number of   steps, or when an event in the simulation occurs, we recommend looking at our   multi-agent example environments (such as FoodCollector). In our examples,   groups of Agents can be reset through an \"Area\" that can reset groups of   Agents.</li> <li>The import for <code>mlagents.envs.UnityEnvironment</code> was removed. If you are using   the Python API, change <code>from mlagents_envs import UnityEnvironment</code> to   <code>from mlagents_envs.environment import UnityEnvironment</code>.</li> </ul>"},{"location":"Migrating/#migrating-from-ml-agents-toolkit-v08-to-v09","title":"Migrating from ML-Agents Toolkit v0.8 to v0.9","text":""},{"location":"Migrating/#important-changes_10","title":"Important Changes","text":"<ul> <li>We have changed the way reward signals (including Curiosity) are defined in   the <code>trainer_config.yaml</code>.</li> <li>When using multiple environments, every \"step\" is recorded in TensorBoard.</li> <li>The steps in the command line console corresponds to a single step of a single   environment. Previously, each step corresponded to one step for all   environments (i.e., <code>num_envs</code> steps).</li> </ul>"},{"location":"Migrating/#steps-to-migrate_10","title":"Steps to Migrate","text":"<ul> <li>If you were overriding any of these following parameters in your config file,   remove them from the top-level config and follow the steps below:</li> <li><code>gamma</code>: Define a new <code>extrinsic</code> reward signal and set it's <code>gamma</code> to your     new gamma.</li> <li><code>use_curiosity</code>, <code>curiosity_strength</code>, <code>curiosity_enc_size</code>: Define a     <code>curiosity</code> reward signal and set its <code>strength</code> to <code>curiosity_strength</code>,     and <code>encoding_size</code> to <code>curiosity_enc_size</code>. Give it the same <code>gamma</code> as     your <code>extrinsic</code> signal to mimic previous behavior.</li> <li>TensorBoards generated when running multiple environments in v0.8 are not   comparable to those generated in v0.9 in terms of step count. Multiply your   v0.8 step count by <code>num_envs</code> for an approximate comparison. You may need to   change <code>max_steps</code> in your config as appropriate as well.</li> </ul>"},{"location":"Migrating/#migrating-from-ml-agents-toolkit-v07-to-v08","title":"Migrating from ML-Agents Toolkit v0.7 to v0.8","text":""},{"location":"Migrating/#important-changes_11","title":"Important Changes","text":"<ul> <li>We have split the Python packages into two separate packages <code>ml-agents</code> and   <code>ml-agents-envs</code>.</li> <li><code>--worker-id</code> option of <code>learn.py</code> has been removed, use <code>--base-port</code> instead   if you'd like to run multiple instances of <code>learn.py</code>.</li> </ul>"},{"location":"Migrating/#steps-to-migrate_11","title":"Steps to Migrate","text":"<ul> <li>If you are installing via PyPI, there is no change.</li> <li>If you intend to make modifications to <code>ml-agents</code> or <code>ml-agents-envs</code> please   check the Installing for Development in the   Installation documentation.</li> </ul>"},{"location":"Migrating/#migrating-from-ml-agents-toolkit-v06-to-v07","title":"Migrating from ML-Agents Toolkit v0.6 to v0.7","text":""},{"location":"Migrating/#important-changes_12","title":"Important Changes","text":"<ul> <li>We no longer support TFS and are now using the   Unity Inference Engine</li> </ul>"},{"location":"Migrating/#steps-to-migrate_12","title":"Steps to Migrate","text":"<ul> <li>Make sure to remove the <code>ENABLE_TENSORFLOW</code> flag in your Unity Project   settings</li> </ul>"},{"location":"Migrating/#migrating-from-ml-agents-toolkit-v05-to-v06","title":"Migrating from ML-Agents Toolkit v0.5 to v0.6","text":""},{"location":"Migrating/#important-changes_13","title":"Important Changes","text":"<ul> <li>Brains are now Scriptable Objects instead of MonoBehaviors.</li> <li> <p>You can no longer modify the type of a Brain. If you want to switch between   <code>PlayerBrain</code> and <code>LearningBrain</code> for multiple agents, you will need to assign   a new Brain to each agent separately. Note: You can pass the same Brain to   multiple agents in a scene by leveraging Unity's prefab system or look for all   the agents in a scene using the search bar of the <code>Hierarchy</code> window with the   word <code>Agent</code>.</p> </li> <li> <p>We replaced the Internal and External Brain with Learning Brain.   When you need to train a model, you need to drag it into the <code>Broadcast Hub</code>   inside the <code>Academy</code> and check the <code>Control</code> checkbox.</p> </li> <li>We removed the <code>Broadcast</code> checkbox of the Brain, to use the broadcast   functionality, you need to drag the Brain into the <code>Broadcast Hub</code>.</li> <li>When training multiple Brains at the same time, each model is now stored into   a separate model file rather than in the same file under different graph   scopes.</li> <li>The Learning Brain graph scope, placeholder names, output names and custom   placeholders can no longer be modified.</li> </ul>"},{"location":"Migrating/#steps-to-migrate_13","title":"Steps to Migrate","text":"<ul> <li>To update a scene from v0.5 to v0.6, you must:</li> <li>Remove the <code>Brain</code> GameObjects in the scene. (Delete all of the Brain     GameObjects under Academy in the scene.)</li> <li>Create new <code>Brain</code> Scriptable Objects using <code>Assets -&gt; Create -&gt; ML-Agents</code>     for each type of the Brain you plan to use, and put the created files under     a folder called Brains within your project.</li> <li>Edit their <code>Brain Parameters</code> to be the same as the parameters used in the     <code>Brain</code> GameObjects.</li> <li>Agents have a <code>Brain</code> field in the Inspector, you need to drag the     appropriate Brain ScriptableObject in it.</li> <li>The Academy has a <code>Broadcast Hub</code> field in the inspector, which is list of     brains used in the scene. To train or control your Brain from the     <code>mlagents-learn</code> Python script, you need to drag the relevant     <code>LearningBrain</code> ScriptableObjects used in your scene into entries into this     list.</li> </ul>"},{"location":"Migrating/#migrating-from-ml-agents-toolkit-v04-to-v05","title":"Migrating from ML-Agents Toolkit v0.4 to v0.5","text":""},{"location":"Migrating/#important","title":"Important","text":"<ul> <li>The Unity project <code>unity-environment</code> has been renamed <code>UnitySDK</code>.</li> <li>The <code>python</code> folder has been renamed to <code>ml-agents</code>. It now contains two   packages, <code>mlagents.env</code> and <code>mlagents.trainers</code>. <code>mlagents.env</code> can be used   to interact directly with a Unity environment, while <code>mlagents.trainers</code>   contains the classes for training agents.</li> <li>The supported Unity version has changed from <code>2017.1 or later</code> to   <code>2017.4 or later</code>. 2017.4 is an LTS (Long Term Support) version that helps us   maintain good quality and support. Earlier versions of Unity might still work,   but you may encounter an   error listed here.</li> </ul>"},{"location":"Migrating/#unity-api","title":"Unity API","text":"<ul> <li>Discrete Actions now use branches. You can   now specify concurrent discrete actions. You will need to update the Brain   Parameters in the Brain Inspector in all your environments that use discrete   actions. Refer to the   discrete action documentation   for more information.</li> </ul>"},{"location":"Migrating/#python-api","title":"Python API","text":"<ul> <li>In order to run a training session, you can now use the command   <code>mlagents-learn</code> instead of <code>python3 learn.py</code> after installing the <code>mlagents</code>   packages. This change is documented   here. For example, if we   previously ran</li> </ul> <p><code>sh   python3 learn.py 3DBall --train</code></p> <p>from the <code>python</code> subdirectory (which is changed to <code>ml-agents</code> subdirectory   in v0.5), we now run</p> <p><code>sh   mlagents-learn config/trainer_config.yaml --env=3DBall --train</code></p> <p>from the root directory where we installed the ML-Agents Toolkit.</p> <ul> <li>It is now required to specify the path to the yaml trainer configuration file   when running <code>mlagents-learn</code>. For an example trainer configuration file, see   trainer_config.yaml. An example of passing a   trainer configuration to <code>mlagents-learn</code> is shown above.</li> <li>The environment name is now passed through the <code>--env</code> option.</li> <li>Curriculum learning has been changed. In summary:</li> <li>Curriculum files for the same environment must now be placed into a folder.     Each curriculum file should be named after the Brain whose curriculum it     specifies.</li> <li><code>min_lesson_length</code> now specifies the minimum number of episodes in a lesson     and affects reward thresholding.</li> <li>It is no longer necessary to specify the <code>Max Steps</code> of the Academy to use     curriculum learning.</li> </ul>"},{"location":"Migrating/#migrating-from-ml-agents-toolkit-v03-to-v04","title":"Migrating from ML-Agents Toolkit v0.3 to v0.4","text":""},{"location":"Migrating/#unity-api_1","title":"Unity API","text":"<ul> <li><code>using MLAgents;</code> needs to be added in all of the C# scripts that use   ML-Agents.</li> </ul>"},{"location":"Migrating/#python-api_1","title":"Python API","text":"<ul> <li>We've changed some of the Python packages dependencies in requirement.txt   file. Make sure to run <code>pip3 install -e .</code> within your <code>ml-agents/python</code>   folder to update your Python packages.</li> </ul>"},{"location":"Migrating/#migrating-from-ml-agents-toolkit-v02-to-v03","title":"Migrating from ML-Agents Toolkit v0.2 to v0.3","text":"<p>There are a large number of new features and improvements in the ML-Agents toolkit v0.3 which change both the training process and Unity API in ways which will cause incompatibilities with environments made using older versions. This page is designed to highlight those changes for users familiar with v0.1 or v0.2 in order to ensure a smooth transition.</p>"},{"location":"Migrating/#important_1","title":"Important","text":"<ul> <li>The ML-Agents Toolkit is no longer compatible with Python 2.</li> </ul>"},{"location":"Migrating/#python-training","title":"Python Training","text":"<ul> <li>The training script <code>ppo.py</code> and <code>PPO.ipynb</code> Python notebook have been   replaced with a single <code>learn.py</code> script as the launching point for training   with ML-Agents. For more information on using <code>learn.py</code>, see   here.</li> <li>Hyperparameters for training Brains are now stored in the   <code>trainer_config.yaml</code> file. For more information on using this file, see   here.</li> </ul>"},{"location":"Migrating/#unity-api_2","title":"Unity API","text":"<ul> <li>Modifications to an Agent's rewards must now be done using either   <code>AddReward()</code> or <code>SetReward()</code>.</li> <li>Setting an Agent to done now requires the use of the <code>Done()</code> method.</li> <li><code>CollectStates()</code> has been replaced by <code>CollectObservations()</code>, which now no   longer returns a list of floats.</li> <li>To collect observations, call <code>AddVectorObs()</code> within <code>CollectObservations()</code>.   Note that you can call <code>AddVectorObs()</code> with floats, integers, lists and   arrays of floats, Vector3 and Quaternions.</li> <li><code>AgentStep()</code> has been replaced by <code>AgentAction()</code>.</li> <li><code>WaitTime()</code> has been removed.</li> <li>The <code>Frame Skip</code> field of the Academy is replaced by the Agent's   <code>Decision Frequency</code> field, enabling the Agent to make decisions at different   frequencies.</li> <li>The names of the inputs in the Internal Brain have been changed. You must   replace <code>state</code> with <code>vector_observation</code> and <code>observation</code> with   <code>visual_observation</code>. In addition, you must remove the <code>epsilon</code> placeholder.</li> </ul>"},{"location":"Migrating/#semantics","title":"Semantics","text":"<p>In order to more closely align with the terminology used in the Reinforcement Learning field, and to be more descriptive, we have changed the names of some of the concepts used in ML-Agents. The changes are highlighted in the table below.</p> Old - v0.2 and earlier New - v0.3 and later State Vector Observation Observation Visual Observation Action Vector Action N/A Text Observation N/A Text Action"},{"location":"Package-Settings/","title":"ML-Agents Package Settings","text":"<p>ML-Agents Package Settings contains settings that apply to the whole project. It allows you to configure ML-Agents-specific settings in the Editor.  These settings are available for use in both the Editor and Player.</p> <p>You can find them at <code>Edit</code> &gt; <code>Project Settings...</code> &gt; <code>ML-Agents</code>. It lists out all the available settings and their default values.</p>"},{"location":"Package-Settings/#create-custom-settings","title":"Create Custom Settings","text":"<p>In order to to use your own settings for your project, you'll need to create a settings asset.</p> <p>You can do this by clicking the <code>Create Settings Asset</code> buttom or clicking the gear on the top right and select <code>New Settings Asset...</code>. The asset file can be placed anywhere in the <code>Asset/</code> folder in your project. After Creating the settings asset, you'll be able to modify the settings for your project and your settings will be saved in the asset.</p> <p></p>"},{"location":"Package-Settings/#multiple-custom-settings-for-different-scenarios","title":"Multiple Custom Settings for Different Scenarios","text":"<p>You can create multiple settings assets in one project.</p> <p>By clicking the gear on the top right you'll see all available settings listed in the drop-down menu to choose from.</p> <p>This allows you to create different settings for different scenatios. For example, you can create two separate settings for training and inference, and specify which one you want to use according to what you're currently running.</p> <p></p>"},{"location":"Profiling-Python/","title":"Profiling in Python","text":"<p>As part of the ML-Agents Tookit, we provide a lightweight profiling system, in order to identity hotspots in the training process and help spot regressions from changes.</p> <p>Timers are hierarchical, meaning that the time tracked in a block of code can be further split into other blocks if desired. This also means that a function that is called from multiple places in the code will appear in multiple places in the timing output.</p> <p>All timers operate using a \"global\" instance by default, but this can be overridden if necessary (mainly for testing).</p>"},{"location":"Profiling-Python/#adding-profiling","title":"Adding Profiling","text":"<p>There are two ways to indicate code should be included in profiling. The simplest way is to add the <code>@timed</code> decorator to a function or method of interested.</p> <pre><code>class TrainerController:\n    # ....\n    @timed\n    def advance(self, env: EnvManager) -&gt; int:\n        # do stuff\n</code></pre> <p>You can also used the <code>hierarchical_timer</code> context manager.</p> <pre><code>with hierarchical_timer(\"communicator.exchange\"):\n    outputs = self.communicator.exchange(step_input)\n</code></pre> <p>The context manager may be easier than the <code>@timed</code> decorator for profiling different parts of a large function, or profiling calls to abstract methods that might not use decorator.</p>"},{"location":"Profiling-Python/#output","title":"Output","text":"<p>By default, at the end of training, timers are collected and written in json format to <code>{summaries_dir}/{run_id}_timers.json</code>. The output consists of node objects with the following keys:</p> <ul> <li>total (float): The total time in seconds spent in the block, including child   calls.</li> <li>count (int): The number of times the block was called.</li> <li>self (float): The total time in seconds spent in the block, excluding child   calls.</li> <li>children (dictionary): A dictionary of child nodes, keyed by the node name.</li> <li>is_parallel (bool): Indicates that the block of code was executed in multiple   threads or processes (see below). This is optional and defaults to false.</li> </ul>"},{"location":"Profiling-Python/#parallel-execution","title":"Parallel execution","text":""},{"location":"Profiling-Python/#subprocesses","title":"Subprocesses","text":"<p>For code that executes in multiple processes (for example, SubprocessEnvManager), we periodically send the timer information back to the \"main\" process, aggregate the timers there, and flush them in the subprocess. Note that (depending on the number of processes) this can result in timers where the total time may exceed the parent's total time. This is analogous to the difference between \"real\" and \"user\" values reported from the unix <code>time</code> command. In the timer output, blocks that were run in parallel are indicated by the <code>is_parallel</code> flag.</p>"},{"location":"Profiling-Python/#threads","title":"Threads","text":"<p>Timers currently use <code>time.perf_counter()</code> to track time spent, which may not give accurate results for multiple threads. If this is problematic, set <code>threaded: false</code> in your trainer configuration.</p>"},{"location":"Python-Custom-Trainer-Plugin/","title":"Unity Ml-Agents Custom trainers Plugin","text":"<p>As an attempt to bring a wider variety of reinforcement learning algorithms to our users, we have added custom trainers capabilities. we introduce an extensible plugin system to define new trainers based on the High level trainer API in <code>Ml-agents</code> Package. This will allow rerouting <code>mlagents-learn</code> CLI to custom trainers and extending the config files with hyper-parameters specific to your new trainers. We will expose a high-level extensible trainer (both on-policy, and off-policy trainers) optimizer and hyperparameter classes with documentation for the use of this plugin. For more infromation on how python plugin system works see Plugin interfaces.</p>"},{"location":"Python-Custom-Trainer-Plugin/#overview","title":"Overview","text":"<p>Model-free RL algorithms generally fall into two broad categories: on-policy and off-policy. On-policy algorithms perform updates based on data gathered from the current policy. Off-policy algorithms learn a Q function from a buffer of previous data, then use this Q function to make decisions. Off-policy algorithms have three key benefits in the context of ML-Agents: They tend to use fewer samples than on-policy as they can pull and re-use data from the buffer many times. They allow player demonstrations to be inserted in-line with RL data into the buffer, enabling new ways of doing imitation learning by streaming player data.</p> <p>To add new custom trainers to ML-agents, you would need to create a new python package. To give you an idea of how to structure your package, we have created a mlagents_trainer_plugin package ourselves as an example, with implementation of <code>A2c</code> and <code>DQN</code> algorithms. You would need a <code>setup.py</code> file to list extra requirements and register the new RL algorithm in ml-agents ecosystem and be able to call <code>mlagents-learn</code> CLI with your customized configuration.</p> <pre><code>\u251c\u2500\u2500 mlagents_trainer_plugin\n\u2502    \u251c\u2500\u2500 __init__.py\n\u2502    \u251c\u2500\u2500 a2c\n\u2502    \u2502    \u251c\u2500\u2500 __init__.py\n\u2502    \u2502    \u251c\u2500\u2500 a2c_3DBall.yaml\n\u2502    \u2502    \u251c\u2500\u2500 a2c_optimizer.py\n\u2502    \u2502    \u2514\u2500\u2500 a2c_trainer.py\n\u2502    \u2514\u2500\u2500 dqn\n\u2502        \u251c\u2500\u2500 __init__.py\n\u2502        \u251c\u2500\u2500 dqn_basic.yaml\n\u2502        \u251c\u2500\u2500 dqn_optimizer.py\n\u2502        \u2514\u2500\u2500 dqn_trainer.py\n\u2514\u2500\u2500 setup.py\n</code></pre>"},{"location":"Python-Custom-Trainer-Plugin/#installation-and-execution","title":"Installation and Execution","text":"<p>If you haven't already, follow the installation instructions. Once you have the <code>ml-agents-env</code> and <code>ml-agents</code> packages you can install the plugin package. From the repository's root directory install <code>ml-agents-trainer-plugin</code> (or replace with the name of your plugin folder).</p> <pre><code>pip3 install -e &lt;./ml-agents-trainer-plugin&gt;\n</code></pre> <p>Following the previous installations your package is added as an entrypoint and you can use a config file with new trainers:</p> <pre><code>mlagents-learn ml-agents-trainer-plugin/mlagents_trainer_plugin/a2c/a2c_3DBall.yaml --run-id &lt;run-id-name&gt;\n--env &lt;env-executable&gt;\n</code></pre>"},{"location":"Python-Custom-Trainer-Plugin/#tutorial","title":"Tutorial","text":"<p>Here\u2019s a step-by-step tutorial on how to write a setup file and extend ml-agents trainers, optimizers, and hyperparameter settings.To extend ML-agents classes see references on trainers and Optimizer.</p>"},{"location":"Python-Gym-API-Documentation/","title":"Table of Contents","text":"<ul> <li>mlagents_envs.envs.unity_gym_env</li> <li>UnityGymException</li> <li>UnityToGymWrapper<ul> <li>__init__</li> <li>reset</li> <li>step</li> <li>render</li> <li>close</li> <li>seed</li> </ul> </li> <li>ActionFlattener<ul> <li>__init__</li> <li>lookup_action</li> </ul> </li> </ul>"},{"location":"Python-Gym-API-Documentation/#mlagents_envsenvsunity_gym_env","title":"mlagents_envs.envs.unity_gym_env","text":""},{"location":"Python-Gym-API-Documentation/#unitygymexception-objects","title":"UnityGymException Objects","text":"<pre><code>class UnityGymException(error.Error)\n</code></pre> <p>Any error related to the gym wrapper of ml-agents.</p> <p></p>"},{"location":"Python-Gym-API-Documentation/#unitytogymwrapper-objects","title":"UnityToGymWrapper Objects","text":"<pre><code>class UnityToGymWrapper(gym.Env)\n</code></pre> <p>Provides Gym wrapper for Unity Learning Environments.</p> <p></p>"},{"location":"Python-Gym-API-Documentation/#__init__","title":"__init__","text":"<pre><code> | __init__(unity_env: BaseEnv, uint8_visual: bool = False, flatten_branched: bool = False, allow_multiple_obs: bool = False, action_space_seed: Optional[int] = None)\n</code></pre> <p>Environment initialization</p> <p>Arguments:</p> <ul> <li><code>unity_env</code>: The Unity BaseEnv to be wrapped in the gym. Will be closed when the UnityToGymWrapper closes.</li> <li><code>uint8_visual</code>: Return visual observations as uint8 (0-255) matrices instead of float (0.0-1.0).</li> <li><code>flatten_branched</code>: If True, turn branched discrete action spaces into a Discrete space rather than     MultiDiscrete.</li> <li><code>allow_multiple_obs</code>: If True, return a list of np.ndarrays as observations with the first elements     containing the visual observations and the last element containing the array of vector observations.     If False, returns a single np.ndarray containing either only a single visual observation or the array of     vector observations.</li> <li><code>action_space_seed</code>: If non-None, will be used to set the random seed on created gym.Space instances.</li> </ul> <p></p>"},{"location":"Python-Gym-API-Documentation/#reset","title":"reset","text":"<pre><code> | reset() -&gt; Union[List[np.ndarray], np.ndarray]\n</code></pre> <p>Resets the state of the environment and returns an initial observation. Returns: observation (object/list): the initial observation of the space.</p> <p></p>"},{"location":"Python-Gym-API-Documentation/#step","title":"step","text":"<pre><code> | step(action: List[Any]) -&gt; GymStepResult\n</code></pre> <p>Run one timestep of the environment's dynamics. When end of episode is reached, you are responsible for calling <code>reset()</code> to reset this environment's state. Accepts an action and returns a tuple (observation, reward, done, info).</p> <p>Arguments:</p> <ul> <li><code>action</code> object/list - an action provided by the environment</li> </ul> <p>Returns:</p> <ul> <li><code>observation</code> object/list - agent's observation of the current environment   reward (float/list) : amount of reward returned after previous action</li> <li><code>done</code> boolean/list - whether the episode has ended.</li> <li><code>info</code> dict - contains auxiliary diagnostic information.</li> </ul> <p></p>"},{"location":"Python-Gym-API-Documentation/#render","title":"render","text":"<pre><code> | render(mode=\"rgb_array\")\n</code></pre> <p>Return the latest visual observations. Note that it will not render a new frame of the environment.</p> <p></p>"},{"location":"Python-Gym-API-Documentation/#close","title":"close","text":"<pre><code> | close() -&gt; None\n</code></pre> <p>Override _close in your subclass to perform any necessary cleanup. Environments will automatically close() themselves when garbage collected or when the program exits.</p> <p></p>"},{"location":"Python-Gym-API-Documentation/#seed","title":"seed","text":"<pre><code> | seed(seed: Any = None) -&gt; None\n</code></pre> <p>Sets the seed for this env's random number generator(s). Currently not implemented.</p> <p></p>"},{"location":"Python-Gym-API-Documentation/#actionflattener-objects","title":"ActionFlattener Objects","text":"<pre><code>class ActionFlattener()\n</code></pre> <p>Flattens branched discrete action spaces into single-branch discrete action spaces.</p> <p></p>"},{"location":"Python-Gym-API-Documentation/#__init___1","title":"__init__","text":"<pre><code> | __init__(branched_action_space)\n</code></pre> <p>Initialize the flattener.</p> <p>Arguments:</p> <ul> <li><code>branched_action_space</code>: A List containing the sizes of each branch of the action space, e.g. [2,3,3] for three branches with size 2, 3, and 3 respectively.</li> </ul> <p></p>"},{"location":"Python-Gym-API-Documentation/#lookup_action","title":"lookup_action","text":"<pre><code> | lookup_action(action)\n</code></pre> <p>Convert a scalar discrete action into a unique set of branched actions.</p> <p>Arguments:</p> <ul> <li><code>action</code>: A scalar value representing one of the discrete actions.</li> </ul> <p>Returns:</p> <p>The List containing the branched actions.</p>"},{"location":"Python-Gym-API/","title":"Unity ML-Agents Gym Wrapper","text":"<p>A common way in which machine learning researchers interact with simulation environments is via a wrapper provided by OpenAI called <code>gym</code>. For more information on the gym interface, see here.</p> <p>We provide a gym wrapper and instructions for using it with existing machine learning algorithms which utilize gym. Our wrapper provides interfaces on top of our <code>UnityEnvironment</code> class, which is the default way of interfacing with a Unity environment via Python.</p>"},{"location":"Python-Gym-API/#installation","title":"Installation","text":"<p>The gym wrapper is part of the <code>mlgents_envs</code> package. Please refer to the mlagents_envs installation instructions.</p>"},{"location":"Python-Gym-API/#using-the-gym-wrapper","title":"Using the Gym Wrapper","text":"<p>The gym interface is available from <code>gym_unity.envs</code>. To launch an environment from the root of the project repository use:</p> <pre><code>from mlagents_envs.envs.unity_gym_env import UnityToGymWrapper\n\nenv = UnityToGymWrapper(unity_env, uint8_visual, flatten_branched, allow_multiple_obs)\n</code></pre> <ul> <li> <p><code>unity_env</code> refers to the Unity environment to be wrapped.</p> </li> <li> <p><code>uint8_visual</code> refers to whether to output visual observations as <code>uint8</code>   values (0-255). Many common Gym environments (e.g. Atari) do this. By default   they will be floats (0.0-1.0). Defaults to <code>False</code>.</p> </li> <li> <p><code>flatten_branched</code> will flatten a branched discrete action space into a Gym   Discrete. Otherwise, it will be converted into a MultiDiscrete. Defaults to   <code>False</code>.</p> </li> <li> <p><code>allow_multiple_obs</code> will return a list of observations. The first elements   contain the visual observations and the last element contains the array of   vector observations. If False the environment returns a single array (containing   a single visual observations, if present, otherwise the vector observation).   Defaults to <code>False</code>.</p> </li> <li> <p><code>action_space_seed</code> is the optional seed for action sampling. If non-None, will   be used to set the random seed on created gym.Space instances.</p> </li> </ul> <p>The returned environment <code>env</code> will function as a gym.</p>"},{"location":"Python-Gym-API/#limitations","title":"Limitations","text":"<ul> <li>It is only possible to use an environment with a single Agent.</li> <li>By default, the first visual observation is provided as the <code>observation</code>, if   present. Otherwise, vector observations are provided. You can receive all   visual and vector observations by using the <code>allow_multiple_obs=True</code> option in   the gym parameters. If set to <code>True</code>, you will receive a list of <code>observation</code>   instead of only one.</li> <li>The <code>TerminalSteps</code> or <code>DecisionSteps</code> output from the environment can still   be accessed from the <code>info</code> provided by <code>env.step(action)</code>.</li> <li>Stacked vector observations are not supported.</li> <li>Environment registration for use with <code>gym.make()</code> is currently not supported.</li> <li>Calling env.render() will not render a new frame of the environment. It will   return the latest visual observation if using visual observations.</li> </ul>"},{"location":"Python-Gym-API/#running-openai-baselines-algorithms","title":"Running OpenAI Baselines Algorithms","text":"<p>OpenAI provides a set of open-source maintained and tested Reinforcement Learning algorithms called the Baselines.</p> <p>Using the provided Gym wrapper, it is possible to train ML-Agents environments using these algorithms. This requires the creation of custom training scripts to launch each algorithm. In most cases these scripts can be created by making slight modifications to the ones provided for Atari and Mujoco environments.</p> <p>These examples were tested with baselines version 0.1.6.</p>"},{"location":"Python-Gym-API/#example-dqn-baseline","title":"Example - DQN Baseline","text":"<p>In order to train an agent to play the <code>GridWorld</code> environment using the Baselines DQN algorithm, you first need to install the baselines package using pip:</p> <pre><code>pip install git+git://github.com/openai/baselines\n</code></pre> <p>Next, create a file called <code>train_unity.py</code>. Then create an <code>/envs/</code> directory and build the environment to that directory. For more information on building Unity environments, see here. Note that because of limitations of the DQN baseline, the environment must have a single visual observation, a single discrete action and a single Agent in the scene. Add the following code to the <code>train_unity.py</code> file:</p> <pre><code>import gym\n\nfrom baselines import deepq\nfrom baselines import logger\n\nfrom mlagents_envs.environment import UnityEnvironment\nfrom mlagents_envs.envs.unity_gym_env import UnityToGymWrapper\n\n\ndef main():\n  unity_env = UnityEnvironment( &lt; path - to - environment &gt;)\n  env = UnityToGymWrapper(unity_env, uint8_visual=True)\n  logger.configure('./logs')  # Change to log in a different directory\n  act = deepq.learn(\n    env,\n    \"cnn\",  # For visual inputs\n    lr=2.5e-4,\n    total_timesteps=1000000,\n    buffer_size=50000,\n    exploration_fraction=0.05,\n    exploration_final_eps=0.1,\n    print_freq=20,\n    train_freq=5,\n    learning_starts=20000,\n    target_network_update_freq=50,\n    gamma=0.99,\n    prioritized_replay=False,\n    checkpoint_freq=1000,\n    checkpoint_path='./logs',  # Change to save model in a different directory\n    dueling=True\n  )\n  print(\"Saving model to unity_model.pkl\")\n  act.save(\"unity_model.pkl\")\n\n\nif __name__ == '__main__':\n  main()\n</code></pre> <p>To start the training process, run the following from the directory containing <code>train_unity.py</code>:</p> <pre><code>python -m train_unity\n</code></pre>"},{"location":"Python-Gym-API/#other-algorithms","title":"Other Algorithms","text":"<p>Other algorithms in the Baselines repository can be run using scripts similar to the examples from the baselines package. In most cases, the primary changes needed to use a Unity environment are to import <code>UnityToGymWrapper</code>, and to replace the environment creation code, typically <code>gym.make()</code>, with a call to <code>UnityToGymWrapper(unity_environment)</code> passing the environment as input.</p> <p>A typical rule of thumb is that for vision-based environments, modification should be done to Atari training scripts, and for vector observation environments, modification should be done to Mujoco scripts.</p> <p>Some algorithms will make use of <code>make_env()</code> or <code>make_mujoco_env()</code> functions. You can define a similar function for Unity environments. An example of such a method using the PPO2 baseline:</p> <pre><code>from mlagents_envs.environment import UnityEnvironment\nfrom mlagents_envs.envs import UnityToGymWrapper\nfrom baselines.common.vec_env.subproc_vec_env import SubprocVecEnv\nfrom baselines.common.vec_env.dummy_vec_env import DummyVecEnv\nfrom baselines.bench import Monitor\nfrom baselines import logger\nimport baselines.ppo2.ppo2 as ppo2\n\nimport os\n\ntry:\n  from mpi4py import MPI\nexcept ImportError:\n  MPI = None\n\n\ndef make_unity_env(env_directory, num_env, visual, start_index=0):\n  \"\"\"\n  Create a wrapped, monitored Unity environment.\n  \"\"\"\n\n  def make_env(rank, use_visual=True):  # pylint: disable=C0111\n    def _thunk():\n      unity_env = UnityEnvironment(env_directory, base_port=5000 + rank)\n      env = UnityToGymWrapper(unity_env, uint8_visual=True)\n      env = Monitor(env, logger.get_dir() and os.path.join(logger.get_dir(), str(rank)))\n      return env\n\n    return _thunk\n\n  if visual:\n    return SubprocVecEnv([make_env(i + start_index) for i in range(num_env)])\n  else:\n    rank = MPI.COMM_WORLD.Get_rank() if MPI else 0\n    return DummyVecEnv([make_env(rank, use_visual=False)])\n\n\ndef main():\n  env = make_unity_env( &lt; path - to - environment &gt;, 4, True)\n  ppo2.learn(\n    network=\"mlp\",\n    env=env,\n    total_timesteps=100000,\n    lr=1e-3,\n  )\n\n\nif __name__ == '__main__':\n  main()\n</code></pre>"},{"location":"Python-Gym-API/#run-google-dopamine-algorithms","title":"Run Google Dopamine Algorithms","text":"<p>Google provides a framework Dopamine, and implementations of algorithms, e.g. DQN, Rainbow, and the C51 variant of Rainbow. Using the Gym wrapper, we can run Unity environments using Dopamine.</p> <p>First, after installing the Gym wrapper, clone the Dopamine repository.</p> <pre><code>git clone https://github.com/google/dopamine\n</code></pre> <p>Then, follow the appropriate install instructions as specified on Dopamine's homepage. Note that the Dopamine guide specifies using a virtualenv. If you choose to do so, make sure your unity_env package is also installed within the same virtualenv as Dopamine.</p>"},{"location":"Python-Gym-API/#adapting-dopamines-scripts","title":"Adapting Dopamine's Scripts","text":"<p>First, open <code>dopamine/atari/run_experiment.py</code>. Alternatively, copy the entire <code>atari</code> folder, and name it something else (e.g. <code>unity</code>). If you choose the copy approach, be sure to change the package names in the import statements in <code>train.py</code> to your new directory.</p> <p>Within <code>run_experiment.py</code>, we will need to make changes to which environment is instantiated, just as in the Baselines example. At the top of the file, insert</p> <pre><code>from mlagents_envs.environment import UnityEnvironment\nfrom mlagents_envs.envs import UnityToGymWrapper\n</code></pre> <p>to import the Gym Wrapper. Navigate to the <code>create_atari_environment</code> method in the same file, and switch to instantiating a Unity environment by replacing the method with the following code.</p> <pre><code>    game_version = 'v0' if sticky_actions else 'v4'\n    full_game_name = '{}NoFrameskip-{}'.format(game_name, game_version)\n    unity_env = UnityEnvironment(&lt;path-to-environment&gt;)\n    env = UnityToGymWrapper(unity_env, uint8_visual=True)\n    return env\n</code></pre> <p><code>&lt;path-to-environment&gt;</code> is the path to your built Unity executable. For more information on building Unity environments, see here, and note the Limitations section below.</p> <p>Note that we are not using the preprocessor from Dopamine, as it uses many Atari-specific calls. Furthermore, frame-skipping can be done from within Unity, rather than on the Python side.</p>"},{"location":"Python-Gym-API/#limitations_1","title":"Limitations","text":"<p>Since Dopamine is designed around variants of DQN, it is only compatible with discrete action spaces, and specifically the Discrete Gym space. For environments that use branched discrete action spaces, you can enable the <code>flatten_branched</code> parameter in <code>UnityToGymWrapper</code>, which treats each combination of branched actions as separate actions.</p> <p>Furthermore, when building your environments, ensure that your Agent is using visual observations with greyscale enabled, and that the dimensions of the visual observations is 84 by 84 (matches the parameter found in <code>dqn_agent.py</code> and <code>rainbow_agent.py</code>). Dopamine's agents currently do not automatically adapt to the observation dimensions or number of channels.</p>"},{"location":"Python-Gym-API/#hyperparameters","title":"Hyperparameters","text":"<p>The hyperparameters provided by Dopamine are tailored to the Atari games, and you will likely need to adjust them for ML-Agents environments. Here is a sample <code>dopamine/agents/rainbow/configs/rainbow.gin</code> file that is known to work with a simple GridWorld.</p> <pre><code>import dopamine.agents.rainbow.rainbow_agent\nimport dopamine.unity.run_experiment\nimport dopamine.replay_memory.prioritized_replay_buffer\nimport gin.tf.external_configurables\n\nRainbowAgent.num_atoms = 51\nRainbowAgent.stack_size = 1\nRainbowAgent.vmax = 10.\nRainbowAgent.gamma = 0.99\nRainbowAgent.update_horizon = 3\nRainbowAgent.min_replay_history = 20000  # agent steps\nRainbowAgent.update_period = 5\nRainbowAgent.target_update_period = 50  # agent steps\nRainbowAgent.epsilon_train = 0.1\nRainbowAgent.epsilon_eval = 0.01\nRainbowAgent.epsilon_decay_period = 50000  # agent steps\nRainbowAgent.replay_scheme = 'prioritized'\nRainbowAgent.tf_device = '/cpu:0'  # use '/cpu:*' for non-GPU version\nRainbowAgent.optimizer = @tf.train.AdamOptimizer()\n\ntf.train.AdamOptimizer.learning_rate = 0.00025\ntf.train.AdamOptimizer.epsilon = 0.0003125\n\nRunner.game_name = \"Unity\" # any name can be used here\nRunner.sticky_actions = False\nRunner.num_iterations = 200\nRunner.training_steps = 10000  # agent steps\nRunner.evaluation_steps = 500  # agent steps\nRunner.max_steps_per_episode = 27000  # agent steps\n\nWrappedPrioritizedReplayBuffer.replay_capacity = 1000000\nWrappedPrioritizedReplayBuffer.batch_size = 32\n</code></pre> <p>This example assumed you copied <code>atari</code> to a separate folder named <code>unity</code>. Replace <code>unity</code> in <code>import dopamine.unity.run_experiment</code> with the folder you copied your <code>run_experiment.py</code> and <code>trainer.py</code> files to. If you directly modified the existing files, then use <code>atari</code> here.</p>"},{"location":"Python-Gym-API/#starting-a-run","title":"Starting a Run","text":"<p>You can now run Dopamine as you would normally:</p> <pre><code>python -um dopamine.unity.train \\\n  --agent_name=rainbow \\\n  --base_dir=/tmp/dopamine \\\n  --gin_files='dopamine/agents/rainbow/configs/rainbow.gin'\n</code></pre> <p>Again, we assume that you've copied <code>atari</code> into a separate folder. Remember to replace <code>unity</code> with the directory you copied your files into. If you edited the Atari files directly, this should be <code>atari</code>.</p>"},{"location":"Python-Gym-API/#example-gridworld","title":"Example: GridWorld","text":"<p>As a baseline, here are rewards over time for the three algorithms provided with Dopamine as run on the GridWorld example environment. All Dopamine (DQN, Rainbow, C51) runs were done with the same epsilon, epsilon decay, replay history, training steps, and buffer settings as specified above. Note that the first 20000 steps are used to pre-fill the training buffer, and no learning happens.</p> <p>We provide results from our PPO implementation and the DQN from Baselines as reference. Note that all runs used the same greyscale GridWorld as Dopamine. For PPO, <code>num_layers</code> was set to 2, and all other hyperparameters are the default for GridWorld in <code>config/ppo/GridWorld.yaml</code>. For Baselines DQN, the provided hyperparameters in the previous section are used. Note that Baselines implements certain features (e.g. dueling-Q) that are not enabled in Dopamine DQN.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/","title":"Table of Contents","text":"<ul> <li>mlagents_envs.base_env</li> <li>DecisionStep</li> <li>DecisionSteps<ul> <li>agent_id_to_index</li> <li>__getitem__</li> <li>empty</li> </ul> </li> <li>TerminalStep</li> <li>TerminalSteps<ul> <li>agent_id_to_index</li> <li>__getitem__</li> <li>empty</li> </ul> </li> <li>ActionTuple<ul> <li>discrete_dtype</li> </ul> </li> <li>ActionSpec<ul> <li>is_discrete</li> <li>is_continuous</li> <li>discrete_size</li> <li>empty_action</li> <li>random_action</li> <li>create_continuous</li> <li>create_discrete</li> </ul> </li> <li>DimensionProperty<ul> <li>UNSPECIFIED</li> <li>NONE</li> <li>TRANSLATIONAL_EQUIVARIANCE</li> <li>VARIABLE_SIZE</li> </ul> </li> <li>ObservationType<ul> <li>DEFAULT</li> <li>GOAL_SIGNAL</li> </ul> </li> <li>ObservationSpec</li> <li>BehaviorSpec</li> <li>BaseEnv<ul> <li>step</li> <li>reset</li> <li>close</li> <li>behavior_specs</li> <li>set_actions</li> <li>set_action_for_agent</li> <li>get_steps</li> </ul> </li> <li>mlagents_envs.environment</li> <li>UnityEnvironment<ul> <li>__init__</li> <li>close</li> </ul> </li> <li>mlagents_envs.registry</li> <li>mlagents_envs.registry.unity_env_registry</li> <li>UnityEnvRegistry<ul> <li>register</li> <li>register_from_yaml</li> <li>clear</li> <li>__getitem__</li> </ul> </li> <li>mlagents_envs.side_channel</li> <li>mlagents_envs.side_channel.raw_bytes_channel</li> <li>RawBytesChannel<ul> <li>on_message_received</li> <li>get_and_clear_received_messages</li> <li>send_raw_data</li> </ul> </li> <li>mlagents_envs.side_channel.outgoing_message</li> <li>OutgoingMessage<ul> <li>__init__</li> <li>write_bool</li> <li>write_int32</li> <li>write_float32</li> <li>write_float32_list</li> <li>write_string</li> <li>set_raw_bytes</li> </ul> </li> <li>mlagents_envs.side_channel.engine_configuration_channel</li> <li>EngineConfigurationChannel<ul> <li>on_message_received</li> <li>set_configuration_parameters</li> <li>set_configuration</li> </ul> </li> <li>mlagents_envs.side_channel.side_channel_manager</li> <li>SideChannelManager<ul> <li>process_side_channel_message</li> <li>generate_side_channel_messages</li> </ul> </li> <li>mlagents_envs.side_channel.stats_side_channel</li> <li>StatsSideChannel<ul> <li>on_message_received</li> <li>get_and_reset_stats</li> </ul> </li> <li>mlagents_envs.side_channel.incoming_message</li> <li>IncomingMessage<ul> <li>__init__</li> <li>read_bool</li> <li>read_int32</li> <li>read_float32</li> <li>read_float32_list</li> <li>read_string</li> <li>get_raw_bytes</li> </ul> </li> <li>mlagents_envs.side_channel.float_properties_channel</li> <li>FloatPropertiesChannel<ul> <li>on_message_received</li> <li>set_property</li> <li>get_property</li> <li>list_properties</li> <li>get_property_dict_copy</li> </ul> </li> <li>mlagents_envs.side_channel.environment_parameters_channel</li> <li>EnvironmentParametersChannel<ul> <li>set_float_parameter</li> <li>set_uniform_sampler_parameters</li> <li>set_gaussian_sampler_parameters</li> <li>set_multirangeuniform_sampler_parameters</li> </ul> </li> <li>mlagents_envs.side_channel.side_channel</li> <li>SideChannel<ul> <li>queue_message_to_send</li> <li>on_message_received</li> <li>channel_id</li> </ul> </li> </ul>"},{"location":"Python-LLAPI-Documentation/#mlagents_envsbase_env","title":"mlagents_envs.base_env","text":"<p>Python Environment API for the ML-Agents Toolkit The aim of this API is to expose Agents evolving in a simulation to perform reinforcement learning on. This API supports multi-agent scenarios and groups similar Agents (same observations, actions spaces and behavior) together. These groups of Agents are identified by their BehaviorName. For performance reasons, the data of each group of agents is processed in a batched manner. Agents are identified by a unique AgentId identifier that allows tracking of Agents across simulation steps. Note that there is no guarantee that the number or order of the Agents in the state will be consistent across simulation steps. A simulation steps corresponds to moving the simulation forward until at least one agent in the simulation sends its observations to Python again. Since Agents can request decisions at different frequencies, a simulation step does not necessarily correspond to a fixed simulation time increment.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#decisionstep-objects","title":"DecisionStep Objects","text":"<pre><code>class DecisionStep(NamedTuple)\n</code></pre> <p>Contains the data a single Agent collected since the last simulation step.  - obs is a list of numpy arrays observations collected by the agent.  - reward is a float. Corresponds to the rewards collected by the agent  since the last simulation step.  - agent_id is an int and an unique identifier for the corresponding Agent.  - action_mask is an optional list of one dimensional array of booleans.  Only available when using multi-discrete actions.  Each array corresponds to an action branch. Each array contains a mask  for each action of the branch. If true, the action is not available for  the agent during this simulation step.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#decisionsteps-objects","title":"DecisionSteps Objects","text":"<pre><code>class DecisionSteps(Mapping)\n</code></pre> <p>Contains the data a batch of similar Agents collected since the last simulation step. Note that all Agents do not necessarily have new information to send at each simulation step. Therefore, the ordering of agents and the batch size of the DecisionSteps are not fixed across simulation steps.  - obs is a list of numpy arrays observations collected by the batch of  agent. Each obs has one extra dimension compared to DecisionStep: the  first dimension of the array corresponds to the batch size of the batch.  - reward is a float vector of length batch size. Corresponds to the  rewards collected by each agent since the last simulation step.  - agent_id is an int vector of length batch size containing unique  identifier for the corresponding Agent. This is used to track Agents  across simulation steps.  - action_mask is an optional list of two dimensional array of booleans.  Only available when using multi-discrete actions.  Each array corresponds to an action branch. The first dimension of each  array is the batch size and the second contains a mask for each action of  the branch. If true, the action is not available for the agent during  this simulation step.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#agent_id_to_index","title":"agent_id_to_index","text":"<pre><code> | @property\n | agent_id_to_index() -&gt; Dict[AgentId, int]\n</code></pre> <p>Returns:</p> <p>A Dict that maps agent_id to the index of those agents in this DecisionSteps.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#__getitem__","title":"__getitem__","text":"<pre><code> | __getitem__(agent_id: AgentId) -&gt; DecisionStep\n</code></pre> <p>returns the DecisionStep for a specific agent.</p> <p>Arguments:</p> <ul> <li><code>agent_id</code>: The id of the agent</li> </ul> <p>Returns:</p> <p>The DecisionStep</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#empty","title":"empty","text":"<pre><code> | @staticmethod\n | empty(spec: \"BehaviorSpec\") -&gt; \"DecisionSteps\"\n</code></pre> <p>Returns an empty DecisionSteps.</p> <p>Arguments:</p> <ul> <li><code>spec</code>: The BehaviorSpec for the DecisionSteps</li> </ul> <p></p>"},{"location":"Python-LLAPI-Documentation/#terminalstep-objects","title":"TerminalStep Objects","text":"<pre><code>class TerminalStep(NamedTuple)\n</code></pre> <p>Contains the data a single Agent collected when its episode ended.  - obs is a list of numpy arrays observations collected by the agent.  - reward is a float. Corresponds to the rewards collected by the agent  since the last simulation step.  - interrupted is a bool. Is true if the Agent was interrupted since the last  decision step. For example, if the Agent reached the maximum number of steps for  the episode.  - agent_id is an int and an unique identifier for the corresponding Agent.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#terminalsteps-objects","title":"TerminalSteps Objects","text":"<pre><code>class TerminalSteps(Mapping)\n</code></pre> <p>Contains the data a batch of Agents collected when their episode terminated. All Agents present in the TerminalSteps have ended their episode.  - obs is a list of numpy arrays observations collected by the batch of  agent. Each obs has one extra dimension compared to DecisionStep: the  first dimension of the array corresponds to the batch size of the batch.  - reward is a float vector of length batch size. Corresponds to the  rewards collected by each agent since the last simulation step.  - interrupted is an array of booleans of length batch size. Is true if the  associated Agent was interrupted since the last decision step. For example, if the  Agent reached the maximum number of steps for the episode.  - agent_id is an int vector of length batch size containing unique  identifier for the corresponding Agent. This is used to track Agents  across simulation steps.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#agent_id_to_index_1","title":"agent_id_to_index","text":"<pre><code> | @property\n | agent_id_to_index() -&gt; Dict[AgentId, int]\n</code></pre> <p>Returns:</p> <p>A Dict that maps agent_id to the index of those agents in this TerminalSteps.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#__getitem___1","title":"__getitem__","text":"<pre><code> | __getitem__(agent_id: AgentId) -&gt; TerminalStep\n</code></pre> <p>returns the TerminalStep for a specific agent.</p> <p>Arguments:</p> <ul> <li><code>agent_id</code>: The id of the agent</li> </ul> <p>Returns:</p> <p>obs, reward, done, agent_id and optional action mask for a specific agent</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#empty_1","title":"empty","text":"<pre><code> | @staticmethod\n | empty(spec: \"BehaviorSpec\") -&gt; \"TerminalSteps\"\n</code></pre> <p>Returns an empty TerminalSteps.</p> <p>Arguments:</p> <ul> <li><code>spec</code>: The BehaviorSpec for the TerminalSteps</li> </ul> <p></p>"},{"location":"Python-LLAPI-Documentation/#actiontuple-objects","title":"ActionTuple Objects","text":"<pre><code>class ActionTuple(_ActionTupleBase)\n</code></pre> <p>An object whose fields correspond to actions of different types. Continuous and discrete actions are numpy arrays of type float32 and int32, respectively and are type checked on construction. Dimensions are of (n_agents, continuous_size) and (n_agents, discrete_size), respectively. Note, this also holds when continuous or discrete size is zero.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#discrete_dtype","title":"discrete_dtype","text":"<pre><code> | @property\n | discrete_dtype() -&gt; np.dtype\n</code></pre> <p>The dtype of a discrete action.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#actionspec-objects","title":"ActionSpec Objects","text":"<pre><code>class ActionSpec(NamedTuple)\n</code></pre> <p>A NamedTuple containing utility functions and information about the action spaces for a group of Agents under the same behavior. - num_continuous_actions is an int corresponding to the number of floats which constitute the action. - discrete_branch_sizes is a Tuple of int where each int corresponds to the number of discrete actions available to the agent on an independent action branch.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#is_discrete","title":"is_discrete","text":"<pre><code> | is_discrete() -&gt; bool\n</code></pre> <p>Returns true if this Behavior uses discrete actions</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#is_continuous","title":"is_continuous","text":"<pre><code> | is_continuous() -&gt; bool\n</code></pre> <p>Returns true if this Behavior uses continuous actions</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#discrete_size","title":"discrete_size","text":"<pre><code> | @property\n | discrete_size() -&gt; int\n</code></pre> <p>Returns a an int corresponding to the number of discrete branches.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#empty_action","title":"empty_action","text":"<pre><code> | empty_action(n_agents: int) -&gt; ActionTuple\n</code></pre> <p>Generates ActionTuple corresponding to an empty action (all zeros) for a number of agents.</p> <p>Arguments:</p> <ul> <li><code>n_agents</code>: The number of agents that will have actions generated</li> </ul> <p></p>"},{"location":"Python-LLAPI-Documentation/#random_action","title":"random_action","text":"<pre><code> | random_action(n_agents: int) -&gt; ActionTuple\n</code></pre> <p>Generates ActionTuple corresponding to a random action (either discrete or continuous) for a number of agents.</p> <p>Arguments:</p> <ul> <li><code>n_agents</code>: The number of agents that will have actions generated</li> </ul> <p></p>"},{"location":"Python-LLAPI-Documentation/#create_continuous","title":"create_continuous","text":"<pre><code> | @staticmethod\n | create_continuous(continuous_size: int) -&gt; \"ActionSpec\"\n</code></pre> <p>Creates an ActionSpec that is homogenously continuous</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#create_discrete","title":"create_discrete","text":"<pre><code> | @staticmethod\n | create_discrete(discrete_branches: Tuple[int]) -&gt; \"ActionSpec\"\n</code></pre> <p>Creates an ActionSpec that is homogenously discrete</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#dimensionproperty-objects","title":"DimensionProperty Objects","text":"<pre><code>class DimensionProperty(IntFlag)\n</code></pre> <p>The dimension property of a dimension of an observation.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#unspecified","title":"UNSPECIFIED","text":"<p>No properties specified.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#none","title":"NONE","text":"<p>No Property of the observation in that dimension. Observation can be processed with Fully connected networks.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#translational_equivariance","title":"TRANSLATIONAL_EQUIVARIANCE","text":"<p>Means it is suitable to do a convolution in this dimension.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#variable_size","title":"VARIABLE_SIZE","text":"<p>Means that there can be a variable number of observations in this dimension. The observations are unordered.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#observationtype-objects","title":"ObservationType Objects","text":"<pre><code>class ObservationType(Enum)\n</code></pre> <p>An Enum which defines the type of information carried in the observation of the agent.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#default","title":"DEFAULT","text":"<p>Observation information is generic.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#goal_signal","title":"GOAL_SIGNAL","text":"<p>Observation contains goal information for current task.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#observationspec-objects","title":"ObservationSpec Objects","text":"<pre><code>class ObservationSpec(NamedTuple)\n</code></pre> <p>A NamedTuple containing information about the observation of Agents. - shape is a Tuple of int : It corresponds to the shape of an observation's dimensions. - dimension_property is a Tuple of DimensionProperties flag, one flag for each dimension. - observation_type is an enum of ObservationType.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#behaviorspec-objects","title":"BehaviorSpec Objects","text":"<pre><code>class BehaviorSpec(NamedTuple)\n</code></pre> <p>A NamedTuple containing information about the observation and action spaces for a group of Agents under the same behavior. - observation_specs is a List of ObservationSpec NamedTuple containing information about the information of the Agent's observations such as their shapes. The order of the ObservationSpec is the same as the order of the observations of an agent. - action_spec is an ActionSpec NamedTuple.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#baseenv-objects","title":"BaseEnv Objects","text":"<pre><code>class BaseEnv(ABC)\n</code></pre>"},{"location":"Python-LLAPI-Documentation/#step","title":"step","text":"<pre><code> | @abstractmethod\n | step() -&gt; None\n</code></pre> <p>Signals the environment that it must move the simulation forward by one step.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#reset","title":"reset","text":"<pre><code> | @abstractmethod\n | reset() -&gt; None\n</code></pre> <p>Signals the environment that it must reset the simulation.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#close","title":"close","text":"<pre><code> | @abstractmethod\n | close() -&gt; None\n</code></pre> <p>Signals the environment that it must close.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#behavior_specs","title":"behavior_specs","text":"<pre><code> | @property\n | @abstractmethod\n | behavior_specs() -&gt; MappingType[str, BehaviorSpec]\n</code></pre> <p>Returns a Mapping from behavior names to behavior specs. Agents grouped under the same behavior name have the same action and observation specs, and are expected to behave similarly in the environment. Note that new keys can be added to this mapping as new policies are instantiated.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#set_actions","title":"set_actions","text":"<pre><code> | @abstractmethod\n | set_actions(behavior_name: BehaviorName, action: ActionTuple) -&gt; None\n</code></pre> <p>Sets the action for all of the agents in the simulation for the next step. The Actions must be in the same order as the order received in the DecisionSteps.</p> <p>Arguments:</p> <ul> <li><code>behavior_name</code>: The name of the behavior the agents are part of</li> <li><code>action</code>: ActionTuple tuple of continuous and/or discrete action. Actions are np.arrays with dimensions  (n_agents, continuous_size) and (n_agents, discrete_size), respectively.</li> </ul> <p></p>"},{"location":"Python-LLAPI-Documentation/#set_action_for_agent","title":"set_action_for_agent","text":"<pre><code> | @abstractmethod\n | set_action_for_agent(behavior_name: BehaviorName, agent_id: AgentId, action: ActionTuple) -&gt; None\n</code></pre> <p>Sets the action for one of the agents in the simulation for the next step.</p> <p>Arguments:</p> <ul> <li><code>behavior_name</code>: The name of the behavior the agent is part of</li> <li><code>agent_id</code>: The id of the agent the action is set for</li> <li><code>action</code>: ActionTuple tuple of continuous and/or discrete action Actions are np.arrays with dimensions  (1, continuous_size) and (1, discrete_size), respectively. Note, this initial dimensions of 1 is because this action is meant for a single agent.</li> </ul> <p></p>"},{"location":"Python-LLAPI-Documentation/#get_steps","title":"get_steps","text":"<pre><code> | @abstractmethod\n | get_steps(behavior_name: BehaviorName) -&gt; Tuple[DecisionSteps, TerminalSteps]\n</code></pre> <p>Retrieves the steps of the agents that requested a step in the simulation.</p> <p>Arguments:</p> <ul> <li><code>behavior_name</code>: The name of the behavior the agents are part of</li> </ul> <p>Returns:</p> <p>A tuple containing : - A DecisionSteps NamedTuple containing the observations, the rewards, the agent ids and the action masks for the Agents of the specified behavior. These Agents need an action this step. - A TerminalSteps NamedTuple containing the observations, rewards, agent ids and interrupted flags of the agents that had their episode terminated last step.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#mlagents_envsenvironment","title":"mlagents_envs.environment","text":""},{"location":"Python-LLAPI-Documentation/#unityenvironment-objects","title":"UnityEnvironment Objects","text":"<pre><code>class UnityEnvironment(BaseEnv)\n</code></pre>"},{"location":"Python-LLAPI-Documentation/#__init__","title":"__init__","text":"<pre><code> | __init__(file_name: Optional[str] = None, worker_id: int = 0, base_port: Optional[int] = None, seed: int = 0, no_graphics: bool = False, timeout_wait: int = 60, additional_args: Optional[List[str]] = None, side_channels: Optional[List[SideChannel]] = None, log_folder: Optional[str] = None, num_areas: int = 1)\n</code></pre> <p>Starts a new unity environment and establishes a connection with the environment. Notice: Currently communication between Unity and Python takes place over an open socket without authentication. Ensure that the network where training takes place is secure.</p> <p>:string file_name: Name of Unity environment binary. :int base_port: Baseline port number to connect to Unity environment over. worker_id increments over this. If no environment is specified (i.e. file_name is None), the DEFAULT_EDITOR_PORT will be used. :int worker_id: Offset from base_port. Used for training multiple environments simultaneously. :bool no_graphics: Whether to run the Unity simulator in no-graphics mode :int timeout_wait: Time (in seconds) to wait for connection from environment. :list args: Addition Unity command line arguments :list side_channels: Additional side channel for no-rl communication with Unity :str log_folder: Optional folder to write the Unity Player log file into.  Requires absolute path.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#close_1","title":"close","text":"<pre><code> | close()\n</code></pre> <p>Sends a shutdown signal to the unity environment, and closes the socket connection.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#mlagents_envsregistry","title":"mlagents_envs.registry","text":""},{"location":"Python-LLAPI-Documentation/#mlagents_envsregistryunity_env_registry","title":"mlagents_envs.registry.unity_env_registry","text":""},{"location":"Python-LLAPI-Documentation/#unityenvregistry-objects","title":"UnityEnvRegistry Objects","text":"<pre><code>class UnityEnvRegistry(Mapping)\n</code></pre>"},{"location":"Python-LLAPI-Documentation/#unityenvregistry","title":"UnityEnvRegistry","text":"<p>Provides a library of Unity environments that can be launched without the need of downloading the Unity Editor. The UnityEnvRegistry implements a Map, to access an entry of the Registry, use:</p> <pre><code>registry = UnityEnvRegistry()\nentry = registry[&lt;environment_identifyier&gt;]\n</code></pre> <p>An entry has the following properties :  * <code>identifier</code> : Uniquely identifies this environment  * <code>expected_reward</code> : Corresponds to the reward an agent must obtained for the task  to be considered completed.  * <code>description</code> : A human readable description of the environment.</p> <p>To launch a Unity environment from a registry entry, use the <code>make</code> method:</p> <pre><code>registry = UnityEnvRegistry()\nenv = registry[&lt;environment_identifyier&gt;].make()\n</code></pre> <p></p>"},{"location":"Python-LLAPI-Documentation/#register","title":"register","text":"<pre><code> | register(new_entry: BaseRegistryEntry) -&gt; None\n</code></pre> <p>Registers a new BaseRegistryEntry to the registry. The BaseRegistryEntry.identifier value will be used as indexing key. If two are more environments are registered under the same key, the most recentry added will replace the others.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#register_from_yaml","title":"register_from_yaml","text":"<pre><code> | register_from_yaml(path_to_yaml: str) -&gt; None\n</code></pre> <p>Registers the environments listed in a yaml file (either local or remote). Note that the entries are registered lazily: the registration will only happen when an environment is accessed. The yaml file must have the following format :</p> <pre><code>environments:\n- &lt;identifier of the first environment&gt;:\n    expected_reward: &lt;expected reward of the environment&gt;\n    description: | &lt;a multi line description of the environment&gt;\n      &lt;continued multi line description&gt;\n    linux_url: &lt;The url for the Linux executable zip file&gt;\n    darwin_url: &lt;The url for the OSX executable zip file&gt;\n    win_url: &lt;The url for the Windows executable zip file&gt;\n\n- &lt;identifier of the second environment&gt;:\n    expected_reward: &lt;expected reward of the environment&gt;\n    description: | &lt;a multi line description of the environment&gt;\n      &lt;continued multi line description&gt;\n    linux_url: &lt;The url for the Linux executable zip file&gt;\n    darwin_url: &lt;The url for the OSX executable zip file&gt;\n    win_url: &lt;The url for the Windows executable zip file&gt;\n\n- ...\n</code></pre> <p>Arguments:</p> <ul> <li><code>path_to_yaml</code>: A local path or url to the yaml file</li> </ul> <p></p>"},{"location":"Python-LLAPI-Documentation/#clear","title":"clear","text":"<pre><code> | clear() -&gt; None\n</code></pre> <p>Deletes all entries in the registry.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#__getitem___2","title":"__getitem__","text":"<pre><code> | __getitem__(identifier: str) -&gt; BaseRegistryEntry\n</code></pre> <p>Returns the BaseRegistryEntry with the provided identifier. BaseRegistryEntry can then be used to make a Unity Environment.</p> <p>Arguments:</p> <ul> <li><code>identifier</code>: The identifier of the BaseRegistryEntry</li> </ul> <p>Returns:</p> <p>The associated BaseRegistryEntry</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#mlagents_envsside_channel","title":"mlagents_envs.side_channel","text":""},{"location":"Python-LLAPI-Documentation/#mlagents_envsside_channelraw_bytes_channel","title":"mlagents_envs.side_channel.raw_bytes_channel","text":""},{"location":"Python-LLAPI-Documentation/#rawbyteschannel-objects","title":"RawBytesChannel Objects","text":"<pre><code>class RawBytesChannel(SideChannel)\n</code></pre> <p>This is an example of what the SideChannel for raw bytes exchange would look like. Is meant to be used for general research purpose.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#on_message_received","title":"on_message_received","text":"<pre><code> | on_message_received(msg: IncomingMessage) -&gt; None\n</code></pre> <p>Is called by the environment to the side channel. Can be called multiple times per step if multiple messages are meant for that SideChannel.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#get_and_clear_received_messages","title":"get_and_clear_received_messages","text":"<pre><code> | get_and_clear_received_messages() -&gt; List[bytes]\n</code></pre> <p>returns a list of bytearray received from the environment.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#send_raw_data","title":"send_raw_data","text":"<pre><code> | send_raw_data(data: bytearray) -&gt; None\n</code></pre> <p>Queues a message to be sent by the environment at the next call to step.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#mlagents_envsside_channeloutgoing_message","title":"mlagents_envs.side_channel.outgoing_message","text":""},{"location":"Python-LLAPI-Documentation/#outgoingmessage-objects","title":"OutgoingMessage Objects","text":"<pre><code>class OutgoingMessage()\n</code></pre> <p>Utility class for forming the message that is written to a SideChannel. All data is written in little-endian format using the struct module.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#__init___1","title":"__init__","text":"<pre><code> | __init__()\n</code></pre> <p>Create an OutgoingMessage with an empty buffer.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#write_bool","title":"write_bool","text":"<pre><code> | write_bool(b: bool) -&gt; None\n</code></pre> <p>Append a boolean value.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#write_int32","title":"write_int32","text":"<pre><code> | write_int32(i: int) -&gt; None\n</code></pre> <p>Append an integer value.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#write_float32","title":"write_float32","text":"<pre><code> | write_float32(f: float) -&gt; None\n</code></pre> <p>Append a float value. It will be truncated to 32-bit precision.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#write_float32_list","title":"write_float32_list","text":"<pre><code> | write_float32_list(float_list: List[float]) -&gt; None\n</code></pre> <p>Append a list of float values. They will be truncated to 32-bit precision.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#write_string","title":"write_string","text":"<pre><code> | write_string(s: str) -&gt; None\n</code></pre> <p>Append a string value. Internally, it will be encoded to ascii, and the encoded length will also be written to the message.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#set_raw_bytes","title":"set_raw_bytes","text":"<pre><code> | set_raw_bytes(buffer: bytearray) -&gt; None\n</code></pre> <p>Set the internal buffer to a new bytearray. This will overwrite any existing data.</p> <p>Arguments:</p> <ul> <li><code>buffer</code>:</li> </ul> <p>Returns:</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#mlagents_envsside_channelengine_configuration_channel","title":"mlagents_envs.side_channel.engine_configuration_channel","text":""},{"location":"Python-LLAPI-Documentation/#engineconfigurationchannel-objects","title":"EngineConfigurationChannel Objects","text":"<pre><code>class EngineConfigurationChannel(SideChannel)\n</code></pre> <p>This is the SideChannel for engine configuration exchange. The data in the engine configuration is as follows :  - int width;  - int height;  - int qualityLevel;  - float timeScale;  - int targetFrameRate;  - int captureFrameRate;</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#on_message_received_1","title":"on_message_received","text":"<pre><code> | on_message_received(msg: IncomingMessage) -&gt; None\n</code></pre> <p>Is called by the environment to the side channel. Can be called multiple times per step if multiple messages are meant for that SideChannel. Note that Python should never receive an engine configuration from Unity</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#set_configuration_parameters","title":"set_configuration_parameters","text":"<pre><code> | set_configuration_parameters(width: Optional[int] = None, height: Optional[int] = None, quality_level: Optional[int] = None, time_scale: Optional[float] = None, target_frame_rate: Optional[int] = None, capture_frame_rate: Optional[int] = None) -&gt; None\n</code></pre> <p>Sets the engine configuration. Takes as input the configurations of the engine.</p> <p>Arguments:</p> <ul> <li><code>width</code>: Defines the width of the display. (Must be set alongside height)</li> <li><code>height</code>: Defines the height of the display. (Must be set alongside width)</li> <li><code>quality_level</code>: Defines the quality level of the simulation.</li> <li><code>time_scale</code>: Defines the multiplier for the deltatime in the simulation. If set to a higher value, time will pass faster in the simulation but the physics might break.</li> <li><code>target_frame_rate</code>: Instructs simulation to try to render at a specified frame rate.</li> <li><code>capture_frame_rate</code>: Instructs the simulation to consider time between updates to always be constant, regardless of the actual frame rate.</li> </ul> <p></p>"},{"location":"Python-LLAPI-Documentation/#set_configuration","title":"set_configuration","text":"<pre><code> | set_configuration(config: EngineConfig) -&gt; None\n</code></pre> <p>Sets the engine configuration. Takes as input an EngineConfig.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#mlagents_envsside_channelside_channel_manager","title":"mlagents_envs.side_channel.side_channel_manager","text":""},{"location":"Python-LLAPI-Documentation/#sidechannelmanager-objects","title":"SideChannelManager Objects","text":"<pre><code>class SideChannelManager()\n</code></pre>"},{"location":"Python-LLAPI-Documentation/#process_side_channel_message","title":"process_side_channel_message","text":"<pre><code> | process_side_channel_message(data: bytes) -&gt; None\n</code></pre> <p>Separates the data received from Python into individual messages for each registered side channel and calls on_message_received on them.</p> <p>Arguments:</p> <ul> <li><code>data</code>: The packed message sent by Unity</li> </ul> <p></p>"},{"location":"Python-LLAPI-Documentation/#generate_side_channel_messages","title":"generate_side_channel_messages","text":"<pre><code> | generate_side_channel_messages() -&gt; bytearray\n</code></pre> <p>Gathers the messages that the registered side channels will send to Unity and combines them into a single message ready to be sent.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#mlagents_envsside_channelstats_side_channel","title":"mlagents_envs.side_channel.stats_side_channel","text":""},{"location":"Python-LLAPI-Documentation/#statssidechannel-objects","title":"StatsSideChannel Objects","text":"<pre><code>class StatsSideChannel(SideChannel)\n</code></pre> <p>Side channel that receives (string, float) pairs from the environment, so that they can eventually be passed to a StatsReporter.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#on_message_received_2","title":"on_message_received","text":"<pre><code> | on_message_received(msg: IncomingMessage) -&gt; None\n</code></pre> <p>Receive the message from the environment, and save it for later retrieval.</p> <p>Arguments:</p> <ul> <li><code>msg</code>:</li> </ul> <p>Returns:</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#get_and_reset_stats","title":"get_and_reset_stats","text":"<pre><code> | get_and_reset_stats() -&gt; EnvironmentStats\n</code></pre> <p>Returns the current stats, and resets the internal storage of the stats.</p> <p>Returns:</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#mlagents_envsside_channelincoming_message","title":"mlagents_envs.side_channel.incoming_message","text":""},{"location":"Python-LLAPI-Documentation/#incomingmessage-objects","title":"IncomingMessage Objects","text":"<pre><code>class IncomingMessage()\n</code></pre> <p>Utility class for reading the message written to a SideChannel. Values must be read in the order they were written.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#__init___2","title":"__init__","text":"<pre><code> | __init__(buffer: bytes, offset: int = 0)\n</code></pre> <p>Create a new IncomingMessage from the bytes.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#read_bool","title":"read_bool","text":"<pre><code> | read_bool(default_value: bool = False) -&gt; bool\n</code></pre> <p>Read a boolean value from the message buffer.</p> <p>Arguments:</p> <ul> <li><code>default_value</code>: Default value to use if the end of the message is reached.</li> </ul> <p>Returns:</p> <p>The value read from the message, or the default value if the end was reached.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#read_int32","title":"read_int32","text":"<pre><code> | read_int32(default_value: int = 0) -&gt; int\n</code></pre> <p>Read an integer value from the message buffer.</p> <p>Arguments:</p> <ul> <li><code>default_value</code>: Default value to use if the end of the message is reached.</li> </ul> <p>Returns:</p> <p>The value read from the message, or the default value if the end was reached.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#read_float32","title":"read_float32","text":"<pre><code> | read_float32(default_value: float = 0.0) -&gt; float\n</code></pre> <p>Read a float value from the message buffer.</p> <p>Arguments:</p> <ul> <li><code>default_value</code>: Default value to use if the end of the message is reached.</li> </ul> <p>Returns:</p> <p>The value read from the message, or the default value if the end was reached.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#read_float32_list","title":"read_float32_list","text":"<pre><code> | read_float32_list(default_value: List[float] = None) -&gt; List[float]\n</code></pre> <p>Read a list of float values from the message buffer.</p> <p>Arguments:</p> <ul> <li><code>default_value</code>: Default value to use if the end of the message is reached.</li> </ul> <p>Returns:</p> <p>The value read from the message, or the default value if the end was reached.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#read_string","title":"read_string","text":"<pre><code> | read_string(default_value: str = \"\") -&gt; str\n</code></pre> <p>Read a string value from the message buffer.</p> <p>Arguments:</p> <ul> <li><code>default_value</code>: Default value to use if the end of the message is reached.</li> </ul> <p>Returns:</p> <p>The value read from the message, or the default value if the end was reached.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#get_raw_bytes","title":"get_raw_bytes","text":"<pre><code> | get_raw_bytes() -&gt; bytes\n</code></pre> <p>Get a copy of the internal bytes used by the message.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#mlagents_envsside_channelfloat_properties_channel","title":"mlagents_envs.side_channel.float_properties_channel","text":""},{"location":"Python-LLAPI-Documentation/#floatpropertieschannel-objects","title":"FloatPropertiesChannel Objects","text":"<pre><code>class FloatPropertiesChannel(SideChannel)\n</code></pre> <p>This is the SideChannel for float properties shared with Unity. You can modify the float properties of an environment with the commands set_property, get_property and list_properties.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#on_message_received_3","title":"on_message_received","text":"<pre><code> | on_message_received(msg: IncomingMessage) -&gt; None\n</code></pre> <p>Is called by the environment to the side channel. Can be called multiple times per step if multiple messages are meant for that SideChannel.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#set_property","title":"set_property","text":"<pre><code> | set_property(key: str, value: float) -&gt; None\n</code></pre> <p>Sets a property in the Unity Environment.</p> <p>Arguments:</p> <ul> <li><code>key</code>: The string identifier of the property.</li> <li><code>value</code>: The float value of the property.</li> </ul> <p></p>"},{"location":"Python-LLAPI-Documentation/#get_property","title":"get_property","text":"<pre><code> | get_property(key: str) -&gt; Optional[float]\n</code></pre> <p>Gets a property in the Unity Environment. If the property was not found, will return None.</p> <p>Arguments:</p> <ul> <li><code>key</code>: The string identifier of the property.</li> </ul> <p>Returns:</p> <p>The float value of the property or None.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#list_properties","title":"list_properties","text":"<pre><code> | list_properties() -&gt; List[str]\n</code></pre> <p>Returns a list of all the string identifiers of the properties currently present in the Unity Environment.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#get_property_dict_copy","title":"get_property_dict_copy","text":"<pre><code> | get_property_dict_copy() -&gt; Dict[str, float]\n</code></pre> <p>Returns a copy of the float properties.</p> <p>Returns:</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#mlagents_envsside_channelenvironment_parameters_channel","title":"mlagents_envs.side_channel.environment_parameters_channel","text":""},{"location":"Python-LLAPI-Documentation/#environmentparameterschannel-objects","title":"EnvironmentParametersChannel Objects","text":"<pre><code>class EnvironmentParametersChannel(SideChannel)\n</code></pre> <p>This is the SideChannel for sending environment parameters to Unity. You can send parameters to an environment with the command set_float_parameter.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#set_float_parameter","title":"set_float_parameter","text":"<pre><code> | set_float_parameter(key: str, value: float) -&gt; None\n</code></pre> <p>Sets a float environment parameter in the Unity Environment.</p> <p>Arguments:</p> <ul> <li><code>key</code>: The string identifier of the parameter.</li> <li><code>value</code>: The float value of the parameter.</li> </ul> <p></p>"},{"location":"Python-LLAPI-Documentation/#set_uniform_sampler_parameters","title":"set_uniform_sampler_parameters","text":"<pre><code> | set_uniform_sampler_parameters(key: str, min_value: float, max_value: float, seed: int) -&gt; None\n</code></pre> <p>Sets a uniform environment parameter sampler.</p> <p>Arguments:</p> <ul> <li><code>key</code>: The string identifier of the parameter.</li> <li><code>min_value</code>: The minimum of the sampling distribution.</li> <li><code>max_value</code>: The maximum of the sampling distribution.</li> <li><code>seed</code>: The random seed to initialize the sampler.</li> </ul> <p></p>"},{"location":"Python-LLAPI-Documentation/#set_gaussian_sampler_parameters","title":"set_gaussian_sampler_parameters","text":"<pre><code> | set_gaussian_sampler_parameters(key: str, mean: float, st_dev: float, seed: int) -&gt; None\n</code></pre> <p>Sets a gaussian environment parameter sampler.</p> <p>Arguments:</p> <ul> <li><code>key</code>: The string identifier of the parameter.</li> <li><code>mean</code>: The mean of the sampling distribution.</li> <li><code>st_dev</code>: The standard deviation of the sampling distribution.</li> <li><code>seed</code>: The random seed to initialize the sampler.</li> </ul> <p></p>"},{"location":"Python-LLAPI-Documentation/#set_multirangeuniform_sampler_parameters","title":"set_multirangeuniform_sampler_parameters","text":"<pre><code> | set_multirangeuniform_sampler_parameters(key: str, intervals: List[Tuple[float, float]], seed: int) -&gt; None\n</code></pre> <p>Sets a multirangeuniform environment parameter sampler.</p> <p>Arguments:</p> <ul> <li><code>key</code>: The string identifier of the parameter.</li> <li><code>intervals</code>: The lists of min and max that define each uniform distribution.</li> <li><code>seed</code>: The random seed to initialize the sampler.</li> </ul> <p></p>"},{"location":"Python-LLAPI-Documentation/#mlagents_envsside_channelside_channel","title":"mlagents_envs.side_channel.side_channel","text":""},{"location":"Python-LLAPI-Documentation/#sidechannel-objects","title":"SideChannel Objects","text":"<pre><code>class SideChannel(ABC)\n</code></pre> <p>The side channel just get access to a bytes buffer that will be shared between C# and Python. For example, We will create a specific side channel for properties that will be a list of string (fixed size) to float number, that can be modified by both C# and Python. All side channels are passed to the Env object at construction.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#queue_message_to_send","title":"queue_message_to_send","text":"<pre><code> | queue_message_to_send(msg: OutgoingMessage) -&gt; None\n</code></pre> <p>Queues a message to be sent by the environment at the next call to step.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#on_message_received_4","title":"on_message_received","text":"<pre><code> | @abstractmethod\n | on_message_received(msg: IncomingMessage) -&gt; None\n</code></pre> <p>Is called by the environment to the side channel. Can be called multiple times per step if multiple messages are meant for that SideChannel.</p> <p></p>"},{"location":"Python-LLAPI-Documentation/#channel_id","title":"channel_id","text":"<pre><code> | @property\n | channel_id() -&gt; uuid.UUID\n</code></pre> <p>Returns:</p> <p>The type of side channel used. Will influence how the data is processed in the environment.</p>"},{"location":"Python-LLAPI/","title":"Unity ML-Agents Python Low Level API","text":"<p>The <code>mlagents</code> Python package contains two components: a low level API which allows you to interact directly with a Unity Environment (<code>mlagents_envs</code>) and an entry point to train (<code>mlagents-learn</code>) which allows you to train agents in Unity Environments using our implementations of reinforcement learning or imitation learning. This document describes how to use the <code>mlagents_envs</code> API. For information on using <code>mlagents-learn</code>, see here. For Python Low Level API documentation, see here.</p> <p>The Python Low Level API can be used to interact directly with your Unity learning environment. As such, it can serve as the basis for developing and evaluating new learning algorithms.</p>"},{"location":"Python-LLAPI/#mlagents_envs","title":"mlagents_envs","text":"<p>The ML-Agents Toolkit Low Level API is a Python API for controlling the simulation loop of an environment or game built with Unity. This API is used by the training algorithms inside the ML-Agent Toolkit, but you can also write your own Python programs using this API.</p> <p>The key objects in the Python API include:</p> <ul> <li>UnityEnvironment \u2014 the main interface between the Unity application and   your code. Use UnityEnvironment to start and control a simulation or training   session.</li> <li>BehaviorName - is a string that identifies a behavior in the simulation.</li> <li>AgentId - is an <code>int</code> that serves as unique identifier for Agents in the   simulation.</li> <li>DecisionSteps \u2014 contains the data from Agents belonging to the same   \"Behavior\" in the simulation, such as observations and rewards. Only Agents   that requested a decision since the last call to <code>env.step()</code> are in the   DecisionSteps object.</li> <li>TerminalSteps \u2014 contains the data from Agents belonging to the same   \"Behavior\" in the simulation, such as observations and rewards. Only Agents   whose episode ended since the last call to <code>env.step()</code> are in the   TerminalSteps object.</li> <li>BehaviorSpec \u2014 describes the shape of the observation data inside   DecisionSteps and TerminalSteps as well as the expected action shapes.</li> </ul> <p>These classes are all defined in the base_env script.</p> <p>An Agent \"Behavior\" is a group of Agents identified by a <code>BehaviorName</code> that share the same observations and action types (described in their <code>BehaviorSpec</code>). You can think about Agent Behavior as a group of agents that will share the same policy. All Agents with the same behavior have the same goal and reward signals.</p> <p>To communicate with an Agent in a Unity environment from a Python program, the Agent in the simulation must have <code>Behavior Parameters</code> set to communicate. You must set the <code>Behavior Type</code> to <code>Default</code> and give it a <code>Behavior Name</code>.</p> <p>Notice: Currently communication between Unity and Python takes place over an open socket without authentication. As such, please make sure that the network where training takes place is secure. This will be addressed in a future release.</p>"},{"location":"Python-LLAPI/#loading-a-unity-environment","title":"Loading a Unity Environment","text":"<p>Python-side communication happens through <code>UnityEnvironment</code> which is located in <code>environment.py</code>. To load a Unity environment from a built binary file, put the file in the same directory as <code>envs</code>. For example, if the filename of your Unity environment is <code>3DBall</code>, in python, run:</p> <pre><code>from mlagents_envs.environment import UnityEnvironment\n# This is a non-blocking call that only loads the environment.\nenv = UnityEnvironment(file_name=\"3DBall\", seed=1, side_channels=[])\n# Start interacting with the environment.\nenv.reset()\nbehavior_names = env.behavior_specs.keys()\n...\n</code></pre> <p>NOTE: Please read Interacting with a Unity Environment to read more about how you can interact with the Unity environment from Python.</p> <ul> <li><code>file_name</code> is the name of the environment binary (located in the root   directory of the python project).</li> <li><code>worker_id</code> indicates which port to use for communication with the   environment. For use in parallel training regimes such as A3C.</li> <li><code>seed</code> indicates the seed to use when generating random numbers during the   training process. In environments which are stochastic, setting the seed   enables reproducible experimentation by ensuring that the environment and   trainers utilize the same random seed.</li> <li><code>side_channels</code> provides a way to exchange data with the Unity simulation that   is not related to the reinforcement learning loop. For example: configurations   or properties. More on them in the Side Channels doc.</li> </ul> <p>If you want to directly interact with the Editor, you need to use <code>file_name=None</code>, then press the Play button in the Editor when the message \"Start training by pressing the Play button in the Unity Editor\" is displayed on the screen</p>"},{"location":"Python-LLAPI/#interacting-with-a-unity-environment","title":"Interacting with a Unity Environment","text":""},{"location":"Python-LLAPI/#the-baseenv-interface","title":"The BaseEnv interface","text":"<p>A <code>BaseEnv</code> has the following methods:</p> <ul> <li>Reset : <code>env.reset()</code> Sends a signal to reset the environment. Returns   None.</li> <li>Step : <code>env.step()</code> Sends a signal to step the environment. Returns None.   Note that a \"step\" for Python does not correspond to either Unity <code>Update</code> nor   <code>FixedUpdate</code>. When <code>step()</code> or <code>reset()</code> is called, the Unity simulation will   move forward until an Agent in the simulation needs a input from Python to   act.</li> <li>Close : <code>env.close()</code> Sends a shutdown signal to the environment and   terminates the communication.</li> <li>Behavior Specs : <code>env.behavior_specs</code> Returns a Mapping of   <code>BehaviorName</code> to <code>BehaviorSpec</code> objects (read only).   A <code>BehaviorSpec</code> contains the observation shapes and the   <code>ActionSpec</code> (which defines the action shape). Note that   the <code>BehaviorSpec</code> for a specific group is fixed throughout the simulation.   The number of entries in the Mapping can change over time in the simulation   if new Agent behaviors are created in the simulation.</li> <li>Get Steps : <code>env.get_steps(behavior_name: str)</code> Returns a tuple   <code>DecisionSteps, TerminalSteps</code> corresponding to the behavior_name given as   input. The <code>DecisionSteps</code> contains information about the state of the agents   that need an action this step and have the behavior behavior_name. The   <code>TerminalSteps</code> contains information about the state of the agents whose   episode ended and have the behavior behavior_name. Both <code>DecisionSteps</code> and   <code>TerminalSteps</code> contain information such as the observations, the rewards and   the agent identifiers. <code>DecisionSteps</code> also contains action masks for the next   action while <code>TerminalSteps</code> contains the reason for termination (did the   Agent reach its maximum step and was interrupted). The data is in <code>np.array</code>   of which the first dimension is always the number of agents note that the   number of agents is not guaranteed to remain constant during the simulation   and it is not unusual to have either <code>DecisionSteps</code> or <code>TerminalSteps</code>   contain no Agents at all.</li> <li>Set Actions :<code>env.set_actions(behavior_name: str, action: ActionTuple)</code> Sets   the actions for a whole agent group. <code>action</code> is an <code>ActionTuple</code>, which   is made up of a 2D <code>np.array</code> of <code>dtype=np.int32</code> for discrete actions, and   <code>dtype=np.float32</code> for continuous actions. The first dimension of <code>np.array</code>   in the tuple is the number of agents that requested a decision since the   last call to <code>env.step()</code>. The second dimension is the number of discrete or   continuous actions for the corresponding array.</li> <li>Set Action for Agent :   <code>env.set_action_for_agent(agent_group: str, agent_id: int, action: ActionTuple)</code>   Sets the action for a specific Agent in an agent group. <code>agent_group</code> is the   name of the group the Agent belongs to and <code>agent_id</code> is the integer   identifier of the Agent. <code>action</code> is an <code>ActionTuple</code> as described above. Note: If no action is provided for an agent group between two calls to <code>env.step()</code> then the default action will be all zeros.</li> </ul>"},{"location":"Python-LLAPI/#decisionsteps-and-decisionstep","title":"DecisionSteps and DecisionStep","text":"<p><code>DecisionSteps</code> (with <code>s</code>) contains information about a whole batch of Agents while <code>DecisionStep</code> (no <code>s</code>) only contains information about a single Agent.</p> <p>A <code>DecisionSteps</code> has the following fields :</p> <ul> <li><code>obs</code> is a list of numpy arrays observations collected by the group of agent.   The first dimension of the array corresponds to the batch size of the group   (number of agents requesting a decision since the last call to <code>env.step()</code>).</li> <li><code>reward</code> is a float vector of length batch size. Corresponds to the rewards   collected by each agent since the last simulation step.</li> <li><code>agent_id</code> is an int vector of length batch size containing unique identifier   for the corresponding Agent. This is used to track Agents across simulation   steps.</li> <li><code>action_mask</code> is an optional list of two dimensional arrays of booleans which is only   available when using multi-discrete actions. Each array corresponds to an   action branch. The first dimension of each array is the batch size and the   second contains a mask for each action of the branch. If true, the action is   not available for the agent during this simulation step.</li> </ul> <p>It also has the two following methods:</p> <ul> <li><code>len(DecisionSteps)</code> Returns the number of agents requesting a decision since   the last call to <code>env.step()</code>.</li> <li><code>DecisionSteps[agent_id]</code> Returns a <code>DecisionStep</code> for the Agent with the   <code>agent_id</code> unique identifier.</li> </ul> <p>A <code>DecisionStep</code> has the following fields:</p> <ul> <li><code>obs</code> is a list of numpy arrays observations collected by the agent. (Each   array has one less dimension than the arrays in <code>DecisionSteps</code>)</li> <li><code>reward</code> is a float. Corresponds to the rewards collected by the agent since   the last simulation step.</li> <li><code>agent_id</code> is an int and an unique identifier for the corresponding Agent.</li> <li><code>action_mask</code> is an optional list of one dimensional arrays of booleans which is only   available when using multi-discrete actions. Each array corresponds to an   action branch. Each array contains a mask for each action of the branch. If   true, the action is not available for the agent during this simulation step.</li> </ul>"},{"location":"Python-LLAPI/#terminalsteps-and-terminalstep","title":"TerminalSteps and TerminalStep","text":"<p>Similarly to <code>DecisionSteps</code> and <code>DecisionStep</code>, <code>TerminalSteps</code> (with <code>s</code>) contains information about a whole batch of Agents while <code>TerminalStep</code> (no <code>s</code>) only contains information about a single Agent.</p> <p>A <code>TerminalSteps</code> has the following fields :</p> <ul> <li><code>obs</code> is a list of numpy arrays observations collected by the group of agent.   The first dimension of the array corresponds to the batch size of the group   (number of agents requesting a decision since the last call to <code>env.step()</code>).</li> <li><code>reward</code> is a float vector of length batch size. Corresponds to the rewards   collected by each agent since the last simulation step.</li> <li><code>agent_id</code> is an int vector of length batch size containing unique identifier   for the corresponding Agent. This is used to track Agents across simulation   steps.</li> <li><code>interrupted</code> is an array of booleans of length batch size. Is true if the  associated Agent was interrupted since the last decision step. For example,  if the Agent reached the maximum number of steps for the episode.</li> </ul> <p>It also has the two following methods:</p> <ul> <li><code>len(TerminalSteps)</code> Returns the number of agents requesting a decision since   the last call to <code>env.step()</code>.</li> <li><code>TerminalSteps[agent_id]</code> Returns a <code>TerminalStep</code> for the Agent with the   <code>agent_id</code> unique identifier.</li> </ul> <p>A <code>TerminalStep</code> has the following fields:</p> <ul> <li><code>obs</code> is a list of numpy arrays observations collected by the agent. (Each   array has one less dimension than the arrays in <code>TerminalSteps</code>)</li> <li><code>reward</code> is a float. Corresponds to the rewards collected by the agent since   the last simulation step.</li> <li><code>agent_id</code> is an int and an unique identifier for the corresponding Agent.</li> <li><code>interrupted</code> is a bool. Is true if the Agent was interrupted since the last  decision step. For example, if the Agent reached the maximum number of steps for  the episode.</li> </ul>"},{"location":"Python-LLAPI/#behaviorspec","title":"BehaviorSpec","text":"<p>A <code>BehaviorSpec</code> has the following fields :</p> <ul> <li><code>observation_specs</code> is a List of <code>ObservationSpec</code> objects : Each <code>ObservationSpec</code>   corresponds to an observation's properties: <code>shape</code> is a tuple of ints that   corresponds to the shape of the observation (without the number of agents dimension).   <code>dimension_property</code> is a tuple of flags containing extra information about how the   data should be processed in the corresponding dimension. <code>observation_type</code> is an enum   corresponding to what type of observation is generating the data (i.e., default, goal,   etc). Note that the <code>ObservationSpec</code> have the same ordering as the ordering of observations   in the DecisionSteps, DecisionStep, TerminalSteps and TerminalStep.</li> <li><code>action_spec</code> is an <code>ActionSpec</code> namedtuple that defines the number and types   of actions for the Agent.</li> </ul> <p>An <code>ActionSpec</code> has the following fields and properties: - <code>continuous_size</code> is the number of floats that constitute the continuous actions. - <code>discrete_size</code> is the number of branches (the number of independent actions) that   constitute the multi-discrete actions. - <code>discrete_branches</code> is a Tuple of ints. Each int corresponds to the number of   different options for each branch of the action. For example:   In a game direction input (no movement, left, right) and   jump input (no jump, jump) there will be two branches (direction and jump),   the first one with 3 options and the second with 2 options. (<code>discrete_size = 2</code>   and <code>discrete_action_branches = (3,2,)</code>)</p>"},{"location":"Python-LLAPI/#communicating-additional-information-with-the-environment","title":"Communicating additional information with the Environment","text":"<p>In addition to the means of communicating between Unity and python described above, we also provide methods for sharing agent-agnostic information. These additional methods are referred to as side channels. ML-Agents includes two ready-made side channels, described below. It is also possible to create custom side channels to communicate any additional data between a Unity environment and Python. Instructions for creating custom side channels can be found here.</p> <p>Side channels exist as separate classes which are instantiated, and then passed as list to the <code>side_channels</code> argument of the constructor of the <code>UnityEnvironment</code> class.</p> <pre><code>channel = MyChannel()\n\nenv = UnityEnvironment(side_channels = [channel])\n</code></pre> <p>Note : A side channel will only send/receive messages when <code>env.step</code> or <code>env.reset()</code> is called.</p>"},{"location":"Python-LLAPI/#engineconfigurationchannel","title":"EngineConfigurationChannel","text":"<p>The <code>EngineConfiguration</code> side channel allows you to modify the time-scale, resolution, and graphics quality of the environment. This can be useful for adjusting the environment to perform better during training, or be more interpretable during inference.</p> <p><code>EngineConfigurationChannel</code> has two methods :</p> <ul> <li><code>set_configuration_parameters</code> which takes the following arguments:</li> <li><code>width</code>: Defines the width of the display. (Must be set alongside height)</li> <li><code>height</code>: Defines the height of the display. (Must be set alongside width)</li> <li><code>quality_level</code>: Defines the quality level of the simulation.</li> <li><code>time_scale</code>: Defines the multiplier for the deltatime in the simulation. If     set to a higher value, time will pass faster in the simulation but the     physics may perform unpredictably.</li> <li><code>target_frame_rate</code>: Instructs simulation to try to render at a specified     frame rate.</li> <li><code>capture_frame_rate</code> Instructs the simulation to consider time between     updates to always be constant, regardless of the actual frame rate.</li> <li><code>set_configuration</code> with argument config which is an <code>EngineConfig</code> NamedTuple   object.</li> </ul> <p>For example, the following code would adjust the time-scale of the simulation to be 2x realtime.</p> <pre><code>from mlagents_envs.environment import UnityEnvironment\nfrom mlagents_envs.side_channel.engine_configuration_channel import EngineConfigurationChannel\n\nchannel = EngineConfigurationChannel()\n\nenv = UnityEnvironment(side_channels=[channel])\n\nchannel.set_configuration_parameters(time_scale = 2.0)\n\ni = env.reset()\n...\n</code></pre>"},{"location":"Python-LLAPI/#environmentparameters","title":"EnvironmentParameters","text":"<p>The <code>EnvironmentParameters</code> will allow you to get and set pre-defined numerical values in the environment. This can be useful for adjusting environment-specific settings, or for reading non-agent related information from the environment. You can call <code>get_property</code> and <code>set_property</code> on the side channel to read and write properties.</p> <p><code>EnvironmentParametersChannel</code> has one methods:</p> <ul> <li><code>set_float_parameter</code> Sets a float parameter in the Unity Environment.</li> <li>key: The string identifier of the property.</li> <li>value: The float value of the property.</li> </ul> <pre><code>from mlagents_envs.environment import UnityEnvironment\nfrom mlagents_envs.side_channel.environment_parameters_channel import EnvironmentParametersChannel\n\nchannel = EnvironmentParametersChannel()\n\nenv = UnityEnvironment(side_channels=[channel])\n\nchannel.set_float_parameter(\"parameter_1\", 2.0)\n\ni = env.reset()\n...\n</code></pre> <p>Once a property has been modified in Python, you can access it in C# after the next call to <code>step</code> as follows:</p> <pre><code>var envParameters = Academy.Instance.EnvironmentParameters;\nfloat property1 = envParameters.GetWithDefault(\"parameter_1\", 0.0f);\n</code></pre>"},{"location":"Python-LLAPI/#custom-side-channels","title":"Custom side channels","text":"<p>For information on how to make custom side channels for sending additional data types, see the documentation here.</p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/","title":"Table of Contents","text":"<ul> <li>mlagents.trainers.trainer.on_policy_trainer</li> <li>OnPolicyTrainer<ul> <li>__init__</li> <li>add_policy</li> </ul> </li> <li>mlagents.trainers.trainer.off_policy_trainer</li> <li>OffPolicyTrainer<ul> <li>__init__</li> <li>save_model</li> <li>save_replay_buffer</li> <li>load_replay_buffer</li> <li>add_policy</li> </ul> </li> <li>mlagents.trainers.trainer.rl_trainer</li> <li>RLTrainer<ul> <li>end_episode</li> <li>create_optimizer</li> <li>save_model</li> <li>advance</li> </ul> </li> <li>mlagents.trainers.trainer.trainer</li> <li>Trainer<ul> <li>__init__</li> <li>stats_reporter</li> <li>parameters</li> <li>get_max_steps</li> <li>get_step</li> <li>threaded</li> <li>should_still_train</li> <li>reward_buffer</li> <li>save_model</li> <li>end_episode</li> <li>create_policy</li> <li>add_policy</li> <li>get_policy</li> <li>advance</li> <li>publish_policy_queue</li> <li>subscribe_trajectory_queue</li> </ul> </li> <li>mlagents.trainers.settings</li> <li>deep_update_dict</li> <li>RewardSignalSettings<ul> <li>structure</li> </ul> </li> <li>ParameterRandomizationSettings<ul> <li>__str__</li> <li>structure</li> <li>unstructure</li> <li>apply</li> </ul> </li> <li>ConstantSettings<ul> <li>__str__</li> <li>apply</li> </ul> </li> <li>UniformSettings<ul> <li>__str__</li> <li>apply</li> </ul> </li> <li>GaussianSettings<ul> <li>__str__</li> <li>apply</li> </ul> </li> <li>MultiRangeUniformSettings<ul> <li>__str__</li> <li>apply</li> </ul> </li> <li>CompletionCriteriaSettings<ul> <li>need_increment</li> </ul> </li> <li>Lesson</li> <li>EnvironmentParameterSettings<ul> <li>structure</li> </ul> </li> <li>TrainerSettings<ul> <li>structure</li> </ul> </li> <li>CheckpointSettings<ul> <li>prioritize_resume_init</li> </ul> </li> <li>RunOptions<ul> <li>from_argparse</li> </ul> </li> </ul>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#mlagentstrainerstraineron_policy_trainer","title":"mlagents.trainers.trainer.on_policy_trainer","text":""},{"location":"Python-On-Off-Policy-Trainer-Documentation/#onpolicytrainer-objects","title":"OnPolicyTrainer Objects","text":"<pre><code>class OnPolicyTrainer(RLTrainer)\n</code></pre> <p>The PPOTrainer is an implementation of the PPO algorithm.</p> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#__init__","title":"__init__","text":"<pre><code> | __init__(behavior_name: str, reward_buff_cap: int, trainer_settings: TrainerSettings, training: bool, load: bool, seed: int, artifact_path: str)\n</code></pre> <p>Responsible for collecting experiences and training an on-policy model.</p> <p>Arguments:</p> <ul> <li><code>behavior_name</code>: The name of the behavior associated with trainer config</li> <li><code>reward_buff_cap</code>: Max reward history to track in the reward buffer</li> <li><code>trainer_settings</code>: The parameters for the trainer.</li> <li><code>training</code>: Whether the trainer is set for training.</li> <li><code>load</code>: Whether the model should be loaded.</li> <li><code>seed</code>: The seed the model will be initialized with</li> <li><code>artifact_path</code>: The directory within which to store artifacts from this trainer.</li> </ul> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#add_policy","title":"add_policy","text":"<pre><code> | add_policy(parsed_behavior_id: BehaviorIdentifiers, policy: Policy) -&gt; None\n</code></pre> <p>Adds policy to trainer.</p> <p>Arguments:</p> <ul> <li><code>parsed_behavior_id</code>: Behavior identifiers that the policy should belong to.</li> <li><code>policy</code>: Policy to associate with name_behavior_id.</li> </ul> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#mlagentstrainerstraineroff_policy_trainer","title":"mlagents.trainers.trainer.off_policy_trainer","text":""},{"location":"Python-On-Off-Policy-Trainer-Documentation/#offpolicytrainer-objects","title":"OffPolicyTrainer Objects","text":"<pre><code>class OffPolicyTrainer(RLTrainer)\n</code></pre> <p>The SACTrainer is an implementation of the SAC algorithm, with support for discrete actions and recurrent networks.</p> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#__init___1","title":"__init__","text":"<pre><code> | __init__(behavior_name: str, reward_buff_cap: int, trainer_settings: TrainerSettings, training: bool, load: bool, seed: int, artifact_path: str)\n</code></pre> <p>Responsible for collecting experiences and training an off-policy model.</p> <p>Arguments:</p> <ul> <li><code>behavior_name</code>: The name of the behavior associated with trainer config</li> <li><code>reward_buff_cap</code>: Max reward history to track in the reward buffer</li> <li><code>trainer_settings</code>: The parameters for the trainer.</li> <li><code>training</code>: Whether the trainer is set for training.</li> <li><code>load</code>: Whether the model should be loaded.</li> <li><code>seed</code>: The seed the model will be initialized with</li> <li><code>artifact_path</code>: The directory within which to store artifacts from this trainer.</li> </ul> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#save_model","title":"save_model","text":"<pre><code> | save_model() -&gt; None\n</code></pre> <p>Saves the final training model to memory Overrides the default to save the replay buffer.</p> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#save_replay_buffer","title":"save_replay_buffer","text":"<pre><code> | save_replay_buffer() -&gt; None\n</code></pre> <p>Save the training buffer's update buffer to a pickle file.</p> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#load_replay_buffer","title":"load_replay_buffer","text":"<pre><code> | load_replay_buffer() -&gt; None\n</code></pre> <p>Loads the last saved replay buffer from a file.</p> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#add_policy_1","title":"add_policy","text":"<pre><code> | add_policy(parsed_behavior_id: BehaviorIdentifiers, policy: Policy) -&gt; None\n</code></pre> <p>Adds policy to trainer.</p> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#mlagentstrainerstrainerrl_trainer","title":"mlagents.trainers.trainer.rl_trainer","text":""},{"location":"Python-On-Off-Policy-Trainer-Documentation/#rltrainer-objects","title":"RLTrainer Objects","text":"<pre><code>class RLTrainer(Trainer)\n</code></pre> <p>This class is the base class for trainers that use Reward Signals.</p> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#end_episode","title":"end_episode","text":"<pre><code> | end_episode() -&gt; None\n</code></pre> <p>A signal that the Episode has ended. The buffer must be reset. Get only called when the academy resets.</p> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#create_optimizer","title":"create_optimizer","text":"<pre><code> | @abc.abstractmethod\n | create_optimizer() -&gt; TorchOptimizer\n</code></pre> <p>Creates an Optimizer object</p> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#save_model_1","title":"save_model","text":"<pre><code> | save_model() -&gt; None\n</code></pre> <p>Saves the policy associated with this trainer.</p> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#advance","title":"advance","text":"<pre><code> | advance() -&gt; None\n</code></pre> <p>Steps the trainer, taking in trajectories and updates if ready. Will block and wait briefly if there are no trajectories.</p> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#mlagentstrainerstrainertrainer","title":"mlagents.trainers.trainer.trainer","text":""},{"location":"Python-On-Off-Policy-Trainer-Documentation/#trainer-objects","title":"Trainer Objects","text":"<pre><code>class Trainer(abc.ABC)\n</code></pre> <p>This class is the base class for the mlagents_envs.trainers</p> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#__init___2","title":"__init__","text":"<pre><code> | __init__(brain_name: str, trainer_settings: TrainerSettings, training: bool, load: bool, artifact_path: str, reward_buff_cap: int = 1)\n</code></pre> <p>Responsible for collecting experiences and training a neural network model.</p> <p>Arguments:</p> <ul> <li><code>brain_name</code>: Brain name of brain to be trained.</li> <li><code>trainer_settings</code>: The parameters for the trainer (dictionary).</li> <li><code>training</code>: Whether the trainer is set for training.</li> <li><code>artifact_path</code>: The directory within which to store artifacts from this trainer</li> <li><code>reward_buff_cap</code>:</li> </ul> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#stats_reporter","title":"stats_reporter","text":"<pre><code> | @property\n | stats_reporter()\n</code></pre> <p>Returns the stats reporter associated with this Trainer.</p> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#parameters","title":"parameters","text":"<pre><code> | @property\n | parameters() -&gt; TrainerSettings\n</code></pre> <p>Returns the trainer parameters of the trainer.</p> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#get_max_steps","title":"get_max_steps","text":"<pre><code> | @property\n | get_max_steps() -&gt; int\n</code></pre> <p>Returns the maximum number of steps. Is used to know when the trainer should be stopped.</p> <p>Returns:</p> <p>The maximum number of steps of the trainer</p> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#get_step","title":"get_step","text":"<pre><code> | @property\n | get_step() -&gt; int\n</code></pre> <p>Returns the number of steps the trainer has performed</p> <p>Returns:</p> <p>the step count of the trainer</p> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#threaded","title":"threaded","text":"<pre><code> | @property\n | threaded() -&gt; bool\n</code></pre> <p>Whether or not to run the trainer in a thread. True allows the trainer to update the policy while the environment is taking steps. Set to False to enforce strict on-policy updates (i.e. don't update the policy when taking steps.)</p> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#should_still_train","title":"should_still_train","text":"<pre><code> | @property\n | should_still_train() -&gt; bool\n</code></pre> <p>Returns whether or not the trainer should train. A Trainer could stop training if it wasn't training to begin with, or if max_steps is reached.</p> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#reward_buffer","title":"reward_buffer","text":"<pre><code> | @property\n | reward_buffer() -&gt; Deque[float]\n</code></pre> <p>Returns the reward buffer. The reward buffer contains the cumulative rewards of the most recent episodes completed by agents using this trainer.</p> <p>Returns:</p> <p>the reward buffer.</p> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#save_model_2","title":"save_model","text":"<pre><code> | @abc.abstractmethod\n | save_model() -&gt; None\n</code></pre> <p>Saves model file(s) for the policy or policies associated with this trainer.</p> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#end_episode_1","title":"end_episode","text":"<pre><code> | @abc.abstractmethod\n | end_episode()\n</code></pre> <p>A signal that the Episode has ended. The buffer must be reset. Get only called when the academy resets.</p> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#create_policy","title":"create_policy","text":"<pre><code> | @abc.abstractmethod\n | create_policy(parsed_behavior_id: BehaviorIdentifiers, behavior_spec: BehaviorSpec) -&gt; Policy\n</code></pre> <p>Creates a Policy object</p> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#add_policy_2","title":"add_policy","text":"<pre><code> | @abc.abstractmethod\n | add_policy(parsed_behavior_id: BehaviorIdentifiers, policy: Policy) -&gt; None\n</code></pre> <p>Adds policy to trainer.</p> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#get_policy","title":"get_policy","text":"<pre><code> | get_policy(name_behavior_id: str) -&gt; Policy\n</code></pre> <p>Gets policy associated with name_behavior_id</p> <p>Arguments:</p> <ul> <li><code>name_behavior_id</code>: Fully qualified behavior name</li> </ul> <p>Returns:</p> <p>Policy associated with name_behavior_id</p> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#advance_1","title":"advance","text":"<pre><code> | @abc.abstractmethod\n | advance() -&gt; None\n</code></pre> <p>Advances the trainer. Typically, this means grabbing trajectories from all subscribed trajectory queues (self.trajectory_queues), and updating a policy using the steps in them, and if needed pushing a new policy onto the right policy queues (self.policy_queues).</p> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#publish_policy_queue","title":"publish_policy_queue","text":"<pre><code> | publish_policy_queue(policy_queue: AgentManagerQueue[Policy]) -&gt; None\n</code></pre> <p>Adds a policy queue to the list of queues to publish to when this Trainer makes a policy update</p> <p>Arguments:</p> <ul> <li><code>policy_queue</code>: Policy queue to publish to.</li> </ul> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#subscribe_trajectory_queue","title":"subscribe_trajectory_queue","text":"<pre><code> | subscribe_trajectory_queue(trajectory_queue: AgentManagerQueue[Trajectory]) -&gt; None\n</code></pre> <p>Adds a trajectory queue to the list of queues for the trainer to ingest Trajectories from.</p> <p>Arguments:</p> <ul> <li><code>trajectory_queue</code>: Trajectory queue to read from.</li> </ul> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#mlagentstrainerssettings","title":"mlagents.trainers.settings","text":""},{"location":"Python-On-Off-Policy-Trainer-Documentation/#deep_update_dict","title":"deep_update_dict","text":"<pre><code>deep_update_dict(d: Dict, update_d: Mapping) -&gt; None\n</code></pre> <p>Similar to dict.update(), but works for nested dicts of dicts as well.</p> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#rewardsignalsettings-objects","title":"RewardSignalSettings Objects","text":"<pre><code>@attr.s(auto_attribs=True)\nclass RewardSignalSettings()\n</code></pre>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#structure","title":"structure","text":"<pre><code> | @staticmethod\n | structure(d: Mapping, t: type) -&gt; Any\n</code></pre> <p>Helper method to structure a Dict of RewardSignalSettings class. Meant to be registered with cattr.register_structure_hook() and called with cattr.structure(). This is needed to handle the special Enum selection of RewardSignalSettings classes.</p> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#parameterrandomizationsettings-objects","title":"ParameterRandomizationSettings Objects","text":"<pre><code>@attr.s(auto_attribs=True)\nclass ParameterRandomizationSettings(abc.ABC)\n</code></pre>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#__str__","title":"__str__","text":"<pre><code> | __str__() -&gt; str\n</code></pre> <p>Helper method to output sampler stats to console.</p> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#structure_1","title":"structure","text":"<pre><code> | @staticmethod\n | structure(d: Union[Mapping, float], t: type) -&gt; \"ParameterRandomizationSettings\"\n</code></pre> <p>Helper method to a ParameterRandomizationSettings class. Meant to be registered with cattr.register_structure_hook() and called with cattr.structure(). This is needed to handle the special Enum selection of ParameterRandomizationSettings classes.</p> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#unstructure","title":"unstructure","text":"<pre><code> | @staticmethod\n | unstructure(d: \"ParameterRandomizationSettings\") -&gt; Mapping\n</code></pre> <p>Helper method to a ParameterRandomizationSettings class. Meant to be registered with cattr.register_unstructure_hook() and called with cattr.unstructure().</p> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#apply","title":"apply","text":"<pre><code> | @abc.abstractmethod\n | apply(key: str, env_channel: EnvironmentParametersChannel) -&gt; None\n</code></pre> <p>Helper method to send sampler settings over EnvironmentParametersChannel Calls the appropriate sampler type set method.</p> <p>Arguments:</p> <ul> <li><code>key</code>: environment parameter to be sampled</li> <li><code>env_channel</code>: The EnvironmentParametersChannel to communicate sampler settings to environment</li> </ul> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#constantsettings-objects","title":"ConstantSettings Objects","text":"<pre><code>@attr.s(auto_attribs=True)\nclass ConstantSettings(ParameterRandomizationSettings)\n</code></pre>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#__str___1","title":"__str__","text":"<pre><code> | __str__() -&gt; str\n</code></pre> <p>Helper method to output sampler stats to console.</p> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#apply_1","title":"apply","text":"<pre><code> | apply(key: str, env_channel: EnvironmentParametersChannel) -&gt; None\n</code></pre> <p>Helper method to send sampler settings over EnvironmentParametersChannel Calls the constant sampler type set method.</p> <p>Arguments:</p> <ul> <li><code>key</code>: environment parameter to be sampled</li> <li><code>env_channel</code>: The EnvironmentParametersChannel to communicate sampler settings to environment</li> </ul> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#uniformsettings-objects","title":"UniformSettings Objects","text":"<pre><code>@attr.s(auto_attribs=True)\nclass UniformSettings(ParameterRandomizationSettings)\n</code></pre>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#__str___2","title":"__str__","text":"<pre><code> | __str__() -&gt; str\n</code></pre> <p>Helper method to output sampler stats to console.</p> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#apply_2","title":"apply","text":"<pre><code> | apply(key: str, env_channel: EnvironmentParametersChannel) -&gt; None\n</code></pre> <p>Helper method to send sampler settings over EnvironmentParametersChannel Calls the uniform sampler type set method.</p> <p>Arguments:</p> <ul> <li><code>key</code>: environment parameter to be sampled</li> <li><code>env_channel</code>: The EnvironmentParametersChannel to communicate sampler settings to environment</li> </ul> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#gaussiansettings-objects","title":"GaussianSettings Objects","text":"<pre><code>@attr.s(auto_attribs=True)\nclass GaussianSettings(ParameterRandomizationSettings)\n</code></pre>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#__str___3","title":"__str__","text":"<pre><code> | __str__() -&gt; str\n</code></pre> <p>Helper method to output sampler stats to console.</p> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#apply_3","title":"apply","text":"<pre><code> | apply(key: str, env_channel: EnvironmentParametersChannel) -&gt; None\n</code></pre> <p>Helper method to send sampler settings over EnvironmentParametersChannel Calls the gaussian sampler type set method.</p> <p>Arguments:</p> <ul> <li><code>key</code>: environment parameter to be sampled</li> <li><code>env_channel</code>: The EnvironmentParametersChannel to communicate sampler settings to environment</li> </ul> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#multirangeuniformsettings-objects","title":"MultiRangeUniformSettings Objects","text":"<pre><code>@attr.s(auto_attribs=True)\nclass MultiRangeUniformSettings(ParameterRandomizationSettings)\n</code></pre>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#__str___4","title":"__str__","text":"<pre><code> | __str__() -&gt; str\n</code></pre> <p>Helper method to output sampler stats to console.</p> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#apply_4","title":"apply","text":"<pre><code> | apply(key: str, env_channel: EnvironmentParametersChannel) -&gt; None\n</code></pre> <p>Helper method to send sampler settings over EnvironmentParametersChannel Calls the multirangeuniform sampler type set method.</p> <p>Arguments:</p> <ul> <li><code>key</code>: environment parameter to be sampled</li> <li><code>env_channel</code>: The EnvironmentParametersChannel to communicate sampler settings to environment</li> </ul> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#completioncriteriasettings-objects","title":"CompletionCriteriaSettings Objects","text":"<pre><code>@attr.s(auto_attribs=True)\nclass CompletionCriteriaSettings()\n</code></pre> <p>CompletionCriteriaSettings contains the information needed to figure out if the next lesson must start.</p> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#need_increment","title":"need_increment","text":"<pre><code> | need_increment(progress: float, reward_buffer: List[float], smoothing: float) -&gt; Tuple[bool, float]\n</code></pre> <p>Given measures, this method returns a boolean indicating if the lesson needs to change now, and a float corresponding to the new smoothed value.</p> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#lesson-objects","title":"Lesson Objects","text":"<pre><code>@attr.s(auto_attribs=True)\nclass Lesson()\n</code></pre> <p>Gathers the data of one lesson for one environment parameter including its name, the condition that must be fullfiled for the lesson to be completed and a sampler for the environment parameter. If the completion_criteria is None, then this is the last lesson in the curriculum.</p> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#environmentparametersettings-objects","title":"EnvironmentParameterSettings Objects","text":"<pre><code>@attr.s(auto_attribs=True)\nclass EnvironmentParameterSettings()\n</code></pre> <p>EnvironmentParameterSettings is an ordered list of lessons for one environment parameter.</p> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#structure_2","title":"structure","text":"<pre><code> | @staticmethod\n | structure(d: Mapping, t: type) -&gt; Dict[str, \"EnvironmentParameterSettings\"]\n</code></pre> <p>Helper method to structure a Dict of EnvironmentParameterSettings class. Meant to be registered with cattr.register_structure_hook() and called with cattr.structure().</p> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#trainersettings-objects","title":"TrainerSettings Objects","text":"<pre><code>@attr.s(auto_attribs=True)\nclass TrainerSettings(ExportableSettings)\n</code></pre>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#structure_3","title":"structure","text":"<pre><code> | @staticmethod\n | structure(d: Mapping, t: type) -&gt; Any\n</code></pre> <p>Helper method to structure a TrainerSettings class. Meant to be registered with cattr.register_structure_hook() and called with cattr.structure().</p> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#checkpointsettings-objects","title":"CheckpointSettings Objects","text":"<pre><code>@attr.s(auto_attribs=True)\nclass CheckpointSettings()\n</code></pre>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#prioritize_resume_init","title":"prioritize_resume_init","text":"<pre><code> | prioritize_resume_init() -&gt; None\n</code></pre> <p>Prioritize explicit command line resume/init over conflicting yaml options. if both resume/init are set at one place use resume</p> <p></p>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#runoptions-objects","title":"RunOptions Objects","text":"<pre><code>@attr.s(auto_attribs=True)\nclass RunOptions(ExportableSettings)\n</code></pre>"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#from_argparse","title":"from_argparse","text":"<pre><code> | @staticmethod\n | from_argparse(args: argparse.Namespace) -&gt; \"RunOptions\"\n</code></pre> <p>Takes an argparse.Namespace as specified in <code>parse_command_line</code>, loads input configuration files from file paths, and converts to a RunOptions instance.</p> <p>Arguments:</p> <ul> <li><code>args</code>: collection of command-line parameters passed to mlagents-learn</li> </ul> <p>Returns:</p> <p>RunOptions representing the passed in arguments, with trainer config, curriculum and sampler configs loaded from files.</p>"},{"location":"Python-Optimizer-Documentation/","title":"Table of Contents","text":"<ul> <li>mlagents.trainers.optimizer.torch_optimizer</li> <li>TorchOptimizer<ul> <li>create_reward_signals</li> <li>get_trajectory_value_estimates</li> </ul> </li> <li>mlagents.trainers.optimizer.optimizer</li> <li>Optimizer<ul> <li>update</li> </ul> </li> </ul>"},{"location":"Python-Optimizer-Documentation/#mlagentstrainersoptimizertorch_optimizer","title":"mlagents.trainers.optimizer.torch_optimizer","text":""},{"location":"Python-Optimizer-Documentation/#torchoptimizer-objects","title":"TorchOptimizer Objects","text":"<pre><code>class TorchOptimizer(Optimizer)\n</code></pre>"},{"location":"Python-Optimizer-Documentation/#create_reward_signals","title":"create_reward_signals","text":"<pre><code> | create_reward_signals(reward_signal_configs: Dict[RewardSignalType, RewardSignalSettings]) -&gt; None\n</code></pre> <p>Create reward signals</p> <p>Arguments:</p> <ul> <li><code>reward_signal_configs</code>: Reward signal config.</li> </ul> <p></p>"},{"location":"Python-Optimizer-Documentation/#get_trajectory_value_estimates","title":"get_trajectory_value_estimates","text":"<pre><code> | get_trajectory_value_estimates(batch: AgentBuffer, next_obs: List[np.ndarray], done: bool, agent_id: str = \"\") -&gt; Tuple[Dict[str, np.ndarray], Dict[str, float], Optional[AgentBufferField]]\n</code></pre> <p>Get value estimates and memories for a trajectory, in batch form.</p> <p>Arguments:</p> <ul> <li><code>batch</code>: An AgentBuffer that consists of a trajectory.</li> <li><code>next_obs</code>: the next observation (after the trajectory). Used for boostrapping     if this is not a termiinal trajectory.</li> <li><code>done</code>: Set true if this is a terminal trajectory.</li> <li><code>agent_id</code>: Agent ID of the agent that this trajectory belongs to.</li> </ul> <p>Returns:</p> <p>A Tuple of the Value Estimates as a Dict of [name, np.ndarray(trajectory_len)],     the final value estimate as a Dict of [name, float], and optionally (if using memories)     an AgentBufferField of initial critic memories to be used during update.</p> <p></p>"},{"location":"Python-Optimizer-Documentation/#mlagentstrainersoptimizeroptimizer","title":"mlagents.trainers.optimizer.optimizer","text":""},{"location":"Python-Optimizer-Documentation/#optimizer-objects","title":"Optimizer Objects","text":"<pre><code>class Optimizer(abc.ABC)\n</code></pre> <p>Creates loss functions and auxillary networks (e.g. Q or Value) needed for training. Provides methods to update the Policy.</p> <p></p>"},{"location":"Python-Optimizer-Documentation/#update","title":"update","text":"<pre><code> | @abc.abstractmethod\n | update(batch: AgentBuffer, num_sequences: int) -&gt; Dict[str, float]\n</code></pre> <p>Update the Policy based on the batch that was passed in.</p> <p>Arguments:</p> <ul> <li><code>batch</code>: AgentBuffer that contains the minibatch of data used for this update.</li> <li><code>num_sequences</code>: Number of recurrent sequences found in the minibatch.</li> </ul> <p>Returns:</p> <p>A Dict containing statistics (name, value) from the update (e.g. loss)</p>"},{"location":"Python-PettingZoo-API-Documentation/","title":"Table of Contents","text":"<ul> <li>mlagents_envs.envs.pettingzoo_env_factory</li> <li>PettingZooEnvFactory<ul> <li>env</li> </ul> </li> <li>mlagents_envs.envs.unity_aec_env</li> <li>UnityAECEnv<ul> <li>__init__</li> <li>step</li> <li>observe</li> <li>last</li> </ul> </li> <li>mlagents_envs.envs.unity_parallel_env</li> <li>UnityParallelEnv<ul> <li>__init__</li> <li>reset</li> </ul> </li> <li>mlagents_envs.envs.unity_pettingzoo_base_env</li> <li>UnityPettingzooBaseEnv<ul> <li>observation_spaces</li> <li>observation_space</li> <li>action_spaces</li> <li>action_space</li> <li>side_channel</li> <li>reset</li> <li>seed</li> <li>render</li> <li>close</li> </ul> </li> </ul>"},{"location":"Python-PettingZoo-API-Documentation/#mlagents_envsenvspettingzoo_env_factory","title":"mlagents_envs.envs.pettingzoo_env_factory","text":""},{"location":"Python-PettingZoo-API-Documentation/#pettingzooenvfactory-objects","title":"PettingZooEnvFactory Objects","text":"<pre><code>class PettingZooEnvFactory()\n</code></pre>"},{"location":"Python-PettingZoo-API-Documentation/#env","title":"env","text":"<pre><code> | env(seed: Optional[int] = None, **kwargs: Union[List, int, bool, None]) -&gt; UnityAECEnv\n</code></pre> <p>Creates the environment with env_id from unity's default_registry and wraps it in a UnityToPettingZooWrapper</p> <p>Arguments:</p> <ul> <li><code>seed</code>: The seed for the action spaces of the agents.</li> <li><code>kwargs</code>: Any argument accepted by <code>UnityEnvironment</code>class except file_name</li> </ul> <p></p>"},{"location":"Python-PettingZoo-API-Documentation/#mlagents_envsenvsunity_aec_env","title":"mlagents_envs.envs.unity_aec_env","text":""},{"location":"Python-PettingZoo-API-Documentation/#unityaecenv-objects","title":"UnityAECEnv Objects","text":"<pre><code>class UnityAECEnv(UnityPettingzooBaseEnv,  AECEnv)\n</code></pre> <p>Unity AEC (PettingZoo) environment wrapper.</p> <p></p>"},{"location":"Python-PettingZoo-API-Documentation/#__init__","title":"__init__","text":"<pre><code> | __init__(env: BaseEnv, seed: Optional[int] = None)\n</code></pre> <p>Initializes a Unity AEC environment wrapper.</p> <p>Arguments:</p> <ul> <li><code>env</code>: The UnityEnvironment that is being wrapped.</li> <li><code>seed</code>: The seed for the action spaces of the agents.</li> </ul> <p></p>"},{"location":"Python-PettingZoo-API-Documentation/#step","title":"step","text":"<pre><code> | step(action: Any) -&gt; None\n</code></pre> <p>Sets the action of the active agent and get the observation, reward, done and info of the next agent.</p> <p>Arguments:</p> <ul> <li><code>action</code>: The action for the active agent</li> </ul> <p></p>"},{"location":"Python-PettingZoo-API-Documentation/#observe","title":"observe","text":"<pre><code> | observe(agent_id)\n</code></pre> <p>Returns the observation an agent currently can make. <code>last()</code> calls this function.</p> <p></p>"},{"location":"Python-PettingZoo-API-Documentation/#last","title":"last","text":"<pre><code> | last(observe=True)\n</code></pre> <p>returns observation, cumulative reward, done, info for the current agent (specified by self.agent_selection)</p> <p></p>"},{"location":"Python-PettingZoo-API-Documentation/#mlagents_envsenvsunity_parallel_env","title":"mlagents_envs.envs.unity_parallel_env","text":""},{"location":"Python-PettingZoo-API-Documentation/#unityparallelenv-objects","title":"UnityParallelEnv Objects","text":"<pre><code>class UnityParallelEnv(UnityPettingzooBaseEnv,  ParallelEnv)\n</code></pre> <p>Unity Parallel (PettingZoo) environment wrapper.</p> <p></p>"},{"location":"Python-PettingZoo-API-Documentation/#__init___1","title":"__init__","text":"<pre><code> | __init__(env: BaseEnv, seed: Optional[int] = None)\n</code></pre> <p>Initializes a Unity Parallel environment wrapper.</p> <p>Arguments:</p> <ul> <li><code>env</code>: The UnityEnvironment that is being wrapped.</li> <li><code>seed</code>: The seed for the action spaces of the agents.</li> </ul> <p></p>"},{"location":"Python-PettingZoo-API-Documentation/#reset","title":"reset","text":"<pre><code> | reset() -&gt; Dict[str, Any]\n</code></pre> <p>Resets the environment.</p> <p></p>"},{"location":"Python-PettingZoo-API-Documentation/#mlagents_envsenvsunity_pettingzoo_base_env","title":"mlagents_envs.envs.unity_pettingzoo_base_env","text":""},{"location":"Python-PettingZoo-API-Documentation/#unitypettingzoobaseenv-objects","title":"UnityPettingzooBaseEnv Objects","text":"<pre><code>class UnityPettingzooBaseEnv()\n</code></pre> <p>Unity Petting Zoo base environment.</p> <p></p>"},{"location":"Python-PettingZoo-API-Documentation/#observation_spaces","title":"observation_spaces","text":"<pre><code> | @property\n | observation_spaces() -&gt; Dict[str, spaces.Space]\n</code></pre> <p>Return the observation spaces of all the agents.</p> <p></p>"},{"location":"Python-PettingZoo-API-Documentation/#observation_space","title":"observation_space","text":"<pre><code> | observation_space(agent: str) -&gt; Optional[spaces.Space]\n</code></pre> <p>The observation space of the current agent.</p> <p></p>"},{"location":"Python-PettingZoo-API-Documentation/#action_spaces","title":"action_spaces","text":"<pre><code> | @property\n | action_spaces() -&gt; Dict[str, spaces.Space]\n</code></pre> <p>Return the action spaces of all the agents.</p> <p></p>"},{"location":"Python-PettingZoo-API-Documentation/#action_space","title":"action_space","text":"<pre><code> | action_space(agent: str) -&gt; Optional[spaces.Space]\n</code></pre> <p>The action space of the current agent.</p> <p></p>"},{"location":"Python-PettingZoo-API-Documentation/#side_channel","title":"side_channel","text":"<pre><code> | @property\n | side_channel() -&gt; Dict[str, Any]\n</code></pre> <p>The side channels of the environment. You can access the side channels of an environment with <code>env.side_channel[&lt;name-of-channel&gt;]</code>.</p> <p></p>"},{"location":"Python-PettingZoo-API-Documentation/#reset_1","title":"reset","text":"<pre><code> | reset()\n</code></pre> <p>Resets the environment.</p> <p></p>"},{"location":"Python-PettingZoo-API-Documentation/#seed","title":"seed","text":"<pre><code> | seed(seed=None)\n</code></pre> <p>Reseeds the environment (making the resulting environment deterministic). <code>reset()</code> must be called after <code>seed()</code>, and before <code>step()</code>.</p> <p></p>"},{"location":"Python-PettingZoo-API-Documentation/#render","title":"render","text":"<pre><code> | render(mode=\"human\")\n</code></pre> <p>NOT SUPPORTED.</p> <p>Displays a rendered frame from the environment, if supported. Alternate render modes in the default environments are <code>'rgb_array'</code> which returns a numpy array and is supported by all environments outside of classic, and <code>'ansi'</code> which returns the strings printed (specific to classic environments).</p> <p></p>"},{"location":"Python-PettingZoo-API-Documentation/#close","title":"close","text":"<pre><code> | close() -&gt; None\n</code></pre> <p>Close the environment.</p>"},{"location":"Python-PettingZoo-API/","title":"Unity ML-Agents PettingZoo Wrapper","text":"<p>With the increasing interest in multi-agent training with a gym-like API, we provide a PettingZoo Wrapper around the Petting Zoo API. Our wrapper provides interfaces on top of our <code>UnityEnvironment</code> class, which is the default way of interfacing with a Unity environment via Python.</p>"},{"location":"Python-PettingZoo-API/#installation-and-examples","title":"Installation and Examples","text":"<p>The PettingZoo wrapper is part of the <code>mlgents_envs</code> package. Please refer to the mlagents_envs installation instructions.</p> <p>[Colab] PettingZoo Wrapper Example</p> <p>This colab notebook demonstrates the example usage of the wrapper, including installation, basic usages, and an example with our Striker vs Goalie environment which is a multi-agents environment with multiple different behavior names.</p>"},{"location":"Python-PettingZoo-API/#api-interface","title":"API interface","text":"<p>This wrapper is compatible with PettingZoo API. Please check out PettingZoo API page for more details. Here's an example of interacting with wrapped environment:</p> <pre><code>from mlagents_envs.environment import UnityEnvironment\nfrom mlagents_envs.envs import UnityToPettingZooWrapper\n\nunity_env = UnityEnvironment(\"StrikersVsGoalie\")\nenv = UnityToPettingZooWrapper(unity_env)\nenv.reset()\nfor agent in env.agent_iter():\n    observation, reward, done, info = env.last()\n    action = policy(observation, agent)\n    env.step(action)\n</code></pre>"},{"location":"Python-PettingZoo-API/#notes","title":"Notes","text":"<ul> <li>There is support for both AEC   and Parallel PettingZoo APIs.</li> <li>The AEC wrapper is compatible with PettingZoo (PZ) API interface but works in a slightly   different way under the hood. For the AEC API, Instead of stepping the environment in every <code>env.step(action)</code>,   the PZ wrapper will store the action, and will only perform environment stepping when all the   agents requesting for actions in the current step have been assigned an action. This is for   performance, considering that the communication between Unity and python is more efficient   when data are sent in batches.</li> <li>Since the actions for the AEC wrapper are stored without applying them to the environment until   all the actions are queued, some components of the API might behave in unexpected way. For example, a call   to <code>env.reward</code> should return the instantaneous reward for that particular step, but the true   reward would only be available when an actual environment step is performed. It's recommended that   you follow the API definition for training (access rewards from <code>env.last()</code> instead of   <code>env.reward</code>) and the underlying mechanism shouldn't affect training results.</li> <li>The environments will automatically reset when it's done, so <code>env.agent_iter(max_step)</code> will   keep going on until the specified max step is reached (default: <code>2**63</code>). There is no need to   call <code>env.reset()</code> except for the very beginning of instantiating an environment.</li> </ul>"},{"location":"Readme/","title":"Unity ML-Agents Toolkit","text":"<p>(latest release) (all releases)</p> <p>The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents. We provide implementations (based on PyTorch) of state-of-the-art algorithms to enable game developers and hobbyists to easily train intelligent agents for 2D, 3D and VR/AR games. Researchers can also use the provided simple-to-use Python API to train Agents using reinforcement learning, imitation learning, neuroevolution, or any other methods. These trained agents can be used for multiple purposes, including controlling NPC behavior (in a variety of settings such as multi-agent and adversarial), automated testing of game builds and evaluating different game design decisions pre-release. The ML-Agents Toolkit is mutually beneficial for both game developers and AI researchers as it provides a central platform where advances in AI can be evaluated on Unity\u2019s rich environments and then made accessible to the wider research and game developer communities.</p>"},{"location":"Readme/#features","title":"Features","text":"<ul> <li>17+ example Unity environments</li> <li>Support for multiple environment configurations and training scenarios</li> <li>Flexible Unity SDK that can be integrated into your game or custom Unity scene</li> <li>Support for training single-agent, multi-agent cooperative, and multi-agent   competitive scenarios via several Deep Reinforcement Learning algorithms (PPO, SAC, MA-POCA, self-play).</li> <li>Support for learning from demonstrations through two Imitation Learning algorithms (BC and GAIL).</li> <li>Quickly and easily add your own custom training algorithm and/or components.</li> <li>Easily definable Curriculum Learning scenarios for complex tasks</li> <li>Train robust agents using environment randomization</li> <li>Flexible agent control with On Demand Decision Making</li> <li>Train using multiple concurrent Unity environment instances</li> <li>Utilizes the Unity Inference Engine to   provide native cross-platform support</li> <li>Unity environment control from Python</li> <li>Wrap Unity learning environments as a gym environment</li> <li>Wrap Unity learning environments as a PettingZoo environment</li> </ul> <p>See our ML-Agents Overview page for detailed descriptions of all these features. Or go straight to our web docs.</p>"},{"location":"Readme/#releases-documentation","title":"Releases &amp; Documentation","text":"<p>Our latest, stable release is <code>Release 20</code>. Click here to get started with the latest release of ML-Agents.</p> <p>You can also check out our new web docs!</p> <p>The table below lists all our releases, including our <code>main</code> branch which is under active development and may be unstable. A few helpful guidelines: - The Versioning page overviews how we manage our GitHub   releases and the versioning process for each of the ML-Agents components. - The Releases page   contains details of the changes between releases. - The Migration page contains details on how to upgrade   from earlier releases of the ML-Agents Toolkit. - The Documentation links in the table below include installation and usage   instructions specific to each release. Remember to always use the   documentation that corresponds to the release version you're using. - The <code>com.unity.ml-agents</code> package is verified   for Unity 2020.1 and later. Verified packages releases are numbered 1.0.x.</p> Version Release Date Source Documentation Download Python Package Unity Package Release 20 November 21, 2022 source docs download 0.30.0 2.3.0 main (unstable) -- source docs download -- -- Verified Package 1.0.8 May 26, 2021 source docs download 0.16.1 1.0.8 <p>If you are a researcher interested in a discussion of Unity as an AI platform, see a pre-print of our reference paper on Unity and the ML-Agents Toolkit.</p> <p>If you use Unity or the ML-Agents Toolkit to conduct research, we ask that you cite the following paper as a reference:</p> <pre><code>@article{juliani2020,\n  title={Unity: A general platform for intelligent agents},\n  author={Juliani, Arthur and Berges, Vincent-Pierre and Teng, Ervin and Cohen, Andrew and Harper, Jonathan and Elion, Chris and Goy, Chris and Gao, Yuan and Henry, Hunter and Mattar, Marwan and Lange, Danny},\n  journal={arXiv preprint arXiv:1809.02627},\n  url={https://arxiv.org/pdf/1809.02627.pdf},\n  year={2020}\n}\n</code></pre> <p>Additionally, if you use the MA-POCA trainer in your research, we ask that you cite the following paper as a reference:</p> <pre><code>@article{cohen2022,\n  title={On the Use and Misuse of Absorbing States in Multi-agent Reinforcement Learning},\n  author={Cohen, Andrew and Teng, Ervin and Berges, Vincent-Pierre and Dong, Ruo-Ping and Henry, Hunter and Mattar, Marwan and Zook, Alexander and Ganguly, Sujoy},\n  journal={RL in Games Workshop AAAI 2022},\n  url={http://aaai-rlg.mlanctot.info/papers/AAAI22-RLG_paper_32.pdf},\n  year={2022}\n}\n</code></pre>"},{"location":"Readme/#additional-resources","title":"Additional Resources","text":"<p>We have a Unity Learn course, ML-Agents: Hummingbirds, that provides a gentle introduction to Unity and the ML-Agents Toolkit.</p> <p>We've also partnered with CodeMonkeyUnity to create a series of tutorial videos on how to implement and use the ML-Agents Toolkit.</p> <p>We have also published a series of blog posts that are relevant for ML-Agents:</p> <ul> <li>(July 12, 2021)   ML-Agents plays Dodgeball</li> <li>(May 5, 2021)   ML-Agents v2.0 release: Now supports training complex cooperative behaviors</li> <li>(December 28, 2020)   Happy holidays from the Unity ML-Agents team!</li> <li>(November 20, 2020)   How Eidos-Montr\u00e9al created Grid Sensors to improve observations for training agents</li> <li>(November 11, 2020)   2020 AI@Unity interns shoutout</li> <li>(May 12, 2020)   Announcing ML-Agents Unity Package v1.0!</li> <li>(February 28, 2020)   Training intelligent adversaries using self-play with ML-Agents</li> <li>(November 11, 2019)   Training your agents 7 times faster with ML-Agents</li> <li>(October 21, 2019)   The AI@Unity interns help shape the world</li> <li>(April 15, 2019)   Unity ML-Agents Toolkit v0.8: Faster training on real games</li> <li>(March 1, 2019)   Unity ML-Agents Toolkit v0.7: A leap towards cross-platform inference</li> <li>(December 17, 2018)   ML-Agents Toolkit v0.6: Improved usability of Brains and Imitation Learning</li> <li>(October 2, 2018)   Puppo, The Corgi: Cuteness Overload with the Unity ML-Agents Toolkit</li> <li>(September 11, 2018)   ML-Agents Toolkit v0.5, new resources for AI researchers available now</li> <li>(June 26, 2018)   Solving sparse-reward tasks with Curiosity</li> <li>(June 19, 2018)   Unity ML-Agents Toolkit v0.4 and Udacity Deep Reinforcement Learning Nanodegree</li> <li>(May 24, 2018)   Imitation Learning in Unity: The Workflow</li> <li>(March 15, 2018)   ML-Agents Toolkit v0.3 Beta released: Imitation Learning, feedback-driven features, and more</li> <li>(December 11, 2017)   Using Machine Learning Agents in a real game: a beginner\u2019s guide</li> <li>(December 8, 2017)   Introducing ML-Agents Toolkit v0.2: Curriculum Learning, new environments, and more</li> <li>(September 19, 2017)   Introducing: Unity Machine Learning Agents Toolkit</li> <li>Overviewing reinforcement learning concepts   (multi-armed bandit   and   Q-learning)</li> </ul>"},{"location":"Readme/#more-from-unity","title":"More from Unity","text":"<ul> <li>Unity Robotics</li> <li>Unity Computer Vision</li> </ul>"},{"location":"Readme/#community-and-feedback","title":"Community and Feedback","text":"<p>The ML-Agents Toolkit is an open-source project and we encourage and welcome contributions. If you wish to contribute, be sure to review our contribution guidelines and code of conduct.</p> <p>For problems with the installation and setup of the ML-Agents Toolkit, or discussions about how to best setup or train your agents, please create a new thread on the Unity ML-Agents forum and make sure to include as much detail as possible. If you run into any other problems using the ML-Agents Toolkit or have a specific feature request, please submit a GitHub issue.</p> <p>Please tell us which samples you would like to see shipped with the ML-Agents Unity package by replying to this forum thread.</p> <p>Your opinion matters a great deal to us. Only by hearing your thoughts on the Unity ML-Agents Toolkit can we continue to improve and grow. Please take a few minutes to let us know about it.</p> <p>For any other questions or feedback, connect directly with the ML-Agents team at ml-agents@unity3d.com.</p>"},{"location":"Readme/#privacy","title":"Privacy","text":"<p>In order to improve the developer experience for Unity ML-Agents Toolkit, we have added in-editor analytics. Please refer to \"Information that is passively collected by Unity\" in the Unity Privacy Policy.</p>"},{"location":"Training-Configuration-File/","title":"Training Configuration File","text":"<p>Table of Contents</p> <ul> <li>Common Trainer Configurations</li> <li>Trainer-specific Configurations</li> <li>PPO-specific Configurations</li> <li>SAC-specific Configurations</li> <li>Reward Signals</li> <li>Extrinsic Rewards</li> <li>Curiosity Intrinsic Reward</li> <li>GAIL Intrinsic Reward</li> <li>RND Intrinsic Reward</li> <li>Reward Signal Settings for SAC</li> <li>Behavioral Cloning</li> <li>Memory-enhanced Agents using Recurrent Neural Networks</li> <li>Self-Play</li> <li>Note on Reward Signals</li> <li>Note on Swap Steps</li> </ul>"},{"location":"Training-Configuration-File/#common-trainer-configurations","title":"Common Trainer Configurations","text":"<p>One of the first decisions you need to make regarding your training run is which trainer to use: PPO, SAC, or POCA. There are some training configurations that are common to both trainers (which we review now) and others that depend on the choice of the trainer (which we review on subsequent sections).</p> Setting Description <code>trainer_type</code> (default = <code>ppo</code>) The type of trainer to use: <code>ppo</code>,  <code>sac</code>, or <code>poca</code>. <code>summary_freq</code> (default = <code>50000</code>) Number of experiences that needs to be collected before generating and displaying training statistics. This determines the granularity of the graphs in Tensorboard. <code>time_horizon</code> (default = <code>64</code>) How many steps of experience to collect per-agent before adding it to the experience buffer. When this limit is reached before the end of an episode, a value estimate is used to predict the overall expected reward from the agent's current state. As such, this parameter trades off between a less biased, but higher variance estimate (long time horizon) and more biased, but less varied estimate (short time horizon). In cases where there are frequent rewards within an episode, or episodes are prohibitively large, a smaller number can be more ideal. This number should be large enough to capture all the important behavior within a sequence of an agent's actions.  Typical range: <code>32</code> - <code>2048</code> <code>max_steps</code> (default = <code>500000</code>) Total number of steps (i.e., observation collected and action taken) that must be taken in the environment (or across all environments if using multiple in parallel) before ending the training process. If you have multiple agents with the same behavior name within your environment, all steps taken by those agents will contribute to the same <code>max_steps</code> count. Typical range: <code>5e5</code> - <code>1e7</code> <code>keep_checkpoints</code> (default = <code>5</code>) The maximum number of model checkpoints to keep. Checkpoints are saved after the number of steps specified by the checkpoint_interval option. Once the maximum number of checkpoints has been reached, the oldest checkpoint is deleted when saving a new checkpoint. <code>even_checkpoints</code> (default = <code>false</code>) If set to true, ignores <code>checkpoint_interval</code> and evenly distributes checkpoints throughout training based on <code>keep_checkpoints</code>and <code>max_steps</code>, i.e. <code>checkpoint_interval = max_steps / keep_checkpoints</code>. Useful for cataloging agent behavior throughout training. <code>checkpoint_interval</code> (default = <code>500000</code>) The number of experiences collected between each checkpoint by the trainer. A maximum of <code>keep_checkpoints</code> checkpoints are saved before old ones are deleted. Each checkpoint saves the <code>.onnx</code> files in <code>results/</code> folder. <code>init_path</code> (default = None) Initialize trainer from a previously saved model. Note that the prior run should have used the same trainer configurations as the current run, and have been saved with the same version of ML-Agents. You can provide either the file name or the full path to the checkpoint, e.g. <code>{checkpoint_name.pt}</code> or <code>./models/{run-id}/{behavior_name}/{checkpoint_name.pt}</code>. This option is provided in case you want to initialize different behaviors from different runs or initialize from an older checkpoint; in most cases, it is sufficient to use the <code>--initialize-from</code> CLI parameter to initialize all models from the same run. <code>threaded</code> (default = <code>false</code>) Allow environments to step while updating the model. This might result in a training speedup, especially when using SAC. For best performance, leave setting to <code>false</code> when using self-play. <code>hyperparameters -&gt; learning_rate</code> (default = <code>3e-4</code>) Initial learning rate for gradient descent. Corresponds to the strength of each gradient descent update step. This should typically be decreased if training is unstable, and the reward does not consistently increase. Typical range: <code>1e-5</code> - <code>1e-3</code> <code>hyperparameters -&gt; batch_size</code> Number of experiences in each iteration of gradient descent. This should always be multiple times smaller than <code>buffer_size</code>. If you are using continuous actions, this value should be large (on the order of 1000s). If you are using only discrete actions, this value should be smaller (on the order of 10s).  Typical range: (Continuous - PPO): <code>512</code> - <code>5120</code>; (Continuous - SAC): <code>128</code> - <code>1024</code>; (Discrete, PPO &amp; SAC): <code>32</code> - <code>512</code>. <code>hyperparameters -&gt; buffer_size</code> (default = <code>10240</code> for PPO and <code>50000</code> for SAC) PPO: Number of experiences to collect before updating the policy model. Corresponds to how many experiences should be collected before we do any learning or updating of the model. This should be multiple times larger than <code>batch_size</code>. Typically a larger <code>buffer_size</code> corresponds to more stable training updates.  SAC: The max size of the experience buffer - on the order of thousands of times longer than your episodes, so that SAC can learn from old as well as new experiences. Typical range: PPO: <code>2048</code> - <code>409600</code>; SAC: <code>50000</code> - <code>1000000</code> <code>hyperparameters -&gt; learning_rate_schedule</code> (default = <code>linear</code> for PPO and <code>constant</code> for SAC) Determines how learning rate changes over time. For PPO, we recommend decaying learning rate until max_steps so learning converges more stably. However, for some cases (e.g. training for an unknown amount of time) this feature can be disabled. For SAC, we recommend holding learning rate constant so that the agent can continue to learn until its Q function converges naturally. <code>linear</code> decays the learning_rate linearly, reaching 0 at max_steps, while <code>constant</code> keeps the learning rate constant for the entire training run. <code>network_settings -&gt; hidden_units</code> (default = <code>128</code>) Number of units in the hidden layers of the neural network. Correspond to how many units are in each fully connected layer of the neural network. For simple problems where the correct action is a straightforward combination of the observation inputs, this should be small. For problems where the action is a very complex interaction between the observation variables, this should be larger.  Typical range: <code>32</code> - <code>512</code> <code>network_settings -&gt; num_layers</code> (default = <code>2</code>) The number of hidden layers in the neural network. Corresponds to how many hidden layers are present after the observation input, or after the CNN encoding of the visual observation. For simple problems, fewer layers are likely to train faster and more efficiently. More layers may be necessary for more complex control problems.  Typical range: <code>1</code> - <code>3</code> <code>network_settings -&gt; normalize</code> (default = <code>false</code>) Whether normalization is applied to the vector observation inputs. This normalization is based on the running average and variance of the vector observation. Normalization can be helpful in cases with complex continuous control problems, but may be harmful with simpler discrete control problems. <code>network_settings -&gt; vis_encode_type</code> (default = <code>simple</code>) Encoder type for encoding visual observations.  <code>simple</code> (default) uses a simple encoder which consists of two convolutional layers, <code>nature_cnn</code> uses the CNN implementation proposed by Mnih et al., consisting of three convolutional layers, and <code>resnet</code> uses the IMPALA Resnet consisting of three stacked layers, each with two residual blocks, making a much larger network than the other two. <code>match3</code> is a smaller CNN (Gudmundsoon et al.) that can capture more granular spatial relationships and is optimized for board games. <code>fully_connected</code> uses a single fully connected dense layer as encoder without any convolutional layers.  Due to the size of convolution kernel, there is a minimum observation size limitation that each encoder type can handle - <code>simple</code>: 20x20, <code>nature_cnn</code>: 36x36, <code>resnet</code>: 15 x 15, <code>match3</code>: 5x5.  <code>fully_connected</code> doesn't have convolutional layers and thus no size limits, but since it has less representation power it should be reserved for very small inputs. Note that using the <code>match3</code> CNN with very large visual input might result in a huge observation encoding and thus potentially slow down training or cause memory issues. <code>network_settings -&gt; conditioning_type</code> (default = <code>hyper</code>) Conditioning type for the policy using goal observations.  <code>none</code> treats the goal observations as regular observations, <code>hyper</code> (default) uses a HyperNetwork with goal observations as input to generate some of the weights of the policy. Note that when using <code>hyper</code> the number of parameters of the network increases greatly. Therefore, it is recommended to reduce the number of <code>hidden_units</code> when using this <code>conditioning_type</code>"},{"location":"Training-Configuration-File/#trainer-specific-configurations","title":"Trainer-specific Configurations","text":"<p>Depending on your choice of a trainer, there are additional trainer-specific configurations. We present them below in two separate tables, but keep in mind that you only need to include the configurations for the trainer selected (i.e. the <code>trainer</code> setting above).</p>"},{"location":"Training-Configuration-File/#ppo-specific-configurations","title":"PPO-specific Configurations","text":"Setting Description <code>hyperparameters -&gt; beta</code> (default = <code>5.0e-3</code>) Strength of the entropy regularization, which makes the policy \"more random.\" This ensures that agents properly explore the action space during training. Increasing this will ensure more random actions are taken. This should be adjusted such that the entropy (measurable from TensorBoard) slowly decreases alongside increases in reward. If entropy drops too quickly, increase beta. If entropy drops too slowly, decrease <code>beta</code>. Typical range: <code>1e-4</code> - <code>1e-2</code> <code>hyperparameters -&gt; epsilon</code> (default = <code>0.2</code>) Influences how rapidly the policy can evolve during training. Corresponds to the acceptable threshold of divergence between the old and new policies during gradient descent updating. Setting this value small will result in more stable updates, but will also slow the training process. Typical range: <code>0.1</code> - <code>0.3</code> <code>hyperparameters -&gt; beta_schedule</code> (default = <code>learning_rate_schedule</code>) Determines how beta changes over time. <code>linear</code> decays beta linearly, reaching 0 at max_steps, while <code>constant</code> keeps beta constant for the entire training run. If not explicitly set, the default beta schedule will be set to <code>hyperparameters -&gt; learning_rate_schedule</code>. <code>hyperparameters -&gt; epsilon_schedule</code> (default = <code>learning_rate_schedule</code>) Determines how epsilon changes over time (PPO only). <code>linear</code> decays epsilon linearly, reaching 0 at max_steps, while <code>constant</code> keeps the epsilon constant for the entire training run. If not explicitly set, the default epsilon schedule will be set to <code>hyperparameters -&gt; learning_rate_schedule</code>. <code>hyperparameters -&gt; lambd</code> (default = <code>0.95</code>) Regularization parameter (lambda) used when calculating the Generalized Advantage Estimate (GAE). This can be thought of as how much the agent relies on its current value estimate when calculating an updated value estimate. Low values correspond to relying more on the current value estimate (which can be high bias), and high values correspond to relying more on the actual rewards received in the environment (which can be high variance). The parameter provides a trade-off between the two, and the right value can lead to a more stable training process. Typical range: <code>0.9</code> - <code>0.95</code> <code>hyperparameters -&gt; num_epoch</code> (default = <code>3</code>) Number of passes to make through the experience buffer when performing gradient descent optimization.The larger the batch_size, the larger it is acceptable to make this. Decreasing this will ensure more stable updates, at the cost of slower learning. Typical range: <code>3</code> - <code>10</code> <code>hyperparameters -&gt; shared_critic</code> (default = <code>False</code>) Whether or not the policy and value function networks share a backbone. It may be useful to use a shared backbone when learning from image observations."},{"location":"Training-Configuration-File/#sac-specific-configurations","title":"SAC-specific Configurations","text":"Setting Description <code>hyperparameters -&gt; buffer_init_steps</code> (default = <code>0</code>) Number of experiences to collect into the buffer before updating the policy model. As the untrained policy is fairly random, pre-filling the buffer with random actions is useful for exploration. Typically, at least several episodes of experiences should be pre-filled. Typical range: <code>1000</code> - <code>10000</code> <code>hyperparameters -&gt; init_entcoef</code> (default = <code>1.0</code>) How much the agent should explore in the beginning of training. Corresponds to the initial entropy coefficient set at the beginning of training. In SAC, the agent is incentivized to make its actions entropic to facilitate better exploration. The entropy coefficient weighs the true reward with a bonus entropy reward. The entropy coefficient is automatically adjusted to a preset target entropy, so the <code>init_entcoef</code> only corresponds to the starting value of the entropy bonus. Increase init_entcoef to explore more in the beginning, decrease to converge to a solution faster. Typical range: (Continuous): <code>0.5</code> - <code>1.0</code>; (Discrete): <code>0.05</code> - <code>0.5</code> <code>hyperparameters -&gt; save_replay_buffer</code> (default = <code>false</code>) Whether to save and load the experience replay buffer as well as the model when quitting and re-starting training. This may help resumes go more smoothly, as the experiences collected won't be wiped. Note that replay buffers can be very large, and will take up a considerable amount of disk space. For that reason, we disable this feature by default. <code>hyperparameters -&gt; tau</code> (default = <code>0.005</code>) How aggressively to update the target network used for bootstrapping value estimation in SAC. Corresponds to the magnitude of the target Q update during the SAC model update. In SAC, there are two neural networks: the target and the policy. The target network is used to bootstrap the policy's estimate of the future rewards at a given state, and is fixed while the policy is being updated. This target is then slowly updated according to tau. Typically, this value should be left at 0.005. For simple problems, increasing tau to 0.01 might reduce the time it takes to learn, at the cost of stability. Typical range: <code>0.005</code> - <code>0.01</code> <code>hyperparameters -&gt; steps_per_update</code> (default = <code>1</code>) Average ratio of agent steps (actions) taken to updates made of the agent's policy. In SAC, a single \"update\" corresponds to grabbing a batch of size <code>batch_size</code> from the experience replay buffer, and using this mini batch to update the models. Note that it is not guaranteed that after exactly <code>steps_per_update</code> steps an update will be made, only that the ratio will hold true over many steps. Typically, <code>steps_per_update</code> should be greater than or equal to 1. Note that setting <code>steps_per_update</code> lower will improve sample efficiency (reduce the number of steps required to train) but increase the CPU time spent performing updates. For most environments where steps are fairly fast (e.g. our example environments) <code>steps_per_update</code> equal to the number of agents in the scene is a good balance. For slow environments (steps take 0.1 seconds or more) reducing <code>steps_per_update</code> may improve training speed. We can also change <code>steps_per_update</code> to lower than 1 to update more often than once per step, though this will usually result in a slowdown unless the environment is very slow. Typical range: <code>1</code> - <code>20</code> <code>hyperparameters -&gt; reward_signal_num_update</code> (default = <code>steps_per_update</code>) Number of steps per mini batch sampled and used for updating the reward signals. By default, we update the reward signals once every time the main policy is updated. However, to imitate the training procedure in certain imitation learning papers (e.g. Kostrikov et. al, Blond\u00e9 et. al), we may want to update the reward signal (GAIL) M times for every update of the policy. We can change <code>steps_per_update</code> of SAC to N, as well as <code>reward_signal_steps_per_update</code> under <code>reward_signals</code> to N / M to accomplish this. By default, <code>reward_signal_steps_per_update</code> is set to <code>steps_per_update</code>."},{"location":"Training-Configuration-File/#ma-poca-specific-configurations","title":"MA-POCA-specific Configurations","text":"<p>MA-POCA uses the same configurations as PPO, and there are no additional POCA-specific parameters.</p> <p>NOTE: Reward signals other than Extrinsic Rewards have not been extensively tested with MA-POCA, though they can still be added and used for training on a your-mileage-may-vary basis.</p>"},{"location":"Training-Configuration-File/#reward-signals","title":"Reward Signals","text":"<p>The <code>reward_signals</code> section enables the specification of settings for both extrinsic (i.e. environment-based) and intrinsic reward signals (e.g. curiosity and GAIL). Each reward signal should define at least two parameters, <code>strength</code> and <code>gamma</code>, in addition to any class-specific hyperparameters. Note that to remove a reward signal, you should delete its entry entirely from <code>reward_signals</code>. At least one reward signal should be left defined at all times. Provide the following configurations to design the reward signal for your training run.</p>"},{"location":"Training-Configuration-File/#extrinsic-rewards","title":"Extrinsic Rewards","text":"<p>Enable these settings to ensure that your training run incorporates your environment-based reward signal:</p> Setting Description <code>extrinsic -&gt; strength</code> (default = <code>1.0</code>) Factor by which to multiply the reward given by the environment. Typical ranges will vary depending on the reward signal. Typical range: <code>1.00</code> <code>extrinsic -&gt; gamma</code> (default = <code>0.99</code>) Discount factor for future rewards coming from the environment. This can be thought of as how far into the future the agent should care about possible rewards. In situations when the agent should be acting in the present in order to prepare for rewards in the distant future, this value should be large. In cases when rewards are more immediate, it can be smaller. Must be strictly smaller than 1. Typical range: <code>0.8</code> - <code>0.995</code>"},{"location":"Training-Configuration-File/#curiosity-intrinsic-reward","title":"Curiosity Intrinsic Reward","text":"<p>To enable curiosity, provide these settings:</p> Setting Description <code>curiosity -&gt; strength</code> (default = <code>1.0</code>) Magnitude of the curiosity reward generated by the intrinsic curiosity module. This should be scaled in order to ensure it is large enough to not be overwhelmed by extrinsic reward signals in the environment. Likewise it should not be too large to overwhelm the extrinsic reward signal. Typical range: <code>0.001</code> - <code>0.1</code> <code>curiosity -&gt; gamma</code> (default = <code>0.99</code>) Discount factor for future rewards. Typical range: <code>0.8</code> - <code>0.995</code> <code>curiosity -&gt; network_settings</code> Please see the documentation for <code>network_settings</code> under Common Trainer Configurations. The network specs used by the intrinsic curiosity model. The value should of <code>hidden_units</code> should be small enough to encourage the ICM to compress the original observation, but also not too small to prevent it from learning to differentiate between expected and actual observations. Typical range: <code>64</code> - <code>256</code> <code>curiosity -&gt; learning_rate</code> (default = <code>3e-4</code>) Learning rate used to update the intrinsic curiosity module. This should typically be decreased if training is unstable, and the curiosity loss is unstable. Typical range: <code>1e-5</code> - <code>1e-3</code>"},{"location":"Training-Configuration-File/#gail-intrinsic-reward","title":"GAIL Intrinsic Reward","text":"<p>To enable GAIL (assuming you have recorded demonstrations), provide these settings:</p> Setting Description <code>gail -&gt; strength</code> (default = <code>1.0</code>) Factor by which to multiply the raw reward. Note that when using GAIL with an Extrinsic Signal, this value should be set lower if your demonstrations are suboptimal (e.g. from a human), so that a trained agent will focus on receiving extrinsic rewards instead of exactly copying the demonstrations. Keep the strength below about 0.1 in those cases. Typical range: <code>0.01</code> - <code>1.0</code> <code>gail -&gt; gamma</code> (default = <code>0.99</code>) Discount factor for future rewards. Typical range: <code>0.8</code> - <code>0.9</code> <code>gail -&gt; demo_path</code> (Required, no default) The path to your .demo file or directory of .demo files. <code>gail -&gt; network_settings</code> Please see the documentation for <code>network_settings</code> under Common Trainer Configurations. The network specs for the GAIL discriminator. The value of <code>hidden_units</code> should be small enough to encourage the discriminator to compress the original observation, but also not too small to prevent it from learning to differentiate between demonstrated and actual behavior. Dramatically increasing this size will also negatively affect training times. Typical range: <code>64</code> - <code>256</code> <code>gail -&gt; learning_rate</code> (Optional, default = <code>3e-4</code>) Learning rate used to update the discriminator. This should typically be decreased if training is unstable, and the GAIL loss is unstable. Typical range: <code>1e-5</code> - <code>1e-3</code> <code>gail -&gt; use_actions</code> (default = <code>false</code>) Determines whether the discriminator should discriminate based on both observations and actions, or just observations. Set to True if you want the agent to mimic the actions from the demonstrations, and False if you'd rather have the agent visit the same states as in the demonstrations but with possibly different actions. Setting to False is more likely to be stable, especially with imperfect demonstrations, but may learn slower. <code>gail -&gt; use_vail</code> (default = <code>false</code>) Enables a variational bottleneck within the GAIL discriminator. This forces the discriminator to learn a more general representation and reduces its tendency to be \"too good\" at discriminating, making learning more stable. However, it does increase training time. Enable this if you notice your imitation learning is unstable, or unable to learn the task at hand."},{"location":"Training-Configuration-File/#rnd-intrinsic-reward","title":"RND Intrinsic Reward","text":"<p>Random Network Distillation (RND) is only available for the PyTorch trainers. To enable RND, provide these settings:</p> Setting Description <code>rnd -&gt; strength</code> (default = <code>1.0</code>) Magnitude of the curiosity reward generated by the intrinsic rnd module. This should be scaled in order to ensure it is large enough to not be overwhelmed by extrinsic reward signals in the environment. Likewise it should not be too large to overwhelm the extrinsic reward signal. Typical range: <code>0.001</code> - <code>0.01</code> <code>rnd -&gt; gamma</code> (default = <code>0.99</code>) Discount factor for future rewards. Typical range: <code>0.8</code> - <code>0.995</code> <code>rnd -&gt; network_settings</code> Please see the documentation for <code>network_settings</code> under Common Trainer Configurations. The network specs for the RND model. <code>curiosity -&gt; learning_rate</code> (default = <code>3e-4</code>) Learning rate used to update the RND module. This should be large enough for the RND module to quickly learn the state representation, but small enough to allow for stable learning. Typical range: <code>1e-5</code> - <code>1e-3</code>"},{"location":"Training-Configuration-File/#behavioral-cloning","title":"Behavioral Cloning","text":"<p>To enable Behavioral Cloning as a pre-training option (assuming you have recorded demonstrations), provide the following configurations under the <code>behavioral_cloning</code> section:</p> Setting Description <code>demo_path</code> (Required, no default) The path to your .demo file or directory of .demo files. <code>strength</code> (default = <code>1.0</code>) Learning rate of the imitation relative to the learning rate of PPO, and roughly corresponds to how strongly we allow BC to influence the policy. Typical range: <code>0.1</code> - <code>0.5</code> <code>steps</code> (default = <code>0</code>) During BC, it is often desirable to stop using demonstrations after the agent has \"seen\" rewards, and allow it to optimize past the available demonstrations and/or generalize outside of the provided demonstrations. steps corresponds to the training steps over which BC is active. The learning rate of BC will anneal over the steps. Set the steps to 0 for constant imitation over the entire training run. <code>batch_size</code> (default = <code>batch_size</code> of trainer) Number of demonstration experiences used for one iteration of a gradient descent update. If not specified, it will default to the <code>batch_size</code> of the trainer. Typical range: (Continuous): <code>512</code> - <code>5120</code>; (Discrete): <code>32</code> - <code>512</code> <code>num_epoch</code> (default = <code>num_epoch</code> of trainer) Number of passes through the experience buffer during gradient descent. If not specified, it will default to the number of epochs set for PPO. Typical range: <code>3</code> - <code>10</code> <code>samples_per_update</code> (default = <code>0</code>) Maximum number of samples to use during each imitation update. You may want to lower this if your demonstration dataset is very large to avoid overfitting the policy on demonstrations. Set to 0 to train over all of the demonstrations at each update step. Typical range: <code>buffer_size</code>"},{"location":"Training-Configuration-File/#memory-enhanced-agents-using-recurrent-neural-networks","title":"Memory-enhanced Agents using Recurrent Neural Networks","text":"<p>You can enable your agents to use memory by adding a <code>memory</code> section under <code>network_settings</code>, and setting <code>memory_size</code> and <code>sequence_length</code>:</p> Setting Description <code>network_settings -&gt; memory -&gt; memory_size</code> (default = <code>128</code>) Size of the memory an agent must keep. In order to use a LSTM, training requires a sequence of experiences instead of single experiences. Corresponds to the size of the array of floating point numbers used to store the hidden state of the recurrent neural network of the policy. This value must be a multiple of 2, and should scale with the amount of information you expect the agent will need to remember in order to successfully complete the task. Typical range: <code>32</code> - <code>256</code> <code>network_settings -&gt; memory -&gt; sequence_length</code> (default = <code>64</code>) Defines how long the sequences of experiences must be while training. Note that if this number is too small, the agent will not be able to remember things over longer periods of time. If this number is too large, the neural network will take longer to train. Typical range: <code>4</code> - <code>128</code> <p>A few considerations when deciding to use memory:</p> <ul> <li>LSTM does not work well with continuous actions. Please use   discrete actions for better results.</li> <li>Adding a recurrent layer increases the complexity of the neural network, it is   recommended to decrease <code>num_layers</code> when using recurrent.</li> <li>It is required that <code>memory_size</code> be divisible by 2.</li> </ul>"},{"location":"Training-Configuration-File/#self-play","title":"Self-Play","text":"<p>Training with self-play adds additional confounding factors to the usual issues faced by reinforcement learning. In general, the tradeoff is between the skill level and generality of the final policy and the stability of learning. Training against a set of slowly or unchanging adversaries with low diversity results in a more stable learning process than training against a set of quickly changing adversaries with high diversity. With this context, this guide discusses the exposed self-play hyperparameters and intuitions for tuning them.</p> <p>If your environment contains multiple agents that are divided into teams, you can leverage our self-play training option by providing these configurations for each Behavior:</p> Setting Description <code>save_steps</code> (default = <code>20000</code>) Number of trainer steps between snapshots. For example, if <code>save_steps=10000</code> then a snapshot of the current policy will be saved every <code>10000</code> trainer steps. Note, trainer steps are counted per agent. For more information, please see the migration doc after v0.13. A larger value of <code>save_steps</code> will yield a set of opponents that cover a wider range of skill levels and possibly play styles since the policy receives more training. As a result, the agent trains against a wider variety of opponents. Learning a policy to defeat more diverse opponents is a harder problem and so may require more overall training steps but also may lead to more general and robust policy at the end of training. This value is also dependent on how intrinsically difficult the environment is for the agent.  Typical range: <code>10000</code> - <code>100000</code> <code>team_change</code> (default = <code>5 * save_steps</code>) Number of trainer_steps between switching the learning team. This is the number of trainer steps the teams associated with a specific ghost trainer will train before a different team becomes the new learning team. It is possible that, in asymmetric games, opposing teams require fewer trainer steps to make similar performance gains. This enables users to train a more complicated team of agents for more trainer steps than a simpler team of agents per team switch. A larger value of <code>team-change</code> will allow the agent to train longer against it's opponents. The longer an agent trains against the same set of opponents the more able it will be to defeat them. However, training against them for too long may result in overfitting to the particular opponent strategies and so the agent may fail against the next batch of opponents.  The value of <code>team-change</code> will determine how many snapshots of the agent's policy are saved to be used as opponents for the other team. So, we recommend setting this value as a function of the <code>save_steps</code> parameter discussed previously.  Typical range: 4x-10x where x=<code>save_steps</code> <code>swap_steps</code> (default = <code>10000</code>) Number of ghost steps (not trainer steps) between swapping the opponents policy with a different snapshot. A 'ghost step' refers to a step taken by an agent that is following a fixed policy and not learning. The reason for this distinction is that in asymmetric games, we may have teams with an unequal number of agents e.g. a 2v1 scenario like our Strikers Vs Goalie example environment. The team with two agents collects twice as many agent steps per environment step as the team with one agent. Thus, these two values will need to be distinct to ensure that the same number of trainer steps corresponds to the same number of opponent swaps for each team. The formula for <code>swap_steps</code> if a user desires <code>x</code> swaps of a team with <code>num_agents</code> agents against an opponent team with <code>num_opponent_agents</code> agents during <code>team-change</code> total steps is: <code>(num_agents / num_opponent_agents) * (team_change / x)</code>  Typical range: <code>10000</code> - <code>100000</code> <code>play_against_latest_model_ratio</code> (default = <code>0.5</code>) Probability an agent will play against the latest opponent policy. With probability 1 - <code>play_against_latest_model_ratio</code>, the agent will play against a snapshot of its opponent from a past iteration.  A larger value of <code>play_against_latest_model_ratio</code> indicates that an agent will be playing against the current opponent more often. Since the agent is updating it's policy, the opponent will be different from iteration to iteration. This can lead to an unstable learning environment, but poses the agent with an auto-curricula of more increasingly challenging situations which may lead to a stronger final policy.  Typical range: <code>0.0</code> - <code>1.0</code> <code>window</code> (default = <code>10</code>) Size of the sliding window of past snapshots from which the agent's opponents are sampled. For example, a <code>window</code> size of 5 will save the last 5 snapshots taken. Each time a new snapshot is taken, the oldest is discarded. A larger value of <code>window</code> means that an agent's pool of opponents will contain a larger diversity of behaviors since it will contain policies from earlier in the training run. Like in the <code>save_steps</code> hyperparameter, the agent trains against a wider variety of opponents. Learning a policy to defeat more diverse opponents is a harder problem and so may require more overall training steps but also may lead to more general and robust policy at the end of training.  Typical range: <code>5</code> - <code>30</code>"},{"location":"Training-Configuration-File/#note-on-reward-signals","title":"Note on Reward Signals","text":"<p>We make the assumption that the final reward in a trajectory corresponds to the outcome of an episode. A final reward of +1 indicates winning, -1 indicates losing and 0 indicates a draw. The ELO calculation (discussed below) depends on this final reward being either +1, 0, -1.</p> <p>The reward signal should still be used as described in the documentation for the other trainers. However, we encourage users to be a bit more conservative when shaping reward functions due to the instability and non-stationarity of learning in adversarial games. Specifically, we encourage users to begin with the simplest possible reward function (+1 winning, -1 losing) and to allow for more iterations of training to compensate for the sparsity of reward.</p>"},{"location":"Training-Configuration-File/#note-on-swap-steps","title":"Note on Swap Steps","text":"<p>As an example, in a 2v1 scenario, if we want the swap to occur x=4 times during team-change=200000 steps, the swap_steps for the team of one agent is:</p> <p>swap_steps = (1 / 2) * (200000 / 4) = 25000 The swap_steps for the team of two agents is:</p> <p>swap_steps = (2 / 1) * (200000 / 4) = 100000 Note, with equal team sizes, the first term is equal to 1 and swap_steps can be calculated by just dividing the total steps by the desired number of swaps.</p> <p>A larger value of swap_steps means that an agent will play against the same fixed opponent for a longer number of training iterations. This results in a more stable training scenario, but leaves the agent open to the risk of overfitting it's behavior for this particular opponent. Thus, when a new opponent is swapped, the agent may lose more often than expected.</p>"},{"location":"Training-ML-Agents/","title":"Training ML-Agents","text":"<p>Table of Contents</p> <ul> <li>Training ML-Agents</li> <li>Training with mlagents-learn<ul> <li>Starting Training</li> <li>Observing Training</li> <li>Stopping and Resuming Training</li> <li>Loading an Existing Model</li> </ul> </li> <li>Training Configurations<ul> <li>Adding CLI Arguments to the Training Configuration file</li> <li>Environment settings</li> <li>Engine settings</li> <li>Checkpoint settings</li> <li>Torch settings:</li> <li>Behavior Configurations</li> <li>Default Behavior Settings</li> <li>Environment Parameters</li> <li>Environment Parameter Randomization<ul> <li>Supported Sampler Types</li> <li>Training with Environment Parameter Randomization</li> </ul> </li> <li>Curriculum<ul> <li>Training with a Curriculum</li> </ul> </li> <li>Training Using Concurrent Unity Instances</li> </ul> </li> </ul> <p>For a broad overview of reinforcement learning, imitation learning and all the training scenarios, methods and options within the ML-Agents Toolkit, see ML-Agents Toolkit Overview.</p> <p>Once your learning environment has been created and is ready for training, the next step is to initiate a training run. Training in the ML-Agents Toolkit is powered by a dedicated Python package, <code>mlagents</code>. This package exposes a command <code>mlagents-learn</code> that is the single entry point for all training workflows (e.g. reinforcement leaning, imitation learning, curriculum learning). Its implementation can be found at ml-agents/mlagents/trainers/learn.py.</p>"},{"location":"Training-ML-Agents/#training-with-mlagents-learn","title":"Training with mlagents-learn","text":""},{"location":"Training-ML-Agents/#starting-training","title":"Starting Training","text":"<p><code>mlagents-learn</code> is the main training utility provided by the ML-Agents Toolkit. It accepts a number of CLI options in addition to a YAML configuration file that contains all the configurations and hyperparameters to be used during training. The set of configurations and hyperparameters to include in this file depend on the agents in your environment and the specific training method you wish to utilize. Keep in mind that the hyperparameter values can have a big impact on the training performance (i.e. your agent's ability to learn a policy that solves the task). In this page, we will review all the hyperparameters for all training methods and provide guidelines and advice on their values.</p> <p>To view a description of all the CLI options accepted by <code>mlagents-learn</code>, use the <code>--help</code>:</p> <pre><code>mlagents-learn --help\n</code></pre> <p>The basic command for training is:</p> <pre><code>mlagents-learn &lt;trainer-config-file&gt; --env=&lt;env_name&gt; --run-id=&lt;run-identifier&gt;\n</code></pre> <p>where</p> <ul> <li><code>&lt;trainer-config-file&gt;</code> is the file path of the trainer configuration YAML.   This contains all the hyperparameter values. We offer a detailed guide on the   structure of this file and the meaning of the hyperparameters (and advice on   how to set them) in the dedicated   Training Configurations section below.</li> <li><code>&lt;env_name&gt;</code>(Optional) is the name (including path) of your   Unity executable containing the agents   to be trained. If <code>&lt;env_name&gt;</code> is not passed, the training will happen in the   Editor. Press the Play button in Unity when the message \"Start training   by pressing the Play button in the Unity Editor\" is displayed on the screen.</li> <li><code>&lt;run-identifier&gt;</code> is a unique name you can use to identify the results of   your training runs.</li> </ul> <p>See the Getting Started Guide for a sample execution of the <code>mlagents-learn</code> command.</p>"},{"location":"Training-ML-Agents/#observing-training","title":"Observing Training","text":"<p>Regardless of which training methods, configurations or hyperparameters you provide, the training process will always generate three artifacts, all found in the <code>results/&lt;run-identifier&gt;</code> folder:</p> <ol> <li>Summaries: these are training metrics that    are updated throughout the training process. They are helpful to monitor your    training performance and may help inform how to update your hyperparameter    values. See Using TensorBoard for more details on how    to visualize the training metrics.</li> <li>Models: these contain the model checkpoints that    are updated throughout training and the final model file (<code>.onnx</code>). This final    model file is generated once either when training completes or is    interrupted.</li> <li>Timers file (under <code>results/&lt;run-identifier&gt;/run_logs</code>): this contains aggregated    metrics on your training process, including time spent on specific code    blocks. See Profiling in Python for more information    on the timers generated.</li> </ol> <p>These artifacts are updated throughout the training process and finalized when training is completed or is interrupted.</p>"},{"location":"Training-ML-Agents/#stopping-and-resuming-training","title":"Stopping and Resuming Training","text":"<p>To interrupt training and save the current progress, hit <code>Ctrl+C</code> once and wait for the model(s) to be saved out.</p> <p>To resume a previously interrupted or completed training run, use the <code>--resume</code> flag and make sure to specify the previously used run ID.</p> <p>If you would like to re-run a previously interrupted or completed training run and re-use the same run ID (in this case, overwriting the previously generated artifacts), then use the <code>--force</code> flag.</p>"},{"location":"Training-ML-Agents/#loading-an-existing-model","title":"Loading an Existing Model","text":"<p>You can also use this mode to run inference of an already-trained model in Python by using both the <code>--resume</code> and <code>--inference</code> flags. Note that if you want to run inference in Unity, you should use the Unity Inference Engine.</p> <p>Additionally, if the network architecture changes, you may still load an existing model, but ML-Agents will only load the parts of the model it can load and ignore all others. For instance, if you add a new reward signal, the existing model will load but the new reward signal will be initialized from scratch. If you have a model with a visual encoder (CNN) but change the <code>hidden_units</code>, the CNN will be loaded but the body of the network will be initialized from scratch.</p> <p>Alternatively, you might want to start a new training run but initialize it using an already-trained model. You may want to do this, for instance, if your environment changed and you want a new model, but the old behavior is still better than random. You can do this by specifying <code>--initialize-from=&lt;run-identifier&gt;</code>, where <code>&lt;run-identifier&gt;</code> is the old run ID.</p>"},{"location":"Training-ML-Agents/#training-configurations","title":"Training Configurations","text":"<p>The Unity ML-Agents Toolkit provides a wide range of training scenarios, methods and options. As such, specific training runs may require different training configurations and may generate different artifacts and TensorBoard statistics. This section offers a detailed guide into how to manage the different training set-ups withing the toolkit.</p> <p>More specifically, this section offers a detailed guide on the command-line flags for <code>mlagents-learn</code> that control the training configurations:</p> <ul> <li><code>&lt;trainer-config-file&gt;</code>: defines the training hyperparameters for each   Behavior in the scene, and the set-ups for the environment parameters   (Curriculum Learning and Environment Parameter Randomization)</li> </ul> <p>It is important to highlight that successfully training a Behavior in the ML-Agents Toolkit involves tuning the training hyperparameters and configuration. This guide contains some best practices for tuning the training process when the default parameters don't seem to be giving the level of performance you would like. We provide sample configuration files for our example environments in the config/ directory. The <code>config/ppo/3DBall.yaml</code> was used to train the 3D Balance Ball in the Getting Started guide. That configuration file uses the PPO trainer, but we also have configuration files for SAC and GAIL.</p> <p>Additionally, the set of configurations you provide depend on the training functionalities you use (see ML-Agents Toolkit Overview for a description of all the training functionalities). Each functionality you add typically has its own training configurations. For instance:</p> <ul> <li>Use PPO or SAC?</li> <li>Use Recurrent Neural Networks for adding memory to your agents?</li> <li>Use the intrinsic curiosity module?</li> <li>Ignore the environment reward signal?</li> <li>Pre-train using behavioral cloning? (Assuming you have recorded   demonstrations.)</li> <li>Include the GAIL intrinsic reward signals? (Assuming you have recorded   demonstrations.)</li> <li>Use self-play? (Assuming your environment includes multiple agents.)</li> </ul> <p>The trainer config file, <code>&lt;trainer-config-file&gt;</code>, determines the features you will use during training, and the answers to the above questions will dictate its contents. The rest of this guide breaks down the different sub-sections of the trainer config file and explains the possible settings for each. If you need a list of all the trainer configurations, please see Training Configuration File.</p> <p>NOTE: The configuration file format has been changed between 0.17.0 and 0.18.0 and between 0.18.0 and onwards. To convert an old set of configuration files (trainer config, curriculum, and sampler files) to the new format, a script has been provided. Run <code>python -m mlagents.trainers.upgrade_config -h</code> in your console to see the script's usage.</p>"},{"location":"Training-ML-Agents/#adding-cli-arguments-to-the-training-configuration-file","title":"Adding CLI Arguments to the Training Configuration file","text":"<p>Additionally, within the training configuration YAML file, you can also add the CLI arguments (such as <code>--num-envs</code>).</p> <p>Reminder that a detailed description of all the CLI arguments can be found by using the help utility:</p> <pre><code>mlagents-learn --help\n</code></pre> <p>These additional CLI arguments are grouped into environment, engine, checkpoint and torch. The available settings and example values are shown below.</p>"},{"location":"Training-ML-Agents/#environment-settings","title":"Environment settings","text":"<pre><code>env_settings:\n  env_path: FoodCollector\n  env_args: null\n  base_port: 5005\n  num_envs: 1\n  seed: -1\n  max_lifetime_restarts: 10\n  restarts_rate_limit_n: 1\n  restarts_rate_limit_period_s: 60\n</code></pre>"},{"location":"Training-ML-Agents/#engine-settings","title":"Engine settings","text":"<pre><code>engine_settings:\n  width: 84\n  height: 84\n  quality_level: 5\n  time_scale: 20\n  target_frame_rate: -1\n  capture_frame_rate: 60\n  no_graphics: false\n</code></pre>"},{"location":"Training-ML-Agents/#checkpoint-settings","title":"Checkpoint settings","text":"<pre><code>checkpoint_settings:\n  run_id: foodtorch\n  initialize_from: null\n  load_model: false\n  resume: false\n  force: true\n  train_model: false\n  inference: false\n</code></pre>"},{"location":"Training-ML-Agents/#torch-settings","title":"Torch settings:","text":"<pre><code>torch_settings:\n  device: cpu\n</code></pre>"},{"location":"Training-ML-Agents/#behavior-configurations","title":"Behavior Configurations","text":"<p>The primary section of the trainer config file is a set of configurations for each Behavior in your scene. These are defined under the sub-section <code>behaviors</code> in your trainer config file. Some of the configurations are required while others are optional. To help us get started, below is a sample file that includes all the possible settings if we're using a PPO trainer with all the possible training functionalities enabled (memory, behavioral cloning, curiosity, GAIL and self-play). You will notice that curriculum and environment parameter randomization settings are not part of the <code>behaviors</code> configuration, but in their own section called <code>environment_parameters</code>.</p> <pre><code>behaviors:\n  BehaviorPPO:\n    trainer_type: ppo\n\n    hyperparameters:\n      # Hyperparameters common to PPO and SAC\n      batch_size: 1024\n      buffer_size: 10240\n      learning_rate: 3.0e-4\n      learning_rate_schedule: linear\n\n      # PPO-specific hyperparameters\n      # Replaces the \"PPO-specific hyperparameters\" section above\n      beta: 5.0e-3\n      beta_schedule: constant\n      epsilon: 0.2\n      epsilon_schedule: linear\n      lambd: 0.95\n      num_epoch: 3\n      shared_critic: False\n\n    # Configuration of the neural network (common to PPO/SAC)\n    network_settings:\n      vis_encode_type: simple\n      normalize: false\n      hidden_units: 128\n      num_layers: 2\n      # memory\n      memory:\n        sequence_length: 64\n        memory_size: 256\n\n    # Trainer configurations common to all trainers\n    max_steps: 5.0e5\n    time_horizon: 64\n    summary_freq: 10000\n    keep_checkpoints: 5\n    checkpoint_interval: 50000\n    threaded: false\n    init_path: null\n\n    # behavior cloning\n    behavioral_cloning:\n      demo_path: Project/Assets/ML-Agents/Examples/Pyramids/Demos/ExpertPyramid.demo\n      strength: 0.5\n      steps: 150000\n      batch_size: 512\n      num_epoch: 3\n      samples_per_update: 0\n\n    reward_signals:\n      # environment reward (default)\n      extrinsic:\n        strength: 1.0\n        gamma: 0.99\n\n      # curiosity module\n      curiosity:\n        strength: 0.02\n        gamma: 0.99\n        encoding_size: 256\n        learning_rate: 3.0e-4\n\n      # GAIL\n      gail:\n        strength: 0.01\n        gamma: 0.99\n        encoding_size: 128\n        demo_path: Project/Assets/ML-Agents/Examples/Pyramids/Demos/ExpertPyramid.demo\n        learning_rate: 3.0e-4\n        use_actions: false\n        use_vail: false\n\n    # self-play\n    self_play:\n      window: 10\n      play_against_latest_model_ratio: 0.5\n      save_steps: 50000\n      swap_steps: 2000\n      team_change: 100000\n</code></pre> <p>Here is an equivalent file if we use an SAC trainer instead. Notice that the configurations for the additional functionalities (memory, behavioral cloning, curiosity and self-play) remain unchanged.</p> <pre><code>behaviors:\n  BehaviorSAC:\n    trainer_type: sac\n\n    # Trainer configs common to PPO/SAC (excluding reward signals)\n    # same as PPO config\n\n    # SAC-specific configs (replaces the hyperparameters section above)\n    hyperparameters:\n      # Hyperparameters common to PPO and SAC\n      # Same as PPO config\n\n      # SAC-specific hyperparameters\n      # Replaces the \"PPO-specific hyperparameters\" section above\n      buffer_init_steps: 0\n      tau: 0.005\n      steps_per_update: 10.0\n      save_replay_buffer: false\n      init_entcoef: 0.5\n      reward_signal_steps_per_update: 10.0\n\n    # Configuration of the neural network (common to PPO/SAC)\n    network_settings:\n      # Same as PPO config\n\n    # Trainer configurations common to all trainers\n      # &lt;Same as PPO config&gt;\n\n    # pre-training using behavior cloning\n    behavioral_cloning:\n      # same as PPO config\n\n    reward_signals:\n      # environment reward\n      extrinsic:\n        # same as PPO config\n\n      # curiosity module\n      curiosity:\n        # same as PPO config\n\n      # GAIL\n      gail:\n        # same as PPO config\n\n    # self-play\n    self_play:\n      # same as PPO config\n</code></pre> <p>We now break apart the components of the configuration file and describe what each of these parameters mean and provide guidelines on how to set them. See Training Configuration File for a detailed description of all the configurations listed above, along with their defaults. Unless otherwise specified, omitting a configuration will revert it to its default.</p>"},{"location":"Training-ML-Agents/#default-behavior-settings","title":"Default Behavior Settings","text":"<p>In some cases, you may want to specify a set of default configurations for your Behaviors. This may be useful, for instance, if your Behavior names are generated procedurally by the environment and not known before runtime, or if you have many Behaviors with very similar settings. To specify a default configuraton, insert a <code>default_settings</code> section in your YAML. This section should be formatted exactly like a configuration for a Behavior.</p> <pre><code>default_settings:\n  # &lt; Same as Behavior configuration &gt;\nbehaviors:\n  # &lt; Same as above &gt;\n</code></pre> <p>Behaviors found in the environment that aren't specified in the YAML will now use the <code>default_settings</code>, and unspecified settings in behavior configurations will default to the values in <code>default_settings</code> if specified there.</p>"},{"location":"Training-ML-Agents/#environment-parameters","title":"Environment Parameters","text":"<p>In order to control the <code>EnvironmentParameters</code> in the Unity simulation during training, you need to add a section called <code>environment_parameters</code>. For example you can set the value of an <code>EnvironmentParameter</code> called <code>my_environment_parameter</code> to <code>3.0</code> with the following code :</p> <pre><code>behaviors:\n  BehaviorY:\n    # &lt; Same as above &gt;\n\n# Add this section\nenvironment_parameters:\n  my_environment_parameter: 3.0\n</code></pre> <p>Inside the Unity simulation, you can access your Environment Parameters by doing :</p> <pre><code>Academy.Instance.EnvironmentParameters.GetWithDefault(\"my_environment_parameter\", 0.0f);\n</code></pre>"},{"location":"Training-ML-Agents/#environment-parameter-randomization","title":"Environment Parameter Randomization","text":"<p>To enable environment parameter randomization, you need to edit the <code>environment_parameters</code> section of your training configuration yaml file. Instead of providing a single float value for your environment parameter, you can specify a sampler instead. Here is an example with three environment parameters called <code>mass</code>, <code>length</code> and <code>scale</code>:</p> <pre><code>behaviors:\n  BehaviorY:\n    # &lt; Same as above &gt;\n\n# Add this section\nenvironment_parameters:\n  mass:\n    sampler_type: uniform\n    sampler_parameters:\n        min_value: 0.5\n        max_value: 10\n\n  length:\n    sampler_type: multirangeuniform\n    sampler_parameters:\n        intervals: [[7, 10], [15, 20]]\n\n  scale:\n    sampler_type: gaussian\n    sampler_parameters:\n        mean: 2\n        st_dev: .3\n</code></pre> Setting Description <code>sampler_type</code> A string identifier for the type of sampler to use for this <code>Environment Parameter</code>. <code>sampler_parameters</code> The parameters for a given <code>sampler_type</code>. Samplers of different types can have different <code>sampler_parameters</code>"},{"location":"Training-ML-Agents/#supported-sampler-types","title":"Supported Sampler Types","text":"<p>Below is a list of the <code>sampler_type</code> values supported by the toolkit.</p> <ul> <li><code>uniform</code> - Uniform sampler</li> <li>Uniformly samples a single float value from a range with a given minimum     and maximum value (inclusive).</li> <li>parameters - <code>min_value</code>, <code>max_value</code></li> <li><code>gaussian</code> - Gaussian sampler</li> <li>Samples a single float value from a normal distribution with a given mean     and standard deviation.</li> <li>parameters - <code>mean</code>, <code>st_dev</code></li> <li><code>multirange_uniform</code> - Multirange uniform sampler</li> <li>First, samples an interval from a set of intervals in proportion to relative     length of the intervals. Then, uniformly samples a single float value from the     sampled interval (inclusive). This sampler can take an arbitrary number of     intervals in a list in the following format:     [[<code>interval_1_min</code>, <code>interval_1_max</code>], [<code>interval_2_min</code>,     <code>interval_2_max</code>], ...]</li> <li>parameters - <code>intervals</code></li> </ul> <p>The implementation of the samplers can be found in the Samplers.cs file.</p>"},{"location":"Training-ML-Agents/#training-with-environment-parameter-randomization","title":"Training with Environment Parameter Randomization","text":"<p>After the sampler configuration is defined, we proceed by launching <code>mlagents-learn</code> and specify trainer configuration with  parameter randomization enabled. For example, if we wanted to train the 3D ball agent with parameter randomization, we would run</p> <pre><code>mlagents-learn config/ppo/3DBall_randomize.yaml --run-id=3D-Ball-randomize\n</code></pre> <p>We can observe progress and metrics via TensorBoard.</p>"},{"location":"Training-ML-Agents/#curriculum","title":"Curriculum","text":"<p>To enable curriculum learning, you need to add a <code>curriculum</code> sub-section to your environment parameter. Here is one example with the environment parameter <code>my_environment_parameter</code> :</p> <pre><code>behaviors:\n  BehaviorY:\n    # &lt; Same as above &gt;\n\n# Add this section\nenvironment_parameters:\n  my_environment_parameter:\n    curriculum:\n      - name: MyFirstLesson # The '-' is important as this is a list\n        completion_criteria:\n          measure: progress\n          behavior: my_behavior\n          signal_smoothing: true\n          min_lesson_length: 100\n          threshold: 0.2\n        value: 0.0\n      - name: MySecondLesson # This is the start of the second lesson\n        completion_criteria:\n          measure: progress\n          behavior: my_behavior\n          signal_smoothing: true\n          min_lesson_length: 100\n          threshold: 0.6\n          require_reset: true\n        value:\n          sampler_type: uniform\n          sampler_parameters:\n            min_value: 4.0\n            max_value: 7.0\n      - name: MyLastLesson\n        value: 8.0\n</code></pre> <p>Note that this curriculum only applies to <code>my_environment_parameter</code>. The <code>curriculum</code> section contains a list of <code>Lessons</code>. In the example, the lessons are named <code>MyFirstLesson</code>, <code>MySecondLesson</code> and <code>MyLastLesson</code>. Each <code>Lesson</code> has 3 fields :</p> <ul> <li><code>name</code> which is a user defined name for the lesson (The name of the lesson will be displayed in  the console when the lesson changes)</li> <li><code>completion_criteria</code> which determines what needs to happen in the simulation before the lesson  can be considered complete. When that condition is met, the curriculum moves on to the next  <code>Lesson</code>. Note that you do not need to specify a <code>completion_criteria</code> for the last <code>Lesson</code></li> <li><code>value</code> which is the value the environment parameter will take during the lesson. Note that this  can be a float or a sampler.</li> </ul> <p>There are the different settings of the <code>completion_criteria</code> :</p> Setting Description <code>measure</code> What to measure learning progress, and advancement in lessons by. <code>reward</code> uses a measure of received reward, <code>progress</code> uses the ratio of steps/max_steps, while <code>Elo</code> is available only for self-play situations and uses Elo score as a curriculum completion measure. <code>behavior</code> Specifies which behavior is being tracked. There can be multiple behaviors with different names, each at different points of training. This setting allows the curriculum to track only one of them. <code>threshold</code> Determines at what point in value of <code>measure</code> the lesson should be increased. <code>min_lesson_length</code> The minimum number of episodes that should be completed before the lesson can change. If <code>measure</code> is set to <code>reward</code>, the average cumulative reward of the last <code>min_lesson_length</code> episodes will be used to determine if the lesson should change. Must be nonnegative.  Important: the average reward that is compared to the thresholds is different than the mean reward that is logged to the console. For example, if <code>min_lesson_length</code> is <code>100</code>, the lesson will increment after the average cumulative reward of the last <code>100</code> episodes exceeds the current threshold. The mean reward logged to the console is dictated by the <code>summary_freq</code> parameter defined above. <code>signal_smoothing</code> Whether to weight the current progress measure by previous values. <code>require_reset</code> Whether changing lesson requires the environment to reset (default: false) ##### Training with a Curriculum <p>Once we have specified our metacurriculum and curricula, we can launch <code>mlagents-learn</code> to point to the config file containing our curricula and PPO will train using Curriculum Learning. For example, to train agents in the Wall Jump environment with curriculum learning, we can run:</p> <pre><code>mlagents-learn config/ppo/WallJump_curriculum.yaml --run-id=wall-jump-curriculum\n</code></pre> <p>We can then keep track of the current lessons and progresses via TensorBoard. If you've terminated the run, you can resume it using <code>--resume</code> and lesson progress will start off where it ended.</p>"},{"location":"Training-ML-Agents/#training-using-concurrent-unity-instances","title":"Training Using Concurrent Unity Instances","text":"<p>In order to run concurrent Unity instances during training, set the number of environment instances using the command line option <code>--num-envs=&lt;n&gt;</code> when you invoke <code>mlagents-learn</code>. Optionally, you can also set the <code>--base-port</code>, which is the starting port used for the concurrent Unity instances.</p> <p>Some considerations:</p> <ul> <li>Buffer Size - If you are having trouble getting an agent to train, even   with multiple concurrent Unity instances, you could increase <code>buffer_size</code> in   the trainer config file. A common practice is to multiply   <code>buffer_size</code> by <code>num-envs</code>.</li> <li>Resource Constraints - Invoking concurrent Unity instances is constrained   by the resources on the machine. Please use discretion when setting   <code>--num-envs=&lt;n&gt;</code>.</li> <li>Result Variation Using Concurrent Unity Instances - If you keep all the   hyperparameters the same, but change <code>--num-envs=&lt;n&gt;</code>, the results and model   would likely change.</li> </ul>"},{"location":"Training-Plugins/","title":"Customizing Training via Plugins","text":"<p>ML-Agents provides support for running your own python implementations of specific interfaces during the training process. These interfaces are currently fairly limited, but will be expanded in the future.</p> <p>Note: Plugin interfaces should currently be considered \"in beta\", and they may change in future releases.</p>"},{"location":"Training-Plugins/#how-to-write-your-own-plugin","title":"How to Write Your Own Plugin","text":"<p>This video explains the basics of how to create a plugin system using setuptools, and is the same approach that ML-Agents' plugin system is based on.</p> <p>The <code>ml-agents-plugin-examples</code> directory contains a reference implementation of each plugin interface, so it's a good starting point.</p>"},{"location":"Training-Plugins/#setuppy","title":"setup.py","text":"<p>If you don't already have a <code>setup.py</code> file for your python code, you'll need to add one. <code>ml-agents-plugin-examples</code> has a minimal example of this.</p> <p>In the call to <code>setup()</code>, you'll need to add to the <code>entry_points</code> dictionary for each plugin interface that you implement. The form of this is <code>{entry point name}={plugin module}:{plugin function}</code>. For example, in  <code>ml-agents-plugin-examples</code>:</p> <pre><code>entry_points={\n    ML_AGENTS_STATS_WRITER: [\n        \"example=mlagents_plugin_examples.example_stats_writer:get_example_stats_writer\"\n    ]\n}\n</code></pre> <ul> <li><code>ML_AGENTS_STATS_WRITER</code> (which is a string constant, <code>mlagents.stats_writer</code>) is the name of the plugin interface. This must be one of the provided interfaces (see below).</li> <li><code>example</code> is the plugin implementation name. This can be anything.</li> <li><code>mlagents_plugin_examples.example_stats_writer</code> is the plugin module. This points to the module where the plugin registration function is defined.</li> <li><code>get_example_stats_writer</code> is the plugin registration function. This is called when running <code>mlagents-learn</code>. The arguments and expected return type for this are different for each plugin interface.</li> </ul>"},{"location":"Training-Plugins/#local-installation","title":"Local Installation","text":"<p>Once you've defined <code>entry_points</code> in your <code>setup.py</code>, you will need to run</p> <pre><code>pip install -e [path to your plugin code]\n</code></pre> <p>in the same python virtual environment that you have <code>mlagents</code> installed.</p>"},{"location":"Training-Plugins/#plugin-interfaces","title":"Plugin Interfaces","text":""},{"location":"Training-Plugins/#statswriter","title":"StatsWriter","text":"<p>The StatsWriter class receives various information from the training process, such as the average Agent reward in each summary period. By default, we log this information to the console and write it to TensorBoard.</p>"},{"location":"Training-Plugins/#interface","title":"Interface","text":"<p>The <code>StatsWriter.write_stats()</code> method must be implemented in any derived classes. It takes a \"category\" parameter, which typically is the behavior name of the Agents being trained, and a dictionary of <code>StatSummary</code> values with string keys. Additionally, <code>StatsWriter.on_add_stat()</code> may be extended to register a callback handler for each stat emission.</p>"},{"location":"Training-Plugins/#registration","title":"Registration","text":"<p>The <code>StatsWriter</code> registration function takes a <code>RunOptions</code> argument and returns a list of <code>StatsWriter</code>s. An example implementation is provided in <code>mlagents_plugin_examples</code></p>"},{"location":"Training-on-Amazon-Web-Service/","title":"Training on Amazon Web Service","text":"<p>:warning: Note: We no longer use this guide ourselves and so it may not work correctly. We've decided to keep it up just in case it is helpful to you.</p> <p>This page contains instructions for setting up an EC2 instance on Amazon Web Service for training ML-Agents environments.</p>"},{"location":"Training-on-Amazon-Web-Service/#pre-configured-ami","title":"Pre-configured AMI","text":"<p>We've prepared a pre-configured AMI for you with the ID: <code>ami-016ff5559334f8619</code> in the <code>us-east-1</code> region. It was created as a modification of Deep Learning AMI (Ubuntu). The AMI has been tested with p2.xlarge instance. Furthermore, if you want to train without headless mode, you need to enable X Server.</p> <p>After launching your EC2 instance using the ami and ssh into it, run the following commands to enable it:</p> <pre><code># Start the X Server, press Enter to come to the command line\n$ sudo /usr/bin/X :0 &amp;\n\n# Check if Xorg process is running\n# You will have a list of processes running on the GPU, Xorg should be in the\n# list, as shown below\n$ nvidia-smi\n\n# Thu Jun 14 20:27:26 2018\n# +-----------------------------------------------------------------------------+\n# | NVIDIA-SMI 390.67                 Driver Version: 390.67                    |\n# |-------------------------------+----------------------+----------------------+\n# | GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n# | Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n# |===============================+======================+======================|\n# |   0  Tesla K80           On   | 00000000:00:1E.0 Off |                    0 |\n# | N/A   35C    P8    31W / 149W |      9MiB / 11441MiB |      0%      Default |\n# +-------------------------------+----------------------+----------------------+\n#\n# +-----------------------------------------------------------------------------+\n# | Processes:                                                       GPU Memory |\n# |  GPU       PID   Type   Process name                             Usage      |\n# |=============================================================================|\n# |    0      2331      G   /usr/lib/xorg/Xorg                             8MiB |\n# +-----------------------------------------------------------------------------+\n\n# Make the ubuntu use X Server for display\n$ export DISPLAY=:0\n</code></pre>"},{"location":"Training-on-Amazon-Web-Service/#configuring-your-own-instance","title":"Configuring your own instance","text":"<p>You could also choose to configure your own instance. To begin with, you will need an EC2 instance which contains the latest Nvidia drivers, CUDA9, and cuDNN. In this tutorial we used the Deep Learning AMI (Ubuntu) listed under AWS Marketplace with a p2.xlarge instance.</p>"},{"location":"Training-on-Amazon-Web-Service/#installing-the-ml-agents-toolkit-on-the-instance","title":"Installing the ML-Agents Toolkit on the instance","text":"<p>After launching your EC2 instance using the ami and ssh into it:</p> <ol> <li>Activate the python3 environment</li> </ol> <p><code>sh    source activate python3</code></p> <ol> <li>Clone the ML-Agents repo and install the required Python packages</li> </ol> <p><code>sh    git clone --branch release_20 https://github.com/Unity-Technologies/ml-agents.git    cd ml-agents/ml-agents/    pip3 install -e .</code></p>"},{"location":"Training-on-Amazon-Web-Service/#setting-up-x-server-optional","title":"Setting up X Server (optional)","text":"<p>X Server setup is only necessary if you want to do training that requires visual observation input. Instructions here are adapted from this Medium post on running general Unity applications in the cloud.</p> <p>Current limitations of the Unity Engine require that a screen be available to render to when using visual observations. In order to make this possible when training on a remote server, a virtual screen is required. We can do this by installing Xorg and creating a virtual screen. Once installed and created, we can display the Unity environment in the virtual environment, and train as we would on a local machine. Ensure that <code>headless</code> mode is disabled when building linux executables which use visual observations.</p>"},{"location":"Training-on-Amazon-Web-Service/#install-and-setup-xorg","title":"Install and setup Xorg:","text":"<pre><code>```sh\n# Install Xorg\n$ sudo apt-get update\n$ sudo apt-get install -y xserver-xorg mesa-utils\n$ sudo nvidia-xconfig -a --use-display-device=None --virtual=1280x1024\n\n# Get the BusID information\n$ nvidia-xconfig --query-gpu-info\n\n# Add the BusID information to your /etc/X11/xorg.conf file\n$ sudo sed -i 's/    BoardName      \"Tesla K80\"/    BoardName      \"Tesla K80\"\\n    BusID          \"0:30:0\"/g' /etc/X11/xorg.conf\n\n# Remove the Section \"Files\" from the /etc/X11/xorg.conf file\n# And remove two lines that contain Section \"Files\" and EndSection\n$ sudo vim /etc/X11/xorg.conf\n```\n</code></pre>"},{"location":"Training-on-Amazon-Web-Service/#update-and-setup-nvidia-driver","title":"Update and setup Nvidia driver:","text":"<pre><code>```sh\n# Download and install the latest Nvidia driver for ubuntu\n# Please refer to http://download.nvidia.com/XFree86/Linux-#x86_64/latest.txt\n$ wget http://download.nvidia.com/XFree86/Linux-x86_64/390.87/NVIDIA-Linux-x86_64-390.87.run\n$ sudo /bin/bash ./NVIDIA-Linux-x86_64-390.87.run --accept-license --no-questions --ui=none\n\n# Disable Nouveau as it will clash with the Nvidia driver\n$ sudo echo 'blacklist nouveau'  | sudo tee -a /etc/modprobe.d/blacklist.conf\n$ sudo echo 'options nouveau modeset=0'  | sudo tee -a /etc/modprobe.d/blacklist.conf\n$ sudo echo options nouveau modeset=0 | sudo tee -a /etc/modprobe.d/nouveau-kms.conf\n$ sudo update-initramfs -u\n```\n</code></pre>"},{"location":"Training-on-Amazon-Web-Service/#restart-the-ec2-instance","title":"Restart the EC2 instance:","text":"<pre><code>```sh\nsudo reboot now\n```\n</code></pre>"},{"location":"Training-on-Amazon-Web-Service/#make-sure-there-are-no-xorg-processes-running","title":"Make sure there are no Xorg processes running:","text":"<pre><code># Kill any possible running Xorg processes\n# Note that you might have to run this command multiple times depending on\n# how Xorg is configured.\n$ sudo killall Xorg\n\n# Check if there is any Xorg process left\n# You will have a list of processes running on the GPU, Xorg should not be in\n# the list, as shown below.\n$ nvidia-smi\n\n# Thu Jun 14 20:21:11 2018\n# +-----------------------------------------------------------------------------+\n# | NVIDIA-SMI 390.67                 Driver Version: 390.67                    |\n# |-------------------------------+----------------------+----------------------+\n# | GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n# | Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n# |===============================+======================+======================|\n# |   0  Tesla K80           On   | 00000000:00:1E.0 Off |                    0 |\n# | N/A   37C    P8    31W / 149W |      0MiB / 11441MiB |      0%      Default |\n# +-------------------------------+----------------------+----------------------+\n#\n# +-----------------------------------------------------------------------------+\n# | Processes:                                                       GPU Memory |\n# |  GPU       PID   Type   Process name                             Usage      |\n# |=============================================================================|\n# |  No running processes found                                                 |\n# +-----------------------------------------------------------------------------+\n\n</code></pre>"},{"location":"Training-on-Amazon-Web-Service/#start-x-server-and-make-the-ubuntu-use-x-server-for-display","title":"Start X Server and make the ubuntu use X Server for display:","text":"<pre><code>```console\n# Start the X Server, press Enter to come back to the command line\n$ sudo /usr/bin/X :0 &amp;\n\n# Check if Xorg process is running\n# You will have a list of processes running on the GPU, Xorg should be in the list.\n$ nvidia-smi\n\n# Make the ubuntu use X Server for display\n$ export DISPLAY=:0\n```\n</code></pre>"},{"location":"Training-on-Amazon-Web-Service/#ensure-the-xorg-is-correctly-configured","title":"Ensure the Xorg is correctly configured:","text":"<pre><code>```sh\n# For more information on glxgears, see ftp://www.x.org/pub/X11R6.8.1/doc/glxgears.1.html.\n$ glxgears\n# If Xorg is configured correctly, you should see the following message\n\n# Running synchronized to the vertical refresh.  The framerate should be\n# approximately the same as the monitor refresh rate.\n# 137296 frames in 5.0 seconds = 27459.053 FPS\n# 141674 frames in 5.0 seconds = 28334.779 FPS\n# 141490 frames in 5.0 seconds = 28297.875 FPS\n\n```\n</code></pre>"},{"location":"Training-on-Amazon-Web-Service/#training-on-ec2-instance","title":"Training on EC2 instance","text":"<ol> <li>In the Unity Editor, load a project containing an ML-Agents environment (you    can use one of the example environments if you have not created your own).</li> <li>Open the Build Settings window (menu: File &gt; Build Settings).</li> <li>Select Linux as the Target Platform, and x86_64 as the target architecture    (the default x86 currently does not work).</li> <li>Check Headless Mode if you have not setup the X Server. (If you do not use    Headless Mode, you have to setup the X Server to enable training.)</li> <li>Click Build to build the Unity environment executable.</li> <li>Upload the executable to your EC2 instance within <code>ml-agents</code> folder.</li> <li>Change the permissions of the executable.</li> </ol> <p><code>sh    chmod +x &lt;your_env&gt;.x86_64</code></p> <ol> <li>(Without Headless Mode) Start X Server and use it for display:</li> </ol> <p>```sh    # Start the X Server, press Enter to come back to the command line    $ sudo /usr/bin/X :0 &amp;</p> <p># Check if Xorg process is running    # You will have a list of processes running on the GPU, Xorg should be in the list.    $ nvidia-smi</p> <p># Make the ubuntu use X Server for display    $ export DISPLAY=:0    ```</p> <ol> <li>Test the instance setup from Python using:</li> </ol> <p>```python    from mlagents_envs.environment import UnityEnvironment</p> <p>env = UnityEnvironment()    ``` <p>Where <code>&lt;your_env&gt;</code> corresponds to the path to your environment executable.</p> <p>You should receive a message confirming that the environment was loaded    successfully.</p> <ol> <li> <p>Train your models</p> <p><code>console mlagents-learn &lt;trainer-config-file&gt; --env=&lt;your_env&gt; --train</code></p> </li> </ol>"},{"location":"Training-on-Amazon-Web-Service/#faq","title":"FAQ","text":""},{"location":"Training-on-Amazon-Web-Service/#the-_data-folder-hasnt-been-copied-cover","title":"The _Data folder hasn't been copied cover <p>If you've built your Linux executable, but forget to copy over the corresponding _Data folder, you will see error message like the following: <pre><code>Set current directory to /home/ubuntu/ml-agents/ml-agents\nFound path: /home/ubuntu/ml-agents/ml-agents/3dball_linux.x86_64\nno boot config - using default values\n\n(Filename:  Line: 403)\n\nThere is no data folder\n</code></pre>","text":""},{"location":"Training-on-Amazon-Web-Service/#unity-environment-not-responding","title":"Unity Environment not responding <p>If you didn't setup X Server or hasn't launched it properly, or your environment somehow crashes, or you haven't <code>chmod +x</code> your Unity Environment, all of these will cause connection between Unity and Python to fail. Then you will see something like this:</p> <pre><code>Logging to /home/ubuntu/.config/unity3d/&lt;Some_Path&gt;/Player.log\nTraceback (most recent call last):\n  File \"&lt;stdin&gt;\", line 1, in &lt;module&gt;\n  File \"/home/ubuntu/ml-agents/ml-agents/mlagents_envs/environment.py\", line 63, in __init__\n    aca_params = self.send_academy_parameters(rl_init_parameters_in)\n  File \"/home/ubuntu/ml-agents/ml-agents/mlagents_envs/environment.py\", line 489, in send_academy_parameters\n    return self.communicator.initialize(inputs).rl_initialization_output\n  File \"/home/ubuntu/ml-agents/ml-agents/mlagents_envs/rpc_communicator.py\", line 60, in initialize\nmlagents_envs.exception.UnityTimeOutException: The Unity environment took too long to respond. Make sure that :\n         The environment does not need user interaction to launch\n         The environment and the Python interface have compatible versions.\n</code></pre> <p>It would be also really helpful to check your /home/ubuntu/.config/unity3d//Player.log to see what happens with your Unity environment.","text":""},{"location":"Training-on-Amazon-Web-Service/#could-not-launch-x-server","title":"Could not launch X Server <p>When you execute:</p> <pre><code>sudo /usr/bin/X :0 &amp;\n</code></pre> <p>You might see something like:</p> <pre><code>X.Org X Server 1.18.4\n...\n(==) Log file: \"/var/log/Xorg.0.log\", Time: Thu Oct 11 21:10:38 2018\n(==) Using config file: \"/etc/X11/xorg.conf\"\n(==) Using system config directory \"/usr/share/X11/xorg.conf.d\"\n(EE)\nFatal server error:\n(EE) no screens found(EE)\n(EE)\nPlease consult the X.Org Foundation support\n         at http://wiki.x.org\n for help.\n(EE) Please also check the log file at \"/var/log/Xorg.0.log\" for additional information.\n(EE)\n(EE) Server terminated with error (1). Closing log file.\n</code></pre> <p>And when you execute:</p> <pre><code>nvidia-smi\n</code></pre> <p>You might see something like:</p> <pre><code>NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running.\n</code></pre> <p>This means the NVIDIA's driver needs to be updated. Refer to this section for more information.</p>","text":""},{"location":"Training-on-Microsoft-Azure/","title":"Training on Microsoft Azure (works with ML-Agents Toolkit v0.3)","text":"<p>:warning: Note: We no longer use this guide ourselves and so it may not work correctly. We've decided to keep it up just in case it is helpful to you.</p> <p>This page contains instructions for setting up training on Microsoft Azure through either Azure Container Instances or Virtual Machines. Non \"headless\" training has not yet been tested to verify support.</p>"},{"location":"Training-on-Microsoft-Azure/#pre-configured-azure-virtual-machine","title":"Pre-Configured Azure Virtual Machine","text":"<p>A pre-configured virtual machine image is available in the Azure Marketplace and is nearly completely ready for training. You can start by deploying the Data Science Virtual Machine for Linux (Ubuntu) into your Azure subscription.</p> <p>Note that, if you choose to deploy the image to an N-Series GPU optimized VM, training will, by default, run on the GPU. If you choose any other type of VM, training will run on the CPU.</p>"},{"location":"Training-on-Microsoft-Azure/#configuring-your-own-instance","title":"Configuring your own Instance","text":"<p>Setting up your own instance requires a number of package installations. Please view the documentation for doing so here.</p>"},{"location":"Training-on-Microsoft-Azure/#installing-ml-agents","title":"Installing ML-Agents","text":"<ol> <li>Move    the <code>ml-agents</code> sub-folder of this ml-agents repo to the remote Azure    instance, and set it as the working directory.</li> <li>Install the required packages:    Torch: <code>pip3 install torch==1.7.0 -f https://download.pytorch.org/whl/torch_stable.html</code> and    MLAgents: <code>python -m pip install mlagents==0.30.0</code></li> </ol>"},{"location":"Training-on-Microsoft-Azure/#testing","title":"Testing","text":"<p>To verify that all steps worked correctly:</p> <ol> <li>In the Unity Editor, load a project containing an ML-Agents environment (you    can use one of the example environments if you have not created your own).</li> <li>Open the Build Settings window (menu: File &gt; Build Settings).</li> <li>Select Linux as the Target Platform, and x86_64 as the target architecture.</li> <li>Check Headless Mode.</li> <li>Click Build to build the Unity environment executable.</li> <li>Upload the resulting files to your Azure instance.</li> <li>Test the instance setup from Python using:</li> </ol> <pre><code>from mlagents_envs.environment import UnityEnvironment\n\nenv = UnityEnvironment(file_name=\"&lt;your_env&gt;\", seed=1, side_channels=[])\n</code></pre> <p>Where <code>&lt;your_env&gt;</code> corresponds to the path to your environment executable (i.e. <code>/home/UserName/Build/yourFile</code>).</p> <p>You should receive a message confirming that the environment was loaded successfully.</p> <p>Note: When running your environment in headless mode, you must append <code>--no-graphics</code> to your mlagents-learn command, as it won't train otherwise. You can test this simply by aborting a training and check if it says \"Model Saved\" or \"Aborted\", or see if it generated the .onnx in the result folder.</p>"},{"location":"Training-on-Microsoft-Azure/#running-training-on-your-virtual-machine","title":"Running Training on your Virtual Machine","text":"<p>To run your training on the VM:</p> <ol> <li>Move    your built Unity application to your Virtual Machine.</li> <li>Set the directory where the ML-Agents Toolkit was installed to your working    directory.</li> <li>Run the following command:</li> </ol> <pre><code>mlagents-learn &lt;trainer_config&gt; --env=&lt;your_app&gt; --run-id=&lt;run_id&gt; --train\n</code></pre> <p>Where <code>&lt;your_app&gt;</code> is the path to your app (i.e. <code>~/unity-volume/3DBallHeadless</code>) and <code>&lt;run_id&gt;</code> is an identifier you would like to identify your training run with.</p> <p>If you've selected to run on a N-Series VM with GPU support, you can verify that the GPU is being used by running <code>nvidia-smi</code> from the command line.</p>"},{"location":"Training-on-Microsoft-Azure/#monitoring-your-training-run-with-tensorboard","title":"Monitoring your Training Run with TensorBoard","text":"<p>Once you have started training, you can use TensorBoard to observe the training.</p> <ol> <li> <p>Start by    opening the appropriate port for web traffic to connect to your VM.</p> </li> <li> <p>Note that you don't need to generate a new <code>Network Security Group</code> but      instead, go to the Networking tab under Settings for your VM.</p> </li> <li> <p>As an example, you could use the following settings to open the Port with      the following Inbound Rule settings:</p> <ul> <li>Source: Any</li> <li>Source Port Ranges: *</li> <li>Destination: Any</li> <li>Destination Port Ranges: 6006</li> <li>Protocol: Any</li> <li>Action: Allow</li> <li>Priority: (Leave as default)</li> </ul> </li> <li> <p>Unless you started the training as a background process, connect to your VM    from another terminal instance.</p> </li> <li>Run the following command from your terminal    <code>tensorboard --logdir results --host 0.0.0.0</code></li> <li>You should now be able to open a browser and navigate to    <code>&lt;Your_VM_IP_Address&gt;:6060</code> to view the TensorBoard report.</li> </ol>"},{"location":"Training-on-Microsoft-Azure/#running-on-azure-container-instances","title":"Running on Azure Container Instances","text":"<p>Azure Container Instances allow you to spin up a container, on demand, that will run your training and then be shut down. This ensures you aren't leaving a billable VM running when it isn't needed. Using ACI enables you to offload training of your models without needing to install Python and TensorFlow on your own computer.</p>"},{"location":"Training-on-Microsoft-Azure/#custom-instances","title":"Custom Instances","text":"<p>This page contains instructions for setting up a custom Virtual Machine on Microsoft Azure so you can running ML-Agents training in the cloud.</p> <ol> <li>Start by    deploying an Azure VM    with Ubuntu Linux (tests were done with 16.04 LTS). To use GPU support, use a    N-Series VM.</li> <li>SSH into your VM.</li> <li>Start with the following commands to install the Nvidia driver:</li> </ol> <p>```sh    wget http://us.download.nvidia.com/tesla/375.66/nvidia-diag-driver-local-repo-ubuntu1604_375.66-1_amd64.deb</p> <p>sudo dpkg -i nvidia-diag-driver-local-repo-ubuntu1604_375.66-1_amd64.deb</p> <p>sudo apt-get update</p> <p>sudo apt-get install cuda-drivers</p> <p>sudo reboot    ```</p> <ol> <li>After a minute you should be able to reconnect to your VM and install the    CUDA toolkit:</li> </ol> <p>```sh    wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_8.0.61-1_amd64.deb</p> <p>sudo dpkg -i cuda-repo-ubuntu1604_8.0.61-1_amd64.deb</p> <p>sudo apt-get update</p> <p>sudo apt-get install cuda-8-0    ```</p> <ol> <li> <p>You'll next need to download cuDNN from the Nvidia developer site. This    requires a registered account.</p> </li> <li> <p>Navigate to http://developer.nvidia.com and    create an account and verify it.</p> </li> <li> <p>Download (to your own computer) cuDNN from    this url.</p> </li> <li> <p>Copy the deb package to your VM:</p> </li> </ol> <p><code>sh    scp libcudnn6_6.0.21-1+cuda8.0_amd64.deb &lt;VMUserName&gt;@&lt;VMIPAddress&gt;:libcudnn6_6.0.21-1+cuda8.0_amd64.deb</code></p> <ol> <li>SSH back to your VM and execute the following:</li> </ol> <p>```console    sudo dpkg -i libcudnn6_6.0.21-1+cuda8.0_amd64.deb</p> <p>export LD_LIBRARY_PATH=/usr/local/cuda/lib64/:/usr/lib/x86_64-linux-gnu/:$LD_LIBRARY_PATH    . ~/.profile</p> <p>sudo reboot    ```</p> <ol> <li> <p>After a minute, you should be able to SSH back into your VM. After doing so,     run the following:</p> <p><code>sh sudo apt install python-pip sudo apt install python3-pip</code></p> </li> <li> <p>At this point, you need to install TensorFlow. The version you install     should be tied to if you are using GPU to train:</p> <p><code>sh pip3 install tensorflow-gpu==1.4.0 keras==2.0.6</code></p> <p>Or CPU to train:</p> <p><code>sh pip3 install tensorflow==1.4.0 keras==2.0.6</code></p> </li> <li> <p>You'll then need to install additional dependencies:</p> <p><code>sh pip3 install pillow pip3 install numpy</code></p> </li> </ol>"},{"location":"Tutorial-Custom-Trainer-Plugin/","title":"Custom Trainer Plugin","text":""},{"location":"Tutorial-Custom-Trainer-Plugin/#step-1-write-your-custom-trainer-class","title":"Step 1: Write your custom trainer class","text":"<p>Before you start writing your code, make sure to use your favorite environment management tool(e.g. <code>venv</code> or <code>conda</code>) to create and activate a Python virtual environment. The following command uses <code>conda</code>, but other tools work similarly:</p> <pre><code>conda create -n trainer-env python=3.8.13\nconda activate trainer-env\n</code></pre> <p>Users of the plug-in system are responsible for implementing the trainer class subject to the API standard. Let us follow an example by implementing a custom trainer named \"YourCustomTrainer\". You can either extend <code>OnPolicyTrainer</code> or <code>OffPolicyTrainer</code> classes depending on the training strategies you choose.</p> <p>Please refer to the internal PPO implementation for a complete code example. We will not provide a workable code in the document. The purpose of the tutorial is to introduce you to the core components and interfaces of our plugin framework. We use code snippets and patterns to demonstrate the control and data flow.</p> <p>Your custom trainers are responsible for collecting experiences and training the models. Your custom trainer class acts like a co-ordinator to the policy and optimizer. To start implementing methods in the class, create a policy class objects from method <code>create_policy</code>:</p> <pre><code>def create_policy(\n    self, parsed_behavior_id: BehaviorIdentifiers, behavior_spec: BehaviorSpec\n) -&gt; TorchPolicy:\n\n    actor_cls: Union[Type[SimpleActor], Type[SharedActorCritic]] = SimpleActor\n    actor_kwargs: Dict[str, Any] = {\n        \"conditional_sigma\": False,\n        \"tanh_squash\": False,\n    }\n    if self.shared_critic:\n        reward_signal_configs = self.trainer_settings.reward_signals\n        reward_signal_names = [\n            key.value for key, _ in reward_signal_configs.items()\n        ]\n        actor_cls = SharedActorCritic\n        actor_kwargs.update({\"stream_names\": reward_signal_names})\n\n    policy = TorchPolicy(\n        self.seed,\n        behavior_spec,\n        self.trainer_settings.network_settings,\n        actor_cls,\n        actor_kwargs,\n    )\n    return policy\n\n</code></pre> <p>Depending on whether you use shared or separate network architecture for your policy, we provide <code>SimpleActor</code> and <code>SharedActorCritic</code> from <code>mlagents.trainers.torch_entities.networks</code> that you can choose from. In our example above, we use a <code>SimpleActor</code>.</p> <p>Next, create an optimizer class object from <code>create_optimizer</code> method and connect it to the policy object you created above:</p> <pre><code>def create_optimizer(self) -&gt; TorchOptimizer:\n    return TorchPPOOptimizer(  # type: ignore\n        cast(TorchPolicy, self.policy), self.trainer_settings  # type: ignore\n    )  # type: ignore\n\n</code></pre> <p>There are a couple of abstract methods(<code>_process_trajectory</code> and <code>_update_policy</code>) inherited from <code>RLTrainer</code> that you need to implement in your custom trainer class. <code>_process_trajectory</code> takes a trajectory and processes it, putting it into the update buffer. Processing involves calculating value and advantage targets for the model updating step. Given input <code>trajectory: Trajectory</code>, users are responsible for processing the data in the trajectory and append <code>agent_buffer_trajectory</code> to the back of the update buffer by calling <code>self._append_to_update_buffer(agent_buffer_trajectory)</code>, whose output will be used in updating the model in <code>optimizer</code> class.</p> <p>A typical <code>_process_trajectory</code> function(incomplete) will convert a trajectory object to an agent buffer then get all value estimates from the trajectory by calling <code>self.optimizer.get_trajectory_value_estimates</code>. From the returned dictionary of value estimates we extract reward signals keyed by their names:</p> <pre><code>def _process_trajectory(self, trajectory: Trajectory) -&gt; None:\n    super()._process_trajectory(trajectory)\n    agent_id = trajectory.agent_id  # All the agents should have the same ID\n\n    agent_buffer_trajectory = trajectory.to_agentbuffer()\n\n    # Get all value estimates\n    (\n        value_estimates,\n        value_next,\n        value_memories,\n    ) =  self.optimizer.get_trajectory_value_estimates(\n        agent_buffer_trajectory,\n        trajectory.next_obs,\n        trajectory.done_reached and not trajectory.interrupted,\n    )\n\n    for name, v in value_estimates.items():\n        agent_buffer_trajectory[RewardSignalUtil.value_estimates_key(name)].extend(\n            v\n        )\n        self._stats_reporter.add_stat(\n            f\"Policy/{self.optimizer.reward_signals[name].name.capitalize()} Value Estimate\",\n            np.mean(v),\n        )\n\n    # Evaluate all reward functions\n    self.collected_rewards[\"environment\"][agent_id] += np.sum(\n        agent_buffer_trajectory[BufferKey.ENVIRONMENT_REWARDS]\n    )\n    for name, reward_signal in self.optimizer.reward_signals.items():\n        evaluate_result = (\n            reward_signal.evaluate(agent_buffer_trajectory) * reward_signal.strength\n        )\n        agent_buffer_trajectory[RewardSignalUtil.rewards_key(name)].extend(\n            evaluate_result\n        )\n        # Report the reward signals\n        self.collected_rewards[name][agent_id] += np.sum(evaluate_result)\n\n    self._append_to_update_buffer(agent_buffer_trajectory)\n\n</code></pre> <p>A trajectory will be a list of dictionaries of strings mapped to <code>Anything</code>. When calling <code>forward</code> on a policy, the argument will include an \u201cexperience\u201d dictionary from the last step. The <code>forward</code> method will generate an action and the next \u201cexperience\u201d dictionary. Examples of fields in the \u201cexperience\u201d dictionary include observation, action, reward, done status, group_reward, LSTM memory state, etc.</p>"},{"location":"Tutorial-Custom-Trainer-Plugin/#step-2-implement-your-custom-optimizer-for-the-trainer","title":"Step 2: implement your custom optimizer for the trainer.","text":"<p>We will show you an example we implemented - <code>class TorchPPOOptimizer(TorchOptimizer)</code>, which takes a Policy and a Dict of trainer parameters and creates an Optimizer that connects to the policy. Your optimizer should include a value estimator and a loss function in the <code>update</code> method.</p> <p>Before writing your optimizer class, first define setting class <code>class PPOSettings(OnPolicyHyperparamSettings)</code> for your custom optimizer:</p> <pre><code>class PPOSettings(OnPolicyHyperparamSettings):\n    beta: float = 5.0e-3\n    epsilon: float = 0.2\n    lambd: float = 0.95\n    num_epoch: int = 3\n    shared_critic: bool = False\n    learning_rate_schedule: ScheduleType = ScheduleType.LINEAR\n    beta_schedule: ScheduleType = ScheduleType.LINEAR\n    epsilon_schedule: ScheduleType = ScheduleType.LINEAR\n\n</code></pre> <p>You should implement <code>update</code> function following interface:</p> <pre><code>def update(self, batch: AgentBuffer, num_sequences: int) -&gt; Dict[str, float]:\n\n</code></pre> <p>In which losses and other metrics are calculated from an <code>AgentBuffer</code> that is generated from your trainer class, depending on which model you choose to implement the loss functions will be different. In our case we calculate value loss from critic and trust region policy loss. A typical pattern(incomplete) of the calculations will look like the following:</p> <pre><code>run_out = self.policy.actor.get_stats(\n    current_obs,\n    actions,\n    masks=act_masks,\n    memories=memories,\n    sequence_length=self.policy.sequence_length,\n)\n\nlog_probs = run_out[\"log_probs\"]\nentropy = run_out[\"entropy\"]\n\nvalues, _ = self.critic.critic_pass(\n    current_obs,\n    memories=value_memories,\n    sequence_length=self.policy.sequence_length,\n)\npolicy_loss = ModelUtils.trust_region_policy_loss(\n    ModelUtils.list_to_tensor(batch[BufferKey.ADVANTAGES]),\n    log_probs,\n    old_log_probs,\n    loss_masks,\n    decay_eps,\n)\nloss = (\n    policy_loss\n    + 0.5 * value_loss\n    - decay_bet * ModelUtils.masked_mean(entropy, loss_masks)\n)\n\n</code></pre> <p>Finally update the model and return the a dictionary including calculated losses and updated decay learning rate:</p> <pre><code>ModelUtils.update_learning_rate(self.optimizer, decay_lr)\nself.optimizer.zero_grad()\nloss.backward()\n\nself.optimizer.step()\nupdate_stats = {\n    \"Losses/Policy Loss\": torch.abs(policy_loss).item(),\n    \"Losses/Value Loss\": value_loss.item(),\n    \"Policy/Learning Rate\": decay_lr,\n    \"Policy/Epsilon\": decay_eps,\n    \"Policy/Beta\": decay_bet,\n}\n\n</code></pre>"},{"location":"Tutorial-Custom-Trainer-Plugin/#step-3-integrate-your-custom-trainer-into-the-plugin-system","title":"Step 3: Integrate your custom trainer into the plugin system","text":"<p>By integrating a custom trainer into the plugin system, a user can use their published packages which have their implementations. To do that, you need to add a setup.py file. In the call to setup(), you'll need to add to the entry_points dictionary for each plugin interface that you implement. The form of this is {entry point name}={plugin module}:{plugin function}. For example:</p> <pre><code>entry_points={\n        ML_AGENTS_TRAINER_TYPE: [\n            \"your_trainer_type=your_package.your_custom_trainer:get_type_and_setting\"\n        ]\n    },\n</code></pre> <p>Some key elements in the code:</p> <pre><code>ML_AGENTS_TRAINER_TYPE: a string constant for trainer type\nyour_trainer_type: name your trainer type, used in configuration file\nyour_package: your pip installable package containing custom trainer implementation\n</code></pre> <p>Also define <code>get_type_and_setting</code> method in <code>YourCustomTrainer</code> class:</p> <pre><code>def get_type_and_setting():\n    return {YourCustomTrainer.get_trainer_name(): YourCustomTrainer}, {\n        YourCustomTrainer.get_trainer_name(): YourCustomSetting\n    }\n\n</code></pre> <p>Finally, specify trainer type in the config file:</p> <pre><code>behaviors:\n  3DBall:\n    trainer_type: your_trainer_type\n...\n</code></pre>"},{"location":"Tutorial-Custom-Trainer-Plugin/#step-4-install-your-custom-trainer-and-run-training","title":"Step 4: Install your custom trainer and run training:","text":"<p>Before installing your custom trainer package, make sure you have <code>ml-agents-env</code> and <code>ml-agents</code> installed</p> <pre><code>pip3 install -e ./ml-agents-envs &amp;&amp; pip3 install -e ./ml-agents\n</code></pre> <p>Install your cutom trainer package(if your package is pip installable):</p> <pre><code>pip3 install your_custom_package\n</code></pre> <p>Or follow our internal implementations:</p> <pre><code>pip3 install -e ./ml-agents-trainer-plugin\n</code></pre> <p>Following the previous installations your package is added as an entrypoint and you can use a config file with new trainers:</p> <pre><code>mlagents-learn ml-agents-trainer-plugin/mlagents_trainer_plugin/a2c/a2c_3DBall.yaml --run-id &lt;run-id-name&gt;\n--env &lt;env-executable&gt;\n</code></pre>"},{"location":"Tutorial-Custom-Trainer-Plugin/#validate-your-implementations","title":"Validate your implementations:","text":"<p>Create a clean Python environment with Python 3.8+ and activate it before you start, if you haven't done so already:</p> <pre><code>conda create -n trainer-env python=3.8.13\nconda activate trainer-env\n</code></pre> <p>Make sure you follow previous steps and install all required packages. We are testing internal implementations in this tutorial, but ML-Agents users can run similar validations once they have their own implementations installed:</p> <pre><code>pip3 install -e ./ml-agents-envs &amp;&amp; pip3 install -e ./ml-agents\npip3 install -e ./ml-agents-trainer-plugin\n</code></pre> <p>Once your package is added as an <code>entrypoint</code>, you can add to the config file the new trainer type. Check if trainer type is specified in the config file <code>a2c_3DBall.yaml</code>:</p> <pre><code>trainer_type: a2c\n</code></pre> <p>Test if custom trainer package is installed by running:</p> <pre><code>mlagents-learn ml-agents-trainer-plugin/mlagents_trainer_plugin/a2c/a2c_3DBall.yaml --run-id test-trainer\n</code></pre> <p>You can also list all trainers installed in the registry. Type <code>python</code> in your shell to open a REPL session. Run the python code below, you should be able to see all trainer types currently installed:</p> <pre><code>&gt;&gt;&gt; import pkg_resources\n&gt;&gt;&gt; for entry in pkg_resources.iter_entry_points('mlagents.trainer_type'):\n...     print(entry)\n...\ndefault = mlagents.plugins.trainer_type:get_default_trainer_types\na2c = mlagents_trainer_plugin.a2c.a2c_trainer:get_type_and_setting\ndqn = mlagents_trainer_plugin.dqn.dqn_trainer:get_type_and_setting\n</code></pre> <p>If it is properly installed, you will see Unity logo and message indicating training will start:</p> <pre><code>[INFO] Listening on port 5004. Start training by pressing the Play button in the Unity Editor.\n</code></pre> <p>If you see the following error message, it could be due to trainer type is wrong or the trainer type specified is not installed:</p> <pre><code>mlagents.trainers.exception.TrainerConfigError: Invalid trainer type a2c was found\n</code></pre>"},{"location":"Unity-Environment-Registry/","title":"Unity Environment Registry [Experimental]","text":"<p>The Unity Environment Registry is a database of pre-built Unity environments that can be easily used without having to install the Unity Editor. It is a great way to get started with our UnityEnvironment API.</p>"},{"location":"Unity-Environment-Registry/#loading-an-environment-from-the-registry","title":"Loading an Environment from the Registry","text":"<p>To get started, you can access the default registry we provide with our Example Environments. The Unity Environment Registry implements a Mapping, therefore, you can access an entry with its identifier with the square brackets <code>[ ]</code>. Use the following code to list all of the environment identifiers present in the default registry:</p> <pre><code>from mlagents_envs.registry import default_registry\n\nenvironment_names = list(default_registry.keys())\nfor name in environment_names:\n   print(name)\n</code></pre> <p>The <code>make()</code> method on a registry value will return a <code>UnityEnvironment</code> ready to be used. All arguments passed to the make method will be passed to the constructor of the <code>UnityEnvironment</code> as well. Refer to the documentation on the Python-API for more information about the arguments of the <code>UnityEnvironment</code> constructor. For example, the following code will create the environment under the identifier <code>\"my-env\"</code>, reset it, perform a few steps and finally close it:</p> <pre><code>from mlagents_envs.registry import default_registry\n\nenv = default_registry[\"my-env\"].make()\nenv.reset()\nfor _ in range(10):\n  env.step()\nenv.close()\n</code></pre>"},{"location":"Unity-Environment-Registry/#create-and-share-your-own-registry","title":"Create and share your own registry","text":"<p>In order to share the <code>UnityEnvironemnt</code> you created, you must :  - Create a Unity executable of your environment for each platform (Linux, OSX and/or Windows)  - Place each executable in a <code>zip</code> compressed folder  - Upload each zip file online to your preferred hosting platform  - Create a <code>yaml</code> file that will contain the description and path to your environment  - Upload the <code>yaml</code> file online The <code>yaml</code> file must have the following format :</p> <pre><code>environments:\n  - &lt;environment-identifier&gt;:\n     expected_reward: &lt;expected-reward-float&gt;\n     description: &lt;description-of-the-environment&gt;\n     linux_url: &lt;url-to-the-linux-zip-folder&gt;\n     darwin_url: &lt;url-to-the-osx-zip-folder&gt;\n     win_url: &lt;url-to-the-windows-zip-folder&gt;\n     additional_args:\n      - &lt;an-optional-list-of-command-line-arguments-for-the-executable&gt;\n      - ...\n</code></pre> <p>Your users can now use your environment with the following code :</p> <pre><code>from mlagents_envs.registry import UnityEnvRegistry\n\nregistry = UnityEnvRegistry()\nregistry.register_from_yaml(\"url-or-path-to-your-yaml-file\")\n</code></pre> <p>Note: The <code>\"url-or-path-to-your-yaml-file\"</code> can be either a url or a local path.</p>"},{"location":"Unity-Inference-Engine/","title":"Unity Inference Engine","text":"<p>The ML-Agents Toolkit allows you to use pre-trained neural network models inside your Unity games. This support is possible thanks to the Unity Inference Engine (codenamed Barracuda). The Unity Inference Engine uses compute shaders to run the neural network within Unity.</p>"},{"location":"Unity-Inference-Engine/#supported-devices","title":"Supported devices","text":"<p>See the Unity Inference Engine documentation for a list of the supported platforms.</p> <p>Scripting Backends : The Unity Inference Engine is generally faster with IL2CPP than with Mono for Standalone builds. In the Editor, It is not possible to use the Unity Inference Engine with GPU device selected when Editor Graphics Emulation is set to OpenGL(ES) 3.0 or 2.0 emulation. Also there might be non-fatal build time errors when target platform includes Graphics API that does not support Unity Compute Shaders.</p>"},{"location":"Unity-Inference-Engine/#using-the-unity-inference-engine","title":"Using the Unity Inference Engine","text":"<p>When using a model, drag the model file into the Model field in the Inspector of the Agent. Select the Inference Device : CPU or GPU you want to use for Inference.</p> <p>Note: For most of the models generated with the ML-Agents Toolkit, CPU will be faster than GPU. You should use the GPU only if you use the ResNet visual encoder or have a large number of agents with visual observations.</p>"},{"location":"Unity-Inference-Engine/#unsupported-use-cases","title":"Unsupported use cases","text":""},{"location":"Unity-Inference-Engine/#externally-trained-models","title":"Externally trained models","text":"<p>The ML-Agents Toolkit only supports the models created with our trainers. Model loading expects certain conventions for constants and tensor names. While it is possible to construct a model that follows these conventions, we don't provide any additional help for this. More details can be found in TensorNames.cs and BarracudaModelParamLoader.cs.</p> <p>If you wish to run inference on an externally trained model, you should use Barracuda directly, instead of trying to run it through ML-Agents.</p>"},{"location":"Unity-Inference-Engine/#model-inference-outside-of-unity","title":"Model inference outside of Unity","text":"<p>We do not provide support for inference anywhere outside of Unity. The <code>.onnx</code> files produced by training use the open format ONNX; if you wish to convert a <code>.onnx</code> file to another format or run inference with them, refer to their documentation.</p>"},{"location":"Using-Docker/","title":"Using Docker For ML-Agents (Deprecated)","text":"<p>:warning: Note: We no longer use this guide ourselves and so it may not work correctly. We've decided to keep it up just in case it is helpful to you.</p> <p>We currently offer a solution for Windows and Mac users who would like to do training or inference using Docker. This option may be appealing to those who would like to avoid installing Python and TensorFlow themselves. The current setup forces both TensorFlow and Unity to only rely on the CPU for computations. Consequently, our Docker simulation does not use a GPU and uses <code>Xvfb</code> to do visual rendering. <code>Xvfb</code> is a utility that enables <code>ML-Agents</code> (or any other application) to do rendering virtually i.e. it does not assume that the machine running <code>ML-Agents</code> has a GPU or a display attached to it. This means that rich environments which involve agents using camera-based visual observations might be slower.</p>"},{"location":"Using-Docker/#requirements","title":"Requirements","text":"<ul> <li>Docker</li> <li>Unity Linux Build Support Component. Make sure to select the Linux Build   Support component when installing Unity.</li> </ul>"},{"location":"Using-Docker/#setup","title":"Setup","text":"<ul> <li> <p>Download the Unity Installer and add   the Linux Build Support Component</p> </li> <li> <p>Download and install   Docker if you don't have it setup on your machine.</p> </li> <li> <p>Since Docker runs a container in an environment that is isolated from the host   machine, a mounted directory in your host machine is used to share data, e.g.   the trainer configuration file, Unity executable and   TensorFlow graph. For convenience, we created an empty <code>unity-volume</code>   directory at the root of the repository for this purpose, but feel free to use   any other directory. The remainder of this guide assumes that the   <code>unity-volume</code> directory is the one used.</p> </li> </ul>"},{"location":"Using-Docker/#usage","title":"Usage","text":"<p>Using Docker for ML-Agents involves three steps: building the Unity environment with specific flags, building a Docker container and, finally, running the container. If you are not familiar with building a Unity environment for ML-Agents, please read through our Getting Started with the 3D Balance Ball Example guide first.</p>"},{"location":"Using-Docker/#build-the-environment-optional","title":"Build the Environment (Optional)","text":"<p>If you want to used the Editor to perform training, you can skip this step.</p> <p>Since Docker typically runs a container sharing a (linux) kernel with the host machine, the Unity environment has to be built for the linux platform. When building a Unity environment, please select the following options from the the Build Settings window:</p> <ul> <li>Set the Target Platform to <code>Linux</code></li> <li>Set the Architecture to <code>x86_64</code></li> </ul> <p>Then click <code>Build</code>, pick an environment name (e.g. <code>3DBall</code>) and set the output directory to <code>unity-volume</code>. After building, ensure that the file <code>&lt;environment-name&gt;.x86_64</code> and subdirectory <code>&lt;environment-name&gt;_Data/</code> are created under <code>unity-volume</code>.</p> <p></p>"},{"location":"Using-Docker/#build-the-docker-container","title":"Build the Docker Container","text":"<p>First, make sure the Docker engine is running on your machine. Then build the Docker container by calling the following command at the top-level of the repository:</p> <pre><code>docker build -t &lt;image-name&gt; .\n</code></pre> <p>Replace <code>&lt;image-name&gt;</code> with a name for the Docker image, e.g. <code>balance.ball.v0.1</code>.</p>"},{"location":"Using-Docker/#run-the-docker-container","title":"Run the Docker Container","text":"<p>Run the Docker container by calling the following command at the top-level of the repository:</p> <pre><code>docker run -it --name &lt;container-name&gt; \\\n           --mount type=bind,source=\"$(pwd)\"/unity-volume,target=/unity-volume \\\n           -p 5005:5005 \\\n           -p 6006:6006 \\\n           &lt;image-name&gt;:latest \\\n           &lt;trainer-config-file&gt; \\\n           --env=&lt;environment-name&gt; \\\n           --train \\\n           --run-id=&lt;run-id&gt;\n</code></pre> <p>Notes on argument values:</p> <ul> <li><code>&lt;container-name&gt;</code> is used to identify the container (in case you want to   interrupt and terminate it). This is optional and Docker will generate a   random name if this is not set. Note that this must be unique for every run   of a Docker image.</li> <li><code>&lt;image-name&gt;</code> references the image name used when building the container.</li> <li><code>&lt;environment-name&gt;</code> (Optional): If you are training with a linux   executable, this is the name of the executable. If you are training in the   Editor, do not pass a <code>&lt;environment-name&gt;</code> argument and press the Play   button in Unity when the message \"Start training by pressing the Play button   in the Unity Editor\" is displayed on the screen.</li> <li><code>source</code>: Reference to the path in your host OS where you will store the Unity   executable.</li> <li><code>target</code>: Tells Docker to mount the <code>source</code> path as a disk with this name.</li> <li><code>trainer-config-file</code>, <code>train</code>, <code>run-id</code>: ML-Agents arguments passed to   <code>mlagents-learn</code>. <code>trainer-config-file</code> is the filename of the trainer config   file, <code>train</code> trains the algorithm, and <code>run-id</code> is used to tag each   experiment with a unique identifier. We recommend placing the trainer-config   file inside <code>unity-volume</code> so that the container has access to the file.</li> </ul> <p>To train with a <code>3DBall</code> environment executable, the command would be:</p> <pre><code>docker run -it --name 3DBallContainer.first.trial \\\n           --mount type=bind,source=\"$(pwd)\"/unity-volume,target=/unity-volume \\\n           -p 5005:5005 \\\n           -p 6006:6006 \\\n           balance.ball.v0.1:latest 3DBall \\\n           /unity-volume/trainer_config.yaml \\\n           --env=/unity-volume/3DBall \\\n           --train \\\n           --run-id=3dball_first_trial\n</code></pre> <p>For more detail on Docker mounts, check out these docs from Docker.</p> <p>NOTE If you are training using docker for environments that use visual observations, you may need to increase the default memory that Docker allocates for the container. For example, see here for instructions for Docker for Mac.</p>"},{"location":"Using-Docker/#running-tensorboard","title":"Running Tensorboard","text":"<p>You can run Tensorboard to monitor your training instance on http://localhost:6006:</p> <pre><code>docker exec -it &lt;container-name&gt; tensorboard --logdir /unity-volume/results --host 0.0.0.0\n</code></pre> <p>With our previous 3DBall example, this command would look like this:</p> <pre><code>docker exec -it 3DBallContainer.first.trial tensorboard --logdir /unity-volume/results --host 0.0.0.0\n</code></pre> <p>For more details on Tensorboard, check out the documentation about Using Tensorboard.</p>"},{"location":"Using-Docker/#stopping-container-and-saving-state","title":"Stopping Container and Saving State","text":"<p>If you are satisfied with the training progress, you can stop the Docker container while saving state by either using <code>Ctrl+C</code> or <code>\u2318+C</code> (Mac) or by using the following command:</p> <pre><code>docker kill --signal=SIGINT &lt;container-name&gt;\n</code></pre> <p><code>&lt;container-name&gt;</code> is the name of the container specified in the earlier <code>docker run</code> command. If you didn't specify one, you can find the randomly generated identifier by running <code>docker container ls</code>.</p>"},{"location":"Using-Tensorboard/","title":"Using TensorBoard to Observe Training","text":"<p>The ML-Agents Toolkit saves statistics during learning session that you can view with a TensorFlow utility named, TensorBoard.</p> <p>The <code>mlagents-learn</code> command saves training statistics to a folder named <code>results</code>, organized by the <code>run-id</code> value you assign to a training session.</p> <p>In order to observe the training process, either during training or afterward, start TensorBoard:</p> <ol> <li>Open a terminal or console window:</li> <li>Navigate to the directory where the ML-Agents Toolkit is installed.</li> <li>From the command line run: <code>tensorboard --logdir results --port 6006</code></li> <li>Open a browser window and navigate to    localhost:6006.</li> </ol> <p>Note: The default port TensorBoard uses is 6006. If there is an existing session running on port 6006 a new session can be launched on an open port using the --port option.</p> <p>Note: If you don't assign a <code>run-id</code> identifier, <code>mlagents-learn</code> uses the default string, \"ppo\". You can delete the folders under the <code>results</code> directory to clear out old statistics.</p> <p>On the left side of the TensorBoard window, you can select which of the training runs you want to display. You can select multiple run-ids to compare statistics. The TensorBoard window also provides options for how to display and smooth graphs.</p>"},{"location":"Using-Tensorboard/#the-ml-agents-toolkit-training-statistics","title":"The ML-Agents Toolkit training statistics","text":"<p>The ML-Agents training program saves the following statistics:</p> <p></p>"},{"location":"Using-Tensorboard/#environment-statistics","title":"Environment Statistics","text":"<ul> <li> <p><code>Environment/Lesson</code> - Plots the progress from lesson to lesson. Only   interesting when performing curriculum training.</p> </li> <li> <p><code>Environment/Cumulative Reward</code> - The mean cumulative episode reward over all   agents. Should increase during a successful training session.</p> </li> <li> <p><code>Environment/Episode Length</code> - The mean length of each episode in the   environment for all agents.</p> </li> </ul>"},{"location":"Using-Tensorboard/#is-training","title":"Is Training","text":"<ul> <li><code>Is Training</code> - A boolean indicating if the agent is updating its model.</li> </ul>"},{"location":"Using-Tensorboard/#policy-statistics","title":"Policy Statistics","text":"<ul> <li> <p><code>Policy/Entropy</code> (PPO; SAC) - How random the decisions of the model are.   Should slowly decrease during a successful training process. If it decreases   too quickly, the <code>beta</code> hyperparameter should be increased.</p> </li> <li> <p><code>Policy/Learning Rate</code> (PPO; SAC) - How large a step the training algorithm   takes as it searches for the optimal policy. Should decrease over time.</p> </li> <li> <p><code>Policy/Entropy Coefficient</code> (SAC) - Determines the relative importance of the   entropy term. This value is adjusted automatically so that the agent retains   some amount of randomness during training.</p> </li> <li> <p><code>Policy/Extrinsic Reward</code> (PPO; SAC) - This corresponds to the mean cumulative   reward received from the environment per-episode.</p> </li> <li> <p><code>Policy/Value Estimate</code> (PPO; SAC) - The mean value estimate for all states   visited by the agent. Should increase during a successful training session.</p> </li> <li> <p><code>Policy/Curiosity Reward</code> (PPO/SAC+Curiosity) - This corresponds to the mean   cumulative intrinsic reward generated per-episode.</p> </li> <li> <p><code>Policy/Curiosity Value Estimate</code> (PPO/SAC+Curiosity) - The agent's value   estimate for the curiosity reward.</p> </li> <li> <p><code>Policy/GAIL Reward</code> (PPO/SAC+GAIL) - This corresponds to the mean cumulative   discriminator-based reward generated per-episode.</p> </li> <li> <p><code>Policy/GAIL Value Estimate</code> (PPO/SAC+GAIL) - The agent's value estimate for   the GAIL reward.</p> </li> <li> <p><code>Policy/GAIL Policy Estimate</code> (PPO/SAC+GAIL) - The discriminator's estimate   for states and actions generated by the policy.</p> </li> <li> <p><code>Policy/GAIL Expert Estimate</code> (PPO/SAC+GAIL) - The discriminator's estimate   for states and actions drawn from expert demonstrations.</p> </li> </ul>"},{"location":"Using-Tensorboard/#learning-loss-functions","title":"Learning Loss Functions","text":"<ul> <li> <p><code>Losses/Policy Loss</code> (PPO; SAC) - The mean magnitude of policy loss function.   Correlates to how much the policy (process for deciding actions) is changing.   The magnitude of this should decrease during a successful training session.</p> </li> <li> <p><code>Losses/Value Loss</code> (PPO; SAC) - The mean loss of the value function update.   Correlates to how well the model is able to predict the value of each state.   This should increase while the agent is learning, and then decrease once the   reward stabilizes.</p> </li> <li> <p><code>Losses/Forward Loss</code> (PPO/SAC+Curiosity) - The mean magnitude of the forward   model loss function. Corresponds to how well the model is able to predict the   new observation encoding.</p> </li> <li> <p><code>Losses/Inverse Loss</code> (PPO/SAC+Curiosity) - The mean magnitude of the inverse   model loss function. Corresponds to how well the model is able to predict the   action taken between two observations.</p> </li> <li> <p><code>Losses/Pretraining Loss</code> (BC) - The mean magnitude of the behavioral cloning   loss. Corresponds to how well the model imitates the demonstration data.</p> </li> <li> <p><code>Losses/GAIL Loss</code> (GAIL) - The mean magnitude of the GAIL discriminator loss.   Corresponds to how well the model imitates the demonstration data.</p> </li> </ul>"},{"location":"Using-Tensorboard/#self-play","title":"Self-Play","text":"<ul> <li><code>Self-Play/ELO</code> (Self-Play) -   ELO measures the relative   skill level between two players. In a proper training run, the ELO of the   agent should steadily increase.</li> </ul>"},{"location":"Using-Tensorboard/#exporting-data-from-tensorboard","title":"Exporting Data from TensorBoard","text":"<p>To export timeseries data in CSV or JSON format, check the \"Show data download links\" in the upper left. This will enable download links below each chart.</p> <p></p>"},{"location":"Using-Tensorboard/#custom-metrics-from-unity","title":"Custom Metrics from Unity","text":"<p>To get custom metrics from a C# environment into TensorBoard, you can use the <code>StatsRecorder</code>:</p> <pre><code>var statsRecorder = Academy.Instance.StatsRecorder;\nstatsRecorder.Add(\"MyMetric\", 1.0);\n</code></pre>"},{"location":"Using-Virtual-Environment/","title":"Using Virtual Environment","text":""},{"location":"Using-Virtual-Environment/#what-is-a-virtual-environment","title":"What is a Virtual Environment?","text":"<p>A Virtual Environment is a self contained directory tree that contains a Python installation for a particular version of Python, plus a number of additional packages. To learn more about Virtual Environments see here.</p>"},{"location":"Using-Virtual-Environment/#why-should-i-use-a-virtual-environment","title":"Why should I use a Virtual Environment?","text":"<p>A Virtual Environment keeps all dependencies for the Python project separate from dependencies of other projects. This has a few advantages:</p> <ol> <li>It makes dependency management for the project easy.</li> <li>It enables using and testing of different library versions by quickly    spinning up a new environment and verifying the compatibility of the code    with the different version.</li> </ol>"},{"location":"Using-Virtual-Environment/#python-version-requirement-required","title":"Python Version Requirement (Required)","text":"<p>This guide has been tested with Python 3.8.13 through Python 3.10.x. Newer versions might not have support for the dependent libraries, so are not recommended.</p>"},{"location":"Using-Virtual-Environment/#installing-pip-required","title":"Installing Pip (Required)","text":"<ol> <li>Download the <code>get-pip.py</code> file using the command    <code>curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py</code></li> <li>Run the following <code>python3 get-pip.py</code></li> <li>Check pip version using <code>pip3 -V</code></li> </ol> <p>Note (for Ubuntu users): If the <code>ModuleNotFoundError: No module named 'distutils.util'</code> error is encountered, then python3-distutils needs to be installed. Install python3-distutils using <code>sudo apt-get install python3-distutils</code></p>"},{"location":"Using-Virtual-Environment/#mac-os-x-setup","title":"Mac OS X Setup","text":"<ol> <li>Create a folder where the virtual environments will reside    <code>$ mkdir ~/python-envs</code></li> <li>To create a new environment named <code>sample-env</code> execute    <code>$ python3 -m venv ~/python-envs/sample-env</code></li> <li>To activate the environment execute    <code>$ source ~/python-envs/sample-env/bin/activate</code></li> <li>Upgrade to the latest pip version using <code>$ pip3 install --upgrade pip</code></li> <li>Upgrade to the latest setuptools version using    <code>$ pip3 install --upgrade setuptools</code></li> <li>To deactivate the environment execute <code>$ deactivate</code> (you can reactivate the    environment using the same <code>activate</code> command listed above)</li> </ol>"},{"location":"Using-Virtual-Environment/#ubuntu-setup","title":"Ubuntu Setup","text":"<ol> <li>Install the python3-venv package using <code>$ sudo apt-get install python3-venv</code></li> <li>Follow the steps in the Mac OS X installation.</li> </ol>"},{"location":"Using-Virtual-Environment/#windows-setup","title":"Windows Setup","text":"<ol> <li>Create a folder where the virtual environments will reside <code>md python-envs</code></li> <li>To create a new environment named <code>sample-env</code> execute    <code>python -m venv python-envs\\sample-env</code></li> <li>To activate the environment execute <code>python-envs\\sample-env\\Scripts\\activate</code></li> <li>Upgrade to the latest pip version using <code>pip install --upgrade pip</code></li> <li>To deactivate the environment execute <code>deactivate</code> (you can reactivate the    environment using the same <code>activate</code> command listed above)</li> </ol> <p>Note: - Verify that you are using a Python version between 3.8.13 and 3.10.x. Launch a   command prompt using <code>cmd</code> and execute <code>python --version</code> to verify the version. - Python3 installation may require admin privileges on Windows. - This guide is for Windows 10 using a 64-bit architecture only.</p>"},{"location":"Versioning/","title":"ML-Agents Versioning","text":""},{"location":"Versioning/#context","title":"Context","text":"<p>As the ML-Agents project evolves into a more mature product, we want to communicate the process we use to version our packages and the data that flows into, through, and out of them clearly. Our project now has four packages (1 Unity, 3 Python) along with artifacts that are produced as well as consumed.  This document covers the versioning for these packages and artifacts.</p>"},{"location":"Versioning/#github-releases","title":"GitHub Releases","text":"<p>Up until now, all packages were in lockstep in-terms of versioning. As a result, the GitHub releases were tagged with the version of all those packages (e.g. v0.15.0, v0.15.1) and labeled accordingly. With the decoupling of package versions, we now need to revisit our GitHub release tagging. The proposal is that we move towards an integer release numbering for our repo and each such release will call out specific version upgrades of each package. For instance, with the April 30th release, we will have: - GitHub Release 1 (branch name: release_1_branch)   - com.unity.ml-agents release 1.0.0   - ml-agents release 0.16.0   - ml-agents-envs release 0.16.0   - gym-unity release 0.16.0</p> <p>Our release cadence will not be affected by these versioning changes.  We will keep having monthly releases to fix bugs and release new features.</p>"},{"location":"Versioning/#packages","title":"Packages","text":"<p>All of the software packages, and their generated artifacts will be versioned.  Any automation tools will not be versioned.</p>"},{"location":"Versioning/#unity-package","title":"Unity package","text":"<p>Package name: com.unity.ml-agents - Versioned following Semantic Versioning Guidelines - This package consumes an artifact of the training process: the <code>.nn</code> file.  These files     are integer versioned and currently at version 2. The com.unity.ml-agents package     will need to support the version of <code>.nn</code> files which existed at its 1.0.0 release.     For example, consider that com.unity.ml-agents is at version 1.0.0 and the NN files     are at version 2.  If the NN files change to version 3, the next release of     com.unity.ml-agents at version 1.1.0 guarantees it will be able to read both of these     formats.  If the NN files were to change to version 4 and com.unity.ml-agents to     version 2.0.0, support for NN versions 2 and 3 could be dropped for com.unity.ml-agents     version 2.0.0. - This package produces one artifact, the <code>.demo</code> files.  These files will have integer     versioning. This means their version will increment by 1 at each change.  The     com.unity.ml-agents package must be backward compatible with version changes     that occur between minor versions. - To summarize, the artifacts produced and consumed by com.unity.ml-agents are guaranteed     to be supported for 1.x.x versions of com.unity.ml-agents.  We intend to provide stability     for our users by moving to a 1.0.0 release of com.unity.ml-agents.</p>"},{"location":"Versioning/#python-packages","title":"Python Packages","text":"<p>Package names: ml-agents / ml-agents-envs / gym-unity - The python packages remain in \"Beta.\"  This means that breaking changes to the public     API of the python packages can change without having to have a major version bump.     Historically, the python and C# packages were in version lockstep.  This is no longer     the case.  The python packages will remain in lockstep with each other for now, while the     C# package will follow its own versioning as is appropriate.  However, the python package     versions may diverge in the future. - While the python packages will remain in Beta for now, we acknowledge that the most     heavily used portion of our python interface is the <code>mlagents-learn</code> CLI and strive     to make this part of our API backward compatible. We are actively working on this and     expect to have a stable CLI in the next few weeks.</p>"},{"location":"Versioning/#communicator","title":"Communicator","text":"<p>Packages which communicate: com.unity.ml-agents / ml-agents-envs</p> <p>Another entity of the ML-Agents Toolkit that requires versioning is the communication layer between C# and Python, which will follow also semantic versioning.  This guarantees a level of backward compatibility between different versions of C# and Python packages which communicate. Any Communicator version 1.x.x of the Unity package should be compatible with any 1.x.x Communicator Version in Python.</p> <p>An RLCapabilities struct keeps track of which features exist. This struct is passed from C# to Python, and another from Python to C#.  With this feature level granularity, we can notify users more specifically about feature limitations based on what's available in both C# and Python. These notifications will be logged to the python terminal, or to the Unity Editor Console.</p>"},{"location":"Versioning/#side-channels","title":"Side Channels","text":"<p>The communicator is what manages data transfer between Unity and Python for the core training loop. Side Channels are another means of data transfer between Unity and Python. Side Channels are not versioned, but have been designed to support backward compatibility for what they are. As of today, we provide 4 side channels: - FloatProperties: shared float data between Unity - Python (bidirectional) - RawBytes: raw data that can be sent Unity - Python (bidirectional) - EngineConfig: a set of numeric fields in a pre-defined order sent from Python to Unity - Stats: (name, value, agg) messages sent from Unity to Python</p> <p>Aside from the specific implementations of side channels we provide (and use ourselves), the Side Channel interface is made available for users to create their own custom side channels. As such, we guarantee that the built in SideChannel interface between Unity and Python is backward compatible in packages that share the same major version.</p>"},{"location":"com.unity.ml-agents.extensions/","title":"About ML-Agents Extensions package (<code>com.unity.ml-agents.extensions</code>)","text":"<p>The Unity ML-Agents Extensions package contains optional add-ons to the C# SDK for the Unity ML-Agents Toolkit.</p> <p>These extensions are all considered experimental, and their API or behavior may change between versions.</p>"},{"location":"com.unity.ml-agents.extensions/#package-contents","title":"Package contents","text":"<p>The following table describes the package folder structure:</p> Location Description Documentation~ Contains the documentation for the Unity package. Editor Contains utilities for Editor windows and drawers. Runtime Contains core C# APIs for integrating ML-Agents into your Unity scene. Tests Contains the unit tests for the package. <p>The Runtime directory currently contains these features:  * Physics-based sensors  * Input System Package Integration  * Custom Grid-based Sensors</p>"},{"location":"com.unity.ml-agents.extensions/#installation","title":"Installation","text":"<p>The ML-Agents Extensions package is not currently available in the Package Manager. There are two recommended ways to install the package:</p>"},{"location":"com.unity.ml-agents.extensions/#local-installation","title":"Local Installation","text":"<p>Clone the repository and follow the Local Installation for Development directions (substituting <code>com.unity.ml-agents.extensions</code> for the package name).</p>"},{"location":"com.unity.ml-agents.extensions/#github-via-package-manager","title":"Github via Package Manager","text":"<p>In Unity 2019.4 or later, open the Package Manager, hit the \"+\" button, and select \"Add package from git URL\".</p> <p></p> <p>In the dialog that appears, enter  ``` git+https://github.com/Unity-Technologies/ml-agents.git?path=com.unity.ml-agents.extensions#release_20</p> <pre><code>\nYou can also edit your project's `manifest.json` directly and add the following line to the `dependencies`\nsection:\n</code></pre> <p>\"com.unity.ml-agents.extensions\": \"git+https://github.com/Unity-Technologies/ml-agents.git?path=com.unity.ml-agents.extensions#release_20\", ``` See Git dependencies for more information. Note that this may take several minutes to resolve the packages the first time that you add it.</p>"},{"location":"com.unity.ml-agents.extensions/#requirements","title":"Requirements","text":"<p>This version of the Unity ML-Agents package is compatible with the following versions of the Unity Editor:</p> <ul> <li>2019.4 and later</li> </ul> <p>If using the <code>InputActuatorComponent</code> - install the <code>com.unity.inputsystem</code> package version <code>1.1.0-preview.3</code> or later.</p>"},{"location":"com.unity.ml-agents.extensions/#known-limitations","title":"Known Limitations","text":"<ul> <li>For the <code>InputActuatorComponent</code><ul> <li>Limited implementation of <code>InputControls</code></li> <li>No way to customize the action space of the <code>InputActuatorComponent</code></li> </ul> </li> </ul>"},{"location":"com.unity.ml-agents.extensions/#need-help","title":"Need Help?","text":"<p>The main README contains links for contacting the team or getting support.</p>"},{"location":"com.unity.ml-agents/","title":"About ML-Agents package (<code>com.unity.ml-agents</code>)","text":"<p>The ML-Agents package contains the primary C# SDK for the Unity ML-Agents Toolkit.</p> <p>The package allows you to convert any Unity scene into a learning environment and train character behaviors using a variety of machine learning algorithms. Additionally, it allows you to embed these trained behaviors back into Unity scenes to control your characters. More specifically, the package provides the following core functionalities:</p> <ul> <li>Define Agents: entities, or characters, whose behavior will be learned. Agents   are entities that generate observations (through sensors), take actions, and   receive rewards from the environment.</li> <li>Define Behaviors: entities that specify how an agent should act. Multiple   agents can share the same Behavior and a scene may have multiple Behaviors.</li> <li>Record demonstrations of an agent within the Editor. You can use   demonstrations to help train a behavior for that agent.</li> <li>Embedding a trained behavior into the scene via the Unity Inference Engine.   Embedded behaviors allow you to switch an Agent between learning and   inference.</li> </ul> <p>Note that the ML-Agents package does not contain the machine learning algorithms for training behaviors. The ML-Agents package only supports instrumenting a Unity scene, setting it up for training, and then embedding the trained model back into your Unity scene. The machine learning algorithms that orchestrate training are part of the companion Python package.</p> <p>Note that we also provide an ML-Agents Extensions package (<code>com.unity.ml-agents.extensions</code>) that contains early/experimental features that you may find useful. This package is only available from the ML-Agents GitHub repo.</p>"},{"location":"com.unity.ml-agents/#package-contents","title":"Package contents","text":"<p>The following table describes the package folder structure:</p> Location Description Documentation~ Contains the documentation for the Unity package. Editor Contains utilities for Editor windows and drawers. Plugins Contains third-party DLLs. Runtime Contains core C# APIs for integrating ML-Agents into your Unity scene. Runtime/Integrations Contains utilities for integrating ML-Agents into specific game genres. Tests Contains the unit tests for the package. <p></p>"},{"location":"com.unity.ml-agents/#installation","title":"Installation","text":"<p>To install this ML-Agents package, follow the instructions in the Package Manager documentation.</p> <p>To install the companion Python package to enable training behaviors, follow the installation instructions on our GitHub repository.</p>"},{"location":"com.unity.ml-agents/#advanced-installation","title":"Advanced Installation","text":"<p>With the changes to Unity Package Manager in 2021, experimental packages will not show up in the package list and have to be installed manually. There are two recommended ways to install the package manually:</p>"},{"location":"com.unity.ml-agents/#github-via-package-manager","title":"Github via Package Manager","text":"<p>In Unity 2019.4 or later, open the Package Manager, hit the \"+\" button, and select \"Add package from git URL\".</p> <p></p> <p>In the dialog that appears, enter</p> <pre><code>git+https://github.com/Unity-Technologies/ml-agents.git?path=com.unity.ml-agents#release_20\n</code></pre> <p>You can also edit your project's <code>manifest.json</code> directly and add the following line to the <code>dependencies</code> section:</p> <pre><code>\"com.unity.ml-agents\": \"git+https://github.com/Unity-Technologies/ml-agents.git?path=com.unity.ml-agents#release_20\",\n</code></pre> <p>See Git dependencies for more information. Note that this may take several minutes to resolve the packages the first time that you add it.</p>"},{"location":"com.unity.ml-agents/#local-installation-for-development","title":"Local Installation for Development","text":"<p>Clone the repository and follow the Local Installation for Development directions.</p>"},{"location":"com.unity.ml-agents/#requirements","title":"Requirements","text":"<p>This version of the Unity ML-Agents package is compatible with the following versions of the Unity Editor:</p> <ul> <li>2019.4 and later</li> </ul>"},{"location":"com.unity.ml-agents/#known-limitations","title":"Known Limitations","text":""},{"location":"com.unity.ml-agents/#training","title":"Training","text":"<p>Training is limited to the Unity Editor and Standalone builds on Windows, MacOS, and Linux with the Mono scripting backend. Currently, training does not work with the IL2CPP scripting backend. Your environment will default to inference mode if training is not supported or is not currently running.</p>"},{"location":"com.unity.ml-agents/#inference","title":"Inference","text":"<p>Inference is executed via the Unity Inference Engine.</p> <p>CPU</p> <p>All platforms supported.</p> <p>GPU</p> <p>All platforms supported except:</p> <ul> <li>WebGL and GLES 3/2 on Android / iPhone</li> </ul> <p>NOTE: Mobile platform support includes:</p> <ul> <li>Vulkan for Android</li> <li>Metal for iOS.</li> </ul>"},{"location":"com.unity.ml-agents/#headless-mode","title":"Headless Mode","text":"<p>If you enable Headless mode, you will not be able to collect visual observations from your agents.</p>"},{"location":"com.unity.ml-agents/#rendering-speed-and-synchronization","title":"Rendering Speed and Synchronization","text":"<p>Currently the speed of the game physics can only be increased to 100x real-time. The Academy also moves in time with FixedUpdate() rather than Update(), so game behavior implemented in Update() may be out of sync with the agent decision making. See Execution Order of Event Functions for more information.</p> <p>You can control the frequency of Academy stepping by calling <code>Academy.Instance.DisableAutomaticStepping()</code>, and then calling <code>Academy.Instance.EnvironmentStep()</code></p>"},{"location":"com.unity.ml-agents/#unity-inference-engine-models","title":"Unity Inference Engine Models","text":"<p>Currently, only models created with our trainers are supported for running ML-Agents with a neural network behavior.</p>"},{"location":"com.unity.ml-agents/#helpful-links","title":"Helpful links","text":"<p>If you are new to the Unity ML-Agents package, or have a question after reading the documentation, you can checkout our GitHub Repository, which also includes a number of ways to connect with us including our ML-Agents Forum.</p> <p>In order to improve the developer experience for Unity ML-Agents Toolkit, we have added in-editor analytics. Please refer to \"Information that is passively collected by Unity\" in the Unity Privacy Policy.</p>"},{"location":"ml-agents-envs/","title":"Unity ML-Agents Python Interface","text":"<p>The <code>mlagents_envs</code> Python package is part of the ML-Agents Toolkit. <code>mlagents_envs</code> provides three Python APIs that allows direct interaction with the Unity game engine: - A single agent API (Gym API) - A gym-like multi-agent API (PettingZoo API) - A low-level API (LLAPI)</p> <p>The LLAPI is used by the trainer implementation in <code>mlagents</code>. <code>mlagents_envs</code> can be used independently of <code>mlagents</code> for Python communication.</p>"},{"location":"ml-agents-envs/#installation","title":"Installation","text":"<p>Install the <code>mlagents_envs</code> package with:</p> <pre><code>python -m pip install mlagents_envs==0.30.0\n</code></pre>"},{"location":"ml-agents-envs/#usage-more-information","title":"Usage &amp; More Information","text":"<p>See - Gym API Guide - PettingZoo API Guide - Python API Guide</p> <p>for more information on how to use the API to interact with a Unity environment.</p> <p>For more information on the ML-Agents Toolkit and how to instrument a Unity scene with the ML-Agents SDK, check out the main ML-Agents Toolkit documentation.</p>"},{"location":"ml-agents-envs/#limitations","title":"Limitations","text":"<ul> <li><code>mlagents_envs</code> uses localhost ports to exchange data between Unity and   Python. As such, multiple instances can have their ports collide, leading to   errors. Make sure to use a different port if you are using multiple instances   of <code>UnityEnvironment</code>.</li> <li>Communication between Unity and the Python <code>UnityEnvironment</code> is not secure.</li> <li>On Linux, ports are not released immediately after the communication closes.   As such, you cannot reuse ports right after closing a <code>UnityEnvironment</code>.</li> </ul>"},{"location":"ml-agents/","title":"Unity ML-Agents Trainers","text":"<p>The <code>mlagents</code> Python package is part of the ML-Agents Toolkit. <code>mlagents</code> provides a set of reinforcement and imitation learning algorithms designed to be used with Unity environments. The algorithms interface with the Python API provided by the <code>mlagents_envs</code> package. See here for more information on <code>mlagents_envs</code>.</p> <p>The algorithms can be accessed using the: <code>mlagents-learn</code> access point. See here for more information on using this package.</p>"},{"location":"ml-agents/#installation","title":"Installation","text":"<p>Install the <code>mlagents</code> package with:</p> <pre><code>python -m pip install mlagents==0.30.0\n</code></pre>"},{"location":"ml-agents/#usage-more-information","title":"Usage &amp; More Information","text":"<p>For more information on the ML-Agents Toolkit and how to instrument a Unity scene with the ML-Agents SDK, check out the main ML-Agents Toolkit documentation.</p>"},{"location":"ml-agents/#limitations","title":"Limitations","text":"<ul> <li>Resuming self-play from a checkpoint resets the reported ELO to the default   value.</li> </ul>"},{"location":"doxygen/Readme/","title":"Doxygen files","text":"<p>To generate the API reference as HTML files, run:</p> <pre><code>doxygen dox-ml-agents.conf\n</code></pre>"}]}
\ No newline at end of file
+{"config":{"indexing":"full","lang":["en"],"min_search_length":3,"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"","text":"Unity ML-Agents Toolkit ( latest release ) ( all releases ) The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents. We provide implementations (based on PyTorch) of state-of-the-art algorithms to enable game developers and hobbyists to easily train intelligent agents for 2D, 3D and VR/AR games. Researchers can also use the provided simple-to-use Python API to train Agents using reinforcement learning, imitation learning, neuroevolution, or any other methods. These trained agents can be used for multiple purposes, including controlling NPC behavior (in a variety of settings such as multi-agent and adversarial), automated testing of game builds and evaluating different game design decisions pre-release. The ML-Agents Toolkit is mutually beneficial for both game developers and AI researchers as it provides a central platform where advances in AI can be evaluated on Unity\u2019s rich environments and then made accessible to the wider research and game developer communities. Features 17+ example Unity environments Support for multiple environment configurations and training scenarios Flexible Unity SDK that can be integrated into your game or custom Unity scene Support for training single-agent, multi-agent cooperative, and multi-agent competitive scenarios via several Deep Reinforcement Learning algorithms (PPO, SAC, MA-POCA, self-play). Support for learning from demonstrations through two Imitation Learning algorithms (BC and GAIL). Quickly and easily add your own custom training algorithm and/or components. Easily definable Curriculum Learning scenarios for complex tasks Train robust agents using environment randomization Flexible agent control with On Demand Decision Making Train using multiple concurrent Unity environment instances Utilizes the Unity Inference Engine to provide native cross-platform support Unity environment control from Python Wrap Unity learning environments as a gym environment Wrap Unity learning environments as a PettingZoo environment See our ML-Agents Overview page for detailed descriptions of all these features. Or go straight to our web docs . Releases & Documentation Our latest, stable release is Release 20 . Click here to get started with the latest release of ML-Agents. You can also check out our new web docs ! The table below lists all our releases, including our main branch which is under active development and may be unstable. A few helpful guidelines: - The Versioning page overviews how we manage our GitHub releases and the versioning process for each of the ML-Agents components. - The Releases page contains details of the changes between releases. - The Migration page contains details on how to upgrade from earlier releases of the ML-Agents Toolkit. - The Documentation links in the table below include installation and usage instructions specific to each release. Remember to always use the documentation that corresponds to the release version you're using. - The com.unity.ml-agents package is verified for Unity 2020.1 and later. Verified packages releases are numbered 1.0.x. Version Release Date Source Documentation Download Python Package Unity Package Release 20 November 21, 2022 source docs download 0.30.0 2.3.0 main (unstable) -- source docs download -- -- Verified Package 1.0.8 May 26, 2021 source docs download 0.16.1 1.0.8 If you are a researcher interested in a discussion of Unity as an AI platform, see a pre-print of our reference paper on Unity and the ML-Agents Toolkit . If you use Unity or the ML-Agents Toolkit to conduct research, we ask that you cite the following paper as a reference: @article{juliani2020, title={Unity: A general platform for intelligent agents}, author={Juliani, Arthur and Berges, Vincent-Pierre and Teng, Ervin and Cohen, Andrew and Harper, Jonathan and Elion, Chris and Goy, Chris and Gao, Yuan and Henry, Hunter and Mattar, Marwan and Lange, Danny}, journal={arXiv preprint arXiv:1809.02627}, url={https://arxiv.org/pdf/1809.02627.pdf}, year={2020} } Additionally, if you use the MA-POCA trainer in your research, we ask that you cite the following paper as a reference: @article{cohen2022, title={On the Use and Misuse of Absorbing States in Multi-agent Reinforcement Learning}, author={Cohen, Andrew and Teng, Ervin and Berges, Vincent-Pierre and Dong, Ruo-Ping and Henry, Hunter and Mattar, Marwan and Zook, Alexander and Ganguly, Sujoy}, journal={RL in Games Workshop AAAI 2022}, url={http://aaai-rlg.mlanctot.info/papers/AAAI22-RLG_paper_32.pdf}, year={2022} } Additional Resources We have a Unity Learn course, ML-Agents: Hummingbirds , that provides a gentle introduction to Unity and the ML-Agents Toolkit. We've also partnered with CodeMonkeyUnity to create a series of tutorial videos on how to implement and use the ML-Agents Toolkit. We have also published a series of blog posts that are relevant for ML-Agents: (July 12, 2021) ML-Agents plays Dodgeball (May 5, 2021) ML-Agents v2.0 release: Now supports training complex cooperative behaviors (December 28, 2020) Happy holidays from the Unity ML-Agents team! (November 20, 2020) How Eidos-Montr\u00e9al created Grid Sensors to improve observations for training agents (November 11, 2020) 2020 AI@Unity interns shoutout (May 12, 2020) Announcing ML-Agents Unity Package v1.0! (February 28, 2020) Training intelligent adversaries using self-play with ML-Agents (November 11, 2019) Training your agents 7 times faster with ML-Agents (October 21, 2019) The AI@Unity interns help shape the world (April 15, 2019) Unity ML-Agents Toolkit v0.8: Faster training on real games (March 1, 2019) Unity ML-Agents Toolkit v0.7: A leap towards cross-platform inference (December 17, 2018) ML-Agents Toolkit v0.6: Improved usability of Brains and Imitation Learning (October 2, 2018) Puppo, The Corgi: Cuteness Overload with the Unity ML-Agents Toolkit (September 11, 2018) ML-Agents Toolkit v0.5, new resources for AI researchers available now (June 26, 2018) Solving sparse-reward tasks with Curiosity (June 19, 2018) Unity ML-Agents Toolkit v0.4 and Udacity Deep Reinforcement Learning Nanodegree (May 24, 2018) Imitation Learning in Unity: The Workflow (March 15, 2018) ML-Agents Toolkit v0.3 Beta released: Imitation Learning, feedback-driven features, and more (December 11, 2017) Using Machine Learning Agents in a real game: a beginner\u2019s guide (December 8, 2017) Introducing ML-Agents Toolkit v0.2: Curriculum Learning, new environments, and more (September 19, 2017) Introducing: Unity Machine Learning Agents Toolkit Overviewing reinforcement learning concepts ( multi-armed bandit and Q-learning ) More from Unity Unity Robotics Unity Computer Vision Community and Feedback The ML-Agents Toolkit is an open-source project and we encourage and welcome contributions. If you wish to contribute, be sure to review our contribution guidelines and code of conduct . For problems with the installation and setup of the ML-Agents Toolkit, or discussions about how to best setup or train your agents, please create a new thread on the Unity ML-Agents forum and make sure to include as much detail as possible. If you run into any other problems using the ML-Agents Toolkit or have a specific feature request, please submit a GitHub issue . Please tell us which samples you would like to see shipped with the ML-Agents Unity package by replying to this forum thread . Your opinion matters a great deal to us. Only by hearing your thoughts on the Unity ML-Agents Toolkit can we continue to improve and grow. Please take a few minutes to let us know about it . For any other questions or feedback, connect directly with the ML-Agents team at ml-agents@unity3d.com. Privacy In order to improve the developer experience for Unity ML-Agents Toolkit, we have added in-editor analytics. Please refer to \"Information that is passively collected by Unity\" in the Unity Privacy Policy .","title":"Home"},{"location":"#unity-ml-agents-toolkit","text":"( latest release ) ( all releases ) The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents. We provide implementations (based on PyTorch) of state-of-the-art algorithms to enable game developers and hobbyists to easily train intelligent agents for 2D, 3D and VR/AR games. Researchers can also use the provided simple-to-use Python API to train Agents using reinforcement learning, imitation learning, neuroevolution, or any other methods. These trained agents can be used for multiple purposes, including controlling NPC behavior (in a variety of settings such as multi-agent and adversarial), automated testing of game builds and evaluating different game design decisions pre-release. The ML-Agents Toolkit is mutually beneficial for both game developers and AI researchers as it provides a central platform where advances in AI can be evaluated on Unity\u2019s rich environments and then made accessible to the wider research and game developer communities.","title":"Unity ML-Agents Toolkit"},{"location":"#features","text":"17+ example Unity environments Support for multiple environment configurations and training scenarios Flexible Unity SDK that can be integrated into your game or custom Unity scene Support for training single-agent, multi-agent cooperative, and multi-agent competitive scenarios via several Deep Reinforcement Learning algorithms (PPO, SAC, MA-POCA, self-play). Support for learning from demonstrations through two Imitation Learning algorithms (BC and GAIL). Quickly and easily add your own custom training algorithm and/or components. Easily definable Curriculum Learning scenarios for complex tasks Train robust agents using environment randomization Flexible agent control with On Demand Decision Making Train using multiple concurrent Unity environment instances Utilizes the Unity Inference Engine to provide native cross-platform support Unity environment control from Python Wrap Unity learning environments as a gym environment Wrap Unity learning environments as a PettingZoo environment See our ML-Agents Overview page for detailed descriptions of all these features. Or go straight to our web docs .","title":"Features"},{"location":"#releases-documentation","text":"Our latest, stable release is Release 20 . Click here to get started with the latest release of ML-Agents. You can also check out our new web docs ! The table below lists all our releases, including our main branch which is under active development and may be unstable. A few helpful guidelines: - The Versioning page overviews how we manage our GitHub releases and the versioning process for each of the ML-Agents components. - The Releases page contains details of the changes between releases. - The Migration page contains details on how to upgrade from earlier releases of the ML-Agents Toolkit. - The Documentation links in the table below include installation and usage instructions specific to each release. Remember to always use the documentation that corresponds to the release version you're using. - The com.unity.ml-agents package is verified for Unity 2020.1 and later. Verified packages releases are numbered 1.0.x. Version Release Date Source Documentation Download Python Package Unity Package Release 20 November 21, 2022 source docs download 0.30.0 2.3.0 main (unstable) -- source docs download -- -- Verified Package 1.0.8 May 26, 2021 source docs download 0.16.1 1.0.8 If you are a researcher interested in a discussion of Unity as an AI platform, see a pre-print of our reference paper on Unity and the ML-Agents Toolkit . If you use Unity or the ML-Agents Toolkit to conduct research, we ask that you cite the following paper as a reference: @article{juliani2020, title={Unity: A general platform for intelligent agents}, author={Juliani, Arthur and Berges, Vincent-Pierre and Teng, Ervin and Cohen, Andrew and Harper, Jonathan and Elion, Chris and Goy, Chris and Gao, Yuan and Henry, Hunter and Mattar, Marwan and Lange, Danny}, journal={arXiv preprint arXiv:1809.02627}, url={https://arxiv.org/pdf/1809.02627.pdf}, year={2020} } Additionally, if you use the MA-POCA trainer in your research, we ask that you cite the following paper as a reference: @article{cohen2022, title={On the Use and Misuse of Absorbing States in Multi-agent Reinforcement Learning}, author={Cohen, Andrew and Teng, Ervin and Berges, Vincent-Pierre and Dong, Ruo-Ping and Henry, Hunter and Mattar, Marwan and Zook, Alexander and Ganguly, Sujoy}, journal={RL in Games Workshop AAAI 2022}, url={http://aaai-rlg.mlanctot.info/papers/AAAI22-RLG_paper_32.pdf}, year={2022} }","title":"Releases &amp; Documentation"},{"location":"#additional-resources","text":"We have a Unity Learn course, ML-Agents: Hummingbirds , that provides a gentle introduction to Unity and the ML-Agents Toolkit. We've also partnered with CodeMonkeyUnity to create a series of tutorial videos on how to implement and use the ML-Agents Toolkit. We have also published a series of blog posts that are relevant for ML-Agents: (July 12, 2021) ML-Agents plays Dodgeball (May 5, 2021) ML-Agents v2.0 release: Now supports training complex cooperative behaviors (December 28, 2020) Happy holidays from the Unity ML-Agents team! (November 20, 2020) How Eidos-Montr\u00e9al created Grid Sensors to improve observations for training agents (November 11, 2020) 2020 AI@Unity interns shoutout (May 12, 2020) Announcing ML-Agents Unity Package v1.0! (February 28, 2020) Training intelligent adversaries using self-play with ML-Agents (November 11, 2019) Training your agents 7 times faster with ML-Agents (October 21, 2019) The AI@Unity interns help shape the world (April 15, 2019) Unity ML-Agents Toolkit v0.8: Faster training on real games (March 1, 2019) Unity ML-Agents Toolkit v0.7: A leap towards cross-platform inference (December 17, 2018) ML-Agents Toolkit v0.6: Improved usability of Brains and Imitation Learning (October 2, 2018) Puppo, The Corgi: Cuteness Overload with the Unity ML-Agents Toolkit (September 11, 2018) ML-Agents Toolkit v0.5, new resources for AI researchers available now (June 26, 2018) Solving sparse-reward tasks with Curiosity (June 19, 2018) Unity ML-Agents Toolkit v0.4 and Udacity Deep Reinforcement Learning Nanodegree (May 24, 2018) Imitation Learning in Unity: The Workflow (March 15, 2018) ML-Agents Toolkit v0.3 Beta released: Imitation Learning, feedback-driven features, and more (December 11, 2017) Using Machine Learning Agents in a real game: a beginner\u2019s guide (December 8, 2017) Introducing ML-Agents Toolkit v0.2: Curriculum Learning, new environments, and more (September 19, 2017) Introducing: Unity Machine Learning Agents Toolkit Overviewing reinforcement learning concepts ( multi-armed bandit and Q-learning )","title":"Additional Resources"},{"location":"#more-from-unity","text":"Unity Robotics Unity Computer Vision","title":"More from Unity"},{"location":"#community-and-feedback","text":"The ML-Agents Toolkit is an open-source project and we encourage and welcome contributions. If you wish to contribute, be sure to review our contribution guidelines and code of conduct . For problems with the installation and setup of the ML-Agents Toolkit, or discussions about how to best setup or train your agents, please create a new thread on the Unity ML-Agents forum and make sure to include as much detail as possible. If you run into any other problems using the ML-Agents Toolkit or have a specific feature request, please submit a GitHub issue . Please tell us which samples you would like to see shipped with the ML-Agents Unity package by replying to this forum thread . Your opinion matters a great deal to us. Only by hearing your thoughts on the Unity ML-Agents Toolkit can we continue to improve and grow. Please take a few minutes to let us know about it . For any other questions or feedback, connect directly with the ML-Agents team at ml-agents@unity3d.com.","title":"Community and Feedback"},{"location":"#privacy","text":"In order to improve the developer experience for Unity ML-Agents Toolkit, we have added in-editor analytics. Please refer to \"Information that is passively collected by Unity\" in the Unity Privacy Policy .","title":"Privacy"},{"location":"API-Reference/","text":"API Reference Our developer-facing C# classes have been documented to be compatible with Doxygen for auto-generating HTML documentation. To generate the API reference, download Doxygen and run the following command within the docs/ directory: doxygen dox-ml-agents.conf dox-ml-agents.conf is a Doxygen configuration file for the ML-Agents Toolkit that includes the classes that have been properly formatted. The generated HTML files will be placed in the html/ subdirectory. Open index.html within that subdirectory to navigate to the API reference home. Note that html/ is already included in the repository's .gitignore file. In the near future, we aim to expand our documentation to include the Python classes.","title":"API Reference"},{"location":"API-Reference/#api-reference","text":"Our developer-facing C# classes have been documented to be compatible with Doxygen for auto-generating HTML documentation. To generate the API reference, download Doxygen and run the following command within the docs/ directory: doxygen dox-ml-agents.conf dox-ml-agents.conf is a Doxygen configuration file for the ML-Agents Toolkit that includes the classes that have been properly formatted. The generated HTML files will be placed in the html/ subdirectory. Open index.html within that subdirectory to navigate to the API reference home. Note that html/ is already included in the repository's .gitignore file. In the near future, we aim to expand our documentation to include the Python classes.","title":"API Reference"},{"location":"Background-Machine-Learning/","text":"Background: Machine Learning Given that a number of users of the ML-Agents Toolkit might not have a formal machine learning background, this page provides an overview to facilitate the understanding of the ML-Agents Toolkit. However, we will not attempt to provide a thorough treatment of machine learning as there are fantastic resources online. Machine learning, a branch of artificial intelligence, focuses on learning patterns from data. The three main classes of machine learning algorithms include: unsupervised learning, supervised learning and reinforcement learning. Each class of algorithm learns from a different type of data. The following paragraphs provide an overview for each of these classes of machine learning, as well as introductory examples. Unsupervised Learning The goal of unsupervised learning is to group or cluster similar items in a data set. For example, consider the players of a game. We may want to group the players depending on how engaged they are with the game. This would enable us to target different groups (e.g. for highly-engaged players we might invite them to be beta testers for new features, while for unengaged players we might email them helpful tutorials). Say that we wish to split our players into two groups. We would first define basic attributes of the players, such as the number of hours played, total money spent on in-app purchases and number of levels completed. We can then feed this data set (three attributes for every player) to an unsupervised learning algorithm where we specify the number of groups to be two. The algorithm would then split the data set of players into two groups where the players within each group would be similar to each other. Given the attributes we used to describe each player, in this case, the output would be a split of all the players into two groups, where one group would semantically represent the engaged players and the second group would semantically represent the unengaged players. With unsupervised learning, we did not provide specific examples of which players are considered engaged and which are considered unengaged. We just defined the appropriate attributes and relied on the algorithm to uncover the two groups on its own. This type of data set is typically called an unlabeled data set as it is lacking these direct labels. Consequently, unsupervised learning can be helpful in situations where these labels can be expensive or hard to produce. In the next paragraph, we overview supervised learning algorithms which accept input labels in addition to attributes. Supervised Learning In supervised learning , we do not want to just group similar items but directly learn a mapping from each item to the group (or class) that it belongs to. Returning to our earlier example of clustering players, let's say we now wish to predict which of our players are about to churn (that is stop playing the game for the next 30 days). We can look into our historical records and create a data set that contains attributes of our players in addition to a label indicating whether they have churned or not. Note that the player attributes we use for this churn prediction task may be different from the ones we used for our earlier clustering task. We can then feed this data set (attributes and label for each player) into a supervised learning algorithm which would learn a mapping from the player attributes to a label indicating whether that player will churn or not. The intuition is that the supervised learning algorithm will learn which values of these attributes typically correspond to players who have churned and not churned (for example, it may learn that players who spend very little and play for very short periods will most likely churn). Now given this learned model, we can provide it the attributes of a new player (one that recently started playing the game) and it would output a predicted label for that player. This prediction is the algorithms expectation of whether the player will churn or not. We can now use these predictions to target the players who are expected to churn and entice them to continue playing the game. As you may have noticed, for both supervised and unsupervised learning, there are two tasks that need to be performed: attribute selection and model selection. Attribute selection (also called feature selection) pertains to selecting how we wish to represent the entity of interest, in this case, the player. Model selection, on the other hand, pertains to selecting the algorithm (and its parameters) that perform the task well. Both of these tasks are active areas of machine learning research and, in practice, require several iterations to achieve good performance. We now switch to reinforcement learning, the third class of machine learning algorithms, and arguably the one most relevant for the ML-Agents Toolkit. Reinforcement Learning Reinforcement learning can be viewed as a form of learning for sequential decision making that is commonly associated with controlling robots (but is, in fact, much more general). Consider an autonomous firefighting robot that is tasked with navigating into an area, finding the fire and neutralizing it. At any given moment, the robot perceives the environment through its sensors (e.g. camera, heat, touch), processes this information and produces an action (e.g. move to the left, rotate the water hose, turn on the water). In other words, it is continuously making decisions about how to interact in this environment given its view of the world (i.e. sensors input) and objective (i.e. neutralizing the fire). Teaching a robot to be a successful firefighting machine is precisely what reinforcement learning is designed to do. More specifically, the goal of reinforcement learning is to learn a policy , which is essentially a mapping from observations to actions . An observation is what the robot can measure from its environment (in this case, all its sensory inputs) and an action, in its most raw form, is a change to the configuration of the robot (e.g. position of its base, position of its water hose and whether the hose is on or off). The last remaining piece of the reinforcement learning task is the reward signal . The robot is trained to learn a policy that maximizes its overall rewards. When training a robot to be a mean firefighting machine, we provide it with rewards (positive and negative) indicating how well it is doing on completing the task. Note that the robot does not know how to put out fires before it is trained. It learns the objective because it receives a large positive reward when it puts out the fire and a small negative reward for every passing second. The fact that rewards are sparse (i.e. may not be provided at every step, but only when a robot arrives at a success or failure situation), is a defining characteristic of reinforcement learning and precisely why learning good policies can be difficult (and/or time-consuming) for complex environments. Learning a policy usually requires many trials and iterative policy updates. More specifically, the robot is placed in several fire situations and over time learns an optimal policy which allows it to put out fires more effectively. Obviously, we cannot expect to train a robot repeatedly in the real world, particularly when fires are involved. This is precisely why the use of Unity as a simulator serves as the perfect training grounds for learning such behaviors. While our discussion of reinforcement learning has centered around robots, there are strong parallels between robots and characters in a game. In fact, in many ways, one can view a non-playable character (NPC) as a virtual robot, with its own observations about the environment, its own set of actions and a specific objective. Thus it is natural to explore how we can train behaviors within Unity using reinforcement learning. This is precisely what the ML-Agents Toolkit offers. The video linked below includes a reinforcement learning demo showcasing training character behaviors using the ML-Agents Toolkit. Similar to both unsupervised and supervised learning, reinforcement learning also involves two tasks: attribute selection and model selection. Attribute selection is defining the set of observations for the robot that best help it complete its objective, while model selection is defining the form of the policy (mapping from observations to actions) and its parameters. In practice, training behaviors is an iterative process that may require changing the attribute and model choices. Training and Inference One common aspect of all three branches of machine learning is that they all involve a training phase and an inference phase . While the details of the training and inference phases are different for each of the three, at a high-level, the training phase involves building a model using the provided data, while the inference phase involves applying this model to new, previously unseen, data. More specifically: For our unsupervised learning example, the training phase learns the optimal two clusters based on the data describing existing players, while the inference phase assigns a new player to one of these two clusters. For our supervised learning example, the training phase learns the mapping from player attributes to player label (whether they churned or not), and the inference phase predicts whether a new player will churn or not based on that learned mapping. For our reinforcement learning example, the training phase learns the optimal policy through guided trials, and in the inference phase, the agent observes and takes actions in the wild using its learned policy. To briefly summarize: all three classes of algorithms involve training and inference phases in addition to attribute and model selections. What ultimately separates them is the type of data available to learn from. In unsupervised learning our data set was a collection of attributes, in supervised learning our data set was a collection of attribute-label pairs, and, lastly, in reinforcement learning our data set was a collection of observation-action-reward tuples. Deep Learning Deep learning is a family of algorithms that can be used to address any of the problems introduced above. More specifically, they can be used to solve both attribute and model selection tasks. Deep learning has gained popularity in recent years due to its outstanding performance on several challenging machine learning tasks. One example is AlphaGo , a computer Go program, that leverages deep learning, that was able to beat Lee Sedol (a Go world champion). A key characteristic of deep learning algorithms is their ability to learn very complex functions from large amounts of training data. This makes them a natural choice for reinforcement learning tasks when a large amount of data can be generated, say through the use of a simulator or engine such as Unity. By generating hundreds of thousands of simulations of the environment within Unity, we can learn policies for very complex environments (a complex environment is one where the number of observations an agent perceives and the number of actions they can take are large). Many of the algorithms we provide in ML-Agents use some form of deep learning, built on top of the open-source library, PyTorch .","title":"Machine Learning"},{"location":"Background-Machine-Learning/#background-machine-learning","text":"Given that a number of users of the ML-Agents Toolkit might not have a formal machine learning background, this page provides an overview to facilitate the understanding of the ML-Agents Toolkit. However, we will not attempt to provide a thorough treatment of machine learning as there are fantastic resources online. Machine learning, a branch of artificial intelligence, focuses on learning patterns from data. The three main classes of machine learning algorithms include: unsupervised learning, supervised learning and reinforcement learning. Each class of algorithm learns from a different type of data. The following paragraphs provide an overview for each of these classes of machine learning, as well as introductory examples.","title":"Background: Machine Learning"},{"location":"Background-Machine-Learning/#unsupervised-learning","text":"The goal of unsupervised learning is to group or cluster similar items in a data set. For example, consider the players of a game. We may want to group the players depending on how engaged they are with the game. This would enable us to target different groups (e.g. for highly-engaged players we might invite them to be beta testers for new features, while for unengaged players we might email them helpful tutorials). Say that we wish to split our players into two groups. We would first define basic attributes of the players, such as the number of hours played, total money spent on in-app purchases and number of levels completed. We can then feed this data set (three attributes for every player) to an unsupervised learning algorithm where we specify the number of groups to be two. The algorithm would then split the data set of players into two groups where the players within each group would be similar to each other. Given the attributes we used to describe each player, in this case, the output would be a split of all the players into two groups, where one group would semantically represent the engaged players and the second group would semantically represent the unengaged players. With unsupervised learning, we did not provide specific examples of which players are considered engaged and which are considered unengaged. We just defined the appropriate attributes and relied on the algorithm to uncover the two groups on its own. This type of data set is typically called an unlabeled data set as it is lacking these direct labels. Consequently, unsupervised learning can be helpful in situations where these labels can be expensive or hard to produce. In the next paragraph, we overview supervised learning algorithms which accept input labels in addition to attributes.","title":"Unsupervised Learning"},{"location":"Background-Machine-Learning/#supervised-learning","text":"In supervised learning , we do not want to just group similar items but directly learn a mapping from each item to the group (or class) that it belongs to. Returning to our earlier example of clustering players, let's say we now wish to predict which of our players are about to churn (that is stop playing the game for the next 30 days). We can look into our historical records and create a data set that contains attributes of our players in addition to a label indicating whether they have churned or not. Note that the player attributes we use for this churn prediction task may be different from the ones we used for our earlier clustering task. We can then feed this data set (attributes and label for each player) into a supervised learning algorithm which would learn a mapping from the player attributes to a label indicating whether that player will churn or not. The intuition is that the supervised learning algorithm will learn which values of these attributes typically correspond to players who have churned and not churned (for example, it may learn that players who spend very little and play for very short periods will most likely churn). Now given this learned model, we can provide it the attributes of a new player (one that recently started playing the game) and it would output a predicted label for that player. This prediction is the algorithms expectation of whether the player will churn or not. We can now use these predictions to target the players who are expected to churn and entice them to continue playing the game. As you may have noticed, for both supervised and unsupervised learning, there are two tasks that need to be performed: attribute selection and model selection. Attribute selection (also called feature selection) pertains to selecting how we wish to represent the entity of interest, in this case, the player. Model selection, on the other hand, pertains to selecting the algorithm (and its parameters) that perform the task well. Both of these tasks are active areas of machine learning research and, in practice, require several iterations to achieve good performance. We now switch to reinforcement learning, the third class of machine learning algorithms, and arguably the one most relevant for the ML-Agents Toolkit.","title":"Supervised Learning"},{"location":"Background-Machine-Learning/#reinforcement-learning","text":"Reinforcement learning can be viewed as a form of learning for sequential decision making that is commonly associated with controlling robots (but is, in fact, much more general). Consider an autonomous firefighting robot that is tasked with navigating into an area, finding the fire and neutralizing it. At any given moment, the robot perceives the environment through its sensors (e.g. camera, heat, touch), processes this information and produces an action (e.g. move to the left, rotate the water hose, turn on the water). In other words, it is continuously making decisions about how to interact in this environment given its view of the world (i.e. sensors input) and objective (i.e. neutralizing the fire). Teaching a robot to be a successful firefighting machine is precisely what reinforcement learning is designed to do. More specifically, the goal of reinforcement learning is to learn a policy , which is essentially a mapping from observations to actions . An observation is what the robot can measure from its environment (in this case, all its sensory inputs) and an action, in its most raw form, is a change to the configuration of the robot (e.g. position of its base, position of its water hose and whether the hose is on or off). The last remaining piece of the reinforcement learning task is the reward signal . The robot is trained to learn a policy that maximizes its overall rewards. When training a robot to be a mean firefighting machine, we provide it with rewards (positive and negative) indicating how well it is doing on completing the task. Note that the robot does not know how to put out fires before it is trained. It learns the objective because it receives a large positive reward when it puts out the fire and a small negative reward for every passing second. The fact that rewards are sparse (i.e. may not be provided at every step, but only when a robot arrives at a success or failure situation), is a defining characteristic of reinforcement learning and precisely why learning good policies can be difficult (and/or time-consuming) for complex environments. Learning a policy usually requires many trials and iterative policy updates. More specifically, the robot is placed in several fire situations and over time learns an optimal policy which allows it to put out fires more effectively. Obviously, we cannot expect to train a robot repeatedly in the real world, particularly when fires are involved. This is precisely why the use of Unity as a simulator serves as the perfect training grounds for learning such behaviors. While our discussion of reinforcement learning has centered around robots, there are strong parallels between robots and characters in a game. In fact, in many ways, one can view a non-playable character (NPC) as a virtual robot, with its own observations about the environment, its own set of actions and a specific objective. Thus it is natural to explore how we can train behaviors within Unity using reinforcement learning. This is precisely what the ML-Agents Toolkit offers. The video linked below includes a reinforcement learning demo showcasing training character behaviors using the ML-Agents Toolkit. Similar to both unsupervised and supervised learning, reinforcement learning also involves two tasks: attribute selection and model selection. Attribute selection is defining the set of observations for the robot that best help it complete its objective, while model selection is defining the form of the policy (mapping from observations to actions) and its parameters. In practice, training behaviors is an iterative process that may require changing the attribute and model choices.","title":"Reinforcement Learning"},{"location":"Background-Machine-Learning/#training-and-inference","text":"One common aspect of all three branches of machine learning is that they all involve a training phase and an inference phase . While the details of the training and inference phases are different for each of the three, at a high-level, the training phase involves building a model using the provided data, while the inference phase involves applying this model to new, previously unseen, data. More specifically: For our unsupervised learning example, the training phase learns the optimal two clusters based on the data describing existing players, while the inference phase assigns a new player to one of these two clusters. For our supervised learning example, the training phase learns the mapping from player attributes to player label (whether they churned or not), and the inference phase predicts whether a new player will churn or not based on that learned mapping. For our reinforcement learning example, the training phase learns the optimal policy through guided trials, and in the inference phase, the agent observes and takes actions in the wild using its learned policy. To briefly summarize: all three classes of algorithms involve training and inference phases in addition to attribute and model selections. What ultimately separates them is the type of data available to learn from. In unsupervised learning our data set was a collection of attributes, in supervised learning our data set was a collection of attribute-label pairs, and, lastly, in reinforcement learning our data set was a collection of observation-action-reward tuples.","title":"Training and Inference"},{"location":"Background-Machine-Learning/#deep-learning","text":"Deep learning is a family of algorithms that can be used to address any of the problems introduced above. More specifically, they can be used to solve both attribute and model selection tasks. Deep learning has gained popularity in recent years due to its outstanding performance on several challenging machine learning tasks. One example is AlphaGo , a computer Go program, that leverages deep learning, that was able to beat Lee Sedol (a Go world champion). A key characteristic of deep learning algorithms is their ability to learn very complex functions from large amounts of training data. This makes them a natural choice for reinforcement learning tasks when a large amount of data can be generated, say through the use of a simulator or engine such as Unity. By generating hundreds of thousands of simulations of the environment within Unity, we can learn policies for very complex environments (a complex environment is one where the number of observations an agent perceives and the number of actions they can take are large). Many of the algorithms we provide in ML-Agents use some form of deep learning, built on top of the open-source library, PyTorch .","title":"Deep Learning"},{"location":"Background-PyTorch/","text":"Background: PyTorch As discussed in our machine learning background page , many of the algorithms we provide in the ML-Agents Toolkit leverage some form of deep learning. More specifically, our implementations are built on top of the open-source library PyTorch . In this page we provide a brief overview of PyTorch and TensorBoard that we leverage within the ML-Agents Toolkit. PyTorch PyTorch is an open source library for performing computations using data flow graphs, the underlying representation of deep learning models. It facilitates training and inference on CPUs and GPUs in a desktop, server, or mobile device. Within the ML-Agents Toolkit, when you train the behavior of an agent, the output is a model (.onnx) file that you can then associate with an Agent. Unless you implement a new algorithm, the use of PyTorch is mostly abstracted away and behind the scenes. TensorBoard One component of training models with PyTorch is setting the values of certain model attributes (called hyperparameters ). Finding the right values of these hyperparameters can require a few iterations. Consequently, we leverage a visualization tool called TensorBoard . It allows the visualization of certain agent attributes (e.g. reward) throughout training which can be helpful in both building intuitions for the different hyperparameters and setting the optimal values for your Unity environment. We provide more details on setting the hyperparameters in the Training ML-Agents page. If you are unfamiliar with TensorBoard we recommend our guide on using TensorBoard with ML-Agents or this tutorial .","title":"PyTorch"},{"location":"Background-PyTorch/#background-pytorch","text":"As discussed in our machine learning background page , many of the algorithms we provide in the ML-Agents Toolkit leverage some form of deep learning. More specifically, our implementations are built on top of the open-source library PyTorch . In this page we provide a brief overview of PyTorch and TensorBoard that we leverage within the ML-Agents Toolkit.","title":"Background: PyTorch"},{"location":"Background-PyTorch/#pytorch","text":"PyTorch is an open source library for performing computations using data flow graphs, the underlying representation of deep learning models. It facilitates training and inference on CPUs and GPUs in a desktop, server, or mobile device. Within the ML-Agents Toolkit, when you train the behavior of an agent, the output is a model (.onnx) file that you can then associate with an Agent. Unless you implement a new algorithm, the use of PyTorch is mostly abstracted away and behind the scenes.","title":"PyTorch"},{"location":"Background-PyTorch/#tensorboard","text":"One component of training models with PyTorch is setting the values of certain model attributes (called hyperparameters ). Finding the right values of these hyperparameters can require a few iterations. Consequently, we leverage a visualization tool called TensorBoard . It allows the visualization of certain agent attributes (e.g. reward) throughout training which can be helpful in both building intuitions for the different hyperparameters and setting the optimal values for your Unity environment. We provide more details on setting the hyperparameters in the Training ML-Agents page. If you are unfamiliar with TensorBoard we recommend our guide on using TensorBoard with ML-Agents or this tutorial .","title":"TensorBoard"},{"location":"Background-Unity/","text":"Background: Unity If you are not familiar with the Unity Engine , we highly recommend the Unity Manual and Tutorials page . The Roll-a-ball tutorial is a fantastic resource to learn all the basic concepts of Unity to get started with the ML-Agents Toolkit: Editor Scene GameObject Rigidbody Camera Scripting Physics Ordering of event functions (e.g. FixedUpdate, Update) Prefabs","title":"Unity"},{"location":"Background-Unity/#background-unity","text":"If you are not familiar with the Unity Engine , we highly recommend the Unity Manual and Tutorials page . The Roll-a-ball tutorial is a fantastic resource to learn all the basic concepts of Unity to get started with the ML-Agents Toolkit: Editor Scene GameObject Rigidbody Camera Scripting Physics Ordering of event functions (e.g. FixedUpdate, Update) Prefabs","title":"Background: Unity"},{"location":"CODE_OF_CONDUCT/","text":"Contributor Covenant Code of Conduct Our Pledge In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to making participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, gender identity and expression, level of experience, nationality, personal appearance, race, religion, or sexual identity and orientation. Our Standards Examples of behavior that contributes to creating a positive environment include: Using welcoming and inclusive language Being respectful of differing viewpoints and experiences Gracefully accepting constructive criticism Focusing on what is best for the community Showing empathy towards other community members Examples of unacceptable behavior by participants include: The use of sexualized language or imagery and unwelcome sexual attention or advances Trolling, insulting/derogatory comments, and personal or political attacks Public or private harassment Publishing others' private information, such as a physical or electronic address, without explicit permission Other conduct which could reasonably be considered inappropriate in a professional setting Our Responsibilities Project maintainers are responsible for clarifying the standards of acceptable behavior and are expected to take appropriate and fair corrective action in response to any instances of unacceptable behavior. Project maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful. Scope This Code of Conduct applies both within project spaces and in public spaces when an individual is representing the project or its community. Examples of representing a project or community include using an official project e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. Representation of a project may be further defined and clarified by project maintainers. Enforcement Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project team at ml-agents@unity3d.com. All complaints will be reviewed and investigated and will result in a response that is deemed necessary and appropriate to the circumstances. The project team is obligated to maintain confidentiality with regard to the reporter of an incident. Further details of specific enforcement policies may be posted separately. Project maintainers who do not follow or enforce the Code of Conduct in good faith may face temporary or permanent repercussions as determined by other members of the project's leadership. Attribution This Code of Conduct is adapted from the Contributor Covenant , version 1.4, available at https://www.contributor-covenant.org/version/1/4/code-of-conduct/","title":"CODE OF CONDUCT"},{"location":"CODE_OF_CONDUCT/#contributor-covenant-code-of-conduct","text":"","title":"Contributor Covenant Code of Conduct"},{"location":"CODE_OF_CONDUCT/#our-pledge","text":"In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to making participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, gender identity and expression, level of experience, nationality, personal appearance, race, religion, or sexual identity and orientation.","title":"Our Pledge"},{"location":"CODE_OF_CONDUCT/#our-standards","text":"Examples of behavior that contributes to creating a positive environment include: Using welcoming and inclusive language Being respectful of differing viewpoints and experiences Gracefully accepting constructive criticism Focusing on what is best for the community Showing empathy towards other community members Examples of unacceptable behavior by participants include: The use of sexualized language or imagery and unwelcome sexual attention or advances Trolling, insulting/derogatory comments, and personal or political attacks Public or private harassment Publishing others' private information, such as a physical or electronic address, without explicit permission Other conduct which could reasonably be considered inappropriate in a professional setting","title":"Our Standards"},{"location":"CODE_OF_CONDUCT/#our-responsibilities","text":"Project maintainers are responsible for clarifying the standards of acceptable behavior and are expected to take appropriate and fair corrective action in response to any instances of unacceptable behavior. Project maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful.","title":"Our Responsibilities"},{"location":"CODE_OF_CONDUCT/#scope","text":"This Code of Conduct applies both within project spaces and in public spaces when an individual is representing the project or its community. Examples of representing a project or community include using an official project e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. Representation of a project may be further defined and clarified by project maintainers.","title":"Scope"},{"location":"CODE_OF_CONDUCT/#enforcement","text":"Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project team at ml-agents@unity3d.com. All complaints will be reviewed and investigated and will result in a response that is deemed necessary and appropriate to the circumstances. The project team is obligated to maintain confidentiality with regard to the reporter of an incident. Further details of specific enforcement policies may be posted separately. Project maintainers who do not follow or enforce the Code of Conduct in good faith may face temporary or permanent repercussions as determined by other members of the project's leadership.","title":"Enforcement"},{"location":"CODE_OF_CONDUCT/#attribution","text":"This Code of Conduct is adapted from the Contributor Covenant , version 1.4, available at https://www.contributor-covenant.org/version/1/4/code-of-conduct/","title":"Attribution"},{"location":"CONTRIBUTING/","text":"Contribution Guidelines Thank you for your interest in contributing to the ML-Agents Toolkit! We are incredibly excited to see how members of our community will use and extend the ML-Agents Toolkit. To facilitate your contributions, we've outlined a brief set of guidelines to ensure that your extensions can be easily integrated. Communication First, please read through our code of conduct , as we expect all our contributors to follow it. Second, before starting on a project that you intend to contribute to the ML-Agents Toolkit (whether environments or modifications to the codebase), we strongly recommend posting on our Issues page and briefly outlining the changes you plan to make. This will enable us to provide some context that may be helpful for you. This could range from advice and feedback on how to optimally perform your changes or reasons for not doing it. Lastly, if you're looking for input on what to contribute, feel free to reach out to us directly at ml-agents@unity3d.com and/or browse the GitHub issues with the Requests or Bug label. Git Branches The main branch corresponds to the most recent version of the project. Note that this may be newer that the latest release . When contributing to the project, please make sure that your Pull Request (PR) contains the following: Detailed description of the changes performed Corresponding changes to documentation, unit tests and sample environments (if applicable) Summary of the tests performed to validate your changes Issue numbers that the PR resolves (if any) Environments We are currently not accepting environment contributions directly into ML-Agents. However, we believe community created enviornments have a lot of value to the community. If you have an interesting enviornment and are willing to share, feel free to showcase it and share any relevant files in the ML-Agents forum . Continuous Integration (CI) We run continuous integration on all PRs; all tests must be passing before the PR is merged. Several static checks are run on the codebase using the pre-commit framework during CI. To execute the same checks locally, run: pip install pre-commit>=2.8.0 pip install identify>==2.1.3 pre-commit run --all-files Some hooks (for example, black ) will output the corrected version of the code; others (like mypy ) may require more effort to fix. You can optionally run pre-commit install to install it as a git hook; after this it will run on all commits that you make. Code style All python code should be formatted with black . C# code is formatted using dotnet-format . You must have dotnet installed first (but don't need to install dotnet-format - pre-commit will do that for you). Python type annotations We use mypy to perform static type checking on python code. Currently not all code is annotated but we will increase coverage over time. If you are adding or refactoring code, please Add type annotations to the new or refactored code. Make sure that code calling or called by the modified code also has type annotations. The type hint cheat sheet provides a good introduction to adding type hints. Contributor License Agreements When you open a pull request, you will be asked to acknolwedge our Contributor License Agreement. We allow both individual contributions and contributions made on behalf of companies. We use an open source tool called CLA assistant. If you have any questions on our CLA, please submit an issue or email us at ml-agents@unity3d.com.","title":"CONTRIBUTING"},{"location":"CONTRIBUTING/#contribution-guidelines","text":"Thank you for your interest in contributing to the ML-Agents Toolkit! We are incredibly excited to see how members of our community will use and extend the ML-Agents Toolkit. To facilitate your contributions, we've outlined a brief set of guidelines to ensure that your extensions can be easily integrated.","title":"Contribution Guidelines"},{"location":"CONTRIBUTING/#communication","text":"First, please read through our code of conduct , as we expect all our contributors to follow it. Second, before starting on a project that you intend to contribute to the ML-Agents Toolkit (whether environments or modifications to the codebase), we strongly recommend posting on our Issues page and briefly outlining the changes you plan to make. This will enable us to provide some context that may be helpful for you. This could range from advice and feedback on how to optimally perform your changes or reasons for not doing it. Lastly, if you're looking for input on what to contribute, feel free to reach out to us directly at ml-agents@unity3d.com and/or browse the GitHub issues with the Requests or Bug label.","title":"Communication"},{"location":"CONTRIBUTING/#git-branches","text":"The main branch corresponds to the most recent version of the project. Note that this may be newer that the latest release . When contributing to the project, please make sure that your Pull Request (PR) contains the following: Detailed description of the changes performed Corresponding changes to documentation, unit tests and sample environments (if applicable) Summary of the tests performed to validate your changes Issue numbers that the PR resolves (if any)","title":"Git Branches"},{"location":"CONTRIBUTING/#environments","text":"We are currently not accepting environment contributions directly into ML-Agents. However, we believe community created enviornments have a lot of value to the community. If you have an interesting enviornment and are willing to share, feel free to showcase it and share any relevant files in the ML-Agents forum .","title":"Environments"},{"location":"CONTRIBUTING/#continuous-integration-ci","text":"We run continuous integration on all PRs; all tests must be passing before the PR is merged. Several static checks are run on the codebase using the pre-commit framework during CI. To execute the same checks locally, run: pip install pre-commit>=2.8.0 pip install identify>==2.1.3 pre-commit run --all-files Some hooks (for example, black ) will output the corrected version of the code; others (like mypy ) may require more effort to fix. You can optionally run pre-commit install to install it as a git hook; after this it will run on all commits that you make.","title":"Continuous Integration (CI)"},{"location":"CONTRIBUTING/#code-style","text":"All python code should be formatted with black . C# code is formatted using dotnet-format . You must have dotnet installed first (but don't need to install dotnet-format - pre-commit will do that for you).","title":"Code style"},{"location":"CONTRIBUTING/#python-type-annotations","text":"We use mypy to perform static type checking on python code. Currently not all code is annotated but we will increase coverage over time. If you are adding or refactoring code, please Add type annotations to the new or refactored code. Make sure that code calling or called by the modified code also has type annotations. The type hint cheat sheet provides a good introduction to adding type hints.","title":"Python type annotations"},{"location":"CONTRIBUTING/#contributor-license-agreements","text":"When you open a pull request, you will be asked to acknolwedge our Contributor License Agreement. We allow both individual contributions and contributions made on behalf of companies. We use an open source tool called CLA assistant. If you have any questions on our CLA, please submit an issue or email us at ml-agents@unity3d.com.","title":"Contributor License Agreements"},{"location":"Custom-SideChannels/","text":"Custom Side Channels You can create your own side channel in C# and Python and use it to communicate custom data structures between the two. This can be useful for situations in which the data to be sent is too complex or structured for the built-in EnvironmentParameters , or is not related to any specific agent, and therefore inappropriate as an agent observation. Overview In order to use a side channel, it must be implemented as both Unity and Python classes. Unity side The side channel will have to implement the SideChannel abstract class and the following method. OnMessageReceived(IncomingMessage msg) : You must implement this method and read the data from IncomingMessage. The data must be read in the order that it was written. The side channel must also assign a ChannelId property in the constructor. The ChannelId is a Guid (or UUID in Python) used to uniquely identify a side channel. This Guid must be the same on C# and Python. There can only be one side channel of a certain id during communication. To send data from C# to Python, create an OutgoingMessage instance, add data to it, call the base.QueueMessageToSend(msg) method inside the side channel, and call the OutgoingMessage.Dispose() method. To register a side channel on the Unity side, call SideChannelManager.RegisterSideChannel with the side channel as only argument. Python side The side channel will have to implement the SideChannel abstract class. You must implement : on_message_received(self, msg: \"IncomingMessage\") -> None : You must implement this method and read the data from IncomingMessage. The data must be read in the order that it was written. The side channel must also assign a channel_id property in the constructor. The channel_id is a UUID (referred in C# as Guid) used to uniquely identify a side channel. This number must be the same on C# and Python. There can only be one side channel of a certain id during communication. To assign the channel_id call the abstract class constructor with the appropriate channel_id as follows: super().__init__(my_channel_id) To send a byte array from Python to C#, create an OutgoingMessage instance, add data to it, and call the super().queue_message_to_send(msg) method inside the side channel. To register a side channel on the Python side, pass the side channel as argument when creating the UnityEnvironment object. One of the arguments of the constructor ( side_channels ) is a list of side channels. Example implementation Below is a simple implementation of a side channel that will exchange ASCII encoded strings between a Unity environment and Python. Example Unity C# code The first step is to create the StringLogSideChannel class within the Unity project. Here is an implementation of a StringLogSideChannel that will listen for messages from python and print them to the Unity debug log, as well as send error messages from Unity to python. using UnityEngine; using Unity.MLAgents; using Unity.MLAgents.SideChannels; using System.Text; using System; public class StringLogSideChannel : SideChannel { public StringLogSideChannel() { ChannelId = new Guid(\"621f0a70-4f87-11ea-a6bf-784f4387d1f7\"); } protected override void OnMessageReceived(IncomingMessage msg) { var receivedString = msg.ReadString(); Debug.Log(\"From Python : \" + receivedString); } public void SendDebugStatementToPython(string logString, string stackTrace, LogType type) { if (type == LogType.Error) { var stringToSend = type.ToString() + \": \" + logString + \"\\n\" + stackTrace; using (var msgOut = new OutgoingMessage()) { msgOut.WriteString(stringToSend); QueueMessageToSend(msgOut); } } } } Once we have defined our custom side channel class, we need to ensure that it is instantiated and registered. This can typically be done wherever the logic of the side channel makes sense to be associated, for example on a MonoBehaviour object that might need to access data from the side channel. Here we show a simple MonoBehaviour object which instantiates and registers the new side channel. If you have not done it already, make sure that the MonoBehaviour which registers the side channel is attached to a GameObject which will be live in your Unity scene. using UnityEngine; using Unity.MLAgents; public class RegisterStringLogSideChannel : MonoBehaviour { StringLogSideChannel stringChannel; public void Awake() { // We create the Side Channel stringChannel = new StringLogSideChannel(); // When a Debug.Log message is created, we send it to the stringChannel Application.logMessageReceived += stringChannel.SendDebugStatementToPython; // The channel must be registered with the SideChannelManager class SideChannelManager.RegisterSideChannel(stringChannel); } public void OnDestroy() { // De-register the Debug.Log callback Application.logMessageReceived -= stringChannel.SendDebugStatementToPython; if (Academy.IsInitialized){ SideChannelManager.UnregisterSideChannel(stringChannel); } } public void Update() { // Optional : If the space bar is pressed, raise an error ! if (Input.GetKeyDown(KeyCode.Space)) { Debug.LogError(\"This is a fake error. Space bar was pressed in Unity.\"); } } } Example Python code Now that we have created the necessary Unity C# classes, we can create their Python counterparts. from mlagents_envs.environment import UnityEnvironment from mlagents_envs.side_channel.side_channel import ( SideChannel, IncomingMessage, OutgoingMessage, ) import numpy as np import uuid # Create the StringLogChannel class class StringLogChannel(SideChannel): def __init__(self) -> None: super().__init__(uuid.UUID(\"621f0a70-4f87-11ea-a6bf-784f4387d1f7\")) def on_message_received(self, msg: IncomingMessage) -> None: \"\"\" Note: We must implement this method of the SideChannel interface to receive messages from Unity \"\"\" # We simply read a string from the message and print it. print(msg.read_string()) def send_string(self, data: str) -> None: # Add the string to an OutgoingMessage msg = OutgoingMessage() msg.write_string(data) # We call this method to queue the data we want to send super().queue_message_to_send(msg) We can then instantiate the new side channel, launch a UnityEnvironment with that side channel active, and send a series of messages to the Unity environment from Python using it. # Create the channel string_log = StringLogChannel() # We start the communication with the Unity Editor and pass the string_log side channel as input env = UnityEnvironment(side_channels=[string_log]) env.reset() string_log.send_string(\"The environment was reset\") group_name = list(env.behavior_specs.keys())[0] # Get the first group_name group_spec = env.behavior_specs[group_name] for i in range(1000): decision_steps, terminal_steps = env.get_steps(group_name) # We send data to Unity : A string with the number of Agent at each string_log.send_string( f\"Step {i} occurred with {len(decision_steps)} deciding agents and \" f\"{len(terminal_steps)} terminal agents\" ) env.step() # Move the simulation forward env.close() Now, if you run this script and press Play the Unity Editor when prompted, the console in the Unity Editor will display a message at every Python step. Additionally, if you press the Space Bar in the Unity Engine, a message will appear in the terminal.","title":"Custom Side Channels"},{"location":"Custom-SideChannels/#custom-side-channels","text":"You can create your own side channel in C# and Python and use it to communicate custom data structures between the two. This can be useful for situations in which the data to be sent is too complex or structured for the built-in EnvironmentParameters , or is not related to any specific agent, and therefore inappropriate as an agent observation.","title":"Custom Side Channels"},{"location":"Custom-SideChannels/#overview","text":"In order to use a side channel, it must be implemented as both Unity and Python classes.","title":"Overview"},{"location":"Custom-SideChannels/#unity-side","text":"The side channel will have to implement the SideChannel abstract class and the following method. OnMessageReceived(IncomingMessage msg) : You must implement this method and read the data from IncomingMessage. The data must be read in the order that it was written. The side channel must also assign a ChannelId property in the constructor. The ChannelId is a Guid (or UUID in Python) used to uniquely identify a side channel. This Guid must be the same on C# and Python. There can only be one side channel of a certain id during communication. To send data from C# to Python, create an OutgoingMessage instance, add data to it, call the base.QueueMessageToSend(msg) method inside the side channel, and call the OutgoingMessage.Dispose() method. To register a side channel on the Unity side, call SideChannelManager.RegisterSideChannel with the side channel as only argument.","title":"Unity side"},{"location":"Custom-SideChannels/#python-side","text":"The side channel will have to implement the SideChannel abstract class. You must implement : on_message_received(self, msg: \"IncomingMessage\") -> None : You must implement this method and read the data from IncomingMessage. The data must be read in the order that it was written. The side channel must also assign a channel_id property in the constructor. The channel_id is a UUID (referred in C# as Guid) used to uniquely identify a side channel. This number must be the same on C# and Python. There can only be one side channel of a certain id during communication. To assign the channel_id call the abstract class constructor with the appropriate channel_id as follows: super().__init__(my_channel_id) To send a byte array from Python to C#, create an OutgoingMessage instance, add data to it, and call the super().queue_message_to_send(msg) method inside the side channel. To register a side channel on the Python side, pass the side channel as argument when creating the UnityEnvironment object. One of the arguments of the constructor ( side_channels ) is a list of side channels.","title":"Python side"},{"location":"Custom-SideChannels/#example-implementation","text":"Below is a simple implementation of a side channel that will exchange ASCII encoded strings between a Unity environment and Python.","title":"Example implementation"},{"location":"Custom-SideChannels/#example-unity-c-code","text":"The first step is to create the StringLogSideChannel class within the Unity project. Here is an implementation of a StringLogSideChannel that will listen for messages from python and print them to the Unity debug log, as well as send error messages from Unity to python. using UnityEngine; using Unity.MLAgents; using Unity.MLAgents.SideChannels; using System.Text; using System; public class StringLogSideChannel : SideChannel { public StringLogSideChannel() { ChannelId = new Guid(\"621f0a70-4f87-11ea-a6bf-784f4387d1f7\"); } protected override void OnMessageReceived(IncomingMessage msg) { var receivedString = msg.ReadString(); Debug.Log(\"From Python : \" + receivedString); } public void SendDebugStatementToPython(string logString, string stackTrace, LogType type) { if (type == LogType.Error) { var stringToSend = type.ToString() + \": \" + logString + \"\\n\" + stackTrace; using (var msgOut = new OutgoingMessage()) { msgOut.WriteString(stringToSend); QueueMessageToSend(msgOut); } } } } Once we have defined our custom side channel class, we need to ensure that it is instantiated and registered. This can typically be done wherever the logic of the side channel makes sense to be associated, for example on a MonoBehaviour object that might need to access data from the side channel. Here we show a simple MonoBehaviour object which instantiates and registers the new side channel. If you have not done it already, make sure that the MonoBehaviour which registers the side channel is attached to a GameObject which will be live in your Unity scene. using UnityEngine; using Unity.MLAgents; public class RegisterStringLogSideChannel : MonoBehaviour { StringLogSideChannel stringChannel; public void Awake() { // We create the Side Channel stringChannel = new StringLogSideChannel(); // When a Debug.Log message is created, we send it to the stringChannel Application.logMessageReceived += stringChannel.SendDebugStatementToPython; // The channel must be registered with the SideChannelManager class SideChannelManager.RegisterSideChannel(stringChannel); } public void OnDestroy() { // De-register the Debug.Log callback Application.logMessageReceived -= stringChannel.SendDebugStatementToPython; if (Academy.IsInitialized){ SideChannelManager.UnregisterSideChannel(stringChannel); } } public void Update() { // Optional : If the space bar is pressed, raise an error ! if (Input.GetKeyDown(KeyCode.Space)) { Debug.LogError(\"This is a fake error. Space bar was pressed in Unity.\"); } } }","title":"Example Unity C# code"},{"location":"Custom-SideChannels/#example-python-code","text":"Now that we have created the necessary Unity C# classes, we can create their Python counterparts. from mlagents_envs.environment import UnityEnvironment from mlagents_envs.side_channel.side_channel import ( SideChannel, IncomingMessage, OutgoingMessage, ) import numpy as np import uuid # Create the StringLogChannel class class StringLogChannel(SideChannel): def __init__(self) -> None: super().__init__(uuid.UUID(\"621f0a70-4f87-11ea-a6bf-784f4387d1f7\")) def on_message_received(self, msg: IncomingMessage) -> None: \"\"\" Note: We must implement this method of the SideChannel interface to receive messages from Unity \"\"\" # We simply read a string from the message and print it. print(msg.read_string()) def send_string(self, data: str) -> None: # Add the string to an OutgoingMessage msg = OutgoingMessage() msg.write_string(data) # We call this method to queue the data we want to send super().queue_message_to_send(msg) We can then instantiate the new side channel, launch a UnityEnvironment with that side channel active, and send a series of messages to the Unity environment from Python using it. # Create the channel string_log = StringLogChannel() # We start the communication with the Unity Editor and pass the string_log side channel as input env = UnityEnvironment(side_channels=[string_log]) env.reset() string_log.send_string(\"The environment was reset\") group_name = list(env.behavior_specs.keys())[0] # Get the first group_name group_spec = env.behavior_specs[group_name] for i in range(1000): decision_steps, terminal_steps = env.get_steps(group_name) # We send data to Unity : A string with the number of Agent at each string_log.send_string( f\"Step {i} occurred with {len(decision_steps)} deciding agents and \" f\"{len(terminal_steps)} terminal agents\" ) env.step() # Move the simulation forward env.close() Now, if you run this script and press Play the Unity Editor when prompted, the console in the Unity Editor will display a message at every Python step. Additionally, if you press the Space Bar in the Unity Engine, a message will appear in the terminal.","title":"Example Python code"},{"location":"ELO-Rating-System/","text":"ELO Rating System In adversarial games, the cumulative environment reward may not be a meaningful metric by which to track learning progress. This is because the cumulative reward is entirely dependent on the skill of the opponent . An agent at a particular skill level will get more or less reward against a worse or better agent, respectively. Instead, it's better to use ELO rating system, a method to calculate the relative skill level between two players in a zero-sum game . If the training performs correctly, this value should steadily increase . What is a zero-sum game? A zero-sum game is a game where each player's gain or loss of utility is exactly balanced by the gain or loss of the utility of the opponent . Simply explained, we face a zero-sum game when one agent gets +1.0, its opponent gets -1.0 reward . For instance, Tennis is a zero-sum game: if you win the point you get +1.0 and your opponent gets -1.0 reward. How works the ELO Rating System Each player has an initial ELO score . It's defined in the initial_elo trainer config hyperparameter. The difference in rating between the two players serves as the predictor of the outcomes of a match. For instance, if player A has an Elo score of 2100 and player B has an ELO score of 1800 the chance that player A wins is 85% against 15% for player b. We calculate the expected score of each player using this formula: At the end of the game, based on the outcome we update the player\u2019s actual Elo score , we use a linear adjustment proportional to the amount by which the player over-performed or under-performed. The winning player takes points from the losing one: If the higher-rated player wins \u2192 a few points will be taken from the lower-rated player. If the lower-rated player wins \u2192 a lot of points will be taken from the high-rated player. If it\u2019s a draw \u2192 the lower-rated player gains a few points from higher. We update players rating using this formula: The Tennis example We start to train our agents. Both of them have the same skills. So ELO score for each of them that we defined using parameter initial_elo = 1200.0 . We calculate the expected score E: Ea = 0.5 Eb = 0.5 So it means that each player has 50% chances of winning the point. If A wins, the new rating R would be: Ra = 1200 + 16 * (1 - 0.5) \u2192 1208 Rb = 1200 + 16 * (0 - 0.5) \u2192 1192 Player A has now an ELO score of 1208 and Player B an ELO score of 1192. Therefore, Player A is now a little bit better than Player B .","title":"ELO Rating System"},{"location":"ELO-Rating-System/#elo-rating-system","text":"In adversarial games, the cumulative environment reward may not be a meaningful metric by which to track learning progress. This is because the cumulative reward is entirely dependent on the skill of the opponent . An agent at a particular skill level will get more or less reward against a worse or better agent, respectively. Instead, it's better to use ELO rating system, a method to calculate the relative skill level between two players in a zero-sum game . If the training performs correctly, this value should steadily increase .","title":"ELO Rating System"},{"location":"ELO-Rating-System/#what-is-a-zero-sum-game","text":"A zero-sum game is a game where each player's gain or loss of utility is exactly balanced by the gain or loss of the utility of the opponent . Simply explained, we face a zero-sum game when one agent gets +1.0, its opponent gets -1.0 reward . For instance, Tennis is a zero-sum game: if you win the point you get +1.0 and your opponent gets -1.0 reward.","title":"What is a zero-sum game?"},{"location":"ELO-Rating-System/#how-works-the-elo-rating-system","text":"Each player has an initial ELO score . It's defined in the initial_elo trainer config hyperparameter. The difference in rating between the two players serves as the predictor of the outcomes of a match. For instance, if player A has an Elo score of 2100 and player B has an ELO score of 1800 the chance that player A wins is 85% against 15% for player b. We calculate the expected score of each player using this formula: At the end of the game, based on the outcome we update the player\u2019s actual Elo score , we use a linear adjustment proportional to the amount by which the player over-performed or under-performed. The winning player takes points from the losing one: If the higher-rated player wins \u2192 a few points will be taken from the lower-rated player. If the lower-rated player wins \u2192 a lot of points will be taken from the high-rated player. If it\u2019s a draw \u2192 the lower-rated player gains a few points from higher. We update players rating using this formula:","title":"How works the ELO Rating System"},{"location":"ELO-Rating-System/#the-tennis-example","text":"We start to train our agents. Both of them have the same skills. So ELO score for each of them that we defined using parameter initial_elo = 1200.0 . We calculate the expected score E: Ea = 0.5 Eb = 0.5 So it means that each player has 50% chances of winning the point. If A wins, the new rating R would be: Ra = 1200 + 16 * (1 - 0.5) \u2192 1208 Rb = 1200 + 16 * (0 - 0.5) \u2192 1192 Player A has now an ELO score of 1208 and Player B an ELO score of 1192. Therefore, Player A is now a little bit better than Player B .","title":"The Tennis example"},{"location":"FAQ/","text":"Frequently Asked Questions Installation problems Environment Permission Error If you directly import your Unity environment without building it in the editor, you might need to give it additional permissions to execute it. If you receive such a permission error on macOS, run: chmod -R 755 *.app or on Linux: chmod -R 755 *.x86_64 On Windows, you can find instructions . Environment Connection Timeout If you are able to launch the environment from UnityEnvironment but then receive a timeout error like this: UnityAgentsException: The Communicator was unable to connect. Please make sure the External process is ready to accept communication with Unity. There may be a number of possible causes: Cause : There may be no agent in the scene Cause : On OSX, the firewall may be preventing communication with the environment. Solution : Add the built environment binary to the list of exceptions on the firewall by following instructions . Cause : An error happened in the Unity Environment preventing communication. Solution : Look into the log files generated by the Unity Environment to figure what error happened. Cause : You have assigned HTTP_PROXY and HTTPS_PROXY values in your environment variables. Solution : Remove these values and try again. Cause : You are running in a headless environment (e.g. remotely connected to a server). Solution : Pass --no-graphics to mlagents-learn , or no_graphics=True to RemoteRegistryEntry.make() or the UnityEnvironment initializer. If you need graphics for visual observations, you will need to set up xvfb (or equivalent). Communication port {} still in use If you receive an exception \"Couldn't launch new environment because communication port {} is still in use. \" , you can change the worker number in the Python script when calling UnityEnvironment(file_name=filename, worker_id=X) Mean reward : nan If you receive a message Mean reward : nan when attempting to train a model using PPO, this is due to the episodes of the Learning Environment not terminating. In order to address this, set Max Steps for the Agents within the Scene Inspector to a value greater than 0. Alternatively, it is possible to manually set done conditions for episodes from within scripts for custom episode-terminating events. \"File name\" cannot be opened because the developer cannot be verified. If you have downloaded the repository using the github website on macOS 10.15 (Catalina) or later, you may see this error when attempting to play scenes in the Unity project. Workarounds include installing the package using the Unity Package Manager (this is the officially supported approach - see here ), or following the instructions here to verify the relevant files on your machine on a file-by-file basis.","title":"FAQs"},{"location":"FAQ/#frequently-asked-questions","text":"","title":"Frequently Asked Questions"},{"location":"FAQ/#installation-problems","text":"","title":"Installation problems"},{"location":"FAQ/#environment-permission-error","text":"If you directly import your Unity environment without building it in the editor, you might need to give it additional permissions to execute it. If you receive such a permission error on macOS, run: chmod -R 755 *.app or on Linux: chmod -R 755 *.x86_64 On Windows, you can find instructions .","title":"Environment Permission Error"},{"location":"FAQ/#environment-connection-timeout","text":"If you are able to launch the environment from UnityEnvironment but then receive a timeout error like this: UnityAgentsException: The Communicator was unable to connect. Please make sure the External process is ready to accept communication with Unity. There may be a number of possible causes: Cause : There may be no agent in the scene Cause : On OSX, the firewall may be preventing communication with the environment. Solution : Add the built environment binary to the list of exceptions on the firewall by following instructions . Cause : An error happened in the Unity Environment preventing communication. Solution : Look into the log files generated by the Unity Environment to figure what error happened. Cause : You have assigned HTTP_PROXY and HTTPS_PROXY values in your environment variables. Solution : Remove these values and try again. Cause : You are running in a headless environment (e.g. remotely connected to a server). Solution : Pass --no-graphics to mlagents-learn , or no_graphics=True to RemoteRegistryEntry.make() or the UnityEnvironment initializer. If you need graphics for visual observations, you will need to set up xvfb (or equivalent).","title":"Environment Connection Timeout"},{"location":"FAQ/#communication-port-still-in-use","text":"If you receive an exception \"Couldn't launch new environment because communication port {} is still in use. \" , you can change the worker number in the Python script when calling UnityEnvironment(file_name=filename, worker_id=X)","title":"Communication port {} still in use"},{"location":"FAQ/#mean-reward-nan","text":"If you receive a message Mean reward : nan when attempting to train a model using PPO, this is due to the episodes of the Learning Environment not terminating. In order to address this, set Max Steps for the Agents within the Scene Inspector to a value greater than 0. Alternatively, it is possible to manually set done conditions for episodes from within scripts for custom episode-terminating events.","title":"Mean reward : nan"},{"location":"FAQ/#file-name-cannot-be-opened-because-the-developer-cannot-be-verified","text":"If you have downloaded the repository using the github website on macOS 10.15 (Catalina) or later, you may see this error when attempting to play scenes in the Unity project. Workarounds include installing the package using the Unity Package Manager (this is the officially supported approach - see here ), or following the instructions here to verify the relevant files on your machine on a file-by-file basis.","title":"\"File name\" cannot be opened because the developer cannot be verified."},{"location":"Getting-Started/","text":"Getting Started Guide This guide walks through the end-to-end process of opening one of our example environments in Unity, training an Agent in it, and embedding the trained model into the Unity environment. After reading this tutorial, you should be able to train any of the example environments. If you are not familiar with the Unity Engine , view our Background: Unity page for helpful pointers. Additionally, if you're not familiar with machine learning, view our Background: Machine Learning page for a brief overview and helpful pointers. For this guide, we'll use the 3D Balance Ball environment which contains a number of agent cubes and balls (which are all copies of each other). Each agent cube tries to keep its ball from falling by rotating either horizontally or vertically. In this environment, an agent cube is an Agent that receives a reward for every step that it balances the ball. An agent is also penalized with a negative reward for dropping the ball. The goal of the training process is to have the agents learn to balance the ball on their head. Let's get started! Installation If you haven't already, follow the installation instructions . Afterwards, open the Unity Project that contains all the example environments: Open the Package Manager Window by navigating to Window -> Package Manager in the menu. Navigate to the ML-Agents Package and click on it. Find the 3D Ball sample and click Import . In the Project window, go to the Assets/ML-Agents/Examples/3DBall/Scenes folder and open the 3DBall scene file. Understanding a Unity Environment An agent is an autonomous actor that observes and interacts with an environment . In the context of Unity, an environment is a scene containing one or more Agent objects, and, of course, the other entities that an agent interacts with. Note: In Unity, the base object of everything in a scene is the GameObject . The GameObject is essentially a container for everything else, including behaviors, graphics, physics, etc. To see the components that make up a GameObject, select the GameObject in the Scene window, and open the Inspector window. The Inspector shows every component on a GameObject. The first thing you may notice after opening the 3D Balance Ball scene is that it contains not one, but several agent cubes. Each agent cube in the scene is an independent agent, but they all share the same Behavior. 3D Balance Ball does this to speed up training since all twelve agents contribute to training in parallel. Agent The Agent is the actor that observes and takes actions in the environment. In the 3D Balance Ball environment, the Agent components are placed on the twelve \"Agent\" GameObjects. The base Agent object has a few properties that affect its behavior: Behavior Parameters \u2014 Every Agent must have a Behavior. The Behavior determines how an Agent makes decisions. Max Step \u2014 Defines how many simulation steps can occur before the Agent's episode ends. In 3D Balance Ball, an Agent restarts after 5000 steps. Behavior Parameters : Vector Observation Space Before making a decision, an agent collects its observation about its state in the world. The vector observation is a vector of floating point numbers which contain relevant information for the agent to make decisions. The Behavior Parameters of the 3D Balance Ball example uses a Space Size of 8. This means that the feature vector containing the Agent's observations contains eight elements: the x and z components of the agent cube's rotation and the x , y , and z components of the ball's relative position and velocity. Behavior Parameters : Actions An Agent is given instructions in the form of actions. ML-Agents Toolkit classifies actions into two types: continuous and discrete. The 3D Balance Ball example is programmed to use continuous actions, which are a vector of floating-point numbers that can vary continuously. More specifically, it uses a Space Size of 2 to control the amount of x and z rotations to apply to itself to keep the ball balanced on its head. Running a pre-trained model We include pre-trained models for our agents ( .onnx files) and we use the Unity Inference Engine to run these models inside Unity. In this section, we will use the pre-trained model for the 3D Ball example. In the Project window, go to the Assets/ML-Agents/Examples/3DBall/Prefabs folder. Expand 3DBall and click on the Agent prefab. You should see the Agent prefab in the Inspector window. Note : The platforms in the 3DBall scene were created using the 3DBall prefab. Instead of updating all 12 platforms individually, you can update the 3DBall prefab instead. In the Project window, drag the 3DBall Model located in Assets/ML-Agents/Examples/3DBall/TFModels into the Model property under Behavior Parameters (Script) component in the Agent GameObject Inspector window. You should notice that each Agent under each 3DBall in the Hierarchy windows now contains 3DBall as Model on the Behavior Parameters . Note : You can modify multiple game objects in a scene by selecting them all at once using the search bar in the Scene Hierarchy. Set the Inference Device to use for this model as CPU . Click the Play button in the Unity Editor and you will see the platforms balance the balls using the pre-trained model. Training a new model with Reinforcement Learning While we provide pre-trained models for the agents in this environment, any environment you make yourself will require training agents from scratch to generate a new model file. In this section we will demonstrate how to use the reinforcement learning algorithms that are part of the ML-Agents Python package to accomplish this. We have provided a convenient command mlagents-learn which accepts arguments used to configure both training and inference phases. Training the environment Open a command or terminal window. Navigate to the folder where you cloned the ml-agents repository. Note : If you followed the default installation , then you should be able to run mlagents-learn from any directory. Run mlagents-learn config/ppo/3DBall.yaml --run-id=first3DBallRun . config/ppo/3DBall.yaml is the path to a default training configuration file that we provide. The config/ppo folder includes training configuration files for all our example environments, including 3DBall. run-id is a unique name for this training session. When the message \"Start training by pressing the Play button in the Unity Editor\" is displayed on the screen, you can press the Play button in Unity to start training in the Editor. If mlagents-learn runs correctly and starts training, you should see something like this: INFO:mlagents_envs: 'Ball3DAcademy' started successfully! Unity Academy name: Ball3DAcademy INFO:mlagents_envs:Connected new brain: Unity brain name: 3DBallLearning Number of Visual Observations (per agent): 0 Vector Observation space size (per agent): 8 Number of stacked Vector Observation: 1 INFO:mlagents_envs:Hyperparameters for the PPO Trainer of brain 3DBallLearning: batch_size: 64 beta: 0.001 buffer_size: 12000 epsilon: 0.2 gamma: 0.995 hidden_units: 128 lambd: 0.99 learning_rate: 0.0003 max_steps: 5.0e4 normalize: True num_epoch: 3 num_layers: 2 time_horizon: 1000 sequence_length: 64 summary_freq: 1000 use_recurrent: False memory_size: 256 use_curiosity: False curiosity_strength: 0.01 curiosity_enc_size: 128 output_path: ./results/first3DBallRun/3DBallLearning INFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 1000. Mean Reward: 1.242. Std of Reward: 0.746. Training. INFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 2000. Mean Reward: 1.319. Std of Reward: 0.693. Training. INFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 3000. Mean Reward: 1.804. Std of Reward: 1.056. Training. INFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 4000. Mean Reward: 2.151. Std of Reward: 1.432. Training. INFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 5000. Mean Reward: 3.175. Std of Reward: 2.250. Training. INFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 6000. Mean Reward: 4.898. Std of Reward: 4.019. Training. INFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 7000. Mean Reward: 6.716. Std of Reward: 5.125. Training. INFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 8000. Mean Reward: 12.124. Std of Reward: 11.929. Training. INFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 9000. Mean Reward: 18.151. Std of Reward: 16.871. Training. INFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 10000. Mean Reward: 27.284. Std of Reward: 28.667. Training. Note how the Mean Reward value printed to the screen increases as training progresses. This is a positive sign that training is succeeding. Note : You can train using an executable rather than the Editor. To do so, follow the instructions in Using an Executable . Observing Training Progress Once you start training using mlagents-learn in the way described in the previous section, the ml-agents directory will contain a results directory. In order to observe the training process in more detail, you can use TensorBoard. From the command line run: tensorboard --logdir results Then navigate to localhost:6006 in your browser to view the TensorBoard summary statistics as shown below. For the purposes of this section, the most important statistic is Environment/Cumulative Reward which should increase throughout training, eventually converging close to 100 which is the maximum reward the agent can accumulate. Embedding the model into the Unity Environment Once the training process completes, and the training process saves the model (denoted by the Saved Model message) you can add it to the Unity project and use it with compatible Agents (the Agents that generated the model). Note: Do not just close the Unity Window once the Saved Model message appears. Either wait for the training process to close the window or press Ctrl+C at the command-line prompt. If you close the window manually, the .onnx file containing the trained model is not exported into the ml-agents folder. If you've quit the training early using Ctrl+C and want to resume training, run the same command again, appending the --resume flag: mlagents-learn config/ppo/3DBall.yaml --run-id=first3DBallRun --resume Your trained model will be at results/<run-identifier>/<behavior_name>.onnx where <behavior_name> is the name of the Behavior Name of the agents corresponding to the model. This file corresponds to your model's latest checkpoint. You can now embed this trained model into your Agents by following the steps below, which is similar to the steps described above . Move your model file into Project/Assets/ML-Agents/Examples/3DBall/TFModels/ . Open the Unity Editor, and select the 3DBall scene as described above. Select the 3DBall prefab Agent object. Drag the <behavior_name>.onnx file from the Project window of the Editor to the Model placeholder in the Ball3DAgent inspector window. Press the Play button at the top of the Editor. Next Steps For more information on the ML-Agents Toolkit, in addition to helpful background, check out the ML-Agents Toolkit Overview page. For a \"Hello World\" introduction to creating your own Learning Environment, check out the Making a New Learning Environment page. For an overview on the more complex example environments that are provided in this toolkit, check out the Example Environments page. For more information on the various training options available, check out the Training ML-Agents page.","title":"Getting Started Guide"},{"location":"Getting-Started/#getting-started-guide","text":"This guide walks through the end-to-end process of opening one of our example environments in Unity, training an Agent in it, and embedding the trained model into the Unity environment. After reading this tutorial, you should be able to train any of the example environments. If you are not familiar with the Unity Engine , view our Background: Unity page for helpful pointers. Additionally, if you're not familiar with machine learning, view our Background: Machine Learning page for a brief overview and helpful pointers. For this guide, we'll use the 3D Balance Ball environment which contains a number of agent cubes and balls (which are all copies of each other). Each agent cube tries to keep its ball from falling by rotating either horizontally or vertically. In this environment, an agent cube is an Agent that receives a reward for every step that it balances the ball. An agent is also penalized with a negative reward for dropping the ball. The goal of the training process is to have the agents learn to balance the ball on their head. Let's get started!","title":"Getting Started Guide"},{"location":"Getting-Started/#installation","text":"If you haven't already, follow the installation instructions . Afterwards, open the Unity Project that contains all the example environments: Open the Package Manager Window by navigating to Window -> Package Manager in the menu. Navigate to the ML-Agents Package and click on it. Find the 3D Ball sample and click Import . In the Project window, go to the Assets/ML-Agents/Examples/3DBall/Scenes folder and open the 3DBall scene file.","title":"Installation"},{"location":"Getting-Started/#understanding-a-unity-environment","text":"An agent is an autonomous actor that observes and interacts with an environment . In the context of Unity, an environment is a scene containing one or more Agent objects, and, of course, the other entities that an agent interacts with. Note: In Unity, the base object of everything in a scene is the GameObject . The GameObject is essentially a container for everything else, including behaviors, graphics, physics, etc. To see the components that make up a GameObject, select the GameObject in the Scene window, and open the Inspector window. The Inspector shows every component on a GameObject. The first thing you may notice after opening the 3D Balance Ball scene is that it contains not one, but several agent cubes. Each agent cube in the scene is an independent agent, but they all share the same Behavior. 3D Balance Ball does this to speed up training since all twelve agents contribute to training in parallel.","title":"Understanding a Unity Environment"},{"location":"Getting-Started/#agent","text":"The Agent is the actor that observes and takes actions in the environment. In the 3D Balance Ball environment, the Agent components are placed on the twelve \"Agent\" GameObjects. The base Agent object has a few properties that affect its behavior: Behavior Parameters \u2014 Every Agent must have a Behavior. The Behavior determines how an Agent makes decisions. Max Step \u2014 Defines how many simulation steps can occur before the Agent's episode ends. In 3D Balance Ball, an Agent restarts after 5000 steps.","title":"Agent"},{"location":"Getting-Started/#behavior-parameters-vector-observation-space","text":"Before making a decision, an agent collects its observation about its state in the world. The vector observation is a vector of floating point numbers which contain relevant information for the agent to make decisions. The Behavior Parameters of the 3D Balance Ball example uses a Space Size of 8. This means that the feature vector containing the Agent's observations contains eight elements: the x and z components of the agent cube's rotation and the x , y , and z components of the ball's relative position and velocity.","title":"Behavior Parameters : Vector Observation Space"},{"location":"Getting-Started/#behavior-parameters-actions","text":"An Agent is given instructions in the form of actions. ML-Agents Toolkit classifies actions into two types: continuous and discrete. The 3D Balance Ball example is programmed to use continuous actions, which are a vector of floating-point numbers that can vary continuously. More specifically, it uses a Space Size of 2 to control the amount of x and z rotations to apply to itself to keep the ball balanced on its head.","title":"Behavior Parameters : Actions"},{"location":"Getting-Started/#running-a-pre-trained-model","text":"We include pre-trained models for our agents ( .onnx files) and we use the Unity Inference Engine to run these models inside Unity. In this section, we will use the pre-trained model for the 3D Ball example. In the Project window, go to the Assets/ML-Agents/Examples/3DBall/Prefabs folder. Expand 3DBall and click on the Agent prefab. You should see the Agent prefab in the Inspector window. Note : The platforms in the 3DBall scene were created using the 3DBall prefab. Instead of updating all 12 platforms individually, you can update the 3DBall prefab instead. In the Project window, drag the 3DBall Model located in Assets/ML-Agents/Examples/3DBall/TFModels into the Model property under Behavior Parameters (Script) component in the Agent GameObject Inspector window. You should notice that each Agent under each 3DBall in the Hierarchy windows now contains 3DBall as Model on the Behavior Parameters . Note : You can modify multiple game objects in a scene by selecting them all at once using the search bar in the Scene Hierarchy. Set the Inference Device to use for this model as CPU . Click the Play button in the Unity Editor and you will see the platforms balance the balls using the pre-trained model.","title":"Running a pre-trained model"},{"location":"Getting-Started/#training-a-new-model-with-reinforcement-learning","text":"While we provide pre-trained models for the agents in this environment, any environment you make yourself will require training agents from scratch to generate a new model file. In this section we will demonstrate how to use the reinforcement learning algorithms that are part of the ML-Agents Python package to accomplish this. We have provided a convenient command mlagents-learn which accepts arguments used to configure both training and inference phases.","title":"Training a new model with Reinforcement Learning"},{"location":"Getting-Started/#training-the-environment","text":"Open a command or terminal window. Navigate to the folder where you cloned the ml-agents repository. Note : If you followed the default installation , then you should be able to run mlagents-learn from any directory. Run mlagents-learn config/ppo/3DBall.yaml --run-id=first3DBallRun . config/ppo/3DBall.yaml is the path to a default training configuration file that we provide. The config/ppo folder includes training configuration files for all our example environments, including 3DBall. run-id is a unique name for this training session. When the message \"Start training by pressing the Play button in the Unity Editor\" is displayed on the screen, you can press the Play button in Unity to start training in the Editor. If mlagents-learn runs correctly and starts training, you should see something like this: INFO:mlagents_envs: 'Ball3DAcademy' started successfully! Unity Academy name: Ball3DAcademy INFO:mlagents_envs:Connected new brain: Unity brain name: 3DBallLearning Number of Visual Observations (per agent): 0 Vector Observation space size (per agent): 8 Number of stacked Vector Observation: 1 INFO:mlagents_envs:Hyperparameters for the PPO Trainer of brain 3DBallLearning: batch_size: 64 beta: 0.001 buffer_size: 12000 epsilon: 0.2 gamma: 0.995 hidden_units: 128 lambd: 0.99 learning_rate: 0.0003 max_steps: 5.0e4 normalize: True num_epoch: 3 num_layers: 2 time_horizon: 1000 sequence_length: 64 summary_freq: 1000 use_recurrent: False memory_size: 256 use_curiosity: False curiosity_strength: 0.01 curiosity_enc_size: 128 output_path: ./results/first3DBallRun/3DBallLearning INFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 1000. Mean Reward: 1.242. Std of Reward: 0.746. Training. INFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 2000. Mean Reward: 1.319. Std of Reward: 0.693. Training. INFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 3000. Mean Reward: 1.804. Std of Reward: 1.056. Training. INFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 4000. Mean Reward: 2.151. Std of Reward: 1.432. Training. INFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 5000. Mean Reward: 3.175. Std of Reward: 2.250. Training. INFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 6000. Mean Reward: 4.898. Std of Reward: 4.019. Training. INFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 7000. Mean Reward: 6.716. Std of Reward: 5.125. Training. INFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 8000. Mean Reward: 12.124. Std of Reward: 11.929. Training. INFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 9000. Mean Reward: 18.151. Std of Reward: 16.871. Training. INFO:mlagents.trainers: first3DBallRun: 3DBallLearning: Step: 10000. Mean Reward: 27.284. Std of Reward: 28.667. Training. Note how the Mean Reward value printed to the screen increases as training progresses. This is a positive sign that training is succeeding. Note : You can train using an executable rather than the Editor. To do so, follow the instructions in Using an Executable .","title":"Training the environment"},{"location":"Getting-Started/#observing-training-progress","text":"Once you start training using mlagents-learn in the way described in the previous section, the ml-agents directory will contain a results directory. In order to observe the training process in more detail, you can use TensorBoard. From the command line run: tensorboard --logdir results Then navigate to localhost:6006 in your browser to view the TensorBoard summary statistics as shown below. For the purposes of this section, the most important statistic is Environment/Cumulative Reward which should increase throughout training, eventually converging close to 100 which is the maximum reward the agent can accumulate.","title":"Observing Training Progress"},{"location":"Getting-Started/#embedding-the-model-into-the-unity-environment","text":"Once the training process completes, and the training process saves the model (denoted by the Saved Model message) you can add it to the Unity project and use it with compatible Agents (the Agents that generated the model). Note: Do not just close the Unity Window once the Saved Model message appears. Either wait for the training process to close the window or press Ctrl+C at the command-line prompt. If you close the window manually, the .onnx file containing the trained model is not exported into the ml-agents folder. If you've quit the training early using Ctrl+C and want to resume training, run the same command again, appending the --resume flag: mlagents-learn config/ppo/3DBall.yaml --run-id=first3DBallRun --resume Your trained model will be at results/<run-identifier>/<behavior_name>.onnx where <behavior_name> is the name of the Behavior Name of the agents corresponding to the model. This file corresponds to your model's latest checkpoint. You can now embed this trained model into your Agents by following the steps below, which is similar to the steps described above . Move your model file into Project/Assets/ML-Agents/Examples/3DBall/TFModels/ . Open the Unity Editor, and select the 3DBall scene as described above. Select the 3DBall prefab Agent object. Drag the <behavior_name>.onnx file from the Project window of the Editor to the Model placeholder in the Ball3DAgent inspector window. Press the Play button at the top of the Editor.","title":"Embedding the model into the Unity Environment"},{"location":"Getting-Started/#next-steps","text":"For more information on the ML-Agents Toolkit, in addition to helpful background, check out the ML-Agents Toolkit Overview page. For a \"Hello World\" introduction to creating your own Learning Environment, check out the Making a New Learning Environment page. For an overview on the more complex example environments that are provided in this toolkit, check out the Example Environments page. For more information on the various training options available, check out the Training ML-Agents page.","title":"Next Steps"},{"location":"Glossary/","text":"ML-Agents Toolkit Glossary Academy - Singleton object which controls timing, reset, and training/inference settings of the environment. Action - The carrying-out of a decision on the part of an agent within the environment. Agent - Unity Component which produces observations and takes actions in the environment. Agents actions are determined by decisions produced by a Policy. Decision - The specification produced by a Policy for an action to be carried out given an observation. Editor - The Unity Editor, which may include any pane (e.g. Hierarchy, Scene, Inspector). Environment - The Unity scene which contains Agents. Experience - Corresponds to a tuple of [Agent observations, actions, rewards] of a single Agent obtained after a Step. External Coordinator - ML-Agents class responsible for communication with outside processes (in this case, the Python API). FixedUpdate - Unity method called each time the game engine is stepped. ML-Agents logic should be placed here. Frame - An instance of rendering the main camera for the display. Corresponds to each Update call of the game engine. Observation - Partial information describing the state of the environment available to a given agent. (e.g. Vector, Visual) Policy - The decision making mechanism for producing decisions from observations, typically a neural network model. Reward - Signal provided at every step used to indicate desirability of an agent\u2019s action within the current state of the environment. State - The underlying properties of the environment (including all agents within it) at a given time. Step - Corresponds to an atomic change of the engine that happens between Agent decisions. Trainer - Python class which is responsible for training a given group of Agents. Update - Unity function called each time a frame is rendered. ML-Agents logic should not be placed here.","title":"ML-Agents Toolkit Glossary"},{"location":"Glossary/#ml-agents-toolkit-glossary","text":"Academy - Singleton object which controls timing, reset, and training/inference settings of the environment. Action - The carrying-out of a decision on the part of an agent within the environment. Agent - Unity Component which produces observations and takes actions in the environment. Agents actions are determined by decisions produced by a Policy. Decision - The specification produced by a Policy for an action to be carried out given an observation. Editor - The Unity Editor, which may include any pane (e.g. Hierarchy, Scene, Inspector). Environment - The Unity scene which contains Agents. Experience - Corresponds to a tuple of [Agent observations, actions, rewards] of a single Agent obtained after a Step. External Coordinator - ML-Agents class responsible for communication with outside processes (in this case, the Python API). FixedUpdate - Unity method called each time the game engine is stepped. ML-Agents logic should be placed here. Frame - An instance of rendering the main camera for the display. Corresponds to each Update call of the game engine. Observation - Partial information describing the state of the environment available to a given agent. (e.g. Vector, Visual) Policy - The decision making mechanism for producing decisions from observations, typically a neural network model. Reward - Signal provided at every step used to indicate desirability of an agent\u2019s action within the current state of the environment. State - The underlying properties of the environment (including all agents within it) at a given time. Step - Corresponds to an atomic change of the engine that happens between Agent decisions. Trainer - Python class which is responsible for training a given group of Agents. Update - Unity function called each time a frame is rendered. ML-Agents logic should not be placed here.","title":"ML-Agents Toolkit Glossary"},{"location":"Hugging-Face-Integration/","text":"The Hugging Face Integration The Hugging Face Hub \ud83e\udd17 is a central place where anyone can share and download models . It allows you to: - Host your trained models. - Download trained models from the community. - Visualize your agents playing directly on your browser . You can see the list of ml-agents models here . We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub : A short tutorial where you teach Huggy the Dog to fetch the stick and then play with him directly in your browser A more in-depth tutorial Download a model from the Hub You can simply download a model from the Hub using mlagents-load-from-hf . You need to define two parameters: --repo-id : the name of the Hugging Face repo you want to download. --local-dir : the path to download the model. For instance, I want to load the model with model-id \"ThomasSimonini/MLAgents-Pyramids\" and put it in the downloads directory: mlagents-load-from-hf --repo-id=\"ThomasSimonini/MLAgents-Pyramids\" --local-dir=\"./downloads\" Upload a model to the Hub You can simply upload a model to the Hub using mlagents-push-to-hf You need to define four parameters: --run-id : the name of the training run id. --local-dir : where the model was saved --repo-id : the name of the Hugging Face repo you want to create or update. It\u2019s always / If the repo does not exist it will be created automatically --commit-message : since HF repos are git repositories you need to give a commit message. For instance, I want to upload my model trained with run-id \"SnowballTarget1\" to the repo-id: ThomasSimonini/ppo-SnowballTarget: mlagents-push-to-hf --run-id=\"SnowballTarget1\" --local-dir=\"./results/SnowballTarget1\" --repo-id=\"ThomasSimonini/ppo-SnowballTarget\" --commit-message=\"First Push\" Visualize an agent playing You can watch your agent playing directly in your browser (if the environment is from the ML-Agents official environments ) Step 1: Go to https://huggingface.co/unity and select the environment demo. Step 2: Find your model_id in the list. Step 3: Select your .nn /.onnx file. Step 4: Click on Watch the agent play","title":"The Hugging Face Integration"},{"location":"Hugging-Face-Integration/#the-hugging-face-integration","text":"The Hugging Face Hub \ud83e\udd17 is a central place where anyone can share and download models . It allows you to: - Host your trained models. - Download trained models from the community. - Visualize your agents playing directly on your browser . You can see the list of ml-agents models here . We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub : A short tutorial where you teach Huggy the Dog to fetch the stick and then play with him directly in your browser A more in-depth tutorial","title":"The Hugging Face Integration"},{"location":"Hugging-Face-Integration/#download-a-model-from-the-hub","text":"You can simply download a model from the Hub using mlagents-load-from-hf . You need to define two parameters: --repo-id : the name of the Hugging Face repo you want to download. --local-dir : the path to download the model. For instance, I want to load the model with model-id \"ThomasSimonini/MLAgents-Pyramids\" and put it in the downloads directory: mlagents-load-from-hf --repo-id=\"ThomasSimonini/MLAgents-Pyramids\" --local-dir=\"./downloads\"","title":"Download a model from the Hub"},{"location":"Hugging-Face-Integration/#upload-a-model-to-the-hub","text":"You can simply upload a model to the Hub using mlagents-push-to-hf You need to define four parameters: --run-id : the name of the training run id. --local-dir : where the model was saved --repo-id : the name of the Hugging Face repo you want to create or update. It\u2019s always / If the repo does not exist it will be created automatically --commit-message : since HF repos are git repositories you need to give a commit message. For instance, I want to upload my model trained with run-id \"SnowballTarget1\" to the repo-id: ThomasSimonini/ppo-SnowballTarget: mlagents-push-to-hf --run-id=\"SnowballTarget1\" --local-dir=\"./results/SnowballTarget1\" --repo-id=\"ThomasSimonini/ppo-SnowballTarget\" --commit-message=\"First Push\"","title":"Upload a model to the Hub"},{"location":"Hugging-Face-Integration/#visualize-an-agent-playing","text":"You can watch your agent playing directly in your browser (if the environment is from the ML-Agents official environments ) Step 1: Go to https://huggingface.co/unity and select the environment demo. Step 2: Find your model_id in the list. Step 3: Select your .nn /.onnx file. Step 4: Click on Watch the agent play","title":"Visualize an agent playing"},{"location":"Installation-Anaconda-Windows/","text":"Installing ML-Agents Toolkit for Windows (Deprecated) :warning: Note: We no longer use this guide ourselves and so it may not work correctly. We've decided to keep it up just in case it is helpful to you. The ML-Agents Toolkit supports Windows 10. While it might be possible to run the ML-Agents Toolkit using other versions of Windows, it has not been tested on other versions. Furthermore, the ML-Agents Toolkit has not been tested on a Windows VM such as Bootcamp or Parallels. To use the ML-Agents Toolkit, you install Python and the required Python packages as outlined below. This guide also covers how set up GPU-based training (for advanced users). GPU-based training is not currently required for the ML-Agents Toolkit. However, training on a GPU might be required by future versions and features. Step 1: Install Python via Anaconda Download and install Anaconda for Windows. By using Anaconda, you can manage separate environments for different distributions of Python. Python 3.7.2 or higher is required as we no longer support Python 2. In this guide, we are using Python version 3.7 and Anaconda version 5.1 ( 64-bit or 32-bit direct links). We recommend the default advanced installation options . However, select the options appropriate for your specific situation. After installation, you must open Anaconda Navigator to finish the setup. From the Windows search bar, type anaconda navigator . You can close Anaconda Navigator after it opens. If environment variables were not created, you will see error \"conda is not recognized as internal or external command\" when you type conda into the command line. To solve this you will need to set the environment variable correctly. Type environment variables in the search bar (this can be reached by hitting the Windows key or the bottom left Windows button). You should see an option called Edit the system environment variables . From here, click the Environment Variables button. Double click \"Path\" under System variable to edit the \"Path\" variable, click New to add the following new paths. %UserProfile%\\Anaconda3\\Scripts %UserProfile%\\Anaconda3\\Scripts\\conda.exe %UserProfile%\\Anaconda3 %UserProfile%\\Anaconda3\\python.exe Step 2: Setup and Activate a New Conda Environment You will create a new Conda environment to be used with the ML-Agents Toolkit. This means that all the packages that you install are localized to just this environment. It will not affect any other installation of Python or other environments. Whenever you want to run ML-Agents, you will need activate this Conda environment. To create a new Conda environment, open a new Anaconda Prompt ( Anaconda Prompt in the search bar) and type in the following command: conda create -n ml-agents python=3.7 You may be asked to install new packages. Type y and press enter (make sure you are connected to the Internet) . You must install these required packages. The new Conda environment is called ml-agents and uses Python version 3.7. To use this environment, you must activate it. (To use this environment In the future, you can run the same command) . In the same Anaconda Prompt, type in the following command: activate ml-agents You should see (ml-agents) prepended on the last line. Next, install tensorflow . Install this package using pip - which is a package management system used to install Python packages. Latest versions of TensorFlow won't work, so you will need to make sure that you install version 1.7.1. In the same Anaconda Prompt, type in the following command (make sure you are connected to the Internet) : pip install tensorflow==1.7.1 Step 3: Install Required Python Packages The ML-Agents Toolkit depends on a number of Python packages. Use pip to install these Python dependencies. If you haven't already, clone the ML-Agents Toolkit Github repository to your local computer. You can do this using Git ( download here ) and running the following commands in an Anaconda Prompt (if you open a new prompt, be sure to activate the ml-agents Conda environment by typing activate ml-agents ) : git clone --branch release_19 https://github.com/Unity-Technologies/ml-agents.git The --branch release_19 option will switch to the tag of the latest stable release. Omitting that will get the main branch which is potentially unstable. If you don't want to use Git, you can find download links on the releases page . The com.unity.ml-agents subdirectory contains the core code to add to your projects. The Project subdirectory contains many example environments to help you get started. The ml-agents subdirectory contains a Python package which provides deep reinforcement learning trainers to use with Unity environments. The ml-agents-envs subdirectory contains a Python API to interface with Unity, which the ml-agents package depends on. The gym-unity subdirectory contains a package to interface with OpenAI Gym. Keep in mind where the files were downloaded, as you will need the trainer config files in this directory when running mlagents-learn . Make sure you are connected to the Internet and then type in the Anaconda Prompt: python -m pip install mlagents==0.30.0 This will complete the installation of all the required Python packages to run the ML-Agents Toolkit. Sometimes on Windows, when you use pip to install certain Python packages, the pip will get stuck when trying to read the cache of the package. If you see this, you can try: python -m pip install mlagents==0.30.0 --no-cache-dir This --no-cache-dir tells the pip to disable the cache. Installing for Development If you intend to make modifications to ml-agents or ml-agents-envs , you should install the packages from the cloned repo rather than from PyPi. To do this, you will need to install ml-agents and ml-agents-envs separately. In our example, the files are located in C:\\Downloads . After you have either cloned or downloaded the files, from the Anaconda Prompt, change to the ml-agents subdirectory inside the ml-agents directory: cd C:\\Downloads\\ml-agents From the repo's main directory, now run: cd ml-agents-envs pip install -e . cd .. cd ml-agents pip install -e . Running pip with the -e flag will let you make changes to the Python files directly and have those reflected when you run mlagents-learn . It is important to install these packages in this order as the mlagents package depends on mlagents_envs , and installing it in the other order will download mlagents_envs from PyPi. (Optional) Step 4: GPU Training using The ML-Agents Toolkit GPU is not required for the ML-Agents Toolkit and won't speed up the PPO algorithm a lot during training(but something in the future will benefit from GPU). This is a guide for advanced users who want to train using GPUs. Additionally, you will need to check if your GPU is CUDA compatible. Please check Nvidia's page here . Currently for the ML-Agents Toolkit, only CUDA v9.0 and cuDNN v7.0.5 is supported. Install Nvidia CUDA toolkit Download and install the CUDA toolkit 9.0 from Nvidia's archive. The toolkit includes GPU-accelerated libraries, debugging and optimization tools, a C/C++ (Step Visual Studio 2017) compiler and a runtime library and is needed to run the ML-Agents Toolkit. In this guide, we are using version 9.0.176 ). Before installing, please make sure you close any running instances of Unity or Visual Studio . Run the installer and select the Express option. Note the directory where you installed the CUDA toolkit. In this guide, we installed in the directory C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v9.0 Install Nvidia cuDNN library Download and install the cuDNN library from Nvidia. cuDNN is a GPU-accelerated library of primitives for deep neural networks. Before you can download, you will need to sign up for free to the Nvidia Developer Program. Once you've signed up, go back to the cuDNN downloads page . You may or may not be asked to fill out a short survey. When you get to the list cuDNN releases, make sure you are downloading the right version for the CUDA toolkit you installed in Step 1. In this guide, we are using version 7.0.5 for CUDA toolkit version 9.0 ( direct link ). After you have downloaded the cuDNN files, you will need to extract the files into the CUDA toolkit directory. In the cuDNN zip file, there are three folders called bin , include , and lib . Copy these three folders into the CUDA toolkit directory. The CUDA toolkit directory is located at C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v9.0 Set Environment Variables You will need to add one environment variable and two path variables. To set the environment variable, type environment variables in the search bar (this can be reached by hitting the Windows key or the bottom left Windows button). You should see an option called Edit the system environment variables . From here, click the Environment Variables button. Click New to add a new system variable (make sure you do this under System variables and not User variables . For Variable Name , enter CUDA_HOME . For the variable value, put the directory location for the CUDA toolkit. In this guide, the directory location is C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v9.0 . Press OK once. To set the two path variables, inside the same Environment Variables window and under the second box called System Variables , find a variable called Path and click Edit . You will add two directories to the list. For this guide, the two entries would look like: C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v9.0\\lib\\x64 C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v9.0\\extras\\CUPTI\\libx64 Make sure to replace the relevant directory location with the one you have installed. Please note that case sensitivity matters . Install TensorFlow GPU Next, install tensorflow-gpu using pip . You'll need version 1.7.1. In an Anaconda Prompt with the Conda environment ml-agents activated, type in the following command to uninstall TensorFlow for cpu and install TensorFlow for gpu (make sure you are connected to the Internet) : pip uninstall tensorflow pip install tensorflow-gpu==1.7.1 Lastly, you should test to see if everything installed properly and that TensorFlow can identify your GPU. In the same Anaconda Prompt, open Python in the Prompt by calling: python And then type the following commands: import tensorflow as tf sess = tf.Session(config=tf.ConfigProto(log_device_placement=True)) You should see something similar to: Found device 0 with properties ... Acknowledgments We would like to thank Jason Weimann and Nitish S. Mutha for writing the original articles which were used to create this guide.","title":"Installing ML-Agents Toolkit for Windows (Deprecated)"},{"location":"Installation-Anaconda-Windows/#installing-ml-agents-toolkit-for-windows-deprecated","text":":warning: Note: We no longer use this guide ourselves and so it may not work correctly. We've decided to keep it up just in case it is helpful to you. The ML-Agents Toolkit supports Windows 10. While it might be possible to run the ML-Agents Toolkit using other versions of Windows, it has not been tested on other versions. Furthermore, the ML-Agents Toolkit has not been tested on a Windows VM such as Bootcamp or Parallels. To use the ML-Agents Toolkit, you install Python and the required Python packages as outlined below. This guide also covers how set up GPU-based training (for advanced users). GPU-based training is not currently required for the ML-Agents Toolkit. However, training on a GPU might be required by future versions and features.","title":"Installing ML-Agents Toolkit for Windows (Deprecated)"},{"location":"Installation-Anaconda-Windows/#step-1-install-python-via-anaconda","text":"Download and install Anaconda for Windows. By using Anaconda, you can manage separate environments for different distributions of Python. Python 3.7.2 or higher is required as we no longer support Python 2. In this guide, we are using Python version 3.7 and Anaconda version 5.1 ( 64-bit or 32-bit direct links). We recommend the default advanced installation options . However, select the options appropriate for your specific situation. After installation, you must open Anaconda Navigator to finish the setup. From the Windows search bar, type anaconda navigator . You can close Anaconda Navigator after it opens. If environment variables were not created, you will see error \"conda is not recognized as internal or external command\" when you type conda into the command line. To solve this you will need to set the environment variable correctly. Type environment variables in the search bar (this can be reached by hitting the Windows key or the bottom left Windows button). You should see an option called Edit the system environment variables . From here, click the Environment Variables button. Double click \"Path\" under System variable to edit the \"Path\" variable, click New to add the following new paths. %UserProfile%\\Anaconda3\\Scripts %UserProfile%\\Anaconda3\\Scripts\\conda.exe %UserProfile%\\Anaconda3 %UserProfile%\\Anaconda3\\python.exe","title":"Step 1: Install Python via Anaconda"},{"location":"Installation-Anaconda-Windows/#step-2-setup-and-activate-a-new-conda-environment","text":"You will create a new Conda environment to be used with the ML-Agents Toolkit. This means that all the packages that you install are localized to just this environment. It will not affect any other installation of Python or other environments. Whenever you want to run ML-Agents, you will need activate this Conda environment. To create a new Conda environment, open a new Anaconda Prompt ( Anaconda Prompt in the search bar) and type in the following command: conda create -n ml-agents python=3.7 You may be asked to install new packages. Type y and press enter (make sure you are connected to the Internet) . You must install these required packages. The new Conda environment is called ml-agents and uses Python version 3.7. To use this environment, you must activate it. (To use this environment In the future, you can run the same command) . In the same Anaconda Prompt, type in the following command: activate ml-agents You should see (ml-agents) prepended on the last line. Next, install tensorflow . Install this package using pip - which is a package management system used to install Python packages. Latest versions of TensorFlow won't work, so you will need to make sure that you install version 1.7.1. In the same Anaconda Prompt, type in the following command (make sure you are connected to the Internet) : pip install tensorflow==1.7.1","title":"Step 2: Setup and Activate a New Conda Environment"},{"location":"Installation-Anaconda-Windows/#step-3-install-required-python-packages","text":"The ML-Agents Toolkit depends on a number of Python packages. Use pip to install these Python dependencies. If you haven't already, clone the ML-Agents Toolkit Github repository to your local computer. You can do this using Git ( download here ) and running the following commands in an Anaconda Prompt (if you open a new prompt, be sure to activate the ml-agents Conda environment by typing activate ml-agents ) : git clone --branch release_19 https://github.com/Unity-Technologies/ml-agents.git The --branch release_19 option will switch to the tag of the latest stable release. Omitting that will get the main branch which is potentially unstable. If you don't want to use Git, you can find download links on the releases page . The com.unity.ml-agents subdirectory contains the core code to add to your projects. The Project subdirectory contains many example environments to help you get started. The ml-agents subdirectory contains a Python package which provides deep reinforcement learning trainers to use with Unity environments. The ml-agents-envs subdirectory contains a Python API to interface with Unity, which the ml-agents package depends on. The gym-unity subdirectory contains a package to interface with OpenAI Gym. Keep in mind where the files were downloaded, as you will need the trainer config files in this directory when running mlagents-learn . Make sure you are connected to the Internet and then type in the Anaconda Prompt: python -m pip install mlagents==0.30.0 This will complete the installation of all the required Python packages to run the ML-Agents Toolkit. Sometimes on Windows, when you use pip to install certain Python packages, the pip will get stuck when trying to read the cache of the package. If you see this, you can try: python -m pip install mlagents==0.30.0 --no-cache-dir This --no-cache-dir tells the pip to disable the cache.","title":"Step 3: Install Required Python Packages"},{"location":"Installation-Anaconda-Windows/#installing-for-development","text":"If you intend to make modifications to ml-agents or ml-agents-envs , you should install the packages from the cloned repo rather than from PyPi. To do this, you will need to install ml-agents and ml-agents-envs separately. In our example, the files are located in C:\\Downloads . After you have either cloned or downloaded the files, from the Anaconda Prompt, change to the ml-agents subdirectory inside the ml-agents directory: cd C:\\Downloads\\ml-agents From the repo's main directory, now run: cd ml-agents-envs pip install -e . cd .. cd ml-agents pip install -e . Running pip with the -e flag will let you make changes to the Python files directly and have those reflected when you run mlagents-learn . It is important to install these packages in this order as the mlagents package depends on mlagents_envs , and installing it in the other order will download mlagents_envs from PyPi.","title":"Installing for Development"},{"location":"Installation-Anaconda-Windows/#optional-step-4-gpu-training-using-the-ml-agents-toolkit","text":"GPU is not required for the ML-Agents Toolkit and won't speed up the PPO algorithm a lot during training(but something in the future will benefit from GPU). This is a guide for advanced users who want to train using GPUs. Additionally, you will need to check if your GPU is CUDA compatible. Please check Nvidia's page here . Currently for the ML-Agents Toolkit, only CUDA v9.0 and cuDNN v7.0.5 is supported.","title":"(Optional) Step 4: GPU Training using The ML-Agents Toolkit"},{"location":"Installation-Anaconda-Windows/#install-nvidia-cuda-toolkit","text":"Download and install the CUDA toolkit 9.0 from Nvidia's archive. The toolkit includes GPU-accelerated libraries, debugging and optimization tools, a C/C++ (Step Visual Studio 2017) compiler and a runtime library and is needed to run the ML-Agents Toolkit. In this guide, we are using version 9.0.176 ). Before installing, please make sure you close any running instances of Unity or Visual Studio . Run the installer and select the Express option. Note the directory where you installed the CUDA toolkit. In this guide, we installed in the directory C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v9.0","title":"Install Nvidia CUDA toolkit"},{"location":"Installation-Anaconda-Windows/#install-nvidia-cudnn-library","text":"Download and install the cuDNN library from Nvidia. cuDNN is a GPU-accelerated library of primitives for deep neural networks. Before you can download, you will need to sign up for free to the Nvidia Developer Program. Once you've signed up, go back to the cuDNN downloads page . You may or may not be asked to fill out a short survey. When you get to the list cuDNN releases, make sure you are downloading the right version for the CUDA toolkit you installed in Step 1. In this guide, we are using version 7.0.5 for CUDA toolkit version 9.0 ( direct link ). After you have downloaded the cuDNN files, you will need to extract the files into the CUDA toolkit directory. In the cuDNN zip file, there are three folders called bin , include , and lib . Copy these three folders into the CUDA toolkit directory. The CUDA toolkit directory is located at C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v9.0","title":"Install Nvidia cuDNN library"},{"location":"Installation-Anaconda-Windows/#set-environment-variables","text":"You will need to add one environment variable and two path variables. To set the environment variable, type environment variables in the search bar (this can be reached by hitting the Windows key or the bottom left Windows button). You should see an option called Edit the system environment variables . From here, click the Environment Variables button. Click New to add a new system variable (make sure you do this under System variables and not User variables . For Variable Name , enter CUDA_HOME . For the variable value, put the directory location for the CUDA toolkit. In this guide, the directory location is C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v9.0 . Press OK once. To set the two path variables, inside the same Environment Variables window and under the second box called System Variables , find a variable called Path and click Edit . You will add two directories to the list. For this guide, the two entries would look like: C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v9.0\\lib\\x64 C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v9.0\\extras\\CUPTI\\libx64 Make sure to replace the relevant directory location with the one you have installed. Please note that case sensitivity matters .","title":"Set Environment Variables"},{"location":"Installation-Anaconda-Windows/#install-tensorflow-gpu","text":"Next, install tensorflow-gpu using pip . You'll need version 1.7.1. In an Anaconda Prompt with the Conda environment ml-agents activated, type in the following command to uninstall TensorFlow for cpu and install TensorFlow for gpu (make sure you are connected to the Internet) : pip uninstall tensorflow pip install tensorflow-gpu==1.7.1 Lastly, you should test to see if everything installed properly and that TensorFlow can identify your GPU. In the same Anaconda Prompt, open Python in the Prompt by calling: python And then type the following commands: import tensorflow as tf sess = tf.Session(config=tf.ConfigProto(log_device_placement=True)) You should see something similar to: Found device 0 with properties ...","title":"Install TensorFlow GPU"},{"location":"Installation-Anaconda-Windows/#acknowledgments","text":"We would like to thank Jason Weimann and Nitish S. Mutha for writing the original articles which were used to create this guide.","title":"Acknowledgments"},{"location":"Installation/","text":"Installation The ML-Agents Toolkit contains several components: Unity package ( com.unity.ml-agents ) contains the Unity C# SDK that will be integrated into your Unity project. This package contains a sample to help you get started with ML-Agents. Unity package ( com.unity.ml-agents.extensions ) contains experimental C#/Unity components that are not yet ready to be part of the base com.unity.ml-agents package. com.unity.ml-agents.extensions has a direct dependency on com.unity.ml-agents . Two Python packages: mlagents contains the machine learning algorithms that enables you to train behaviors in your Unity scene. Most users of ML-Agents will only need to directly install mlagents . mlagents_envs contains a set of Python APIs to interact with a Unity scene. It is a foundational layer that facilitates data messaging between Unity scene and the Python machine learning algorithms. Consequently, mlagents depends on mlagents_envs . Unity Project that contains several example environments that highlight the various features of the toolkit to help you get started. Consequently, to install and use the ML-Agents Toolkit you will need to: Install Unity (2021.3 or later) Install Python (3.8.13 or higher) Clone this repository (Optional) Note: If you do not clone the repository, then you will not be able to access the example environments and training configurations or the com.unity.ml-agents.extensions package. Additionally, the Getting Started Guide assumes that you have cloned the repository. Install the com.unity.ml-agents Unity package Install the com.unity.ml-agents.extensions Unity package (Optional) Install the mlagents Python package Install Unity 2021.3 or Later Download and install Unity. We strongly recommend that you install Unity through the Unity Hub as it will enable you to manage multiple Unity versions. Install Python 3.7.2 or Higher We recommend installing Python 3.7. If you are using Windows, please install the x86-64 version and not x86. If your Python environment doesn't include pip3 , see these instructions on installing it. Clone the ML-Agents Toolkit Repository (Optional) Now that you have installed Unity and Python, you can now install the Unity and Python packages. You do not need to clone the repository to install those packages, but you may choose to clone the repository if you'd like download our example environments and training configurations to experiment with them (some of our tutorials / guides assume you have access to our example environments). NOTE: There are samples shipped with the Unity Package. You only need to clone the repository if you would like to explore more examples. git clone --branch release_20 https://github.com/Unity-Technologies/ml-agents.git The --branch release_20 option will switch to the tag of the latest stable release. Omitting that will get the main branch which is potentially unstable. Advanced: Local Installation for Development You will need to clone the repository if you plan to modify or extend the ML-Agents Toolkit for your purposes. If you plan to contribute those changes back, make sure to clone the main branch (by omitting --branch release_20 from the command above). See our Contributions Guidelines for more information on contributing to the ML-Agents Toolkit. Install the com.unity.ml-agents Unity package The Unity ML-Agents C# SDK is a Unity Package. You can install the com.unity.ml-agents package directly from the Package Manager registry . Please make sure you enable 'Preview Packages' in the 'Advanced' dropdown in order to find the latest Preview release of the package. NOTE: If you do not see the ML-Agents package listed in the Package Manager please follow the advanced installation instructions below. Advanced: Local Installation for Development You can add the local com.unity.ml-agents package (from the repository that you just cloned) to your project by: navigating to the menu Window -> Package Manager . In the package manager window click on the + button on the top left of the packages list). Select Add package from disk... Navigate into the com.unity.ml-agents folder. Select the package.json file. If you are going to follow the examples from our documentation, you can open the Project folder in Unity and start tinkering immediately. Install the com.unity.ml-agents.extensions Unity package (Optional) To install the com.unity.ml-agents.extensions package, you need to first clone the repo and then complete a local installation similar to what was outlined in the previous Advanced: Local Installation for Development section. Complete installation steps can be found in the package documentation . Install the mlagents Python package Installing the mlagents Python package involves installing other Python packages that mlagents depends on. So you may run into installation issues if your machine has older versions of any of those dependencies already installed. Consequently, our supported path for installing mlagents is to leverage Python Virtual Environments. Virtual Environments provide a mechanism for isolating the dependencies for each project and are supported on Mac / Windows / Linux. We offer a dedicated guide on Virtual Environments . (Windows) Installing PyTorch On Windows, you'll have to install the PyTorch package separately prior to installing ML-Agents. Activate your virtual environment and run from the command line: pip3 install torch~=1.7.1 -f https://download.pytorch.org/whl/torch_stable.html Note that on Windows, you may also need Microsoft's Visual C++ Redistributable if you don't have it already. See the PyTorch installation guide for more installation options and versions. Installing mlagents To install the mlagents Python package, activate your virtual environment and run from the command line: python -m pip install mlagents==0.30.0 Note that this will install mlagents from PyPi, not from the cloned repository. If you installed this correctly, you should be able to run mlagents-learn --help , after which you will see the command line parameters you can use with mlagents-learn . By installing the mlagents package, the dependencies listed in the setup.py file are also installed. These include PyTorch (Requires a CPU w/ AVX support). Advanced: Local Installation for Development If you intend to make modifications to mlagents or mlagents_envs , you should install the packages from the cloned repository rather than from PyPi. To do this, you will need to install mlagents and mlagents_envs separately. From the repository's root directory, run: pip3 install torch -f https://download.pytorch.org/whl/torch_stable.html pip3 install -e ./ml-agents-envs pip3 install -e ./ml-agents Running pip with the -e flag will let you make changes to the Python files directly and have those reflected when you run mlagents-learn . It is important to install these packages in this order as the mlagents package depends on mlagents_envs , and installing it in the other order will download mlagents_envs from PyPi. Next Steps The Getting Started guide contains several short tutorials on setting up the ML-Agents Toolkit within Unity, running a pre-trained model, in addition to building and training environments. Help If you run into any problems regarding ML-Agents, refer to our FAQ and our Limitations pages. If you can't find anything please submit an issue and make sure to cite relevant information on OS, Python version, and exact error message (whenever possible).","title":"Installation"},{"location":"Installation/#installation","text":"The ML-Agents Toolkit contains several components: Unity package ( com.unity.ml-agents ) contains the Unity C# SDK that will be integrated into your Unity project. This package contains a sample to help you get started with ML-Agents. Unity package ( com.unity.ml-agents.extensions ) contains experimental C#/Unity components that are not yet ready to be part of the base com.unity.ml-agents package. com.unity.ml-agents.extensions has a direct dependency on com.unity.ml-agents . Two Python packages: mlagents contains the machine learning algorithms that enables you to train behaviors in your Unity scene. Most users of ML-Agents will only need to directly install mlagents . mlagents_envs contains a set of Python APIs to interact with a Unity scene. It is a foundational layer that facilitates data messaging between Unity scene and the Python machine learning algorithms. Consequently, mlagents depends on mlagents_envs . Unity Project that contains several example environments that highlight the various features of the toolkit to help you get started. Consequently, to install and use the ML-Agents Toolkit you will need to: Install Unity (2021.3 or later) Install Python (3.8.13 or higher) Clone this repository (Optional) Note: If you do not clone the repository, then you will not be able to access the example environments and training configurations or the com.unity.ml-agents.extensions package. Additionally, the Getting Started Guide assumes that you have cloned the repository. Install the com.unity.ml-agents Unity package Install the com.unity.ml-agents.extensions Unity package (Optional) Install the mlagents Python package","title":"Installation"},{"location":"Installation/#install-unity-20213-or-later","text":"Download and install Unity. We strongly recommend that you install Unity through the Unity Hub as it will enable you to manage multiple Unity versions.","title":"Install Unity 2021.3 or Later"},{"location":"Installation/#install-python-372-or-higher","text":"We recommend installing Python 3.7. If you are using Windows, please install the x86-64 version and not x86. If your Python environment doesn't include pip3 , see these instructions on installing it.","title":"Install Python 3.7.2 or Higher"},{"location":"Installation/#clone-the-ml-agents-toolkit-repository-optional","text":"Now that you have installed Unity and Python, you can now install the Unity and Python packages. You do not need to clone the repository to install those packages, but you may choose to clone the repository if you'd like download our example environments and training configurations to experiment with them (some of our tutorials / guides assume you have access to our example environments). NOTE: There are samples shipped with the Unity Package. You only need to clone the repository if you would like to explore more examples. git clone --branch release_20 https://github.com/Unity-Technologies/ml-agents.git The --branch release_20 option will switch to the tag of the latest stable release. Omitting that will get the main branch which is potentially unstable.","title":"Clone the ML-Agents Toolkit Repository (Optional)"},{"location":"Installation/#advanced-local-installation-for-development","text":"You will need to clone the repository if you plan to modify or extend the ML-Agents Toolkit for your purposes. If you plan to contribute those changes back, make sure to clone the main branch (by omitting --branch release_20 from the command above). See our Contributions Guidelines for more information on contributing to the ML-Agents Toolkit.","title":"Advanced: Local Installation for Development"},{"location":"Installation/#install-the-comunityml-agents-unity-package","text":"The Unity ML-Agents C# SDK is a Unity Package. You can install the com.unity.ml-agents package directly from the Package Manager registry . Please make sure you enable 'Preview Packages' in the 'Advanced' dropdown in order to find the latest Preview release of the package. NOTE: If you do not see the ML-Agents package listed in the Package Manager please follow the advanced installation instructions below.","title":"Install the com.unity.ml-agents Unity package"},{"location":"Installation/#advanced-local-installation-for-development_1","text":"You can add the local com.unity.ml-agents package (from the repository that you just cloned) to your project by: navigating to the menu Window -> Package Manager . In the package manager window click on the + button on the top left of the packages list). Select Add package from disk... Navigate into the com.unity.ml-agents folder. Select the package.json file. If you are going to follow the examples from our documentation, you can open the Project folder in Unity and start tinkering immediately.","title":"Advanced: Local Installation for Development"},{"location":"Installation/#install-the-comunityml-agentsextensions-unity-package-optional","text":"To install the com.unity.ml-agents.extensions package, you need to first clone the repo and then complete a local installation similar to what was outlined in the previous Advanced: Local Installation for Development section. Complete installation steps can be found in the package documentation .","title":"Install the com.unity.ml-agents.extensions Unity package (Optional)"},{"location":"Installation/#install-the-mlagents-python-package","text":"Installing the mlagents Python package involves installing other Python packages that mlagents depends on. So you may run into installation issues if your machine has older versions of any of those dependencies already installed. Consequently, our supported path for installing mlagents is to leverage Python Virtual Environments. Virtual Environments provide a mechanism for isolating the dependencies for each project and are supported on Mac / Windows / Linux. We offer a dedicated guide on Virtual Environments .","title":"Install the mlagents Python package"},{"location":"Installation/#windows-installing-pytorch","text":"On Windows, you'll have to install the PyTorch package separately prior to installing ML-Agents. Activate your virtual environment and run from the command line: pip3 install torch~=1.7.1 -f https://download.pytorch.org/whl/torch_stable.html Note that on Windows, you may also need Microsoft's Visual C++ Redistributable if you don't have it already. See the PyTorch installation guide for more installation options and versions.","title":"(Windows) Installing PyTorch"},{"location":"Installation/#installing-mlagents","text":"To install the mlagents Python package, activate your virtual environment and run from the command line: python -m pip install mlagents==0.30.0 Note that this will install mlagents from PyPi, not from the cloned repository. If you installed this correctly, you should be able to run mlagents-learn --help , after which you will see the command line parameters you can use with mlagents-learn . By installing the mlagents package, the dependencies listed in the setup.py file are also installed. These include PyTorch (Requires a CPU w/ AVX support).","title":"Installing mlagents"},{"location":"Installation/#advanced-local-installation-for-development_2","text":"If you intend to make modifications to mlagents or mlagents_envs , you should install the packages from the cloned repository rather than from PyPi. To do this, you will need to install mlagents and mlagents_envs separately. From the repository's root directory, run: pip3 install torch -f https://download.pytorch.org/whl/torch_stable.html pip3 install -e ./ml-agents-envs pip3 install -e ./ml-agents Running pip with the -e flag will let you make changes to the Python files directly and have those reflected when you run mlagents-learn . It is important to install these packages in this order as the mlagents package depends on mlagents_envs , and installing it in the other order will download mlagents_envs from PyPi.","title":"Advanced: Local Installation for Development"},{"location":"Installation/#next-steps","text":"The Getting Started guide contains several short tutorials on setting up the ML-Agents Toolkit within Unity, running a pre-trained model, in addition to building and training environments.","title":"Next Steps"},{"location":"Installation/#help","text":"If you run into any problems regarding ML-Agents, refer to our FAQ and our Limitations pages. If you can't find anything please submit an issue and make sure to cite relevant information on OS, Python version, and exact error message (whenever possible).","title":"Help"},{"location":"Integrations-Match3/","text":"Match-3 with ML-Agents Getting started The C# code for Match-3 exists inside of the Unity package ( com.unity.ml-agents ). The good first step would be to take a look at how we have implemented the C# code in the example Match-3 scene (located under /Project/Assets/ML-Agents/Examples/match3). Once you have some familiarity, then the next step would be to implement the C# code for Match-3 from the extensions package. Additionally, see below for additional technical specifications on the C# code for Match-3. Please note the Match-3 game isn't human playable as implemented and can be only played via training. Technical specifications for Match-3 with ML-Agents AbstractBoard class The AbstractBoard is the bridge between ML-Agents and your game. It allows ML-Agents to * ask your game what the current and maximum sizes (rows, columns, and potential piece types) of the board are * ask your game what the \"color\" of a cell is * ask whether the cell is a \"special\" piece type or not * ask your game whether a move is allowed * request that your game make a move These are handled by implementing the abstract methods of AbstractBoard . public abstract BoardSize GetMaxBoardSize() Returns the largest BoardSize that the game can use. This is used to determine the sizes of observations and sensors, so don't make it larger than necessary. public virtual BoardSize GetCurrentBoardSize() Returns the current size of the board. Each field on this BoardSize must be less than or equal to the corresponding field returned by GetMaxBoardSize() . This method is optional; if your always use the same size board, you don't need to override it. If the current board size is smaller than the maximum board size, GetCellType() and GetSpecialType() will not be called for cells outside the current board size, and IsValidMove won't be called for moves that would go outside of the current board size. public abstract int GetCellType(int row, int col) Returns the \"color\" of piece at the given row and column. This should be between 0 and BoardSize.NumCellTypes-1 (inclusive). The actual order of the values doesn't matter. public abstract int GetSpecialType(int row, int col) Returns the special type of the piece at the given row and column. This should be between 0 and BoardSize.NumSpecialTypes (inclusive). The actual order of the values doesn't matter. public abstract bool IsMoveValid(Move m) Check whether the particular Move is valid for the game. The actual results will depend on the rules of the game, but we provide the SimpleIsMoveValid() method that handles basic match3 rules with no special or immovable pieces. public abstract bool MakeMove(Move m) Instruct the game to make the given move. Returns true if the move was made. Note that during training, a move that was marked as invalid may occasionally still be requested. If this happens, it is safe to do nothing and request another move. Move struct The Move struct encapsulates a swap of two adjacent cells. You can get the number of potential moves for a board of a given size with. Move.NumPotentialMoves(maxBoardSize) . There are two helper functions to create a new Move : * public static Move FromMoveIndex(int moveIndex, BoardSize maxBoardSize) can be used to iterate over all potential moves for the board by looping from 0 to Move.NumPotentialMoves() * public static Move FromPositionAndDirection(int row, int col, Direction dir, BoardSize maxBoardSize) creates a Move from a row, column, and direction (and board size). BoardSize struct Describes the \"size\" of the board, including the number of potential piece types that the board can have. This is returned by the AbstractBoard.GetMaxBoardSize() and GetCurrentBoardSize() methods. Match3Sensor and Match3SensorComponent classes The Match3Sensor generates observations about the state using the AbstractBoard interface. You can choose whether to use vector or \"visual\" observations; in theory, visual observations should perform better because they are 2-dimensional like the board, but we need to experiment more on this. A Match3SensorComponent generates Match3Sensor s (the exact number of sensors depends on your configuration) at runtime, and should be added to the same GameObject as your Agent implementation. You do not need to write any additional code to use them. Match3Actuator and Match3ActuatorComponent classes The Match3Actuator converts actions from training or inference into a Move that is sent to AbstractBoard.MakeMove() It also checks AbstractBoard.IsMoveValid for each potential move and uses this to set the action mask for Agent. A Match3ActuatorComponent generates a Match3Actuator at runtime, and should be added to the same GameObject as your Agent implementation. You do not need to write any additional code to use them. Setting up Match-3 simulation Implement the AbstractBoard methods to integrate with your game. Give the Agent rewards when it does what you want it to (match multiple pieces in a row, clears pieces of a certain type, etc). Add the Agent , AbstractBoard implementation, Match3SensorComponent , and Match3ActuatorComponent to the same GameObject . Call Agent.RequestDecision() when you're ready for the Agent to make a move on the next Academy step. During the next Academy step, the MakeMove() method on the board will be called. Implementation Details Action Space The indexing for actions is the same as described in Human Like Playtesting with Deep Learning (for example, Figure 2b). The horizontal moves are enumerated first, then the vertical ones. Feedback If you are a Match-3 developer and are trying to leverage ML-Agents for this scenario, we want to hear from you . Additionally, we are also looking for interested Match-3 teams to speak with us for 45 minutes. If you are interested, please indicate that in the form . If selected, we will provide gift cards as a token of appreciation.","title":"Match-3 with ML-Agents"},{"location":"Integrations-Match3/#match-3-with-ml-agents","text":"","title":"Match-3 with ML-Agents"},{"location":"Integrations-Match3/#getting-started","text":"The C# code for Match-3 exists inside of the Unity package ( com.unity.ml-agents ). The good first step would be to take a look at how we have implemented the C# code in the example Match-3 scene (located under /Project/Assets/ML-Agents/Examples/match3). Once you have some familiarity, then the next step would be to implement the C# code for Match-3 from the extensions package. Additionally, see below for additional technical specifications on the C# code for Match-3. Please note the Match-3 game isn't human playable as implemented and can be only played via training.","title":"Getting started"},{"location":"Integrations-Match3/#technical-specifications-for-match-3-with-ml-agents","text":"","title":"Technical specifications for Match-3 with ML-Agents"},{"location":"Integrations-Match3/#abstractboard-class","text":"The AbstractBoard is the bridge between ML-Agents and your game. It allows ML-Agents to * ask your game what the current and maximum sizes (rows, columns, and potential piece types) of the board are * ask your game what the \"color\" of a cell is * ask whether the cell is a \"special\" piece type or not * ask your game whether a move is allowed * request that your game make a move These are handled by implementing the abstract methods of AbstractBoard .","title":"AbstractBoard class"},{"location":"Integrations-Match3/#public-abstract-boardsize-getmaxboardsize","text":"Returns the largest BoardSize that the game can use. This is used to determine the sizes of observations and sensors, so don't make it larger than necessary.","title":"public abstract BoardSize GetMaxBoardSize()"},{"location":"Integrations-Match3/#public-virtual-boardsize-getcurrentboardsize","text":"Returns the current size of the board. Each field on this BoardSize must be less than or equal to the corresponding field returned by GetMaxBoardSize() . This method is optional; if your always use the same size board, you don't need to override it. If the current board size is smaller than the maximum board size, GetCellType() and GetSpecialType() will not be called for cells outside the current board size, and IsValidMove won't be called for moves that would go outside of the current board size.","title":"public virtual BoardSize GetCurrentBoardSize()"},{"location":"Integrations-Match3/#public-abstract-int-getcelltypeint-row-int-col","text":"Returns the \"color\" of piece at the given row and column. This should be between 0 and BoardSize.NumCellTypes-1 (inclusive). The actual order of the values doesn't matter.","title":"public abstract int GetCellType(int row, int col)"},{"location":"Integrations-Match3/#public-abstract-int-getspecialtypeint-row-int-col","text":"Returns the special type of the piece at the given row and column. This should be between 0 and BoardSize.NumSpecialTypes (inclusive). The actual order of the values doesn't matter.","title":"public abstract int GetSpecialType(int row, int col)"},{"location":"Integrations-Match3/#public-abstract-bool-ismovevalidmove-m","text":"Check whether the particular Move is valid for the game. The actual results will depend on the rules of the game, but we provide the SimpleIsMoveValid() method that handles basic match3 rules with no special or immovable pieces.","title":"public abstract bool IsMoveValid(Move m)"},{"location":"Integrations-Match3/#public-abstract-bool-makemovemove-m","text":"Instruct the game to make the given move. Returns true if the move was made. Note that during training, a move that was marked as invalid may occasionally still be requested. If this happens, it is safe to do nothing and request another move.","title":"public abstract bool MakeMove(Move m)"},{"location":"Integrations-Match3/#move-struct","text":"The Move struct encapsulates a swap of two adjacent cells. You can get the number of potential moves for a board of a given size with. Move.NumPotentialMoves(maxBoardSize) . There are two helper functions to create a new Move : * public static Move FromMoveIndex(int moveIndex, BoardSize maxBoardSize) can be used to iterate over all potential moves for the board by looping from 0 to Move.NumPotentialMoves() * public static Move FromPositionAndDirection(int row, int col, Direction dir, BoardSize maxBoardSize) creates a Move from a row, column, and direction (and board size).","title":"Move struct"},{"location":"Integrations-Match3/#boardsize-struct","text":"Describes the \"size\" of the board, including the number of potential piece types that the board can have. This is returned by the AbstractBoard.GetMaxBoardSize() and GetCurrentBoardSize() methods.","title":"BoardSize struct"},{"location":"Integrations-Match3/#match3sensor-and-match3sensorcomponent-classes","text":"The Match3Sensor generates observations about the state using the AbstractBoard interface. You can choose whether to use vector or \"visual\" observations; in theory, visual observations should perform better because they are 2-dimensional like the board, but we need to experiment more on this. A Match3SensorComponent generates Match3Sensor s (the exact number of sensors depends on your configuration) at runtime, and should be added to the same GameObject as your Agent implementation. You do not need to write any additional code to use them.","title":"Match3Sensor and Match3SensorComponent classes"},{"location":"Integrations-Match3/#match3actuator-and-match3actuatorcomponent-classes","text":"The Match3Actuator converts actions from training or inference into a Move that is sent to AbstractBoard.MakeMove() It also checks AbstractBoard.IsMoveValid for each potential move and uses this to set the action mask for Agent. A Match3ActuatorComponent generates a Match3Actuator at runtime, and should be added to the same GameObject as your Agent implementation. You do not need to write any additional code to use them.","title":"Match3Actuator and Match3ActuatorComponent classes"},{"location":"Integrations-Match3/#setting-up-match-3-simulation","text":"Implement the AbstractBoard methods to integrate with your game. Give the Agent rewards when it does what you want it to (match multiple pieces in a row, clears pieces of a certain type, etc). Add the Agent , AbstractBoard implementation, Match3SensorComponent , and Match3ActuatorComponent to the same GameObject . Call Agent.RequestDecision() when you're ready for the Agent to make a move on the next Academy step. During the next Academy step, the MakeMove() method on the board will be called.","title":"Setting up Match-3 simulation"},{"location":"Integrations-Match3/#implementation-details","text":"","title":"Implementation Details"},{"location":"Integrations-Match3/#action-space","text":"The indexing for actions is the same as described in Human Like Playtesting with Deep Learning (for example, Figure 2b). The horizontal moves are enumerated first, then the vertical ones.","title":"Action Space"},{"location":"Integrations-Match3/#feedback","text":"If you are a Match-3 developer and are trying to leverage ML-Agents for this scenario, we want to hear from you . Additionally, we are also looking for interested Match-3 teams to speak with us for 45 minutes. If you are interested, please indicate that in the form . If selected, we will provide gift cards as a token of appreciation.","title":"Feedback"},{"location":"Integrations/","text":"Game Integrations ML-Agents provides some utilities to make it easier to integrate with some common genres of games. Match-3 The Match-3 integration provides an abstraction of a match-3 game board and moves, along with a sensor to observe the game state, and an actuator to translate the ML-Agent actions into game moves. Interested in more game templates? Do you have a type of game you are interested for ML-Agents? If so, please post a forum issue with [GAME TEMPLATE] in the title.","title":"Game Integrations"},{"location":"Integrations/#game-integrations","text":"ML-Agents provides some utilities to make it easier to integrate with some common genres of games.","title":"Game Integrations"},{"location":"Integrations/#match-3","text":"The Match-3 integration provides an abstraction of a match-3 game board and moves, along with a sensor to observe the game state, and an actuator to translate the ML-Agent actions into game moves.","title":"Match-3"},{"location":"Integrations/#interested-in-more-game-templates","text":"Do you have a type of game you are interested for ML-Agents? If so, please post a forum issue with [GAME TEMPLATE] in the title.","title":"Interested in more game templates?"},{"location":"LICENSE/","text":"Copyright 2017-2021 Unity Technologies Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. 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We also recommend that a file or class name and description of purpose be included on the same \"printed page\" as the copyright notice for easier identification within third-party archives.","title":"LICENSE"},{"location":"Learning-Environment-Create-New/","text":"Making a New Learning Environment This tutorial walks through the process of creating a Unity Environment from scratch. We recommend first reading the Getting Started guide to understand the concepts presented here first in an already-built environment. In this example, we will create an agent capable of controlling a ball on a platform. We will then train the agent to roll the ball toward the cube while avoiding falling off the platform. Overview Using the ML-Agents Toolkit in a Unity project involves the following basic steps: Create an environment for your agents to live in. An environment can range from a simple physical simulation containing a few objects to an entire game or ecosystem. Implement your Agent subclasses. An Agent subclass defines the code an Agent uses to observe its environment, to carry out assigned actions, and to calculate the rewards used for reinforcement training. You can also implement optional methods to reset the Agent when it has finished or failed its task. Add your Agent subclasses to appropriate GameObjects, typically, the object in the scene that represents the Agent in the simulation. Note: If you are unfamiliar with Unity, refer to the Unity manual if an Editor task isn't explained sufficiently in this tutorial. If you haven't already, follow the installation instructions . Set Up the Unity Project The first task to accomplish is simply creating a new Unity project and importing the ML-Agents assets into it: Launch Unity Hub and create a new 3D project named \"RollerBall\". Add the ML-Agents Unity package to your project. Your Unity Project window should contain the following assets: Create the Environment Next, we will create a very simple scene to act as our learning environment. The \"physical\" components of the environment include a Plane to act as the floor for the Agent to move around on, a Cube to act as the goal or target for the agent to seek, and a Sphere to represent the Agent itself. Create the Floor Plane Right click in Hierarchy window, select 3D Object > Plane. Name the GameObject \"Floor\". Select the Floor Plane to view its properties in the Inspector window. Set Transform to Position = (0, 0, 0) , Rotation = (0, 0, 0) , Scale = (1, 1, 1) . Add the Target Cube Right click in Hierarchy window, select 3D Object > Cube. Name the GameObject \"Target\". Select the Target Cube to view its properties in the Inspector window. Set Transform to Position = (3, 0.5, 3) , Rotation = (0, 0, 0) , Scale = (1, 1, 1) . Add the Agent Sphere Right click in Hierarchy window, select 3D Object > Sphere. Name the GameObject \"RollerAgent\". Select the RollerAgent Sphere to view its properties in the Inspector window. Set Transform to Position = (0, 0.5, 0) , Rotation = (0, 0, 0) , Scale = (1, 1, 1) . Click Add Component . Add the Rigidbody component to the Sphere. Group into Training Area Group the floor, target and agent under a single, empty, GameObject. This will simplify some of our subsequent steps. To do so: Right-click on your Project Hierarchy and create a new empty GameObject. Name it TrainingArea. Reset the TrainingArea\u2019s Transform so that it is at (0,0,0) with Rotation (0,0,0) and Scale (1,1,1) . Drag the Floor, Target, and RollerAgent GameObjects in the Hierarchy into the TrainingArea GameObject. Implement an Agent To create the Agent Script: Select the RollerAgent GameObject to view it in the Inspector window. Click Add Component . Click New Script in the list of components (at the bottom). Name the script \"RollerAgent\". Click Create and Add . Then, edit the new RollerAgent script: In the Unity Project window, double-click the RollerAgent script to open it in your code editor. Import ML-Agent package by adding csharp using Unity.MLAgents; using Unity.MLAgents.Sensors; using Unity.MLAgents.Actuators; then change the base class from MonoBehaviour to Agent . 1. Delete Update() since we are not using it, but keep Start() . So far, these are the basic steps that you would use to add ML-Agents to any Unity project. Next, we will add the logic that will let our Agent learn to roll to the cube using reinforcement learning. More specifically, we will need to extend three methods from the Agent base class: OnEpisodeBegin() CollectObservations(VectorSensor sensor) OnActionReceived(ActionBuffers actionBuffers) We overview each of these in more detail in the dedicated subsections below. Initialization and Resetting the Agent The process of training in the ML-Agents Toolkit involves running episodes where the Agent (Sphere) attempts to solve the task. Each episode lasts until the Agents solves the task (i.e. reaches the cube), fails (rolls off the platform) or times out (takes too long to solve or fail at the task). At the start of each episode, OnEpisodeBegin() is called to set-up the environment for a new episode. Typically the scene is initialized in a random manner to enable the agent to learn to solve the task under a variety of conditions. In this example, each time the Agent (Sphere) reaches its target (Cube), the episode ends and the target (Cube) is moved to a new random location; and if the Agent rolls off the platform, it will be put back onto the floor. These are all handled in OnEpisodeBegin() . To move the target (Cube), we need a reference to its Transform (which stores a GameObject's position, orientation and scale in the 3D world). To get this reference, add a public field of type Transform to the RollerAgent class. Public fields of a component in Unity get displayed in the Inspector window, allowing you to choose which GameObject to use as the target in the Unity Editor. To reset the Agent's velocity (and later to apply force to move the agent) we need a reference to the Rigidbody component. A Rigidbody is Unity's primary element for physics simulation. (See Physics for full documentation of Unity physics.) Since the Rigidbody component is on the same GameObject as our Agent script, the best way to get this reference is using GameObject.GetComponent<T>() , which we can call in our script's Start() method. So far, our RollerAgent script looks like: using System.Collections.Generic; using UnityEngine; using Unity.MLAgents; using Unity.MLAgents.Sensors; public class RollerAgent : Agent { Rigidbody rBody; void Start () { rBody = GetComponent<Rigidbody>(); } public Transform Target; public override void OnEpisodeBegin() { // If the Agent fell, zero its momentum if (this.transform.localPosition.y < 0) { this.rBody.angularVelocity = Vector3.zero; this.rBody.velocity = Vector3.zero; this.transform.localPosition = new Vector3( 0, 0.5f, 0); } // Move the target to a new spot Target.localPosition = new Vector3(Random.value * 8 - 4, 0.5f, Random.value * 8 - 4); } } Next, let's implement the Agent.CollectObservations(VectorSensor sensor) method. Observing the Environment The Agent sends the information we collect to the Brain, which uses it to make a decision. When you train the Agent (or use a trained model), the data is fed into a neural network as a feature vector. For an Agent to successfully learn a task, we need to provide the correct information. A good rule of thumb for deciding what information to collect is to consider what you would need to calculate an analytical solution to the problem. In our case, the information our Agent collects includes the position of the target, the position of the agent itself, and the velocity of the agent. This helps the Agent learn to control its speed so it doesn't overshoot the target and roll off the platform. In total, the agent observation contains 8 values as implemented below: public override void CollectObservations(VectorSensor sensor) { // Target and Agent positions sensor.AddObservation(Target.localPosition); sensor.AddObservation(this.transform.localPosition); // Agent velocity sensor.AddObservation(rBody.velocity.x); sensor.AddObservation(rBody.velocity.z); } Taking Actions and Assigning Rewards The final part of the Agent code is the Agent.OnActionReceived() method, which receives actions and assigns the reward. Actions To solve the task of moving towards the target, the Agent (Sphere) needs to be able to move in the x and z directions. As such, the agent needs 2 actions: the first determines the force applied along the x-axis; and the second determines the force applied along the z-axis. (If we allowed the Agent to move in three dimensions, then we would need a third action.) The RollerAgent applies the values from the action[] array to its Rigidbody component rBody , using Rigidbody.AddForce() : Vector3 controlSignal = Vector3.zero; controlSignal.x = action[0]; controlSignal.z = action[1]; rBody.AddForce(controlSignal * forceMultiplier); Rewards Reinforcement learning requires rewards to signal which decisions are good and which are bad. The learning algorithm uses the rewards to determine whether it is giving the Agent the optimal actions. You want to reward an Agent for completing the assigned task. In this case, the Agent is given a reward of 1.0 for reaching the Target cube. Rewards are assigned in OnActionReceived() . The RollerAgent calculates the distance to detect when it reaches the target. When it does, the code calls Agent.SetReward() to assign a reward of 1.0 and marks the agent as finished by calling EndEpisode() on the Agent. float distanceToTarget = Vector3.Distance(this.transform.localPosition, Target.localPosition); // Reached target if (distanceToTarget < 1.42f) { SetReward(1.0f); EndEpisode(); } Finally, if the Agent falls off the platform, end the episode so that it can reset itself: // Fell off platform if (this.transform.localPosition.y < 0) { EndEpisode(); } OnActionReceived() With the action and reward logic outlined above, the final version of OnActionReceived() looks like: public float forceMultiplier = 10; public override void OnActionReceived(ActionBuffers actionBuffers) { // Actions, size = 2 Vector3 controlSignal = Vector3.zero; controlSignal.x = actionBuffers.ContinuousActions[0]; controlSignal.z = actionBuffers.ContinuousActions[1]; rBody.AddForce(controlSignal * forceMultiplier); // Rewards float distanceToTarget = Vector3.Distance(this.transform.localPosition, Target.localPosition); // Reached target if (distanceToTarget < 1.42f) { SetReward(1.0f); EndEpisode(); } // Fell off platform else if (this.transform.localPosition.y < 0) { EndEpisode(); } } Note the forceMultiplier class variable is defined before the method definition. Since forceMultiplier is public, you can set the value from the Inspector window. Final Agent Setup in Editor Now that all the GameObjects and ML-Agent components are in place, it is time to connect everything together in the Unity Editor. This involves adding and setting some of the Agent Component's properties so that they are compatible with our Agent script. Select the RollerAgent GameObject to show its properties in the Inspector window. Drag the Target GameObject in the Hierarchy into the Target field in RollerAgent Script. Add a Decision Requester script with the Add Component button. Set the Decision Period to 10 . For more information on decisions, see the Agent documentation Add a Behavior Parameters script with the Add Component button. Set the Behavior Parameters of the Agent to the following: Behavior Name : RollerBall Vector Observation > Space Size = 8 Actions > Continuous Actions = 2 In the inspector, the RollerAgent should look like this now: Now you are ready to test the environment before training. Testing the Environment It is always a good idea to first test your environment by controlling the Agent using the keyboard. To do so, you will need to extend the Heuristic() method in the RollerAgent class. For our example, the heuristic will generate an action corresponding to the values of the \"Horizontal\" and \"Vertical\" input axis (which correspond to the keyboard arrow keys): public override void Heuristic(in ActionBuffers actionsOut) { var continuousActionsOut = actionsOut.ContinuousActions; continuousActionsOut[0] = Input.GetAxis(\"Horizontal\"); continuousActionsOut[1] = Input.GetAxis(\"Vertical\"); } In order for the Agent to use the Heuristic, You will need to set the Behavior Type to Heuristic Only in the Behavior Parameters of the RollerAgent. Press Play to run the scene and use the arrows keys to move the Agent around the platform. Make sure that there are no errors displayed in the Unity Editor Console window and that the Agent resets when it reaches its target or falls from the platform. Training the Environment The process is the same as described in the Getting Started Guide . The hyperparameters for training are specified in a configuration file that you pass to the mlagents-learn program. Create a new rollerball_config.yaml file under config/ and include the following hyperparameter values: behaviors: RollerBall: trainer_type: ppo hyperparameters: batch_size: 10 buffer_size: 100 learning_rate: 3.0e-4 beta: 5.0e-4 epsilon: 0.2 lambd: 0.99 num_epoch: 3 learning_rate_schedule: linear beta_schedule: constant epsilon_schedule: linear network_settings: normalize: false hidden_units: 128 num_layers: 2 reward_signals: extrinsic: gamma: 0.99 strength: 1.0 max_steps: 500000 time_horizon: 64 summary_freq: 10000 Hyperparameters are explained in the training configuration file documentation Since this example creates a very simple training environment with only a few inputs and outputs, using small batch and buffer sizes speeds up the training considerably. However, if you add more complexity to the environment or change the reward or observation functions, you might also find that training performs better with different hyperparameter values. In addition to setting these hyperparameter values, the Agent DecisionFrequency parameter has a large effect on training time and success. A larger value reduces the number of decisions the training algorithm has to consider and, in this simple environment, speeds up training. To train your agent, run the following command before pressing Play in the Editor: mlagents-learn config/rollerball_config.yaml --run-id=RollerBall To monitor the statistics of Agent performance during training, use TensorBoard . In particular, the cumulative_reward and value_estimate statistics show how well the Agent is achieving the task. In this example, the maximum reward an Agent can earn is 1.0, so these statistics approach that value when the Agent has successfully solved the problem. Optional: Multiple Training Areas within the Same Scene In many of the example environments , many copies of the training area are instantiated in the scene. This generally speeds up training, allowing the environment to gather many experiences in parallel. This can be achieved simply by instantiating many Agents with the same Behavior Name . Note that we've already simplified our transition to using multiple areas by creating the TrainingArea GameObject and relying on local positions in RollerAgent.cs . Use the following steps to parallelize your RollerBall environment: Drag the TrainingArea GameObject, along with its attached GameObjects, into your Assets browser, turning it into a prefab. You can now instantiate copies of the TrainingArea prefab. Drag them into your scene, positioning them so that they do not overlap. Alternatively, you can use the TrainingAreaReplicator to replicate training areas. Use the following steps: Create a new empty Game Object in the scene. Click on the new object and add a TrainingAreaReplicator component to the empty Game Object through the inspector. Drag the training area to Base Area in the Training Area Replicator. Specify the number of areas to replicate and the separation between areas. Hit play and the areas will be replicated automatically! Optional: Training Using Concurrent Unity Instances Another level of parallelization comes by training using concurrent Unity instances . For example, mlagents-learn config/rollerball_config.yaml --run-id=RollerBall --num-envs=2 will start ML Agents training with two environment instances. Combining multiple training areas within the same scene, with concurrent Unity instances, effectively gives you two levels of parallelism to speed up training. The command line option --num-envs=<n> controls the number of concurrent Unity instances that are executed in parallel during training.","title":"Making a New Learning Environment"},{"location":"Learning-Environment-Create-New/#making-a-new-learning-environment","text":"This tutorial walks through the process of creating a Unity Environment from scratch. We recommend first reading the Getting Started guide to understand the concepts presented here first in an already-built environment. In this example, we will create an agent capable of controlling a ball on a platform. We will then train the agent to roll the ball toward the cube while avoiding falling off the platform.","title":"Making a New Learning Environment"},{"location":"Learning-Environment-Create-New/#overview","text":"Using the ML-Agents Toolkit in a Unity project involves the following basic steps: Create an environment for your agents to live in. An environment can range from a simple physical simulation containing a few objects to an entire game or ecosystem. Implement your Agent subclasses. An Agent subclass defines the code an Agent uses to observe its environment, to carry out assigned actions, and to calculate the rewards used for reinforcement training. You can also implement optional methods to reset the Agent when it has finished or failed its task. Add your Agent subclasses to appropriate GameObjects, typically, the object in the scene that represents the Agent in the simulation. Note: If you are unfamiliar with Unity, refer to the Unity manual if an Editor task isn't explained sufficiently in this tutorial. If you haven't already, follow the installation instructions .","title":"Overview"},{"location":"Learning-Environment-Create-New/#set-up-the-unity-project","text":"The first task to accomplish is simply creating a new Unity project and importing the ML-Agents assets into it: Launch Unity Hub and create a new 3D project named \"RollerBall\". Add the ML-Agents Unity package to your project. Your Unity Project window should contain the following assets:","title":"Set Up the Unity Project"},{"location":"Learning-Environment-Create-New/#create-the-environment","text":"Next, we will create a very simple scene to act as our learning environment. The \"physical\" components of the environment include a Plane to act as the floor for the Agent to move around on, a Cube to act as the goal or target for the agent to seek, and a Sphere to represent the Agent itself.","title":"Create the Environment"},{"location":"Learning-Environment-Create-New/#create-the-floor-plane","text":"Right click in Hierarchy window, select 3D Object > Plane. Name the GameObject \"Floor\". Select the Floor Plane to view its properties in the Inspector window. Set Transform to Position = (0, 0, 0) , Rotation = (0, 0, 0) , Scale = (1, 1, 1) .","title":"Create the Floor Plane"},{"location":"Learning-Environment-Create-New/#add-the-target-cube","text":"Right click in Hierarchy window, select 3D Object > Cube. Name the GameObject \"Target\". Select the Target Cube to view its properties in the Inspector window. Set Transform to Position = (3, 0.5, 3) , Rotation = (0, 0, 0) , Scale = (1, 1, 1) .","title":"Add the Target Cube"},{"location":"Learning-Environment-Create-New/#add-the-agent-sphere","text":"Right click in Hierarchy window, select 3D Object > Sphere. Name the GameObject \"RollerAgent\". Select the RollerAgent Sphere to view its properties in the Inspector window. Set Transform to Position = (0, 0.5, 0) , Rotation = (0, 0, 0) , Scale = (1, 1, 1) . Click Add Component . Add the Rigidbody component to the Sphere.","title":"Add the Agent Sphere"},{"location":"Learning-Environment-Create-New/#group-into-training-area","text":"Group the floor, target and agent under a single, empty, GameObject. This will simplify some of our subsequent steps. To do so: Right-click on your Project Hierarchy and create a new empty GameObject. Name it TrainingArea. Reset the TrainingArea\u2019s Transform so that it is at (0,0,0) with Rotation (0,0,0) and Scale (1,1,1) . Drag the Floor, Target, and RollerAgent GameObjects in the Hierarchy into the TrainingArea GameObject.","title":"Group into Training Area"},{"location":"Learning-Environment-Create-New/#implement-an-agent","text":"To create the Agent Script: Select the RollerAgent GameObject to view it in the Inspector window. Click Add Component . Click New Script in the list of components (at the bottom). Name the script \"RollerAgent\". Click Create and Add . Then, edit the new RollerAgent script: In the Unity Project window, double-click the RollerAgent script to open it in your code editor. Import ML-Agent package by adding csharp using Unity.MLAgents; using Unity.MLAgents.Sensors; using Unity.MLAgents.Actuators; then change the base class from MonoBehaviour to Agent . 1. Delete Update() since we are not using it, but keep Start() . So far, these are the basic steps that you would use to add ML-Agents to any Unity project. Next, we will add the logic that will let our Agent learn to roll to the cube using reinforcement learning. More specifically, we will need to extend three methods from the Agent base class: OnEpisodeBegin() CollectObservations(VectorSensor sensor) OnActionReceived(ActionBuffers actionBuffers) We overview each of these in more detail in the dedicated subsections below.","title":"Implement an Agent"},{"location":"Learning-Environment-Create-New/#initialization-and-resetting-the-agent","text":"The process of training in the ML-Agents Toolkit involves running episodes where the Agent (Sphere) attempts to solve the task. Each episode lasts until the Agents solves the task (i.e. reaches the cube), fails (rolls off the platform) or times out (takes too long to solve or fail at the task). At the start of each episode, OnEpisodeBegin() is called to set-up the environment for a new episode. Typically the scene is initialized in a random manner to enable the agent to learn to solve the task under a variety of conditions. In this example, each time the Agent (Sphere) reaches its target (Cube), the episode ends and the target (Cube) is moved to a new random location; and if the Agent rolls off the platform, it will be put back onto the floor. These are all handled in OnEpisodeBegin() . To move the target (Cube), we need a reference to its Transform (which stores a GameObject's position, orientation and scale in the 3D world). To get this reference, add a public field of type Transform to the RollerAgent class. Public fields of a component in Unity get displayed in the Inspector window, allowing you to choose which GameObject to use as the target in the Unity Editor. To reset the Agent's velocity (and later to apply force to move the agent) we need a reference to the Rigidbody component. A Rigidbody is Unity's primary element for physics simulation. (See Physics for full documentation of Unity physics.) Since the Rigidbody component is on the same GameObject as our Agent script, the best way to get this reference is using GameObject.GetComponent<T>() , which we can call in our script's Start() method. So far, our RollerAgent script looks like: using System.Collections.Generic; using UnityEngine; using Unity.MLAgents; using Unity.MLAgents.Sensors; public class RollerAgent : Agent { Rigidbody rBody; void Start () { rBody = GetComponent<Rigidbody>(); } public Transform Target; public override void OnEpisodeBegin() { // If the Agent fell, zero its momentum if (this.transform.localPosition.y < 0) { this.rBody.angularVelocity = Vector3.zero; this.rBody.velocity = Vector3.zero; this.transform.localPosition = new Vector3( 0, 0.5f, 0); } // Move the target to a new spot Target.localPosition = new Vector3(Random.value * 8 - 4, 0.5f, Random.value * 8 - 4); } } Next, let's implement the Agent.CollectObservations(VectorSensor sensor) method.","title":"Initialization and Resetting the Agent"},{"location":"Learning-Environment-Create-New/#observing-the-environment","text":"The Agent sends the information we collect to the Brain, which uses it to make a decision. When you train the Agent (or use a trained model), the data is fed into a neural network as a feature vector. For an Agent to successfully learn a task, we need to provide the correct information. A good rule of thumb for deciding what information to collect is to consider what you would need to calculate an analytical solution to the problem. In our case, the information our Agent collects includes the position of the target, the position of the agent itself, and the velocity of the agent. This helps the Agent learn to control its speed so it doesn't overshoot the target and roll off the platform. In total, the agent observation contains 8 values as implemented below: public override void CollectObservations(VectorSensor sensor) { // Target and Agent positions sensor.AddObservation(Target.localPosition); sensor.AddObservation(this.transform.localPosition); // Agent velocity sensor.AddObservation(rBody.velocity.x); sensor.AddObservation(rBody.velocity.z); }","title":"Observing the Environment"},{"location":"Learning-Environment-Create-New/#taking-actions-and-assigning-rewards","text":"The final part of the Agent code is the Agent.OnActionReceived() method, which receives actions and assigns the reward.","title":"Taking Actions and Assigning Rewards"},{"location":"Learning-Environment-Create-New/#actions","text":"To solve the task of moving towards the target, the Agent (Sphere) needs to be able to move in the x and z directions. As such, the agent needs 2 actions: the first determines the force applied along the x-axis; and the second determines the force applied along the z-axis. (If we allowed the Agent to move in three dimensions, then we would need a third action.) The RollerAgent applies the values from the action[] array to its Rigidbody component rBody , using Rigidbody.AddForce() : Vector3 controlSignal = Vector3.zero; controlSignal.x = action[0]; controlSignal.z = action[1]; rBody.AddForce(controlSignal * forceMultiplier);","title":"Actions"},{"location":"Learning-Environment-Create-New/#rewards","text":"Reinforcement learning requires rewards to signal which decisions are good and which are bad. The learning algorithm uses the rewards to determine whether it is giving the Agent the optimal actions. You want to reward an Agent for completing the assigned task. In this case, the Agent is given a reward of 1.0 for reaching the Target cube. Rewards are assigned in OnActionReceived() . The RollerAgent calculates the distance to detect when it reaches the target. When it does, the code calls Agent.SetReward() to assign a reward of 1.0 and marks the agent as finished by calling EndEpisode() on the Agent. float distanceToTarget = Vector3.Distance(this.transform.localPosition, Target.localPosition); // Reached target if (distanceToTarget < 1.42f) { SetReward(1.0f); EndEpisode(); } Finally, if the Agent falls off the platform, end the episode so that it can reset itself: // Fell off platform if (this.transform.localPosition.y < 0) { EndEpisode(); }","title":"Rewards"},{"location":"Learning-Environment-Create-New/#onactionreceived","text":"With the action and reward logic outlined above, the final version of OnActionReceived() looks like: public float forceMultiplier = 10; public override void OnActionReceived(ActionBuffers actionBuffers) { // Actions, size = 2 Vector3 controlSignal = Vector3.zero; controlSignal.x = actionBuffers.ContinuousActions[0]; controlSignal.z = actionBuffers.ContinuousActions[1]; rBody.AddForce(controlSignal * forceMultiplier); // Rewards float distanceToTarget = Vector3.Distance(this.transform.localPosition, Target.localPosition); // Reached target if (distanceToTarget < 1.42f) { SetReward(1.0f); EndEpisode(); } // Fell off platform else if (this.transform.localPosition.y < 0) { EndEpisode(); } } Note the forceMultiplier class variable is defined before the method definition. Since forceMultiplier is public, you can set the value from the Inspector window.","title":"OnActionReceived()"},{"location":"Learning-Environment-Create-New/#final-agent-setup-in-editor","text":"Now that all the GameObjects and ML-Agent components are in place, it is time to connect everything together in the Unity Editor. This involves adding and setting some of the Agent Component's properties so that they are compatible with our Agent script. Select the RollerAgent GameObject to show its properties in the Inspector window. Drag the Target GameObject in the Hierarchy into the Target field in RollerAgent Script. Add a Decision Requester script with the Add Component button. Set the Decision Period to 10 . For more information on decisions, see the Agent documentation Add a Behavior Parameters script with the Add Component button. Set the Behavior Parameters of the Agent to the following: Behavior Name : RollerBall Vector Observation > Space Size = 8 Actions > Continuous Actions = 2 In the inspector, the RollerAgent should look like this now: Now you are ready to test the environment before training.","title":"Final Agent Setup in Editor"},{"location":"Learning-Environment-Create-New/#testing-the-environment","text":"It is always a good idea to first test your environment by controlling the Agent using the keyboard. To do so, you will need to extend the Heuristic() method in the RollerAgent class. For our example, the heuristic will generate an action corresponding to the values of the \"Horizontal\" and \"Vertical\" input axis (which correspond to the keyboard arrow keys): public override void Heuristic(in ActionBuffers actionsOut) { var continuousActionsOut = actionsOut.ContinuousActions; continuousActionsOut[0] = Input.GetAxis(\"Horizontal\"); continuousActionsOut[1] = Input.GetAxis(\"Vertical\"); } In order for the Agent to use the Heuristic, You will need to set the Behavior Type to Heuristic Only in the Behavior Parameters of the RollerAgent. Press Play to run the scene and use the arrows keys to move the Agent around the platform. Make sure that there are no errors displayed in the Unity Editor Console window and that the Agent resets when it reaches its target or falls from the platform.","title":"Testing the Environment"},{"location":"Learning-Environment-Create-New/#training-the-environment","text":"The process is the same as described in the Getting Started Guide . The hyperparameters for training are specified in a configuration file that you pass to the mlagents-learn program. Create a new rollerball_config.yaml file under config/ and include the following hyperparameter values: behaviors: RollerBall: trainer_type: ppo hyperparameters: batch_size: 10 buffer_size: 100 learning_rate: 3.0e-4 beta: 5.0e-4 epsilon: 0.2 lambd: 0.99 num_epoch: 3 learning_rate_schedule: linear beta_schedule: constant epsilon_schedule: linear network_settings: normalize: false hidden_units: 128 num_layers: 2 reward_signals: extrinsic: gamma: 0.99 strength: 1.0 max_steps: 500000 time_horizon: 64 summary_freq: 10000 Hyperparameters are explained in the training configuration file documentation Since this example creates a very simple training environment with only a few inputs and outputs, using small batch and buffer sizes speeds up the training considerably. However, if you add more complexity to the environment or change the reward or observation functions, you might also find that training performs better with different hyperparameter values. In addition to setting these hyperparameter values, the Agent DecisionFrequency parameter has a large effect on training time and success. A larger value reduces the number of decisions the training algorithm has to consider and, in this simple environment, speeds up training. To train your agent, run the following command before pressing Play in the Editor: mlagents-learn config/rollerball_config.yaml --run-id=RollerBall To monitor the statistics of Agent performance during training, use TensorBoard . In particular, the cumulative_reward and value_estimate statistics show how well the Agent is achieving the task. In this example, the maximum reward an Agent can earn is 1.0, so these statistics approach that value when the Agent has successfully solved the problem.","title":"Training the Environment"},{"location":"Learning-Environment-Create-New/#optional-multiple-training-areas-within-the-same-scene","text":"In many of the example environments , many copies of the training area are instantiated in the scene. This generally speeds up training, allowing the environment to gather many experiences in parallel. This can be achieved simply by instantiating many Agents with the same Behavior Name . Note that we've already simplified our transition to using multiple areas by creating the TrainingArea GameObject and relying on local positions in RollerAgent.cs . Use the following steps to parallelize your RollerBall environment: Drag the TrainingArea GameObject, along with its attached GameObjects, into your Assets browser, turning it into a prefab. You can now instantiate copies of the TrainingArea prefab. Drag them into your scene, positioning them so that they do not overlap. Alternatively, you can use the TrainingAreaReplicator to replicate training areas. Use the following steps: Create a new empty Game Object in the scene. Click on the new object and add a TrainingAreaReplicator component to the empty Game Object through the inspector. Drag the training area to Base Area in the Training Area Replicator. Specify the number of areas to replicate and the separation between areas. Hit play and the areas will be replicated automatically!","title":"Optional: Multiple Training Areas within the Same Scene"},{"location":"Learning-Environment-Create-New/#optional-training-using-concurrent-unity-instances","text":"Another level of parallelization comes by training using concurrent Unity instances . For example, mlagents-learn config/rollerball_config.yaml --run-id=RollerBall --num-envs=2 will start ML Agents training with two environment instances. Combining multiple training areas within the same scene, with concurrent Unity instances, effectively gives you two levels of parallelism to speed up training. The command line option --num-envs=<n> controls the number of concurrent Unity instances that are executed in parallel during training.","title":"Optional: Training Using Concurrent Unity Instances"},{"location":"Learning-Environment-Design-Agents/","text":"Agents Table of Contents: Decisions Observations and Sensors Generating Observations Agent.CollectObservations() Observable Fields and Properties ISensor interface and SensorComponents Vector Observations One-hot encoding categorical information Normalization Stacking Vector Observation Summary & Best Practices Visual Observations Visual Observation Summary & Best Practices Raycast Observations RayCast Observation Summary & Best Practices Variable Length Observations Variable Length Observation Summary & Best Practices Goal Signal Goal Signal Summary & Best Practices Actions and Actuators Continuous Actions Discrete Actions Masking Discrete Actions Actions Summary & Best Practices Rewards Examples Rewards Summary & Best Practices Agent Properties Destroying an Agent Defining Multi-agent Scenarios Teams for Adversarial Scenarios Groups for Cooperative Scenarios Recording Demonstrations An agent is an entity that can observe its environment, decide on the best course of action using those observations, and execute those actions within its environment. Agents can be created in Unity by extending the Agent class. The most important aspects of creating agents that can successfully learn are the observations the agent collects, and the reward you assign to estimate the value of the agent's current state toward accomplishing its tasks. An Agent passes its observations to its Policy. The Policy then makes a decision and passes the chosen action back to the agent. Your agent code must execute the action, for example, move the agent in one direction or another. In order to train an agent using reinforcement learning , your agent must calculate a reward value at each action. The reward is used to discover the optimal decision-making policy. The Policy class abstracts out the decision making logic from the Agent itself so that you can use the same Policy in multiple Agents. How a Policy makes its decisions depends on the Behavior Parameters associated with the agent. If you set Behavior Type to Heuristic Only , the Agent will use its Heuristic() method to make decisions which can allow you to control the Agent manually or write your own Policy. If the Agent has a Model file, its Policy will use the neural network Model to take decisions. When you create an Agent, you should usually extend the base Agent class. This includes implementing the following methods: Agent.OnEpisodeBegin() \u2014 Called at the beginning of an Agent's episode, including at the beginning of the simulation. Agent.CollectObservations(VectorSensor sensor) \u2014 Called every step that the Agent requests a decision. This is one possible way for collecting the Agent's observations of the environment; see Generating Observations below for more options. Agent.OnActionReceived() \u2014 Called every time the Agent receives an action to take. Receives the action chosen by the Agent. It is also common to assign a reward in this method. Agent.Heuristic() - When the Behavior Type is set to Heuristic Only in the Behavior Parameters of the Agent, the Agent will use the Heuristic() method to generate the actions of the Agent. As such, the Heuristic() method writes to the array of floats provided to the Heuristic method as argument. Note : Do not create a new float array of action in the Heuristic() method, as this will prevent writing floats to the original action array. As a concrete example, here is how the Ball3DAgent class implements these methods: Agent.OnEpisodeBegin() \u2014 Resets the agent cube and ball to their starting positions. The function randomizes the reset values so that the training generalizes to more than a specific starting position and agent cube orientation. Agent.CollectObservations(VectorSensor sensor) \u2014 Adds information about the orientation of the agent cube, the ball velocity, and the relative position between the ball and the cube. Since the CollectObservations() method calls VectorSensor.AddObservation() such that vector size adds up to 8, the Behavior Parameters of the Agent are set with vector observation space with a state size of 8. Agent.OnActionReceived() \u2014 The action results in a small change in the agent cube's rotation at each step. In this example, an Agent receives a small positive reward for each step it keeps the ball on the agent cube's head and a larger, negative reward for dropping the ball. An Agent's episode is also ended when it drops the ball so that it will reset with a new ball for the next simulation step. Agent.Heuristic() - Converts the keyboard inputs into actions. Decisions The observation-decision-action-reward cycle repeats each time the Agent request a decision. Agents will request a decision when Agent.RequestDecision() is called. If you need the Agent to request decisions on its own at regular intervals, add a Decision Requester component to the Agent's GameObject. Making decisions at regular step intervals is generally most appropriate for physics-based simulations. For example, an agent in a robotic simulator that must provide fine-control of joint torques should make its decisions every step of the simulation. In games such as real-time strategy, where many agents make their decisions at regular intervals, the decision timing for each agent can be staggered by setting the DecisionStep parameter in the Decision Requester component for each agent. On the other hand, an agent that only needs to make decisions when certain game or simulation events occur, such as in a turn-based game, should call Agent.RequestDecision() manually. Observations and Sensors In order for an agent to learn, the observations should include all the information an agent needs to accomplish its task. Without sufficient and relevant information, an agent may learn poorly or may not learn at all. A reasonable approach for determining what information should be included is to consider what you would need to calculate an analytical solution to the problem, or what you would expect a human to be able to use to solve the problem. Generating Observations ML-Agents provides multiple ways for an Agent to make observations: 1. Overriding the Agent.CollectObservations() method and passing the observations to the provided VectorSensor . 1. Adding the [Observable] attribute to fields and properties on the Agent. 1. Implementing the ISensor interface, using a SensorComponent attached to the Agent to create the ISensor . Agent.CollectObservations() Agent.CollectObservations() is best used for aspects of the environment which are numerical and non-visual. The Policy class calls the CollectObservations(VectorSensor sensor) method of each Agent. Your implementation of this function must call VectorSensor.AddObservation to add vector observations. The VectorSensor.AddObservation method provides a number of overloads for adding common types of data to your observation vector. You can add Integers and booleans directly to the observation vector, as well as some common Unity data types such as Vector2 , Vector3 , and Quaternion . For examples of various state observation functions, you can look at the example environments included in the ML-Agents SDK. For instance, the 3DBall example uses the rotation of the platform, the relative position of the ball, and the velocity of the ball as its state observation. public GameObject ball; public override void CollectObservations(VectorSensor sensor) { // Orientation of the cube (2 floats) sensor.AddObservation(gameObject.transform.rotation.z); sensor.AddObservation(gameObject.transform.rotation.x); // Relative position of the ball to the cube (3 floats) sensor.AddObservation(ball.transform.position - gameObject.transform.position); // Velocity of the ball (3 floats) sensor.AddObservation(m_BallRb.velocity); // 8 floats total } As an experiment, you can remove the velocity components from the observation and retrain the 3DBall agent. While it will learn to balance the ball reasonably well, the performance of the agent without using velocity is noticeably worse. The observations passed to VectorSensor.AddObservation() must always contain the same number of elements must always be in the same order. If the number of observed entities in an environment can vary, you can pad the calls with zeros for any missing entities in a specific observation, or you can limit an agent's observations to a fixed subset. For example, instead of observing every enemy in an environment, you could only observe the closest five. Additionally, when you set up an Agent's Behavior Parameters in the Unity Editor, you must set the Vector Observations > Space Size to equal the number of floats that are written by CollectObservations() . Observable Fields and Properties Another approach is to define the relevant observations as fields or properties on your Agent class, and annotate them with an ObservableAttribute . For example, in the Ball3DHardAgent, the difference between positions could be observed by adding a property to the Agent: using Unity.MLAgents.Sensors.Reflection; public class Ball3DHardAgent : Agent { [Observable(numStackedObservations: 9)] Vector3 PositionDelta { get { return ball.transform.position - gameObject.transform.position; } } } ObservableAttribute currently supports most basic types (e.g. floats, ints, bools), as well as Vector2 , Vector3 , Vector4 , Quaternion , and enums. The behavior of ObservableAttribute s are controlled by the \"Observable Attribute Handling\" in the Agent's Behavior Parameters . The possible values for this are: * Ignore (default) - All ObservableAttributes on the Agent will be ignored. If there are no ObservableAttributes on the Agent, this will result in the fastest initialization time. * Exclude Inherited - Only members on the declared class will be examined; members that are inherited are ignored. This is a reasonable tradeoff between performance and flexibility. * Examine All All members on the class will be examined. This can lead to slower startup times. \"Exclude Inherited\" is generally sufficient, but if your Agent inherits from another Agent implementation that has Observable members, you will need to use \"Examine All\". Internally, ObservableAttribute uses reflection to determine which members of the Agent have ObservableAttributes, and also uses reflection to access the fields or invoke the properties at runtime. This may be slower than using CollectObservations or an ISensor, although this might not be enough to noticeably affect performance. NOTE : you do not need to adjust the Space Size in the Agent's Behavior Parameters when you add [Observable] fields or properties to an Agent, since their size can be computed before they are used. ISensor interface and SensorComponents The ISensor interface is generally intended for advanced users. The Write() method is used to actually generate the observation, but some other methods such as returning the shape of the observations must also be implemented. The SensorComponent abstract class is used to create the actual ISensor at runtime. It must be attached to the same GameObject as the Agent , or to a child GameObject . There are several SensorComponents provided in the API, including: - CameraSensorComponent - Uses images from a Camera as observations. - RenderTextureSensorComponent - Uses the content of a RenderTexture as observations. - RayPerceptionSensorComponent - Uses the information from set of ray casts as observations. - Match3SensorComponent - Uses the board of a Match-3 game as observations. - GridSensorComponent - Uses a set of box queries in a grid shape as observations. NOTE : you do not need to adjust the Space Size in the Agent's Behavior Parameters when using SensorComponents s. Internally, both Agent.CollectObservations and [Observable] attribute use an ISensors to write observations, although this is mostly abstracted from the user. Vector Observations Both Agent.CollectObservations() and ObservableAttribute s produce vector observations, which are represented at lists of float s. ISensor s can produce both vector observations and visual observations, which are multi-dimensional arrays of floats. Below are some additional considerations when dealing with vector observations: One-hot encoding categorical information Type enumerations should be encoded in the one-hot style. That is, add an element to the feature vector for each element of enumeration, setting the element representing the observed member to one and set the rest to zero. For example, if your enumeration contains [Sword, Shield, Bow] and the agent observes that the current item is a Bow, you would add the elements: 0, 0, 1 to the feature vector. The following code example illustrates how to add. enum ItemType { Sword, Shield, Bow, LastItem } public override void CollectObservations(VectorSensor sensor) { for (int ci = 0; ci < (int)ItemType.LastItem; ci++) { sensor.AddObservation((int)currentItem == ci ? 1.0f : 0.0f); } } VectorSensor also provides a two-argument function AddOneHotObservation() as a shortcut for one-hot style observations. The following example is identical to the previous one. enum ItemType { Sword, Shield, Bow, LastItem } const int NUM_ITEM_TYPES = (int)ItemType.LastItem + 1; public override void CollectObservations(VectorSensor sensor) { // The first argument is the selection index; the second is the // number of possibilities sensor.AddOneHotObservation((int)currentItem, NUM_ITEM_TYPES); } ObservableAttribute has built-in support for enums. Note that you don't need the LastItem placeholder in this case: enum ItemType { Sword, Shield, Bow } public class HeroAgent : Agent { [Observable] ItemType m_CurrentItem; } Normalization For the best results when training, you should normalize the components of your feature vector to the range [-1, +1] or [0, 1]. When you normalize the values, the PPO neural network can often converge to a solution faster. Note that it isn't always necessary to normalize to these recommended ranges, but it is considered a best practice when using neural networks. The greater the variation in ranges between the components of your observation, the more likely that training will be affected. To normalize a value to [0, 1], you can use the following formula: normalizedValue = (currentValue - minValue)/(maxValue - minValue) :warning: For vectors, you should apply the above formula to each component (x, y, and z). Note that this is not the same as using the Vector3.normalized property or Vector3.Normalize() method in Unity (and similar for Vector2 ). Rotations and angles should also be normalized. For angles between 0 and 360 degrees, you can use the following formulas: Quaternion rotation = transform.rotation; Vector3 normalized = rotation.eulerAngles / 180.0f - Vector3.one; // [-1,1] Vector3 normalized = rotation.eulerAngles / 360.0f; // [0,1] For angles that can be outside the range [0,360], you can either reduce the angle, or, if the number of turns is significant, increase the maximum value used in your normalization formula. Stacking Stacking refers to repeating observations from previous steps as part of a larger observation. For example, consider an Agent that generates these observations in four steps step 1: [0.1] step 2: [0.2] step 3: [0.3] step 4: [0.4] If we use a stack size of 3, the observations would instead be: step 1: [0.1, 0.0, 0.0] step 2: [0.2, 0.1, 0.0] step 3: [0.3, 0.2, 0.1] step 4: [0.4, 0.3, 0.2] (The observations are padded with zeroes for the first stackSize-1 steps). This is a simple way to give an Agent limited \"memory\" without the complexity of adding a recurrent neural network (RNN). The steps for enabling stacking depends on how you generate observations: * For Agent.CollectObservations(), set \"Stacked Vectors\" on the Agent's Behavior Parameters to a value greater than 1. * For ObservableAttribute, set the numStackedObservations parameter in the constructor, e.g. [Observable(numStackedObservations: 2)] . * For ISensor s, wrap them in a StackingSensor (which is also an ISensor ). Generally, this should happen in the CreateSensor() method of your SensorComponent . Vector Observation Summary & Best Practices Vector Observations should include all variables relevant for allowing the agent to take the optimally informed decision, and ideally no extraneous information. In cases where Vector Observations need to be remembered or compared over time, either an RNN should be used in the model, or the Stacked Vectors value in the agent GameObject's Behavior Parameters should be changed. Categorical variables such as type of object (Sword, Shield, Bow) should be encoded in one-hot fashion (i.e. 3 -> 0, 0, 1 ). This can be done automatically using the AddOneHotObservation() method of the VectorSensor , or using [Observable] on an enum field or property of the Agent. In general, all inputs should be normalized to be in the range 0 to +1 (or -1 to 1). For example, the x position information of an agent where the maximum possible value is maxValue should be recorded as VectorSensor.AddObservation(transform.position.x / maxValue); rather than VectorSensor.AddObservation(transform.position.x); . Positional information of relevant GameObjects should be encoded in relative coordinates wherever possible. This is often relative to the agent position. Visual Observations Visual observations are generally provided to agent via either a CameraSensor or RenderTextureSensor . These collect image information and transforms it into a 3D Tensor which can be fed into the convolutional neural network (CNN) of the agent policy. For more information on CNNs, see this guide . This allows agents to learn from spatial regularities in the observation images. It is possible to use visual and vector observations with the same agent. Agents using visual observations can capture state of arbitrary complexity and are useful when the state is difficult to describe numerically. However, they are also typically less efficient and slower to train, and sometimes don't succeed at all as compared to vector observations. As such, they should only be used when it is not possible to properly define the problem using vector or ray-cast observations. Visual observations can be derived from Cameras or RenderTextures within your scene. To add a visual observation to an Agent, add either a Camera Sensor Component or RenderTextures Sensor Component to the Agent. Then drag the camera or render texture you want to add to the Camera or RenderTexture field. You can have more than one camera or render texture and even use a combination of both attached to an Agent. For each visual observation, set the width and height of the image (in pixels) and whether or not the observation is color or grayscale. or Each Agent that uses the same Policy must have the same number of visual observations, and they must all have the same resolutions (including whether or not they are grayscale). Additionally, each Sensor Component on an Agent must have a unique name so that they can be sorted deterministically (the name must be unique for that Agent, but multiple Agents can have a Sensor Component with the same name). Visual observations also support stacking, by specifying Observation Stacks to a value greater than 1. The visual observations from the last stackSize steps will be stacked on the last dimension (channel dimension). When using RenderTexture visual observations, a handy feature for debugging is adding a Canvas , then adding a Raw Image with it's texture set to the Agent's RenderTexture . This will render the agent observation on the game screen. The GridWorld environment is an example on how to use a RenderTexture for both debugging and observation. Note that in this example, a Camera is rendered to a RenderTexture, which is then used for observations and debugging. To update the RenderTexture, the Camera must be asked to render every time a decision is requested within the game code. When using Cameras as observations directly, this is done automatically by the Agent. Visual Observation Summary & Best Practices To collect visual observations, attach CameraSensor or RenderTextureSensor components to the agent GameObject. Visual observations should generally only be used when vector observations are not sufficient. Image size should be kept as small as possible, without the loss of needed details for decision making. Images should be made grayscale in situations where color information is not needed for making informed decisions. Raycast Observations Raycasts are another possible method for providing observations to an agent. This can be easily implemented by adding a RayPerceptionSensorComponent3D (or RayPerceptionSensorComponent2D ) to the Agent GameObject. During observations, several rays (or spheres, depending on settings) are cast into the physics world, and the objects that are hit determine the observation vector that is produced. Both sensor components have several settings: Detectable Tags A list of strings corresponding to the types of objects that the Agent should be able to distinguish between. For example, in the WallJump example, we use \"wall\", \"goal\", and \"block\" as the list of objects to detect. Rays Per Direction Determines the number of rays that are cast. One ray is always cast forward, and this many rays are cast to the left and right. Max Ray Degrees The angle (in degrees) for the outermost rays. 90 degrees corresponds to the left and right of the agent. Sphere Cast Radius The size of the sphere used for sphere casting. If set to 0, rays will be used instead of spheres. Rays may be more efficient, especially in complex scenes. Ray Length The length of the casts Ray Layer Mask The LayerMask passed to the raycast or spherecast. This can be used to ignore certain types of objects when casting. Observation Stacks The number of previous results to \"stack\" with the cast results. Note that this can be independent of the \"Stacked Vectors\" setting in Behavior Parameters . Start Vertical Offset (3D only) The vertical offset of the ray start point. End Vertical Offset (3D only) The vertical offset of the ray end point. Alternating Ray Order Alternating is the default, it gives an order of (0, -delta, delta, -2 delta, 2 delta, ..., -n delta, n delta). If alternating is disabled the order is left to right (-n delta, -(n-1) delta, ..., -delta, 0, delta, ..., (n-1) delta, n delta). For general usage there is no difference but if using custom models the left-to-right layout that matches the spatial structuring can be preferred (e.g. for processing with conv nets). In the example image above, the Agent has two RayPerceptionSensorComponent3D s. Both use 3 Rays Per Direction and 90 Max Ray Degrees. One of the components had a vertical offset, so the Agent can tell whether it's clear to jump over the wall. The total size of the created observations is (Observation Stacks) * (1 + 2 * Rays Per Direction) * (Num Detectable Tags + 2) so the number of rays and tags should be kept as small as possible to reduce the amount of data used. Note that this is separate from the State Size defined in Behavior Parameters , so you don't need to worry about the formula above when setting the State Size. RayCast Observation Summary & Best Practices Attach RayPerceptionSensorComponent3D or RayPerceptionSensorComponent2D to use. This observation type is best used when there is relevant spatial information for the agent that doesn't require a fully rendered image to convey. Use as few rays and tags as necessary to solve the problem in order to improve learning stability and agent performance. Grid Observations Grid-base observations combine the advantages of 2D spatial representation in visual observations, and the flexibility of defining detectable objects in RayCast observations. The sensor uses a set of box queries in a grid shape and gives a top-down 2D view around the agent. This can be implemented by adding a GridSensorComponent to the Agent GameObject. During observations, the sensor detects the presence of detectable objects in each cell and encode that into one-hot representation. The collected information from each cell forms a 3D tensor observation and will be fed into the convolutional neural network (CNN) of the agent policy just like visual observations. The sensor component has the following settings: - Cell Scale The scale of each cell in the grid. - Grid Size Number of cells on each side of the grid. - Agent Game Object The Agent that holds the grid sensor. This is used to disambiguate objects with the same tag as the agent so that the agent doesn't detect itself. - Rotate With Agent Whether the grid rotates with the Agent. - Detectable Tags A list of strings corresponding to the types of objects that the Agent should be able to distinguish between. - Collider Mask The LayerMask passed to the collider detection. This can be used to ignore certain types of objects. - Initial Collider Buffer Size The initial size of the Collider buffer used in the non-allocating Physics calls for each cell. - Max Collider Buffer Size The max size of the Collider buffer used in the non-allocating Physics calls for each cell. The observation for each grid cell is a one-hot encoding of the detected object. The total size of the created observations is GridSize.x * GridSize.z * Num Detectable Tags so the number of detectable tags and size of the grid should be kept as small as possible to reduce the amount of data used. This makes a trade-off between the granularity of the observation and training speed. To allow more variety of observations that grid sensor can capture, the GridSensorComponent and the underlying GridSensorBase also provides interfaces that can be overridden to collect customized observation from detected objects. See the doc on extending grid Sensors for more details on custom grid sensors. Note : The GridSensor only works in 3D environments and will not behave properly in 2D environments. Grid Observation Summary & Best Practices Attach GridSensorComponent to use. This observation type is best used when there is relevant non-visual spatial information that can be best captured in 2D representations. Use as small grid size and as few tags as necessary to solve the problem in order to improve learning stability and agent performance. Do not use GridSensor in a 2D game. Variable Length Observations It is possible for agents to collect observations from a varying number of GameObjects by using a BufferSensor . You can add a BufferSensor to your Agent by adding a BufferSensorComponent to its GameObject. The BufferSensor can be useful in situations in which the Agent must pay attention to a varying number of entities (for example, a varying number of enemies or projectiles). On the trainer side, the BufferSensor is processed using an attention module. More information about attention mechanisms can be found here . Training or doing inference with variable length observations can be slower than using a flat vector observation. However, attention mechanisms enable solving problems that require comparative reasoning between entities in a scene such as our Sorter environment . Note that even though the BufferSensor can process a variable number of entities, you still need to define a maximum number of entities. This is because our network architecture requires to know what the shape of the observations will be. If fewer entities are observed than the maximum, the observation will be padded with zeros and the trainer will ignore the padded observations. Note that attention layers are invariant to the order of the entities, so there is no need to properly \"order\" the entities before feeding them into the BufferSensor . The BufferSensorComponent Editor inspector has two arguments: - Observation Size : This is how many floats each entities will be represented with. This number is fixed and all entities must have the same representation. For example, if the entities you want to put into the BufferSensor have for relevant information position and speed, then the Observation Size should be 6 floats. - Maximum Number of Entities : This is the maximum number of entities the BufferSensor will be able to collect. To add an entity's observations to a BufferSensorComponent , you need to call BufferSensorComponent.AppendObservation() in the Agent.CollectObservations() method with a float array of size Observation Size as argument. Note : Currently, the observations put into the BufferSensor are not normalized, you will need to normalize your observations manually between -1 and 1. Variable Length Observation Summary & Best Practices Attach BufferSensorComponent to use. Call BufferSensorComponent.AppendObservation() in the Agent.CollectObservations() methodto add the observations of an entity to the BufferSensor . Normalize the entities observations before feeding them into the BufferSensor . Goal Signal It is possible for agents to collect observations that will be treated as \"goal signal\". A goal signal is used to condition the policy of the agent, meaning that if the goal changes, the policy (i.e. the mapping from observations to actions) will change as well. Note that this is true for any observation since all observations influence the policy of the Agent to some degree. But by specifying a goal signal explicitly, we can make this conditioning more important to the agent. This feature can be used in settings where an agent must learn to solve different tasks that are similar by some aspects because the agent will learn to reuse learnings from different tasks to generalize better. In Unity, you can specify that a VectorSensor or a CameraSensor is a goal by attaching a VectorSensorComponent or a CameraSensorComponent to the Agent and selecting Goal Signal as Observation Type . On the trainer side, there are two different ways to condition the policy. This setting is determined by the conditioning_type parameter . If set to hyper (default) a HyperNetwork will be used to generate some of the weights of the policy using the goal observations as input. Note that using a HyperNetwork requires a lot of computations, it is recommended to use a smaller number of hidden units in the policy to alleviate this. If set to none the goal signal will be considered as regular observations. For an example on how to use a goal signal, see the GridWorld example . Goal Signal Summary & Best Practices Attach a VectorSensorComponent or CameraSensorComponent to an agent and set the observation type to goal to use the feature. Set the conditioning_type parameter in the training configuration. Reduce the number of hidden units in the network when using the HyperNetwork conditioning type. Actions and Actuators An action is an instruction from the Policy that the agent carries out. The action is passed to the an IActionReceiver (either an Agent or an IActuator ) as the ActionBuffers parameter when the Academy invokes the IActionReciever.OnActionReceived() function. There are two types of actions supported: Continuous and Discrete . Neither the Policy nor the training algorithm know anything about what the action values themselves mean. The training algorithm simply tries different values for the action list and observes the affect on the accumulated rewards over time and many training episodes. Thus, the only place actions are defined for an Agent is in the OnActionReceived() function. For example, if you designed an agent to move in two dimensions, you could use either continuous or the discrete actions. In the continuous case, you would set the action size to two (one for each dimension), and the agent's Policy would output an action with two floating point values. In the discrete case, you would use one Branch with a size of four (one for each direction), and the Policy would create an action array containing a single element with a value ranging from zero to three. Alternatively, you could create two branches of size two (one for horizontal movement and one for vertical movement), and the Policy would output an action array containing two elements with values ranging from zero to one. You could alternatively use a combination of continuous and discrete actions e.g., using one continuous action for horizontal movement and a discrete branch of size two for the vertical movement. Note that when you are programming actions for an agent, it is often helpful to test your action logic using the Heuristic() method of the Agent, which lets you map keyboard commands to actions. Continuous Actions When an Agent's Policy has Continuous actions, the ActionBuffers.ContinuousActions passed to the Agent's OnActionReceived() function is an array with length equal to the Continuous Action Size property value. The individual values in the array have whatever meanings that you ascribe to them. If you assign an element in the array as the speed of an Agent, for example, the training process learns to control the speed of the Agent through this parameter. The 3DBall example uses continuous actions with two control values. These control values are applied as rotation to the cube: public override void OnActionReceived(ActionBuffers actionBuffers) { var actionZ = 2f * Mathf.Clamp(actionBuffers.ContinuousActions[0], -1f, 1f); var actionX = 2f * Mathf.Clamp(actionBuffers.ContinuousActions[1], -1f, 1f); gameObject.transform.Rotate(new Vector3(0, 0, 1), actionZ); gameObject.transform.Rotate(new Vector3(1, 0, 0), actionX); } By default the output from our provided PPO algorithm pre-clamps the values of ActionBuffers.ContinuousActions into the [-1, 1] range. It is a best practice to manually clip these as well, if you plan to use a 3rd party algorithm with your environment. As shown above, you can scale the control values as needed after clamping them. Discrete Actions When an Agent's Policy uses discrete actions, the ActionBuffers.DiscreteActions passed to the Agent's OnActionReceived() function is an array of integers with length equal to Discrete Branch Size . When defining the discrete actions, Branches is an array of integers, each value corresponds to the number of possibilities for each branch. For example, if we wanted an Agent that can move in a plane and jump, we could define two branches (one for motion and one for jumping) because we want our agent be able to move and jump concurrently. We define the first branch to have 5 possible actions (don't move, go left, go right, go backward, go forward) and the second one to have 2 possible actions (don't jump, jump). The OnActionReceived() method would look something like: // Get the action index for movement int movement = actionBuffers.DiscreteActions[0]; // Get the action index for jumping int jump = actionBuffers.DiscreteActions[1]; // Look up the index in the movement action list: if (movement == 1) { directionX = -1; } if (movement == 2) { directionX = 1; } if (movement == 3) { directionZ = -1; } if (movement == 4) { directionZ = 1; } // Look up the index in the jump action list: if (jump == 1 && IsGrounded()) { directionY = 1; } // Apply the action results to move the Agent gameObject.GetComponent<Rigidbody>().AddForce( new Vector3( directionX * 40f, directionY * 300f, directionZ * 40f)); Masking Discrete Actions When using Discrete Actions, it is possible to specify that some actions are impossible for the next decision. When the Agent is controlled by a neural network, the Agent will be unable to perform the specified action. Note that when the Agent is controlled by its Heuristic, the Agent will still be able to decide to perform the masked action. In order to disallow an action, override the Agent.WriteDiscreteActionMask() virtual method, and call SetActionEnabled() on the provided IDiscreteActionMask : public override void WriteDiscreteActionMask(IDiscreteActionMask actionMask) { actionMask.SetActionEnabled(branch, actionIndex, isEnabled); } Where: branch is the index (starting at 0) of the branch on which you want to allow or disallow the action actionIndex is the index of the action that you want to allow or disallow. isEnabled is a bool indicating whether the action should be allowed or now. For example, if you have an Agent with 2 branches and on the first branch (branch 0) there are 4 possible actions : \"do nothing\" , \"jump\" , \"shoot\" and \"change weapon\" . Then with the code bellow, the Agent will either \"do nothing\" or \"change weapon\" for their next decision (since action index 1 and 2 are masked) actionMask.SetActionEnabled(0, 1, false); actionMask.SetActionEnabled(0, 2, false); Notes: You can call SetActionEnabled multiple times if you want to put masks on multiple branches. At each step, the state of an action is reset and enabled by default. You cannot mask all the actions of a branch. You cannot mask actions in continuous control. IActuator interface and ActuatorComponents The Actuator API allows users to abstract behavior out of Agents and in to components (similar to the ISensor API). The IActuator interface and Agent class both implement the IActionReceiver interface to allow for backward compatibility with the current Agent.OnActionReceived . This means you will not have to change your code until you decide to use the IActuator API. Like the ISensor interface, the IActuator interface is intended for advanced users. The ActuatorComponent abstract class is used to create the actual IActuator at runtime. It must be attached to the same GameObject as the Agent , or to a child GameObject . Actuators and all of their data structures are initialized during Agent.Initialize . This was done to prevent an unexpected allocations at runtime. You can find an example of an IActuator implementation in the Basic example scene. NOTE : you do not need to adjust the Actions in the Agent's Behavior Parameters when using an IActuator and ActuatorComponents . Internally, Agent.OnActionReceived uses an IActuator to send actions to the Agent, although this is mostly abstracted from the user. Actions Summary & Best Practices Agents can use Discrete and/or Continuous actions. Discrete actions can have multiple action branches, and it's possible to mask certain actions so that they won't be taken. In general, fewer actions will make for easier learning. Be sure to set the Continuous Action Size and Discrete Branch Size to the desired number for each type of action, and not greater, as doing the latter can interfere with the efficiency of the training process. Continuous action values should be clipped to an appropriate range. The provided PPO model automatically clips these values between -1 and 1, but third party training systems may not do so. Rewards In reinforcement learning, the reward is a signal that the agent has done something right. The PPO reinforcement learning algorithm works by optimizing the choices an agent makes such that the agent earns the highest cumulative reward over time. The better your reward mechanism, the better your agent will learn. Note: Rewards are not used during inference by an Agent using a trained model and is also not used during imitation learning. Perhaps the best advice is to start simple and only add complexity as needed. In general, you should reward results rather than actions you think will lead to the desired results. You can even use the Agent's Heuristic to control the Agent while watching how it accumulates rewards. Allocate rewards to an Agent by calling the AddReward() or SetReward() methods on the agent. The reward assigned between each decision should be in the range [-1,1]. Values outside this range can lead to unstable training. The reward value is reset to zero when the agent receives a new decision. If there are multiple calls to AddReward() for a single agent decision, the rewards will be summed together to evaluate how good the previous decision was. The SetReward() will override all previous rewards given to an agent since the previous decision. Examples You can examine the OnActionReceived() functions defined in the example environments to see how those projects allocate rewards. The GridAgent class in the GridWorld example uses a very simple reward system: Collider[] hitObjects = Physics.OverlapBox(trueAgent.transform.position, new Vector3(0.3f, 0.3f, 0.3f)); if (hitObjects.Where(col => col.gameObject.tag == \"goal\").ToArray().Length == 1) { AddReward(1.0f); EndEpisode(); } else if (hitObjects.Where(col => col.gameObject.tag == \"pit\").ToArray().Length == 1) { AddReward(-1f); EndEpisode(); } The agent receives a positive reward when it reaches the goal and a negative reward when it falls into the pit. Otherwise, it gets no rewards. This is an example of a sparse reward system. The agent must explore a lot to find the infrequent reward. In contrast, the AreaAgent in the Area example gets a small negative reward every step. In order to get the maximum reward, the agent must finish its task of reaching the goal square as quickly as possible: AddReward( -0.005f); MoveAgent(act); if (gameObject.transform.position.y < 0.0f || Mathf.Abs(gameObject.transform.position.x - area.transform.position.x) > 8f || Mathf.Abs(gameObject.transform.position.z + 5 - area.transform.position.z) > 8) { AddReward(-1f); EndEpisode(); } The agent also gets a larger negative penalty if it falls off the playing surface. The Ball3DAgent in the 3DBall takes a similar approach, but allocates a small positive reward as long as the agent balances the ball. The agent can maximize its rewards by keeping the ball on the platform: SetReward(0.1f); // When ball falls mark Agent as finished and give a negative penalty if ((ball.transform.position.y - gameObject.transform.position.y) < -2f || Mathf.Abs(ball.transform.position.x - gameObject.transform.position.x) > 3f || Mathf.Abs(ball.transform.position.z - gameObject.transform.position.z) > 3f) { SetReward(-1f); EndEpisode(); } The Ball3DAgent also assigns a negative penalty when the ball falls off the platform. Note that all of these environments make use of the EndEpisode() method, which manually terminates an episode when a termination condition is reached. This can be called independently of the Max Step property. Rewards Summary & Best Practices Use AddReward() to accumulate rewards between decisions. Use SetReward() to overwrite any previous rewards accumulate between decisions. The magnitude of any given reward should typically not be greater than 1.0 in order to ensure a more stable learning process. Positive rewards are often more helpful to shaping the desired behavior of an agent than negative rewards. Excessive negative rewards can result in the agent failing to learn any meaningful behavior. For locomotion tasks, a small positive reward (+0.1) for forward velocity is typically used. If you want the agent to finish a task quickly, it is often helpful to provide a small penalty every step (-0.05) that the agent does not complete the task. In this case completion of the task should also coincide with the end of the episode by calling EndEpisode() on the agent when it has accomplished its goal. Agent Properties Behavior Parameters - The parameters dictating what Policy the Agent will receive. Behavior Name - The identifier for the behavior. Agents with the same behavior name will learn the same policy. Vector Observation Space Size - Length of vector observation for the Agent. Stacked Vectors - The number of previous vector observations that will be stacked and used collectively for decision making. This results in the effective size of the vector observation being passed to the Policy being: Space Size x Stacked Vectors . Actions Continuous Actions - The number of concurrent continuous actions that the Agent can take. Discrete Branches - An array of integers, defines multiple concurrent discrete actions. The values in the Discrete Branches array correspond to the number of possible discrete values for each action branch. Model - The neural network model used for inference (obtained after training) Inference Device - Whether to use CPU or GPU to run the model during inference Behavior Type - Determines whether the Agent will do training, inference, or use its Heuristic() method: Default - the Agent will train if they connect to a python trainer, otherwise they will perform inference. Heuristic Only - the Agent will always use the Heuristic() method. Inference Only - the Agent will always perform inference. Team ID - Used to define the team for self-play Use Child Sensors - Whether to use all Sensor components attached to child GameObjects of this Agent. Max Step - The per-agent maximum number of steps. Once this number is reached, the Agent will be reset. Destroying an Agent You can destroy an Agent GameObject during the simulation. Make sure that there is always at least one Agent training at all times by either spawning a new Agent every time one is destroyed or by re-spawning new Agents when the whole environment resets. Defining Multi-agent Scenarios Teams for Adversarial Scenarios Self-play is triggered by including the self-play hyperparameter hierarchy in the trainer configuration . To distinguish opposing agents, set the team ID to different integer values in the behavior parameters script on the agent prefab. Team ID must be 0 or an integer greater than 0. In symmetric games, since all agents (even on opposing teams) will share the same policy, they should have the same 'Behavior Name' in their Behavior Parameters Script. In asymmetric games, they should have a different Behavior Name in their Behavior Parameters script. Note, in asymmetric games, the agents must have both different Behavior Names and different team IDs! For examples of how to use this feature, you can see the trainer configurations and agent prefabs for our Tennis and Soccer environments. Tennis and Soccer provide examples of symmetric games. To train an asymmetric game, specify trainer configurations for each of your behavior names and include the self-play hyperparameter hierarchy in both. Groups for Cooperative Scenarios Cooperative behavior in ML-Agents can be enabled by instantiating a SimpleMultiAgentGroup , typically in an environment controller or similar script, and adding agents to it using the RegisterAgent(Agent agent) method. Note that all agents added to the same SimpleMultiAgentGroup must have the same behavior name and Behavior Parameters. Using SimpleMultiAgentGroup enables the agents within a group to learn how to work together to achieve a common goal (i.e., maximize a group-given reward), even if one or more of the group members are removed before the episode ends. You can then use this group to add/set rewards, end or interrupt episodes at a group level using the AddGroupReward() , SetGroupReward() , EndGroupEpisode() , and GroupEpisodeInterrupted() methods. For example: // Create a Multi Agent Group in Start() or Initialize() m_AgentGroup = new SimpleMultiAgentGroup(); // Register agents in group at the beginning of an episode for (var agent in AgentList) { m_AgentGroup.RegisterAgent(agent); } // if the team scores a goal m_AgentGroup.AddGroupReward(rewardForGoal); // If the goal is reached and the episode is over m_AgentGroup.EndGroupEpisode(); ResetScene(); // If time ran out and we need to interrupt the episode m_AgentGroup.GroupEpisodeInterrupted(); ResetScene(); Multi Agent Groups should be used with the MA-POCA trainer, which is explicitly designed to train cooperative environments. This can be enabled by using the poca trainer - see the training configurations doc for more information on configuring MA-POCA. When using MA-POCA, agents which are deactivated or removed from the Scene during the episode will still learn to contribute to the group's long term rewards, even if they are not active in the scene to experience them. See the Cooperative Push Block environment for an example of how to use Multi Agent Groups, and the Dungeon Escape environment for an example of how the Multi Agent Group can be used with agents that are removed from the scene mid-episode. NOTE : Groups differ from Teams (for competitive settings) in the following way - Agents working together should be added to the same Group, while agents playing against each other should be given different Team Ids. If in the Scene there is one playing field and two teams, there should be two Groups, one for each team, and each team should be assigned a different Team Id. If this playing field is duplicated many times in the Scene (e.g. for training speedup), there should be two Groups per playing field , and two unique Team Ids for the entire Scene . In environments with both Groups and Team Ids configured, MA-POCA and self-play can be used together for training. In the diagram below, there are two agents on each team, and two playing fields where teams are pitted against each other. All the blue agents should share a Team Id (and the orange ones a different ID), and there should be four group managers, one per pair of agents. Please see the SoccerTwos environment for an example. Cooperative Behaviors Notes and Best Practices An agent can only be registered to one MultiAgentGroup at a time. If you want to re-assign an agent from one group to another, you have to unregister it from the current group first. Agents with different behavior names in the same group are not supported. Agents within groups should always set the Max Steps parameter in the Agent script to 0. Instead, handle Max Steps using the MultiAgentGroup by ending the episode for the entire Group using GroupEpisodeInterrupted() . EndGroupEpisode and GroupEpisodeInterrupted do the same job in the game, but has slightly different effect on the training. If the episode is completed, you would want to call EndGroupEpisode . But if the episode is not over but it has been running for enough steps, i.e. reaching max step, you would call GroupEpisodeInterrupted . If an agent finished earlier, e.g. completed tasks/be removed/be killed in the game, do not call EndEpisode() on the Agent. Instead, disable the agent and re-enable it when the next episode starts, or destroy the agent entirely. This is because calling EndEpisode() will call OnEpisodeBegin() , which will reset the agent immediately. While it is possible to call EndEpisode() in this way, it is usually not the desired behavior when training groups of agents. If an agent that was disabled in a scene needs to be re-enabled, it must be re-registered to the MultiAgentGroup. Group rewards are meant to reinforce agents to act in the group's best interest instead of individual ones, and are treated differently than individual agent rewards during training. So calling AddGroupReward() is not equivalent to calling agent.AddReward() on each agent in the group. You can still add incremental rewards to agents using Agent.AddReward() if they are in a Group. These rewards will only be given to those agents and are received when the Agent is active. Environments which use Multi Agent Groups can be trained using PPO or SAC, but agents will not be able to learn from group rewards after deactivation/removal, nor will they behave as cooperatively. Recording Demonstrations In order to record demonstrations from an agent, add the Demonstration Recorder component to a GameObject in the scene which contains an Agent component. Once added, it is possible to name the demonstration that will be recorded from the agent. When Record is checked, a demonstration will be created whenever the scene is played from the Editor. Depending on the complexity of the task, anywhere from a few minutes or a few hours of demonstration data may be necessary to be useful for imitation learning. To specify an exact number of steps you want to record use the Num Steps To Record field and the editor will end your play session automatically once that many steps are recorded. If you set Num Steps To Record to 0 then recording will continue until you manually end the play session. Once the play session ends a .demo file will be created in the Assets/Demonstrations folder (by default). This file contains the demonstrations. Clicking on the file will provide metadata about the demonstration in the inspector. You can then specify the path to this file in your training configurations .","title":"Agents"},{"location":"Learning-Environment-Design-Agents/#agents","text":"Table of Contents: Decisions Observations and Sensors Generating Observations Agent.CollectObservations() Observable Fields and Properties ISensor interface and SensorComponents Vector Observations One-hot encoding categorical information Normalization Stacking Vector Observation Summary & Best Practices Visual Observations Visual Observation Summary & Best Practices Raycast Observations RayCast Observation Summary & Best Practices Variable Length Observations Variable Length Observation Summary & Best Practices Goal Signal Goal Signal Summary & Best Practices Actions and Actuators Continuous Actions Discrete Actions Masking Discrete Actions Actions Summary & Best Practices Rewards Examples Rewards Summary & Best Practices Agent Properties Destroying an Agent Defining Multi-agent Scenarios Teams for Adversarial Scenarios Groups for Cooperative Scenarios Recording Demonstrations An agent is an entity that can observe its environment, decide on the best course of action using those observations, and execute those actions within its environment. Agents can be created in Unity by extending the Agent class. The most important aspects of creating agents that can successfully learn are the observations the agent collects, and the reward you assign to estimate the value of the agent's current state toward accomplishing its tasks. An Agent passes its observations to its Policy. The Policy then makes a decision and passes the chosen action back to the agent. Your agent code must execute the action, for example, move the agent in one direction or another. In order to train an agent using reinforcement learning , your agent must calculate a reward value at each action. The reward is used to discover the optimal decision-making policy. The Policy class abstracts out the decision making logic from the Agent itself so that you can use the same Policy in multiple Agents. How a Policy makes its decisions depends on the Behavior Parameters associated with the agent. If you set Behavior Type to Heuristic Only , the Agent will use its Heuristic() method to make decisions which can allow you to control the Agent manually or write your own Policy. If the Agent has a Model file, its Policy will use the neural network Model to take decisions. When you create an Agent, you should usually extend the base Agent class. This includes implementing the following methods: Agent.OnEpisodeBegin() \u2014 Called at the beginning of an Agent's episode, including at the beginning of the simulation. Agent.CollectObservations(VectorSensor sensor) \u2014 Called every step that the Agent requests a decision. This is one possible way for collecting the Agent's observations of the environment; see Generating Observations below for more options. Agent.OnActionReceived() \u2014 Called every time the Agent receives an action to take. Receives the action chosen by the Agent. It is also common to assign a reward in this method. Agent.Heuristic() - When the Behavior Type is set to Heuristic Only in the Behavior Parameters of the Agent, the Agent will use the Heuristic() method to generate the actions of the Agent. As such, the Heuristic() method writes to the array of floats provided to the Heuristic method as argument. Note : Do not create a new float array of action in the Heuristic() method, as this will prevent writing floats to the original action array. As a concrete example, here is how the Ball3DAgent class implements these methods: Agent.OnEpisodeBegin() \u2014 Resets the agent cube and ball to their starting positions. The function randomizes the reset values so that the training generalizes to more than a specific starting position and agent cube orientation. Agent.CollectObservations(VectorSensor sensor) \u2014 Adds information about the orientation of the agent cube, the ball velocity, and the relative position between the ball and the cube. Since the CollectObservations() method calls VectorSensor.AddObservation() such that vector size adds up to 8, the Behavior Parameters of the Agent are set with vector observation space with a state size of 8. Agent.OnActionReceived() \u2014 The action results in a small change in the agent cube's rotation at each step. In this example, an Agent receives a small positive reward for each step it keeps the ball on the agent cube's head and a larger, negative reward for dropping the ball. An Agent's episode is also ended when it drops the ball so that it will reset with a new ball for the next simulation step. Agent.Heuristic() - Converts the keyboard inputs into actions.","title":"Agents"},{"location":"Learning-Environment-Design-Agents/#decisions","text":"The observation-decision-action-reward cycle repeats each time the Agent request a decision. Agents will request a decision when Agent.RequestDecision() is called. If you need the Agent to request decisions on its own at regular intervals, add a Decision Requester component to the Agent's GameObject. Making decisions at regular step intervals is generally most appropriate for physics-based simulations. For example, an agent in a robotic simulator that must provide fine-control of joint torques should make its decisions every step of the simulation. In games such as real-time strategy, where many agents make their decisions at regular intervals, the decision timing for each agent can be staggered by setting the DecisionStep parameter in the Decision Requester component for each agent. On the other hand, an agent that only needs to make decisions when certain game or simulation events occur, such as in a turn-based game, should call Agent.RequestDecision() manually.","title":"Decisions"},{"location":"Learning-Environment-Design-Agents/#observations-and-sensors","text":"In order for an agent to learn, the observations should include all the information an agent needs to accomplish its task. Without sufficient and relevant information, an agent may learn poorly or may not learn at all. A reasonable approach for determining what information should be included is to consider what you would need to calculate an analytical solution to the problem, or what you would expect a human to be able to use to solve the problem.","title":"Observations and Sensors"},{"location":"Learning-Environment-Design-Agents/#generating-observations","text":"ML-Agents provides multiple ways for an Agent to make observations: 1. Overriding the Agent.CollectObservations() method and passing the observations to the provided VectorSensor . 1. Adding the [Observable] attribute to fields and properties on the Agent. 1. Implementing the ISensor interface, using a SensorComponent attached to the Agent to create the ISensor .","title":"Generating Observations"},{"location":"Learning-Environment-Design-Agents/#agentcollectobservations","text":"Agent.CollectObservations() is best used for aspects of the environment which are numerical and non-visual. The Policy class calls the CollectObservations(VectorSensor sensor) method of each Agent. Your implementation of this function must call VectorSensor.AddObservation to add vector observations. The VectorSensor.AddObservation method provides a number of overloads for adding common types of data to your observation vector. You can add Integers and booleans directly to the observation vector, as well as some common Unity data types such as Vector2 , Vector3 , and Quaternion . For examples of various state observation functions, you can look at the example environments included in the ML-Agents SDK. For instance, the 3DBall example uses the rotation of the platform, the relative position of the ball, and the velocity of the ball as its state observation. public GameObject ball; public override void CollectObservations(VectorSensor sensor) { // Orientation of the cube (2 floats) sensor.AddObservation(gameObject.transform.rotation.z); sensor.AddObservation(gameObject.transform.rotation.x); // Relative position of the ball to the cube (3 floats) sensor.AddObservation(ball.transform.position - gameObject.transform.position); // Velocity of the ball (3 floats) sensor.AddObservation(m_BallRb.velocity); // 8 floats total } As an experiment, you can remove the velocity components from the observation and retrain the 3DBall agent. While it will learn to balance the ball reasonably well, the performance of the agent without using velocity is noticeably worse. The observations passed to VectorSensor.AddObservation() must always contain the same number of elements must always be in the same order. If the number of observed entities in an environment can vary, you can pad the calls with zeros for any missing entities in a specific observation, or you can limit an agent's observations to a fixed subset. For example, instead of observing every enemy in an environment, you could only observe the closest five. Additionally, when you set up an Agent's Behavior Parameters in the Unity Editor, you must set the Vector Observations > Space Size to equal the number of floats that are written by CollectObservations() .","title":"Agent.CollectObservations()"},{"location":"Learning-Environment-Design-Agents/#observable-fields-and-properties","text":"Another approach is to define the relevant observations as fields or properties on your Agent class, and annotate them with an ObservableAttribute . For example, in the Ball3DHardAgent, the difference between positions could be observed by adding a property to the Agent: using Unity.MLAgents.Sensors.Reflection; public class Ball3DHardAgent : Agent { [Observable(numStackedObservations: 9)] Vector3 PositionDelta { get { return ball.transform.position - gameObject.transform.position; } } } ObservableAttribute currently supports most basic types (e.g. floats, ints, bools), as well as Vector2 , Vector3 , Vector4 , Quaternion , and enums. The behavior of ObservableAttribute s are controlled by the \"Observable Attribute Handling\" in the Agent's Behavior Parameters . The possible values for this are: * Ignore (default) - All ObservableAttributes on the Agent will be ignored. If there are no ObservableAttributes on the Agent, this will result in the fastest initialization time. * Exclude Inherited - Only members on the declared class will be examined; members that are inherited are ignored. This is a reasonable tradeoff between performance and flexibility. * Examine All All members on the class will be examined. This can lead to slower startup times. \"Exclude Inherited\" is generally sufficient, but if your Agent inherits from another Agent implementation that has Observable members, you will need to use \"Examine All\". Internally, ObservableAttribute uses reflection to determine which members of the Agent have ObservableAttributes, and also uses reflection to access the fields or invoke the properties at runtime. This may be slower than using CollectObservations or an ISensor, although this might not be enough to noticeably affect performance. NOTE : you do not need to adjust the Space Size in the Agent's Behavior Parameters when you add [Observable] fields or properties to an Agent, since their size can be computed before they are used.","title":"Observable Fields and Properties"},{"location":"Learning-Environment-Design-Agents/#isensor-interface-and-sensorcomponents","text":"The ISensor interface is generally intended for advanced users. The Write() method is used to actually generate the observation, but some other methods such as returning the shape of the observations must also be implemented. The SensorComponent abstract class is used to create the actual ISensor at runtime. It must be attached to the same GameObject as the Agent , or to a child GameObject . There are several SensorComponents provided in the API, including: - CameraSensorComponent - Uses images from a Camera as observations. - RenderTextureSensorComponent - Uses the content of a RenderTexture as observations. - RayPerceptionSensorComponent - Uses the information from set of ray casts as observations. - Match3SensorComponent - Uses the board of a Match-3 game as observations. - GridSensorComponent - Uses a set of box queries in a grid shape as observations. NOTE : you do not need to adjust the Space Size in the Agent's Behavior Parameters when using SensorComponents s. Internally, both Agent.CollectObservations and [Observable] attribute use an ISensors to write observations, although this is mostly abstracted from the user.","title":"ISensor interface and SensorComponents"},{"location":"Learning-Environment-Design-Agents/#vector-observations","text":"Both Agent.CollectObservations() and ObservableAttribute s produce vector observations, which are represented at lists of float s. ISensor s can produce both vector observations and visual observations, which are multi-dimensional arrays of floats. Below are some additional considerations when dealing with vector observations:","title":"Vector Observations"},{"location":"Learning-Environment-Design-Agents/#one-hot-encoding-categorical-information","text":"Type enumerations should be encoded in the one-hot style. That is, add an element to the feature vector for each element of enumeration, setting the element representing the observed member to one and set the rest to zero. For example, if your enumeration contains [Sword, Shield, Bow] and the agent observes that the current item is a Bow, you would add the elements: 0, 0, 1 to the feature vector. The following code example illustrates how to add. enum ItemType { Sword, Shield, Bow, LastItem } public override void CollectObservations(VectorSensor sensor) { for (int ci = 0; ci < (int)ItemType.LastItem; ci++) { sensor.AddObservation((int)currentItem == ci ? 1.0f : 0.0f); } } VectorSensor also provides a two-argument function AddOneHotObservation() as a shortcut for one-hot style observations. The following example is identical to the previous one. enum ItemType { Sword, Shield, Bow, LastItem } const int NUM_ITEM_TYPES = (int)ItemType.LastItem + 1; public override void CollectObservations(VectorSensor sensor) { // The first argument is the selection index; the second is the // number of possibilities sensor.AddOneHotObservation((int)currentItem, NUM_ITEM_TYPES); } ObservableAttribute has built-in support for enums. Note that you don't need the LastItem placeholder in this case: enum ItemType { Sword, Shield, Bow } public class HeroAgent : Agent { [Observable] ItemType m_CurrentItem; }","title":"One-hot encoding categorical information"},{"location":"Learning-Environment-Design-Agents/#normalization","text":"For the best results when training, you should normalize the components of your feature vector to the range [-1, +1] or [0, 1]. When you normalize the values, the PPO neural network can often converge to a solution faster. Note that it isn't always necessary to normalize to these recommended ranges, but it is considered a best practice when using neural networks. The greater the variation in ranges between the components of your observation, the more likely that training will be affected. To normalize a value to [0, 1], you can use the following formula: normalizedValue = (currentValue - minValue)/(maxValue - minValue) :warning: For vectors, you should apply the above formula to each component (x, y, and z). Note that this is not the same as using the Vector3.normalized property or Vector3.Normalize() method in Unity (and similar for Vector2 ). Rotations and angles should also be normalized. For angles between 0 and 360 degrees, you can use the following formulas: Quaternion rotation = transform.rotation; Vector3 normalized = rotation.eulerAngles / 180.0f - Vector3.one; // [-1,1] Vector3 normalized = rotation.eulerAngles / 360.0f; // [0,1] For angles that can be outside the range [0,360], you can either reduce the angle, or, if the number of turns is significant, increase the maximum value used in your normalization formula.","title":"Normalization"},{"location":"Learning-Environment-Design-Agents/#stacking","text":"Stacking refers to repeating observations from previous steps as part of a larger observation. For example, consider an Agent that generates these observations in four steps step 1: [0.1] step 2: [0.2] step 3: [0.3] step 4: [0.4] If we use a stack size of 3, the observations would instead be: step 1: [0.1, 0.0, 0.0] step 2: [0.2, 0.1, 0.0] step 3: [0.3, 0.2, 0.1] step 4: [0.4, 0.3, 0.2] (The observations are padded with zeroes for the first stackSize-1 steps). This is a simple way to give an Agent limited \"memory\" without the complexity of adding a recurrent neural network (RNN). The steps for enabling stacking depends on how you generate observations: * For Agent.CollectObservations(), set \"Stacked Vectors\" on the Agent's Behavior Parameters to a value greater than 1. * For ObservableAttribute, set the numStackedObservations parameter in the constructor, e.g. [Observable(numStackedObservations: 2)] . * For ISensor s, wrap them in a StackingSensor (which is also an ISensor ). Generally, this should happen in the CreateSensor() method of your SensorComponent .","title":"Stacking"},{"location":"Learning-Environment-Design-Agents/#vector-observation-summary-best-practices","text":"Vector Observations should include all variables relevant for allowing the agent to take the optimally informed decision, and ideally no extraneous information. In cases where Vector Observations need to be remembered or compared over time, either an RNN should be used in the model, or the Stacked Vectors value in the agent GameObject's Behavior Parameters should be changed. Categorical variables such as type of object (Sword, Shield, Bow) should be encoded in one-hot fashion (i.e. 3 -> 0, 0, 1 ). This can be done automatically using the AddOneHotObservation() method of the VectorSensor , or using [Observable] on an enum field or property of the Agent. In general, all inputs should be normalized to be in the range 0 to +1 (or -1 to 1). For example, the x position information of an agent where the maximum possible value is maxValue should be recorded as VectorSensor.AddObservation(transform.position.x / maxValue); rather than VectorSensor.AddObservation(transform.position.x); . Positional information of relevant GameObjects should be encoded in relative coordinates wherever possible. This is often relative to the agent position.","title":"Vector Observation Summary &amp; Best Practices"},{"location":"Learning-Environment-Design-Agents/#visual-observations","text":"Visual observations are generally provided to agent via either a CameraSensor or RenderTextureSensor . These collect image information and transforms it into a 3D Tensor which can be fed into the convolutional neural network (CNN) of the agent policy. For more information on CNNs, see this guide . This allows agents to learn from spatial regularities in the observation images. It is possible to use visual and vector observations with the same agent. Agents using visual observations can capture state of arbitrary complexity and are useful when the state is difficult to describe numerically. However, they are also typically less efficient and slower to train, and sometimes don't succeed at all as compared to vector observations. As such, they should only be used when it is not possible to properly define the problem using vector or ray-cast observations. Visual observations can be derived from Cameras or RenderTextures within your scene. To add a visual observation to an Agent, add either a Camera Sensor Component or RenderTextures Sensor Component to the Agent. Then drag the camera or render texture you want to add to the Camera or RenderTexture field. You can have more than one camera or render texture and even use a combination of both attached to an Agent. For each visual observation, set the width and height of the image (in pixels) and whether or not the observation is color or grayscale. or Each Agent that uses the same Policy must have the same number of visual observations, and they must all have the same resolutions (including whether or not they are grayscale). Additionally, each Sensor Component on an Agent must have a unique name so that they can be sorted deterministically (the name must be unique for that Agent, but multiple Agents can have a Sensor Component with the same name). Visual observations also support stacking, by specifying Observation Stacks to a value greater than 1. The visual observations from the last stackSize steps will be stacked on the last dimension (channel dimension). When using RenderTexture visual observations, a handy feature for debugging is adding a Canvas , then adding a Raw Image with it's texture set to the Agent's RenderTexture . This will render the agent observation on the game screen. The GridWorld environment is an example on how to use a RenderTexture for both debugging and observation. Note that in this example, a Camera is rendered to a RenderTexture, which is then used for observations and debugging. To update the RenderTexture, the Camera must be asked to render every time a decision is requested within the game code. When using Cameras as observations directly, this is done automatically by the Agent.","title":"Visual Observations"},{"location":"Learning-Environment-Design-Agents/#visual-observation-summary-best-practices","text":"To collect visual observations, attach CameraSensor or RenderTextureSensor components to the agent GameObject. Visual observations should generally only be used when vector observations are not sufficient. Image size should be kept as small as possible, without the loss of needed details for decision making. Images should be made grayscale in situations where color information is not needed for making informed decisions.","title":"Visual Observation Summary &amp; Best Practices"},{"location":"Learning-Environment-Design-Agents/#raycast-observations","text":"Raycasts are another possible method for providing observations to an agent. This can be easily implemented by adding a RayPerceptionSensorComponent3D (or RayPerceptionSensorComponent2D ) to the Agent GameObject. During observations, several rays (or spheres, depending on settings) are cast into the physics world, and the objects that are hit determine the observation vector that is produced. Both sensor components have several settings: Detectable Tags A list of strings corresponding to the types of objects that the Agent should be able to distinguish between. For example, in the WallJump example, we use \"wall\", \"goal\", and \"block\" as the list of objects to detect. Rays Per Direction Determines the number of rays that are cast. One ray is always cast forward, and this many rays are cast to the left and right. Max Ray Degrees The angle (in degrees) for the outermost rays. 90 degrees corresponds to the left and right of the agent. Sphere Cast Radius The size of the sphere used for sphere casting. If set to 0, rays will be used instead of spheres. Rays may be more efficient, especially in complex scenes. Ray Length The length of the casts Ray Layer Mask The LayerMask passed to the raycast or spherecast. This can be used to ignore certain types of objects when casting. Observation Stacks The number of previous results to \"stack\" with the cast results. Note that this can be independent of the \"Stacked Vectors\" setting in Behavior Parameters . Start Vertical Offset (3D only) The vertical offset of the ray start point. End Vertical Offset (3D only) The vertical offset of the ray end point. Alternating Ray Order Alternating is the default, it gives an order of (0, -delta, delta, -2 delta, 2 delta, ..., -n delta, n delta). If alternating is disabled the order is left to right (-n delta, -(n-1) delta, ..., -delta, 0, delta, ..., (n-1) delta, n delta). For general usage there is no difference but if using custom models the left-to-right layout that matches the spatial structuring can be preferred (e.g. for processing with conv nets). In the example image above, the Agent has two RayPerceptionSensorComponent3D s. Both use 3 Rays Per Direction and 90 Max Ray Degrees. One of the components had a vertical offset, so the Agent can tell whether it's clear to jump over the wall. The total size of the created observations is (Observation Stacks) * (1 + 2 * Rays Per Direction) * (Num Detectable Tags + 2) so the number of rays and tags should be kept as small as possible to reduce the amount of data used. Note that this is separate from the State Size defined in Behavior Parameters , so you don't need to worry about the formula above when setting the State Size.","title":"Raycast Observations"},{"location":"Learning-Environment-Design-Agents/#raycast-observation-summary-best-practices","text":"Attach RayPerceptionSensorComponent3D or RayPerceptionSensorComponent2D to use. This observation type is best used when there is relevant spatial information for the agent that doesn't require a fully rendered image to convey. Use as few rays and tags as necessary to solve the problem in order to improve learning stability and agent performance.","title":"RayCast Observation Summary &amp; Best Practices"},{"location":"Learning-Environment-Design-Agents/#grid-observations","text":"Grid-base observations combine the advantages of 2D spatial representation in visual observations, and the flexibility of defining detectable objects in RayCast observations. The sensor uses a set of box queries in a grid shape and gives a top-down 2D view around the agent. This can be implemented by adding a GridSensorComponent to the Agent GameObject. During observations, the sensor detects the presence of detectable objects in each cell and encode that into one-hot representation. The collected information from each cell forms a 3D tensor observation and will be fed into the convolutional neural network (CNN) of the agent policy just like visual observations. The sensor component has the following settings: - Cell Scale The scale of each cell in the grid. - Grid Size Number of cells on each side of the grid. - Agent Game Object The Agent that holds the grid sensor. This is used to disambiguate objects with the same tag as the agent so that the agent doesn't detect itself. - Rotate With Agent Whether the grid rotates with the Agent. - Detectable Tags A list of strings corresponding to the types of objects that the Agent should be able to distinguish between. - Collider Mask The LayerMask passed to the collider detection. This can be used to ignore certain types of objects. - Initial Collider Buffer Size The initial size of the Collider buffer used in the non-allocating Physics calls for each cell. - Max Collider Buffer Size The max size of the Collider buffer used in the non-allocating Physics calls for each cell. The observation for each grid cell is a one-hot encoding of the detected object. The total size of the created observations is GridSize.x * GridSize.z * Num Detectable Tags so the number of detectable tags and size of the grid should be kept as small as possible to reduce the amount of data used. This makes a trade-off between the granularity of the observation and training speed. To allow more variety of observations that grid sensor can capture, the GridSensorComponent and the underlying GridSensorBase also provides interfaces that can be overridden to collect customized observation from detected objects. See the doc on extending grid Sensors for more details on custom grid sensors. Note : The GridSensor only works in 3D environments and will not behave properly in 2D environments.","title":"Grid Observations"},{"location":"Learning-Environment-Design-Agents/#grid-observation-summary-best-practices","text":"Attach GridSensorComponent to use. This observation type is best used when there is relevant non-visual spatial information that can be best captured in 2D representations. Use as small grid size and as few tags as necessary to solve the problem in order to improve learning stability and agent performance. Do not use GridSensor in a 2D game.","title":"Grid Observation Summary &amp; Best Practices"},{"location":"Learning-Environment-Design-Agents/#variable-length-observations","text":"It is possible for agents to collect observations from a varying number of GameObjects by using a BufferSensor . You can add a BufferSensor to your Agent by adding a BufferSensorComponent to its GameObject. The BufferSensor can be useful in situations in which the Agent must pay attention to a varying number of entities (for example, a varying number of enemies or projectiles). On the trainer side, the BufferSensor is processed using an attention module. More information about attention mechanisms can be found here . Training or doing inference with variable length observations can be slower than using a flat vector observation. However, attention mechanisms enable solving problems that require comparative reasoning between entities in a scene such as our Sorter environment . Note that even though the BufferSensor can process a variable number of entities, you still need to define a maximum number of entities. This is because our network architecture requires to know what the shape of the observations will be. If fewer entities are observed than the maximum, the observation will be padded with zeros and the trainer will ignore the padded observations. Note that attention layers are invariant to the order of the entities, so there is no need to properly \"order\" the entities before feeding them into the BufferSensor . The BufferSensorComponent Editor inspector has two arguments: - Observation Size : This is how many floats each entities will be represented with. This number is fixed and all entities must have the same representation. For example, if the entities you want to put into the BufferSensor have for relevant information position and speed, then the Observation Size should be 6 floats. - Maximum Number of Entities : This is the maximum number of entities the BufferSensor will be able to collect. To add an entity's observations to a BufferSensorComponent , you need to call BufferSensorComponent.AppendObservation() in the Agent.CollectObservations() method with a float array of size Observation Size as argument. Note : Currently, the observations put into the BufferSensor are not normalized, you will need to normalize your observations manually between -1 and 1.","title":"Variable Length Observations"},{"location":"Learning-Environment-Design-Agents/#variable-length-observation-summary-best-practices","text":"Attach BufferSensorComponent to use. Call BufferSensorComponent.AppendObservation() in the Agent.CollectObservations() methodto add the observations of an entity to the BufferSensor . Normalize the entities observations before feeding them into the BufferSensor .","title":"Variable Length Observation Summary &amp; Best Practices"},{"location":"Learning-Environment-Design-Agents/#goal-signal","text":"It is possible for agents to collect observations that will be treated as \"goal signal\". A goal signal is used to condition the policy of the agent, meaning that if the goal changes, the policy (i.e. the mapping from observations to actions) will change as well. Note that this is true for any observation since all observations influence the policy of the Agent to some degree. But by specifying a goal signal explicitly, we can make this conditioning more important to the agent. This feature can be used in settings where an agent must learn to solve different tasks that are similar by some aspects because the agent will learn to reuse learnings from different tasks to generalize better. In Unity, you can specify that a VectorSensor or a CameraSensor is a goal by attaching a VectorSensorComponent or a CameraSensorComponent to the Agent and selecting Goal Signal as Observation Type . On the trainer side, there are two different ways to condition the policy. This setting is determined by the conditioning_type parameter . If set to hyper (default) a HyperNetwork will be used to generate some of the weights of the policy using the goal observations as input. Note that using a HyperNetwork requires a lot of computations, it is recommended to use a smaller number of hidden units in the policy to alleviate this. If set to none the goal signal will be considered as regular observations. For an example on how to use a goal signal, see the GridWorld example .","title":"Goal Signal"},{"location":"Learning-Environment-Design-Agents/#goal-signal-summary-best-practices","text":"Attach a VectorSensorComponent or CameraSensorComponent to an agent and set the observation type to goal to use the feature. Set the conditioning_type parameter in the training configuration. Reduce the number of hidden units in the network when using the HyperNetwork conditioning type.","title":"Goal Signal Summary &amp; Best Practices"},{"location":"Learning-Environment-Design-Agents/#actions-and-actuators","text":"An action is an instruction from the Policy that the agent carries out. The action is passed to the an IActionReceiver (either an Agent or an IActuator ) as the ActionBuffers parameter when the Academy invokes the IActionReciever.OnActionReceived() function. There are two types of actions supported: Continuous and Discrete . Neither the Policy nor the training algorithm know anything about what the action values themselves mean. The training algorithm simply tries different values for the action list and observes the affect on the accumulated rewards over time and many training episodes. Thus, the only place actions are defined for an Agent is in the OnActionReceived() function. For example, if you designed an agent to move in two dimensions, you could use either continuous or the discrete actions. In the continuous case, you would set the action size to two (one for each dimension), and the agent's Policy would output an action with two floating point values. In the discrete case, you would use one Branch with a size of four (one for each direction), and the Policy would create an action array containing a single element with a value ranging from zero to three. Alternatively, you could create two branches of size two (one for horizontal movement and one for vertical movement), and the Policy would output an action array containing two elements with values ranging from zero to one. You could alternatively use a combination of continuous and discrete actions e.g., using one continuous action for horizontal movement and a discrete branch of size two for the vertical movement. Note that when you are programming actions for an agent, it is often helpful to test your action logic using the Heuristic() method of the Agent, which lets you map keyboard commands to actions.","title":"Actions and Actuators"},{"location":"Learning-Environment-Design-Agents/#continuous-actions","text":"When an Agent's Policy has Continuous actions, the ActionBuffers.ContinuousActions passed to the Agent's OnActionReceived() function is an array with length equal to the Continuous Action Size property value. The individual values in the array have whatever meanings that you ascribe to them. If you assign an element in the array as the speed of an Agent, for example, the training process learns to control the speed of the Agent through this parameter. The 3DBall example uses continuous actions with two control values. These control values are applied as rotation to the cube: public override void OnActionReceived(ActionBuffers actionBuffers) { var actionZ = 2f * Mathf.Clamp(actionBuffers.ContinuousActions[0], -1f, 1f); var actionX = 2f * Mathf.Clamp(actionBuffers.ContinuousActions[1], -1f, 1f); gameObject.transform.Rotate(new Vector3(0, 0, 1), actionZ); gameObject.transform.Rotate(new Vector3(1, 0, 0), actionX); } By default the output from our provided PPO algorithm pre-clamps the values of ActionBuffers.ContinuousActions into the [-1, 1] range. It is a best practice to manually clip these as well, if you plan to use a 3rd party algorithm with your environment. As shown above, you can scale the control values as needed after clamping them.","title":"Continuous Actions"},{"location":"Learning-Environment-Design-Agents/#discrete-actions","text":"When an Agent's Policy uses discrete actions, the ActionBuffers.DiscreteActions passed to the Agent's OnActionReceived() function is an array of integers with length equal to Discrete Branch Size . When defining the discrete actions, Branches is an array of integers, each value corresponds to the number of possibilities for each branch. For example, if we wanted an Agent that can move in a plane and jump, we could define two branches (one for motion and one for jumping) because we want our agent be able to move and jump concurrently. We define the first branch to have 5 possible actions (don't move, go left, go right, go backward, go forward) and the second one to have 2 possible actions (don't jump, jump). The OnActionReceived() method would look something like: // Get the action index for movement int movement = actionBuffers.DiscreteActions[0]; // Get the action index for jumping int jump = actionBuffers.DiscreteActions[1]; // Look up the index in the movement action list: if (movement == 1) { directionX = -1; } if (movement == 2) { directionX = 1; } if (movement == 3) { directionZ = -1; } if (movement == 4) { directionZ = 1; } // Look up the index in the jump action list: if (jump == 1 && IsGrounded()) { directionY = 1; } // Apply the action results to move the Agent gameObject.GetComponent<Rigidbody>().AddForce( new Vector3( directionX * 40f, directionY * 300f, directionZ * 40f));","title":"Discrete Actions"},{"location":"Learning-Environment-Design-Agents/#masking-discrete-actions","text":"When using Discrete Actions, it is possible to specify that some actions are impossible for the next decision. When the Agent is controlled by a neural network, the Agent will be unable to perform the specified action. Note that when the Agent is controlled by its Heuristic, the Agent will still be able to decide to perform the masked action. In order to disallow an action, override the Agent.WriteDiscreteActionMask() virtual method, and call SetActionEnabled() on the provided IDiscreteActionMask : public override void WriteDiscreteActionMask(IDiscreteActionMask actionMask) { actionMask.SetActionEnabled(branch, actionIndex, isEnabled); } Where: branch is the index (starting at 0) of the branch on which you want to allow or disallow the action actionIndex is the index of the action that you want to allow or disallow. isEnabled is a bool indicating whether the action should be allowed or now. For example, if you have an Agent with 2 branches and on the first branch (branch 0) there are 4 possible actions : \"do nothing\" , \"jump\" , \"shoot\" and \"change weapon\" . Then with the code bellow, the Agent will either \"do nothing\" or \"change weapon\" for their next decision (since action index 1 and 2 are masked) actionMask.SetActionEnabled(0, 1, false); actionMask.SetActionEnabled(0, 2, false); Notes: You can call SetActionEnabled multiple times if you want to put masks on multiple branches. At each step, the state of an action is reset and enabled by default. You cannot mask all the actions of a branch. You cannot mask actions in continuous control.","title":"Masking Discrete Actions"},{"location":"Learning-Environment-Design-Agents/#iactuator-interface-and-actuatorcomponents","text":"The Actuator API allows users to abstract behavior out of Agents and in to components (similar to the ISensor API). The IActuator interface and Agent class both implement the IActionReceiver interface to allow for backward compatibility with the current Agent.OnActionReceived . This means you will not have to change your code until you decide to use the IActuator API. Like the ISensor interface, the IActuator interface is intended for advanced users. The ActuatorComponent abstract class is used to create the actual IActuator at runtime. It must be attached to the same GameObject as the Agent , or to a child GameObject . Actuators and all of their data structures are initialized during Agent.Initialize . This was done to prevent an unexpected allocations at runtime. You can find an example of an IActuator implementation in the Basic example scene. NOTE : you do not need to adjust the Actions in the Agent's Behavior Parameters when using an IActuator and ActuatorComponents . Internally, Agent.OnActionReceived uses an IActuator to send actions to the Agent, although this is mostly abstracted from the user.","title":"IActuator interface and ActuatorComponents"},{"location":"Learning-Environment-Design-Agents/#actions-summary-best-practices","text":"Agents can use Discrete and/or Continuous actions. Discrete actions can have multiple action branches, and it's possible to mask certain actions so that they won't be taken. In general, fewer actions will make for easier learning. Be sure to set the Continuous Action Size and Discrete Branch Size to the desired number for each type of action, and not greater, as doing the latter can interfere with the efficiency of the training process. Continuous action values should be clipped to an appropriate range. The provided PPO model automatically clips these values between -1 and 1, but third party training systems may not do so.","title":"Actions Summary &amp; Best Practices"},{"location":"Learning-Environment-Design-Agents/#rewards","text":"In reinforcement learning, the reward is a signal that the agent has done something right. The PPO reinforcement learning algorithm works by optimizing the choices an agent makes such that the agent earns the highest cumulative reward over time. The better your reward mechanism, the better your agent will learn. Note: Rewards are not used during inference by an Agent using a trained model and is also not used during imitation learning. Perhaps the best advice is to start simple and only add complexity as needed. In general, you should reward results rather than actions you think will lead to the desired results. You can even use the Agent's Heuristic to control the Agent while watching how it accumulates rewards. Allocate rewards to an Agent by calling the AddReward() or SetReward() methods on the agent. The reward assigned between each decision should be in the range [-1,1]. Values outside this range can lead to unstable training. The reward value is reset to zero when the agent receives a new decision. If there are multiple calls to AddReward() for a single agent decision, the rewards will be summed together to evaluate how good the previous decision was. The SetReward() will override all previous rewards given to an agent since the previous decision.","title":"Rewards"},{"location":"Learning-Environment-Design-Agents/#examples","text":"You can examine the OnActionReceived() functions defined in the example environments to see how those projects allocate rewards. The GridAgent class in the GridWorld example uses a very simple reward system: Collider[] hitObjects = Physics.OverlapBox(trueAgent.transform.position, new Vector3(0.3f, 0.3f, 0.3f)); if (hitObjects.Where(col => col.gameObject.tag == \"goal\").ToArray().Length == 1) { AddReward(1.0f); EndEpisode(); } else if (hitObjects.Where(col => col.gameObject.tag == \"pit\").ToArray().Length == 1) { AddReward(-1f); EndEpisode(); } The agent receives a positive reward when it reaches the goal and a negative reward when it falls into the pit. Otherwise, it gets no rewards. This is an example of a sparse reward system. The agent must explore a lot to find the infrequent reward. In contrast, the AreaAgent in the Area example gets a small negative reward every step. In order to get the maximum reward, the agent must finish its task of reaching the goal square as quickly as possible: AddReward( -0.005f); MoveAgent(act); if (gameObject.transform.position.y < 0.0f || Mathf.Abs(gameObject.transform.position.x - area.transform.position.x) > 8f || Mathf.Abs(gameObject.transform.position.z + 5 - area.transform.position.z) > 8) { AddReward(-1f); EndEpisode(); } The agent also gets a larger negative penalty if it falls off the playing surface. The Ball3DAgent in the 3DBall takes a similar approach, but allocates a small positive reward as long as the agent balances the ball. The agent can maximize its rewards by keeping the ball on the platform: SetReward(0.1f); // When ball falls mark Agent as finished and give a negative penalty if ((ball.transform.position.y - gameObject.transform.position.y) < -2f || Mathf.Abs(ball.transform.position.x - gameObject.transform.position.x) > 3f || Mathf.Abs(ball.transform.position.z - gameObject.transform.position.z) > 3f) { SetReward(-1f); EndEpisode(); } The Ball3DAgent also assigns a negative penalty when the ball falls off the platform. Note that all of these environments make use of the EndEpisode() method, which manually terminates an episode when a termination condition is reached. This can be called independently of the Max Step property.","title":"Examples"},{"location":"Learning-Environment-Design-Agents/#rewards-summary-best-practices","text":"Use AddReward() to accumulate rewards between decisions. Use SetReward() to overwrite any previous rewards accumulate between decisions. The magnitude of any given reward should typically not be greater than 1.0 in order to ensure a more stable learning process. Positive rewards are often more helpful to shaping the desired behavior of an agent than negative rewards. Excessive negative rewards can result in the agent failing to learn any meaningful behavior. For locomotion tasks, a small positive reward (+0.1) for forward velocity is typically used. If you want the agent to finish a task quickly, it is often helpful to provide a small penalty every step (-0.05) that the agent does not complete the task. In this case completion of the task should also coincide with the end of the episode by calling EndEpisode() on the agent when it has accomplished its goal.","title":"Rewards Summary &amp; Best Practices"},{"location":"Learning-Environment-Design-Agents/#agent-properties","text":"Behavior Parameters - The parameters dictating what Policy the Agent will receive. Behavior Name - The identifier for the behavior. Agents with the same behavior name will learn the same policy. Vector Observation Space Size - Length of vector observation for the Agent. Stacked Vectors - The number of previous vector observations that will be stacked and used collectively for decision making. This results in the effective size of the vector observation being passed to the Policy being: Space Size x Stacked Vectors . Actions Continuous Actions - The number of concurrent continuous actions that the Agent can take. Discrete Branches - An array of integers, defines multiple concurrent discrete actions. The values in the Discrete Branches array correspond to the number of possible discrete values for each action branch. Model - The neural network model used for inference (obtained after training) Inference Device - Whether to use CPU or GPU to run the model during inference Behavior Type - Determines whether the Agent will do training, inference, or use its Heuristic() method: Default - the Agent will train if they connect to a python trainer, otherwise they will perform inference. Heuristic Only - the Agent will always use the Heuristic() method. Inference Only - the Agent will always perform inference. Team ID - Used to define the team for self-play Use Child Sensors - Whether to use all Sensor components attached to child GameObjects of this Agent. Max Step - The per-agent maximum number of steps. Once this number is reached, the Agent will be reset.","title":"Agent Properties"},{"location":"Learning-Environment-Design-Agents/#destroying-an-agent","text":"You can destroy an Agent GameObject during the simulation. Make sure that there is always at least one Agent training at all times by either spawning a new Agent every time one is destroyed or by re-spawning new Agents when the whole environment resets.","title":"Destroying an Agent"},{"location":"Learning-Environment-Design-Agents/#defining-multi-agent-scenarios","text":"","title":"Defining Multi-agent Scenarios"},{"location":"Learning-Environment-Design-Agents/#teams-for-adversarial-scenarios","text":"Self-play is triggered by including the self-play hyperparameter hierarchy in the trainer configuration . To distinguish opposing agents, set the team ID to different integer values in the behavior parameters script on the agent prefab. Team ID must be 0 or an integer greater than 0. In symmetric games, since all agents (even on opposing teams) will share the same policy, they should have the same 'Behavior Name' in their Behavior Parameters Script. In asymmetric games, they should have a different Behavior Name in their Behavior Parameters script. Note, in asymmetric games, the agents must have both different Behavior Names and different team IDs! For examples of how to use this feature, you can see the trainer configurations and agent prefabs for our Tennis and Soccer environments. Tennis and Soccer provide examples of symmetric games. To train an asymmetric game, specify trainer configurations for each of your behavior names and include the self-play hyperparameter hierarchy in both.","title":"Teams for Adversarial Scenarios"},{"location":"Learning-Environment-Design-Agents/#groups-for-cooperative-scenarios","text":"Cooperative behavior in ML-Agents can be enabled by instantiating a SimpleMultiAgentGroup , typically in an environment controller or similar script, and adding agents to it using the RegisterAgent(Agent agent) method. Note that all agents added to the same SimpleMultiAgentGroup must have the same behavior name and Behavior Parameters. Using SimpleMultiAgentGroup enables the agents within a group to learn how to work together to achieve a common goal (i.e., maximize a group-given reward), even if one or more of the group members are removed before the episode ends. You can then use this group to add/set rewards, end or interrupt episodes at a group level using the AddGroupReward() , SetGroupReward() , EndGroupEpisode() , and GroupEpisodeInterrupted() methods. For example: // Create a Multi Agent Group in Start() or Initialize() m_AgentGroup = new SimpleMultiAgentGroup(); // Register agents in group at the beginning of an episode for (var agent in AgentList) { m_AgentGroup.RegisterAgent(agent); } // if the team scores a goal m_AgentGroup.AddGroupReward(rewardForGoal); // If the goal is reached and the episode is over m_AgentGroup.EndGroupEpisode(); ResetScene(); // If time ran out and we need to interrupt the episode m_AgentGroup.GroupEpisodeInterrupted(); ResetScene(); Multi Agent Groups should be used with the MA-POCA trainer, which is explicitly designed to train cooperative environments. This can be enabled by using the poca trainer - see the training configurations doc for more information on configuring MA-POCA. When using MA-POCA, agents which are deactivated or removed from the Scene during the episode will still learn to contribute to the group's long term rewards, even if they are not active in the scene to experience them. See the Cooperative Push Block environment for an example of how to use Multi Agent Groups, and the Dungeon Escape environment for an example of how the Multi Agent Group can be used with agents that are removed from the scene mid-episode. NOTE : Groups differ from Teams (for competitive settings) in the following way - Agents working together should be added to the same Group, while agents playing against each other should be given different Team Ids. If in the Scene there is one playing field and two teams, there should be two Groups, one for each team, and each team should be assigned a different Team Id. If this playing field is duplicated many times in the Scene (e.g. for training speedup), there should be two Groups per playing field , and two unique Team Ids for the entire Scene . In environments with both Groups and Team Ids configured, MA-POCA and self-play can be used together for training. In the diagram below, there are two agents on each team, and two playing fields where teams are pitted against each other. All the blue agents should share a Team Id (and the orange ones a different ID), and there should be four group managers, one per pair of agents. Please see the SoccerTwos environment for an example.","title":"Groups for Cooperative Scenarios"},{"location":"Learning-Environment-Design-Agents/#cooperative-behaviors-notes-and-best-practices","text":"An agent can only be registered to one MultiAgentGroup at a time. If you want to re-assign an agent from one group to another, you have to unregister it from the current group first. Agents with different behavior names in the same group are not supported. Agents within groups should always set the Max Steps parameter in the Agent script to 0. Instead, handle Max Steps using the MultiAgentGroup by ending the episode for the entire Group using GroupEpisodeInterrupted() . EndGroupEpisode and GroupEpisodeInterrupted do the same job in the game, but has slightly different effect on the training. If the episode is completed, you would want to call EndGroupEpisode . But if the episode is not over but it has been running for enough steps, i.e. reaching max step, you would call GroupEpisodeInterrupted . If an agent finished earlier, e.g. completed tasks/be removed/be killed in the game, do not call EndEpisode() on the Agent. Instead, disable the agent and re-enable it when the next episode starts, or destroy the agent entirely. This is because calling EndEpisode() will call OnEpisodeBegin() , which will reset the agent immediately. While it is possible to call EndEpisode() in this way, it is usually not the desired behavior when training groups of agents. If an agent that was disabled in a scene needs to be re-enabled, it must be re-registered to the MultiAgentGroup. Group rewards are meant to reinforce agents to act in the group's best interest instead of individual ones, and are treated differently than individual agent rewards during training. So calling AddGroupReward() is not equivalent to calling agent.AddReward() on each agent in the group. You can still add incremental rewards to agents using Agent.AddReward() if they are in a Group. These rewards will only be given to those agents and are received when the Agent is active. Environments which use Multi Agent Groups can be trained using PPO or SAC, but agents will not be able to learn from group rewards after deactivation/removal, nor will they behave as cooperatively.","title":"Cooperative Behaviors Notes and Best Practices"},{"location":"Learning-Environment-Design-Agents/#recording-demonstrations","text":"In order to record demonstrations from an agent, add the Demonstration Recorder component to a GameObject in the scene which contains an Agent component. Once added, it is possible to name the demonstration that will be recorded from the agent. When Record is checked, a demonstration will be created whenever the scene is played from the Editor. Depending on the complexity of the task, anywhere from a few minutes or a few hours of demonstration data may be necessary to be useful for imitation learning. To specify an exact number of steps you want to record use the Num Steps To Record field and the editor will end your play session automatically once that many steps are recorded. If you set Num Steps To Record to 0 then recording will continue until you manually end the play session. Once the play session ends a .demo file will be created in the Assets/Demonstrations folder (by default). This file contains the demonstrations. Clicking on the file will provide metadata about the demonstration in the inspector. You can then specify the path to this file in your training configurations .","title":"Recording Demonstrations"},{"location":"Learning-Environment-Design/","text":"Designing a Learning Environment This page contains general advice on how to design your learning environment, in addition to overviewing aspects of the ML-Agents Unity SDK that pertain to setting up your scene and simulation as opposed to designing your agents within the scene. We have a dedicated page on Designing Agents which includes how to instrument observations, actions and rewards, define teams for multi-agent scenarios and record agent demonstrations for imitation learning. To help on-board to the entire set of functionality provided by the ML-Agents Toolkit, we recommend exploring our API documentation . Additionally, our example environments are a great resource as they provide sample usage of almost all of our features. The Simulation and Training Process Training and simulation proceed in steps orchestrated by the ML-Agents Academy class. The Academy works with Agent objects in the scene to step through the simulation. During training, the external Python training process communicates with the Academy to run a series of episodes while it collects data and optimizes its neural network model. When training is completed successfully, you can add the trained model file to your Unity project for later use. The ML-Agents Academy class orchestrates the agent simulation loop as follows: Calls your Academy's OnEnvironmentReset delegate. Calls the OnEpisodeBegin() function for each Agent in the scene. Gathers information about the scene. This is done by calling the CollectObservations(VectorSensor sensor) function for each Agent in the scene, as well as updating their sensor and collecting the resulting observations. Uses each Agent's Policy to decide on the Agent's next action. Calls the OnActionReceived() function for each Agent in the scene, passing in the action chosen by the Agent's Policy. Calls the Agent's OnEpisodeBegin() function if the Agent has reached its Max Step count or has otherwise marked itself as EndEpisode() . To create a training environment, extend the Agent class to implement the above methods whether you need to implement them or not depends on your specific scenario. Organizing the Unity Scene To train and use the ML-Agents Toolkit in a Unity scene, the scene as many Agent subclasses as you need. Agent instances should be attached to the GameObject representing that Agent. Academy The Academy is a singleton which orchestrates Agents and their decision making processes. Only a single Academy exists at a time. Academy resetting To alter the environment at the start of each episode, add your method to the Academy's OnEnvironmentReset action. public class MySceneBehavior : MonoBehaviour { public void Awake() { Academy.Instance.OnEnvironmentReset += EnvironmentReset; } void EnvironmentReset() { // Reset the scene here } } For example, you might want to reset an Agent to its starting position or move a goal to a random position. An environment resets when the reset() method is called on the Python UnityEnvironment . When you reset an environment, consider the factors that should change so that training is generalizable to different conditions. For example, if you were training a maze-solving agent, you would probably want to change the maze itself for each training episode. Otherwise, the agent would probably on learn to solve one, particular maze, not mazes in general. Multiple Areas In many of the example environments, many copies of the training area are instantiated in the scene. This generally speeds up training, allowing the environment to gather many experiences in parallel. This can be achieved simply by instantiating many Agents with the same Behavior Name. If possible, consider designing your scene to support multiple areas. Check out our example environments to see examples of multiple areas. Additionally, the Making a New Learning Environment guide demonstrates this option. Environments When you create a training environment in Unity, you must set up the scene so that it can be controlled by the external training process. Considerations include: The training scene must start automatically when your Unity application is launched by the training process. The Academy must reset the scene to a valid starting point for each episode of training. A training episode must have a definite end \u2014 either using Max Steps or by each Agent ending its episode manually with EndEpisode() . Environment Parameters Curriculum learning and environment parameter randomization are two training methods that control specific parameters in your environment. As such, it is important to ensure that your environment parameters are updated at each step to the correct values. To enable this, we expose a EnvironmentParameters C# class that you can use to retrieve the values of the parameters defined in the training configurations for both of those features. Please see our documentation for curriculum learning and environment parameter randomization for details. We recommend modifying the environment from the Agent's OnEpisodeBegin() function by leveraging Academy.Instance.EnvironmentParameters . See the WallJump example environment for a sample usage (specifically, WallJumpAgent.cs ). Agent The Agent class represents an actor in the scene that collects observations and carries out actions. The Agent class is typically attached to the GameObject in the scene that otherwise represents the actor \u2014 for example, to a player object in a football game or a car object in a vehicle simulation. Every Agent must have appropriate Behavior Parameters . Generally, when creating an Agent, you should extend the Agent class and implement the CollectObservations(VectorSensor sensor) and OnActionReceived() methods: CollectObservations(VectorSensor sensor) \u2014 Collects the Agent's observation of its environment. OnActionReceived() \u2014 Carries out the action chosen by the Agent's Policy and assigns a reward to the current state. Your implementations of these functions determine how the Behavior Parameters assigned to this Agent must be set. You must also determine how an Agent finishes its task or times out. You can manually terminate an Agent episode in your OnActionReceived() function when the Agent has finished (or irrevocably failed) its task by calling the EndEpisode() function. You can also set the Agent's Max Steps property to a positive value and the Agent will consider the episode over after it has taken that many steps. You can use the Agent.OnEpisodeBegin() function to prepare the Agent to start again. See Agents for detailed information about programming your own Agents. Recording Statistics We offer developers a mechanism to record statistics from within their Unity environments. These statistics are aggregated and generated during the training process. To record statistics, see the StatsRecorder C# class. See the FoodCollector example environment for a sample usage (specifically, FoodCollectorSettings.cs ).","title":"Designing a Learning Environment"},{"location":"Learning-Environment-Design/#designing-a-learning-environment","text":"This page contains general advice on how to design your learning environment, in addition to overviewing aspects of the ML-Agents Unity SDK that pertain to setting up your scene and simulation as opposed to designing your agents within the scene. We have a dedicated page on Designing Agents which includes how to instrument observations, actions and rewards, define teams for multi-agent scenarios and record agent demonstrations for imitation learning. To help on-board to the entire set of functionality provided by the ML-Agents Toolkit, we recommend exploring our API documentation . Additionally, our example environments are a great resource as they provide sample usage of almost all of our features.","title":"Designing a Learning Environment"},{"location":"Learning-Environment-Design/#the-simulation-and-training-process","text":"Training and simulation proceed in steps orchestrated by the ML-Agents Academy class. The Academy works with Agent objects in the scene to step through the simulation. During training, the external Python training process communicates with the Academy to run a series of episodes while it collects data and optimizes its neural network model. When training is completed successfully, you can add the trained model file to your Unity project for later use. The ML-Agents Academy class orchestrates the agent simulation loop as follows: Calls your Academy's OnEnvironmentReset delegate. Calls the OnEpisodeBegin() function for each Agent in the scene. Gathers information about the scene. This is done by calling the CollectObservations(VectorSensor sensor) function for each Agent in the scene, as well as updating their sensor and collecting the resulting observations. Uses each Agent's Policy to decide on the Agent's next action. Calls the OnActionReceived() function for each Agent in the scene, passing in the action chosen by the Agent's Policy. Calls the Agent's OnEpisodeBegin() function if the Agent has reached its Max Step count or has otherwise marked itself as EndEpisode() . To create a training environment, extend the Agent class to implement the above methods whether you need to implement them or not depends on your specific scenario.","title":"The Simulation and Training Process"},{"location":"Learning-Environment-Design/#organizing-the-unity-scene","text":"To train and use the ML-Agents Toolkit in a Unity scene, the scene as many Agent subclasses as you need. Agent instances should be attached to the GameObject representing that Agent.","title":"Organizing the Unity Scene"},{"location":"Learning-Environment-Design/#academy","text":"The Academy is a singleton which orchestrates Agents and their decision making processes. Only a single Academy exists at a time.","title":"Academy"},{"location":"Learning-Environment-Design/#academy-resetting","text":"To alter the environment at the start of each episode, add your method to the Academy's OnEnvironmentReset action. public class MySceneBehavior : MonoBehaviour { public void Awake() { Academy.Instance.OnEnvironmentReset += EnvironmentReset; } void EnvironmentReset() { // Reset the scene here } } For example, you might want to reset an Agent to its starting position or move a goal to a random position. An environment resets when the reset() method is called on the Python UnityEnvironment . When you reset an environment, consider the factors that should change so that training is generalizable to different conditions. For example, if you were training a maze-solving agent, you would probably want to change the maze itself for each training episode. Otherwise, the agent would probably on learn to solve one, particular maze, not mazes in general.","title":"Academy resetting"},{"location":"Learning-Environment-Design/#multiple-areas","text":"In many of the example environments, many copies of the training area are instantiated in the scene. This generally speeds up training, allowing the environment to gather many experiences in parallel. This can be achieved simply by instantiating many Agents with the same Behavior Name. If possible, consider designing your scene to support multiple areas. Check out our example environments to see examples of multiple areas. Additionally, the Making a New Learning Environment guide demonstrates this option.","title":"Multiple Areas"},{"location":"Learning-Environment-Design/#environments","text":"When you create a training environment in Unity, you must set up the scene so that it can be controlled by the external training process. Considerations include: The training scene must start automatically when your Unity application is launched by the training process. The Academy must reset the scene to a valid starting point for each episode of training. A training episode must have a definite end \u2014 either using Max Steps or by each Agent ending its episode manually with EndEpisode() .","title":"Environments"},{"location":"Learning-Environment-Design/#environment-parameters","text":"Curriculum learning and environment parameter randomization are two training methods that control specific parameters in your environment. As such, it is important to ensure that your environment parameters are updated at each step to the correct values. To enable this, we expose a EnvironmentParameters C# class that you can use to retrieve the values of the parameters defined in the training configurations for both of those features. Please see our documentation for curriculum learning and environment parameter randomization for details. We recommend modifying the environment from the Agent's OnEpisodeBegin() function by leveraging Academy.Instance.EnvironmentParameters . See the WallJump example environment for a sample usage (specifically, WallJumpAgent.cs ).","title":"Environment Parameters"},{"location":"Learning-Environment-Design/#agent","text":"The Agent class represents an actor in the scene that collects observations and carries out actions. The Agent class is typically attached to the GameObject in the scene that otherwise represents the actor \u2014 for example, to a player object in a football game or a car object in a vehicle simulation. Every Agent must have appropriate Behavior Parameters . Generally, when creating an Agent, you should extend the Agent class and implement the CollectObservations(VectorSensor sensor) and OnActionReceived() methods: CollectObservations(VectorSensor sensor) \u2014 Collects the Agent's observation of its environment. OnActionReceived() \u2014 Carries out the action chosen by the Agent's Policy and assigns a reward to the current state. Your implementations of these functions determine how the Behavior Parameters assigned to this Agent must be set. You must also determine how an Agent finishes its task or times out. You can manually terminate an Agent episode in your OnActionReceived() function when the Agent has finished (or irrevocably failed) its task by calling the EndEpisode() function. You can also set the Agent's Max Steps property to a positive value and the Agent will consider the episode over after it has taken that many steps. You can use the Agent.OnEpisodeBegin() function to prepare the Agent to start again. See Agents for detailed information about programming your own Agents.","title":"Agent"},{"location":"Learning-Environment-Design/#recording-statistics","text":"We offer developers a mechanism to record statistics from within their Unity environments. These statistics are aggregated and generated during the training process. To record statistics, see the StatsRecorder C# class. See the FoodCollector example environment for a sample usage (specifically, FoodCollectorSettings.cs ).","title":"Recording Statistics"},{"location":"Learning-Environment-Examples/","text":"Example Learning Environments The Unity ML-Agents Toolkit includes an expanding set of example environments that highlight the various features of the toolkit. These environments can also serve as templates for new environments or as ways to test new ML algorithms. Environments are located in Project/Assets/ML-Agents/Examples and summarized below. For the environments that highlight specific features of the toolkit, we provide the pre-trained model files and the training config file that enables you to train the scene yourself. The environments that are designed to serve as challenges for researchers do not have accompanying pre-trained model files or training configs and are marked as Optional below. This page only overviews the example environments we provide. To learn more on how to design and build your own environments see our Making a New Learning Environment page. If you would like to contribute environments, please see our contribution guidelines page. Basic Set-up: A linear movement task where the agent must move left or right to rewarding states. Goal: Move to the most reward state. Agents: The environment contains one agent. Agent Reward Function: -0.01 at each step +0.1 for arriving at suboptimal state. +1.0 for arriving at optimal state. Behavior Parameters: Vector Observation space: One variable corresponding to current state. Actions: 1 discrete action branch with 3 actions (Move left, do nothing, move right). Visual Observations: None Float Properties: None Benchmark Mean Reward: 0.93 3DBall: 3D Balance Ball Set-up: A balance-ball task, where the agent balances the ball on it's head. Goal: The agent must balance the ball on it's head for as long as possible. Agents: The environment contains 12 agents of the same kind, all using the same Behavior Parameters. Agent Reward Function: +0.1 for every step the ball remains on it's head. -1.0 if the ball falls off. Behavior Parameters: Vector Observation space: 8 variables corresponding to rotation of the agent cube, and position and velocity of ball. Vector Observation space (Hard Version): 5 variables corresponding to rotation of the agent cube and position of ball. Actions: 2 continuous actions, with one value corresponding to X-rotation, and the other to Z-rotation. Visual Observations: Third-person view from the upper-front of the agent. Use Visual3DBall scene. Float Properties: Three scale: Specifies the scale of the ball in the 3 dimensions (equal across the three dimensions) Default: 1 Recommended Minimum: 0.2 Recommended Maximum: 5 gravity: Magnitude of gravity Default: 9.81 Recommended Minimum: 4 Recommended Maximum: 105 mass: Specifies mass of the ball Default: 1 Recommended Minimum: 0.1 Recommended Maximum: 20 Benchmark Mean Reward: 100 GridWorld Set-up: A multi-goal version of the grid-world task. Scene contains agent, goal, and obstacles. Goal: The agent must navigate the grid to the appropriate goal while avoiding the obstacles. Agents: The environment contains nine agents with the same Behavior Parameters. Agent Reward Function: -0.01 for every step. +1.0 if the agent navigates to the correct goal (episode ends). -1.0 if the agent navigates to an incorrect goal (episode ends). Behavior Parameters: Vector Observation space: None Actions: 1 discrete action branch with 5 actions, corresponding to movement in cardinal directions or not moving. Note that for this environment, action masking is turned on by default (this option can be toggled using the Mask Actions checkbox within the trueAgent GameObject). The trained model file provided was generated with action masking turned on. Visual Observations: One corresponding to top-down view of GridWorld. Goal Signal : A one hot vector corresponding to which color is the correct goal for the Agent Float Properties: Three, corresponding to grid size, number of green goals, and number of red goals. Benchmark Mean Reward: 0.8 Push Block Set-up: A platforming environment where the agent can push a block around. Goal: The agent must push the block to the goal. Agents: The environment contains one agent. Agent Reward Function: -0.0025 for every step. +1.0 if the block touches the goal. Behavior Parameters: Vector Observation space: (Continuous) 70 variables corresponding to 14 ray-casts each detecting one of three possible objects (wall, goal, or block). Actions: 1 discrete action branch with 7 actions, corresponding to turn clockwise and counterclockwise, move along four different face directions, or do nothing. Float Properties: Four block_scale: Scale of the block along the x and z dimensions Default: 2 Recommended Minimum: 0.5 Recommended Maximum: 4 dynamic_friction: Coefficient of friction for the ground material acting on moving objects Default: 0 Recommended Minimum: 0 Recommended Maximum: 1 static_friction: Coefficient of friction for the ground material acting on stationary objects Default: 0 Recommended Minimum: 0 Recommended Maximum: 1 block_drag: Effect of air resistance on block Default: 0.5 Recommended Minimum: 0 Recommended Maximum: 2000 Benchmark Mean Reward: 4.5 Wall Jump Set-up: A platforming environment where the agent can jump over a wall. Goal: The agent must use the block to scale the wall and reach the goal. Agents: The environment contains one agent linked to two different Models. The Policy the agent is linked to changes depending on the height of the wall. The change of Policy is done in the WallJumpAgent class. Agent Reward Function: -0.0005 for every step. +1.0 if the agent touches the goal. -1.0 if the agent falls off the platform. Behavior Parameters: Vector Observation space: Size of 74, corresponding to 14 ray casts each detecting 4 possible objects. plus the global position of the agent and whether or not the agent is grounded. Actions: 4 discrete action branches: Forward Motion (3 possible actions: Forward, Backwards, No Action) Rotation (3 possible actions: Rotate Left, Rotate Right, No Action) Side Motion (3 possible actions: Left, Right, No Action) Jump (2 possible actions: Jump, No Action) Visual Observations: None Float Properties: Four Benchmark Mean Reward (Big & Small Wall): 0.8 Crawler Set-up: A creature with 4 arms and 4 forearms. Goal: The agents must move its body toward the goal direction without falling. Agents: The environment contains 10 agents with same Behavior Parameters. Agent Reward Function (independent): The reward function is now geometric meaning the reward each step is a product of all the rewards instead of a sum, this helps the agent try to maximize all rewards instead of the easiest rewards. Body velocity matches goal velocity. (normalized between (0,1)) Head direction alignment with goal direction. (normalized between (0,1)) Behavior Parameters: Vector Observation space: 172 variables corresponding to position, rotation, velocity, and angular velocities of each limb plus the acceleration and angular acceleration of the body. Actions: 20 continuous actions, corresponding to target rotations for joints. Visual Observations: None Float Properties: None Benchmark Mean Reward: 3000 Worm Set-up: A worm with a head and 3 body segments. Goal: The agents must move its body toward the goal direction. Agents: The environment contains 10 agents with same Behavior Parameters. Agent Reward Function (independent): The reward function is now geometric meaning the reward each step is a product of all the rewards instead of a sum, this helps the agent try to maximize all rewards instead of the easiest rewards. Body velocity matches goal velocity. (normalized between (0,1)) Body direction alignment with goal direction. (normalized between (0,1)) Behavior Parameters: Vector Observation space: 64 variables corresponding to position, rotation, velocity, and angular velocities of each limb plus the acceleration and angular acceleration of the body. Actions: 9 continuous actions, corresponding to target rotations for joints. Visual Observations: None Float Properties: None Benchmark Mean Reward: 800 Food Collector Set-up: A multi-agent environment where agents compete to collect food. Goal: The agents must learn to collect as many green food spheres as possible while avoiding red spheres. Agents: The environment contains 5 agents with same Behavior Parameters. Agent Reward Function (independent): +1 for interaction with green spheres -1 for interaction with red spheres Behavior Parameters: Vector Observation space: 53 corresponding to velocity of agent (2), whether agent is frozen and/or shot its laser (2), plus grid based perception of objects around agent's forward direction (40 by 40 with 6 different categories). Actions: 3 continuous actions correspond to Forward Motion, Side Motion and Rotation 1 discrete acion branch for Laser with 2 possible actions corresponding to Shoot Laser or No Action Visual Observations (Optional): First-person camera per-agent, plus one vector flag representing the frozen state of the agent. This scene uses a combination of vector and visual observations and the training will not succeed without the frozen vector flag. Use VisualFoodCollector scene. Float Properties: Two laser_length: Length of the laser used by the agent Default: 1 Recommended Minimum: 0.2 Recommended Maximum: 7 agent_scale: Specifies the scale of the agent in the 3 dimensions (equal across the three dimensions) Default: 1 Recommended Minimum: 0.5 Recommended Maximum: 5 Benchmark Mean Reward: 10 Hallway Set-up: Environment where the agent needs to find information in a room, remember it, and use it to move to the correct goal. Goal: Move to the goal which corresponds to the color of the block in the room. Agents: The environment contains one agent. Agent Reward Function (independent): +1 For moving to correct goal. -0.1 For moving to incorrect goal. -0.0003 Existential penalty. Behavior Parameters: Vector Observation space: 30 corresponding to local ray-casts detecting objects, goals, and walls. Actions: 1 discrete action Branch, with 4 actions corresponding to agent rotation and forward/backward movement. Float Properties: None Benchmark Mean Reward: 0.7 To train this environment, you can enable curiosity by adding the curiosity reward signal in config/ppo/Hallway.yaml Soccer Twos Set-up: Environment where four agents compete in a 2 vs 2 toy soccer game. Goal: Get the ball into the opponent's goal while preventing the ball from entering own goal. Agents: The environment contains two different Multi Agent Groups with two agents in each. Parameters : SoccerTwos. Agent Reward Function (dependent): (1 - accumulated time penalty ) When ball enters opponent's goal accumulated time penalty is incremented by (1 / MaxStep ) every fixed update and is reset to 0 at the beginning of an episode. -1 When ball enters team's goal. Behavior Parameters: Vector Observation space: 336 corresponding to 11 ray-casts forward distributed over 120 degrees and 3 ray-casts backward distributed over 90 degrees each detecting 6 possible object types, along with the object's distance. The forward ray-casts contribute 264 state dimensions and backward 72 state dimensions over three observation stacks. Actions: 3 discrete branched actions corresponding to forward, backward, sideways movement, as well as rotation. Visual Observations: None Float Properties: Two ball_scale: Specifies the scale of the ball in the 3 dimensions (equal across the three dimensions) Default: 7.5 Recommended minimum: 4 Recommended maximum: 10 gravity: Magnitude of the gravity Default: 9.81 Recommended minimum: 6 Recommended maximum: 20 Strikers Vs. Goalie Set-up: Environment where two agents compete in a 2 vs 1 soccer variant. Goal: Striker: Get the ball into the opponent's goal. Goalie: Keep the ball out of the goal. Agents: The environment contains two different Multi Agent Groups. One with two Strikers and the other one Goalie. Behavior Parameters : Striker, Goalie. Striker Agent Reward Function (dependent): +1 When ball enters opponent's goal. -0.001 Existential penalty. Goalie Agent Reward Function (dependent): -1 When ball enters goal. 0.001 Existential bonus. Behavior Parameters: Striker Vector Observation space: 294 corresponding to 11 ray-casts forward distributed over 120 degrees and 3 ray-casts backward distributed over 90 degrees each detecting 5 possible object types, along with the object's distance. The forward ray-casts contribute 231 state dimensions and backward 63 state dimensions over three observation stacks. Striker Actions: 3 discrete branched actions corresponding to forward, backward, sideways movement, as well as rotation. Goalie Vector Observation space: 738 corresponding to 41 ray-casts distributed over 360 degrees each detecting 4 possible object types, along with the object's distance and 3 observation stacks. Goalie Actions: 3 discrete branched actions corresponding to forward, backward, sideways movement, as well as rotation. Visual Observations: None Float Properties: Two ball_scale: Specifies the scale of the ball in the 3 dimensions (equal across the three dimensions) Default: 7.5 Recommended minimum: 4 Recommended maximum: 10 gravity: Magnitude of the gravity Default: 9.81 Recommended minimum: 6 Recommended maximum: 20 Walker Set-up: Physics-based Humanoid agents with 26 degrees of freedom. These DOFs correspond to articulation of the following body-parts: hips, chest, spine, head, thighs, shins, feet, arms, forearms and hands. Goal: The agents must move its body toward the goal direction without falling. Agents: The environment contains 10 independent agents with same Behavior Parameters. Agent Reward Function (independent): The reward function is now geometric meaning the reward each step is a product of all the rewards instead of a sum, this helps the agent try to maximize all rewards instead of the easiest rewards. Body velocity matches goal velocity. (normalized between (0,1)) Head direction alignment with goal direction. (normalized between (0,1)) Behavior Parameters: Vector Observation space: 243 variables corresponding to position, rotation, velocity, and angular velocities of each limb, along with goal direction. Actions: 39 continuous actions, corresponding to target rotations and strength applicable to the joints. Visual Observations: None Float Properties: Four gravity: Magnitude of gravity Default: 9.81 Recommended Minimum: Recommended Maximum: hip_mass: Mass of the hip component of the walker Default: 8 Recommended Minimum: 7 Recommended Maximum: 28 chest_mass: Mass of the chest component of the walker Default: 8 Recommended Minimum: 3 Recommended Maximum: 20 spine_mass: Mass of the spine component of the walker Default: 8 Recommended Minimum: 3 Recommended Maximum: 20 Benchmark Mean Reward : 2500 Pyramids Set-up: Environment where the agent needs to press a button to spawn a pyramid, then navigate to the pyramid, knock it over, and move to the gold brick at the top. Goal: Move to the golden brick on top of the spawned pyramid. Agents: The environment contains one agent. Agent Reward Function (independent): +2 For moving to golden brick (minus 0.001 per step). Behavior Parameters: Vector Observation space: 148 corresponding to local ray-casts detecting switch, bricks, golden brick, and walls, plus variable indicating switch state. Actions: 1 discrete action branch, with 4 actions corresponding to agent rotation and forward/backward movement. Float Properties: None Benchmark Mean Reward: 1.75 Match 3 Set-up: Simple match-3 game. Matched pieces are removed, and remaining pieces drop down. New pieces are spawned randomly at the top, with a chance of being \"special\". Goal: Maximize score from matching pieces. Agents: The environment contains several independent Agents. Agent Reward Function (independent): .01 for each normal piece cleared. Special pieces are worth 2x or 3x. Behavior Parameters: None Observations and actions are defined with a sensor and actuator respectively. Float Properties: None Benchmark Mean Reward: 39.5 for visual observations 38.5 for vector observations 34.2 for simple heuristic (pick a random valid move) 37.0 for greedy heuristic (pick the highest-scoring valid move) Sorter Set-up: The Agent is in a circular room with numbered tiles. The values of the tiles are random between 1 and 20. The tiles present in the room are randomized at each episode. When the Agent visits a tile, it turns green. Goal: Visit all the tiles in ascending order. Agents: The environment contains a single Agent Agent Reward Function: -.0002 Existential penalty. +1 For visiting the right tile -1 For visiting the wrong tile BehaviorParameters: Vector Observations : 4 : 2 floats for Position and 2 floats for orientation Variable Length Observations : Between 1 and 20 entities (one for each tile) each with 22 observations, the first 20 are one hot encoding of the value of the tile, the 21st and 22nd represent the position of the tile relative to the Agent and the 23rd is 1 if the tile was visited and 0 otherwise. Actions: 3 discrete branched actions corresponding to forward, backward, sideways movement, as well as rotation. Float Properties: One num_tiles: The maximum number of tiles to sample. Default: 2 Recommended Minimum: 1 Recommended Maximum: 20 Benchmark Mean Reward: Depends on the number of tiles. Cooperative Push Block Set-up: Similar to Push Block, the agents are in an area with blocks that need to be pushed into a goal. Small blocks can be pushed by one agents and are worth +1 value, medium blocks require two agents to push in and are worth +2, and large blocks require all 3 agents to push and are worth +3. Goal: Push all blocks into the goal. Agents: The environment contains three Agents in a Multi Agent Group. Agent Reward Function: -0.0001 Existential penalty, as a group reward. +1, +2, or +3 for pushing in a block, added as a group reward. Behavior Parameters: Observation space: A single Grid Sensor with separate tags for each block size, the goal, the walls, and other agents. Actions: 1 discrete action branch with 7 actions, corresponding to turn clockwise and counterclockwise, move along four different face directions, or do nothing. Float Properties: None Benchmark Mean Reward: 11 (Group Reward) Dungeon Escape Set-up: Agents are trapped in a dungeon with a dragon, and must work together to escape. To retrieve the key, one of the agents must find and slay the dragon, sacrificing itself to do so. The dragon will drop a key for the others to use. The other agents can then pick up this key and unlock the dungeon door. If the agents take too long, the dragon will escape through a portal and the environment resets. Goal: Unlock the dungeon door and leave. Agents: The environment contains three Agents in a Multi Agent Group and one Dragon, which moves in a predetermined pattern. Agent Reward Function: +1 group reward if any agent successfully unlocks the door and leaves the dungeon. Behavior Parameters: Observation space: A Ray Perception Sensor with separate tags for the walls, other agents, the door, key, the dragon, and the dragon's portal. A single Vector Observation which indicates whether the agent is holding a key. Actions: 1 discrete action branch with 7 actions, corresponding to turn clockwise and counterclockwise, move along four different face directions, or do nothing. Float Properties: None Benchmark Mean Reward: 1.0 (Group Reward)","title":"Example Learning Environments"},{"location":"Learning-Environment-Examples/#example-learning-environments","text":"The Unity ML-Agents Toolkit includes an expanding set of example environments that highlight the various features of the toolkit. These environments can also serve as templates for new environments or as ways to test new ML algorithms. Environments are located in Project/Assets/ML-Agents/Examples and summarized below. For the environments that highlight specific features of the toolkit, we provide the pre-trained model files and the training config file that enables you to train the scene yourself. The environments that are designed to serve as challenges for researchers do not have accompanying pre-trained model files or training configs and are marked as Optional below. This page only overviews the example environments we provide. To learn more on how to design and build your own environments see our Making a New Learning Environment page. If you would like to contribute environments, please see our contribution guidelines page.","title":"Example Learning Environments"},{"location":"Learning-Environment-Examples/#basic","text":"Set-up: A linear movement task where the agent must move left or right to rewarding states. Goal: Move to the most reward state. Agents: The environment contains one agent. Agent Reward Function: -0.01 at each step +0.1 for arriving at suboptimal state. +1.0 for arriving at optimal state. Behavior Parameters: Vector Observation space: One variable corresponding to current state. Actions: 1 discrete action branch with 3 actions (Move left, do nothing, move right). Visual Observations: None Float Properties: None Benchmark Mean Reward: 0.93","title":"Basic"},{"location":"Learning-Environment-Examples/#3dball-3d-balance-ball","text":"Set-up: A balance-ball task, where the agent balances the ball on it's head. Goal: The agent must balance the ball on it's head for as long as possible. Agents: The environment contains 12 agents of the same kind, all using the same Behavior Parameters. Agent Reward Function: +0.1 for every step the ball remains on it's head. -1.0 if the ball falls off. Behavior Parameters: Vector Observation space: 8 variables corresponding to rotation of the agent cube, and position and velocity of ball. Vector Observation space (Hard Version): 5 variables corresponding to rotation of the agent cube and position of ball. Actions: 2 continuous actions, with one value corresponding to X-rotation, and the other to Z-rotation. Visual Observations: Third-person view from the upper-front of the agent. Use Visual3DBall scene. Float Properties: Three scale: Specifies the scale of the ball in the 3 dimensions (equal across the three dimensions) Default: 1 Recommended Minimum: 0.2 Recommended Maximum: 5 gravity: Magnitude of gravity Default: 9.81 Recommended Minimum: 4 Recommended Maximum: 105 mass: Specifies mass of the ball Default: 1 Recommended Minimum: 0.1 Recommended Maximum: 20 Benchmark Mean Reward: 100","title":"3DBall: 3D Balance Ball"},{"location":"Learning-Environment-Examples/#gridworld","text":"Set-up: A multi-goal version of the grid-world task. Scene contains agent, goal, and obstacles. Goal: The agent must navigate the grid to the appropriate goal while avoiding the obstacles. Agents: The environment contains nine agents with the same Behavior Parameters. Agent Reward Function: -0.01 for every step. +1.0 if the agent navigates to the correct goal (episode ends). -1.0 if the agent navigates to an incorrect goal (episode ends). Behavior Parameters: Vector Observation space: None Actions: 1 discrete action branch with 5 actions, corresponding to movement in cardinal directions or not moving. Note that for this environment, action masking is turned on by default (this option can be toggled using the Mask Actions checkbox within the trueAgent GameObject). The trained model file provided was generated with action masking turned on. Visual Observations: One corresponding to top-down view of GridWorld. Goal Signal : A one hot vector corresponding to which color is the correct goal for the Agent Float Properties: Three, corresponding to grid size, number of green goals, and number of red goals. Benchmark Mean Reward: 0.8","title":"GridWorld"},{"location":"Learning-Environment-Examples/#push-block","text":"Set-up: A platforming environment where the agent can push a block around. Goal: The agent must push the block to the goal. Agents: The environment contains one agent. Agent Reward Function: -0.0025 for every step. +1.0 if the block touches the goal. Behavior Parameters: Vector Observation space: (Continuous) 70 variables corresponding to 14 ray-casts each detecting one of three possible objects (wall, goal, or block). Actions: 1 discrete action branch with 7 actions, corresponding to turn clockwise and counterclockwise, move along four different face directions, or do nothing. Float Properties: Four block_scale: Scale of the block along the x and z dimensions Default: 2 Recommended Minimum: 0.5 Recommended Maximum: 4 dynamic_friction: Coefficient of friction for the ground material acting on moving objects Default: 0 Recommended Minimum: 0 Recommended Maximum: 1 static_friction: Coefficient of friction for the ground material acting on stationary objects Default: 0 Recommended Minimum: 0 Recommended Maximum: 1 block_drag: Effect of air resistance on block Default: 0.5 Recommended Minimum: 0 Recommended Maximum: 2000 Benchmark Mean Reward: 4.5","title":"Push Block"},{"location":"Learning-Environment-Examples/#wall-jump","text":"Set-up: A platforming environment where the agent can jump over a wall. Goal: The agent must use the block to scale the wall and reach the goal. Agents: The environment contains one agent linked to two different Models. The Policy the agent is linked to changes depending on the height of the wall. The change of Policy is done in the WallJumpAgent class. Agent Reward Function: -0.0005 for every step. +1.0 if the agent touches the goal. -1.0 if the agent falls off the platform. Behavior Parameters: Vector Observation space: Size of 74, corresponding to 14 ray casts each detecting 4 possible objects. plus the global position of the agent and whether or not the agent is grounded. Actions: 4 discrete action branches: Forward Motion (3 possible actions: Forward, Backwards, No Action) Rotation (3 possible actions: Rotate Left, Rotate Right, No Action) Side Motion (3 possible actions: Left, Right, No Action) Jump (2 possible actions: Jump, No Action) Visual Observations: None Float Properties: Four Benchmark Mean Reward (Big & Small Wall): 0.8","title":"Wall Jump"},{"location":"Learning-Environment-Examples/#crawler","text":"Set-up: A creature with 4 arms and 4 forearms. Goal: The agents must move its body toward the goal direction without falling. Agents: The environment contains 10 agents with same Behavior Parameters. Agent Reward Function (independent): The reward function is now geometric meaning the reward each step is a product of all the rewards instead of a sum, this helps the agent try to maximize all rewards instead of the easiest rewards. Body velocity matches goal velocity. (normalized between (0,1)) Head direction alignment with goal direction. (normalized between (0,1)) Behavior Parameters: Vector Observation space: 172 variables corresponding to position, rotation, velocity, and angular velocities of each limb plus the acceleration and angular acceleration of the body. Actions: 20 continuous actions, corresponding to target rotations for joints. Visual Observations: None Float Properties: None Benchmark Mean Reward: 3000","title":"Crawler"},{"location":"Learning-Environment-Examples/#worm","text":"Set-up: A worm with a head and 3 body segments. Goal: The agents must move its body toward the goal direction. Agents: The environment contains 10 agents with same Behavior Parameters. Agent Reward Function (independent): The reward function is now geometric meaning the reward each step is a product of all the rewards instead of a sum, this helps the agent try to maximize all rewards instead of the easiest rewards. Body velocity matches goal velocity. (normalized between (0,1)) Body direction alignment with goal direction. (normalized between (0,1)) Behavior Parameters: Vector Observation space: 64 variables corresponding to position, rotation, velocity, and angular velocities of each limb plus the acceleration and angular acceleration of the body. Actions: 9 continuous actions, corresponding to target rotations for joints. Visual Observations: None Float Properties: None Benchmark Mean Reward: 800","title":"Worm"},{"location":"Learning-Environment-Examples/#food-collector","text":"Set-up: A multi-agent environment where agents compete to collect food. Goal: The agents must learn to collect as many green food spheres as possible while avoiding red spheres. Agents: The environment contains 5 agents with same Behavior Parameters. Agent Reward Function (independent): +1 for interaction with green spheres -1 for interaction with red spheres Behavior Parameters: Vector Observation space: 53 corresponding to velocity of agent (2), whether agent is frozen and/or shot its laser (2), plus grid based perception of objects around agent's forward direction (40 by 40 with 6 different categories). Actions: 3 continuous actions correspond to Forward Motion, Side Motion and Rotation 1 discrete acion branch for Laser with 2 possible actions corresponding to Shoot Laser or No Action Visual Observations (Optional): First-person camera per-agent, plus one vector flag representing the frozen state of the agent. This scene uses a combination of vector and visual observations and the training will not succeed without the frozen vector flag. Use VisualFoodCollector scene. Float Properties: Two laser_length: Length of the laser used by the agent Default: 1 Recommended Minimum: 0.2 Recommended Maximum: 7 agent_scale: Specifies the scale of the agent in the 3 dimensions (equal across the three dimensions) Default: 1 Recommended Minimum: 0.5 Recommended Maximum: 5 Benchmark Mean Reward: 10","title":"Food Collector"},{"location":"Learning-Environment-Examples/#hallway","text":"Set-up: Environment where the agent needs to find information in a room, remember it, and use it to move to the correct goal. Goal: Move to the goal which corresponds to the color of the block in the room. Agents: The environment contains one agent. Agent Reward Function (independent): +1 For moving to correct goal. -0.1 For moving to incorrect goal. -0.0003 Existential penalty. Behavior Parameters: Vector Observation space: 30 corresponding to local ray-casts detecting objects, goals, and walls. Actions: 1 discrete action Branch, with 4 actions corresponding to agent rotation and forward/backward movement. Float Properties: None Benchmark Mean Reward: 0.7 To train this environment, you can enable curiosity by adding the curiosity reward signal in config/ppo/Hallway.yaml","title":"Hallway"},{"location":"Learning-Environment-Examples/#soccer-twos","text":"Set-up: Environment where four agents compete in a 2 vs 2 toy soccer game. Goal: Get the ball into the opponent's goal while preventing the ball from entering own goal. Agents: The environment contains two different Multi Agent Groups with two agents in each. Parameters : SoccerTwos. Agent Reward Function (dependent): (1 - accumulated time penalty ) When ball enters opponent's goal accumulated time penalty is incremented by (1 / MaxStep ) every fixed update and is reset to 0 at the beginning of an episode. -1 When ball enters team's goal. Behavior Parameters: Vector Observation space: 336 corresponding to 11 ray-casts forward distributed over 120 degrees and 3 ray-casts backward distributed over 90 degrees each detecting 6 possible object types, along with the object's distance. The forward ray-casts contribute 264 state dimensions and backward 72 state dimensions over three observation stacks. Actions: 3 discrete branched actions corresponding to forward, backward, sideways movement, as well as rotation. Visual Observations: None Float Properties: Two ball_scale: Specifies the scale of the ball in the 3 dimensions (equal across the three dimensions) Default: 7.5 Recommended minimum: 4 Recommended maximum: 10 gravity: Magnitude of the gravity Default: 9.81 Recommended minimum: 6 Recommended maximum: 20","title":"Soccer Twos"},{"location":"Learning-Environment-Examples/#strikers-vs-goalie","text":"Set-up: Environment where two agents compete in a 2 vs 1 soccer variant. Goal: Striker: Get the ball into the opponent's goal. Goalie: Keep the ball out of the goal. Agents: The environment contains two different Multi Agent Groups. One with two Strikers and the other one Goalie. Behavior Parameters : Striker, Goalie. Striker Agent Reward Function (dependent): +1 When ball enters opponent's goal. -0.001 Existential penalty. Goalie Agent Reward Function (dependent): -1 When ball enters goal. 0.001 Existential bonus. Behavior Parameters: Striker Vector Observation space: 294 corresponding to 11 ray-casts forward distributed over 120 degrees and 3 ray-casts backward distributed over 90 degrees each detecting 5 possible object types, along with the object's distance. The forward ray-casts contribute 231 state dimensions and backward 63 state dimensions over three observation stacks. Striker Actions: 3 discrete branched actions corresponding to forward, backward, sideways movement, as well as rotation. Goalie Vector Observation space: 738 corresponding to 41 ray-casts distributed over 360 degrees each detecting 4 possible object types, along with the object's distance and 3 observation stacks. Goalie Actions: 3 discrete branched actions corresponding to forward, backward, sideways movement, as well as rotation. Visual Observations: None Float Properties: Two ball_scale: Specifies the scale of the ball in the 3 dimensions (equal across the three dimensions) Default: 7.5 Recommended minimum: 4 Recommended maximum: 10 gravity: Magnitude of the gravity Default: 9.81 Recommended minimum: 6 Recommended maximum: 20","title":"Strikers Vs. Goalie"},{"location":"Learning-Environment-Examples/#walker","text":"Set-up: Physics-based Humanoid agents with 26 degrees of freedom. These DOFs correspond to articulation of the following body-parts: hips, chest, spine, head, thighs, shins, feet, arms, forearms and hands. Goal: The agents must move its body toward the goal direction without falling. Agents: The environment contains 10 independent agents with same Behavior Parameters. Agent Reward Function (independent): The reward function is now geometric meaning the reward each step is a product of all the rewards instead of a sum, this helps the agent try to maximize all rewards instead of the easiest rewards. Body velocity matches goal velocity. (normalized between (0,1)) Head direction alignment with goal direction. (normalized between (0,1)) Behavior Parameters: Vector Observation space: 243 variables corresponding to position, rotation, velocity, and angular velocities of each limb, along with goal direction. Actions: 39 continuous actions, corresponding to target rotations and strength applicable to the joints. Visual Observations: None Float Properties: Four gravity: Magnitude of gravity Default: 9.81 Recommended Minimum: Recommended Maximum: hip_mass: Mass of the hip component of the walker Default: 8 Recommended Minimum: 7 Recommended Maximum: 28 chest_mass: Mass of the chest component of the walker Default: 8 Recommended Minimum: 3 Recommended Maximum: 20 spine_mass: Mass of the spine component of the walker Default: 8 Recommended Minimum: 3 Recommended Maximum: 20 Benchmark Mean Reward : 2500","title":"Walker"},{"location":"Learning-Environment-Examples/#pyramids","text":"Set-up: Environment where the agent needs to press a button to spawn a pyramid, then navigate to the pyramid, knock it over, and move to the gold brick at the top. Goal: Move to the golden brick on top of the spawned pyramid. Agents: The environment contains one agent. Agent Reward Function (independent): +2 For moving to golden brick (minus 0.001 per step). Behavior Parameters: Vector Observation space: 148 corresponding to local ray-casts detecting switch, bricks, golden brick, and walls, plus variable indicating switch state. Actions: 1 discrete action branch, with 4 actions corresponding to agent rotation and forward/backward movement. Float Properties: None Benchmark Mean Reward: 1.75","title":"Pyramids"},{"location":"Learning-Environment-Examples/#match-3","text":"Set-up: Simple match-3 game. Matched pieces are removed, and remaining pieces drop down. New pieces are spawned randomly at the top, with a chance of being \"special\". Goal: Maximize score from matching pieces. Agents: The environment contains several independent Agents. Agent Reward Function (independent): .01 for each normal piece cleared. Special pieces are worth 2x or 3x. Behavior Parameters: None Observations and actions are defined with a sensor and actuator respectively. Float Properties: None Benchmark Mean Reward: 39.5 for visual observations 38.5 for vector observations 34.2 for simple heuristic (pick a random valid move) 37.0 for greedy heuristic (pick the highest-scoring valid move)","title":"Match 3"},{"location":"Learning-Environment-Examples/#sorter","text":"Set-up: The Agent is in a circular room with numbered tiles. The values of the tiles are random between 1 and 20. The tiles present in the room are randomized at each episode. When the Agent visits a tile, it turns green. Goal: Visit all the tiles in ascending order. Agents: The environment contains a single Agent Agent Reward Function: -.0002 Existential penalty. +1 For visiting the right tile -1 For visiting the wrong tile BehaviorParameters: Vector Observations : 4 : 2 floats for Position and 2 floats for orientation Variable Length Observations : Between 1 and 20 entities (one for each tile) each with 22 observations, the first 20 are one hot encoding of the value of the tile, the 21st and 22nd represent the position of the tile relative to the Agent and the 23rd is 1 if the tile was visited and 0 otherwise. Actions: 3 discrete branched actions corresponding to forward, backward, sideways movement, as well as rotation. Float Properties: One num_tiles: The maximum number of tiles to sample. Default: 2 Recommended Minimum: 1 Recommended Maximum: 20 Benchmark Mean Reward: Depends on the number of tiles.","title":"Sorter"},{"location":"Learning-Environment-Examples/#cooperative-push-block","text":"Set-up: Similar to Push Block, the agents are in an area with blocks that need to be pushed into a goal. Small blocks can be pushed by one agents and are worth +1 value, medium blocks require two agents to push in and are worth +2, and large blocks require all 3 agents to push and are worth +3. Goal: Push all blocks into the goal. Agents: The environment contains three Agents in a Multi Agent Group. Agent Reward Function: -0.0001 Existential penalty, as a group reward. +1, +2, or +3 for pushing in a block, added as a group reward. Behavior Parameters: Observation space: A single Grid Sensor with separate tags for each block size, the goal, the walls, and other agents. Actions: 1 discrete action branch with 7 actions, corresponding to turn clockwise and counterclockwise, move along four different face directions, or do nothing. Float Properties: None Benchmark Mean Reward: 11 (Group Reward)","title":"Cooperative Push Block"},{"location":"Learning-Environment-Examples/#dungeon-escape","text":"Set-up: Agents are trapped in a dungeon with a dragon, and must work together to escape. To retrieve the key, one of the agents must find and slay the dragon, sacrificing itself to do so. The dragon will drop a key for the others to use. The other agents can then pick up this key and unlock the dungeon door. If the agents take too long, the dragon will escape through a portal and the environment resets. Goal: Unlock the dungeon door and leave. Agents: The environment contains three Agents in a Multi Agent Group and one Dragon, which moves in a predetermined pattern. Agent Reward Function: +1 group reward if any agent successfully unlocks the door and leaves the dungeon. Behavior Parameters: Observation space: A Ray Perception Sensor with separate tags for the walls, other agents, the door, key, the dragon, and the dragon's portal. A single Vector Observation which indicates whether the agent is holding a key. Actions: 1 discrete action branch with 7 actions, corresponding to turn clockwise and counterclockwise, move along four different face directions, or do nothing. Float Properties: None Benchmark Mean Reward: 1.0 (Group Reward)","title":"Dungeon Escape"},{"location":"Learning-Environment-Executable/","text":"Using an Environment Executable This section will help you create and use built environments rather than the Editor to interact with an environment. Using an executable has some advantages over using the Editor: You can exchange executable with other people without having to share your entire repository. You can put your executable on a remote machine for faster training. You can use Server Build ( Headless ) mode for faster training (as long as the executable does not need rendering). You can keep using the Unity Editor for other tasks while the agents are training. Building the 3DBall environment The first step is to open the Unity scene containing the 3D Balance Ball environment: Launch Unity. On the Projects dialog, choose the Open option at the top of the window. Using the file dialog that opens, locate the Project folder within the ML-Agents project and click Open . In the Project window, navigate to the folder Assets/ML-Agents/Examples/3DBall/Scenes/ . Double-click the 3DBall file to load the scene containing the Balance Ball environment. Next, we want the set up scene to play correctly when the training process launches our environment executable. This means: The environment application runs in the background. No dialogs require interaction. The correct scene loads automatically. Open Player Settings (menu: Edit > Project Settings > Player ). Under Resolution and Presentation : Ensure that Run in Background is Checked. Ensure that Display Resolution Dialog is set to Disabled. (Note: this setting may not be available in newer versions of the editor.) Open the Build Settings window (menu: File > Build Settings ). Choose your target platform. (optional) Select \u201cDevelopment Build\u201d to log debug messages . If any scenes are shown in the Scenes in Build list, make sure that the 3DBall Scene is the only one checked. (If the list is empty, then only the current scene is included in the build). Click Build : In the File dialog, navigate to your ML-Agents directory. Assign a file name and click Save . (For Windows\uff09With Unity 2018.1, it will ask you to select a folder instead of a file name. Create a subfolder within the root directory and select that folder to build. In the following steps you will refer to this subfolder's name as env_name . You cannot create builds in the Assets folder Now that we have a Unity executable containing the simulation environment, we can interact with it. Interacting with the Environment If you want to use the Python API to interact with your executable, you can pass the name of the executable with the argument 'file_name' of the UnityEnvironment . For instance: from mlagents_envs.environment import UnityEnvironment env = UnityEnvironment(file_name=<env_name>) Training the Environment Open a command or terminal window. Navigate to the folder where you installed the ML-Agents Toolkit. If you followed the default installation , then navigate to the ml-agents/ folder. Run mlagents-learn <trainer-config-file> --env=<env_name> --run-id=<run-identifier> Where: <trainer-config-file> is the file path of the trainer configuration yaml <env_name> is the name and path to the executable you exported from Unity (without extension) <run-identifier> is a string used to separate the results of different training runs For example, if you are training with a 3DBall executable, and you saved it to the directory where you installed the ML-Agents Toolkit, run: mlagents-learn config/ppo/3DBall.yaml --env=3DBall --run-id=firstRun And you should see something like ml-agents$ mlagents-learn config/ppo/3DBall.yaml --env=3DBall --run-id=first-run \u2584\u2584\u2584\u2593\u2593\u2593\u2593 \u2553\u2593\u2593\u2593\u2593\u2593\u2593\u2588\u2593\u2593\u2593\u2593\u2593 ,\u2584\u2584\u2584m\u2580\u2580\u2580' ,\u2593\u2593\u2593\u2580\u2593\u2593\u2584 \u2593\u2593\u2593 \u2593\u2593\u258c \u2584\u2593\u2593\u2593\u2580' \u2584\u2593\u2593\u2580 \u2593\u2593\u2593 \u2584\u2584 \u2584\u2584 ,\u2584\u2584 \u2584\u2584\u2584\u2584 ,\u2584\u2584 \u2584\u2593\u2593\u258c\u2584 \u2584\u2584\u2584 ,\u2584\u2584 \u2584\u2593\u2593\u2593\u2580 \u2584\u2593\u2593\u2580 \u2590\u2593\u2593\u258c \u2593\u2593\u258c \u2590\u2593\u2593 \u2590\u2593\u2593\u2593\u2580\u2580\u2580\u2593\u2593\u258c \u2593\u2593\u2593 \u2580\u2593\u2593\u258c\u2580 ^\u2593\u2593\u258c \u2552\u2593\u2593\u258c \u2584\u2593\u2593\u2593\u2593\u2593\u2584\u2584\u2584\u2584\u2584\u2584\u2584\u2584\u2593\u2593\u2593 \u2593\u2580 \u2593\u2593\u258c \u2590\u2593\u2593 \u2590\u2593\u2593 \u2593\u2593\u2593 \u2593\u2593\u2593 \u2593\u2593\u258c \u2590\u2593\u2593\u2584 \u2593\u2593\u258c \u2580\u2593\u2593\u2593\u2593\u2580\u2580\u2580\u2580\u2580\u2580\u2580\u2580\u2580\u2580\u2593\u2593\u2584 \u2593\u2593 \u2593\u2593\u258c \u2590\u2593\u2593 \u2590\u2593\u2593 \u2593\u2593\u2593 \u2593\u2593\u2593 \u2593\u2593\u258c \u2590\u2593\u2593\u2590\u2593\u2593 ^\u2588\u2593\u2593\u2593 \u2580\u2593\u2593\u2584 \u2590\u2593\u2593\u258c \u2593\u2593\u2593\u2593\u2584\u2593\u2593\u2593\u2593 \u2590\u2593\u2593 \u2593\u2593\u2593 \u2593\u2593\u2593 \u2593\u2593\u2593\u2584 \u2593\u2593\u2593\u2593` '\u2580\u2593\u2593\u2593\u2584 ^\u2593\u2593\u2593 \u2593\u2593\u2593 \u2514\u2580\u2580\u2580\u2580 \u2580\u2580 ^\u2580\u2580 `\u2580\u2580 `\u2580\u2580 '\u2580\u2580 \u2590\u2593\u2593\u258c \u2580\u2580\u2580\u2580\u2593\u2584\u2584\u2584 \u2593\u2593\u2593\u2593\u2593\u2593, \u2593\u2593\u2593\u2593\u2580 `\u2580\u2588\u2593\u2593\u2593\u2593\u2593\u2593\u2593\u2593\u2593\u258c \u00ac`\u2580\u2580\u2580\u2588\u2593 Note : If you're using Anaconda, don't forget to activate the ml-agents environment first. If mlagents-learn runs correctly and starts training, you should see something like this: CrashReporter: initialized Mono path[0] = '/Users/dericp/workspace/ml-agents/3DBall.app/Contents/Resources/Data/Managed' Mono config path = '/Users/dericp/workspace/ml-agents/3DBall.app/Contents/MonoBleedingEdge/etc' INFO:mlagents_envs: 'Ball3DAcademy' started successfully! Unity Academy name: Ball3DAcademy INFO:mlagents_envs:Connected new brain: Unity brain name: Ball3DLearning Number of Visual Observations (per agent): 0 Vector Observation space size (per agent): 8 Number of stacked Vector Observation: 1 INFO:mlagents_envs:Hyperparameters for the PPO Trainer of brain Ball3DLearning: batch_size: 64 beta: 0.001 buffer_size: 12000 epsilon: 0.2 gamma: 0.995 hidden_units: 128 lambd: 0.99 learning_rate: 0.0003 max_steps: 5.0e4 normalize: True num_epoch: 3 num_layers: 2 time_horizon: 1000 sequence_length: 64 summary_freq: 1000 use_recurrent: False memory_size: 256 use_curiosity: False curiosity_strength: 0.01 curiosity_enc_size: 128 output_path: ./results/first-run-0/Ball3DLearning INFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 1000. Mean Reward: 1.242. Std of Reward: 0.746. Training. INFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 2000. Mean Reward: 1.319. Std of Reward: 0.693. Training. INFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 3000. Mean Reward: 1.804. Std of Reward: 1.056. Training. INFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 4000. Mean Reward: 2.151. Std of Reward: 1.432. Training. INFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 5000. Mean Reward: 3.175. Std of Reward: 2.250. Training. INFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 6000. Mean Reward: 4.898. Std of Reward: 4.019. Training. INFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 7000. Mean Reward: 6.716. Std of Reward: 5.125. Training. INFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 8000. Mean Reward: 12.124. Std of Reward: 11.929. Training. INFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 9000. Mean Reward: 18.151. Std of Reward: 16.871. Training. INFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 10000. Mean Reward: 27.284. Std of Reward: 28.667. Training. You can press Ctrl+C to stop the training, and your trained model will be at results/<run-identifier>/<behavior_name>.onnx , which corresponds to your model's latest checkpoint. ( Note: There is a known bug on Windows that causes the saving of the model to fail when you early terminate the training, it's recommended to wait until Step has reached the max_steps parameter you set in your config YAML.) You can now embed this trained model into your Agent by following the steps below: Move your model file into Project/Assets/ML-Agents/Examples/3DBall/TFModels/ . Open the Unity Editor, and select the 3DBall scene as described above. Select the 3DBall prefab from the Project window and select Agent . Drag the <behavior_name>.onnx file from the Project window of the Editor to the Model placeholder in the Ball3DAgent inspector window. Press the Play button at the top of the Editor. Training on Headless Server To run training on headless server with no graphics rendering support, you need to turn off graphics display in the Unity executable. There are two ways to achieve this: 1. Pass --no-graphics option to mlagents-learn training command. This is equivalent to adding -nographics -batchmode to the Unity executable's commandline. 2. Build your Unity executable with Server Build . You can find this setting in Build Settings in the Unity Editor. If you want to train with graphics (for example, using camera and visual observations), you'll need to set up display rendering support (e.g. xvfb) on you server machine. In our Colab Notebook Tutorials , the Setup section has examples of setting up xvfb on servers.","title":"Using an Environment Executable"},{"location":"Learning-Environment-Executable/#using-an-environment-executable","text":"This section will help you create and use built environments rather than the Editor to interact with an environment. Using an executable has some advantages over using the Editor: You can exchange executable with other people without having to share your entire repository. You can put your executable on a remote machine for faster training. You can use Server Build ( Headless ) mode for faster training (as long as the executable does not need rendering). You can keep using the Unity Editor for other tasks while the agents are training.","title":"Using an Environment Executable"},{"location":"Learning-Environment-Executable/#building-the-3dball-environment","text":"The first step is to open the Unity scene containing the 3D Balance Ball environment: Launch Unity. On the Projects dialog, choose the Open option at the top of the window. Using the file dialog that opens, locate the Project folder within the ML-Agents project and click Open . In the Project window, navigate to the folder Assets/ML-Agents/Examples/3DBall/Scenes/ . Double-click the 3DBall file to load the scene containing the Balance Ball environment. Next, we want the set up scene to play correctly when the training process launches our environment executable. This means: The environment application runs in the background. No dialogs require interaction. The correct scene loads automatically. Open Player Settings (menu: Edit > Project Settings > Player ). Under Resolution and Presentation : Ensure that Run in Background is Checked. Ensure that Display Resolution Dialog is set to Disabled. (Note: this setting may not be available in newer versions of the editor.) Open the Build Settings window (menu: File > Build Settings ). Choose your target platform. (optional) Select \u201cDevelopment Build\u201d to log debug messages . If any scenes are shown in the Scenes in Build list, make sure that the 3DBall Scene is the only one checked. (If the list is empty, then only the current scene is included in the build). Click Build : In the File dialog, navigate to your ML-Agents directory. Assign a file name and click Save . (For Windows\uff09With Unity 2018.1, it will ask you to select a folder instead of a file name. Create a subfolder within the root directory and select that folder to build. In the following steps you will refer to this subfolder's name as env_name . You cannot create builds in the Assets folder Now that we have a Unity executable containing the simulation environment, we can interact with it.","title":"Building the 3DBall environment"},{"location":"Learning-Environment-Executable/#interacting-with-the-environment","text":"If you want to use the Python API to interact with your executable, you can pass the name of the executable with the argument 'file_name' of the UnityEnvironment . For instance: from mlagents_envs.environment import UnityEnvironment env = UnityEnvironment(file_name=<env_name>)","title":"Interacting with the Environment"},{"location":"Learning-Environment-Executable/#training-the-environment","text":"Open a command or terminal window. Navigate to the folder where you installed the ML-Agents Toolkit. If you followed the default installation , then navigate to the ml-agents/ folder. Run mlagents-learn <trainer-config-file> --env=<env_name> --run-id=<run-identifier> Where: <trainer-config-file> is the file path of the trainer configuration yaml <env_name> is the name and path to the executable you exported from Unity (without extension) <run-identifier> is a string used to separate the results of different training runs For example, if you are training with a 3DBall executable, and you saved it to the directory where you installed the ML-Agents Toolkit, run: mlagents-learn config/ppo/3DBall.yaml --env=3DBall --run-id=firstRun And you should see something like ml-agents$ mlagents-learn config/ppo/3DBall.yaml --env=3DBall --run-id=first-run \u2584\u2584\u2584\u2593\u2593\u2593\u2593 \u2553\u2593\u2593\u2593\u2593\u2593\u2593\u2588\u2593\u2593\u2593\u2593\u2593 ,\u2584\u2584\u2584m\u2580\u2580\u2580' ,\u2593\u2593\u2593\u2580\u2593\u2593\u2584 \u2593\u2593\u2593 \u2593\u2593\u258c \u2584\u2593\u2593\u2593\u2580' \u2584\u2593\u2593\u2580 \u2593\u2593\u2593 \u2584\u2584 \u2584\u2584 ,\u2584\u2584 \u2584\u2584\u2584\u2584 ,\u2584\u2584 \u2584\u2593\u2593\u258c\u2584 \u2584\u2584\u2584 ,\u2584\u2584 \u2584\u2593\u2593\u2593\u2580 \u2584\u2593\u2593\u2580 \u2590\u2593\u2593\u258c \u2593\u2593\u258c \u2590\u2593\u2593 \u2590\u2593\u2593\u2593\u2580\u2580\u2580\u2593\u2593\u258c \u2593\u2593\u2593 \u2580\u2593\u2593\u258c\u2580 ^\u2593\u2593\u258c \u2552\u2593\u2593\u258c \u2584\u2593\u2593\u2593\u2593\u2593\u2584\u2584\u2584\u2584\u2584\u2584\u2584\u2584\u2593\u2593\u2593 \u2593\u2580 \u2593\u2593\u258c \u2590\u2593\u2593 \u2590\u2593\u2593 \u2593\u2593\u2593 \u2593\u2593\u2593 \u2593\u2593\u258c \u2590\u2593\u2593\u2584 \u2593\u2593\u258c \u2580\u2593\u2593\u2593\u2593\u2580\u2580\u2580\u2580\u2580\u2580\u2580\u2580\u2580\u2580\u2593\u2593\u2584 \u2593\u2593 \u2593\u2593\u258c \u2590\u2593\u2593 \u2590\u2593\u2593 \u2593\u2593\u2593 \u2593\u2593\u2593 \u2593\u2593\u258c \u2590\u2593\u2593\u2590\u2593\u2593 ^\u2588\u2593\u2593\u2593 \u2580\u2593\u2593\u2584 \u2590\u2593\u2593\u258c \u2593\u2593\u2593\u2593\u2584\u2593\u2593\u2593\u2593 \u2590\u2593\u2593 \u2593\u2593\u2593 \u2593\u2593\u2593 \u2593\u2593\u2593\u2584 \u2593\u2593\u2593\u2593` '\u2580\u2593\u2593\u2593\u2584 ^\u2593\u2593\u2593 \u2593\u2593\u2593 \u2514\u2580\u2580\u2580\u2580 \u2580\u2580 ^\u2580\u2580 `\u2580\u2580 `\u2580\u2580 '\u2580\u2580 \u2590\u2593\u2593\u258c \u2580\u2580\u2580\u2580\u2593\u2584\u2584\u2584 \u2593\u2593\u2593\u2593\u2593\u2593, \u2593\u2593\u2593\u2593\u2580 `\u2580\u2588\u2593\u2593\u2593\u2593\u2593\u2593\u2593\u2593\u2593\u258c \u00ac`\u2580\u2580\u2580\u2588\u2593 Note : If you're using Anaconda, don't forget to activate the ml-agents environment first. If mlagents-learn runs correctly and starts training, you should see something like this: CrashReporter: initialized Mono path[0] = '/Users/dericp/workspace/ml-agents/3DBall.app/Contents/Resources/Data/Managed' Mono config path = '/Users/dericp/workspace/ml-agents/3DBall.app/Contents/MonoBleedingEdge/etc' INFO:mlagents_envs: 'Ball3DAcademy' started successfully! Unity Academy name: Ball3DAcademy INFO:mlagents_envs:Connected new brain: Unity brain name: Ball3DLearning Number of Visual Observations (per agent): 0 Vector Observation space size (per agent): 8 Number of stacked Vector Observation: 1 INFO:mlagents_envs:Hyperparameters for the PPO Trainer of brain Ball3DLearning: batch_size: 64 beta: 0.001 buffer_size: 12000 epsilon: 0.2 gamma: 0.995 hidden_units: 128 lambd: 0.99 learning_rate: 0.0003 max_steps: 5.0e4 normalize: True num_epoch: 3 num_layers: 2 time_horizon: 1000 sequence_length: 64 summary_freq: 1000 use_recurrent: False memory_size: 256 use_curiosity: False curiosity_strength: 0.01 curiosity_enc_size: 128 output_path: ./results/first-run-0/Ball3DLearning INFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 1000. Mean Reward: 1.242. Std of Reward: 0.746. Training. INFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 2000. Mean Reward: 1.319. Std of Reward: 0.693. Training. INFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 3000. Mean Reward: 1.804. Std of Reward: 1.056. Training. INFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 4000. Mean Reward: 2.151. Std of Reward: 1.432. Training. INFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 5000. Mean Reward: 3.175. Std of Reward: 2.250. Training. INFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 6000. Mean Reward: 4.898. Std of Reward: 4.019. Training. INFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 7000. Mean Reward: 6.716. Std of Reward: 5.125. Training. INFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 8000. Mean Reward: 12.124. Std of Reward: 11.929. Training. INFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 9000. Mean Reward: 18.151. Std of Reward: 16.871. Training. INFO:mlagents.trainers: first-run-0: Ball3DLearning: Step: 10000. Mean Reward: 27.284. Std of Reward: 28.667. Training. You can press Ctrl+C to stop the training, and your trained model will be at results/<run-identifier>/<behavior_name>.onnx , which corresponds to your model's latest checkpoint. ( Note: There is a known bug on Windows that causes the saving of the model to fail when you early terminate the training, it's recommended to wait until Step has reached the max_steps parameter you set in your config YAML.) You can now embed this trained model into your Agent by following the steps below: Move your model file into Project/Assets/ML-Agents/Examples/3DBall/TFModels/ . Open the Unity Editor, and select the 3DBall scene as described above. Select the 3DBall prefab from the Project window and select Agent . Drag the <behavior_name>.onnx file from the Project window of the Editor to the Model placeholder in the Ball3DAgent inspector window. Press the Play button at the top of the Editor.","title":"Training the Environment"},{"location":"Learning-Environment-Executable/#training-on-headless-server","text":"To run training on headless server with no graphics rendering support, you need to turn off graphics display in the Unity executable. There are two ways to achieve this: 1. Pass --no-graphics option to mlagents-learn training command. This is equivalent to adding -nographics -batchmode to the Unity executable's commandline. 2. Build your Unity executable with Server Build . You can find this setting in Build Settings in the Unity Editor. If you want to train with graphics (for example, using camera and visual observations), you'll need to set up display rendering support (e.g. xvfb) on you server machine. In our Colab Notebook Tutorials , the Setup section has examples of setting up xvfb on servers.","title":"Training on Headless Server"},{"location":"Limitations/","text":"Limitations See the package-specific Limitations pages: com.unity.mlagents Unity package mlagents Python package mlagents_envs Python package","title":"Limitations"},{"location":"Limitations/#limitations","text":"See the package-specific Limitations pages: com.unity.mlagents Unity package mlagents Python package mlagents_envs Python package","title":"Limitations"},{"location":"ML-Agents-Envs-README/","text":"Unity ML-Agents Python Interface The mlagents_envs Python package is part of the ML-Agents Toolkit . mlagents_envs provides three Python APIs that allows direct interaction with the Unity game engine: - A single agent API (Gym API) - A gym-like multi-agent API (PettingZoo API) - A low-level API (LLAPI) The LLAPI is used by the trainer implementation in mlagents . mlagents_envs can be used independently of mlagents for Python communication. Installation Install the mlagents_envs package with: python -m pip install mlagents_envs==0.30.0 Usage & More Information See - Gym API Guide - PettingZoo API Guide - Python API Guide for more information on how to use the API to interact with a Unity environment. For more information on the ML-Agents Toolkit and how to instrument a Unity scene with the ML-Agents SDK, check out the main ML-Agents Toolkit documentation . Limitations mlagents_envs uses localhost ports to exchange data between Unity and Python. As such, multiple instances can have their ports collide, leading to errors. Make sure to use a different port if you are using multiple instances of UnityEnvironment . Communication between Unity and the Python UnityEnvironment is not secure. On Linux, ports are not released immediately after the communication closes. As such, you cannot reuse ports right after closing a UnityEnvironment .","title":"ML Agents Envs README"},{"location":"ML-Agents-Envs-README/#unity-ml-agents-python-interface","text":"The mlagents_envs Python package is part of the ML-Agents Toolkit . mlagents_envs provides three Python APIs that allows direct interaction with the Unity game engine: - A single agent API (Gym API) - A gym-like multi-agent API (PettingZoo API) - A low-level API (LLAPI) The LLAPI is used by the trainer implementation in mlagents . mlagents_envs can be used independently of mlagents for Python communication.","title":"Unity ML-Agents Python Interface"},{"location":"ML-Agents-Envs-README/#installation","text":"Install the mlagents_envs package with: python -m pip install mlagents_envs==0.30.0","title":"Installation"},{"location":"ML-Agents-Envs-README/#usage-more-information","text":"See - Gym API Guide - PettingZoo API Guide - Python API Guide for more information on how to use the API to interact with a Unity environment. For more information on the ML-Agents Toolkit and how to instrument a Unity scene with the ML-Agents SDK, check out the main ML-Agents Toolkit documentation .","title":"Usage &amp; More Information"},{"location":"ML-Agents-Envs-README/#limitations","text":"mlagents_envs uses localhost ports to exchange data between Unity and Python. As such, multiple instances can have their ports collide, leading to errors. Make sure to use a different port if you are using multiple instances of UnityEnvironment . Communication between Unity and the Python UnityEnvironment is not secure. On Linux, ports are not released immediately after the communication closes. As such, you cannot reuse ports right after closing a UnityEnvironment .","title":"Limitations"},{"location":"ML-Agents-Overview/","text":"ML-Agents Toolkit Overview Table of Contents Running Example: Training NPC Behaviors Key Components Training Modes Built-in Training and Inference Cross-Platform Inference Custom Training and Inference Flexible Training Scenarios Training Methods: Environment-agnostic A Quick Note on Reward Signals Deep Reinforcement Learning Curiosity for Sparse-reward Environments RND for Sparse-reward Environments Imitation Learning GAIL (Generative Adversarial Imitation Learning) Behavioral Cloning (BC) Recording Demonstrations Summary Training Methods: Environment-specific Training in Competitive Multi-Agent Environments with Self-Play Training in Cooperative Multi-Agent Environments with MA-POCA Solving Complex Tasks using Curriculum Learning Training Robust Agents using Environment Parameter Randomization Model Types Learning from Vector Observations Learning from Cameras using Convolutional Neural Networks Learning from Variable Length Observations using Attention Memory-enhanced Agents using Recurrent Neural Networks Additional Features Summary and Next Steps The Unity Machine Learning Agents Toolkit (ML-Agents Toolkit) is an open-source project that enables games and simulations to serve as environments for training intelligent agents. Agents can be trained using reinforcement learning, imitation learning, neuroevolution, or other machine learning methods through a simple-to-use Python API. We also provide implementations (based on PyTorch) of state-of-the-art algorithms to enable game developers and hobbyists to easily train intelligent agents for 2D, 3D and VR/AR games. These trained agents can be used for multiple purposes, including controlling NPC behavior (in a variety of settings such as multi-agent and adversarial), automated testing of game builds and evaluating different game design decisions pre-release. The ML-Agents Toolkit is mutually beneficial for both game developers and AI researchers as it provides a central platform where advances in AI can be evaluated on Unity\u2019s rich environments and then made accessible to the wider research and game developer communities. Depending on your background (i.e. researcher, game developer, hobbyist), you may have very different questions on your mind at the moment. To make your transition to the ML-Agents Toolkit easier, we provide several background pages that include overviews and helpful resources on the Unity Engine , machine learning and PyTorch . We strongly recommend browsing the relevant background pages if you're not familiar with a Unity scene, basic machine learning concepts or have not previously heard of PyTorch. The remainder of this page contains a deep dive into ML-Agents, its key components, different training modes and scenarios. By the end of it, you should have a good sense of what the ML-Agents Toolkit allows you to do. The subsequent documentation pages provide examples of how to use ML-Agents. To get started, watch this demo video of ML-Agents in action . Running Example: Training NPC Behaviors To help explain the material and terminology in this page, we'll use a hypothetical, running example throughout. We will explore the problem of training the behavior of a non-playable character (NPC) in a game. (An NPC is a game character that is never controlled by a human player and its behavior is pre-defined by the game developer.) More specifically, let's assume we're building a multi-player, war-themed game in which players control the soldiers. In this game, we have a single NPC who serves as a medic, finding and reviving wounded players. Lastly, let us assume that there are two teams, each with five players and one NPC medic. The behavior of a medic is quite complex. It first needs to avoid getting injured, which requires detecting when it is in danger and moving to a safe location. Second, it needs to be aware of which of its team members are injured and require assistance. In the case of multiple injuries, it needs to assess the degree of injury and decide who to help first. Lastly, a good medic will always place itself in a position where it can quickly help its team members. Factoring in all of these traits means that at every instance, the medic needs to measure several attributes of the environment (e.g. position of team members, position of enemies, which of its team members are injured and to what degree) and then decide on an action (e.g. hide from enemy fire, move to help one of its members). Given the large number of settings of the environment and the large number of actions that the medic can take, defining and implementing such complex behaviors by hand is challenging and prone to errors. With ML-Agents, it is possible to train the behaviors of such NPCs (called Agents ) using a variety of methods. The basic idea is quite simple. We need to define three entities at every moment of the game (called environment ): Observations - what the medic perceives about the environment. Observations can be numeric and/or visual. Numeric observations measure attributes of the environment from the point of view of the agent. For our medic this would be attributes of the battlefield that are visible to it. For most interesting environments, an agent will require several continuous numeric observations. Visual observations, on the other hand, are images generated from the cameras attached to the agent and represent what the agent is seeing at that point in time. It is common to confuse an agent's observation with the environment (or game) state . The environment state represents information about the entire scene containing all the game characters. The agents observation, however, only contains information that the agent is aware of and is typically a subset of the environment state. For example, the medic observation cannot include information about an enemy in hiding that the medic is unaware of. Actions - what actions the medic can take. Similar to observations, actions can either be continuous or discrete depending on the complexity of the environment and agent. In the case of the medic, if the environment is a simple grid world where only their location matters, then a discrete action taking on one of four values (north, south, east, west) suffices. However, if the environment is more complex and the medic can move freely then using two continuous actions (one for direction and another for speed) is more appropriate. Reward signals - a scalar value indicating how well the medic is doing. Note that the reward signal need not be provided at every moment, but only when the medic performs an action that is good or bad. For example, it can receive a large negative reward if it dies, a modest positive reward whenever it revives a wounded team member, and a modest negative reward when a wounded team member dies due to lack of assistance. Note that the reward signal is how the objectives of the task are communicated to the agent, so they need to be set up in a manner where maximizing reward generates the desired optimal behavior. After defining these three entities (the building blocks of a reinforcement learning task ), we can now train the medic's behavior. This is achieved by simulating the environment for many trials where the medic, over time, learns what is the optimal action to take for every observation it measures by maximizing its future reward. The key is that by learning the actions that maximize its reward, the medic is learning the behaviors that make it a good medic (i.e. one who saves the most number of lives). In reinforcement learning terminology, the behavior that is learned is called a policy , which is essentially a (optimal) mapping from observations to actions. Note that the process of learning a policy through running simulations is called the training phase , while playing the game with an NPC that is using its learned policy is called the inference phase . The ML-Agents Toolkit provides all the necessary tools for using Unity as the simulation engine for learning the policies of different objects in a Unity environment. In the next few sections, we discuss how the ML-Agents Toolkit achieves this and what features it provides. Key Components The ML-Agents Toolkit contains five high-level components: Learning Environment - which contains the Unity scene and all the game characters. The Unity scene provides the environment in which agents observe, act, and learn. How you set up the Unity scene to serve as a learning environment really depends on your goal. You may be trying to solve a specific reinforcement learning problem of limited scope, in which case you can use the same scene for both training and for testing trained agents. Or, you may be training agents to operate in a complex game or simulation. In this case, it might be more efficient and practical to create a purpose-built training scene. The ML-Agents Toolkit includes an ML-Agents Unity SDK ( com.unity.ml-agents package) that enables you to transform any Unity scene into a learning environment by defining the agents and their behaviors. Python Low-Level API - which contains a low-level Python interface for interacting and manipulating a learning environment. Note that, unlike the Learning Environment, the Python API is not part of Unity, but lives outside and communicates with Unity through the Communicator. This API is contained in a dedicated mlagents_envs Python package and is used by the Python training process to communicate with and control the Academy during training. However, it can be used for other purposes as well. For example, you could use the API to use Unity as the simulation engine for your own machine learning algorithms. See Python API for more information. External Communicator - which connects the Learning Environment with the Python Low-Level API. It lives within the Learning Environment. Python Trainers which contains all the machine learning algorithms that enable training agents. The algorithms are implemented in Python and are part of their own mlagents Python package. The package exposes a single command-line utility mlagents-learn that supports all the training methods and options outlined in this document. The Python Trainers interface solely with the Python Low-Level API. Gym Wrapper (not pictured). A common way in which machine learning researchers interact with simulation environments is via a wrapper provided by OpenAI called gym . We provide a gym wrapper in the ml-agents-envs package and instructions for using it with existing machine learning algorithms which utilize gym. PettingZoo Wrapper (not pictured) PettingZoo is python API for interacting with multi-agent simulation environments that provides a gym-like interface. We provide a PettingZoo wrapper for Unity ML-Agents environments in the ml-agents-envs package and instructions for using it with machine learning algorithms. Simplified block diagram of ML-Agents. The Learning Environment contains two Unity Components that help organize the Unity scene: Agents - which is attached to a Unity GameObject (any character within a scene) and handles generating its observations, performing the actions it receives and assigning a reward (positive / negative) when appropriate. Each Agent is linked to a Behavior. Behavior - defines specific attributes of the agent such as the number of actions that agent can take. Each Behavior is uniquely identified by a Behavior Name field. A Behavior can be thought as a function that receives observations and rewards from the Agent and returns actions. A Behavior can be of one of three types: Learning, Heuristic or Inference. A Learning Behavior is one that is not, yet, defined but about to be trained. A Heuristic Behavior is one that is defined by a hard-coded set of rules implemented in code. An Inference Behavior is one that includes a trained Neural Network file. In essence, after a Learning Behavior is trained, it becomes an Inference Behavior. Every Learning Environment will always have one Agent for every character in the scene. While each Agent must be linked to a Behavior, it is possible for Agents that have similar observations and actions to have the same Behavior. In our sample game, we have two teams each with their own medic. Thus we will have two Agents in our Learning Environment, one for each medic, but both of these medics can have the same Behavior. This does not mean that at each instance they will have identical observation and action values . Example block diagram of ML-Agents Toolkit for our sample game. Note that in a single environment, there can be multiple Agents and multiple Behaviors at the same time. For example, if we expanded our game to include tank driver NPCs, then the Agent attached to those characters cannot share its Behavior with the Agent linked to the medics (medics and drivers have different actions). The Learning Environment through the Academy (not represented in the diagram) ensures that all the Agents are in sync in addition to controlling environment-wide settings. Lastly, it is possible to exchange data between Unity and Python outside of the machine learning loop through Side Channels . One example of using Side Channels is to exchange data with Python about Environment Parameters . The following diagram illustrates the above. Training Modes Given the flexibility of ML-Agents, there are a few ways in which training and inference can proceed. Built-in Training and Inference As mentioned previously, the ML-Agents Toolkit ships with several implementations of state-of-the-art algorithms for training intelligent agents. More specifically, during training, all the medics in the scene send their observations to the Python API through the External Communicator. The Python API processes these observations and sends back actions for each medic to take. During training these actions are mostly exploratory to help the Python API learn the best policy for each medic. Once training concludes, the learned policy for each medic can be exported as a model file. Then during the inference phase, the medics still continue to generate their observations, but instead of being sent to the Python API, they will be fed into their (internal, embedded) model to generate the optimal action for each medic to take at every point in time. The Getting Started Guide tutorial covers this training mode with the 3D Balance Ball sample environment. Cross-Platform Inference It is important to note that the ML-Agents Toolkit leverages the Unity Inference Engine to run the models within a Unity scene such that an agent can take the optimal action at each step. Given that the Unity Inference Engine support most platforms that Unity does, this means that any model you train with the ML-Agents Toolkit can be embedded into your Unity application that runs on any platform. See our dedicated blog post for additional information. Custom Training and Inference In the previous mode, the Agents were used for training to generate a PyTorch model that the Agents can later use. However, any user of the ML-Agents Toolkit can leverage their own algorithms for training. In this case, the behaviors of all the Agents in the scene will be controlled within Python. You can even turn your environment into a gym. We do not currently have a tutorial highlighting this mode, but you can learn more about the Python API here . Flexible Training Scenarios While the discussion so-far has mostly focused on training a single agent, with ML-Agents, several training scenarios are possible. We are excited to see what kinds of novel and fun environments the community creates. For those new to training intelligent agents, below are a few examples that can serve as inspiration: Single-Agent. A single agent, with its own reward signal. The traditional way of training an agent. An example is any single-player game, such as Chicken. Simultaneous Single-Agent. Multiple independent agents with independent reward signals with same Behavior Parameters . A parallelized version of the traditional training scenario, which can speed-up and stabilize the training process. Helpful when you have multiple versions of the same character in an environment who should learn similar behaviors. An example might be training a dozen robot-arms to each open a door simultaneously. Adversarial Self-Play. Two interacting agents with inverse reward signals. In two-player games, adversarial self-play can allow an agent to become increasingly more skilled, while always having the perfectly matched opponent: itself. This was the strategy employed when training AlphaGo, and more recently used by OpenAI to train a human-beating 1-vs-1 Dota 2 agent. Cooperative Multi-Agent. Multiple interacting agents with a shared reward signal with same or different Behavior Parameters . In this scenario, all agents must work together to accomplish a task that cannot be done alone. Examples include environments where each agent only has access to partial information, which needs to be shared in order to accomplish the task or collaboratively solve a puzzle. Competitive Multi-Agent. Multiple interacting agents with inverse reward signals with same or different Behavior Parameters . In this scenario, agents must compete with one another to either win a competition, or obtain some limited set of resources. All team sports fall into this scenario. Ecosystem. Multiple interacting agents with independent reward signals with same or different Behavior Parameters . This scenario can be thought of as creating a small world in which animals with different goals all interact, such as a savanna in which there might be zebras, elephants and giraffes, or an autonomous driving simulation within an urban environment. Training Methods: Environment-agnostic The remaining sections overview the various state-of-the-art machine learning algorithms that are part of the ML-Agents Toolkit. If you aren't studying machine and reinforcement learning as a subject and just want to train agents to accomplish tasks, you can treat these algorithms as black boxes . There are a few training-related parameters to adjust inside Unity as well as on the Python training side, but you do not need in-depth knowledge of the algorithms themselves to successfully create and train agents. Step-by-step procedures for running the training process are provided in the Training ML-Agents page. This section specifically focuses on the training methods that are available regardless of the specifics of your learning environment. A Quick Note on Reward Signals In this section we introduce the concepts of intrinsic and extrinsic rewards, which helps explain some of the training methods. In reinforcement learning, the end goal for the Agent is to discover a behavior (a Policy) that maximizes a reward. You will need to provide the agent one or more reward signals to use during training. Typically, a reward is defined by your environment, and corresponds to reaching some goal. These are what we refer to as extrinsic rewards, as they are defined external of the learning algorithm. Rewards, however, can be defined outside of the environment as well, to encourage the agent to behave in certain ways, or to aid the learning of the true extrinsic reward. We refer to these rewards as intrinsic reward signals. The total reward that the agent will learn to maximize can be a mix of extrinsic and intrinsic reward signals. The ML-Agents Toolkit allows reward signals to be defined in a modular way, and we provide four reward signals that can the mixed and matched to help shape your agent's behavior: extrinsic : represents the rewards defined in your environment, and is enabled by default gail : represents an intrinsic reward signal that is defined by GAIL (see below) curiosity : represents an intrinsic reward signal that encourages exploration in sparse-reward environments that is defined by the Curiosity module (see below). rnd : represents an intrinsic reward signal that encourages exploration in sparse-reward environments that is defined by the Curiosity module (see below). Deep Reinforcement Learning ML-Agents provide an implementation of two reinforcement learning algorithms: Proximal Policy Optimization (PPO) Soft Actor-Critic (SAC) The default algorithm is PPO. This is a method that has been shown to be more general purpose and stable than many other RL algorithms. In contrast with PPO, SAC is off-policy , which means it can learn from experiences collected at any time during the past. As experiences are collected, they are placed in an experience replay buffer and randomly drawn during training. This makes SAC significantly more sample-efficient, often requiring 5-10 times less samples to learn the same task as PPO. However, SAC tends to require more model updates. SAC is a good choice for heavier or slower environments (about 0.1 seconds per step or more). SAC is also a \"maximum entropy\" algorithm, and enables exploration in an intrinsic way. Read more about maximum entropy RL here . Curiosity for Sparse-reward Environments In environments where the agent receives rare or infrequent rewards (i.e. sparse-reward), an agent may never receive a reward signal on which to bootstrap its training process. This is a scenario where the use of an intrinsic reward signals can be valuable. Curiosity is one such signal which can help the agent explore when extrinsic rewards are sparse. The curiosity Reward Signal enables the Intrinsic Curiosity Module. This is an implementation of the approach described in Curiosity-driven Exploration by Self-supervised Prediction by Pathak, et al. It trains two networks: an inverse model, which takes the current and next observation of the agent, encodes them, and uses the encoding to predict the action that was taken between the observations a forward model, which takes the encoded current observation and action, and predicts the next encoded observation. The loss of the forward model (the difference between the predicted and actual encoded observations) is used as the intrinsic reward, so the more surprised the model is, the larger the reward will be. For more information, see our dedicated blog post on the Curiosity module . RND for Sparse-reward Environments Similarly to Curiosity, Random Network Distillation (RND) is useful in sparse or rare reward environments as it helps the Agent explore. The RND Module is implemented following the paper Exploration by Random Network Distillation . RND uses two networks: - The first is a network with fixed random weights that takes observations as inputs and generates an encoding - The second is a network with similar architecture that is trained to predict the outputs of the first network and uses the observations the Agent collects as training data. The loss (the squared difference between the predicted and actual encoded observations) of the trained model is used as intrinsic reward. The more an Agent visits a state, the more accurate the predictions and the lower the rewards which encourages the Agent to explore new states with higher prediction errors. Imitation Learning It is often more intuitive to simply demonstrate the behavior we want an agent to perform, rather than attempting to have it learn via trial-and-error methods. For example, instead of indirectly training a medic with the help of a reward function, we can give the medic real world examples of observations from the game and actions from a game controller to guide the medic's behavior. Imitation Learning uses pairs of observations and actions from a demonstration to learn a policy. See this video demo of imitation learning . Imitation learning can either be used alone or in conjunction with reinforcement learning. If used alone it can provide a mechanism for learning a specific type of behavior (i.e. a specific style of solving the task). If used in conjunction with reinforcement learning it can dramatically reduce the time the agent takes to solve the environment. This can be especially pronounced in sparse-reward environments. For instance, on the Pyramids environment , using 6 episodes of demonstrations can reduce training steps by more than 4 times. See Behavioral Cloning + GAIL + Curiosity + RL below. The ML-Agents Toolkit provides a way to learn directly from demonstrations, as well as use them to help speed up reward-based training (RL). We include two algorithms called Behavioral Cloning (BC) and Generative Adversarial Imitation Learning (GAIL). In most scenarios, you can combine these two features: If you want to help your agents learn (especially with environments that have sparse rewards) using pre-recorded demonstrations, you can generally enable both GAIL and Behavioral Cloning at low strengths in addition to having an extrinsic reward. An example of this is provided for the PushBlock example environment in config/imitation/PushBlock.yaml . If you want to train purely from demonstrations with GAIL and BC without an extrinsic reward signal, please see the CrawlerStatic example environment under in config/imitation/CrawlerStatic.yaml . Note: GAIL introduces a survivor bias to the learning process. That is, by giving positive rewards based on similarity to the expert, the agent is incentivized to remain alive for as long as possible. This can directly conflict with goal-oriented tasks like our PushBlock or Pyramids example environments where an agent must reach a goal state thus ending the episode as quickly as possible. In these cases, we strongly recommend that you use a low strength GAIL reward signal and a sparse extrinisic signal when the agent achieves the task. This way, the GAIL reward signal will guide the agent until it discovers the extrnisic signal and will not overpower it. If the agent appears to be ignoring the extrinsic reward signal, you should reduce the strength of GAIL. GAIL (Generative Adversarial Imitation Learning) GAIL, or Generative Adversarial Imitation Learning , uses an adversarial approach to reward your Agent for behaving similar to a set of demonstrations. GAIL can be used with or without environment rewards, and works well when there are a limited number of demonstrations. In this framework, a second neural network, the discriminator, is taught to distinguish whether an observation/action is from a demonstration or produced by the agent. This discriminator can then examine a new observation/action and provide it a reward based on how close it believes this new observation/action is to the provided demonstrations. At each training step, the agent tries to learn how to maximize this reward. Then, the discriminator is trained to better distinguish between demonstrations and agent state/actions. In this way, while the agent gets better and better at mimicking the demonstrations, the discriminator keeps getting stricter and stricter and the agent must try harder to \"fool\" it. This approach learns a policy that produces states and actions similar to the demonstrations, requiring fewer demonstrations than direct cloning of the actions. In addition to learning purely from demonstrations, the GAIL reward signal can be mixed with an extrinsic reward signal to guide the learning process. Behavioral Cloning (BC) BC trains the Agent's policy to exactly mimic the actions shown in a set of demonstrations. The BC feature can be enabled on the PPO or SAC trainers. As BC cannot generalize past the examples shown in the demonstrations, BC tends to work best when there exists demonstrations for nearly all of the states that the agent can experience, or in conjunction with GAIL and/or an extrinsic reward. Recording Demonstrations Demonstrations of agent behavior can be recorded from the Unity Editor or build, and saved as assets. These demonstrations contain information on the observations, actions, and rewards for a given agent during the recording session. They can be managed in the Editor, as well as used for training with BC and GAIL. See the Designing Agents page for more information on how to record demonstrations for your agent. Summary To summarize, we provide 3 training methods: BC, GAIL and RL (PPO or SAC) that can be used independently or together: BC can be used on its own or as a pre-training step before GAIL and/or RL GAIL can be used with or without extrinsic rewards RL can be used on its own (either PPO or SAC) or in conjunction with BC and/or GAIL. Leveraging either BC or GAIL requires recording demonstrations to be provided as input to the training algorithms. Training Methods: Environment-specific In addition to the three environment-agnostic training methods introduced in the previous section, the ML-Agents Toolkit provides additional methods that can aid in training behaviors for specific types of environments. Training in Competitive Multi-Agent Environments with Self-Play ML-Agents provides the functionality to train both symmetric and asymmetric adversarial games with Self-Play . A symmetric game is one in which opposing agents are equal in form, function and objective. Examples of symmetric games are our Tennis and Soccer example environments. In reinforcement learning, this means both agents have the same observation and actions and learn from the same reward function and so they can share the same policy . In asymmetric games, this is not the case. An example of an asymmetric games are Hide and Seek. Agents in these types of games do not always have the same observation or actions and so sharing policy networks is not necessarily ideal. With self-play, an agent learns in adversarial games by competing against fixed, past versions of its opponent (which could be itself as in symmetric games) to provide a more stable, stationary learning environment. This is compared to competing against the current, best opponent in every episode, which is constantly changing (because it's learning). Self-play can be used with our implementations of both Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC). However, from the perspective of an individual agent, these scenarios appear to have non-stationary dynamics because the opponent is often changing. This can cause significant issues in the experience replay mechanism used by SAC. Thus, we recommend that users use PPO. For further reading on this issue in particular, see the paper Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning . See our Designing Agents page for more information on setting up teams in your Unity scene. Also, read our blog post on self-play for additional information. Additionally, check ELO Rating System the method we use to calculate the relative skill level between two players. Training In Cooperative Multi-Agent Environments with MA-POCA ML-Agents provides the functionality for training cooperative behaviors - i.e., groups of agents working towards a common goal, where the success of the individual is linked to the success of the whole group. In such a scenario, agents typically receive rewards as a group. For instance, if a team of agents wins a game against an opposing team, everyone is rewarded - even agents who did not directly contribute to the win. This makes learning what to do as an individual difficult - you may get a win for doing nothing, and a loss for doing your best. In ML-Agents, we provide MA-POCA (MultiAgent POsthumous Credit Assignment), which is a novel multi-agent trainer that trains a centralized critic , a neural network that acts as a \"coach\" for a whole group of agents. You can then give rewards to the team as a whole, and the agents will learn how best to contribute to achieving that reward. Agents can also be given rewards individually, and the team will work together to help the individual achieve those goals. During an episode, agents can be added or removed from the group, such as when agents spawn or die in a game. If agents are removed mid-episode (e.g., if teammates die or are removed from the game), they will still learn whether their actions contributed to the team winning later, enabling agents to take group-beneficial actions even if they result in the individual being removed from the game (i.e., self-sacrifice). MA-POCA can also be combined with self-play to train teams of agents to play against each other. To learn more about enabling cooperative behaviors for agents in an ML-Agents environment, check out this page . To learn more about MA-POCA, please see our paper On the Use and Misuse of Absorbing States in Multi-Agent Reinforcement Learning . For further reading, MA-POCA builds on previous work in multi-agent cooperative learning ( Lowe et al. , Foerster et al. , among others) to enable the above use-cases. Solving Complex Tasks using Curriculum Learning Curriculum learning is a way of training a machine learning model where more difficult aspects of a problem are gradually introduced in such a way that the model is always optimally challenged. This idea has been around for a long time, and it is how we humans typically learn. If you imagine any childhood primary school education, there is an ordering of classes and topics. Arithmetic is taught before algebra, for example. Likewise, algebra is taught before calculus. The skills and knowledge learned in the earlier subjects provide a scaffolding for later lessons. The same principle can be applied to machine learning, where training on easier tasks can provide a scaffolding for harder tasks in the future. Imagine training the medic to scale a wall to arrive at a wounded team member. The starting point when training a medic to accomplish this task will be a random policy. That starting policy will have the medic running in circles, and will likely never, or very rarely scale the wall properly to revive their team member (and achieve the reward). If we start with a simpler task, such as moving toward an unobstructed team member, then the medic can easily learn to accomplish the task. From there, we can slowly add to the difficulty of the task by increasing the size of the wall until the medic can complete the initially near-impossible task of scaling the wall. We have included an environment to demonstrate this with ML-Agents, called Wall Jump . Demonstration of a hypothetical curriculum training scenario in which a progressively taller wall obstructs the path to the goal. [ Note : The example provided above is for instructional purposes, and was based on an early version of the Wall Jump example environment . As such, it is not possible to directly replicate the results here using that environment.] The ML-Agents Toolkit supports modifying custom environment parameters during the training process to aid in learning. This allows elements of the environment related to difficulty or complexity to be dynamically adjusted based on training progress. The Training ML-Agents page has more information on defining training curriculums. Training Robust Agents using Environment Parameter Randomization An agent trained on a specific environment, may be unable to generalize to any tweaks or variations in the environment (in machine learning this is referred to as overfitting). This becomes problematic in cases where environments are instantiated with varying objects or properties. One mechanism to alleviate this and train more robust agents that can generalize to unseen variations of the environment is to expose them to these variations during training. Similar to Curriculum Learning, where environments become more difficult as the agent learns, the ML-Agents Toolkit provides a way to randomly sample parameters of the environment during training. We refer to this approach as Environment Parameter Randomization . For those familiar with Reinforcement Learning research, this approach is based on the concept of Domain Randomization . By using parameter randomization during training , the agent can be better suited to adapt (with higher performance) to future unseen variations of the environment. Ball scale of 0.5 Ball scale of 4 Example of variations of the 3D Ball environment. The environment parameters are gravity , ball_mass and ball_scale . Model Types Regardless of the training method deployed, there are a few model types that users can train using the ML-Agents Toolkit. This is due to the flexibility in defining agent observations, which include vector, ray cast and visual observations. You can learn more about how to instrument an agent's observation in the Designing Agents guide. Learning from Vector Observations Whether an agent's observations are ray cast or vector, the ML-Agents Toolkit provides a fully connected neural network model to learn from those observations. At training time you can configure different aspects of this model such as the number of hidden units and number of layers. Learning from Cameras using Convolutional Neural Networks Unlike other platforms, where the agent\u2019s observation might be limited to a single vector or image, the ML-Agents Toolkit allows multiple cameras to be used for observations per agent. This enables agents to learn to integrate information from multiple visual streams. This can be helpful in several scenarios such as training a self-driving car which requires multiple cameras with different viewpoints, or a navigational agent which might need to integrate aerial and first-person visuals. You can learn more about adding visual observations to an agent here . When visual observations are utilized, the ML-Agents Toolkit leverages convolutional neural networks (CNN) to learn from the input images. We offer three network architectures: a simple encoder which consists of two convolutional layers the implementation proposed by Mnih et al. , consisting of three convolutional layers, the IMPALA Resnet consisting of three stacked layers, each with two residual blocks, making a much larger network than the other two. The choice of the architecture depends on the visual complexity of the scene and the available computational resources. Learning from Variable Length Observations using Attention Using the ML-Agents Toolkit, it is possible to have agents learn from a varying number of inputs. To do so, each agent can keep track of a buffer of vector observations. At each step, the agent will go through all the elements in the buffer and extract information but the elements in the buffer can change at every step. This can be useful in scenarios in which the agents must keep track of a varying number of elements throughout the episode. For example in a game where an agent must learn to avoid projectiles, but the projectiles can vary in numbers. You can learn more about variable length observations here . When variable length observations are utilized, the ML-Agents Toolkit leverages attention networks to learn from a varying number of entities. Agents using attention will ignore entities that are deemed not relevant and pay special attention to entities relevant to the current situation based on context. Memory-enhanced Agents using Recurrent Neural Networks Have you ever entered a room to get something and immediately forgot what you were looking for? Don't let that happen to your agents. In some scenarios, agents must learn to remember the past in order to take the best decision. When an agent only has partial observability of the environment, keeping track of past observations can help the agent learn. Deciding what the agents should remember in order to solve a task is not easy to do by hand, but our training algorithms can learn to keep track of what is important to remember with LSTM . Additional Features Beyond the flexible training scenarios available, the ML-Agents Toolkit includes additional features which improve the flexibility and interpretability of the training process. Concurrent Unity Instances - We enable developers to run concurrent, parallel instances of the Unity executable during training. For certain scenarios, this should speed up training. Check out our dedicated page on creating a Unity executable and the Training ML-Agents page for instructions on how to set the number of concurrent instances. Recording Statistics from Unity - We enable developers to record statistics from within their Unity environments. These statistics are aggregated and generated during the training process. Custom Side Channels - We enable developers to create custom side channels to manage data transfer between Unity and Python that is unique to their training workflow and/or environment. Custom Samplers - We enable developers to create custom sampling methods for Environment Parameter Randomization. This enables users to customize this training method for their particular environment. Summary and Next Steps To briefly summarize: The ML-Agents Toolkit enables games and simulations built in Unity to serve as the platform for training intelligent agents. It is designed to enable a large variety of training modes and scenarios and comes packed with several features to enable researchers and developers to leverage (and enhance) machine learning within Unity. In terms of next steps: For a walkthrough of running ML-Agents with a simple scene, check out the Getting Started guide. For a \"Hello World\" introduction to creating your own Learning Environment, check out the Making a New Learning Environment page. For an overview on the more complex example environments that are provided in this toolkit, check out the Example Environments page. For more information on the various training options available, check out the Training ML-Agents page.","title":"ML-Agents Overview"},{"location":"ML-Agents-Overview/#ml-agents-toolkit-overview","text":"Table of Contents Running Example: Training NPC Behaviors Key Components Training Modes Built-in Training and Inference Cross-Platform Inference Custom Training and Inference Flexible Training Scenarios Training Methods: Environment-agnostic A Quick Note on Reward Signals Deep Reinforcement Learning Curiosity for Sparse-reward Environments RND for Sparse-reward Environments Imitation Learning GAIL (Generative Adversarial Imitation Learning) Behavioral Cloning (BC) Recording Demonstrations Summary Training Methods: Environment-specific Training in Competitive Multi-Agent Environments with Self-Play Training in Cooperative Multi-Agent Environments with MA-POCA Solving Complex Tasks using Curriculum Learning Training Robust Agents using Environment Parameter Randomization Model Types Learning from Vector Observations Learning from Cameras using Convolutional Neural Networks Learning from Variable Length Observations using Attention Memory-enhanced Agents using Recurrent Neural Networks Additional Features Summary and Next Steps The Unity Machine Learning Agents Toolkit (ML-Agents Toolkit) is an open-source project that enables games and simulations to serve as environments for training intelligent agents. Agents can be trained using reinforcement learning, imitation learning, neuroevolution, or other machine learning methods through a simple-to-use Python API. We also provide implementations (based on PyTorch) of state-of-the-art algorithms to enable game developers and hobbyists to easily train intelligent agents for 2D, 3D and VR/AR games. These trained agents can be used for multiple purposes, including controlling NPC behavior (in a variety of settings such as multi-agent and adversarial), automated testing of game builds and evaluating different game design decisions pre-release. The ML-Agents Toolkit is mutually beneficial for both game developers and AI researchers as it provides a central platform where advances in AI can be evaluated on Unity\u2019s rich environments and then made accessible to the wider research and game developer communities. Depending on your background (i.e. researcher, game developer, hobbyist), you may have very different questions on your mind at the moment. To make your transition to the ML-Agents Toolkit easier, we provide several background pages that include overviews and helpful resources on the Unity Engine , machine learning and PyTorch . We strongly recommend browsing the relevant background pages if you're not familiar with a Unity scene, basic machine learning concepts or have not previously heard of PyTorch. The remainder of this page contains a deep dive into ML-Agents, its key components, different training modes and scenarios. By the end of it, you should have a good sense of what the ML-Agents Toolkit allows you to do. The subsequent documentation pages provide examples of how to use ML-Agents. To get started, watch this demo video of ML-Agents in action .","title":"ML-Agents Toolkit Overview"},{"location":"ML-Agents-Overview/#running-example-training-npc-behaviors","text":"To help explain the material and terminology in this page, we'll use a hypothetical, running example throughout. We will explore the problem of training the behavior of a non-playable character (NPC) in a game. (An NPC is a game character that is never controlled by a human player and its behavior is pre-defined by the game developer.) More specifically, let's assume we're building a multi-player, war-themed game in which players control the soldiers. In this game, we have a single NPC who serves as a medic, finding and reviving wounded players. Lastly, let us assume that there are two teams, each with five players and one NPC medic. The behavior of a medic is quite complex. It first needs to avoid getting injured, which requires detecting when it is in danger and moving to a safe location. Second, it needs to be aware of which of its team members are injured and require assistance. In the case of multiple injuries, it needs to assess the degree of injury and decide who to help first. Lastly, a good medic will always place itself in a position where it can quickly help its team members. Factoring in all of these traits means that at every instance, the medic needs to measure several attributes of the environment (e.g. position of team members, position of enemies, which of its team members are injured and to what degree) and then decide on an action (e.g. hide from enemy fire, move to help one of its members). Given the large number of settings of the environment and the large number of actions that the medic can take, defining and implementing such complex behaviors by hand is challenging and prone to errors. With ML-Agents, it is possible to train the behaviors of such NPCs (called Agents ) using a variety of methods. The basic idea is quite simple. We need to define three entities at every moment of the game (called environment ): Observations - what the medic perceives about the environment. Observations can be numeric and/or visual. Numeric observations measure attributes of the environment from the point of view of the agent. For our medic this would be attributes of the battlefield that are visible to it. For most interesting environments, an agent will require several continuous numeric observations. Visual observations, on the other hand, are images generated from the cameras attached to the agent and represent what the agent is seeing at that point in time. It is common to confuse an agent's observation with the environment (or game) state . The environment state represents information about the entire scene containing all the game characters. The agents observation, however, only contains information that the agent is aware of and is typically a subset of the environment state. For example, the medic observation cannot include information about an enemy in hiding that the medic is unaware of. Actions - what actions the medic can take. Similar to observations, actions can either be continuous or discrete depending on the complexity of the environment and agent. In the case of the medic, if the environment is a simple grid world where only their location matters, then a discrete action taking on one of four values (north, south, east, west) suffices. However, if the environment is more complex and the medic can move freely then using two continuous actions (one for direction and another for speed) is more appropriate. Reward signals - a scalar value indicating how well the medic is doing. Note that the reward signal need not be provided at every moment, but only when the medic performs an action that is good or bad. For example, it can receive a large negative reward if it dies, a modest positive reward whenever it revives a wounded team member, and a modest negative reward when a wounded team member dies due to lack of assistance. Note that the reward signal is how the objectives of the task are communicated to the agent, so they need to be set up in a manner where maximizing reward generates the desired optimal behavior. After defining these three entities (the building blocks of a reinforcement learning task ), we can now train the medic's behavior. This is achieved by simulating the environment for many trials where the medic, over time, learns what is the optimal action to take for every observation it measures by maximizing its future reward. The key is that by learning the actions that maximize its reward, the medic is learning the behaviors that make it a good medic (i.e. one who saves the most number of lives). In reinforcement learning terminology, the behavior that is learned is called a policy , which is essentially a (optimal) mapping from observations to actions. Note that the process of learning a policy through running simulations is called the training phase , while playing the game with an NPC that is using its learned policy is called the inference phase . The ML-Agents Toolkit provides all the necessary tools for using Unity as the simulation engine for learning the policies of different objects in a Unity environment. In the next few sections, we discuss how the ML-Agents Toolkit achieves this and what features it provides.","title":"Running Example: Training NPC Behaviors"},{"location":"ML-Agents-Overview/#key-components","text":"The ML-Agents Toolkit contains five high-level components: Learning Environment - which contains the Unity scene and all the game characters. The Unity scene provides the environment in which agents observe, act, and learn. How you set up the Unity scene to serve as a learning environment really depends on your goal. You may be trying to solve a specific reinforcement learning problem of limited scope, in which case you can use the same scene for both training and for testing trained agents. Or, you may be training agents to operate in a complex game or simulation. In this case, it might be more efficient and practical to create a purpose-built training scene. The ML-Agents Toolkit includes an ML-Agents Unity SDK ( com.unity.ml-agents package) that enables you to transform any Unity scene into a learning environment by defining the agents and their behaviors. Python Low-Level API - which contains a low-level Python interface for interacting and manipulating a learning environment. Note that, unlike the Learning Environment, the Python API is not part of Unity, but lives outside and communicates with Unity through the Communicator. This API is contained in a dedicated mlagents_envs Python package and is used by the Python training process to communicate with and control the Academy during training. However, it can be used for other purposes as well. For example, you could use the API to use Unity as the simulation engine for your own machine learning algorithms. See Python API for more information. External Communicator - which connects the Learning Environment with the Python Low-Level API. It lives within the Learning Environment. Python Trainers which contains all the machine learning algorithms that enable training agents. The algorithms are implemented in Python and are part of their own mlagents Python package. The package exposes a single command-line utility mlagents-learn that supports all the training methods and options outlined in this document. The Python Trainers interface solely with the Python Low-Level API. Gym Wrapper (not pictured). A common way in which machine learning researchers interact with simulation environments is via a wrapper provided by OpenAI called gym . We provide a gym wrapper in the ml-agents-envs package and instructions for using it with existing machine learning algorithms which utilize gym. PettingZoo Wrapper (not pictured) PettingZoo is python API for interacting with multi-agent simulation environments that provides a gym-like interface. We provide a PettingZoo wrapper for Unity ML-Agents environments in the ml-agents-envs package and instructions for using it with machine learning algorithms. Simplified block diagram of ML-Agents. The Learning Environment contains two Unity Components that help organize the Unity scene: Agents - which is attached to a Unity GameObject (any character within a scene) and handles generating its observations, performing the actions it receives and assigning a reward (positive / negative) when appropriate. Each Agent is linked to a Behavior. Behavior - defines specific attributes of the agent such as the number of actions that agent can take. Each Behavior is uniquely identified by a Behavior Name field. A Behavior can be thought as a function that receives observations and rewards from the Agent and returns actions. A Behavior can be of one of three types: Learning, Heuristic or Inference. A Learning Behavior is one that is not, yet, defined but about to be trained. A Heuristic Behavior is one that is defined by a hard-coded set of rules implemented in code. An Inference Behavior is one that includes a trained Neural Network file. In essence, after a Learning Behavior is trained, it becomes an Inference Behavior. Every Learning Environment will always have one Agent for every character in the scene. While each Agent must be linked to a Behavior, it is possible for Agents that have similar observations and actions to have the same Behavior. In our sample game, we have two teams each with their own medic. Thus we will have two Agents in our Learning Environment, one for each medic, but both of these medics can have the same Behavior. This does not mean that at each instance they will have identical observation and action values . Example block diagram of ML-Agents Toolkit for our sample game. Note that in a single environment, there can be multiple Agents and multiple Behaviors at the same time. For example, if we expanded our game to include tank driver NPCs, then the Agent attached to those characters cannot share its Behavior with the Agent linked to the medics (medics and drivers have different actions). The Learning Environment through the Academy (not represented in the diagram) ensures that all the Agents are in sync in addition to controlling environment-wide settings. Lastly, it is possible to exchange data between Unity and Python outside of the machine learning loop through Side Channels . One example of using Side Channels is to exchange data with Python about Environment Parameters . The following diagram illustrates the above.","title":"Key Components"},{"location":"ML-Agents-Overview/#training-modes","text":"Given the flexibility of ML-Agents, there are a few ways in which training and inference can proceed.","title":"Training Modes"},{"location":"ML-Agents-Overview/#built-in-training-and-inference","text":"As mentioned previously, the ML-Agents Toolkit ships with several implementations of state-of-the-art algorithms for training intelligent agents. More specifically, during training, all the medics in the scene send their observations to the Python API through the External Communicator. The Python API processes these observations and sends back actions for each medic to take. During training these actions are mostly exploratory to help the Python API learn the best policy for each medic. Once training concludes, the learned policy for each medic can be exported as a model file. Then during the inference phase, the medics still continue to generate their observations, but instead of being sent to the Python API, they will be fed into their (internal, embedded) model to generate the optimal action for each medic to take at every point in time. The Getting Started Guide tutorial covers this training mode with the 3D Balance Ball sample environment.","title":"Built-in Training and Inference"},{"location":"ML-Agents-Overview/#cross-platform-inference","text":"It is important to note that the ML-Agents Toolkit leverages the Unity Inference Engine to run the models within a Unity scene such that an agent can take the optimal action at each step. Given that the Unity Inference Engine support most platforms that Unity does, this means that any model you train with the ML-Agents Toolkit can be embedded into your Unity application that runs on any platform. See our dedicated blog post for additional information.","title":"Cross-Platform Inference"},{"location":"ML-Agents-Overview/#custom-training-and-inference","text":"In the previous mode, the Agents were used for training to generate a PyTorch model that the Agents can later use. However, any user of the ML-Agents Toolkit can leverage their own algorithms for training. In this case, the behaviors of all the Agents in the scene will be controlled within Python. You can even turn your environment into a gym. We do not currently have a tutorial highlighting this mode, but you can learn more about the Python API here .","title":"Custom Training and Inference"},{"location":"ML-Agents-Overview/#flexible-training-scenarios","text":"While the discussion so-far has mostly focused on training a single agent, with ML-Agents, several training scenarios are possible. We are excited to see what kinds of novel and fun environments the community creates. For those new to training intelligent agents, below are a few examples that can serve as inspiration: Single-Agent. A single agent, with its own reward signal. The traditional way of training an agent. An example is any single-player game, such as Chicken. Simultaneous Single-Agent. Multiple independent agents with independent reward signals with same Behavior Parameters . A parallelized version of the traditional training scenario, which can speed-up and stabilize the training process. Helpful when you have multiple versions of the same character in an environment who should learn similar behaviors. An example might be training a dozen robot-arms to each open a door simultaneously. Adversarial Self-Play. Two interacting agents with inverse reward signals. In two-player games, adversarial self-play can allow an agent to become increasingly more skilled, while always having the perfectly matched opponent: itself. This was the strategy employed when training AlphaGo, and more recently used by OpenAI to train a human-beating 1-vs-1 Dota 2 agent. Cooperative Multi-Agent. Multiple interacting agents with a shared reward signal with same or different Behavior Parameters . In this scenario, all agents must work together to accomplish a task that cannot be done alone. Examples include environments where each agent only has access to partial information, which needs to be shared in order to accomplish the task or collaboratively solve a puzzle. Competitive Multi-Agent. Multiple interacting agents with inverse reward signals with same or different Behavior Parameters . In this scenario, agents must compete with one another to either win a competition, or obtain some limited set of resources. All team sports fall into this scenario. Ecosystem. Multiple interacting agents with independent reward signals with same or different Behavior Parameters . This scenario can be thought of as creating a small world in which animals with different goals all interact, such as a savanna in which there might be zebras, elephants and giraffes, or an autonomous driving simulation within an urban environment.","title":"Flexible Training Scenarios"},{"location":"ML-Agents-Overview/#training-methods-environment-agnostic","text":"The remaining sections overview the various state-of-the-art machine learning algorithms that are part of the ML-Agents Toolkit. If you aren't studying machine and reinforcement learning as a subject and just want to train agents to accomplish tasks, you can treat these algorithms as black boxes . There are a few training-related parameters to adjust inside Unity as well as on the Python training side, but you do not need in-depth knowledge of the algorithms themselves to successfully create and train agents. Step-by-step procedures for running the training process are provided in the Training ML-Agents page. This section specifically focuses on the training methods that are available regardless of the specifics of your learning environment.","title":"Training Methods: Environment-agnostic"},{"location":"ML-Agents-Overview/#a-quick-note-on-reward-signals","text":"In this section we introduce the concepts of intrinsic and extrinsic rewards, which helps explain some of the training methods. In reinforcement learning, the end goal for the Agent is to discover a behavior (a Policy) that maximizes a reward. You will need to provide the agent one or more reward signals to use during training. Typically, a reward is defined by your environment, and corresponds to reaching some goal. These are what we refer to as extrinsic rewards, as they are defined external of the learning algorithm. Rewards, however, can be defined outside of the environment as well, to encourage the agent to behave in certain ways, or to aid the learning of the true extrinsic reward. We refer to these rewards as intrinsic reward signals. The total reward that the agent will learn to maximize can be a mix of extrinsic and intrinsic reward signals. The ML-Agents Toolkit allows reward signals to be defined in a modular way, and we provide four reward signals that can the mixed and matched to help shape your agent's behavior: extrinsic : represents the rewards defined in your environment, and is enabled by default gail : represents an intrinsic reward signal that is defined by GAIL (see below) curiosity : represents an intrinsic reward signal that encourages exploration in sparse-reward environments that is defined by the Curiosity module (see below). rnd : represents an intrinsic reward signal that encourages exploration in sparse-reward environments that is defined by the Curiosity module (see below).","title":"A Quick Note on Reward Signals"},{"location":"ML-Agents-Overview/#deep-reinforcement-learning","text":"ML-Agents provide an implementation of two reinforcement learning algorithms: Proximal Policy Optimization (PPO) Soft Actor-Critic (SAC) The default algorithm is PPO. This is a method that has been shown to be more general purpose and stable than many other RL algorithms. In contrast with PPO, SAC is off-policy , which means it can learn from experiences collected at any time during the past. As experiences are collected, they are placed in an experience replay buffer and randomly drawn during training. This makes SAC significantly more sample-efficient, often requiring 5-10 times less samples to learn the same task as PPO. However, SAC tends to require more model updates. SAC is a good choice for heavier or slower environments (about 0.1 seconds per step or more). SAC is also a \"maximum entropy\" algorithm, and enables exploration in an intrinsic way. Read more about maximum entropy RL here .","title":"Deep Reinforcement Learning"},{"location":"ML-Agents-Overview/#curiosity-for-sparse-reward-environments","text":"In environments where the agent receives rare or infrequent rewards (i.e. sparse-reward), an agent may never receive a reward signal on which to bootstrap its training process. This is a scenario where the use of an intrinsic reward signals can be valuable. Curiosity is one such signal which can help the agent explore when extrinsic rewards are sparse. The curiosity Reward Signal enables the Intrinsic Curiosity Module. This is an implementation of the approach described in Curiosity-driven Exploration by Self-supervised Prediction by Pathak, et al. It trains two networks: an inverse model, which takes the current and next observation of the agent, encodes them, and uses the encoding to predict the action that was taken between the observations a forward model, which takes the encoded current observation and action, and predicts the next encoded observation. The loss of the forward model (the difference between the predicted and actual encoded observations) is used as the intrinsic reward, so the more surprised the model is, the larger the reward will be. For more information, see our dedicated blog post on the Curiosity module .","title":"Curiosity for Sparse-reward Environments"},{"location":"ML-Agents-Overview/#rnd-for-sparse-reward-environments","text":"Similarly to Curiosity, Random Network Distillation (RND) is useful in sparse or rare reward environments as it helps the Agent explore. The RND Module is implemented following the paper Exploration by Random Network Distillation . RND uses two networks: - The first is a network with fixed random weights that takes observations as inputs and generates an encoding - The second is a network with similar architecture that is trained to predict the outputs of the first network and uses the observations the Agent collects as training data. The loss (the squared difference between the predicted and actual encoded observations) of the trained model is used as intrinsic reward. The more an Agent visits a state, the more accurate the predictions and the lower the rewards which encourages the Agent to explore new states with higher prediction errors.","title":"RND for Sparse-reward Environments"},{"location":"ML-Agents-Overview/#imitation-learning","text":"It is often more intuitive to simply demonstrate the behavior we want an agent to perform, rather than attempting to have it learn via trial-and-error methods. For example, instead of indirectly training a medic with the help of a reward function, we can give the medic real world examples of observations from the game and actions from a game controller to guide the medic's behavior. Imitation Learning uses pairs of observations and actions from a demonstration to learn a policy. See this video demo of imitation learning . Imitation learning can either be used alone or in conjunction with reinforcement learning. If used alone it can provide a mechanism for learning a specific type of behavior (i.e. a specific style of solving the task). If used in conjunction with reinforcement learning it can dramatically reduce the time the agent takes to solve the environment. This can be especially pronounced in sparse-reward environments. For instance, on the Pyramids environment , using 6 episodes of demonstrations can reduce training steps by more than 4 times. See Behavioral Cloning + GAIL + Curiosity + RL below. The ML-Agents Toolkit provides a way to learn directly from demonstrations, as well as use them to help speed up reward-based training (RL). We include two algorithms called Behavioral Cloning (BC) and Generative Adversarial Imitation Learning (GAIL). In most scenarios, you can combine these two features: If you want to help your agents learn (especially with environments that have sparse rewards) using pre-recorded demonstrations, you can generally enable both GAIL and Behavioral Cloning at low strengths in addition to having an extrinsic reward. An example of this is provided for the PushBlock example environment in config/imitation/PushBlock.yaml . If you want to train purely from demonstrations with GAIL and BC without an extrinsic reward signal, please see the CrawlerStatic example environment under in config/imitation/CrawlerStatic.yaml . Note: GAIL introduces a survivor bias to the learning process. That is, by giving positive rewards based on similarity to the expert, the agent is incentivized to remain alive for as long as possible. This can directly conflict with goal-oriented tasks like our PushBlock or Pyramids example environments where an agent must reach a goal state thus ending the episode as quickly as possible. In these cases, we strongly recommend that you use a low strength GAIL reward signal and a sparse extrinisic signal when the agent achieves the task. This way, the GAIL reward signal will guide the agent until it discovers the extrnisic signal and will not overpower it. If the agent appears to be ignoring the extrinsic reward signal, you should reduce the strength of GAIL.","title":"Imitation Learning"},{"location":"ML-Agents-Overview/#gail-generative-adversarial-imitation-learning","text":"GAIL, or Generative Adversarial Imitation Learning , uses an adversarial approach to reward your Agent for behaving similar to a set of demonstrations. GAIL can be used with or without environment rewards, and works well when there are a limited number of demonstrations. In this framework, a second neural network, the discriminator, is taught to distinguish whether an observation/action is from a demonstration or produced by the agent. This discriminator can then examine a new observation/action and provide it a reward based on how close it believes this new observation/action is to the provided demonstrations. At each training step, the agent tries to learn how to maximize this reward. Then, the discriminator is trained to better distinguish between demonstrations and agent state/actions. In this way, while the agent gets better and better at mimicking the demonstrations, the discriminator keeps getting stricter and stricter and the agent must try harder to \"fool\" it. This approach learns a policy that produces states and actions similar to the demonstrations, requiring fewer demonstrations than direct cloning of the actions. In addition to learning purely from demonstrations, the GAIL reward signal can be mixed with an extrinsic reward signal to guide the learning process.","title":"GAIL (Generative Adversarial Imitation Learning)"},{"location":"ML-Agents-Overview/#behavioral-cloning-bc","text":"BC trains the Agent's policy to exactly mimic the actions shown in a set of demonstrations. The BC feature can be enabled on the PPO or SAC trainers. As BC cannot generalize past the examples shown in the demonstrations, BC tends to work best when there exists demonstrations for nearly all of the states that the agent can experience, or in conjunction with GAIL and/or an extrinsic reward.","title":"Behavioral Cloning (BC)"},{"location":"ML-Agents-Overview/#recording-demonstrations","text":"Demonstrations of agent behavior can be recorded from the Unity Editor or build, and saved as assets. These demonstrations contain information on the observations, actions, and rewards for a given agent during the recording session. They can be managed in the Editor, as well as used for training with BC and GAIL. See the Designing Agents page for more information on how to record demonstrations for your agent.","title":"Recording Demonstrations"},{"location":"ML-Agents-Overview/#summary","text":"To summarize, we provide 3 training methods: BC, GAIL and RL (PPO or SAC) that can be used independently or together: BC can be used on its own or as a pre-training step before GAIL and/or RL GAIL can be used with or without extrinsic rewards RL can be used on its own (either PPO or SAC) or in conjunction with BC and/or GAIL. Leveraging either BC or GAIL requires recording demonstrations to be provided as input to the training algorithms.","title":"Summary"},{"location":"ML-Agents-Overview/#training-methods-environment-specific","text":"In addition to the three environment-agnostic training methods introduced in the previous section, the ML-Agents Toolkit provides additional methods that can aid in training behaviors for specific types of environments.","title":"Training Methods: Environment-specific"},{"location":"ML-Agents-Overview/#training-in-competitive-multi-agent-environments-with-self-play","text":"ML-Agents provides the functionality to train both symmetric and asymmetric adversarial games with Self-Play . A symmetric game is one in which opposing agents are equal in form, function and objective. Examples of symmetric games are our Tennis and Soccer example environments. In reinforcement learning, this means both agents have the same observation and actions and learn from the same reward function and so they can share the same policy . In asymmetric games, this is not the case. An example of an asymmetric games are Hide and Seek. Agents in these types of games do not always have the same observation or actions and so sharing policy networks is not necessarily ideal. With self-play, an agent learns in adversarial games by competing against fixed, past versions of its opponent (which could be itself as in symmetric games) to provide a more stable, stationary learning environment. This is compared to competing against the current, best opponent in every episode, which is constantly changing (because it's learning). Self-play can be used with our implementations of both Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC). However, from the perspective of an individual agent, these scenarios appear to have non-stationary dynamics because the opponent is often changing. This can cause significant issues in the experience replay mechanism used by SAC. Thus, we recommend that users use PPO. For further reading on this issue in particular, see the paper Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning . See our Designing Agents page for more information on setting up teams in your Unity scene. Also, read our blog post on self-play for additional information. Additionally, check ELO Rating System the method we use to calculate the relative skill level between two players.","title":"Training in Competitive Multi-Agent Environments with Self-Play"},{"location":"ML-Agents-Overview/#training-in-cooperative-multi-agent-environments-with-ma-poca","text":"ML-Agents provides the functionality for training cooperative behaviors - i.e., groups of agents working towards a common goal, where the success of the individual is linked to the success of the whole group. In such a scenario, agents typically receive rewards as a group. For instance, if a team of agents wins a game against an opposing team, everyone is rewarded - even agents who did not directly contribute to the win. This makes learning what to do as an individual difficult - you may get a win for doing nothing, and a loss for doing your best. In ML-Agents, we provide MA-POCA (MultiAgent POsthumous Credit Assignment), which is a novel multi-agent trainer that trains a centralized critic , a neural network that acts as a \"coach\" for a whole group of agents. You can then give rewards to the team as a whole, and the agents will learn how best to contribute to achieving that reward. Agents can also be given rewards individually, and the team will work together to help the individual achieve those goals. During an episode, agents can be added or removed from the group, such as when agents spawn or die in a game. If agents are removed mid-episode (e.g., if teammates die or are removed from the game), they will still learn whether their actions contributed to the team winning later, enabling agents to take group-beneficial actions even if they result in the individual being removed from the game (i.e., self-sacrifice). MA-POCA can also be combined with self-play to train teams of agents to play against each other. To learn more about enabling cooperative behaviors for agents in an ML-Agents environment, check out this page . To learn more about MA-POCA, please see our paper On the Use and Misuse of Absorbing States in Multi-Agent Reinforcement Learning . For further reading, MA-POCA builds on previous work in multi-agent cooperative learning ( Lowe et al. , Foerster et al. , among others) to enable the above use-cases.","title":"Training In Cooperative Multi-Agent Environments with MA-POCA"},{"location":"ML-Agents-Overview/#solving-complex-tasks-using-curriculum-learning","text":"Curriculum learning is a way of training a machine learning model where more difficult aspects of a problem are gradually introduced in such a way that the model is always optimally challenged. This idea has been around for a long time, and it is how we humans typically learn. If you imagine any childhood primary school education, there is an ordering of classes and topics. Arithmetic is taught before algebra, for example. Likewise, algebra is taught before calculus. The skills and knowledge learned in the earlier subjects provide a scaffolding for later lessons. The same principle can be applied to machine learning, where training on easier tasks can provide a scaffolding for harder tasks in the future. Imagine training the medic to scale a wall to arrive at a wounded team member. The starting point when training a medic to accomplish this task will be a random policy. That starting policy will have the medic running in circles, and will likely never, or very rarely scale the wall properly to revive their team member (and achieve the reward). If we start with a simpler task, such as moving toward an unobstructed team member, then the medic can easily learn to accomplish the task. From there, we can slowly add to the difficulty of the task by increasing the size of the wall until the medic can complete the initially near-impossible task of scaling the wall. We have included an environment to demonstrate this with ML-Agents, called Wall Jump . Demonstration of a hypothetical curriculum training scenario in which a progressively taller wall obstructs the path to the goal. [ Note : The example provided above is for instructional purposes, and was based on an early version of the Wall Jump example environment . As such, it is not possible to directly replicate the results here using that environment.] The ML-Agents Toolkit supports modifying custom environment parameters during the training process to aid in learning. This allows elements of the environment related to difficulty or complexity to be dynamically adjusted based on training progress. The Training ML-Agents page has more information on defining training curriculums.","title":"Solving Complex Tasks using Curriculum Learning"},{"location":"ML-Agents-Overview/#training-robust-agents-using-environment-parameter-randomization","text":"An agent trained on a specific environment, may be unable to generalize to any tweaks or variations in the environment (in machine learning this is referred to as overfitting). This becomes problematic in cases where environments are instantiated with varying objects or properties. One mechanism to alleviate this and train more robust agents that can generalize to unseen variations of the environment is to expose them to these variations during training. Similar to Curriculum Learning, where environments become more difficult as the agent learns, the ML-Agents Toolkit provides a way to randomly sample parameters of the environment during training. We refer to this approach as Environment Parameter Randomization . For those familiar with Reinforcement Learning research, this approach is based on the concept of Domain Randomization . By using parameter randomization during training , the agent can be better suited to adapt (with higher performance) to future unseen variations of the environment. Ball scale of 0.5 Ball scale of 4 Example of variations of the 3D Ball environment. The environment parameters are gravity , ball_mass and ball_scale .","title":"Training Robust Agents using Environment Parameter Randomization"},{"location":"ML-Agents-Overview/#model-types","text":"Regardless of the training method deployed, there are a few model types that users can train using the ML-Agents Toolkit. This is due to the flexibility in defining agent observations, which include vector, ray cast and visual observations. You can learn more about how to instrument an agent's observation in the Designing Agents guide.","title":"Model Types"},{"location":"ML-Agents-Overview/#learning-from-vector-observations","text":"Whether an agent's observations are ray cast or vector, the ML-Agents Toolkit provides a fully connected neural network model to learn from those observations. At training time you can configure different aspects of this model such as the number of hidden units and number of layers.","title":"Learning from Vector Observations"},{"location":"ML-Agents-Overview/#learning-from-cameras-using-convolutional-neural-networks","text":"Unlike other platforms, where the agent\u2019s observation might be limited to a single vector or image, the ML-Agents Toolkit allows multiple cameras to be used for observations per agent. This enables agents to learn to integrate information from multiple visual streams. This can be helpful in several scenarios such as training a self-driving car which requires multiple cameras with different viewpoints, or a navigational agent which might need to integrate aerial and first-person visuals. You can learn more about adding visual observations to an agent here . When visual observations are utilized, the ML-Agents Toolkit leverages convolutional neural networks (CNN) to learn from the input images. We offer three network architectures: a simple encoder which consists of two convolutional layers the implementation proposed by Mnih et al. , consisting of three convolutional layers, the IMPALA Resnet consisting of three stacked layers, each with two residual blocks, making a much larger network than the other two. The choice of the architecture depends on the visual complexity of the scene and the available computational resources.","title":"Learning from Cameras using Convolutional Neural Networks"},{"location":"ML-Agents-Overview/#learning-from-variable-length-observations-using-attention","text":"Using the ML-Agents Toolkit, it is possible to have agents learn from a varying number of inputs. To do so, each agent can keep track of a buffer of vector observations. At each step, the agent will go through all the elements in the buffer and extract information but the elements in the buffer can change at every step. This can be useful in scenarios in which the agents must keep track of a varying number of elements throughout the episode. For example in a game where an agent must learn to avoid projectiles, but the projectiles can vary in numbers. You can learn more about variable length observations here . When variable length observations are utilized, the ML-Agents Toolkit leverages attention networks to learn from a varying number of entities. Agents using attention will ignore entities that are deemed not relevant and pay special attention to entities relevant to the current situation based on context.","title":"Learning from Variable Length Observations using Attention"},{"location":"ML-Agents-Overview/#memory-enhanced-agents-using-recurrent-neural-networks","text":"Have you ever entered a room to get something and immediately forgot what you were looking for? Don't let that happen to your agents. In some scenarios, agents must learn to remember the past in order to take the best decision. When an agent only has partial observability of the environment, keeping track of past observations can help the agent learn. Deciding what the agents should remember in order to solve a task is not easy to do by hand, but our training algorithms can learn to keep track of what is important to remember with LSTM .","title":"Memory-enhanced Agents using Recurrent Neural Networks"},{"location":"ML-Agents-Overview/#additional-features","text":"Beyond the flexible training scenarios available, the ML-Agents Toolkit includes additional features which improve the flexibility and interpretability of the training process. Concurrent Unity Instances - We enable developers to run concurrent, parallel instances of the Unity executable during training. For certain scenarios, this should speed up training. Check out our dedicated page on creating a Unity executable and the Training ML-Agents page for instructions on how to set the number of concurrent instances. Recording Statistics from Unity - We enable developers to record statistics from within their Unity environments. These statistics are aggregated and generated during the training process. Custom Side Channels - We enable developers to create custom side channels to manage data transfer between Unity and Python that is unique to their training workflow and/or environment. Custom Samplers - We enable developers to create custom sampling methods for Environment Parameter Randomization. This enables users to customize this training method for their particular environment.","title":"Additional Features"},{"location":"ML-Agents-Overview/#summary-and-next-steps","text":"To briefly summarize: The ML-Agents Toolkit enables games and simulations built in Unity to serve as the platform for training intelligent agents. It is designed to enable a large variety of training modes and scenarios and comes packed with several features to enable researchers and developers to leverage (and enhance) machine learning within Unity. In terms of next steps: For a walkthrough of running ML-Agents with a simple scene, check out the Getting Started guide. For a \"Hello World\" introduction to creating your own Learning Environment, check out the Making a New Learning Environment page. For an overview on the more complex example environments that are provided in this toolkit, check out the Example Environments page. For more information on the various training options available, check out the Training ML-Agents page.","title":"Summary and Next Steps"},{"location":"ML-Agents-README/","text":"Unity ML-Agents Trainers The mlagents Python package is part of the ML-Agents Toolkit . mlagents provides a set of reinforcement and imitation learning algorithms designed to be used with Unity environments. The algorithms interface with the Python API provided by the mlagents_envs package. See here for more information on mlagents_envs . The algorithms can be accessed using the: mlagents-learn access point. See here for more information on using this package. Installation Install the mlagents package with: python -m pip install mlagents==0.30.0 Usage & More Information For more information on the ML-Agents Toolkit and how to instrument a Unity scene with the ML-Agents SDK, check out the main ML-Agents Toolkit documentation . Limitations Resuming self-play from a checkpoint resets the reported ELO to the default value.","title":"ML Agents README"},{"location":"ML-Agents-README/#unity-ml-agents-trainers","text":"The mlagents Python package is part of the ML-Agents Toolkit . mlagents provides a set of reinforcement and imitation learning algorithms designed to be used with Unity environments. The algorithms interface with the Python API provided by the mlagents_envs package. See here for more information on mlagents_envs . The algorithms can be accessed using the: mlagents-learn access point. See here for more information on using this package.","title":"Unity ML-Agents Trainers"},{"location":"ML-Agents-README/#installation","text":"Install the mlagents package with: python -m pip install mlagents==0.30.0","title":"Installation"},{"location":"ML-Agents-README/#usage-more-information","text":"For more information on the ML-Agents Toolkit and how to instrument a Unity scene with the ML-Agents SDK, check out the main ML-Agents Toolkit documentation .","title":"Usage &amp; More Information"},{"location":"ML-Agents-README/#limitations","text":"Resuming self-play from a checkpoint resets the reported ELO to the default value.","title":"Limitations"},{"location":"ML-Agents-Toolkit-Documentation/","text":"Unity ML-Agents Toolkit Documentation Installation & Set-up Installation Using Virtual Environment Getting Started Getting Started Guide ML-Agents Toolkit Overview Background: Unity Background: Machine Learning Background: PyTorch Example Environments Creating Learning Environments Making a New Learning Environment Designing a Learning Environment Designing Agents Using an Executable Environment ML-Agents Package Settings Training & Inference Training ML-Agents Training Configuration File Using TensorBoard to Observe Training Profiling Trainers Unity Inference Engine Extending ML-Agents Creating Custom Side Channels Creating Custom Samplers for Environment Parameter Randomization Hugging Face Integration Using Hugging Face to download and upload trained models Python Tutorial with Google Colab Using a UnityEnvironment Q-Learning with a UnityEnvironment Using Side Channels on a UnityEnvironment Help Migrating from earlier versions of ML-Agents Frequently Asked Questions ML-Agents Glossary Limitations API Docs API Reference Python API Documentation How to use the Python API How to use the Unity Environment Registry Wrapping Learning Environment as a Gym (+Baselines/Dopamine Integration) Translations To make the Unity ML-Agents Toolkit accessible to the global research and Unity developer communities, we're attempting to create and maintain translations of our documentation. We've started with translating a subset of the documentation to one language (Chinese), but we hope to continue translating more pages and to other languages. Consequently, we welcome any enhancements and improvements from the community. Chinese Korean Deprecated Docs We no longer use them ourselves and so they may not be up-to-date. We've decided to keep them up just in case they are helpful to you. Windows Anaconda Installation Using Docker Training on the Cloud with Amazon Web Services Training on the Cloud with Microsoft Azure Using the Video Recorder","title":"Toolkit Documentation"},{"location":"ML-Agents-Toolkit-Documentation/#unity-ml-agents-toolkit-documentation","text":"","title":"Unity ML-Agents Toolkit Documentation"},{"location":"ML-Agents-Toolkit-Documentation/#installation-set-up","text":"Installation Using Virtual Environment","title":"Installation &amp; Set-up"},{"location":"ML-Agents-Toolkit-Documentation/#getting-started","text":"Getting Started Guide ML-Agents Toolkit Overview Background: Unity Background: Machine Learning Background: PyTorch Example Environments","title":"Getting Started"},{"location":"ML-Agents-Toolkit-Documentation/#creating-learning-environments","text":"Making a New Learning Environment Designing a Learning Environment Designing Agents Using an Executable Environment ML-Agents Package Settings","title":"Creating Learning Environments"},{"location":"ML-Agents-Toolkit-Documentation/#training-inference","text":"Training ML-Agents Training Configuration File Using TensorBoard to Observe Training Profiling Trainers Unity Inference Engine","title":"Training &amp; Inference"},{"location":"ML-Agents-Toolkit-Documentation/#extending-ml-agents","text":"Creating Custom Side Channels Creating Custom Samplers for Environment Parameter Randomization","title":"Extending ML-Agents"},{"location":"ML-Agents-Toolkit-Documentation/#hugging-face-integration","text":"Using Hugging Face to download and upload trained models","title":"Hugging Face Integration"},{"location":"ML-Agents-Toolkit-Documentation/#python-tutorial-with-google-colab","text":"Using a UnityEnvironment Q-Learning with a UnityEnvironment Using Side Channels on a UnityEnvironment","title":"Python Tutorial with Google Colab"},{"location":"ML-Agents-Toolkit-Documentation/#help","text":"Migrating from earlier versions of ML-Agents Frequently Asked Questions ML-Agents Glossary Limitations","title":"Help"},{"location":"ML-Agents-Toolkit-Documentation/#api-docs","text":"API Reference Python API Documentation How to use the Python API How to use the Unity Environment Registry Wrapping Learning Environment as a Gym (+Baselines/Dopamine Integration)","title":"API Docs"},{"location":"ML-Agents-Toolkit-Documentation/#translations","text":"To make the Unity ML-Agents Toolkit accessible to the global research and Unity developer communities, we're attempting to create and maintain translations of our documentation. We've started with translating a subset of the documentation to one language (Chinese), but we hope to continue translating more pages and to other languages. Consequently, we welcome any enhancements and improvements from the community. Chinese Korean","title":"Translations"},{"location":"ML-Agents-Toolkit-Documentation/#deprecated-docs","text":"We no longer use them ourselves and so they may not be up-to-date. We've decided to keep them up just in case they are helpful to you. Windows Anaconda Installation Using Docker Training on the Cloud with Amazon Web Services Training on the Cloud with Microsoft Azure Using the Video Recorder","title":"Deprecated Docs"},{"location":"Migrating/","text":"Upgrading Migrating Migrating to the ml-agents-envs 0.29.0 package Python 3.8 is now the minimum version of python supported due to python3.6 EOL . Please update your python installation to 3.8.13 or higher. The gym-unity package has been refactored into the ml-agents-envs package. Please update your imports accordingly. Example: Before from gym_unity.unity_gym_env import UnityToGymWrapper After: from mlagents_envs.envs.unity_gym_env import UnityToGymWrapper Migrating the package to version 2.0 The official version of Unity ML-Agents supports is now 2021.3 LTS. If you run into issues, please consider deleting your project's Library folder and reponening your project. If you used any of the APIs that were deprecated before version 2.0, you need to use their replacement. These deprecated APIs have been removed. See the migration steps bellow for specific API replacements. Deprecated methods removed Deprecated API Suggested Replacement IActuator ActuatorComponent.CreateActuator() IActuator[] ActuatorComponent.CreateActuators() IActionReceiver.PackActions(in float[] destination) none Agent.CollectDiscreteActionMasks(DiscreteActionMasker actionMasker) Agent.WriteDiscreteActionMask(IDiscreteActionMask actionMask) Agent.Heuristic(float[] actionsOut) Agent.Heuristic(in ActionBuffers actionsOut) Agent.OnActionReceived(float[] vectorAction) Agent.OnActionReceived(ActionBuffers actions) Agent.GetAction() Agent.GetStoredActionBuffers() BrainParameters.SpaceType , VectorActionSize , VectorActionSpaceType , and NumActions BrainParameters.ActionSpec ObservationWriter.AddRange(IEnumerable<float> data, int writeOffset = 0) ObservationWriter. AddList(IList<float> data, int writeOffset = 0 SensorComponent.IsVisual() and IsVector() none VectorSensor.AddObservation(IEnumerable<float> observation) VectorSensor.AddObservation(IList<float> observation) SideChannelsManager SideChannelManager IDiscreteActionMask changes The interface for disabling specific discrete actions has changed. IDiscreteActionMask.WriteMask() was removed, and replaced with SetActionEnabled() . Instead of returning an IEnumerable with indices to disable, you can now call SetActionEnabled for each index to disable (or enable). As an example, if you overrode Agent.WriteDiscreteActionMask() with something that looked like: public override void WriteDiscreteActionMask(IDiscreteActionMask actionMask) { var branch = 2; var actionsToDisable = new[] {1, 3}; actionMask.WriteMask(branch, actionsToDisable); } the equivalent code would now be public override void WriteDiscreteActionMask(IDiscreteActionMask actionMask) { var branch = 2; actionMask.SetActionEnabled(branch, 1, false); actionMask.SetActionEnabled(branch, 3, false); } IActuator changes The IActuator interface now implements IHeuristicProvider . Please add the corresponding Heuristic(in ActionBuffers) method to your custom Actuator classes. ISensor and SensorComponent changes The ISensor.GetObservationShape() method and ITypedSensor and IDimensionPropertiesSensor interfaces were removed, and GetObservationSpec() was added. You can use ObservationSpec.Vector() or ObservationSpec.Visual() to generate ObservationSpec s that are equivalent to the previous shape. For example, if your old ISensor looked like: public override int[] GetObservationShape() { return new[] { m_Height, m_Width, m_NumChannels }; } the equivalent code would now be public override ObservationSpec GetObservationSpec() { return ObservationSpec.Visual(m_Height, m_Width, m_NumChannels); } The ISensor.GetCompressionType() method and ISparseChannelSensor interface was removed, and GetCompressionSpec() was added. You can use CompressionSpec.Default() or CompressionSpec.Compressed() to generate CompressionSpec s that are equivalent to the previous values. For example, if your old ISensor looked like: ```csharp public virtual SensorCompressionType GetCompressionType() { return SensorCompressionType.None; } the equivalent code would now be ```csharp public CompressionSpec GetCompressionSpec() { return CompressionSpec.Default(); } The abstract method SensorComponent.GetObservationShape() was removed. The abstract method SensorComponent.CreateSensor() was replaced with CreateSensors() , which returns an ISensor[] . Match3 integration changes The Match-3 integration utilities were moved from com.unity.ml-agents.extensions to com.unity.ml-agents . The AbstractBoard interface was changed: * AbstractBoard no longer contains Rows , Columns , NumCellTypes , and NumSpecialTypes fields. * public abstract BoardSize GetMaxBoardSize() was added as an abstract method. BoardSize is a new struct that contains Rows , Columns , NumCellTypes , and NumSpecialTypes fields, with the same meanings as the old AbstractBoard fields. * public virtual BoardSize GetCurrentBoardSize() is an optional method; by default it returns GetMaxBoardSize() . If you wish to use a single behavior to work with multiple board sizes, override GetCurrentBoardSize() to return the current BoardSize . The values returned by GetCurrentBoardSize() must be less than or equal to the corresponding values from GetMaxBoardSize() . GridSensor changes The sensor configuration has changed: * The sensor implementation has been refactored and exsisting GridSensor created from extension package will not work in newer version. Some errors might show up when loading the old sensor in the scene. You'll need to remove the old sensor and create a new GridSensor. * These parameters names have changed but still refer to the same concept in the sensor: GridNumSide -> GridSize , RotateToAgent -> RotateWithAgent , ObserveMask -> ColliderMask , DetectableObjects -> DetectableTags * DepthType ( ChanelBase / ChannelHot ) option and ChannelDepth are removed. Now the default is one-hot encoding for detected tag. If you were using original GridSensor without overriding any method, switching to new GridSensor will produce similar effect for training although the actual observations will be slightly different. For creating your GridSensor implementation with custom data: * To create custom GridSensor, derive from GridSensorBase instead of GridSensor . Besides overriding GetObjectData() , you will also need to consider override GetCellObservationSize() , IsDataNormalized() and GetProcessCollidersMethod() according to the data you collect. Also you'll need to override GridSensorComponent.GetGridSensors() and return your custom GridSensor. * The input argument tagIndex in GetObjectData() has changed from 1-indexed to 0-indexed and the data type changed from float to int . The index of first detectable tag will be 0 instead of 1. normalizedDistance was removed from input. * The observation data should be written to the input dataBuffer instead of creating and returning a new array. * Removed the constraint of all data required to be normalized. You should specify it in IsDataNormalized() . Sensors with non-normalized data cannot use PNG compression type. * The sensor will not further encode the data recieved from GetObjectData() anymore. The values recieved from GetObjectData() will be the observation sent to the trainer. LSTM models from previous releases no longer supported The way the Unity Inference Engine processes LSTM (recurrent neural networks) has changed. As a result, models trained with previous versions of ML-Agents will not be usable at inference if they were trained with a memory setting in the .yaml config file. If you want to use a model that has a recurrent neural network in this release of ML-Agents, you need to train the model using the python trainer from this release. Migrating to Release 13 Implementing IHeuristic in your IActuator implementations If you have any custom actuators, you can now implement the IHeuristicProvider interface to have your actuator handle the generation of actions when an Agent is running in heuristic mode. VectorSensor.AddObservation(IEnumerable<float>) is deprecated. Use VectorSensor.AddObservation(IList<float>) instead. ObservationWriter.AddRange() is deprecated. Use ObservationWriter.AddList() instead. ActuatorComponent.CreateAcuator() is deprecated. Please use override ActuatorComponent.CreateActuators instead. Since ActuatorComponent.CreateActuator() is abstract, you will still need to override it in your class until it is removed. It is only ever called if you don't override ActuatorComponent.CreateActuators . You can suppress the warnings by surrounding the method with the following pragma: c# #pragma warning disable 672 public IActuator CreateActuator() { ... } #pragma warning restore 672 Migrating Migrating to Release 11 Agent virtual method deprecation Agent.CollectDiscreteActionMasks() was deprecated and should be replaced with Agent.WriteDiscreteActionMask() Agent.Heuristic(float[]) was deprecated and should be replaced with Agent.Heuristic(ActionBuffers) . Agent.OnActionReceived(float[]) was deprecated and should be replaced with Agent.OnActionReceived(ActionBuffers) . Agent.GetAction() was deprecated and should be replaced with Agent.GetStoredActionBuffers() . The default implementation of these will continue to call the deprecated versions where appropriate. However, the deprecated versions may not be compatible with continuous and discrete actions on the same Agent. BrainParameters field and method deprecation BrainParameters.VectorActionSize was deprecated; you can now set BrainParameters.ActionSpec.NumContinuousActions or BrainParameters.ActionSpec.BranchSizes instead. BrainParameters.VectorActionSpaceType was deprecated, since both continuous and discrete actions can now be used. BrainParameters.NumActions() was deprecated. Use BrainParameters.ActionSpec.NumContinuousActions and BrainParameters.ActionSpec.NumDiscreteActions instead. Migrating from Release 7 to latest Important changes Some trainer files were moved. If you were using the TrainerFactory class, it was moved to the trainers/trainer folder. The components folder containing bc and reward_signals code was moved to the trainers/tf folder Steps to Migrate Replace calls to from mlagents.trainers.trainer_util import TrainerFactory to from mlagents.trainers.trainer import TrainerFactory Replace calls to from mlagents.trainers.trainer_util import handle_existing_directories to from mlagents.trainers.directory_utils import validate_existing_directories Replace mlagents.trainers.components with mlagents.trainers.tf.components in your import statements. Migrating from Release 3 to Release 7 Important changes The Parameter Randomization feature has been merged with the Curriculum feature. It is now possible to specify a sampler in the lesson of a Curriculum. Curriculum has been refactored and is now specified at the level of the parameter, not the behavior. More information here .(#4160) Steps to Migrate The configuration format for curriculum and parameter randomization has changed. To upgrade your configuration files, an upgrade script has been provided. Run python -m mlagents.trainers.upgrade_config -h to see the script usage. Note that you will have had to upgrade to/install the current version of ML-Agents before running the script. To update manually: If your config file used a parameter_randomization section, rename that section to environment_parameters If your config file used a curriculum section, you will need to rewrite your curriculum with this format . Migrating from Release 1 to Release 3 Important changes Training artifacts (trained models, summaries) are now found under results/ instead of summaries/ and models/ . Trainer configuration, curriculum configuration, and parameter randomization configuration have all been moved to a single YAML file. (#3791) Trainer configuration format has changed, and using a \"default\" behavior name has been deprecated. (#3936) max_step in the TerminalStep and TerminalSteps objects was renamed interrupted . On the UnityEnvironment API, get_behavior_names() and get_behavior_specs() methods were combined into the property behavior_specs that contains a mapping from behavior names to behavior spec. use_visual and allow_multiple_visual_obs in the UnityToGymWrapper constructor were replaced by allow_multiple_obs which allows one or more visual observations and vector observations to be used simultaneously. --save-freq has been removed from the CLI and is now configurable in the trainer configuration file. --lesson has been removed from the CLI. Lessons will resume when using --resume . To start at a different lesson, modify your Curriculum configuration. Steps to Migrate To upgrade your configuration files, an upgrade script has been provided. Run python -m mlagents.trainers.upgrade_config -h to see the script usage. Note that you will have had to upgrade to/install the current version of ML-Agents before running the script. To do it manually, copy your <BehaviorName> sections from trainer_config.yaml into a separate trainer configuration file, under a behaviors section. The default section is no longer needed. This new file should be specific to your environment, and not contain configurations for multiple environments (unless they have the same Behavior Names). - You will need to reformat your trainer settings as per the example . - If your training uses curriculum , move those configurations under a curriculum section. - If your training uses parameter randomization , move the contents of the sampler config to parameter_randomization in the main trainer configuration. - If you are using UnityEnvironment directly, replace max_step with interrupted in the TerminalStep and TerminalSteps objects. - Replace usage of get_behavior_names() and get_behavior_specs() in UnityEnvironment with behavior_specs . - If you use the UnityToGymWrapper , remove use_visual and allow_multiple_visual_obs from the constructor and add allow_multiple_obs = True if the environment contains either both visual and vector observations or multiple visual observations. - If you were setting --save-freq in the CLI, add a checkpoint_interval value in your trainer configuration, and set it equal to save-freq * n_agents_in_scene . Migrating from 0.15 to Release 1 Important changes The MLAgents C# namespace was renamed to Unity.MLAgents , and other nested namespaces were similarly renamed (#3843). The --load and --train command-line flags have been deprecated and replaced with --resume and --inference . Running with the same --run-id twice will now throw an error. The play_against_current_self_ratio self-play trainer hyperparameter has been renamed to play_against_latest_model_ratio Removed the multi-agent gym option from the gym wrapper. For multi-agent scenarios, use the Low Level Python API . The low level Python API has changed. You can look at the document Low Level Python API documentation for more information. If you use mlagents-learn for training, this should be a transparent change. The obsolete Agent methods GiveModel , Done , InitializeAgent , AgentAction and AgentReset have been removed. The signature of Agent.Heuristic() was changed to take a float[] as a parameter, instead of returning the array. This was done to prevent a common source of error where users would return arrays of the wrong size. The SideChannel API has changed (#3833, #3660) : Introduced the SideChannelManager to register, unregister and access side channels. EnvironmentParameters replaces the default FloatProperties . You can access the EnvironmentParameters with Academy.Instance.EnvironmentParameters on C#. If you were previously creating a UnityEnvironment in python and passing it a FloatPropertiesChannel , create an EnvironmentParametersChannel instead. SideChannel.OnMessageReceived is now a protected method (was public) SideChannel IncomingMessages methods now take an optional default argument, which is used when trying to read more data than the message contains. Added a feature to allow sending stats from C# environments to TensorBoard (and other python StatsWriters). To do this from your code, use Academy.Instance.StatsRecorder.Add(key, value) (#3660) num_updates and train_interval for SAC have been replaced with steps_per_update . The UnityEnv class from the gym-unity package was renamed UnityToGymWrapper and no longer creates the UnityEnvironment . Instead, the UnityEnvironment must be passed as input to the constructor of UnityToGymWrapper Public fields and properties on several classes were renamed to follow Unity's C# style conventions. All public fields and properties now use \"PascalCase\" instead of \"camelCase\"; for example, Agent.maxStep was renamed to Agent.MaxStep . For a full list of changes, see the pull request. (#3828) WriteAdapter was renamed to ObservationWriter . (#3834) Steps to Migrate In C# code, replace using MLAgents with using Unity.MLAgents . Replace other nested namespaces such as using MLAgents.Sensors with using Unity.MLAgents.Sensors Replace the --load flag with --resume when calling mlagents-learn , and don't use the --train flag as training will happen by default. To run without training, use --inference . To force-overwrite files from a pre-existing run, add the --force command-line flag. The Jupyter notebooks have been removed from the repository. If your Agent class overrides Heuristic() , change the signature to public override void Heuristic(float[] actionsOut) and assign values to actionsOut instead of returning an array. If you used SideChannels you must: Replace Academy.FloatProperties with Academy.Instance.EnvironmentParameters . Academy.RegisterSideChannel and Academy.UnregisterSideChannel were removed. Use SideChannelManager.RegisterSideChannel and SideChannelManager.UnregisterSideChannel instead. Set steps_per_update to be around equal to the number of agents in your environment, times num_updates and divided by train_interval . Replace UnityEnv with UnityToGymWrapper in your code. The constructor no longer takes a file name as input but a fully constructed UnityEnvironment instead. Update uses of \"camelCase\" fields and properties to \"PascalCase\". Migrating from 0.14 to 0.15 Important changes The Agent.CollectObservations() virtual method now takes as input a VectorSensor sensor as argument. The Agent.AddVectorObs() methods were removed. The SetMask was renamed to SetMask method must now be called on the DiscreteActionMasker argument of the CollectDiscreteActionMasks virtual method. We consolidated our API for DiscreteActionMasker . SetMask takes two arguments : the branch index and the list of masked actions for that branch. The Monitor class has been moved to the Examples Project. (It was prone to errors during testing) The MLAgents.Sensors namespace has been introduced. All sensors classes are part of the MLAgents.Sensors namespace. The MLAgents.SideChannels namespace has been introduced. All side channel classes are part of the MLAgents.SideChannels namespace. The interface for RayPerceptionSensor.PerceiveStatic() was changed to take an input class and write to an output class, and the method was renamed to Perceive() . The SetMask method must now be called on the DiscreteActionMasker argument of the CollectDiscreteActionMasks method. The method GetStepCount() on the Agent class has been replaced with the property getter StepCount The --multi-gpu option has been removed temporarily. AgentInfo.actionMasks has been renamed to AgentInfo.discreteActionMasks . BrainParameters and SpaceType have been removed from the public API BehaviorParameters have been removed from the public API. DecisionRequester has been made internal (you can still use the DecisionRequesterComponent from the inspector). RepeatAction was renamed TakeActionsBetweenDecisions for clarity. The following methods in the Agent class have been renamed. The original method names will be removed in a later release: InitializeAgent() was renamed to Initialize() AgentAction() was renamed to OnActionReceived() AgentReset() was renamed to OnEpsiodeBegin() Done() was renamed to EndEpisode() GiveModel() was renamed to SetModel() The IFloatProperties interface has been removed. The interface for SideChannels was changed: In C#, OnMessageReceived now takes a IncomingMessage argument, and QueueMessageToSend takes an OutgoingMessage argument. In python, on_message_received now takes a IncomingMessage argument, and queue_message_to_send takes an OutgoingMessage argument. Automatic stepping for Academy is now controlled from the AutomaticSteppingEnabled property. Steps to Migrate Add the using MLAgents.Sensors; in addition to using MLAgents; on top of your Agent's script. Replace your Agent's implementation of CollectObservations() with CollectObservations(VectorSensor sensor) . In addition, replace all calls to AddVectorObs() with sensor.AddObservation() or sensor.AddOneHotObservation() on the VectorSensor passed as argument. Replace your calls to SetActionMask on your Agent to DiscreteActionMasker.SetActionMask in CollectDiscreteActionMasks . If you call RayPerceptionSensor.PerceiveStatic() manually, add your inputs to a RayPerceptionInput . To get the previous float array output, iterate through RayPerceptionOutput.rayOutputs and call RayPerceptionOutput.RayOutput.ToFloatArray() . Replace all calls to Agent.GetStepCount() with Agent.StepCount We strongly recommend replacing the following methods with their new equivalent as they will be removed in a later release: InitializeAgent() to Initialize() AgentAction() to OnActionReceived() AgentReset() to OnEpisodeBegin() Done() to EndEpisode() GiveModel() to SetModel() Replace IFloatProperties variables with FloatPropertiesChannel variables. If you implemented custom SideChannels , update the signatures of your methods, and add your data to the OutgoingMessage or read it from the IncomingMessage . Replace calls to Academy.EnableAutomaticStepping()/DisableAutomaticStepping() with Academy.AutomaticSteppingEnabled = true/false. Migrating from 0.13 to 0.14 Important changes The UnitySDK folder has been split into a Unity Package ( com.unity.ml-agents ) and an examples project ( Project ). Please follow the Installation Guide to get up and running with this new repo structure. Several changes were made to how agents are reset and marked as done: Calling Done() on the Agent will now reset it immediately and call the AgentReset virtual method. (This is to simplify the previous logic in which the Agent had to wait for the next EnvironmentStep to reset) The \"Reset on Done\" setting in AgentParameters was removed; this is now effectively always true. AgentOnDone virtual method on the Agent has been removed. The Decision Period and On Demand decision checkbox have been removed from the Agent. On demand decision is now the default (calling RequestDecision on the Agent manually.) The Academy class was changed to a singleton, and its virtual methods were removed. Trainer steps are now counted per-Agent, not per-environment as in previous versions. For instance, if you have 10 Agents in the scene, 20 environment steps now corresponds to 200 steps as printed in the terminal and in Tensorboard. Curriculum config files are now YAML formatted and all curricula for a training run are combined into a single file. The --num-runs command-line option has been removed from mlagents-learn . Several fields on the Agent were removed or made private in order to simplify the interface. The agentParameters field of the Agent has been removed. (Contained only maxStep information) maxStep is now a public field on the Agent. (Was moved from agentParameters ) The Info field of the Agent has been made private. (Was only used internally and not meant to be modified outside of the Agent) The GetReward() method on the Agent has been removed. (It was being confused with GetCumulativeReward() ) The AgentAction struct no longer contains a value field. (Value estimates were not set during inference) The GetValueEstimate() method on the Agent has been removed. The UpdateValueAction() method on the Agent has been removed. The deprecated RayPerception3D and RayPerception2D classes were removed, and the legacyHitFractionBehavior argument was removed from RayPerceptionSensor.PerceiveStatic() . RayPerceptionSensor was inconsistent in how it handle scale on the Agent's transform. It now scales the ray length and sphere size for casting as the transform's scale changes. Steps to Migrate Follow the instructions on how to install the com.unity.ml-agents package into your project in the Installation Guide . If your Agent implemented AgentOnDone and did not have the checkbox Reset On Done checked in the inspector, you must call the code that was in AgentOnDone manually. If you give your Agent a reward or penalty at the end of an episode (e.g. for reaching a goal or falling off of a platform), make sure you call AddReward() or SetReward() before calling Done() . Previously, the order didn't matter. If you were not using On Demand Decision for your Agent, you must add a DecisionRequester component to your Agent GameObject and set its Decision Period field to the old Decision Period of the Agent. If you have a class that inherits from Academy: If the class didn't override any of the virtual methods and didn't store any additional data, you can just remove the old script from the scene. If the class had additional data, create a new MonoBehaviour and store the data in the new MonoBehaviour instead. If the class overrode the virtual methods, create a new MonoBehaviour and move the logic to it: Move the InitializeAcademy code to MonoBehaviour.Awake Move the AcademyStep code to MonoBehaviour.FixedUpdate Move the OnDestroy code to MonoBehaviour.OnDestroy. Move the AcademyReset code to a new method and add it to the Academy.OnEnvironmentReset action. Multiply max_steps and summary_freq in your trainer_config.yaml by the number of Agents in the scene. Combine curriculum configs into a single file. See the WallJump curricula for an example of the new curriculum config format. A tool like https://www.json2yaml.com may be useful to help with the conversion. If you have a model trained which uses RayPerceptionSensor and has non-1.0 scale in the Agent's transform, it must be retrained. Migrating from ML-Agents Toolkit v0.12.0 to v0.13.0 Important changes The low level Python API has changed. You can look at the document Low Level Python API documentation for more information. This should only affect you if you're writing a custom trainer; if you use mlagents-learn for training, this should be a transparent change. reset() on the Low-Level Python API no longer takes a train_mode argument. To modify the performance/speed of the engine, you must use an EngineConfigurationChannel reset() on the Low-Level Python API no longer takes a config argument. UnityEnvironment no longer has a reset_parameters field. To modify float properties in the environment, you must use a FloatPropertiesChannel . For more information, refer to the Low Level Python API documentation CustomResetParameters are now removed. The Academy no longer has a Training Configuration nor Inference Configuration field in the inspector. To modify the configuration from the Low-Level Python API, use an EngineConfigurationChannel . To modify it during training, use the new command line arguments --width , --height , --quality-level , --time-scale and --target-frame-rate in mlagents-learn . The Academy no longer has a Default Reset Parameters field in the inspector. The Academy class no longer has a ResetParameters . To access shared float properties with Python, use the new FloatProperties field on the Academy. Offline Behavioral Cloning has been removed. To learn from demonstrations, use the GAIL and Behavioral Cloning features with either PPO or SAC. mlagents.envs was renamed to mlagents_envs . The previous repo layout depended on PEP420 , which caused problems with some of our tooling such as mypy and pylint. The official version of Unity ML-Agents supports is now 2018.4 LTS. If you run into issues, please consider deleting your library folder and reponening your projects. You will need to install the Barracuda package into your project in order to ML-Agents to compile correctly. Steps to Migrate If you had a custom Training Configuration in the Academy inspector, you will need to pass your custom configuration at every training run using the new command line arguments --width , --height , --quality-level , --time-scale and --target-frame-rate . If you were using --slow in mlagents-learn , you will need to pass your old Inference Configuration of the Academy inspector with the new command line arguments --width , --height , --quality-level , --time-scale and --target-frame-rate instead. Any imports from mlagents.envs should be replaced with mlagents_envs . Migrating from ML-Agents Toolkit v0.11.0 to v0.12.0 Important Changes Text actions and observations, and custom action and observation protos have been removed. RayPerception3D and RayPerception2D are marked deprecated, and will be removed in a future release. They can be replaced by RayPerceptionSensorComponent3D and RayPerceptionSensorComponent2D. The Use Heuristic checkbox in Behavior Parameters has been replaced with a Behavior Type dropdown menu. This has the following options: Default corresponds to the previous unchecked behavior, meaning that Agents will train if they connect to a python trainer, otherwise they will perform inference. Heuristic Only means the Agent will always use the Heuristic() method. This corresponds to having \"Use Heuristic\" selected in 0.11.0. Inference Only means the Agent will always perform inference. Barracuda was upgraded to 0.3.2, and it is now installed via the Unity Package Manager. Steps to Migrate We fixed a bug in RayPerception3d.Perceive() that was causing the endOffset to be used incorrectly. However this may produce different behavior from previous versions if you use a non-zero startOffset . To reproduce the old behavior, you should increase the value of endOffset by startOffset . You can verify your raycasts are performing as expected in scene view using the debug rays. If you use RayPerception3D, replace it with RayPerceptionSensorComponent3D (and similarly for 2D). The settings, such as ray angles and detectable tags, are configured on the component now. RayPerception3D would contribute (# of rays) * (# of tags + 2) to the State Size in Behavior Parameters, but this is no longer necessary, so you should reduce the State Size by this amount. Making this change will require retraining your model, since the observations that RayPerceptionSensorComponent3D produces are different from the old behavior. If you see messages such as The type or namespace 'Barracuda' could not be found or The type or namespace 'Google' could not be found , you will need to install the Barracuda preview package . Migrating from ML-Agents Toolkit v0.10 to v0.11.0 Important Changes The definition of the gRPC service has changed. The online BC training feature has been removed. The BroadcastHub has been deprecated. If there is a training Python process, all LearningBrains in the scene will automatically be trained. If there is no Python process, inference will be used. The Brain ScriptableObjects have been deprecated. The Brain Parameters are now on the Agent and are referred to as Behavior Parameters. Make sure the Behavior Parameters is attached to the Agent GameObject. To use a heuristic behavior, implement the Heuristic() method in the Agent class and check the use heuristic checkbox in the Behavior Parameters. Several changes were made to the setup for visual observations (i.e. using Cameras or RenderTextures): Camera resolutions are no longer stored in the Brain Parameters. AgentParameters no longer stores lists of Cameras and RenderTextures To add visual observations to an Agent, you must now attach a CameraSensorComponent or RenderTextureComponent to the agent. The corresponding Camera or RenderTexture can be added to these in the editor, and the resolution and color/grayscale is configured on the component itself. Steps to Migrate In order to be able to train, make sure both your ML-Agents Python package and UnitySDK code come from the v0.11 release. Training will not work, for example, if you update the ML-Agents Python package, and only update the API Version in UnitySDK. If your Agents used visual observations, you must add a CameraSensorComponent corresponding to each old Camera in the Agent's camera list (and similarly for RenderTextures). Since Brain ScriptableObjects have been removed, you will need to delete all the Brain ScriptableObjects from your Assets folder. Then, add a Behavior Parameters component to each Agent GameObject. You will then need to complete the fields on the new Behavior Parameters component with the BrainParameters of the old Brain. Migrating from ML-Agents Toolkit v0.9 to v0.10 Important Changes We have updated the C# code in our repository to be in line with Unity Coding Conventions. This has changed the name of some public facing classes and enums. The example environments have been updated. If you were using these environments to benchmark your training, please note that the resulting rewards may be slightly different in v0.10. Steps to Migrate UnitySDK/Assets/ML-Agents/Scripts/Communicator.cs and its class Communicator have been renamed to UnitySDK/Assets/ML-Agents/Scripts/ICommunicator.cs and ICommunicator respectively. The SpaceType Enums discrete , and continuous have been renamed to Discrete and Continuous . We have removed the Done call as well as the capacity to set Max Steps on the Academy. Therefore an AcademyReset will never be triggered from C# (only from Python). If you want to reset the simulation after a fixed number of steps, or when an event in the simulation occurs, we recommend looking at our multi-agent example environments (such as FoodCollector). In our examples, groups of Agents can be reset through an \"Area\" that can reset groups of Agents. The import for mlagents.envs.UnityEnvironment was removed. If you are using the Python API, change from mlagents_envs import UnityEnvironment to from mlagents_envs.environment import UnityEnvironment . Migrating from ML-Agents Toolkit v0.8 to v0.9 Important Changes We have changed the way reward signals (including Curiosity) are defined in the trainer_config.yaml . When using multiple environments, every \"step\" is recorded in TensorBoard. The steps in the command line console corresponds to a single step of a single environment. Previously, each step corresponded to one step for all environments (i.e., num_envs steps). Steps to Migrate If you were overriding any of these following parameters in your config file, remove them from the top-level config and follow the steps below: gamma : Define a new extrinsic reward signal and set it's gamma to your new gamma. use_curiosity , curiosity_strength , curiosity_enc_size : Define a curiosity reward signal and set its strength to curiosity_strength , and encoding_size to curiosity_enc_size . Give it the same gamma as your extrinsic signal to mimic previous behavior. TensorBoards generated when running multiple environments in v0.8 are not comparable to those generated in v0.9 in terms of step count. Multiply your v0.8 step count by num_envs for an approximate comparison. You may need to change max_steps in your config as appropriate as well. Migrating from ML-Agents Toolkit v0.7 to v0.8 Important Changes We have split the Python packages into two separate packages ml-agents and ml-agents-envs . --worker-id option of learn.py has been removed, use --base-port instead if you'd like to run multiple instances of learn.py . Steps to Migrate If you are installing via PyPI, there is no change. If you intend to make modifications to ml-agents or ml-agents-envs please check the Installing for Development in the Installation documentation . Migrating from ML-Agents Toolkit v0.6 to v0.7 Important Changes We no longer support TFS and are now using the Unity Inference Engine Steps to Migrate Make sure to remove the ENABLE_TENSORFLOW flag in your Unity Project settings Migrating from ML-Agents Toolkit v0.5 to v0.6 Important Changes Brains are now Scriptable Objects instead of MonoBehaviors. You can no longer modify the type of a Brain. If you want to switch between PlayerBrain and LearningBrain for multiple agents, you will need to assign a new Brain to each agent separately. Note: You can pass the same Brain to multiple agents in a scene by leveraging Unity's prefab system or look for all the agents in a scene using the search bar of the Hierarchy window with the word Agent . We replaced the Internal and External Brain with Learning Brain . When you need to train a model, you need to drag it into the Broadcast Hub inside the Academy and check the Control checkbox. We removed the Broadcast checkbox of the Brain, to use the broadcast functionality, you need to drag the Brain into the Broadcast Hub . When training multiple Brains at the same time, each model is now stored into a separate model file rather than in the same file under different graph scopes. The Learning Brain graph scope, placeholder names, output names and custom placeholders can no longer be modified. Steps to Migrate To update a scene from v0.5 to v0.6, you must: Remove the Brain GameObjects in the scene. (Delete all of the Brain GameObjects under Academy in the scene.) Create new Brain Scriptable Objects using Assets -> Create -> ML-Agents for each type of the Brain you plan to use, and put the created files under a folder called Brains within your project. Edit their Brain Parameters to be the same as the parameters used in the Brain GameObjects. Agents have a Brain field in the Inspector, you need to drag the appropriate Brain ScriptableObject in it. The Academy has a Broadcast Hub field in the inspector, which is list of brains used in the scene. To train or control your Brain from the mlagents-learn Python script, you need to drag the relevant LearningBrain ScriptableObjects used in your scene into entries into this list. Migrating from ML-Agents Toolkit v0.4 to v0.5 Important The Unity project unity-environment has been renamed UnitySDK . The python folder has been renamed to ml-agents . It now contains two packages, mlagents.env and mlagents.trainers . mlagents.env can be used to interact directly with a Unity environment, while mlagents.trainers contains the classes for training agents. The supported Unity version has changed from 2017.1 or later to 2017.4 or later . 2017.4 is an LTS (Long Term Support) version that helps us maintain good quality and support. Earlier versions of Unity might still work, but you may encounter an error listed here. Unity API Discrete Actions now use branches . You can now specify concurrent discrete actions. You will need to update the Brain Parameters in the Brain Inspector in all your environments that use discrete actions. Refer to the discrete action documentation for more information. Python API In order to run a training session, you can now use the command mlagents-learn instead of python3 learn.py after installing the mlagents packages. This change is documented here . For example, if we previously ran sh python3 learn.py 3DBall --train from the python subdirectory (which is changed to ml-agents subdirectory in v0.5), we now run sh mlagents-learn config/trainer_config.yaml --env=3DBall --train from the root directory where we installed the ML-Agents Toolkit. It is now required to specify the path to the yaml trainer configuration file when running mlagents-learn . For an example trainer configuration file, see trainer_config.yaml . An example of passing a trainer configuration to mlagents-learn is shown above. The environment name is now passed through the --env option. Curriculum learning has been changed. In summary: Curriculum files for the same environment must now be placed into a folder. Each curriculum file should be named after the Brain whose curriculum it specifies. min_lesson_length now specifies the minimum number of episodes in a lesson and affects reward thresholding. It is no longer necessary to specify the Max Steps of the Academy to use curriculum learning. Migrating from ML-Agents Toolkit v0.3 to v0.4 Unity API using MLAgents; needs to be added in all of the C# scripts that use ML-Agents. Python API We've changed some of the Python packages dependencies in requirement.txt file. Make sure to run pip3 install -e . within your ml-agents/python folder to update your Python packages. Migrating from ML-Agents Toolkit v0.2 to v0.3 There are a large number of new features and improvements in the ML-Agents toolkit v0.3 which change both the training process and Unity API in ways which will cause incompatibilities with environments made using older versions. This page is designed to highlight those changes for users familiar with v0.1 or v0.2 in order to ensure a smooth transition. Important The ML-Agents Toolkit is no longer compatible with Python 2. Python Training The training script ppo.py and PPO.ipynb Python notebook have been replaced with a single learn.py script as the launching point for training with ML-Agents. For more information on using learn.py , see here . Hyperparameters for training Brains are now stored in the trainer_config.yaml file. For more information on using this file, see here . Unity API Modifications to an Agent's rewards must now be done using either AddReward() or SetReward() . Setting an Agent to done now requires the use of the Done() method. CollectStates() has been replaced by CollectObservations() , which now no longer returns a list of floats. To collect observations, call AddVectorObs() within CollectObservations() . Note that you can call AddVectorObs() with floats, integers, lists and arrays of floats, Vector3 and Quaternions. AgentStep() has been replaced by AgentAction() . WaitTime() has been removed. The Frame Skip field of the Academy is replaced by the Agent's Decision Frequency field, enabling the Agent to make decisions at different frequencies. The names of the inputs in the Internal Brain have been changed. You must replace state with vector_observation and observation with visual_observation . In addition, you must remove the epsilon placeholder. Semantics In order to more closely align with the terminology used in the Reinforcement Learning field, and to be more descriptive, we have changed the names of some of the concepts used in ML-Agents. The changes are highlighted in the table below. Old - v0.2 and earlier New - v0.3 and later State Vector Observation Observation Visual Observation Action Vector Action N/A Text Observation N/A Text Action","title":"Migrating"},{"location":"Migrating/#upgrading","text":"","title":"Upgrading"},{"location":"Migrating/#migrating","text":"","title":"Migrating"},{"location":"Migrating/#migrating-to-the-ml-agents-envs-0290-package","text":"Python 3.8 is now the minimum version of python supported due to python3.6 EOL . Please update your python installation to 3.8.13 or higher. The gym-unity package has been refactored into the ml-agents-envs package. Please update your imports accordingly. Example: Before from gym_unity.unity_gym_env import UnityToGymWrapper After: from mlagents_envs.envs.unity_gym_env import UnityToGymWrapper","title":"Migrating to the ml-agents-envs 0.29.0 package"},{"location":"Migrating/#migrating-the-package-to-version-20","text":"The official version of Unity ML-Agents supports is now 2021.3 LTS. If you run into issues, please consider deleting your project's Library folder and reponening your project. If you used any of the APIs that were deprecated before version 2.0, you need to use their replacement. These deprecated APIs have been removed. See the migration steps bellow for specific API replacements.","title":"Migrating the package to version 2.0"},{"location":"Migrating/#deprecated-methods-removed","text":"Deprecated API Suggested Replacement IActuator ActuatorComponent.CreateActuator() IActuator[] ActuatorComponent.CreateActuators() IActionReceiver.PackActions(in float[] destination) none Agent.CollectDiscreteActionMasks(DiscreteActionMasker actionMasker) Agent.WriteDiscreteActionMask(IDiscreteActionMask actionMask) Agent.Heuristic(float[] actionsOut) Agent.Heuristic(in ActionBuffers actionsOut) Agent.OnActionReceived(float[] vectorAction) Agent.OnActionReceived(ActionBuffers actions) Agent.GetAction() Agent.GetStoredActionBuffers() BrainParameters.SpaceType , VectorActionSize , VectorActionSpaceType , and NumActions BrainParameters.ActionSpec ObservationWriter.AddRange(IEnumerable<float> data, int writeOffset = 0) ObservationWriter. AddList(IList<float> data, int writeOffset = 0 SensorComponent.IsVisual() and IsVector() none VectorSensor.AddObservation(IEnumerable<float> observation) VectorSensor.AddObservation(IList<float> observation) SideChannelsManager SideChannelManager","title":"Deprecated methods removed"},{"location":"Migrating/#idiscreteactionmask-changes","text":"The interface for disabling specific discrete actions has changed. IDiscreteActionMask.WriteMask() was removed, and replaced with SetActionEnabled() . Instead of returning an IEnumerable with indices to disable, you can now call SetActionEnabled for each index to disable (or enable). As an example, if you overrode Agent.WriteDiscreteActionMask() with something that looked like: public override void WriteDiscreteActionMask(IDiscreteActionMask actionMask) { var branch = 2; var actionsToDisable = new[] {1, 3}; actionMask.WriteMask(branch, actionsToDisable); } the equivalent code would now be public override void WriteDiscreteActionMask(IDiscreteActionMask actionMask) { var branch = 2; actionMask.SetActionEnabled(branch, 1, false); actionMask.SetActionEnabled(branch, 3, false); }","title":"IDiscreteActionMask changes"},{"location":"Migrating/#iactuator-changes","text":"The IActuator interface now implements IHeuristicProvider . Please add the corresponding Heuristic(in ActionBuffers) method to your custom Actuator classes.","title":"IActuator changes"},{"location":"Migrating/#isensor-and-sensorcomponent-changes","text":"The ISensor.GetObservationShape() method and ITypedSensor and IDimensionPropertiesSensor interfaces were removed, and GetObservationSpec() was added. You can use ObservationSpec.Vector() or ObservationSpec.Visual() to generate ObservationSpec s that are equivalent to the previous shape. For example, if your old ISensor looked like: public override int[] GetObservationShape() { return new[] { m_Height, m_Width, m_NumChannels }; } the equivalent code would now be public override ObservationSpec GetObservationSpec() { return ObservationSpec.Visual(m_Height, m_Width, m_NumChannels); } The ISensor.GetCompressionType() method and ISparseChannelSensor interface was removed, and GetCompressionSpec() was added. You can use CompressionSpec.Default() or CompressionSpec.Compressed() to generate CompressionSpec s that are equivalent to the previous values. For example, if your old ISensor looked like: ```csharp public virtual SensorCompressionType GetCompressionType() { return SensorCompressionType.None; } the equivalent code would now be ```csharp public CompressionSpec GetCompressionSpec() { return CompressionSpec.Default(); } The abstract method SensorComponent.GetObservationShape() was removed. The abstract method SensorComponent.CreateSensor() was replaced with CreateSensors() , which returns an ISensor[] .","title":"ISensor and SensorComponent changes"},{"location":"Migrating/#match3-integration-changes","text":"The Match-3 integration utilities were moved from com.unity.ml-agents.extensions to com.unity.ml-agents . The AbstractBoard interface was changed: * AbstractBoard no longer contains Rows , Columns , NumCellTypes , and NumSpecialTypes fields. * public abstract BoardSize GetMaxBoardSize() was added as an abstract method. BoardSize is a new struct that contains Rows , Columns , NumCellTypes , and NumSpecialTypes fields, with the same meanings as the old AbstractBoard fields. * public virtual BoardSize GetCurrentBoardSize() is an optional method; by default it returns GetMaxBoardSize() . If you wish to use a single behavior to work with multiple board sizes, override GetCurrentBoardSize() to return the current BoardSize . The values returned by GetCurrentBoardSize() must be less than or equal to the corresponding values from GetMaxBoardSize() .","title":"Match3 integration changes"},{"location":"Migrating/#gridsensor-changes","text":"The sensor configuration has changed: * The sensor implementation has been refactored and exsisting GridSensor created from extension package will not work in newer version. Some errors might show up when loading the old sensor in the scene. You'll need to remove the old sensor and create a new GridSensor. * These parameters names have changed but still refer to the same concept in the sensor: GridNumSide -> GridSize , RotateToAgent -> RotateWithAgent , ObserveMask -> ColliderMask , DetectableObjects -> DetectableTags * DepthType ( ChanelBase / ChannelHot ) option and ChannelDepth are removed. Now the default is one-hot encoding for detected tag. If you were using original GridSensor without overriding any method, switching to new GridSensor will produce similar effect for training although the actual observations will be slightly different. For creating your GridSensor implementation with custom data: * To create custom GridSensor, derive from GridSensorBase instead of GridSensor . Besides overriding GetObjectData() , you will also need to consider override GetCellObservationSize() , IsDataNormalized() and GetProcessCollidersMethod() according to the data you collect. Also you'll need to override GridSensorComponent.GetGridSensors() and return your custom GridSensor. * The input argument tagIndex in GetObjectData() has changed from 1-indexed to 0-indexed and the data type changed from float to int . The index of first detectable tag will be 0 instead of 1. normalizedDistance was removed from input. * The observation data should be written to the input dataBuffer instead of creating and returning a new array. * Removed the constraint of all data required to be normalized. You should specify it in IsDataNormalized() . Sensors with non-normalized data cannot use PNG compression type. * The sensor will not further encode the data recieved from GetObjectData() anymore. The values recieved from GetObjectData() will be the observation sent to the trainer.","title":"GridSensor changes"},{"location":"Migrating/#lstm-models-from-previous-releases-no-longer-supported","text":"The way the Unity Inference Engine processes LSTM (recurrent neural networks) has changed. As a result, models trained with previous versions of ML-Agents will not be usable at inference if they were trained with a memory setting in the .yaml config file. If you want to use a model that has a recurrent neural network in this release of ML-Agents, you need to train the model using the python trainer from this release.","title":"LSTM models from previous releases no longer supported"},{"location":"Migrating/#migrating-to-release-13","text":"","title":"Migrating to Release 13"},{"location":"Migrating/#implementing-iheuristic-in-your-iactuator-implementations","text":"If you have any custom actuators, you can now implement the IHeuristicProvider interface to have your actuator handle the generation of actions when an Agent is running in heuristic mode. VectorSensor.AddObservation(IEnumerable<float>) is deprecated. Use VectorSensor.AddObservation(IList<float>) instead. ObservationWriter.AddRange() is deprecated. Use ObservationWriter.AddList() instead. ActuatorComponent.CreateAcuator() is deprecated. Please use override ActuatorComponent.CreateActuators instead. Since ActuatorComponent.CreateActuator() is abstract, you will still need to override it in your class until it is removed. It is only ever called if you don't override ActuatorComponent.CreateActuators . You can suppress the warnings by surrounding the method with the following pragma: c# #pragma warning disable 672 public IActuator CreateActuator() { ... } #pragma warning restore 672","title":"Implementing IHeuristic in your IActuator implementations"},{"location":"Migrating/#migrating_1","text":"","title":"Migrating"},{"location":"Migrating/#migrating-to-release-11","text":"","title":"Migrating to Release 11"},{"location":"Migrating/#agent-virtual-method-deprecation","text":"Agent.CollectDiscreteActionMasks() was deprecated and should be replaced with Agent.WriteDiscreteActionMask() Agent.Heuristic(float[]) was deprecated and should be replaced with Agent.Heuristic(ActionBuffers) . Agent.OnActionReceived(float[]) was deprecated and should be replaced with Agent.OnActionReceived(ActionBuffers) . Agent.GetAction() was deprecated and should be replaced with Agent.GetStoredActionBuffers() . The default implementation of these will continue to call the deprecated versions where appropriate. However, the deprecated versions may not be compatible with continuous and discrete actions on the same Agent.","title":"Agent virtual method deprecation"},{"location":"Migrating/#brainparameters-field-and-method-deprecation","text":"BrainParameters.VectorActionSize was deprecated; you can now set BrainParameters.ActionSpec.NumContinuousActions or BrainParameters.ActionSpec.BranchSizes instead. BrainParameters.VectorActionSpaceType was deprecated, since both continuous and discrete actions can now be used. BrainParameters.NumActions() was deprecated. Use BrainParameters.ActionSpec.NumContinuousActions and BrainParameters.ActionSpec.NumDiscreteActions instead.","title":"BrainParameters field and method deprecation"},{"location":"Migrating/#migrating-from-release-7-to-latest","text":"","title":"Migrating from Release 7 to latest"},{"location":"Migrating/#important-changes","text":"Some trainer files were moved. If you were using the TrainerFactory class, it was moved to the trainers/trainer folder. The components folder containing bc and reward_signals code was moved to the trainers/tf folder","title":"Important changes"},{"location":"Migrating/#steps-to-migrate","text":"Replace calls to from mlagents.trainers.trainer_util import TrainerFactory to from mlagents.trainers.trainer import TrainerFactory Replace calls to from mlagents.trainers.trainer_util import handle_existing_directories to from mlagents.trainers.directory_utils import validate_existing_directories Replace mlagents.trainers.components with mlagents.trainers.tf.components in your import statements.","title":"Steps to Migrate"},{"location":"Migrating/#migrating-from-release-3-to-release-7","text":"","title":"Migrating from Release 3 to Release 7"},{"location":"Migrating/#important-changes_1","text":"The Parameter Randomization feature has been merged with the Curriculum feature. It is now possible to specify a sampler in the lesson of a Curriculum. Curriculum has been refactored and is now specified at the level of the parameter, not the behavior. More information here .(#4160)","title":"Important changes"},{"location":"Migrating/#steps-to-migrate_1","text":"The configuration format for curriculum and parameter randomization has changed. To upgrade your configuration files, an upgrade script has been provided. Run python -m mlagents.trainers.upgrade_config -h to see the script usage. Note that you will have had to upgrade to/install the current version of ML-Agents before running the script. To update manually: If your config file used a parameter_randomization section, rename that section to environment_parameters If your config file used a curriculum section, you will need to rewrite your curriculum with this format .","title":"Steps to Migrate"},{"location":"Migrating/#migrating-from-release-1-to-release-3","text":"","title":"Migrating from Release 1 to Release 3"},{"location":"Migrating/#important-changes_2","text":"Training artifacts (trained models, summaries) are now found under results/ instead of summaries/ and models/ . Trainer configuration, curriculum configuration, and parameter randomization configuration have all been moved to a single YAML file. (#3791) Trainer configuration format has changed, and using a \"default\" behavior name has been deprecated. (#3936) max_step in the TerminalStep and TerminalSteps objects was renamed interrupted . On the UnityEnvironment API, get_behavior_names() and get_behavior_specs() methods were combined into the property behavior_specs that contains a mapping from behavior names to behavior spec. use_visual and allow_multiple_visual_obs in the UnityToGymWrapper constructor were replaced by allow_multiple_obs which allows one or more visual observations and vector observations to be used simultaneously. --save-freq has been removed from the CLI and is now configurable in the trainer configuration file. --lesson has been removed from the CLI. Lessons will resume when using --resume . To start at a different lesson, modify your Curriculum configuration.","title":"Important changes"},{"location":"Migrating/#steps-to-migrate_2","text":"To upgrade your configuration files, an upgrade script has been provided. Run python -m mlagents.trainers.upgrade_config -h to see the script usage. Note that you will have had to upgrade to/install the current version of ML-Agents before running the script. To do it manually, copy your <BehaviorName> sections from trainer_config.yaml into a separate trainer configuration file, under a behaviors section. The default section is no longer needed. This new file should be specific to your environment, and not contain configurations for multiple environments (unless they have the same Behavior Names). - You will need to reformat your trainer settings as per the example . - If your training uses curriculum , move those configurations under a curriculum section. - If your training uses parameter randomization , move the contents of the sampler config to parameter_randomization in the main trainer configuration. - If you are using UnityEnvironment directly, replace max_step with interrupted in the TerminalStep and TerminalSteps objects. - Replace usage of get_behavior_names() and get_behavior_specs() in UnityEnvironment with behavior_specs . - If you use the UnityToGymWrapper , remove use_visual and allow_multiple_visual_obs from the constructor and add allow_multiple_obs = True if the environment contains either both visual and vector observations or multiple visual observations. - If you were setting --save-freq in the CLI, add a checkpoint_interval value in your trainer configuration, and set it equal to save-freq * n_agents_in_scene .","title":"Steps to Migrate"},{"location":"Migrating/#migrating-from-015-to-release-1","text":"","title":"Migrating from 0.15 to Release 1"},{"location":"Migrating/#important-changes_3","text":"The MLAgents C# namespace was renamed to Unity.MLAgents , and other nested namespaces were similarly renamed (#3843). The --load and --train command-line flags have been deprecated and replaced with --resume and --inference . Running with the same --run-id twice will now throw an error. The play_against_current_self_ratio self-play trainer hyperparameter has been renamed to play_against_latest_model_ratio Removed the multi-agent gym option from the gym wrapper. For multi-agent scenarios, use the Low Level Python API . The low level Python API has changed. You can look at the document Low Level Python API documentation for more information. If you use mlagents-learn for training, this should be a transparent change. The obsolete Agent methods GiveModel , Done , InitializeAgent , AgentAction and AgentReset have been removed. The signature of Agent.Heuristic() was changed to take a float[] as a parameter, instead of returning the array. This was done to prevent a common source of error where users would return arrays of the wrong size. The SideChannel API has changed (#3833, #3660) : Introduced the SideChannelManager to register, unregister and access side channels. EnvironmentParameters replaces the default FloatProperties . You can access the EnvironmentParameters with Academy.Instance.EnvironmentParameters on C#. If you were previously creating a UnityEnvironment in python and passing it a FloatPropertiesChannel , create an EnvironmentParametersChannel instead. SideChannel.OnMessageReceived is now a protected method (was public) SideChannel IncomingMessages methods now take an optional default argument, which is used when trying to read more data than the message contains. Added a feature to allow sending stats from C# environments to TensorBoard (and other python StatsWriters). To do this from your code, use Academy.Instance.StatsRecorder.Add(key, value) (#3660) num_updates and train_interval for SAC have been replaced with steps_per_update . The UnityEnv class from the gym-unity package was renamed UnityToGymWrapper and no longer creates the UnityEnvironment . Instead, the UnityEnvironment must be passed as input to the constructor of UnityToGymWrapper Public fields and properties on several classes were renamed to follow Unity's C# style conventions. All public fields and properties now use \"PascalCase\" instead of \"camelCase\"; for example, Agent.maxStep was renamed to Agent.MaxStep . For a full list of changes, see the pull request. (#3828) WriteAdapter was renamed to ObservationWriter . (#3834)","title":"Important changes"},{"location":"Migrating/#steps-to-migrate_3","text":"In C# code, replace using MLAgents with using Unity.MLAgents . Replace other nested namespaces such as using MLAgents.Sensors with using Unity.MLAgents.Sensors Replace the --load flag with --resume when calling mlagents-learn , and don't use the --train flag as training will happen by default. To run without training, use --inference . To force-overwrite files from a pre-existing run, add the --force command-line flag. The Jupyter notebooks have been removed from the repository. If your Agent class overrides Heuristic() , change the signature to public override void Heuristic(float[] actionsOut) and assign values to actionsOut instead of returning an array. If you used SideChannels you must: Replace Academy.FloatProperties with Academy.Instance.EnvironmentParameters . Academy.RegisterSideChannel and Academy.UnregisterSideChannel were removed. Use SideChannelManager.RegisterSideChannel and SideChannelManager.UnregisterSideChannel instead. Set steps_per_update to be around equal to the number of agents in your environment, times num_updates and divided by train_interval . Replace UnityEnv with UnityToGymWrapper in your code. The constructor no longer takes a file name as input but a fully constructed UnityEnvironment instead. Update uses of \"camelCase\" fields and properties to \"PascalCase\".","title":"Steps to Migrate"},{"location":"Migrating/#migrating-from-014-to-015","text":"","title":"Migrating from 0.14 to 0.15"},{"location":"Migrating/#important-changes_4","text":"The Agent.CollectObservations() virtual method now takes as input a VectorSensor sensor as argument. The Agent.AddVectorObs() methods were removed. The SetMask was renamed to SetMask method must now be called on the DiscreteActionMasker argument of the CollectDiscreteActionMasks virtual method. We consolidated our API for DiscreteActionMasker . SetMask takes two arguments : the branch index and the list of masked actions for that branch. The Monitor class has been moved to the Examples Project. (It was prone to errors during testing) The MLAgents.Sensors namespace has been introduced. All sensors classes are part of the MLAgents.Sensors namespace. The MLAgents.SideChannels namespace has been introduced. All side channel classes are part of the MLAgents.SideChannels namespace. The interface for RayPerceptionSensor.PerceiveStatic() was changed to take an input class and write to an output class, and the method was renamed to Perceive() . The SetMask method must now be called on the DiscreteActionMasker argument of the CollectDiscreteActionMasks method. The method GetStepCount() on the Agent class has been replaced with the property getter StepCount The --multi-gpu option has been removed temporarily. AgentInfo.actionMasks has been renamed to AgentInfo.discreteActionMasks . BrainParameters and SpaceType have been removed from the public API BehaviorParameters have been removed from the public API. DecisionRequester has been made internal (you can still use the DecisionRequesterComponent from the inspector). RepeatAction was renamed TakeActionsBetweenDecisions for clarity. The following methods in the Agent class have been renamed. The original method names will be removed in a later release: InitializeAgent() was renamed to Initialize() AgentAction() was renamed to OnActionReceived() AgentReset() was renamed to OnEpsiodeBegin() Done() was renamed to EndEpisode() GiveModel() was renamed to SetModel() The IFloatProperties interface has been removed. The interface for SideChannels was changed: In C#, OnMessageReceived now takes a IncomingMessage argument, and QueueMessageToSend takes an OutgoingMessage argument. In python, on_message_received now takes a IncomingMessage argument, and queue_message_to_send takes an OutgoingMessage argument. Automatic stepping for Academy is now controlled from the AutomaticSteppingEnabled property.","title":"Important changes"},{"location":"Migrating/#steps-to-migrate_4","text":"Add the using MLAgents.Sensors; in addition to using MLAgents; on top of your Agent's script. Replace your Agent's implementation of CollectObservations() with CollectObservations(VectorSensor sensor) . In addition, replace all calls to AddVectorObs() with sensor.AddObservation() or sensor.AddOneHotObservation() on the VectorSensor passed as argument. Replace your calls to SetActionMask on your Agent to DiscreteActionMasker.SetActionMask in CollectDiscreteActionMasks . If you call RayPerceptionSensor.PerceiveStatic() manually, add your inputs to a RayPerceptionInput . To get the previous float array output, iterate through RayPerceptionOutput.rayOutputs and call RayPerceptionOutput.RayOutput.ToFloatArray() . Replace all calls to Agent.GetStepCount() with Agent.StepCount We strongly recommend replacing the following methods with their new equivalent as they will be removed in a later release: InitializeAgent() to Initialize() AgentAction() to OnActionReceived() AgentReset() to OnEpisodeBegin() Done() to EndEpisode() GiveModel() to SetModel() Replace IFloatProperties variables with FloatPropertiesChannel variables. If you implemented custom SideChannels , update the signatures of your methods, and add your data to the OutgoingMessage or read it from the IncomingMessage . Replace calls to Academy.EnableAutomaticStepping()/DisableAutomaticStepping() with Academy.AutomaticSteppingEnabled = true/false.","title":"Steps to Migrate"},{"location":"Migrating/#migrating-from-013-to-014","text":"","title":"Migrating from 0.13 to 0.14"},{"location":"Migrating/#important-changes_5","text":"The UnitySDK folder has been split into a Unity Package ( com.unity.ml-agents ) and an examples project ( Project ). Please follow the Installation Guide to get up and running with this new repo structure. Several changes were made to how agents are reset and marked as done: Calling Done() on the Agent will now reset it immediately and call the AgentReset virtual method. (This is to simplify the previous logic in which the Agent had to wait for the next EnvironmentStep to reset) The \"Reset on Done\" setting in AgentParameters was removed; this is now effectively always true. AgentOnDone virtual method on the Agent has been removed. The Decision Period and On Demand decision checkbox have been removed from the Agent. On demand decision is now the default (calling RequestDecision on the Agent manually.) The Academy class was changed to a singleton, and its virtual methods were removed. Trainer steps are now counted per-Agent, not per-environment as in previous versions. For instance, if you have 10 Agents in the scene, 20 environment steps now corresponds to 200 steps as printed in the terminal and in Tensorboard. Curriculum config files are now YAML formatted and all curricula for a training run are combined into a single file. The --num-runs command-line option has been removed from mlagents-learn . Several fields on the Agent were removed or made private in order to simplify the interface. The agentParameters field of the Agent has been removed. (Contained only maxStep information) maxStep is now a public field on the Agent. (Was moved from agentParameters ) The Info field of the Agent has been made private. (Was only used internally and not meant to be modified outside of the Agent) The GetReward() method on the Agent has been removed. (It was being confused with GetCumulativeReward() ) The AgentAction struct no longer contains a value field. (Value estimates were not set during inference) The GetValueEstimate() method on the Agent has been removed. The UpdateValueAction() method on the Agent has been removed. The deprecated RayPerception3D and RayPerception2D classes were removed, and the legacyHitFractionBehavior argument was removed from RayPerceptionSensor.PerceiveStatic() . RayPerceptionSensor was inconsistent in how it handle scale on the Agent's transform. It now scales the ray length and sphere size for casting as the transform's scale changes.","title":"Important changes"},{"location":"Migrating/#steps-to-migrate_5","text":"Follow the instructions on how to install the com.unity.ml-agents package into your project in the Installation Guide . If your Agent implemented AgentOnDone and did not have the checkbox Reset On Done checked in the inspector, you must call the code that was in AgentOnDone manually. If you give your Agent a reward or penalty at the end of an episode (e.g. for reaching a goal or falling off of a platform), make sure you call AddReward() or SetReward() before calling Done() . Previously, the order didn't matter. If you were not using On Demand Decision for your Agent, you must add a DecisionRequester component to your Agent GameObject and set its Decision Period field to the old Decision Period of the Agent. If you have a class that inherits from Academy: If the class didn't override any of the virtual methods and didn't store any additional data, you can just remove the old script from the scene. If the class had additional data, create a new MonoBehaviour and store the data in the new MonoBehaviour instead. If the class overrode the virtual methods, create a new MonoBehaviour and move the logic to it: Move the InitializeAcademy code to MonoBehaviour.Awake Move the AcademyStep code to MonoBehaviour.FixedUpdate Move the OnDestroy code to MonoBehaviour.OnDestroy. Move the AcademyReset code to a new method and add it to the Academy.OnEnvironmentReset action. Multiply max_steps and summary_freq in your trainer_config.yaml by the number of Agents in the scene. Combine curriculum configs into a single file. See the WallJump curricula for an example of the new curriculum config format. A tool like https://www.json2yaml.com may be useful to help with the conversion. If you have a model trained which uses RayPerceptionSensor and has non-1.0 scale in the Agent's transform, it must be retrained.","title":"Steps to Migrate"},{"location":"Migrating/#migrating-from-ml-agents-toolkit-v0120-to-v0130","text":"","title":"Migrating from ML-Agents Toolkit v0.12.0 to v0.13.0"},{"location":"Migrating/#important-changes_6","text":"The low level Python API has changed. You can look at the document Low Level Python API documentation for more information. This should only affect you if you're writing a custom trainer; if you use mlagents-learn for training, this should be a transparent change. reset() on the Low-Level Python API no longer takes a train_mode argument. To modify the performance/speed of the engine, you must use an EngineConfigurationChannel reset() on the Low-Level Python API no longer takes a config argument. UnityEnvironment no longer has a reset_parameters field. To modify float properties in the environment, you must use a FloatPropertiesChannel . For more information, refer to the Low Level Python API documentation CustomResetParameters are now removed. The Academy no longer has a Training Configuration nor Inference Configuration field in the inspector. To modify the configuration from the Low-Level Python API, use an EngineConfigurationChannel . To modify it during training, use the new command line arguments --width , --height , --quality-level , --time-scale and --target-frame-rate in mlagents-learn . The Academy no longer has a Default Reset Parameters field in the inspector. The Academy class no longer has a ResetParameters . To access shared float properties with Python, use the new FloatProperties field on the Academy. Offline Behavioral Cloning has been removed. To learn from demonstrations, use the GAIL and Behavioral Cloning features with either PPO or SAC. mlagents.envs was renamed to mlagents_envs . The previous repo layout depended on PEP420 , which caused problems with some of our tooling such as mypy and pylint. The official version of Unity ML-Agents supports is now 2018.4 LTS. If you run into issues, please consider deleting your library folder and reponening your projects. You will need to install the Barracuda package into your project in order to ML-Agents to compile correctly.","title":"Important changes"},{"location":"Migrating/#steps-to-migrate_6","text":"If you had a custom Training Configuration in the Academy inspector, you will need to pass your custom configuration at every training run using the new command line arguments --width , --height , --quality-level , --time-scale and --target-frame-rate . If you were using --slow in mlagents-learn , you will need to pass your old Inference Configuration of the Academy inspector with the new command line arguments --width , --height , --quality-level , --time-scale and --target-frame-rate instead. Any imports from mlagents.envs should be replaced with mlagents_envs .","title":"Steps to Migrate"},{"location":"Migrating/#migrating-from-ml-agents-toolkit-v0110-to-v0120","text":"","title":"Migrating from ML-Agents Toolkit v0.11.0 to v0.12.0"},{"location":"Migrating/#important-changes_7","text":"Text actions and observations, and custom action and observation protos have been removed. RayPerception3D and RayPerception2D are marked deprecated, and will be removed in a future release. They can be replaced by RayPerceptionSensorComponent3D and RayPerceptionSensorComponent2D. The Use Heuristic checkbox in Behavior Parameters has been replaced with a Behavior Type dropdown menu. This has the following options: Default corresponds to the previous unchecked behavior, meaning that Agents will train if they connect to a python trainer, otherwise they will perform inference. Heuristic Only means the Agent will always use the Heuristic() method. This corresponds to having \"Use Heuristic\" selected in 0.11.0. Inference Only means the Agent will always perform inference. Barracuda was upgraded to 0.3.2, and it is now installed via the Unity Package Manager.","title":"Important Changes"},{"location":"Migrating/#steps-to-migrate_7","text":"We fixed a bug in RayPerception3d.Perceive() that was causing the endOffset to be used incorrectly. However this may produce different behavior from previous versions if you use a non-zero startOffset . To reproduce the old behavior, you should increase the value of endOffset by startOffset . You can verify your raycasts are performing as expected in scene view using the debug rays. If you use RayPerception3D, replace it with RayPerceptionSensorComponent3D (and similarly for 2D). The settings, such as ray angles and detectable tags, are configured on the component now. RayPerception3D would contribute (# of rays) * (# of tags + 2) to the State Size in Behavior Parameters, but this is no longer necessary, so you should reduce the State Size by this amount. Making this change will require retraining your model, since the observations that RayPerceptionSensorComponent3D produces are different from the old behavior. If you see messages such as The type or namespace 'Barracuda' could not be found or The type or namespace 'Google' could not be found , you will need to install the Barracuda preview package .","title":"Steps to Migrate"},{"location":"Migrating/#migrating-from-ml-agents-toolkit-v010-to-v0110","text":"","title":"Migrating from ML-Agents Toolkit v0.10 to v0.11.0"},{"location":"Migrating/#important-changes_8","text":"The definition of the gRPC service has changed. The online BC training feature has been removed. The BroadcastHub has been deprecated. If there is a training Python process, all LearningBrains in the scene will automatically be trained. If there is no Python process, inference will be used. The Brain ScriptableObjects have been deprecated. The Brain Parameters are now on the Agent and are referred to as Behavior Parameters. Make sure the Behavior Parameters is attached to the Agent GameObject. To use a heuristic behavior, implement the Heuristic() method in the Agent class and check the use heuristic checkbox in the Behavior Parameters. Several changes were made to the setup for visual observations (i.e. using Cameras or RenderTextures): Camera resolutions are no longer stored in the Brain Parameters. AgentParameters no longer stores lists of Cameras and RenderTextures To add visual observations to an Agent, you must now attach a CameraSensorComponent or RenderTextureComponent to the agent. The corresponding Camera or RenderTexture can be added to these in the editor, and the resolution and color/grayscale is configured on the component itself.","title":"Important Changes"},{"location":"Migrating/#steps-to-migrate_8","text":"In order to be able to train, make sure both your ML-Agents Python package and UnitySDK code come from the v0.11 release. Training will not work, for example, if you update the ML-Agents Python package, and only update the API Version in UnitySDK. If your Agents used visual observations, you must add a CameraSensorComponent corresponding to each old Camera in the Agent's camera list (and similarly for RenderTextures). Since Brain ScriptableObjects have been removed, you will need to delete all the Brain ScriptableObjects from your Assets folder. Then, add a Behavior Parameters component to each Agent GameObject. You will then need to complete the fields on the new Behavior Parameters component with the BrainParameters of the old Brain.","title":"Steps to Migrate"},{"location":"Migrating/#migrating-from-ml-agents-toolkit-v09-to-v010","text":"","title":"Migrating from ML-Agents Toolkit v0.9 to v0.10"},{"location":"Migrating/#important-changes_9","text":"We have updated the C# code in our repository to be in line with Unity Coding Conventions. This has changed the name of some public facing classes and enums. The example environments have been updated. If you were using these environments to benchmark your training, please note that the resulting rewards may be slightly different in v0.10.","title":"Important Changes"},{"location":"Migrating/#steps-to-migrate_9","text":"UnitySDK/Assets/ML-Agents/Scripts/Communicator.cs and its class Communicator have been renamed to UnitySDK/Assets/ML-Agents/Scripts/ICommunicator.cs and ICommunicator respectively. The SpaceType Enums discrete , and continuous have been renamed to Discrete and Continuous . We have removed the Done call as well as the capacity to set Max Steps on the Academy. Therefore an AcademyReset will never be triggered from C# (only from Python). If you want to reset the simulation after a fixed number of steps, or when an event in the simulation occurs, we recommend looking at our multi-agent example environments (such as FoodCollector). In our examples, groups of Agents can be reset through an \"Area\" that can reset groups of Agents. The import for mlagents.envs.UnityEnvironment was removed. If you are using the Python API, change from mlagents_envs import UnityEnvironment to from mlagents_envs.environment import UnityEnvironment .","title":"Steps to Migrate"},{"location":"Migrating/#migrating-from-ml-agents-toolkit-v08-to-v09","text":"","title":"Migrating from ML-Agents Toolkit v0.8 to v0.9"},{"location":"Migrating/#important-changes_10","text":"We have changed the way reward signals (including Curiosity) are defined in the trainer_config.yaml . When using multiple environments, every \"step\" is recorded in TensorBoard. The steps in the command line console corresponds to a single step of a single environment. Previously, each step corresponded to one step for all environments (i.e., num_envs steps).","title":"Important Changes"},{"location":"Migrating/#steps-to-migrate_10","text":"If you were overriding any of these following parameters in your config file, remove them from the top-level config and follow the steps below: gamma : Define a new extrinsic reward signal and set it's gamma to your new gamma. use_curiosity , curiosity_strength , curiosity_enc_size : Define a curiosity reward signal and set its strength to curiosity_strength , and encoding_size to curiosity_enc_size . Give it the same gamma as your extrinsic signal to mimic previous behavior. TensorBoards generated when running multiple environments in v0.8 are not comparable to those generated in v0.9 in terms of step count. Multiply your v0.8 step count by num_envs for an approximate comparison. You may need to change max_steps in your config as appropriate as well.","title":"Steps to Migrate"},{"location":"Migrating/#migrating-from-ml-agents-toolkit-v07-to-v08","text":"","title":"Migrating from ML-Agents Toolkit v0.7 to v0.8"},{"location":"Migrating/#important-changes_11","text":"We have split the Python packages into two separate packages ml-agents and ml-agents-envs . --worker-id option of learn.py has been removed, use --base-port instead if you'd like to run multiple instances of learn.py .","title":"Important Changes"},{"location":"Migrating/#steps-to-migrate_11","text":"If you are installing via PyPI, there is no change. If you intend to make modifications to ml-agents or ml-agents-envs please check the Installing for Development in the Installation documentation .","title":"Steps to Migrate"},{"location":"Migrating/#migrating-from-ml-agents-toolkit-v06-to-v07","text":"","title":"Migrating from ML-Agents Toolkit v0.6 to v0.7"},{"location":"Migrating/#important-changes_12","text":"We no longer support TFS and are now using the Unity Inference Engine","title":"Important Changes"},{"location":"Migrating/#steps-to-migrate_12","text":"Make sure to remove the ENABLE_TENSORFLOW flag in your Unity Project settings","title":"Steps to Migrate"},{"location":"Migrating/#migrating-from-ml-agents-toolkit-v05-to-v06","text":"","title":"Migrating from ML-Agents Toolkit v0.5 to v0.6"},{"location":"Migrating/#important-changes_13","text":"Brains are now Scriptable Objects instead of MonoBehaviors. You can no longer modify the type of a Brain. If you want to switch between PlayerBrain and LearningBrain for multiple agents, you will need to assign a new Brain to each agent separately. Note: You can pass the same Brain to multiple agents in a scene by leveraging Unity's prefab system or look for all the agents in a scene using the search bar of the Hierarchy window with the word Agent . We replaced the Internal and External Brain with Learning Brain . When you need to train a model, you need to drag it into the Broadcast Hub inside the Academy and check the Control checkbox. We removed the Broadcast checkbox of the Brain, to use the broadcast functionality, you need to drag the Brain into the Broadcast Hub . When training multiple Brains at the same time, each model is now stored into a separate model file rather than in the same file under different graph scopes. The Learning Brain graph scope, placeholder names, output names and custom placeholders can no longer be modified.","title":"Important Changes"},{"location":"Migrating/#steps-to-migrate_13","text":"To update a scene from v0.5 to v0.6, you must: Remove the Brain GameObjects in the scene. (Delete all of the Brain GameObjects under Academy in the scene.) Create new Brain Scriptable Objects using Assets -> Create -> ML-Agents for each type of the Brain you plan to use, and put the created files under a folder called Brains within your project. Edit their Brain Parameters to be the same as the parameters used in the Brain GameObjects. Agents have a Brain field in the Inspector, you need to drag the appropriate Brain ScriptableObject in it. The Academy has a Broadcast Hub field in the inspector, which is list of brains used in the scene. To train or control your Brain from the mlagents-learn Python script, you need to drag the relevant LearningBrain ScriptableObjects used in your scene into entries into this list.","title":"Steps to Migrate"},{"location":"Migrating/#migrating-from-ml-agents-toolkit-v04-to-v05","text":"","title":"Migrating from ML-Agents Toolkit v0.4 to v0.5"},{"location":"Migrating/#important","text":"The Unity project unity-environment has been renamed UnitySDK . The python folder has been renamed to ml-agents . It now contains two packages, mlagents.env and mlagents.trainers . mlagents.env can be used to interact directly with a Unity environment, while mlagents.trainers contains the classes for training agents. The supported Unity version has changed from 2017.1 or later to 2017.4 or later . 2017.4 is an LTS (Long Term Support) version that helps us maintain good quality and support. Earlier versions of Unity might still work, but you may encounter an error listed here.","title":"Important"},{"location":"Migrating/#unity-api","text":"Discrete Actions now use branches . You can now specify concurrent discrete actions. You will need to update the Brain Parameters in the Brain Inspector in all your environments that use discrete actions. Refer to the discrete action documentation for more information.","title":"Unity API"},{"location":"Migrating/#python-api","text":"In order to run a training session, you can now use the command mlagents-learn instead of python3 learn.py after installing the mlagents packages. This change is documented here . For example, if we previously ran sh python3 learn.py 3DBall --train from the python subdirectory (which is changed to ml-agents subdirectory in v0.5), we now run sh mlagents-learn config/trainer_config.yaml --env=3DBall --train from the root directory where we installed the ML-Agents Toolkit. It is now required to specify the path to the yaml trainer configuration file when running mlagents-learn . For an example trainer configuration file, see trainer_config.yaml . An example of passing a trainer configuration to mlagents-learn is shown above. The environment name is now passed through the --env option. Curriculum learning has been changed. In summary: Curriculum files for the same environment must now be placed into a folder. Each curriculum file should be named after the Brain whose curriculum it specifies. min_lesson_length now specifies the minimum number of episodes in a lesson and affects reward thresholding. It is no longer necessary to specify the Max Steps of the Academy to use curriculum learning.","title":"Python API"},{"location":"Migrating/#migrating-from-ml-agents-toolkit-v03-to-v04","text":"","title":"Migrating from ML-Agents Toolkit v0.3 to v0.4"},{"location":"Migrating/#unity-api_1","text":"using MLAgents; needs to be added in all of the C# scripts that use ML-Agents.","title":"Unity API"},{"location":"Migrating/#python-api_1","text":"We've changed some of the Python packages dependencies in requirement.txt file. Make sure to run pip3 install -e . within your ml-agents/python folder to update your Python packages.","title":"Python API"},{"location":"Migrating/#migrating-from-ml-agents-toolkit-v02-to-v03","text":"There are a large number of new features and improvements in the ML-Agents toolkit v0.3 which change both the training process and Unity API in ways which will cause incompatibilities with environments made using older versions. This page is designed to highlight those changes for users familiar with v0.1 or v0.2 in order to ensure a smooth transition.","title":"Migrating from ML-Agents Toolkit v0.2 to v0.3"},{"location":"Migrating/#important_1","text":"The ML-Agents Toolkit is no longer compatible with Python 2.","title":"Important"},{"location":"Migrating/#python-training","text":"The training script ppo.py and PPO.ipynb Python notebook have been replaced with a single learn.py script as the launching point for training with ML-Agents. For more information on using learn.py , see here . Hyperparameters for training Brains are now stored in the trainer_config.yaml file. For more information on using this file, see here .","title":"Python Training"},{"location":"Migrating/#unity-api_2","text":"Modifications to an Agent's rewards must now be done using either AddReward() or SetReward() . Setting an Agent to done now requires the use of the Done() method. CollectStates() has been replaced by CollectObservations() , which now no longer returns a list of floats. To collect observations, call AddVectorObs() within CollectObservations() . Note that you can call AddVectorObs() with floats, integers, lists and arrays of floats, Vector3 and Quaternions. AgentStep() has been replaced by AgentAction() . WaitTime() has been removed. The Frame Skip field of the Academy is replaced by the Agent's Decision Frequency field, enabling the Agent to make decisions at different frequencies. The names of the inputs in the Internal Brain have been changed. You must replace state with vector_observation and observation with visual_observation . In addition, you must remove the epsilon placeholder.","title":"Unity API"},{"location":"Migrating/#semantics","text":"In order to more closely align with the terminology used in the Reinforcement Learning field, and to be more descriptive, we have changed the names of some of the concepts used in ML-Agents. The changes are highlighted in the table below. Old - v0.2 and earlier New - v0.3 and later State Vector Observation Observation Visual Observation Action Vector Action N/A Text Observation N/A Text Action","title":"Semantics"},{"location":"Package-Settings/","text":"ML-Agents Package Settings ML-Agents Package Settings contains settings that apply to the whole project. It allows you to configure ML-Agents-specific settings in the Editor. These settings are available for use in both the Editor and Player. You can find them at Edit > Project Settings... > ML-Agents . It lists out all the available settings and their default values. Create Custom Settings In order to to use your own settings for your project, you'll need to create a settings asset. You can do this by clicking the Create Settings Asset buttom or clicking the gear on the top right and select New Settings Asset... . The asset file can be placed anywhere in the Asset/ folder in your project. After Creating the settings asset, you'll be able to modify the settings for your project and your settings will be saved in the asset. Multiple Custom Settings for Different Scenarios You can create multiple settings assets in one project. By clicking the gear on the top right you'll see all available settings listed in the drop-down menu to choose from. This allows you to create different settings for different scenatios. For example, you can create two separate settings for training and inference, and specify which one you want to use according to what you're currently running.","title":"ML-Agents Package Settings"},{"location":"Package-Settings/#ml-agents-package-settings","text":"ML-Agents Package Settings contains settings that apply to the whole project. It allows you to configure ML-Agents-specific settings in the Editor. These settings are available for use in both the Editor and Player. You can find them at Edit > Project Settings... > ML-Agents . It lists out all the available settings and their default values.","title":"ML-Agents Package Settings"},{"location":"Package-Settings/#create-custom-settings","text":"In order to to use your own settings for your project, you'll need to create a settings asset. You can do this by clicking the Create Settings Asset buttom or clicking the gear on the top right and select New Settings Asset... . The asset file can be placed anywhere in the Asset/ folder in your project. After Creating the settings asset, you'll be able to modify the settings for your project and your settings will be saved in the asset.","title":"Create Custom Settings"},{"location":"Package-Settings/#multiple-custom-settings-for-different-scenarios","text":"You can create multiple settings assets in one project. By clicking the gear on the top right you'll see all available settings listed in the drop-down menu to choose from. This allows you to create different settings for different scenatios. For example, you can create two separate settings for training and inference, and specify which one you want to use according to what you're currently running.","title":"Multiple Custom Settings for Different Scenarios"},{"location":"Profiling-Python/","text":"Profiling in Python As part of the ML-Agents Tookit, we provide a lightweight profiling system, in order to identity hotspots in the training process and help spot regressions from changes. Timers are hierarchical, meaning that the time tracked in a block of code can be further split into other blocks if desired. This also means that a function that is called from multiple places in the code will appear in multiple places in the timing output. All timers operate using a \"global\" instance by default, but this can be overridden if necessary (mainly for testing). Adding Profiling There are two ways to indicate code should be included in profiling. The simplest way is to add the @timed decorator to a function or method of interested. class TrainerController: # .... @timed def advance(self, env: EnvManager) -> int: # do stuff You can also used the hierarchical_timer context manager. with hierarchical_timer(\"communicator.exchange\"): outputs = self.communicator.exchange(step_input) The context manager may be easier than the @timed decorator for profiling different parts of a large function, or profiling calls to abstract methods that might not use decorator. Output By default, at the end of training, timers are collected and written in json format to {summaries_dir}/{run_id}_timers.json . The output consists of node objects with the following keys: total (float): The total time in seconds spent in the block, including child calls. count (int): The number of times the block was called. self (float): The total time in seconds spent in the block, excluding child calls. children (dictionary): A dictionary of child nodes, keyed by the node name. is_parallel (bool): Indicates that the block of code was executed in multiple threads or processes (see below). This is optional and defaults to false. Parallel execution Subprocesses For code that executes in multiple processes (for example, SubprocessEnvManager), we periodically send the timer information back to the \"main\" process, aggregate the timers there, and flush them in the subprocess. Note that (depending on the number of processes) this can result in timers where the total time may exceed the parent's total time. This is analogous to the difference between \"real\" and \"user\" values reported from the unix time command. In the timer output, blocks that were run in parallel are indicated by the is_parallel flag. Threads Timers currently use time.perf_counter() to track time spent, which may not give accurate results for multiple threads. If this is problematic, set threaded: false in your trainer configuration.","title":"Profiling in Python"},{"location":"Profiling-Python/#profiling-in-python","text":"As part of the ML-Agents Tookit, we provide a lightweight profiling system, in order to identity hotspots in the training process and help spot regressions from changes. Timers are hierarchical, meaning that the time tracked in a block of code can be further split into other blocks if desired. This also means that a function that is called from multiple places in the code will appear in multiple places in the timing output. All timers operate using a \"global\" instance by default, but this can be overridden if necessary (mainly for testing).","title":"Profiling in Python"},{"location":"Profiling-Python/#adding-profiling","text":"There are two ways to indicate code should be included in profiling. The simplest way is to add the @timed decorator to a function or method of interested. class TrainerController: # .... @timed def advance(self, env: EnvManager) -> int: # do stuff You can also used the hierarchical_timer context manager. with hierarchical_timer(\"communicator.exchange\"): outputs = self.communicator.exchange(step_input) The context manager may be easier than the @timed decorator for profiling different parts of a large function, or profiling calls to abstract methods that might not use decorator.","title":"Adding Profiling"},{"location":"Profiling-Python/#output","text":"By default, at the end of training, timers are collected and written in json format to {summaries_dir}/{run_id}_timers.json . The output consists of node objects with the following keys: total (float): The total time in seconds spent in the block, including child calls. count (int): The number of times the block was called. self (float): The total time in seconds spent in the block, excluding child calls. children (dictionary): A dictionary of child nodes, keyed by the node name. is_parallel (bool): Indicates that the block of code was executed in multiple threads or processes (see below). This is optional and defaults to false.","title":"Output"},{"location":"Profiling-Python/#parallel-execution","text":"","title":"Parallel execution"},{"location":"Profiling-Python/#subprocesses","text":"For code that executes in multiple processes (for example, SubprocessEnvManager), we periodically send the timer information back to the \"main\" process, aggregate the timers there, and flush them in the subprocess. Note that (depending on the number of processes) this can result in timers where the total time may exceed the parent's total time. This is analogous to the difference between \"real\" and \"user\" values reported from the unix time command. In the timer output, blocks that were run in parallel are indicated by the is_parallel flag.","title":"Subprocesses"},{"location":"Profiling-Python/#threads","text":"Timers currently use time.perf_counter() to track time spent, which may not give accurate results for multiple threads. If this is problematic, set threaded: false in your trainer configuration.","title":"Threads"},{"location":"Python-Custom-Trainer-Plugin/","text":"Unity Ml-Agents Custom trainers Plugin As an attempt to bring a wider variety of reinforcement learning algorithms to our users, we have added custom trainers capabilities. we introduce an extensible plugin system to define new trainers based on the High level trainer API in Ml-agents Package. This will allow rerouting mlagents-learn CLI to custom trainers and extending the config files with hyper-parameters specific to your new trainers. We will expose a high-level extensible trainer (both on-policy, and off-policy trainers) optimizer and hyperparameter classes with documentation for the use of this plugin. For more infromation on how python plugin system works see Plugin interfaces . Overview Model-free RL algorithms generally fall into two broad categories: on-policy and off-policy. On-policy algorithms perform updates based on data gathered from the current policy. Off-policy algorithms learn a Q function from a buffer of previous data, then use this Q function to make decisions. Off-policy algorithms have three key benefits in the context of ML-Agents: They tend to use fewer samples than on-policy as they can pull and re-use data from the buffer many times. They allow player demonstrations to be inserted in-line with RL data into the buffer, enabling new ways of doing imitation learning by streaming player data. To add new custom trainers to ML-agents, you would need to create a new python package. To give you an idea of how to structure your package, we have created a mlagents_trainer_plugin package ourselves as an example, with implementation of A2c and DQN algorithms. You would need a setup.py file to list extra requirements and register the new RL algorithm in ml-agents ecosystem and be able to call mlagents-learn CLI with your customized configuration. \u251c\u2500\u2500 mlagents_trainer_plugin \u2502 \u251c\u2500\u2500 __init__.py \u2502 \u251c\u2500\u2500 a2c \u2502 \u2502 \u251c\u2500\u2500 __init__.py \u2502 \u2502 \u251c\u2500\u2500 a2c_3DBall.yaml \u2502 \u2502 \u251c\u2500\u2500 a2c_optimizer.py \u2502 \u2502 \u2514\u2500\u2500 a2c_trainer.py \u2502 \u2514\u2500\u2500 dqn \u2502 \u251c\u2500\u2500 __init__.py \u2502 \u251c\u2500\u2500 dqn_basic.yaml \u2502 \u251c\u2500\u2500 dqn_optimizer.py \u2502 \u2514\u2500\u2500 dqn_trainer.py \u2514\u2500\u2500 setup.py Installation and Execution If you haven't already, follow the installation instructions . Once you have the ml-agents-env and ml-agents packages you can install the plugin package. From the repository's root directory install ml-agents-trainer-plugin (or replace with the name of your plugin folder). pip3 install -e <./ml-agents-trainer-plugin> Following the previous installations your package is added as an entrypoint and you can use a config file with new trainers: mlagents-learn ml-agents-trainer-plugin/mlagents_trainer_plugin/a2c/a2c_3DBall.yaml --run-id <run-id-name> --env <env-executable> Tutorial Here\u2019s a step-by-step tutorial on how to write a setup file and extend ml-agents trainers, optimizers, and hyperparameter settings.To extend ML-agents classes see references on trainers and Optimizer .","title":"Unity Ml-Agents Custom trainers Plugin"},{"location":"Python-Custom-Trainer-Plugin/#unity-ml-agents-custom-trainers-plugin","text":"As an attempt to bring a wider variety of reinforcement learning algorithms to our users, we have added custom trainers capabilities. we introduce an extensible plugin system to define new trainers based on the High level trainer API in Ml-agents Package. This will allow rerouting mlagents-learn CLI to custom trainers and extending the config files with hyper-parameters specific to your new trainers. We will expose a high-level extensible trainer (both on-policy, and off-policy trainers) optimizer and hyperparameter classes with documentation for the use of this plugin. For more infromation on how python plugin system works see Plugin interfaces .","title":"Unity Ml-Agents Custom trainers Plugin"},{"location":"Python-Custom-Trainer-Plugin/#overview","text":"Model-free RL algorithms generally fall into two broad categories: on-policy and off-policy. On-policy algorithms perform updates based on data gathered from the current policy. Off-policy algorithms learn a Q function from a buffer of previous data, then use this Q function to make decisions. Off-policy algorithms have three key benefits in the context of ML-Agents: They tend to use fewer samples than on-policy as they can pull and re-use data from the buffer many times. They allow player demonstrations to be inserted in-line with RL data into the buffer, enabling new ways of doing imitation learning by streaming player data. To add new custom trainers to ML-agents, you would need to create a new python package. To give you an idea of how to structure your package, we have created a mlagents_trainer_plugin package ourselves as an example, with implementation of A2c and DQN algorithms. You would need a setup.py file to list extra requirements and register the new RL algorithm in ml-agents ecosystem and be able to call mlagents-learn CLI with your customized configuration. \u251c\u2500\u2500 mlagents_trainer_plugin \u2502 \u251c\u2500\u2500 __init__.py \u2502 \u251c\u2500\u2500 a2c \u2502 \u2502 \u251c\u2500\u2500 __init__.py \u2502 \u2502 \u251c\u2500\u2500 a2c_3DBall.yaml \u2502 \u2502 \u251c\u2500\u2500 a2c_optimizer.py \u2502 \u2502 \u2514\u2500\u2500 a2c_trainer.py \u2502 \u2514\u2500\u2500 dqn \u2502 \u251c\u2500\u2500 __init__.py \u2502 \u251c\u2500\u2500 dqn_basic.yaml \u2502 \u251c\u2500\u2500 dqn_optimizer.py \u2502 \u2514\u2500\u2500 dqn_trainer.py \u2514\u2500\u2500 setup.py","title":"Overview"},{"location":"Python-Custom-Trainer-Plugin/#installation-and-execution","text":"If you haven't already, follow the installation instructions . Once you have the ml-agents-env and ml-agents packages you can install the plugin package. From the repository's root directory install ml-agents-trainer-plugin (or replace with the name of your plugin folder). pip3 install -e <./ml-agents-trainer-plugin> Following the previous installations your package is added as an entrypoint and you can use a config file with new trainers: mlagents-learn ml-agents-trainer-plugin/mlagents_trainer_plugin/a2c/a2c_3DBall.yaml --run-id <run-id-name> --env <env-executable>","title":"Installation and Execution"},{"location":"Python-Custom-Trainer-Plugin/#tutorial","text":"Here\u2019s a step-by-step tutorial on how to write a setup file and extend ml-agents trainers, optimizers, and hyperparameter settings.To extend ML-agents classes see references on trainers and Optimizer .","title":"Tutorial"},{"location":"Python-Gym-API-Documentation/","text":"Table of Contents mlagents_envs.envs.unity_gym_env UnityGymException UnityToGymWrapper __init__ reset step render close seed ActionFlattener __init__ lookup_action mlagents_envs.envs.unity_gym_env UnityGymException Objects class UnityGymException(error.Error) Any error related to the gym wrapper of ml-agents. UnityToGymWrapper Objects class UnityToGymWrapper(gym.Env) Provides Gym wrapper for Unity Learning Environments. __init__ | __init__(unity_env: BaseEnv, uint8_visual: bool = False, flatten_branched: bool = False, allow_multiple_obs: bool = False, action_space_seed: Optional[int] = None) Environment initialization Arguments : unity_env : The Unity BaseEnv to be wrapped in the gym. Will be closed when the UnityToGymWrapper closes. uint8_visual : Return visual observations as uint8 (0-255) matrices instead of float (0.0-1.0). flatten_branched : If True, turn branched discrete action spaces into a Discrete space rather than MultiDiscrete. allow_multiple_obs : If True, return a list of np.ndarrays as observations with the first elements containing the visual observations and the last element containing the array of vector observations. If False, returns a single np.ndarray containing either only a single visual observation or the array of vector observations. action_space_seed : If non-None, will be used to set the random seed on created gym.Space instances. reset | reset() -> Union[List[np.ndarray], np.ndarray] Resets the state of the environment and returns an initial observation. Returns: observation (object/list): the initial observation of the space. step | step(action: List[Any]) -> GymStepResult Run one timestep of the environment's dynamics. When end of episode is reached, you are responsible for calling reset() to reset this environment's state. Accepts an action and returns a tuple (observation, reward, done, info). Arguments : action object/list - an action provided by the environment Returns : observation object/list - agent's observation of the current environment reward (float/list) : amount of reward returned after previous action done boolean/list - whether the episode has ended. info dict - contains auxiliary diagnostic information. render | render(mode=\"rgb_array\") Return the latest visual observations. Note that it will not render a new frame of the environment. close | close() -> None Override _close in your subclass to perform any necessary cleanup. Environments will automatically close() themselves when garbage collected or when the program exits. seed | seed(seed: Any = None) -> None Sets the seed for this env's random number generator(s). Currently not implemented. ActionFlattener Objects class ActionFlattener() Flattens branched discrete action spaces into single-branch discrete action spaces. __init__ | __init__(branched_action_space) Initialize the flattener. Arguments : branched_action_space : A List containing the sizes of each branch of the action space, e.g. [2,3,3] for three branches with size 2, 3, and 3 respectively. lookup_action | lookup_action(action) Convert a scalar discrete action into a unique set of branched actions. Arguments : action : A scalar value representing one of the discrete actions. Returns : The List containing the branched actions.","title":"Gym API Documentation"},{"location":"Python-Gym-API-Documentation/#table-of-contents","text":"mlagents_envs.envs.unity_gym_env UnityGymException UnityToGymWrapper __init__ reset step render close seed ActionFlattener __init__ lookup_action","title":"Table of Contents"},{"location":"Python-Gym-API-Documentation/#mlagents_envsenvsunity_gym_env","text":"","title":"mlagents_envs.envs.unity_gym_env"},{"location":"Python-Gym-API-Documentation/#unitygymexception-objects","text":"class UnityGymException(error.Error) Any error related to the gym wrapper of ml-agents.","title":"UnityGymException Objects"},{"location":"Python-Gym-API-Documentation/#unitytogymwrapper-objects","text":"class UnityToGymWrapper(gym.Env) Provides Gym wrapper for Unity Learning Environments.","title":"UnityToGymWrapper Objects"},{"location":"Python-Gym-API-Documentation/#__init__","text":"| __init__(unity_env: BaseEnv, uint8_visual: bool = False, flatten_branched: bool = False, allow_multiple_obs: bool = False, action_space_seed: Optional[int] = None) Environment initialization Arguments : unity_env : The Unity BaseEnv to be wrapped in the gym. Will be closed when the UnityToGymWrapper closes. uint8_visual : Return visual observations as uint8 (0-255) matrices instead of float (0.0-1.0). flatten_branched : If True, turn branched discrete action spaces into a Discrete space rather than MultiDiscrete. allow_multiple_obs : If True, return a list of np.ndarrays as observations with the first elements containing the visual observations and the last element containing the array of vector observations. If False, returns a single np.ndarray containing either only a single visual observation or the array of vector observations. action_space_seed : If non-None, will be used to set the random seed on created gym.Space instances.","title":"__init__"},{"location":"Python-Gym-API-Documentation/#reset","text":"| reset() -> Union[List[np.ndarray], np.ndarray] Resets the state of the environment and returns an initial observation. Returns: observation (object/list): the initial observation of the space.","title":"reset"},{"location":"Python-Gym-API-Documentation/#step","text":"| step(action: List[Any]) -> GymStepResult Run one timestep of the environment's dynamics. When end of episode is reached, you are responsible for calling reset() to reset this environment's state. Accepts an action and returns a tuple (observation, reward, done, info). Arguments : action object/list - an action provided by the environment Returns : observation object/list - agent's observation of the current environment reward (float/list) : amount of reward returned after previous action done boolean/list - whether the episode has ended. info dict - contains auxiliary diagnostic information.","title":"step"},{"location":"Python-Gym-API-Documentation/#render","text":"| render(mode=\"rgb_array\") Return the latest visual observations. Note that it will not render a new frame of the environment.","title":"render"},{"location":"Python-Gym-API-Documentation/#close","text":"| close() -> None Override _close in your subclass to perform any necessary cleanup. Environments will automatically close() themselves when garbage collected or when the program exits.","title":"close"},{"location":"Python-Gym-API-Documentation/#seed","text":"| seed(seed: Any = None) -> None Sets the seed for this env's random number generator(s). Currently not implemented.","title":"seed"},{"location":"Python-Gym-API-Documentation/#actionflattener-objects","text":"class ActionFlattener() Flattens branched discrete action spaces into single-branch discrete action spaces.","title":"ActionFlattener Objects"},{"location":"Python-Gym-API-Documentation/#__init___1","text":"| __init__(branched_action_space) Initialize the flattener. Arguments : branched_action_space : A List containing the sizes of each branch of the action space, e.g. [2,3,3] for three branches with size 2, 3, and 3 respectively.","title":"__init__"},{"location":"Python-Gym-API-Documentation/#lookup_action","text":"| lookup_action(action) Convert a scalar discrete action into a unique set of branched actions. Arguments : action : A scalar value representing one of the discrete actions. Returns : The List containing the branched actions.","title":"lookup_action"},{"location":"Python-Gym-API/","text":"Unity ML-Agents Gym Wrapper A common way in which machine learning researchers interact with simulation environments is via a wrapper provided by OpenAI called gym . For more information on the gym interface, see here . We provide a gym wrapper and instructions for using it with existing machine learning algorithms which utilize gym. Our wrapper provides interfaces on top of our UnityEnvironment class, which is the default way of interfacing with a Unity environment via Python. Installation The gym wrapper is part of the mlgents_envs package. Please refer to the mlagents_envs installation instructions . Using the Gym Wrapper The gym interface is available from gym_unity.envs . To launch an environment from the root of the project repository use: from mlagents_envs.envs.unity_gym_env import UnityToGymWrapper env = UnityToGymWrapper(unity_env, uint8_visual, flatten_branched, allow_multiple_obs) unity_env refers to the Unity environment to be wrapped. uint8_visual refers to whether to output visual observations as uint8 values (0-255). Many common Gym environments (e.g. Atari) do this. By default they will be floats (0.0-1.0). Defaults to False . flatten_branched will flatten a branched discrete action space into a Gym Discrete. Otherwise, it will be converted into a MultiDiscrete. Defaults to False . allow_multiple_obs will return a list of observations. The first elements contain the visual observations and the last element contains the array of vector observations. If False the environment returns a single array (containing a single visual observations, if present, otherwise the vector observation). Defaults to False . action_space_seed is the optional seed for action sampling. If non-None, will be used to set the random seed on created gym.Space instances. The returned environment env will function as a gym. Limitations It is only possible to use an environment with a single Agent. By default, the first visual observation is provided as the observation , if present. Otherwise, vector observations are provided. You can receive all visual and vector observations by using the allow_multiple_obs=True option in the gym parameters. If set to True , you will receive a list of observation instead of only one. The TerminalSteps or DecisionSteps output from the environment can still be accessed from the info provided by env.step(action) . Stacked vector observations are not supported. Environment registration for use with gym.make() is currently not supported. Calling env.render() will not render a new frame of the environment. It will return the latest visual observation if using visual observations. Running OpenAI Baselines Algorithms OpenAI provides a set of open-source maintained and tested Reinforcement Learning algorithms called the Baselines . Using the provided Gym wrapper, it is possible to train ML-Agents environments using these algorithms. This requires the creation of custom training scripts to launch each algorithm. In most cases these scripts can be created by making slight modifications to the ones provided for Atari and Mujoco environments. These examples were tested with baselines version 0.1.6. Example - DQN Baseline In order to train an agent to play the GridWorld environment using the Baselines DQN algorithm, you first need to install the baselines package using pip: pip install git+git://github.com/openai/baselines Next, create a file called train_unity.py . Then create an /envs/ directory and build the environment to that directory. For more information on building Unity environments, see here . Note that because of limitations of the DQN baseline, the environment must have a single visual observation, a single discrete action and a single Agent in the scene. Add the following code to the train_unity.py file: import gym from baselines import deepq from baselines import logger from mlagents_envs.environment import UnityEnvironment from mlagents_envs.envs.unity_gym_env import UnityToGymWrapper def main(): unity_env = UnityEnvironment( < path - to - environment >) env = UnityToGymWrapper(unity_env, uint8_visual=True) logger.configure('./logs') # Change to log in a different directory act = deepq.learn( env, \"cnn\", # For visual inputs lr=2.5e-4, total_timesteps=1000000, buffer_size=50000, exploration_fraction=0.05, exploration_final_eps=0.1, print_freq=20, train_freq=5, learning_starts=20000, target_network_update_freq=50, gamma=0.99, prioritized_replay=False, checkpoint_freq=1000, checkpoint_path='./logs', # Change to save model in a different directory dueling=True ) print(\"Saving model to unity_model.pkl\") act.save(\"unity_model.pkl\") if __name__ == '__main__': main() To start the training process, run the following from the directory containing train_unity.py : python -m train_unity Other Algorithms Other algorithms in the Baselines repository can be run using scripts similar to the examples from the baselines package. In most cases, the primary changes needed to use a Unity environment are to import UnityToGymWrapper , and to replace the environment creation code, typically gym.make() , with a call to UnityToGymWrapper(unity_environment) passing the environment as input. A typical rule of thumb is that for vision-based environments, modification should be done to Atari training scripts, and for vector observation environments, modification should be done to Mujoco scripts. Some algorithms will make use of make_env() or make_mujoco_env() functions. You can define a similar function for Unity environments. An example of such a method using the PPO2 baseline: from mlagents_envs.environment import UnityEnvironment from mlagents_envs.envs import UnityToGymWrapper from baselines.common.vec_env.subproc_vec_env import SubprocVecEnv from baselines.common.vec_env.dummy_vec_env import DummyVecEnv from baselines.bench import Monitor from baselines import logger import baselines.ppo2.ppo2 as ppo2 import os try: from mpi4py import MPI except ImportError: MPI = None def make_unity_env(env_directory, num_env, visual, start_index=0): \"\"\" Create a wrapped, monitored Unity environment. \"\"\" def make_env(rank, use_visual=True): # pylint: disable=C0111 def _thunk(): unity_env = UnityEnvironment(env_directory, base_port=5000 + rank) env = UnityToGymWrapper(unity_env, uint8_visual=True) env = Monitor(env, logger.get_dir() and os.path.join(logger.get_dir(), str(rank))) return env return _thunk if visual: return SubprocVecEnv([make_env(i + start_index) for i in range(num_env)]) else: rank = MPI.COMM_WORLD.Get_rank() if MPI else 0 return DummyVecEnv([make_env(rank, use_visual=False)]) def main(): env = make_unity_env( < path - to - environment >, 4, True) ppo2.learn( network=\"mlp\", env=env, total_timesteps=100000, lr=1e-3, ) if __name__ == '__main__': main() Run Google Dopamine Algorithms Google provides a framework Dopamine , and implementations of algorithms, e.g. DQN, Rainbow, and the C51 variant of Rainbow. Using the Gym wrapper, we can run Unity environments using Dopamine. First, after installing the Gym wrapper, clone the Dopamine repository. git clone https://github.com/google/dopamine Then, follow the appropriate install instructions as specified on Dopamine's homepage . Note that the Dopamine guide specifies using a virtualenv. If you choose to do so, make sure your unity_env package is also installed within the same virtualenv as Dopamine. Adapting Dopamine's Scripts First, open dopamine/atari/run_experiment.py . Alternatively, copy the entire atari folder, and name it something else (e.g. unity ). If you choose the copy approach, be sure to change the package names in the import statements in train.py to your new directory. Within run_experiment.py , we will need to make changes to which environment is instantiated, just as in the Baselines example. At the top of the file, insert from mlagents_envs.environment import UnityEnvironment from mlagents_envs.envs import UnityToGymWrapper to import the Gym Wrapper. Navigate to the create_atari_environment method in the same file, and switch to instantiating a Unity environment by replacing the method with the following code. game_version = 'v0' if sticky_actions else 'v4' full_game_name = '{}NoFrameskip-{}'.format(game_name, game_version) unity_env = UnityEnvironment(<path-to-environment>) env = UnityToGymWrapper(unity_env, uint8_visual=True) return env <path-to-environment> is the path to your built Unity executable. For more information on building Unity environments, see here , and note the Limitations section below. Note that we are not using the preprocessor from Dopamine, as it uses many Atari-specific calls. Furthermore, frame-skipping can be done from within Unity, rather than on the Python side. Limitations Since Dopamine is designed around variants of DQN, it is only compatible with discrete action spaces, and specifically the Discrete Gym space. For environments that use branched discrete action spaces, you can enable the flatten_branched parameter in UnityToGymWrapper , which treats each combination of branched actions as separate actions. Furthermore, when building your environments, ensure that your Agent is using visual observations with greyscale enabled, and that the dimensions of the visual observations is 84 by 84 (matches the parameter found in dqn_agent.py and rainbow_agent.py ). Dopamine's agents currently do not automatically adapt to the observation dimensions or number of channels. Hyperparameters The hyperparameters provided by Dopamine are tailored to the Atari games, and you will likely need to adjust them for ML-Agents environments. Here is a sample dopamine/agents/rainbow/configs/rainbow.gin file that is known to work with a simple GridWorld. import dopamine.agents.rainbow.rainbow_agent import dopamine.unity.run_experiment import dopamine.replay_memory.prioritized_replay_buffer import gin.tf.external_configurables RainbowAgent.num_atoms = 51 RainbowAgent.stack_size = 1 RainbowAgent.vmax = 10. RainbowAgent.gamma = 0.99 RainbowAgent.update_horizon = 3 RainbowAgent.min_replay_history = 20000 # agent steps RainbowAgent.update_period = 5 RainbowAgent.target_update_period = 50 # agent steps RainbowAgent.epsilon_train = 0.1 RainbowAgent.epsilon_eval = 0.01 RainbowAgent.epsilon_decay_period = 50000 # agent steps RainbowAgent.replay_scheme = 'prioritized' RainbowAgent.tf_device = '/cpu:0' # use '/cpu:*' for non-GPU version RainbowAgent.optimizer = @tf.train.AdamOptimizer() tf.train.AdamOptimizer.learning_rate = 0.00025 tf.train.AdamOptimizer.epsilon = 0.0003125 Runner.game_name = \"Unity\" # any name can be used here Runner.sticky_actions = False Runner.num_iterations = 200 Runner.training_steps = 10000 # agent steps Runner.evaluation_steps = 500 # agent steps Runner.max_steps_per_episode = 27000 # agent steps WrappedPrioritizedReplayBuffer.replay_capacity = 1000000 WrappedPrioritizedReplayBuffer.batch_size = 32 This example assumed you copied atari to a separate folder named unity . Replace unity in import dopamine.unity.run_experiment with the folder you copied your run_experiment.py and trainer.py files to. If you directly modified the existing files, then use atari here. Starting a Run You can now run Dopamine as you would normally: python -um dopamine.unity.train \\ --agent_name=rainbow \\ --base_dir=/tmp/dopamine \\ --gin_files='dopamine/agents/rainbow/configs/rainbow.gin' Again, we assume that you've copied atari into a separate folder. Remember to replace unity with the directory you copied your files into. If you edited the Atari files directly, this should be atari . Example: GridWorld As a baseline, here are rewards over time for the three algorithms provided with Dopamine as run on the GridWorld example environment. All Dopamine (DQN, Rainbow, C51) runs were done with the same epsilon, epsilon decay, replay history, training steps, and buffer settings as specified above. Note that the first 20000 steps are used to pre-fill the training buffer, and no learning happens. We provide results from our PPO implementation and the DQN from Baselines as reference. Note that all runs used the same greyscale GridWorld as Dopamine. For PPO, num_layers was set to 2, and all other hyperparameters are the default for GridWorld in config/ppo/GridWorld.yaml . For Baselines DQN, the provided hyperparameters in the previous section are used. Note that Baselines implements certain features (e.g. dueling-Q) that are not enabled in Dopamine DQN.","title":"Getting started with the Gym API"},{"location":"Python-Gym-API/#unity-ml-agents-gym-wrapper","text":"A common way in which machine learning researchers interact with simulation environments is via a wrapper provided by OpenAI called gym . For more information on the gym interface, see here . We provide a gym wrapper and instructions for using it with existing machine learning algorithms which utilize gym. Our wrapper provides interfaces on top of our UnityEnvironment class, which is the default way of interfacing with a Unity environment via Python.","title":"Unity ML-Agents Gym Wrapper"},{"location":"Python-Gym-API/#installation","text":"The gym wrapper is part of the mlgents_envs package. Please refer to the mlagents_envs installation instructions .","title":"Installation"},{"location":"Python-Gym-API/#using-the-gym-wrapper","text":"The gym interface is available from gym_unity.envs . To launch an environment from the root of the project repository use: from mlagents_envs.envs.unity_gym_env import UnityToGymWrapper env = UnityToGymWrapper(unity_env, uint8_visual, flatten_branched, allow_multiple_obs) unity_env refers to the Unity environment to be wrapped. uint8_visual refers to whether to output visual observations as uint8 values (0-255). Many common Gym environments (e.g. Atari) do this. By default they will be floats (0.0-1.0). Defaults to False . flatten_branched will flatten a branched discrete action space into a Gym Discrete. Otherwise, it will be converted into a MultiDiscrete. Defaults to False . allow_multiple_obs will return a list of observations. The first elements contain the visual observations and the last element contains the array of vector observations. If False the environment returns a single array (containing a single visual observations, if present, otherwise the vector observation). Defaults to False . action_space_seed is the optional seed for action sampling. If non-None, will be used to set the random seed on created gym.Space instances. The returned environment env will function as a gym.","title":"Using the Gym Wrapper"},{"location":"Python-Gym-API/#limitations","text":"It is only possible to use an environment with a single Agent. By default, the first visual observation is provided as the observation , if present. Otherwise, vector observations are provided. You can receive all visual and vector observations by using the allow_multiple_obs=True option in the gym parameters. If set to True , you will receive a list of observation instead of only one. The TerminalSteps or DecisionSteps output from the environment can still be accessed from the info provided by env.step(action) . Stacked vector observations are not supported. Environment registration for use with gym.make() is currently not supported. Calling env.render() will not render a new frame of the environment. It will return the latest visual observation if using visual observations.","title":"Limitations"},{"location":"Python-Gym-API/#running-openai-baselines-algorithms","text":"OpenAI provides a set of open-source maintained and tested Reinforcement Learning algorithms called the Baselines . Using the provided Gym wrapper, it is possible to train ML-Agents environments using these algorithms. This requires the creation of custom training scripts to launch each algorithm. In most cases these scripts can be created by making slight modifications to the ones provided for Atari and Mujoco environments. These examples were tested with baselines version 0.1.6.","title":"Running OpenAI Baselines Algorithms"},{"location":"Python-Gym-API/#example-dqn-baseline","text":"In order to train an agent to play the GridWorld environment using the Baselines DQN algorithm, you first need to install the baselines package using pip: pip install git+git://github.com/openai/baselines Next, create a file called train_unity.py . Then create an /envs/ directory and build the environment to that directory. For more information on building Unity environments, see here . Note that because of limitations of the DQN baseline, the environment must have a single visual observation, a single discrete action and a single Agent in the scene. Add the following code to the train_unity.py file: import gym from baselines import deepq from baselines import logger from mlagents_envs.environment import UnityEnvironment from mlagents_envs.envs.unity_gym_env import UnityToGymWrapper def main(): unity_env = UnityEnvironment( < path - to - environment >) env = UnityToGymWrapper(unity_env, uint8_visual=True) logger.configure('./logs') # Change to log in a different directory act = deepq.learn( env, \"cnn\", # For visual inputs lr=2.5e-4, total_timesteps=1000000, buffer_size=50000, exploration_fraction=0.05, exploration_final_eps=0.1, print_freq=20, train_freq=5, learning_starts=20000, target_network_update_freq=50, gamma=0.99, prioritized_replay=False, checkpoint_freq=1000, checkpoint_path='./logs', # Change to save model in a different directory dueling=True ) print(\"Saving model to unity_model.pkl\") act.save(\"unity_model.pkl\") if __name__ == '__main__': main() To start the training process, run the following from the directory containing train_unity.py : python -m train_unity","title":"Example - DQN Baseline"},{"location":"Python-Gym-API/#other-algorithms","text":"Other algorithms in the Baselines repository can be run using scripts similar to the examples from the baselines package. In most cases, the primary changes needed to use a Unity environment are to import UnityToGymWrapper , and to replace the environment creation code, typically gym.make() , with a call to UnityToGymWrapper(unity_environment) passing the environment as input. A typical rule of thumb is that for vision-based environments, modification should be done to Atari training scripts, and for vector observation environments, modification should be done to Mujoco scripts. Some algorithms will make use of make_env() or make_mujoco_env() functions. You can define a similar function for Unity environments. An example of such a method using the PPO2 baseline: from mlagents_envs.environment import UnityEnvironment from mlagents_envs.envs import UnityToGymWrapper from baselines.common.vec_env.subproc_vec_env import SubprocVecEnv from baselines.common.vec_env.dummy_vec_env import DummyVecEnv from baselines.bench import Monitor from baselines import logger import baselines.ppo2.ppo2 as ppo2 import os try: from mpi4py import MPI except ImportError: MPI = None def make_unity_env(env_directory, num_env, visual, start_index=0): \"\"\" Create a wrapped, monitored Unity environment. \"\"\" def make_env(rank, use_visual=True): # pylint: disable=C0111 def _thunk(): unity_env = UnityEnvironment(env_directory, base_port=5000 + rank) env = UnityToGymWrapper(unity_env, uint8_visual=True) env = Monitor(env, logger.get_dir() and os.path.join(logger.get_dir(), str(rank))) return env return _thunk if visual: return SubprocVecEnv([make_env(i + start_index) for i in range(num_env)]) else: rank = MPI.COMM_WORLD.Get_rank() if MPI else 0 return DummyVecEnv([make_env(rank, use_visual=False)]) def main(): env = make_unity_env( < path - to - environment >, 4, True) ppo2.learn( network=\"mlp\", env=env, total_timesteps=100000, lr=1e-3, ) if __name__ == '__main__': main()","title":"Other Algorithms"},{"location":"Python-Gym-API/#run-google-dopamine-algorithms","text":"Google provides a framework Dopamine , and implementations of algorithms, e.g. DQN, Rainbow, and the C51 variant of Rainbow. Using the Gym wrapper, we can run Unity environments using Dopamine. First, after installing the Gym wrapper, clone the Dopamine repository. git clone https://github.com/google/dopamine Then, follow the appropriate install instructions as specified on Dopamine's homepage . Note that the Dopamine guide specifies using a virtualenv. If you choose to do so, make sure your unity_env package is also installed within the same virtualenv as Dopamine.","title":"Run Google Dopamine Algorithms"},{"location":"Python-Gym-API/#adapting-dopamines-scripts","text":"First, open dopamine/atari/run_experiment.py . Alternatively, copy the entire atari folder, and name it something else (e.g. unity ). If you choose the copy approach, be sure to change the package names in the import statements in train.py to your new directory. Within run_experiment.py , we will need to make changes to which environment is instantiated, just as in the Baselines example. At the top of the file, insert from mlagents_envs.environment import UnityEnvironment from mlagents_envs.envs import UnityToGymWrapper to import the Gym Wrapper. Navigate to the create_atari_environment method in the same file, and switch to instantiating a Unity environment by replacing the method with the following code. game_version = 'v0' if sticky_actions else 'v4' full_game_name = '{}NoFrameskip-{}'.format(game_name, game_version) unity_env = UnityEnvironment(<path-to-environment>) env = UnityToGymWrapper(unity_env, uint8_visual=True) return env <path-to-environment> is the path to your built Unity executable. For more information on building Unity environments, see here , and note the Limitations section below. Note that we are not using the preprocessor from Dopamine, as it uses many Atari-specific calls. Furthermore, frame-skipping can be done from within Unity, rather than on the Python side.","title":"Adapting Dopamine's Scripts"},{"location":"Python-Gym-API/#limitations_1","text":"Since Dopamine is designed around variants of DQN, it is only compatible with discrete action spaces, and specifically the Discrete Gym space. For environments that use branched discrete action spaces, you can enable the flatten_branched parameter in UnityToGymWrapper , which treats each combination of branched actions as separate actions. Furthermore, when building your environments, ensure that your Agent is using visual observations with greyscale enabled, and that the dimensions of the visual observations is 84 by 84 (matches the parameter found in dqn_agent.py and rainbow_agent.py ). Dopamine's agents currently do not automatically adapt to the observation dimensions or number of channels.","title":"Limitations"},{"location":"Python-Gym-API/#hyperparameters","text":"The hyperparameters provided by Dopamine are tailored to the Atari games, and you will likely need to adjust them for ML-Agents environments. Here is a sample dopamine/agents/rainbow/configs/rainbow.gin file that is known to work with a simple GridWorld. import dopamine.agents.rainbow.rainbow_agent import dopamine.unity.run_experiment import dopamine.replay_memory.prioritized_replay_buffer import gin.tf.external_configurables RainbowAgent.num_atoms = 51 RainbowAgent.stack_size = 1 RainbowAgent.vmax = 10. RainbowAgent.gamma = 0.99 RainbowAgent.update_horizon = 3 RainbowAgent.min_replay_history = 20000 # agent steps RainbowAgent.update_period = 5 RainbowAgent.target_update_period = 50 # agent steps RainbowAgent.epsilon_train = 0.1 RainbowAgent.epsilon_eval = 0.01 RainbowAgent.epsilon_decay_period = 50000 # agent steps RainbowAgent.replay_scheme = 'prioritized' RainbowAgent.tf_device = '/cpu:0' # use '/cpu:*' for non-GPU version RainbowAgent.optimizer = @tf.train.AdamOptimizer() tf.train.AdamOptimizer.learning_rate = 0.00025 tf.train.AdamOptimizer.epsilon = 0.0003125 Runner.game_name = \"Unity\" # any name can be used here Runner.sticky_actions = False Runner.num_iterations = 200 Runner.training_steps = 10000 # agent steps Runner.evaluation_steps = 500 # agent steps Runner.max_steps_per_episode = 27000 # agent steps WrappedPrioritizedReplayBuffer.replay_capacity = 1000000 WrappedPrioritizedReplayBuffer.batch_size = 32 This example assumed you copied atari to a separate folder named unity . Replace unity in import dopamine.unity.run_experiment with the folder you copied your run_experiment.py and trainer.py files to. If you directly modified the existing files, then use atari here.","title":"Hyperparameters"},{"location":"Python-Gym-API/#starting-a-run","text":"You can now run Dopamine as you would normally: python -um dopamine.unity.train \\ --agent_name=rainbow \\ --base_dir=/tmp/dopamine \\ --gin_files='dopamine/agents/rainbow/configs/rainbow.gin' Again, we assume that you've copied atari into a separate folder. Remember to replace unity with the directory you copied your files into. If you edited the Atari files directly, this should be atari .","title":"Starting a Run"},{"location":"Python-Gym-API/#example-gridworld","text":"As a baseline, here are rewards over time for the three algorithms provided with Dopamine as run on the GridWorld example environment. All Dopamine (DQN, Rainbow, C51) runs were done with the same epsilon, epsilon decay, replay history, training steps, and buffer settings as specified above. Note that the first 20000 steps are used to pre-fill the training buffer, and no learning happens. We provide results from our PPO implementation and the DQN from Baselines as reference. Note that all runs used the same greyscale GridWorld as Dopamine. For PPO, num_layers was set to 2, and all other hyperparameters are the default for GridWorld in config/ppo/GridWorld.yaml . For Baselines DQN, the provided hyperparameters in the previous section are used. Note that Baselines implements certain features (e.g. dueling-Q) that are not enabled in Dopamine DQN.","title":"Example: GridWorld"},{"location":"Python-LLAPI-Documentation/","text":"Table of Contents mlagents_envs.base_env DecisionStep DecisionSteps agent_id_to_index __getitem__ empty TerminalStep TerminalSteps agent_id_to_index __getitem__ empty ActionTuple discrete_dtype ActionSpec is_discrete is_continuous discrete_size empty_action random_action create_continuous create_discrete DimensionProperty UNSPECIFIED NONE TRANSLATIONAL_EQUIVARIANCE VARIABLE_SIZE ObservationType DEFAULT GOAL_SIGNAL ObservationSpec BehaviorSpec BaseEnv step reset close behavior_specs set_actions set_action_for_agent get_steps mlagents_envs.environment UnityEnvironment __init__ close mlagents_envs.registry mlagents_envs.registry.unity_env_registry UnityEnvRegistry register register_from_yaml clear __getitem__ mlagents_envs.side_channel mlagents_envs.side_channel.raw_bytes_channel RawBytesChannel on_message_received get_and_clear_received_messages send_raw_data mlagents_envs.side_channel.outgoing_message OutgoingMessage __init__ write_bool write_int32 write_float32 write_float32_list write_string set_raw_bytes mlagents_envs.side_channel.engine_configuration_channel EngineConfigurationChannel on_message_received set_configuration_parameters set_configuration mlagents_envs.side_channel.side_channel_manager SideChannelManager process_side_channel_message generate_side_channel_messages mlagents_envs.side_channel.stats_side_channel StatsSideChannel on_message_received get_and_reset_stats mlagents_envs.side_channel.incoming_message IncomingMessage __init__ read_bool read_int32 read_float32 read_float32_list read_string get_raw_bytes mlagents_envs.side_channel.float_properties_channel FloatPropertiesChannel on_message_received set_property get_property list_properties get_property_dict_copy mlagents_envs.side_channel.environment_parameters_channel EnvironmentParametersChannel set_float_parameter set_uniform_sampler_parameters set_gaussian_sampler_parameters set_multirangeuniform_sampler_parameters mlagents_envs.side_channel.side_channel SideChannel queue_message_to_send on_message_received channel_id mlagents_envs.base_env Python Environment API for the ML-Agents Toolkit The aim of this API is to expose Agents evolving in a simulation to perform reinforcement learning on. This API supports multi-agent scenarios and groups similar Agents (same observations, actions spaces and behavior) together. These groups of Agents are identified by their BehaviorName. For performance reasons, the data of each group of agents is processed in a batched manner. Agents are identified by a unique AgentId identifier that allows tracking of Agents across simulation steps. Note that there is no guarantee that the number or order of the Agents in the state will be consistent across simulation steps. A simulation steps corresponds to moving the simulation forward until at least one agent in the simulation sends its observations to Python again. Since Agents can request decisions at different frequencies, a simulation step does not necessarily correspond to a fixed simulation time increment. DecisionStep Objects class DecisionStep(NamedTuple) Contains the data a single Agent collected since the last simulation step. - obs is a list of numpy arrays observations collected by the agent. - reward is a float. Corresponds to the rewards collected by the agent since the last simulation step. - agent_id is an int and an unique identifier for the corresponding Agent. - action_mask is an optional list of one dimensional array of booleans. Only available when using multi-discrete actions. Each array corresponds to an action branch. Each array contains a mask for each action of the branch. If true, the action is not available for the agent during this simulation step. DecisionSteps Objects class DecisionSteps(Mapping) Contains the data a batch of similar Agents collected since the last simulation step. Note that all Agents do not necessarily have new information to send at each simulation step. Therefore, the ordering of agents and the batch size of the DecisionSteps are not fixed across simulation steps. - obs is a list of numpy arrays observations collected by the batch of agent. Each obs has one extra dimension compared to DecisionStep: the first dimension of the array corresponds to the batch size of the batch. - reward is a float vector of length batch size. Corresponds to the rewards collected by each agent since the last simulation step. - agent_id is an int vector of length batch size containing unique identifier for the corresponding Agent. This is used to track Agents across simulation steps. - action_mask is an optional list of two dimensional array of booleans. Only available when using multi-discrete actions. Each array corresponds to an action branch. The first dimension of each array is the batch size and the second contains a mask for each action of the branch. If true, the action is not available for the agent during this simulation step. agent_id_to_index | @property | agent_id_to_index() -> Dict[AgentId, int] Returns : A Dict that maps agent_id to the index of those agents in this DecisionSteps. __getitem__ | __getitem__(agent_id: AgentId) -> DecisionStep returns the DecisionStep for a specific agent. Arguments : agent_id : The id of the agent Returns : The DecisionStep empty | @staticmethod | empty(spec: \"BehaviorSpec\") -> \"DecisionSteps\" Returns an empty DecisionSteps. Arguments : spec : The BehaviorSpec for the DecisionSteps TerminalStep Objects class TerminalStep(NamedTuple) Contains the data a single Agent collected when its episode ended. - obs is a list of numpy arrays observations collected by the agent. - reward is a float. Corresponds to the rewards collected by the agent since the last simulation step. - interrupted is a bool. Is true if the Agent was interrupted since the last decision step. For example, if the Agent reached the maximum number of steps for the episode. - agent_id is an int and an unique identifier for the corresponding Agent. TerminalSteps Objects class TerminalSteps(Mapping) Contains the data a batch of Agents collected when their episode terminated. All Agents present in the TerminalSteps have ended their episode. - obs is a list of numpy arrays observations collected by the batch of agent. Each obs has one extra dimension compared to DecisionStep: the first dimension of the array corresponds to the batch size of the batch. - reward is a float vector of length batch size. Corresponds to the rewards collected by each agent since the last simulation step. - interrupted is an array of booleans of length batch size. Is true if the associated Agent was interrupted since the last decision step. For example, if the Agent reached the maximum number of steps for the episode. - agent_id is an int vector of length batch size containing unique identifier for the corresponding Agent. This is used to track Agents across simulation steps. agent_id_to_index | @property | agent_id_to_index() -> Dict[AgentId, int] Returns : A Dict that maps agent_id to the index of those agents in this TerminalSteps. __getitem__ | __getitem__(agent_id: AgentId) -> TerminalStep returns the TerminalStep for a specific agent. Arguments : agent_id : The id of the agent Returns : obs, reward, done, agent_id and optional action mask for a specific agent empty | @staticmethod | empty(spec: \"BehaviorSpec\") -> \"TerminalSteps\" Returns an empty TerminalSteps. Arguments : spec : The BehaviorSpec for the TerminalSteps ActionTuple Objects class ActionTuple(_ActionTupleBase) An object whose fields correspond to actions of different types. Continuous and discrete actions are numpy arrays of type float32 and int32, respectively and are type checked on construction. Dimensions are of (n_agents, continuous_size) and (n_agents, discrete_size), respectively. Note, this also holds when continuous or discrete size is zero. discrete_dtype | @property | discrete_dtype() -> np.dtype The dtype of a discrete action. ActionSpec Objects class ActionSpec(NamedTuple) A NamedTuple containing utility functions and information about the action spaces for a group of Agents under the same behavior. - num_continuous_actions is an int corresponding to the number of floats which constitute the action. - discrete_branch_sizes is a Tuple of int where each int corresponds to the number of discrete actions available to the agent on an independent action branch. is_discrete | is_discrete() -> bool Returns true if this Behavior uses discrete actions is_continuous | is_continuous() -> bool Returns true if this Behavior uses continuous actions discrete_size | @property | discrete_size() -> int Returns a an int corresponding to the number of discrete branches. empty_action | empty_action(n_agents: int) -> ActionTuple Generates ActionTuple corresponding to an empty action (all zeros) for a number of agents. Arguments : n_agents : The number of agents that will have actions generated random_action | random_action(n_agents: int) -> ActionTuple Generates ActionTuple corresponding to a random action (either discrete or continuous) for a number of agents. Arguments : n_agents : The number of agents that will have actions generated create_continuous | @staticmethod | create_continuous(continuous_size: int) -> \"ActionSpec\" Creates an ActionSpec that is homogenously continuous create_discrete | @staticmethod | create_discrete(discrete_branches: Tuple[int]) -> \"ActionSpec\" Creates an ActionSpec that is homogenously discrete DimensionProperty Objects class DimensionProperty(IntFlag) The dimension property of a dimension of an observation. UNSPECIFIED No properties specified. NONE No Property of the observation in that dimension. Observation can be processed with Fully connected networks. TRANSLATIONAL_EQUIVARIANCE Means it is suitable to do a convolution in this dimension. VARIABLE_SIZE Means that there can be a variable number of observations in this dimension. The observations are unordered. ObservationType Objects class ObservationType(Enum) An Enum which defines the type of information carried in the observation of the agent. DEFAULT Observation information is generic. GOAL_SIGNAL Observation contains goal information for current task. ObservationSpec Objects class ObservationSpec(NamedTuple) A NamedTuple containing information about the observation of Agents. - shape is a Tuple of int : It corresponds to the shape of an observation's dimensions. - dimension_property is a Tuple of DimensionProperties flag, one flag for each dimension. - observation_type is an enum of ObservationType. BehaviorSpec Objects class BehaviorSpec(NamedTuple) A NamedTuple containing information about the observation and action spaces for a group of Agents under the same behavior. - observation_specs is a List of ObservationSpec NamedTuple containing information about the information of the Agent's observations such as their shapes. The order of the ObservationSpec is the same as the order of the observations of an agent. - action_spec is an ActionSpec NamedTuple. BaseEnv Objects class BaseEnv(ABC) step | @abstractmethod | step() -> None Signals the environment that it must move the simulation forward by one step. reset | @abstractmethod | reset() -> None Signals the environment that it must reset the simulation. close | @abstractmethod | close() -> None Signals the environment that it must close. behavior_specs | @property | @abstractmethod | behavior_specs() -> MappingType[str, BehaviorSpec] Returns a Mapping from behavior names to behavior specs. Agents grouped under the same behavior name have the same action and observation specs, and are expected to behave similarly in the environment. Note that new keys can be added to this mapping as new policies are instantiated. set_actions | @abstractmethod | set_actions(behavior_name: BehaviorName, action: ActionTuple) -> None Sets the action for all of the agents in the simulation for the next step. The Actions must be in the same order as the order received in the DecisionSteps. Arguments : behavior_name : The name of the behavior the agents are part of action : ActionTuple tuple of continuous and/or discrete action. Actions are np.arrays with dimensions (n_agents, continuous_size) and (n_agents, discrete_size), respectively. set_action_for_agent | @abstractmethod | set_action_for_agent(behavior_name: BehaviorName, agent_id: AgentId, action: ActionTuple) -> None Sets the action for one of the agents in the simulation for the next step. Arguments : behavior_name : The name of the behavior the agent is part of agent_id : The id of the agent the action is set for action : ActionTuple tuple of continuous and/or discrete action Actions are np.arrays with dimensions (1, continuous_size) and (1, discrete_size), respectively. Note, this initial dimensions of 1 is because this action is meant for a single agent. get_steps | @abstractmethod | get_steps(behavior_name: BehaviorName) -> Tuple[DecisionSteps, TerminalSteps] Retrieves the steps of the agents that requested a step in the simulation. Arguments : behavior_name : The name of the behavior the agents are part of Returns : A tuple containing : - A DecisionSteps NamedTuple containing the observations, the rewards, the agent ids and the action masks for the Agents of the specified behavior. These Agents need an action this step. - A TerminalSteps NamedTuple containing the observations, rewards, agent ids and interrupted flags of the agents that had their episode terminated last step. mlagents_envs.environment UnityEnvironment Objects class UnityEnvironment(BaseEnv) __init__ | __init__(file_name: Optional[str] = None, worker_id: int = 0, base_port: Optional[int] = None, seed: int = 0, no_graphics: bool = False, timeout_wait: int = 60, additional_args: Optional[List[str]] = None, side_channels: Optional[List[SideChannel]] = None, log_folder: Optional[str] = None, num_areas: int = 1) Starts a new unity environment and establishes a connection with the environment. Notice: Currently communication between Unity and Python takes place over an open socket without authentication. Ensure that the network where training takes place is secure. :string file_name: Name of Unity environment binary. :int base_port: Baseline port number to connect to Unity environment over. worker_id increments over this. If no environment is specified (i.e. file_name is None), the DEFAULT_EDITOR_PORT will be used. :int worker_id: Offset from base_port. Used for training multiple environments simultaneously. :bool no_graphics: Whether to run the Unity simulator in no-graphics mode :int timeout_wait: Time (in seconds) to wait for connection from environment. :list args: Addition Unity command line arguments :list side_channels: Additional side channel for no-rl communication with Unity :str log_folder: Optional folder to write the Unity Player log file into. Requires absolute path. close | close() Sends a shutdown signal to the unity environment, and closes the socket connection. mlagents_envs.registry mlagents_envs.registry.unity_env_registry UnityEnvRegistry Objects class UnityEnvRegistry(Mapping) UnityEnvRegistry Provides a library of Unity environments that can be launched without the need of downloading the Unity Editor. The UnityEnvRegistry implements a Map, to access an entry of the Registry, use: registry = UnityEnvRegistry() entry = registry[<environment_identifyier>] An entry has the following properties : * identifier : Uniquely identifies this environment * expected_reward : Corresponds to the reward an agent must obtained for the task to be considered completed. * description : A human readable description of the environment. To launch a Unity environment from a registry entry, use the make method: registry = UnityEnvRegistry() env = registry[<environment_identifyier>].make() register | register(new_entry: BaseRegistryEntry) -> None Registers a new BaseRegistryEntry to the registry. The BaseRegistryEntry.identifier value will be used as indexing key. If two are more environments are registered under the same key, the most recentry added will replace the others. register_from_yaml | register_from_yaml(path_to_yaml: str) -> None Registers the environments listed in a yaml file (either local or remote). Note that the entries are registered lazily: the registration will only happen when an environment is accessed. The yaml file must have the following format : environments: - <identifier of the first environment>: expected_reward: <expected reward of the environment> description: | <a multi line description of the environment> <continued multi line description> linux_url: <The url for the Linux executable zip file> darwin_url: <The url for the OSX executable zip file> win_url: <The url for the Windows executable zip file> - <identifier of the second environment>: expected_reward: <expected reward of the environment> description: | <a multi line description of the environment> <continued multi line description> linux_url: <The url for the Linux executable zip file> darwin_url: <The url for the OSX executable zip file> win_url: <The url for the Windows executable zip file> - ... Arguments : path_to_yaml : A local path or url to the yaml file clear | clear() -> None Deletes all entries in the registry. __getitem__ | __getitem__(identifier: str) -> BaseRegistryEntry Returns the BaseRegistryEntry with the provided identifier. BaseRegistryEntry can then be used to make a Unity Environment. Arguments : identifier : The identifier of the BaseRegistryEntry Returns : The associated BaseRegistryEntry mlagents_envs.side_channel mlagents_envs.side_channel.raw_bytes_channel RawBytesChannel Objects class RawBytesChannel(SideChannel) This is an example of what the SideChannel for raw bytes exchange would look like. Is meant to be used for general research purpose. on_message_received | on_message_received(msg: IncomingMessage) -> None Is called by the environment to the side channel. Can be called multiple times per step if multiple messages are meant for that SideChannel. get_and_clear_received_messages | get_and_clear_received_messages() -> List[bytes] returns a list of bytearray received from the environment. send_raw_data | send_raw_data(data: bytearray) -> None Queues a message to be sent by the environment at the next call to step. mlagents_envs.side_channel.outgoing_message OutgoingMessage Objects class OutgoingMessage() Utility class for forming the message that is written to a SideChannel. All data is written in little-endian format using the struct module. __init__ | __init__() Create an OutgoingMessage with an empty buffer. write_bool | write_bool(b: bool) -> None Append a boolean value. write_int32 | write_int32(i: int) -> None Append an integer value. write_float32 | write_float32(f: float) -> None Append a float value. It will be truncated to 32-bit precision. write_float32_list | write_float32_list(float_list: List[float]) -> None Append a list of float values. They will be truncated to 32-bit precision. write_string | write_string(s: str) -> None Append a string value. Internally, it will be encoded to ascii, and the encoded length will also be written to the message. set_raw_bytes | set_raw_bytes(buffer: bytearray) -> None Set the internal buffer to a new bytearray. This will overwrite any existing data. Arguments : buffer : Returns : mlagents_envs.side_channel.engine_configuration_channel EngineConfigurationChannel Objects class EngineConfigurationChannel(SideChannel) This is the SideChannel for engine configuration exchange. The data in the engine configuration is as follows : - int width; - int height; - int qualityLevel; - float timeScale; - int targetFrameRate; - int captureFrameRate; on_message_received | on_message_received(msg: IncomingMessage) -> None Is called by the environment to the side channel. Can be called multiple times per step if multiple messages are meant for that SideChannel. Note that Python should never receive an engine configuration from Unity set_configuration_parameters | set_configuration_parameters(width: Optional[int] = None, height: Optional[int] = None, quality_level: Optional[int] = None, time_scale: Optional[float] = None, target_frame_rate: Optional[int] = None, capture_frame_rate: Optional[int] = None) -> None Sets the engine configuration. Takes as input the configurations of the engine. Arguments : width : Defines the width of the display. (Must be set alongside height) height : Defines the height of the display. (Must be set alongside width) quality_level : Defines the quality level of the simulation. time_scale : Defines the multiplier for the deltatime in the simulation. If set to a higher value, time will pass faster in the simulation but the physics might break. target_frame_rate : Instructs simulation to try to render at a specified frame rate. capture_frame_rate : Instructs the simulation to consider time between updates to always be constant, regardless of the actual frame rate. set_configuration | set_configuration(config: EngineConfig) -> None Sets the engine configuration. Takes as input an EngineConfig. mlagents_envs.side_channel.side_channel_manager SideChannelManager Objects class SideChannelManager() process_side_channel_message | process_side_channel_message(data: bytes) -> None Separates the data received from Python into individual messages for each registered side channel and calls on_message_received on them. Arguments : data : The packed message sent by Unity generate_side_channel_messages | generate_side_channel_messages() -> bytearray Gathers the messages that the registered side channels will send to Unity and combines them into a single message ready to be sent. mlagents_envs.side_channel.stats_side_channel StatsSideChannel Objects class StatsSideChannel(SideChannel) Side channel that receives (string, float) pairs from the environment, so that they can eventually be passed to a StatsReporter. on_message_received | on_message_received(msg: IncomingMessage) -> None Receive the message from the environment, and save it for later retrieval. Arguments : msg : Returns : get_and_reset_stats | get_and_reset_stats() -> EnvironmentStats Returns the current stats, and resets the internal storage of the stats. Returns : mlagents_envs.side_channel.incoming_message IncomingMessage Objects class IncomingMessage() Utility class for reading the message written to a SideChannel. Values must be read in the order they were written. __init__ | __init__(buffer: bytes, offset: int = 0) Create a new IncomingMessage from the bytes. read_bool | read_bool(default_value: bool = False) -> bool Read a boolean value from the message buffer. Arguments : default_value : Default value to use if the end of the message is reached. Returns : The value read from the message, or the default value if the end was reached. read_int32 | read_int32(default_value: int = 0) -> int Read an integer value from the message buffer. Arguments : default_value : Default value to use if the end of the message is reached. Returns : The value read from the message, or the default value if the end was reached. read_float32 | read_float32(default_value: float = 0.0) -> float Read a float value from the message buffer. Arguments : default_value : Default value to use if the end of the message is reached. Returns : The value read from the message, or the default value if the end was reached. read_float32_list | read_float32_list(default_value: List[float] = None) -> List[float] Read a list of float values from the message buffer. Arguments : default_value : Default value to use if the end of the message is reached. Returns : The value read from the message, or the default value if the end was reached. read_string | read_string(default_value: str = \"\") -> str Read a string value from the message buffer. Arguments : default_value : Default value to use if the end of the message is reached. Returns : The value read from the message, or the default value if the end was reached. get_raw_bytes | get_raw_bytes() -> bytes Get a copy of the internal bytes used by the message. mlagents_envs.side_channel.float_properties_channel FloatPropertiesChannel Objects class FloatPropertiesChannel(SideChannel) This is the SideChannel for float properties shared with Unity. You can modify the float properties of an environment with the commands set_property, get_property and list_properties. on_message_received | on_message_received(msg: IncomingMessage) -> None Is called by the environment to the side channel. Can be called multiple times per step if multiple messages are meant for that SideChannel. set_property | set_property(key: str, value: float) -> None Sets a property in the Unity Environment. Arguments : key : The string identifier of the property. value : The float value of the property. get_property | get_property(key: str) -> Optional[float] Gets a property in the Unity Environment. If the property was not found, will return None. Arguments : key : The string identifier of the property. Returns : The float value of the property or None. list_properties | list_properties() -> List[str] Returns a list of all the string identifiers of the properties currently present in the Unity Environment. get_property_dict_copy | get_property_dict_copy() -> Dict[str, float] Returns a copy of the float properties. Returns : mlagents_envs.side_channel.environment_parameters_channel EnvironmentParametersChannel Objects class EnvironmentParametersChannel(SideChannel) This is the SideChannel for sending environment parameters to Unity. You can send parameters to an environment with the command set_float_parameter. set_float_parameter | set_float_parameter(key: str, value: float) -> None Sets a float environment parameter in the Unity Environment. Arguments : key : The string identifier of the parameter. value : The float value of the parameter. set_uniform_sampler_parameters | set_uniform_sampler_parameters(key: str, min_value: float, max_value: float, seed: int) -> None Sets a uniform environment parameter sampler. Arguments : key : The string identifier of the parameter. min_value : The minimum of the sampling distribution. max_value : The maximum of the sampling distribution. seed : The random seed to initialize the sampler. set_gaussian_sampler_parameters | set_gaussian_sampler_parameters(key: str, mean: float, st_dev: float, seed: int) -> None Sets a gaussian environment parameter sampler. Arguments : key : The string identifier of the parameter. mean : The mean of the sampling distribution. st_dev : The standard deviation of the sampling distribution. seed : The random seed to initialize the sampler. set_multirangeuniform_sampler_parameters | set_multirangeuniform_sampler_parameters(key: str, intervals: List[Tuple[float, float]], seed: int) -> None Sets a multirangeuniform environment parameter sampler. Arguments : key : The string identifier of the parameter. intervals : The lists of min and max that define each uniform distribution. seed : The random seed to initialize the sampler. mlagents_envs.side_channel.side_channel SideChannel Objects class SideChannel(ABC) The side channel just get access to a bytes buffer that will be shared between C# and Python. For example, We will create a specific side channel for properties that will be a list of string (fixed size) to float number, that can be modified by both C# and Python. All side channels are passed to the Env object at construction. queue_message_to_send | queue_message_to_send(msg: OutgoingMessage) -> None Queues a message to be sent by the environment at the next call to step. on_message_received | @abstractmethod | on_message_received(msg: IncomingMessage) -> None Is called by the environment to the side channel. Can be called multiple times per step if multiple messages are meant for that SideChannel. channel_id | @property | channel_id() -> uuid.UUID Returns : The type of side channel used. Will influence how the data is processed in the environment.","title":"LLAPI Documentation"},{"location":"Python-LLAPI-Documentation/#table-of-contents","text":"mlagents_envs.base_env DecisionStep DecisionSteps agent_id_to_index __getitem__ empty TerminalStep TerminalSteps agent_id_to_index __getitem__ empty ActionTuple discrete_dtype ActionSpec is_discrete is_continuous discrete_size empty_action random_action create_continuous create_discrete DimensionProperty UNSPECIFIED NONE TRANSLATIONAL_EQUIVARIANCE VARIABLE_SIZE ObservationType DEFAULT GOAL_SIGNAL ObservationSpec BehaviorSpec BaseEnv step reset close behavior_specs set_actions set_action_for_agent get_steps mlagents_envs.environment UnityEnvironment __init__ close mlagents_envs.registry mlagents_envs.registry.unity_env_registry UnityEnvRegistry register register_from_yaml clear __getitem__ mlagents_envs.side_channel mlagents_envs.side_channel.raw_bytes_channel RawBytesChannel on_message_received get_and_clear_received_messages send_raw_data mlagents_envs.side_channel.outgoing_message OutgoingMessage __init__ write_bool write_int32 write_float32 write_float32_list write_string set_raw_bytes mlagents_envs.side_channel.engine_configuration_channel EngineConfigurationChannel on_message_received set_configuration_parameters set_configuration mlagents_envs.side_channel.side_channel_manager SideChannelManager process_side_channel_message generate_side_channel_messages mlagents_envs.side_channel.stats_side_channel StatsSideChannel on_message_received get_and_reset_stats mlagents_envs.side_channel.incoming_message IncomingMessage __init__ read_bool read_int32 read_float32 read_float32_list read_string get_raw_bytes mlagents_envs.side_channel.float_properties_channel FloatPropertiesChannel on_message_received set_property get_property list_properties get_property_dict_copy mlagents_envs.side_channel.environment_parameters_channel EnvironmentParametersChannel set_float_parameter set_uniform_sampler_parameters set_gaussian_sampler_parameters set_multirangeuniform_sampler_parameters mlagents_envs.side_channel.side_channel SideChannel queue_message_to_send on_message_received channel_id","title":"Table of Contents"},{"location":"Python-LLAPI-Documentation/#mlagents_envsbase_env","text":"Python Environment API for the ML-Agents Toolkit The aim of this API is to expose Agents evolving in a simulation to perform reinforcement learning on. This API supports multi-agent scenarios and groups similar Agents (same observations, actions spaces and behavior) together. These groups of Agents are identified by their BehaviorName. For performance reasons, the data of each group of agents is processed in a batched manner. Agents are identified by a unique AgentId identifier that allows tracking of Agents across simulation steps. Note that there is no guarantee that the number or order of the Agents in the state will be consistent across simulation steps. A simulation steps corresponds to moving the simulation forward until at least one agent in the simulation sends its observations to Python again. Since Agents can request decisions at different frequencies, a simulation step does not necessarily correspond to a fixed simulation time increment.","title":"mlagents_envs.base_env"},{"location":"Python-LLAPI-Documentation/#decisionstep-objects","text":"class DecisionStep(NamedTuple) Contains the data a single Agent collected since the last simulation step. - obs is a list of numpy arrays observations collected by the agent. - reward is a float. Corresponds to the rewards collected by the agent since the last simulation step. - agent_id is an int and an unique identifier for the corresponding Agent. - action_mask is an optional list of one dimensional array of booleans. Only available when using multi-discrete actions. Each array corresponds to an action branch. Each array contains a mask for each action of the branch. If true, the action is not available for the agent during this simulation step.","title":"DecisionStep Objects"},{"location":"Python-LLAPI-Documentation/#decisionsteps-objects","text":"class DecisionSteps(Mapping) Contains the data a batch of similar Agents collected since the last simulation step. Note that all Agents do not necessarily have new information to send at each simulation step. Therefore, the ordering of agents and the batch size of the DecisionSteps are not fixed across simulation steps. - obs is a list of numpy arrays observations collected by the batch of agent. Each obs has one extra dimension compared to DecisionStep: the first dimension of the array corresponds to the batch size of the batch. - reward is a float vector of length batch size. Corresponds to the rewards collected by each agent since the last simulation step. - agent_id is an int vector of length batch size containing unique identifier for the corresponding Agent. This is used to track Agents across simulation steps. - action_mask is an optional list of two dimensional array of booleans. Only available when using multi-discrete actions. Each array corresponds to an action branch. The first dimension of each array is the batch size and the second contains a mask for each action of the branch. If true, the action is not available for the agent during this simulation step.","title":"DecisionSteps Objects"},{"location":"Python-LLAPI-Documentation/#agent_id_to_index","text":"| @property | agent_id_to_index() -> Dict[AgentId, int] Returns : A Dict that maps agent_id to the index of those agents in this DecisionSteps.","title":"agent_id_to_index"},{"location":"Python-LLAPI-Documentation/#__getitem__","text":"| __getitem__(agent_id: AgentId) -> DecisionStep returns the DecisionStep for a specific agent. Arguments : agent_id : The id of the agent Returns : The DecisionStep","title":"__getitem__"},{"location":"Python-LLAPI-Documentation/#empty","text":"| @staticmethod | empty(spec: \"BehaviorSpec\") -> \"DecisionSteps\" Returns an empty DecisionSteps. Arguments : spec : The BehaviorSpec for the DecisionSteps","title":"empty"},{"location":"Python-LLAPI-Documentation/#terminalstep-objects","text":"class TerminalStep(NamedTuple) Contains the data a single Agent collected when its episode ended. - obs is a list of numpy arrays observations collected by the agent. - reward is a float. Corresponds to the rewards collected by the agent since the last simulation step. - interrupted is a bool. Is true if the Agent was interrupted since the last decision step. For example, if the Agent reached the maximum number of steps for the episode. - agent_id is an int and an unique identifier for the corresponding Agent.","title":"TerminalStep Objects"},{"location":"Python-LLAPI-Documentation/#terminalsteps-objects","text":"class TerminalSteps(Mapping) Contains the data a batch of Agents collected when their episode terminated. All Agents present in the TerminalSteps have ended their episode. - obs is a list of numpy arrays observations collected by the batch of agent. Each obs has one extra dimension compared to DecisionStep: the first dimension of the array corresponds to the batch size of the batch. - reward is a float vector of length batch size. Corresponds to the rewards collected by each agent since the last simulation step. - interrupted is an array of booleans of length batch size. Is true if the associated Agent was interrupted since the last decision step. For example, if the Agent reached the maximum number of steps for the episode. - agent_id is an int vector of length batch size containing unique identifier for the corresponding Agent. This is used to track Agents across simulation steps.","title":"TerminalSteps Objects"},{"location":"Python-LLAPI-Documentation/#agent_id_to_index_1","text":"| @property | agent_id_to_index() -> Dict[AgentId, int] Returns : A Dict that maps agent_id to the index of those agents in this TerminalSteps.","title":"agent_id_to_index"},{"location":"Python-LLAPI-Documentation/#__getitem___1","text":"| __getitem__(agent_id: AgentId) -> TerminalStep returns the TerminalStep for a specific agent. Arguments : agent_id : The id of the agent Returns : obs, reward, done, agent_id and optional action mask for a specific agent","title":"__getitem__"},{"location":"Python-LLAPI-Documentation/#empty_1","text":"| @staticmethod | empty(spec: \"BehaviorSpec\") -> \"TerminalSteps\" Returns an empty TerminalSteps. Arguments : spec : The BehaviorSpec for the TerminalSteps","title":"empty"},{"location":"Python-LLAPI-Documentation/#actiontuple-objects","text":"class ActionTuple(_ActionTupleBase) An object whose fields correspond to actions of different types. Continuous and discrete actions are numpy arrays of type float32 and int32, respectively and are type checked on construction. Dimensions are of (n_agents, continuous_size) and (n_agents, discrete_size), respectively. Note, this also holds when continuous or discrete size is zero.","title":"ActionTuple Objects"},{"location":"Python-LLAPI-Documentation/#discrete_dtype","text":"| @property | discrete_dtype() -> np.dtype The dtype of a discrete action.","title":"discrete_dtype"},{"location":"Python-LLAPI-Documentation/#actionspec-objects","text":"class ActionSpec(NamedTuple) A NamedTuple containing utility functions and information about the action spaces for a group of Agents under the same behavior. - num_continuous_actions is an int corresponding to the number of floats which constitute the action. - discrete_branch_sizes is a Tuple of int where each int corresponds to the number of discrete actions available to the agent on an independent action branch.","title":"ActionSpec Objects"},{"location":"Python-LLAPI-Documentation/#is_discrete","text":"| is_discrete() -> bool Returns true if this Behavior uses discrete actions","title":"is_discrete"},{"location":"Python-LLAPI-Documentation/#is_continuous","text":"| is_continuous() -> bool Returns true if this Behavior uses continuous actions","title":"is_continuous"},{"location":"Python-LLAPI-Documentation/#discrete_size","text":"| @property | discrete_size() -> int Returns a an int corresponding to the number of discrete branches.","title":"discrete_size"},{"location":"Python-LLAPI-Documentation/#empty_action","text":"| empty_action(n_agents: int) -> ActionTuple Generates ActionTuple corresponding to an empty action (all zeros) for a number of agents. Arguments : n_agents : The number of agents that will have actions generated","title":"empty_action"},{"location":"Python-LLAPI-Documentation/#random_action","text":"| random_action(n_agents: int) -> ActionTuple Generates ActionTuple corresponding to a random action (either discrete or continuous) for a number of agents. Arguments : n_agents : The number of agents that will have actions generated","title":"random_action"},{"location":"Python-LLAPI-Documentation/#create_continuous","text":"| @staticmethod | create_continuous(continuous_size: int) -> \"ActionSpec\" Creates an ActionSpec that is homogenously continuous","title":"create_continuous"},{"location":"Python-LLAPI-Documentation/#create_discrete","text":"| @staticmethod | create_discrete(discrete_branches: Tuple[int]) -> \"ActionSpec\" Creates an ActionSpec that is homogenously discrete","title":"create_discrete"},{"location":"Python-LLAPI-Documentation/#dimensionproperty-objects","text":"class DimensionProperty(IntFlag) The dimension property of a dimension of an observation.","title":"DimensionProperty Objects"},{"location":"Python-LLAPI-Documentation/#unspecified","text":"No properties specified.","title":"UNSPECIFIED"},{"location":"Python-LLAPI-Documentation/#none","text":"No Property of the observation in that dimension. Observation can be processed with Fully connected networks.","title":"NONE"},{"location":"Python-LLAPI-Documentation/#translational_equivariance","text":"Means it is suitable to do a convolution in this dimension.","title":"TRANSLATIONAL_EQUIVARIANCE"},{"location":"Python-LLAPI-Documentation/#variable_size","text":"Means that there can be a variable number of observations in this dimension. The observations are unordered.","title":"VARIABLE_SIZE"},{"location":"Python-LLAPI-Documentation/#observationtype-objects","text":"class ObservationType(Enum) An Enum which defines the type of information carried in the observation of the agent.","title":"ObservationType Objects"},{"location":"Python-LLAPI-Documentation/#default","text":"Observation information is generic.","title":"DEFAULT"},{"location":"Python-LLAPI-Documentation/#goal_signal","text":"Observation contains goal information for current task.","title":"GOAL_SIGNAL"},{"location":"Python-LLAPI-Documentation/#observationspec-objects","text":"class ObservationSpec(NamedTuple) A NamedTuple containing information about the observation of Agents. - shape is a Tuple of int : It corresponds to the shape of an observation's dimensions. - dimension_property is a Tuple of DimensionProperties flag, one flag for each dimension. - observation_type is an enum of ObservationType.","title":"ObservationSpec Objects"},{"location":"Python-LLAPI-Documentation/#behaviorspec-objects","text":"class BehaviorSpec(NamedTuple) A NamedTuple containing information about the observation and action spaces for a group of Agents under the same behavior. - observation_specs is a List of ObservationSpec NamedTuple containing information about the information of the Agent's observations such as their shapes. The order of the ObservationSpec is the same as the order of the observations of an agent. - action_spec is an ActionSpec NamedTuple.","title":"BehaviorSpec Objects"},{"location":"Python-LLAPI-Documentation/#baseenv-objects","text":"class BaseEnv(ABC)","title":"BaseEnv Objects"},{"location":"Python-LLAPI-Documentation/#step","text":"| @abstractmethod | step() -> None Signals the environment that it must move the simulation forward by one step.","title":"step"},{"location":"Python-LLAPI-Documentation/#reset","text":"| @abstractmethod | reset() -> None Signals the environment that it must reset the simulation.","title":"reset"},{"location":"Python-LLAPI-Documentation/#close","text":"| @abstractmethod | close() -> None Signals the environment that it must close.","title":"close"},{"location":"Python-LLAPI-Documentation/#behavior_specs","text":"| @property | @abstractmethod | behavior_specs() -> MappingType[str, BehaviorSpec] Returns a Mapping from behavior names to behavior specs. Agents grouped under the same behavior name have the same action and observation specs, and are expected to behave similarly in the environment. Note that new keys can be added to this mapping as new policies are instantiated.","title":"behavior_specs"},{"location":"Python-LLAPI-Documentation/#set_actions","text":"| @abstractmethod | set_actions(behavior_name: BehaviorName, action: ActionTuple) -> None Sets the action for all of the agents in the simulation for the next step. The Actions must be in the same order as the order received in the DecisionSteps. Arguments : behavior_name : The name of the behavior the agents are part of action : ActionTuple tuple of continuous and/or discrete action. Actions are np.arrays with dimensions (n_agents, continuous_size) and (n_agents, discrete_size), respectively.","title":"set_actions"},{"location":"Python-LLAPI-Documentation/#set_action_for_agent","text":"| @abstractmethod | set_action_for_agent(behavior_name: BehaviorName, agent_id: AgentId, action: ActionTuple) -> None Sets the action for one of the agents in the simulation for the next step. Arguments : behavior_name : The name of the behavior the agent is part of agent_id : The id of the agent the action is set for action : ActionTuple tuple of continuous and/or discrete action Actions are np.arrays with dimensions (1, continuous_size) and (1, discrete_size), respectively. Note, this initial dimensions of 1 is because this action is meant for a single agent.","title":"set_action_for_agent"},{"location":"Python-LLAPI-Documentation/#get_steps","text":"| @abstractmethod | get_steps(behavior_name: BehaviorName) -> Tuple[DecisionSteps, TerminalSteps] Retrieves the steps of the agents that requested a step in the simulation. Arguments : behavior_name : The name of the behavior the agents are part of Returns : A tuple containing : - A DecisionSteps NamedTuple containing the observations, the rewards, the agent ids and the action masks for the Agents of the specified behavior. These Agents need an action this step. - A TerminalSteps NamedTuple containing the observations, rewards, agent ids and interrupted flags of the agents that had their episode terminated last step.","title":"get_steps"},{"location":"Python-LLAPI-Documentation/#mlagents_envsenvironment","text":"","title":"mlagents_envs.environment"},{"location":"Python-LLAPI-Documentation/#unityenvironment-objects","text":"class UnityEnvironment(BaseEnv)","title":"UnityEnvironment Objects"},{"location":"Python-LLAPI-Documentation/#__init__","text":"| __init__(file_name: Optional[str] = None, worker_id: int = 0, base_port: Optional[int] = None, seed: int = 0, no_graphics: bool = False, timeout_wait: int = 60, additional_args: Optional[List[str]] = None, side_channels: Optional[List[SideChannel]] = None, log_folder: Optional[str] = None, num_areas: int = 1) Starts a new unity environment and establishes a connection with the environment. Notice: Currently communication between Unity and Python takes place over an open socket without authentication. Ensure that the network where training takes place is secure. :string file_name: Name of Unity environment binary. :int base_port: Baseline port number to connect to Unity environment over. worker_id increments over this. If no environment is specified (i.e. file_name is None), the DEFAULT_EDITOR_PORT will be used. :int worker_id: Offset from base_port. Used for training multiple environments simultaneously. :bool no_graphics: Whether to run the Unity simulator in no-graphics mode :int timeout_wait: Time (in seconds) to wait for connection from environment. :list args: Addition Unity command line arguments :list side_channels: Additional side channel for no-rl communication with Unity :str log_folder: Optional folder to write the Unity Player log file into. Requires absolute path.","title":"__init__"},{"location":"Python-LLAPI-Documentation/#close_1","text":"| close() Sends a shutdown signal to the unity environment, and closes the socket connection.","title":"close"},{"location":"Python-LLAPI-Documentation/#mlagents_envsregistry","text":"","title":"mlagents_envs.registry"},{"location":"Python-LLAPI-Documentation/#mlagents_envsregistryunity_env_registry","text":"","title":"mlagents_envs.registry.unity_env_registry"},{"location":"Python-LLAPI-Documentation/#unityenvregistry-objects","text":"class UnityEnvRegistry(Mapping)","title":"UnityEnvRegistry Objects"},{"location":"Python-LLAPI-Documentation/#unityenvregistry","text":"Provides a library of Unity environments that can be launched without the need of downloading the Unity Editor. The UnityEnvRegistry implements a Map, to access an entry of the Registry, use: registry = UnityEnvRegistry() entry = registry[<environment_identifyier>] An entry has the following properties : * identifier : Uniquely identifies this environment * expected_reward : Corresponds to the reward an agent must obtained for the task to be considered completed. * description : A human readable description of the environment. To launch a Unity environment from a registry entry, use the make method: registry = UnityEnvRegistry() env = registry[<environment_identifyier>].make()","title":"UnityEnvRegistry"},{"location":"Python-LLAPI-Documentation/#register","text":"| register(new_entry: BaseRegistryEntry) -> None Registers a new BaseRegistryEntry to the registry. The BaseRegistryEntry.identifier value will be used as indexing key. If two are more environments are registered under the same key, the most recentry added will replace the others.","title":"register"},{"location":"Python-LLAPI-Documentation/#register_from_yaml","text":"| register_from_yaml(path_to_yaml: str) -> None Registers the environments listed in a yaml file (either local or remote). Note that the entries are registered lazily: the registration will only happen when an environment is accessed. The yaml file must have the following format : environments: - <identifier of the first environment>: expected_reward: <expected reward of the environment> description: | <a multi line description of the environment> <continued multi line description> linux_url: <The url for the Linux executable zip file> darwin_url: <The url for the OSX executable zip file> win_url: <The url for the Windows executable zip file> - <identifier of the second environment>: expected_reward: <expected reward of the environment> description: | <a multi line description of the environment> <continued multi line description> linux_url: <The url for the Linux executable zip file> darwin_url: <The url for the OSX executable zip file> win_url: <The url for the Windows executable zip file> - ... Arguments : path_to_yaml : A local path or url to the yaml file","title":"register_from_yaml"},{"location":"Python-LLAPI-Documentation/#clear","text":"| clear() -> None Deletes all entries in the registry.","title":"clear"},{"location":"Python-LLAPI-Documentation/#__getitem___2","text":"| __getitem__(identifier: str) -> BaseRegistryEntry Returns the BaseRegistryEntry with the provided identifier. BaseRegistryEntry can then be used to make a Unity Environment. Arguments : identifier : The identifier of the BaseRegistryEntry Returns : The associated BaseRegistryEntry","title":"__getitem__"},{"location":"Python-LLAPI-Documentation/#mlagents_envsside_channel","text":"","title":"mlagents_envs.side_channel"},{"location":"Python-LLAPI-Documentation/#mlagents_envsside_channelraw_bytes_channel","text":"","title":"mlagents_envs.side_channel.raw_bytes_channel"},{"location":"Python-LLAPI-Documentation/#rawbyteschannel-objects","text":"class RawBytesChannel(SideChannel) This is an example of what the SideChannel for raw bytes exchange would look like. Is meant to be used for general research purpose.","title":"RawBytesChannel Objects"},{"location":"Python-LLAPI-Documentation/#on_message_received","text":"| on_message_received(msg: IncomingMessage) -> None Is called by the environment to the side channel. Can be called multiple times per step if multiple messages are meant for that SideChannel.","title":"on_message_received"},{"location":"Python-LLAPI-Documentation/#get_and_clear_received_messages","text":"| get_and_clear_received_messages() -> List[bytes] returns a list of bytearray received from the environment.","title":"get_and_clear_received_messages"},{"location":"Python-LLAPI-Documentation/#send_raw_data","text":"| send_raw_data(data: bytearray) -> None Queues a message to be sent by the environment at the next call to step.","title":"send_raw_data"},{"location":"Python-LLAPI-Documentation/#mlagents_envsside_channeloutgoing_message","text":"","title":"mlagents_envs.side_channel.outgoing_message"},{"location":"Python-LLAPI-Documentation/#outgoingmessage-objects","text":"class OutgoingMessage() Utility class for forming the message that is written to a SideChannel. All data is written in little-endian format using the struct module.","title":"OutgoingMessage Objects"},{"location":"Python-LLAPI-Documentation/#__init___1","text":"| __init__() Create an OutgoingMessage with an empty buffer.","title":"__init__"},{"location":"Python-LLAPI-Documentation/#write_bool","text":"| write_bool(b: bool) -> None Append a boolean value.","title":"write_bool"},{"location":"Python-LLAPI-Documentation/#write_int32","text":"| write_int32(i: int) -> None Append an integer value.","title":"write_int32"},{"location":"Python-LLAPI-Documentation/#write_float32","text":"| write_float32(f: float) -> None Append a float value. It will be truncated to 32-bit precision.","title":"write_float32"},{"location":"Python-LLAPI-Documentation/#write_float32_list","text":"| write_float32_list(float_list: List[float]) -> None Append a list of float values. They will be truncated to 32-bit precision.","title":"write_float32_list"},{"location":"Python-LLAPI-Documentation/#write_string","text":"| write_string(s: str) -> None Append a string value. Internally, it will be encoded to ascii, and the encoded length will also be written to the message.","title":"write_string"},{"location":"Python-LLAPI-Documentation/#set_raw_bytes","text":"| set_raw_bytes(buffer: bytearray) -> None Set the internal buffer to a new bytearray. This will overwrite any existing data. Arguments : buffer : Returns :","title":"set_raw_bytes"},{"location":"Python-LLAPI-Documentation/#mlagents_envsside_channelengine_configuration_channel","text":"","title":"mlagents_envs.side_channel.engine_configuration_channel"},{"location":"Python-LLAPI-Documentation/#engineconfigurationchannel-objects","text":"class EngineConfigurationChannel(SideChannel) This is the SideChannel for engine configuration exchange. The data in the engine configuration is as follows : - int width; - int height; - int qualityLevel; - float timeScale; - int targetFrameRate; - int captureFrameRate;","title":"EngineConfigurationChannel Objects"},{"location":"Python-LLAPI-Documentation/#on_message_received_1","text":"| on_message_received(msg: IncomingMessage) -> None Is called by the environment to the side channel. Can be called multiple times per step if multiple messages are meant for that SideChannel. Note that Python should never receive an engine configuration from Unity","title":"on_message_received"},{"location":"Python-LLAPI-Documentation/#set_configuration_parameters","text":"| set_configuration_parameters(width: Optional[int] = None, height: Optional[int] = None, quality_level: Optional[int] = None, time_scale: Optional[float] = None, target_frame_rate: Optional[int] = None, capture_frame_rate: Optional[int] = None) -> None Sets the engine configuration. Takes as input the configurations of the engine. Arguments : width : Defines the width of the display. (Must be set alongside height) height : Defines the height of the display. (Must be set alongside width) quality_level : Defines the quality level of the simulation. time_scale : Defines the multiplier for the deltatime in the simulation. If set to a higher value, time will pass faster in the simulation but the physics might break. target_frame_rate : Instructs simulation to try to render at a specified frame rate. capture_frame_rate : Instructs the simulation to consider time between updates to always be constant, regardless of the actual frame rate.","title":"set_configuration_parameters"},{"location":"Python-LLAPI-Documentation/#set_configuration","text":"| set_configuration(config: EngineConfig) -> None Sets the engine configuration. Takes as input an EngineConfig.","title":"set_configuration"},{"location":"Python-LLAPI-Documentation/#mlagents_envsside_channelside_channel_manager","text":"","title":"mlagents_envs.side_channel.side_channel_manager"},{"location":"Python-LLAPI-Documentation/#sidechannelmanager-objects","text":"class SideChannelManager()","title":"SideChannelManager Objects"},{"location":"Python-LLAPI-Documentation/#process_side_channel_message","text":"| process_side_channel_message(data: bytes) -> None Separates the data received from Python into individual messages for each registered side channel and calls on_message_received on them. Arguments : data : The packed message sent by Unity","title":"process_side_channel_message"},{"location":"Python-LLAPI-Documentation/#generate_side_channel_messages","text":"| generate_side_channel_messages() -> bytearray Gathers the messages that the registered side channels will send to Unity and combines them into a single message ready to be sent.","title":"generate_side_channel_messages"},{"location":"Python-LLAPI-Documentation/#mlagents_envsside_channelstats_side_channel","text":"","title":"mlagents_envs.side_channel.stats_side_channel"},{"location":"Python-LLAPI-Documentation/#statssidechannel-objects","text":"class StatsSideChannel(SideChannel) Side channel that receives (string, float) pairs from the environment, so that they can eventually be passed to a StatsReporter.","title":"StatsSideChannel Objects"},{"location":"Python-LLAPI-Documentation/#on_message_received_2","text":"| on_message_received(msg: IncomingMessage) -> None Receive the message from the environment, and save it for later retrieval. Arguments : msg : Returns :","title":"on_message_received"},{"location":"Python-LLAPI-Documentation/#get_and_reset_stats","text":"| get_and_reset_stats() -> EnvironmentStats Returns the current stats, and resets the internal storage of the stats. Returns :","title":"get_and_reset_stats"},{"location":"Python-LLAPI-Documentation/#mlagents_envsside_channelincoming_message","text":"","title":"mlagents_envs.side_channel.incoming_message"},{"location":"Python-LLAPI-Documentation/#incomingmessage-objects","text":"class IncomingMessage() Utility class for reading the message written to a SideChannel. Values must be read in the order they were written.","title":"IncomingMessage Objects"},{"location":"Python-LLAPI-Documentation/#__init___2","text":"| __init__(buffer: bytes, offset: int = 0) Create a new IncomingMessage from the bytes.","title":"__init__"},{"location":"Python-LLAPI-Documentation/#read_bool","text":"| read_bool(default_value: bool = False) -> bool Read a boolean value from the message buffer. Arguments : default_value : Default value to use if the end of the message is reached. Returns : The value read from the message, or the default value if the end was reached.","title":"read_bool"},{"location":"Python-LLAPI-Documentation/#read_int32","text":"| read_int32(default_value: int = 0) -> int Read an integer value from the message buffer. Arguments : default_value : Default value to use if the end of the message is reached. Returns : The value read from the message, or the default value if the end was reached.","title":"read_int32"},{"location":"Python-LLAPI-Documentation/#read_float32","text":"| read_float32(default_value: float = 0.0) -> float Read a float value from the message buffer. Arguments : default_value : Default value to use if the end of the message is reached. Returns : The value read from the message, or the default value if the end was reached.","title":"read_float32"},{"location":"Python-LLAPI-Documentation/#read_float32_list","text":"| read_float32_list(default_value: List[float] = None) -> List[float] Read a list of float values from the message buffer. Arguments : default_value : Default value to use if the end of the message is reached. Returns : The value read from the message, or the default value if the end was reached.","title":"read_float32_list"},{"location":"Python-LLAPI-Documentation/#read_string","text":"| read_string(default_value: str = \"\") -> str Read a string value from the message buffer. Arguments : default_value : Default value to use if the end of the message is reached. Returns : The value read from the message, or the default value if the end was reached.","title":"read_string"},{"location":"Python-LLAPI-Documentation/#get_raw_bytes","text":"| get_raw_bytes() -> bytes Get a copy of the internal bytes used by the message.","title":"get_raw_bytes"},{"location":"Python-LLAPI-Documentation/#mlagents_envsside_channelfloat_properties_channel","text":"","title":"mlagents_envs.side_channel.float_properties_channel"},{"location":"Python-LLAPI-Documentation/#floatpropertieschannel-objects","text":"class FloatPropertiesChannel(SideChannel) This is the SideChannel for float properties shared with Unity. You can modify the float properties of an environment with the commands set_property, get_property and list_properties.","title":"FloatPropertiesChannel Objects"},{"location":"Python-LLAPI-Documentation/#on_message_received_3","text":"| on_message_received(msg: IncomingMessage) -> None Is called by the environment to the side channel. Can be called multiple times per step if multiple messages are meant for that SideChannel.","title":"on_message_received"},{"location":"Python-LLAPI-Documentation/#set_property","text":"| set_property(key: str, value: float) -> None Sets a property in the Unity Environment. Arguments : key : The string identifier of the property. value : The float value of the property.","title":"set_property"},{"location":"Python-LLAPI-Documentation/#get_property","text":"| get_property(key: str) -> Optional[float] Gets a property in the Unity Environment. If the property was not found, will return None. Arguments : key : The string identifier of the property. Returns : The float value of the property or None.","title":"get_property"},{"location":"Python-LLAPI-Documentation/#list_properties","text":"| list_properties() -> List[str] Returns a list of all the string identifiers of the properties currently present in the Unity Environment.","title":"list_properties"},{"location":"Python-LLAPI-Documentation/#get_property_dict_copy","text":"| get_property_dict_copy() -> Dict[str, float] Returns a copy of the float properties. Returns :","title":"get_property_dict_copy"},{"location":"Python-LLAPI-Documentation/#mlagents_envsside_channelenvironment_parameters_channel","text":"","title":"mlagents_envs.side_channel.environment_parameters_channel"},{"location":"Python-LLAPI-Documentation/#environmentparameterschannel-objects","text":"class EnvironmentParametersChannel(SideChannel) This is the SideChannel for sending environment parameters to Unity. You can send parameters to an environment with the command set_float_parameter.","title":"EnvironmentParametersChannel Objects"},{"location":"Python-LLAPI-Documentation/#set_float_parameter","text":"| set_float_parameter(key: str, value: float) -> None Sets a float environment parameter in the Unity Environment. Arguments : key : The string identifier of the parameter. value : The float value of the parameter.","title":"set_float_parameter"},{"location":"Python-LLAPI-Documentation/#set_uniform_sampler_parameters","text":"| set_uniform_sampler_parameters(key: str, min_value: float, max_value: float, seed: int) -> None Sets a uniform environment parameter sampler. Arguments : key : The string identifier of the parameter. min_value : The minimum of the sampling distribution. max_value : The maximum of the sampling distribution. seed : The random seed to initialize the sampler.","title":"set_uniform_sampler_parameters"},{"location":"Python-LLAPI-Documentation/#set_gaussian_sampler_parameters","text":"| set_gaussian_sampler_parameters(key: str, mean: float, st_dev: float, seed: int) -> None Sets a gaussian environment parameter sampler. Arguments : key : The string identifier of the parameter. mean : The mean of the sampling distribution. st_dev : The standard deviation of the sampling distribution. seed : The random seed to initialize the sampler.","title":"set_gaussian_sampler_parameters"},{"location":"Python-LLAPI-Documentation/#set_multirangeuniform_sampler_parameters","text":"| set_multirangeuniform_sampler_parameters(key: str, intervals: List[Tuple[float, float]], seed: int) -> None Sets a multirangeuniform environment parameter sampler. Arguments : key : The string identifier of the parameter. intervals : The lists of min and max that define each uniform distribution. seed : The random seed to initialize the sampler.","title":"set_multirangeuniform_sampler_parameters"},{"location":"Python-LLAPI-Documentation/#mlagents_envsside_channelside_channel","text":"","title":"mlagents_envs.side_channel.side_channel"},{"location":"Python-LLAPI-Documentation/#sidechannel-objects","text":"class SideChannel(ABC) The side channel just get access to a bytes buffer that will be shared between C# and Python. For example, We will create a specific side channel for properties that will be a list of string (fixed size) to float number, that can be modified by both C# and Python. All side channels are passed to the Env object at construction.","title":"SideChannel Objects"},{"location":"Python-LLAPI-Documentation/#queue_message_to_send","text":"| queue_message_to_send(msg: OutgoingMessage) -> None Queues a message to be sent by the environment at the next call to step.","title":"queue_message_to_send"},{"location":"Python-LLAPI-Documentation/#on_message_received_4","text":"| @abstractmethod | on_message_received(msg: IncomingMessage) -> None Is called by the environment to the side channel. Can be called multiple times per step if multiple messages are meant for that SideChannel.","title":"on_message_received"},{"location":"Python-LLAPI-Documentation/#channel_id","text":"| @property | channel_id() -> uuid.UUID Returns : The type of side channel used. Will influence how the data is processed in the environment.","title":"channel_id"},{"location":"Python-LLAPI/","text":"Unity ML-Agents Python Low Level API The mlagents Python package contains two components: a low level API which allows you to interact directly with a Unity Environment ( mlagents_envs ) and an entry point to train ( mlagents-learn ) which allows you to train agents in Unity Environments using our implementations of reinforcement learning or imitation learning. This document describes how to use the mlagents_envs API. For information on using mlagents-learn , see here . For Python Low Level API documentation, see here . The Python Low Level API can be used to interact directly with your Unity learning environment. As such, it can serve as the basis for developing and evaluating new learning algorithms. mlagents_envs The ML-Agents Toolkit Low Level API is a Python API for controlling the simulation loop of an environment or game built with Unity. This API is used by the training algorithms inside the ML-Agent Toolkit, but you can also write your own Python programs using this API. The key objects in the Python API include: UnityEnvironment \u2014 the main interface between the Unity application and your code. Use UnityEnvironment to start and control a simulation or training session. BehaviorName - is a string that identifies a behavior in the simulation. AgentId - is an int that serves as unique identifier for Agents in the simulation. DecisionSteps \u2014 contains the data from Agents belonging to the same \"Behavior\" in the simulation, such as observations and rewards. Only Agents that requested a decision since the last call to env.step() are in the DecisionSteps object. TerminalSteps \u2014 contains the data from Agents belonging to the same \"Behavior\" in the simulation, such as observations and rewards. Only Agents whose episode ended since the last call to env.step() are in the TerminalSteps object. BehaviorSpec \u2014 describes the shape of the observation data inside DecisionSteps and TerminalSteps as well as the expected action shapes. These classes are all defined in the base_env script. An Agent \"Behavior\" is a group of Agents identified by a BehaviorName that share the same observations and action types (described in their BehaviorSpec ). You can think about Agent Behavior as a group of agents that will share the same policy. All Agents with the same behavior have the same goal and reward signals. To communicate with an Agent in a Unity environment from a Python program, the Agent in the simulation must have Behavior Parameters set to communicate. You must set the Behavior Type to Default and give it a Behavior Name . Notice: Currently communication between Unity and Python takes place over an open socket without authentication. As such, please make sure that the network where training takes place is secure. This will be addressed in a future release. Loading a Unity Environment Python-side communication happens through UnityEnvironment which is located in environment.py . To load a Unity environment from a built binary file, put the file in the same directory as envs . For example, if the filename of your Unity environment is 3DBall , in python, run: from mlagents_envs.environment import UnityEnvironment # This is a non-blocking call that only loads the environment. env = UnityEnvironment(file_name=\"3DBall\", seed=1, side_channels=[]) # Start interacting with the environment. env.reset() behavior_names = env.behavior_specs.keys() ... NOTE: Please read Interacting with a Unity Environment to read more about how you can interact with the Unity environment from Python. file_name is the name of the environment binary (located in the root directory of the python project). worker_id indicates which port to use for communication with the environment. For use in parallel training regimes such as A3C. seed indicates the seed to use when generating random numbers during the training process. In environments which are stochastic, setting the seed enables reproducible experimentation by ensuring that the environment and trainers utilize the same random seed. side_channels provides a way to exchange data with the Unity simulation that is not related to the reinforcement learning loop. For example: configurations or properties. More on them in the Side Channels doc. If you want to directly interact with the Editor, you need to use file_name=None , then press the Play button in the Editor when the message \"Start training by pressing the Play button in the Unity Editor\" is displayed on the screen Interacting with a Unity Environment The BaseEnv interface A BaseEnv has the following methods: Reset : env.reset() Sends a signal to reset the environment. Returns None. Step : env.step() Sends a signal to step the environment. Returns None. Note that a \"step\" for Python does not correspond to either Unity Update nor FixedUpdate . When step() or reset() is called, the Unity simulation will move forward until an Agent in the simulation needs a input from Python to act. Close : env.close() Sends a shutdown signal to the environment and terminates the communication. Behavior Specs : env.behavior_specs Returns a Mapping of BehaviorName to BehaviorSpec objects (read only). A BehaviorSpec contains the observation shapes and the ActionSpec (which defines the action shape). Note that the BehaviorSpec for a specific group is fixed throughout the simulation. The number of entries in the Mapping can change over time in the simulation if new Agent behaviors are created in the simulation. Get Steps : env.get_steps(behavior_name: str) Returns a tuple DecisionSteps, TerminalSteps corresponding to the behavior_name given as input. The DecisionSteps contains information about the state of the agents that need an action this step and have the behavior behavior_name. The TerminalSteps contains information about the state of the agents whose episode ended and have the behavior behavior_name. Both DecisionSteps and TerminalSteps contain information such as the observations, the rewards and the agent identifiers. DecisionSteps also contains action masks for the next action while TerminalSteps contains the reason for termination (did the Agent reach its maximum step and was interrupted). The data is in np.array of which the first dimension is always the number of agents note that the number of agents is not guaranteed to remain constant during the simulation and it is not unusual to have either DecisionSteps or TerminalSteps contain no Agents at all. Set Actions : env.set_actions(behavior_name: str, action: ActionTuple) Sets the actions for a whole agent group. action is an ActionTuple , which is made up of a 2D np.array of dtype=np.int32 for discrete actions, and dtype=np.float32 for continuous actions. The first dimension of np.array in the tuple is the number of agents that requested a decision since the last call to env.step() . The second dimension is the number of discrete or continuous actions for the corresponding array. Set Action for Agent : env.set_action_for_agent(agent_group: str, agent_id: int, action: ActionTuple) Sets the action for a specific Agent in an agent group. agent_group is the name of the group the Agent belongs to and agent_id is the integer identifier of the Agent. action is an ActionTuple as described above. Note: If no action is provided for an agent group between two calls to env.step() then the default action will be all zeros. DecisionSteps and DecisionStep DecisionSteps (with s ) contains information about a whole batch of Agents while DecisionStep (no s ) only contains information about a single Agent. A DecisionSteps has the following fields : obs is a list of numpy arrays observations collected by the group of agent. The first dimension of the array corresponds to the batch size of the group (number of agents requesting a decision since the last call to env.step() ). reward is a float vector of length batch size. Corresponds to the rewards collected by each agent since the last simulation step. agent_id is an int vector of length batch size containing unique identifier for the corresponding Agent. This is used to track Agents across simulation steps. action_mask is an optional list of two dimensional arrays of booleans which is only available when using multi-discrete actions. Each array corresponds to an action branch. The first dimension of each array is the batch size and the second contains a mask for each action of the branch. If true, the action is not available for the agent during this simulation step. It also has the two following methods: len(DecisionSteps) Returns the number of agents requesting a decision since the last call to env.step() . DecisionSteps[agent_id] Returns a DecisionStep for the Agent with the agent_id unique identifier. A DecisionStep has the following fields: obs is a list of numpy arrays observations collected by the agent. (Each array has one less dimension than the arrays in DecisionSteps ) reward is a float. Corresponds to the rewards collected by the agent since the last simulation step. agent_id is an int and an unique identifier for the corresponding Agent. action_mask is an optional list of one dimensional arrays of booleans which is only available when using multi-discrete actions. Each array corresponds to an action branch. Each array contains a mask for each action of the branch. If true, the action is not available for the agent during this simulation step. TerminalSteps and TerminalStep Similarly to DecisionSteps and DecisionStep , TerminalSteps (with s ) contains information about a whole batch of Agents while TerminalStep (no s ) only contains information about a single Agent. A TerminalSteps has the following fields : obs is a list of numpy arrays observations collected by the group of agent. The first dimension of the array corresponds to the batch size of the group (number of agents requesting a decision since the last call to env.step() ). reward is a float vector of length batch size. Corresponds to the rewards collected by each agent since the last simulation step. agent_id is an int vector of length batch size containing unique identifier for the corresponding Agent. This is used to track Agents across simulation steps. interrupted is an array of booleans of length batch size. Is true if the associated Agent was interrupted since the last decision step. For example, if the Agent reached the maximum number of steps for the episode. It also has the two following methods: len(TerminalSteps) Returns the number of agents requesting a decision since the last call to env.step() . TerminalSteps[agent_id] Returns a TerminalStep for the Agent with the agent_id unique identifier. A TerminalStep has the following fields: obs is a list of numpy arrays observations collected by the agent. (Each array has one less dimension than the arrays in TerminalSteps ) reward is a float. Corresponds to the rewards collected by the agent since the last simulation step. agent_id is an int and an unique identifier for the corresponding Agent. interrupted is a bool. Is true if the Agent was interrupted since the last decision step. For example, if the Agent reached the maximum number of steps for the episode. BehaviorSpec A BehaviorSpec has the following fields : observation_specs is a List of ObservationSpec objects : Each ObservationSpec corresponds to an observation's properties: shape is a tuple of ints that corresponds to the shape of the observation (without the number of agents dimension). dimension_property is a tuple of flags containing extra information about how the data should be processed in the corresponding dimension. observation_type is an enum corresponding to what type of observation is generating the data (i.e., default, goal, etc). Note that the ObservationSpec have the same ordering as the ordering of observations in the DecisionSteps, DecisionStep, TerminalSteps and TerminalStep. action_spec is an ActionSpec namedtuple that defines the number and types of actions for the Agent. An ActionSpec has the following fields and properties: - continuous_size is the number of floats that constitute the continuous actions. - discrete_size is the number of branches (the number of independent actions) that constitute the multi-discrete actions. - discrete_branches is a Tuple of ints. Each int corresponds to the number of different options for each branch of the action. For example: In a game direction input (no movement, left, right) and jump input (no jump, jump) there will be two branches (direction and jump), the first one with 3 options and the second with 2 options. ( discrete_size = 2 and discrete_action_branches = (3,2,) ) Communicating additional information with the Environment In addition to the means of communicating between Unity and python described above, we also provide methods for sharing agent-agnostic information. These additional methods are referred to as side channels. ML-Agents includes two ready-made side channels, described below. It is also possible to create custom side channels to communicate any additional data between a Unity environment and Python. Instructions for creating custom side channels can be found here . Side channels exist as separate classes which are instantiated, and then passed as list to the side_channels argument of the constructor of the UnityEnvironment class. channel = MyChannel() env = UnityEnvironment(side_channels = [channel]) Note : A side channel will only send/receive messages when env.step or env.reset() is called. EngineConfigurationChannel The EngineConfiguration side channel allows you to modify the time-scale, resolution, and graphics quality of the environment. This can be useful for adjusting the environment to perform better during training, or be more interpretable during inference. EngineConfigurationChannel has two methods : set_configuration_parameters which takes the following arguments: width : Defines the width of the display. (Must be set alongside height) height : Defines the height of the display. (Must be set alongside width) quality_level : Defines the quality level of the simulation. time_scale : Defines the multiplier for the deltatime in the simulation. If set to a higher value, time will pass faster in the simulation but the physics may perform unpredictably. target_frame_rate : Instructs simulation to try to render at a specified frame rate. capture_frame_rate Instructs the simulation to consider time between updates to always be constant, regardless of the actual frame rate. set_configuration with argument config which is an EngineConfig NamedTuple object. For example, the following code would adjust the time-scale of the simulation to be 2x realtime. from mlagents_envs.environment import UnityEnvironment from mlagents_envs.side_channel.engine_configuration_channel import EngineConfigurationChannel channel = EngineConfigurationChannel() env = UnityEnvironment(side_channels=[channel]) channel.set_configuration_parameters(time_scale = 2.0) i = env.reset() ... EnvironmentParameters The EnvironmentParameters will allow you to get and set pre-defined numerical values in the environment. This can be useful for adjusting environment-specific settings, or for reading non-agent related information from the environment. You can call get_property and set_property on the side channel to read and write properties. EnvironmentParametersChannel has one methods: set_float_parameter Sets a float parameter in the Unity Environment. key: The string identifier of the property. value: The float value of the property. from mlagents_envs.environment import UnityEnvironment from mlagents_envs.side_channel.environment_parameters_channel import EnvironmentParametersChannel channel = EnvironmentParametersChannel() env = UnityEnvironment(side_channels=[channel]) channel.set_float_parameter(\"parameter_1\", 2.0) i = env.reset() ... Once a property has been modified in Python, you can access it in C# after the next call to step as follows: var envParameters = Academy.Instance.EnvironmentParameters; float property1 = envParameters.GetWithDefault(\"parameter_1\", 0.0f); Custom side channels For information on how to make custom side channels for sending additional data types, see the documentation here .","title":"Getting started with the LLAPI"},{"location":"Python-LLAPI/#unity-ml-agents-python-low-level-api","text":"The mlagents Python package contains two components: a low level API which allows you to interact directly with a Unity Environment ( mlagents_envs ) and an entry point to train ( mlagents-learn ) which allows you to train agents in Unity Environments using our implementations of reinforcement learning or imitation learning. This document describes how to use the mlagents_envs API. For information on using mlagents-learn , see here . For Python Low Level API documentation, see here . The Python Low Level API can be used to interact directly with your Unity learning environment. As such, it can serve as the basis for developing and evaluating new learning algorithms.","title":"Unity ML-Agents Python Low Level API"},{"location":"Python-LLAPI/#mlagents_envs","text":"The ML-Agents Toolkit Low Level API is a Python API for controlling the simulation loop of an environment or game built with Unity. This API is used by the training algorithms inside the ML-Agent Toolkit, but you can also write your own Python programs using this API. The key objects in the Python API include: UnityEnvironment \u2014 the main interface between the Unity application and your code. Use UnityEnvironment to start and control a simulation or training session. BehaviorName - is a string that identifies a behavior in the simulation. AgentId - is an int that serves as unique identifier for Agents in the simulation. DecisionSteps \u2014 contains the data from Agents belonging to the same \"Behavior\" in the simulation, such as observations and rewards. Only Agents that requested a decision since the last call to env.step() are in the DecisionSteps object. TerminalSteps \u2014 contains the data from Agents belonging to the same \"Behavior\" in the simulation, such as observations and rewards. Only Agents whose episode ended since the last call to env.step() are in the TerminalSteps object. BehaviorSpec \u2014 describes the shape of the observation data inside DecisionSteps and TerminalSteps as well as the expected action shapes. These classes are all defined in the base_env script. An Agent \"Behavior\" is a group of Agents identified by a BehaviorName that share the same observations and action types (described in their BehaviorSpec ). You can think about Agent Behavior as a group of agents that will share the same policy. All Agents with the same behavior have the same goal and reward signals. To communicate with an Agent in a Unity environment from a Python program, the Agent in the simulation must have Behavior Parameters set to communicate. You must set the Behavior Type to Default and give it a Behavior Name . Notice: Currently communication between Unity and Python takes place over an open socket without authentication. As such, please make sure that the network where training takes place is secure. This will be addressed in a future release.","title":"mlagents_envs"},{"location":"Python-LLAPI/#loading-a-unity-environment","text":"Python-side communication happens through UnityEnvironment which is located in environment.py . To load a Unity environment from a built binary file, put the file in the same directory as envs . For example, if the filename of your Unity environment is 3DBall , in python, run: from mlagents_envs.environment import UnityEnvironment # This is a non-blocking call that only loads the environment. env = UnityEnvironment(file_name=\"3DBall\", seed=1, side_channels=[]) # Start interacting with the environment. env.reset() behavior_names = env.behavior_specs.keys() ... NOTE: Please read Interacting with a Unity Environment to read more about how you can interact with the Unity environment from Python. file_name is the name of the environment binary (located in the root directory of the python project). worker_id indicates which port to use for communication with the environment. For use in parallel training regimes such as A3C. seed indicates the seed to use when generating random numbers during the training process. In environments which are stochastic, setting the seed enables reproducible experimentation by ensuring that the environment and trainers utilize the same random seed. side_channels provides a way to exchange data with the Unity simulation that is not related to the reinforcement learning loop. For example: configurations or properties. More on them in the Side Channels doc. If you want to directly interact with the Editor, you need to use file_name=None , then press the Play button in the Editor when the message \"Start training by pressing the Play button in the Unity Editor\" is displayed on the screen","title":"Loading a Unity Environment"},{"location":"Python-LLAPI/#interacting-with-a-unity-environment","text":"","title":"Interacting with a Unity Environment"},{"location":"Python-LLAPI/#the-baseenv-interface","text":"A BaseEnv has the following methods: Reset : env.reset() Sends a signal to reset the environment. Returns None. Step : env.step() Sends a signal to step the environment. Returns None. Note that a \"step\" for Python does not correspond to either Unity Update nor FixedUpdate . When step() or reset() is called, the Unity simulation will move forward until an Agent in the simulation needs a input from Python to act. Close : env.close() Sends a shutdown signal to the environment and terminates the communication. Behavior Specs : env.behavior_specs Returns a Mapping of BehaviorName to BehaviorSpec objects (read only). A BehaviorSpec contains the observation shapes and the ActionSpec (which defines the action shape). Note that the BehaviorSpec for a specific group is fixed throughout the simulation. The number of entries in the Mapping can change over time in the simulation if new Agent behaviors are created in the simulation. Get Steps : env.get_steps(behavior_name: str) Returns a tuple DecisionSteps, TerminalSteps corresponding to the behavior_name given as input. The DecisionSteps contains information about the state of the agents that need an action this step and have the behavior behavior_name. The TerminalSteps contains information about the state of the agents whose episode ended and have the behavior behavior_name. Both DecisionSteps and TerminalSteps contain information such as the observations, the rewards and the agent identifiers. DecisionSteps also contains action masks for the next action while TerminalSteps contains the reason for termination (did the Agent reach its maximum step and was interrupted). The data is in np.array of which the first dimension is always the number of agents note that the number of agents is not guaranteed to remain constant during the simulation and it is not unusual to have either DecisionSteps or TerminalSteps contain no Agents at all. Set Actions : env.set_actions(behavior_name: str, action: ActionTuple) Sets the actions for a whole agent group. action is an ActionTuple , which is made up of a 2D np.array of dtype=np.int32 for discrete actions, and dtype=np.float32 for continuous actions. The first dimension of np.array in the tuple is the number of agents that requested a decision since the last call to env.step() . The second dimension is the number of discrete or continuous actions for the corresponding array. Set Action for Agent : env.set_action_for_agent(agent_group: str, agent_id: int, action: ActionTuple) Sets the action for a specific Agent in an agent group. agent_group is the name of the group the Agent belongs to and agent_id is the integer identifier of the Agent. action is an ActionTuple as described above. Note: If no action is provided for an agent group between two calls to env.step() then the default action will be all zeros.","title":"The BaseEnv interface"},{"location":"Python-LLAPI/#decisionsteps-and-decisionstep","text":"DecisionSteps (with s ) contains information about a whole batch of Agents while DecisionStep (no s ) only contains information about a single Agent. A DecisionSteps has the following fields : obs is a list of numpy arrays observations collected by the group of agent. The first dimension of the array corresponds to the batch size of the group (number of agents requesting a decision since the last call to env.step() ). reward is a float vector of length batch size. Corresponds to the rewards collected by each agent since the last simulation step. agent_id is an int vector of length batch size containing unique identifier for the corresponding Agent. This is used to track Agents across simulation steps. action_mask is an optional list of two dimensional arrays of booleans which is only available when using multi-discrete actions. Each array corresponds to an action branch. The first dimension of each array is the batch size and the second contains a mask for each action of the branch. If true, the action is not available for the agent during this simulation step. It also has the two following methods: len(DecisionSteps) Returns the number of agents requesting a decision since the last call to env.step() . DecisionSteps[agent_id] Returns a DecisionStep for the Agent with the agent_id unique identifier. A DecisionStep has the following fields: obs is a list of numpy arrays observations collected by the agent. (Each array has one less dimension than the arrays in DecisionSteps ) reward is a float. Corresponds to the rewards collected by the agent since the last simulation step. agent_id is an int and an unique identifier for the corresponding Agent. action_mask is an optional list of one dimensional arrays of booleans which is only available when using multi-discrete actions. Each array corresponds to an action branch. Each array contains a mask for each action of the branch. If true, the action is not available for the agent during this simulation step.","title":"DecisionSteps and DecisionStep"},{"location":"Python-LLAPI/#terminalsteps-and-terminalstep","text":"Similarly to DecisionSteps and DecisionStep , TerminalSteps (with s ) contains information about a whole batch of Agents while TerminalStep (no s ) only contains information about a single Agent. A TerminalSteps has the following fields : obs is a list of numpy arrays observations collected by the group of agent. The first dimension of the array corresponds to the batch size of the group (number of agents requesting a decision since the last call to env.step() ). reward is a float vector of length batch size. Corresponds to the rewards collected by each agent since the last simulation step. agent_id is an int vector of length batch size containing unique identifier for the corresponding Agent. This is used to track Agents across simulation steps. interrupted is an array of booleans of length batch size. Is true if the associated Agent was interrupted since the last decision step. For example, if the Agent reached the maximum number of steps for the episode. It also has the two following methods: len(TerminalSteps) Returns the number of agents requesting a decision since the last call to env.step() . TerminalSteps[agent_id] Returns a TerminalStep for the Agent with the agent_id unique identifier. A TerminalStep has the following fields: obs is a list of numpy arrays observations collected by the agent. (Each array has one less dimension than the arrays in TerminalSteps ) reward is a float. Corresponds to the rewards collected by the agent since the last simulation step. agent_id is an int and an unique identifier for the corresponding Agent. interrupted is a bool. Is true if the Agent was interrupted since the last decision step. For example, if the Agent reached the maximum number of steps for the episode.","title":"TerminalSteps and TerminalStep"},{"location":"Python-LLAPI/#behaviorspec","text":"A BehaviorSpec has the following fields : observation_specs is a List of ObservationSpec objects : Each ObservationSpec corresponds to an observation's properties: shape is a tuple of ints that corresponds to the shape of the observation (without the number of agents dimension). dimension_property is a tuple of flags containing extra information about how the data should be processed in the corresponding dimension. observation_type is an enum corresponding to what type of observation is generating the data (i.e., default, goal, etc). Note that the ObservationSpec have the same ordering as the ordering of observations in the DecisionSteps, DecisionStep, TerminalSteps and TerminalStep. action_spec is an ActionSpec namedtuple that defines the number and types of actions for the Agent. An ActionSpec has the following fields and properties: - continuous_size is the number of floats that constitute the continuous actions. - discrete_size is the number of branches (the number of independent actions) that constitute the multi-discrete actions. - discrete_branches is a Tuple of ints. Each int corresponds to the number of different options for each branch of the action. For example: In a game direction input (no movement, left, right) and jump input (no jump, jump) there will be two branches (direction and jump), the first one with 3 options and the second with 2 options. ( discrete_size = 2 and discrete_action_branches = (3,2,) )","title":"BehaviorSpec"},{"location":"Python-LLAPI/#communicating-additional-information-with-the-environment","text":"In addition to the means of communicating between Unity and python described above, we also provide methods for sharing agent-agnostic information. These additional methods are referred to as side channels. ML-Agents includes two ready-made side channels, described below. It is also possible to create custom side channels to communicate any additional data between a Unity environment and Python. Instructions for creating custom side channels can be found here . Side channels exist as separate classes which are instantiated, and then passed as list to the side_channels argument of the constructor of the UnityEnvironment class. channel = MyChannel() env = UnityEnvironment(side_channels = [channel]) Note : A side channel will only send/receive messages when env.step or env.reset() is called.","title":"Communicating additional information with the Environment"},{"location":"Python-LLAPI/#engineconfigurationchannel","text":"The EngineConfiguration side channel allows you to modify the time-scale, resolution, and graphics quality of the environment. This can be useful for adjusting the environment to perform better during training, or be more interpretable during inference. EngineConfigurationChannel has two methods : set_configuration_parameters which takes the following arguments: width : Defines the width of the display. (Must be set alongside height) height : Defines the height of the display. (Must be set alongside width) quality_level : Defines the quality level of the simulation. time_scale : Defines the multiplier for the deltatime in the simulation. If set to a higher value, time will pass faster in the simulation but the physics may perform unpredictably. target_frame_rate : Instructs simulation to try to render at a specified frame rate. capture_frame_rate Instructs the simulation to consider time between updates to always be constant, regardless of the actual frame rate. set_configuration with argument config which is an EngineConfig NamedTuple object. For example, the following code would adjust the time-scale of the simulation to be 2x realtime. from mlagents_envs.environment import UnityEnvironment from mlagents_envs.side_channel.engine_configuration_channel import EngineConfigurationChannel channel = EngineConfigurationChannel() env = UnityEnvironment(side_channels=[channel]) channel.set_configuration_parameters(time_scale = 2.0) i = env.reset() ...","title":"EngineConfigurationChannel"},{"location":"Python-LLAPI/#environmentparameters","text":"The EnvironmentParameters will allow you to get and set pre-defined numerical values in the environment. This can be useful for adjusting environment-specific settings, or for reading non-agent related information from the environment. You can call get_property and set_property on the side channel to read and write properties. EnvironmentParametersChannel has one methods: set_float_parameter Sets a float parameter in the Unity Environment. key: The string identifier of the property. value: The float value of the property. from mlagents_envs.environment import UnityEnvironment from mlagents_envs.side_channel.environment_parameters_channel import EnvironmentParametersChannel channel = EnvironmentParametersChannel() env = UnityEnvironment(side_channels=[channel]) channel.set_float_parameter(\"parameter_1\", 2.0) i = env.reset() ... Once a property has been modified in Python, you can access it in C# after the next call to step as follows: var envParameters = Academy.Instance.EnvironmentParameters; float property1 = envParameters.GetWithDefault(\"parameter_1\", 0.0f);","title":"EnvironmentParameters"},{"location":"Python-LLAPI/#custom-side-channels","text":"For information on how to make custom side channels for sending additional data types, see the documentation here .","title":"Custom side channels"},{"location":"Python-On-Off-Policy-Trainer-Documentation/","text":"Table of Contents mlagents.trainers.trainer.on_policy_trainer OnPolicyTrainer __init__ add_policy mlagents.trainers.trainer.off_policy_trainer OffPolicyTrainer __init__ save_model save_replay_buffer load_replay_buffer add_policy mlagents.trainers.trainer.rl_trainer RLTrainer end_episode create_optimizer save_model advance mlagents.trainers.trainer.trainer Trainer __init__ stats_reporter parameters get_max_steps get_step threaded should_still_train reward_buffer save_model end_episode create_policy add_policy get_policy advance publish_policy_queue subscribe_trajectory_queue mlagents.trainers.settings deep_update_dict RewardSignalSettings structure ParameterRandomizationSettings __str__ structure unstructure apply ConstantSettings __str__ apply UniformSettings __str__ apply GaussianSettings __str__ apply MultiRangeUniformSettings __str__ apply CompletionCriteriaSettings need_increment Lesson EnvironmentParameterSettings structure TrainerSettings structure CheckpointSettings prioritize_resume_init RunOptions from_argparse mlagents.trainers.trainer.on_policy_trainer OnPolicyTrainer Objects class OnPolicyTrainer(RLTrainer) The PPOTrainer is an implementation of the PPO algorithm. __init__ | __init__(behavior_name: str, reward_buff_cap: int, trainer_settings: TrainerSettings, training: bool, load: bool, seed: int, artifact_path: str) Responsible for collecting experiences and training an on-policy model. Arguments : behavior_name : The name of the behavior associated with trainer config reward_buff_cap : Max reward history to track in the reward buffer trainer_settings : The parameters for the trainer. training : Whether the trainer is set for training. load : Whether the model should be loaded. seed : The seed the model will be initialized with artifact_path : The directory within which to store artifacts from this trainer. add_policy | add_policy(parsed_behavior_id: BehaviorIdentifiers, policy: Policy) -> None Adds policy to trainer. Arguments : parsed_behavior_id : Behavior identifiers that the policy should belong to. policy : Policy to associate with name_behavior_id. mlagents.trainers.trainer.off_policy_trainer OffPolicyTrainer Objects class OffPolicyTrainer(RLTrainer) The SACTrainer is an implementation of the SAC algorithm, with support for discrete actions and recurrent networks. __init__ | __init__(behavior_name: str, reward_buff_cap: int, trainer_settings: TrainerSettings, training: bool, load: bool, seed: int, artifact_path: str) Responsible for collecting experiences and training an off-policy model. Arguments : behavior_name : The name of the behavior associated with trainer config reward_buff_cap : Max reward history to track in the reward buffer trainer_settings : The parameters for the trainer. training : Whether the trainer is set for training. load : Whether the model should be loaded. seed : The seed the model will be initialized with artifact_path : The directory within which to store artifacts from this trainer. save_model | save_model() -> None Saves the final training model to memory Overrides the default to save the replay buffer. save_replay_buffer | save_replay_buffer() -> None Save the training buffer's update buffer to a pickle file. load_replay_buffer | load_replay_buffer() -> None Loads the last saved replay buffer from a file. add_policy | add_policy(parsed_behavior_id: BehaviorIdentifiers, policy: Policy) -> None Adds policy to trainer. mlagents.trainers.trainer.rl_trainer RLTrainer Objects class RLTrainer(Trainer) This class is the base class for trainers that use Reward Signals. end_episode | end_episode() -> None A signal that the Episode has ended. The buffer must be reset. Get only called when the academy resets. create_optimizer | @abc.abstractmethod | create_optimizer() -> TorchOptimizer Creates an Optimizer object save_model | save_model() -> None Saves the policy associated with this trainer. advance | advance() -> None Steps the trainer, taking in trajectories and updates if ready. Will block and wait briefly if there are no trajectories. mlagents.trainers.trainer.trainer Trainer Objects class Trainer(abc.ABC) This class is the base class for the mlagents_envs.trainers __init__ | __init__(brain_name: str, trainer_settings: TrainerSettings, training: bool, load: bool, artifact_path: str, reward_buff_cap: int = 1) Responsible for collecting experiences and training a neural network model. Arguments : brain_name : Brain name of brain to be trained. trainer_settings : The parameters for the trainer (dictionary). training : Whether the trainer is set for training. artifact_path : The directory within which to store artifacts from this trainer reward_buff_cap : stats_reporter | @property | stats_reporter() Returns the stats reporter associated with this Trainer. parameters | @property | parameters() -> TrainerSettings Returns the trainer parameters of the trainer. get_max_steps | @property | get_max_steps() -> int Returns the maximum number of steps. Is used to know when the trainer should be stopped. Returns : The maximum number of steps of the trainer get_step | @property | get_step() -> int Returns the number of steps the trainer has performed Returns : the step count of the trainer threaded | @property | threaded() -> bool Whether or not to run the trainer in a thread. True allows the trainer to update the policy while the environment is taking steps. Set to False to enforce strict on-policy updates (i.e. don't update the policy when taking steps.) should_still_train | @property | should_still_train() -> bool Returns whether or not the trainer should train. A Trainer could stop training if it wasn't training to begin with, or if max_steps is reached. reward_buffer | @property | reward_buffer() -> Deque[float] Returns the reward buffer. The reward buffer contains the cumulative rewards of the most recent episodes completed by agents using this trainer. Returns : the reward buffer. save_model | @abc.abstractmethod | save_model() -> None Saves model file(s) for the policy or policies associated with this trainer. end_episode | @abc.abstractmethod | end_episode() A signal that the Episode has ended. The buffer must be reset. Get only called when the academy resets. create_policy | @abc.abstractmethod | create_policy(parsed_behavior_id: BehaviorIdentifiers, behavior_spec: BehaviorSpec) -> Policy Creates a Policy object add_policy | @abc.abstractmethod | add_policy(parsed_behavior_id: BehaviorIdentifiers, policy: Policy) -> None Adds policy to trainer. get_policy | get_policy(name_behavior_id: str) -> Policy Gets policy associated with name_behavior_id Arguments : name_behavior_id : Fully qualified behavior name Returns : Policy associated with name_behavior_id advance | @abc.abstractmethod | advance() -> None Advances the trainer. Typically, this means grabbing trajectories from all subscribed trajectory queues (self.trajectory_queues), and updating a policy using the steps in them, and if needed pushing a new policy onto the right policy queues (self.policy_queues). publish_policy_queue | publish_policy_queue(policy_queue: AgentManagerQueue[Policy]) -> None Adds a policy queue to the list of queues to publish to when this Trainer makes a policy update Arguments : policy_queue : Policy queue to publish to. subscribe_trajectory_queue | subscribe_trajectory_queue(trajectory_queue: AgentManagerQueue[Trajectory]) -> None Adds a trajectory queue to the list of queues for the trainer to ingest Trajectories from. Arguments : trajectory_queue : Trajectory queue to read from. mlagents.trainers.settings deep_update_dict deep_update_dict(d: Dict, update_d: Mapping) -> None Similar to dict.update(), but works for nested dicts of dicts as well. RewardSignalSettings Objects @attr.s(auto_attribs=True) class RewardSignalSettings() structure | @staticmethod | structure(d: Mapping, t: type) -> Any Helper method to structure a Dict of RewardSignalSettings class. Meant to be registered with cattr.register_structure_hook() and called with cattr.structure(). This is needed to handle the special Enum selection of RewardSignalSettings classes. ParameterRandomizationSettings Objects @attr.s(auto_attribs=True) class ParameterRandomizationSettings(abc.ABC) __str__ | __str__() -> str Helper method to output sampler stats to console. structure | @staticmethod | structure(d: Union[Mapping, float], t: type) -> \"ParameterRandomizationSettings\" Helper method to a ParameterRandomizationSettings class. Meant to be registered with cattr.register_structure_hook() and called with cattr.structure(). This is needed to handle the special Enum selection of ParameterRandomizationSettings classes. unstructure | @staticmethod | unstructure(d: \"ParameterRandomizationSettings\") -> Mapping Helper method to a ParameterRandomizationSettings class. Meant to be registered with cattr.register_unstructure_hook() and called with cattr.unstructure(). apply | @abc.abstractmethod | apply(key: str, env_channel: EnvironmentParametersChannel) -> None Helper method to send sampler settings over EnvironmentParametersChannel Calls the appropriate sampler type set method. Arguments : key : environment parameter to be sampled env_channel : The EnvironmentParametersChannel to communicate sampler settings to environment ConstantSettings Objects @attr.s(auto_attribs=True) class ConstantSettings(ParameterRandomizationSettings) __str__ | __str__() -> str Helper method to output sampler stats to console. apply | apply(key: str, env_channel: EnvironmentParametersChannel) -> None Helper method to send sampler settings over EnvironmentParametersChannel Calls the constant sampler type set method. Arguments : key : environment parameter to be sampled env_channel : The EnvironmentParametersChannel to communicate sampler settings to environment UniformSettings Objects @attr.s(auto_attribs=True) class UniformSettings(ParameterRandomizationSettings) __str__ | __str__() -> str Helper method to output sampler stats to console. apply | apply(key: str, env_channel: EnvironmentParametersChannel) -> None Helper method to send sampler settings over EnvironmentParametersChannel Calls the uniform sampler type set method. Arguments : key : environment parameter to be sampled env_channel : The EnvironmentParametersChannel to communicate sampler settings to environment GaussianSettings Objects @attr.s(auto_attribs=True) class GaussianSettings(ParameterRandomizationSettings) __str__ | __str__() -> str Helper method to output sampler stats to console. apply | apply(key: str, env_channel: EnvironmentParametersChannel) -> None Helper method to send sampler settings over EnvironmentParametersChannel Calls the gaussian sampler type set method. Arguments : key : environment parameter to be sampled env_channel : The EnvironmentParametersChannel to communicate sampler settings to environment MultiRangeUniformSettings Objects @attr.s(auto_attribs=True) class MultiRangeUniformSettings(ParameterRandomizationSettings) __str__ | __str__() -> str Helper method to output sampler stats to console. apply | apply(key: str, env_channel: EnvironmentParametersChannel) -> None Helper method to send sampler settings over EnvironmentParametersChannel Calls the multirangeuniform sampler type set method. Arguments : key : environment parameter to be sampled env_channel : The EnvironmentParametersChannel to communicate sampler settings to environment CompletionCriteriaSettings Objects @attr.s(auto_attribs=True) class CompletionCriteriaSettings() CompletionCriteriaSettings contains the information needed to figure out if the next lesson must start. need_increment | need_increment(progress: float, reward_buffer: List[float], smoothing: float) -> Tuple[bool, float] Given measures, this method returns a boolean indicating if the lesson needs to change now, and a float corresponding to the new smoothed value. Lesson Objects @attr.s(auto_attribs=True) class Lesson() Gathers the data of one lesson for one environment parameter including its name, the condition that must be fullfiled for the lesson to be completed and a sampler for the environment parameter. If the completion_criteria is None, then this is the last lesson in the curriculum. EnvironmentParameterSettings Objects @attr.s(auto_attribs=True) class EnvironmentParameterSettings() EnvironmentParameterSettings is an ordered list of lessons for one environment parameter. structure | @staticmethod | structure(d: Mapping, t: type) -> Dict[str, \"EnvironmentParameterSettings\"] Helper method to structure a Dict of EnvironmentParameterSettings class. Meant to be registered with cattr.register_structure_hook() and called with cattr.structure(). TrainerSettings Objects @attr.s(auto_attribs=True) class TrainerSettings(ExportableSettings) structure | @staticmethod | structure(d: Mapping, t: type) -> Any Helper method to structure a TrainerSettings class. Meant to be registered with cattr.register_structure_hook() and called with cattr.structure(). CheckpointSettings Objects @attr.s(auto_attribs=True) class CheckpointSettings() prioritize_resume_init | prioritize_resume_init() -> None Prioritize explicit command line resume/init over conflicting yaml options. if both resume/init are set at one place use resume RunOptions Objects @attr.s(auto_attribs=True) class RunOptions(ExportableSettings) from_argparse | @staticmethod | from_argparse(args: argparse.Namespace) -> \"RunOptions\" Takes an argparse.Namespace as specified in parse_command_line , loads input configuration files from file paths, and converts to a RunOptions instance. Arguments : args : collection of command-line parameters passed to mlagents-learn Returns : RunOptions representing the passed in arguments, with trainer config, curriculum and sampler configs loaded from files.","title":"On/Off Policy Trainer"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#table-of-contents","text":"mlagents.trainers.trainer.on_policy_trainer OnPolicyTrainer __init__ add_policy mlagents.trainers.trainer.off_policy_trainer OffPolicyTrainer __init__ save_model save_replay_buffer load_replay_buffer add_policy mlagents.trainers.trainer.rl_trainer RLTrainer end_episode create_optimizer save_model advance mlagents.trainers.trainer.trainer Trainer __init__ stats_reporter parameters get_max_steps get_step threaded should_still_train reward_buffer save_model end_episode create_policy add_policy get_policy advance publish_policy_queue subscribe_trajectory_queue mlagents.trainers.settings deep_update_dict RewardSignalSettings structure ParameterRandomizationSettings __str__ structure unstructure apply ConstantSettings __str__ apply UniformSettings __str__ apply GaussianSettings __str__ apply MultiRangeUniformSettings __str__ apply CompletionCriteriaSettings need_increment Lesson EnvironmentParameterSettings structure TrainerSettings structure CheckpointSettings prioritize_resume_init RunOptions from_argparse","title":"Table of Contents"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#mlagentstrainerstraineron_policy_trainer","text":"","title":"mlagents.trainers.trainer.on_policy_trainer"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#onpolicytrainer-objects","text":"class OnPolicyTrainer(RLTrainer) The PPOTrainer is an implementation of the PPO algorithm.","title":"OnPolicyTrainer Objects"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#__init__","text":"| __init__(behavior_name: str, reward_buff_cap: int, trainer_settings: TrainerSettings, training: bool, load: bool, seed: int, artifact_path: str) Responsible for collecting experiences and training an on-policy model. Arguments : behavior_name : The name of the behavior associated with trainer config reward_buff_cap : Max reward history to track in the reward buffer trainer_settings : The parameters for the trainer. training : Whether the trainer is set for training. load : Whether the model should be loaded. seed : The seed the model will be initialized with artifact_path : The directory within which to store artifacts from this trainer.","title":"__init__"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#add_policy","text":"| add_policy(parsed_behavior_id: BehaviorIdentifiers, policy: Policy) -> None Adds policy to trainer. Arguments : parsed_behavior_id : Behavior identifiers that the policy should belong to. policy : Policy to associate with name_behavior_id.","title":"add_policy"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#mlagentstrainerstraineroff_policy_trainer","text":"","title":"mlagents.trainers.trainer.off_policy_trainer"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#offpolicytrainer-objects","text":"class OffPolicyTrainer(RLTrainer) The SACTrainer is an implementation of the SAC algorithm, with support for discrete actions and recurrent networks.","title":"OffPolicyTrainer Objects"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#__init___1","text":"| __init__(behavior_name: str, reward_buff_cap: int, trainer_settings: TrainerSettings, training: bool, load: bool, seed: int, artifact_path: str) Responsible for collecting experiences and training an off-policy model. Arguments : behavior_name : The name of the behavior associated with trainer config reward_buff_cap : Max reward history to track in the reward buffer trainer_settings : The parameters for the trainer. training : Whether the trainer is set for training. load : Whether the model should be loaded. seed : The seed the model will be initialized with artifact_path : The directory within which to store artifacts from this trainer.","title":"__init__"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#save_model","text":"| save_model() -> None Saves the final training model to memory Overrides the default to save the replay buffer.","title":"save_model"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#save_replay_buffer","text":"| save_replay_buffer() -> None Save the training buffer's update buffer to a pickle file.","title":"save_replay_buffer"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#load_replay_buffer","text":"| load_replay_buffer() -> None Loads the last saved replay buffer from a file.","title":"load_replay_buffer"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#add_policy_1","text":"| add_policy(parsed_behavior_id: BehaviorIdentifiers, policy: Policy) -> None Adds policy to trainer.","title":"add_policy"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#mlagentstrainerstrainerrl_trainer","text":"","title":"mlagents.trainers.trainer.rl_trainer"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#rltrainer-objects","text":"class RLTrainer(Trainer) This class is the base class for trainers that use Reward Signals.","title":"RLTrainer Objects"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#end_episode","text":"| end_episode() -> None A signal that the Episode has ended. The buffer must be reset. Get only called when the academy resets.","title":"end_episode"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#create_optimizer","text":"| @abc.abstractmethod | create_optimizer() -> TorchOptimizer Creates an Optimizer object","title":"create_optimizer"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#save_model_1","text":"| save_model() -> None Saves the policy associated with this trainer.","title":"save_model"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#advance","text":"| advance() -> None Steps the trainer, taking in trajectories and updates if ready. Will block and wait briefly if there are no trajectories.","title":"advance"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#mlagentstrainerstrainertrainer","text":"","title":"mlagents.trainers.trainer.trainer"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#trainer-objects","text":"class Trainer(abc.ABC) This class is the base class for the mlagents_envs.trainers","title":"Trainer Objects"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#__init___2","text":"| __init__(brain_name: str, trainer_settings: TrainerSettings, training: bool, load: bool, artifact_path: str, reward_buff_cap: int = 1) Responsible for collecting experiences and training a neural network model. Arguments : brain_name : Brain name of brain to be trained. trainer_settings : The parameters for the trainer (dictionary). training : Whether the trainer is set for training. artifact_path : The directory within which to store artifacts from this trainer reward_buff_cap :","title":"__init__"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#stats_reporter","text":"| @property | stats_reporter() Returns the stats reporter associated with this Trainer.","title":"stats_reporter"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#parameters","text":"| @property | parameters() -> TrainerSettings Returns the trainer parameters of the trainer.","title":"parameters"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#get_max_steps","text":"| @property | get_max_steps() -> int Returns the maximum number of steps. Is used to know when the trainer should be stopped. Returns : The maximum number of steps of the trainer","title":"get_max_steps"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#get_step","text":"| @property | get_step() -> int Returns the number of steps the trainer has performed Returns : the step count of the trainer","title":"get_step"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#threaded","text":"| @property | threaded() -> bool Whether or not to run the trainer in a thread. True allows the trainer to update the policy while the environment is taking steps. Set to False to enforce strict on-policy updates (i.e. don't update the policy when taking steps.)","title":"threaded"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#should_still_train","text":"| @property | should_still_train() -> bool Returns whether or not the trainer should train. A Trainer could stop training if it wasn't training to begin with, or if max_steps is reached.","title":"should_still_train"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#reward_buffer","text":"| @property | reward_buffer() -> Deque[float] Returns the reward buffer. The reward buffer contains the cumulative rewards of the most recent episodes completed by agents using this trainer. Returns : the reward buffer.","title":"reward_buffer"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#save_model_2","text":"| @abc.abstractmethod | save_model() -> None Saves model file(s) for the policy or policies associated with this trainer.","title":"save_model"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#end_episode_1","text":"| @abc.abstractmethod | end_episode() A signal that the Episode has ended. The buffer must be reset. Get only called when the academy resets.","title":"end_episode"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#create_policy","text":"| @abc.abstractmethod | create_policy(parsed_behavior_id: BehaviorIdentifiers, behavior_spec: BehaviorSpec) -> Policy Creates a Policy object","title":"create_policy"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#add_policy_2","text":"| @abc.abstractmethod | add_policy(parsed_behavior_id: BehaviorIdentifiers, policy: Policy) -> None Adds policy to trainer.","title":"add_policy"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#get_policy","text":"| get_policy(name_behavior_id: str) -> Policy Gets policy associated with name_behavior_id Arguments : name_behavior_id : Fully qualified behavior name Returns : Policy associated with name_behavior_id","title":"get_policy"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#advance_1","text":"| @abc.abstractmethod | advance() -> None Advances the trainer. Typically, this means grabbing trajectories from all subscribed trajectory queues (self.trajectory_queues), and updating a policy using the steps in them, and if needed pushing a new policy onto the right policy queues (self.policy_queues).","title":"advance"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#publish_policy_queue","text":"| publish_policy_queue(policy_queue: AgentManagerQueue[Policy]) -> None Adds a policy queue to the list of queues to publish to when this Trainer makes a policy update Arguments : policy_queue : Policy queue to publish to.","title":"publish_policy_queue"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#subscribe_trajectory_queue","text":"| subscribe_trajectory_queue(trajectory_queue: AgentManagerQueue[Trajectory]) -> None Adds a trajectory queue to the list of queues for the trainer to ingest Trajectories from. Arguments : trajectory_queue : Trajectory queue to read from.","title":"subscribe_trajectory_queue"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#mlagentstrainerssettings","text":"","title":"mlagents.trainers.settings"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#deep_update_dict","text":"deep_update_dict(d: Dict, update_d: Mapping) -> None Similar to dict.update(), but works for nested dicts of dicts as well.","title":"deep_update_dict"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#rewardsignalsettings-objects","text":"@attr.s(auto_attribs=True) class RewardSignalSettings()","title":"RewardSignalSettings Objects"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#structure","text":"| @staticmethod | structure(d: Mapping, t: type) -> Any Helper method to structure a Dict of RewardSignalSettings class. Meant to be registered with cattr.register_structure_hook() and called with cattr.structure(). This is needed to handle the special Enum selection of RewardSignalSettings classes.","title":"structure"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#parameterrandomizationsettings-objects","text":"@attr.s(auto_attribs=True) class ParameterRandomizationSettings(abc.ABC)","title":"ParameterRandomizationSettings Objects"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#__str__","text":"| __str__() -> str Helper method to output sampler stats to console.","title":"__str__"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#structure_1","text":"| @staticmethod | structure(d: Union[Mapping, float], t: type) -> \"ParameterRandomizationSettings\" Helper method to a ParameterRandomizationSettings class. Meant to be registered with cattr.register_structure_hook() and called with cattr.structure(). This is needed to handle the special Enum selection of ParameterRandomizationSettings classes.","title":"structure"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#unstructure","text":"| @staticmethod | unstructure(d: \"ParameterRandomizationSettings\") -> Mapping Helper method to a ParameterRandomizationSettings class. Meant to be registered with cattr.register_unstructure_hook() and called with cattr.unstructure().","title":"unstructure"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#apply","text":"| @abc.abstractmethod | apply(key: str, env_channel: EnvironmentParametersChannel) -> None Helper method to send sampler settings over EnvironmentParametersChannel Calls the appropriate sampler type set method. Arguments : key : environment parameter to be sampled env_channel : The EnvironmentParametersChannel to communicate sampler settings to environment","title":"apply"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#constantsettings-objects","text":"@attr.s(auto_attribs=True) class ConstantSettings(ParameterRandomizationSettings)","title":"ConstantSettings Objects"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#__str___1","text":"| __str__() -> str Helper method to output sampler stats to console.","title":"__str__"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#apply_1","text":"| apply(key: str, env_channel: EnvironmentParametersChannel) -> None Helper method to send sampler settings over EnvironmentParametersChannel Calls the constant sampler type set method. Arguments : key : environment parameter to be sampled env_channel : The EnvironmentParametersChannel to communicate sampler settings to environment","title":"apply"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#uniformsettings-objects","text":"@attr.s(auto_attribs=True) class UniformSettings(ParameterRandomizationSettings)","title":"UniformSettings Objects"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#__str___2","text":"| __str__() -> str Helper method to output sampler stats to console.","title":"__str__"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#apply_2","text":"| apply(key: str, env_channel: EnvironmentParametersChannel) -> None Helper method to send sampler settings over EnvironmentParametersChannel Calls the uniform sampler type set method. Arguments : key : environment parameter to be sampled env_channel : The EnvironmentParametersChannel to communicate sampler settings to environment","title":"apply"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#gaussiansettings-objects","text":"@attr.s(auto_attribs=True) class GaussianSettings(ParameterRandomizationSettings)","title":"GaussianSettings Objects"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#__str___3","text":"| __str__() -> str Helper method to output sampler stats to console.","title":"__str__"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#apply_3","text":"| apply(key: str, env_channel: EnvironmentParametersChannel) -> None Helper method to send sampler settings over EnvironmentParametersChannel Calls the gaussian sampler type set method. Arguments : key : environment parameter to be sampled env_channel : The EnvironmentParametersChannel to communicate sampler settings to environment","title":"apply"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#multirangeuniformsettings-objects","text":"@attr.s(auto_attribs=True) class MultiRangeUniformSettings(ParameterRandomizationSettings)","title":"MultiRangeUniformSettings Objects"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#__str___4","text":"| __str__() -> str Helper method to output sampler stats to console.","title":"__str__"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#apply_4","text":"| apply(key: str, env_channel: EnvironmentParametersChannel) -> None Helper method to send sampler settings over EnvironmentParametersChannel Calls the multirangeuniform sampler type set method. Arguments : key : environment parameter to be sampled env_channel : The EnvironmentParametersChannel to communicate sampler settings to environment","title":"apply"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#completioncriteriasettings-objects","text":"@attr.s(auto_attribs=True) class CompletionCriteriaSettings() CompletionCriteriaSettings contains the information needed to figure out if the next lesson must start.","title":"CompletionCriteriaSettings Objects"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#need_increment","text":"| need_increment(progress: float, reward_buffer: List[float], smoothing: float) -> Tuple[bool, float] Given measures, this method returns a boolean indicating if the lesson needs to change now, and a float corresponding to the new smoothed value.","title":"need_increment"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#lesson-objects","text":"@attr.s(auto_attribs=True) class Lesson() Gathers the data of one lesson for one environment parameter including its name, the condition that must be fullfiled for the lesson to be completed and a sampler for the environment parameter. If the completion_criteria is None, then this is the last lesson in the curriculum.","title":"Lesson Objects"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#environmentparametersettings-objects","text":"@attr.s(auto_attribs=True) class EnvironmentParameterSettings() EnvironmentParameterSettings is an ordered list of lessons for one environment parameter.","title":"EnvironmentParameterSettings Objects"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#structure_2","text":"| @staticmethod | structure(d: Mapping, t: type) -> Dict[str, \"EnvironmentParameterSettings\"] Helper method to structure a Dict of EnvironmentParameterSettings class. Meant to be registered with cattr.register_structure_hook() and called with cattr.structure().","title":"structure"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#trainersettings-objects","text":"@attr.s(auto_attribs=True) class TrainerSettings(ExportableSettings)","title":"TrainerSettings Objects"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#structure_3","text":"| @staticmethod | structure(d: Mapping, t: type) -> Any Helper method to structure a TrainerSettings class. Meant to be registered with cattr.register_structure_hook() and called with cattr.structure().","title":"structure"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#checkpointsettings-objects","text":"@attr.s(auto_attribs=True) class CheckpointSettings()","title":"CheckpointSettings Objects"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#prioritize_resume_init","text":"| prioritize_resume_init() -> None Prioritize explicit command line resume/init over conflicting yaml options. if both resume/init are set at one place use resume","title":"prioritize_resume_init"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#runoptions-objects","text":"@attr.s(auto_attribs=True) class RunOptions(ExportableSettings)","title":"RunOptions Objects"},{"location":"Python-On-Off-Policy-Trainer-Documentation/#from_argparse","text":"| @staticmethod | from_argparse(args: argparse.Namespace) -> \"RunOptions\" Takes an argparse.Namespace as specified in parse_command_line , loads input configuration files from file paths, and converts to a RunOptions instance. Arguments : args : collection of command-line parameters passed to mlagents-learn Returns : RunOptions representing the passed in arguments, with trainer config, curriculum and sampler configs loaded from files.","title":"from_argparse"},{"location":"Python-Optimizer-Documentation/","text":"Table of Contents mlagents.trainers.optimizer.torch_optimizer TorchOptimizer create_reward_signals get_trajectory_value_estimates mlagents.trainers.optimizer.optimizer Optimizer update mlagents.trainers.optimizer.torch_optimizer TorchOptimizer Objects class TorchOptimizer(Optimizer) create_reward_signals | create_reward_signals(reward_signal_configs: Dict[RewardSignalType, RewardSignalSettings]) -> None Create reward signals Arguments : reward_signal_configs : Reward signal config. get_trajectory_value_estimates | get_trajectory_value_estimates(batch: AgentBuffer, next_obs: List[np.ndarray], done: bool, agent_id: str = \"\") -> Tuple[Dict[str, np.ndarray], Dict[str, float], Optional[AgentBufferField]] Get value estimates and memories for a trajectory, in batch form. Arguments : batch : An AgentBuffer that consists of a trajectory. next_obs : the next observation (after the trajectory). Used for boostrapping if this is not a termiinal trajectory. done : Set true if this is a terminal trajectory. agent_id : Agent ID of the agent that this trajectory belongs to. Returns : A Tuple of the Value Estimates as a Dict of [name, np.ndarray(trajectory_len)], the final value estimate as a Dict of [name, float], and optionally (if using memories) an AgentBufferField of initial critic memories to be used during update. mlagents.trainers.optimizer.optimizer Optimizer Objects class Optimizer(abc.ABC) Creates loss functions and auxillary networks (e.g. Q or Value) needed for training. Provides methods to update the Policy. update | @abc.abstractmethod | update(batch: AgentBuffer, num_sequences: int) -> Dict[str, float] Update the Policy based on the batch that was passed in. Arguments : batch : AgentBuffer that contains the minibatch of data used for this update. num_sequences : Number of recurrent sequences found in the minibatch. Returns : A Dict containing statistics (name, value) from the update (e.g. loss)","title":"Table of Contents"},{"location":"Python-Optimizer-Documentation/#table-of-contents","text":"mlagents.trainers.optimizer.torch_optimizer TorchOptimizer create_reward_signals get_trajectory_value_estimates mlagents.trainers.optimizer.optimizer Optimizer update","title":"Table of Contents"},{"location":"Python-Optimizer-Documentation/#mlagentstrainersoptimizertorch_optimizer","text":"","title":"mlagents.trainers.optimizer.torch_optimizer"},{"location":"Python-Optimizer-Documentation/#torchoptimizer-objects","text":"class TorchOptimizer(Optimizer)","title":"TorchOptimizer Objects"},{"location":"Python-Optimizer-Documentation/#create_reward_signals","text":"| create_reward_signals(reward_signal_configs: Dict[RewardSignalType, RewardSignalSettings]) -> None Create reward signals Arguments : reward_signal_configs : Reward signal config.","title":"create_reward_signals"},{"location":"Python-Optimizer-Documentation/#get_trajectory_value_estimates","text":"| get_trajectory_value_estimates(batch: AgentBuffer, next_obs: List[np.ndarray], done: bool, agent_id: str = \"\") -> Tuple[Dict[str, np.ndarray], Dict[str, float], Optional[AgentBufferField]] Get value estimates and memories for a trajectory, in batch form. Arguments : batch : An AgentBuffer that consists of a trajectory. next_obs : the next observation (after the trajectory). Used for boostrapping if this is not a termiinal trajectory. done : Set true if this is a terminal trajectory. agent_id : Agent ID of the agent that this trajectory belongs to. Returns : A Tuple of the Value Estimates as a Dict of [name, np.ndarray(trajectory_len)], the final value estimate as a Dict of [name, float], and optionally (if using memories) an AgentBufferField of initial critic memories to be used during update.","title":"get_trajectory_value_estimates"},{"location":"Python-Optimizer-Documentation/#mlagentstrainersoptimizeroptimizer","text":"","title":"mlagents.trainers.optimizer.optimizer"},{"location":"Python-Optimizer-Documentation/#optimizer-objects","text":"class Optimizer(abc.ABC) Creates loss functions and auxillary networks (e.g. Q or Value) needed for training. Provides methods to update the Policy.","title":"Optimizer Objects"},{"location":"Python-Optimizer-Documentation/#update","text":"| @abc.abstractmethod | update(batch: AgentBuffer, num_sequences: int) -> Dict[str, float] Update the Policy based on the batch that was passed in. Arguments : batch : AgentBuffer that contains the minibatch of data used for this update. num_sequences : Number of recurrent sequences found in the minibatch. Returns : A Dict containing statistics (name, value) from the update (e.g. loss)","title":"update"},{"location":"Python-PettingZoo-API-Documentation/","text":"Table of Contents mlagents_envs.envs.pettingzoo_env_factory PettingZooEnvFactory env mlagents_envs.envs.unity_aec_env UnityAECEnv __init__ step observe last mlagents_envs.envs.unity_parallel_env UnityParallelEnv __init__ reset mlagents_envs.envs.unity_pettingzoo_base_env UnityPettingzooBaseEnv observation_spaces observation_space action_spaces action_space side_channel reset seed render close mlagents_envs.envs.pettingzoo_env_factory PettingZooEnvFactory Objects class PettingZooEnvFactory() env | env(seed: Optional[int] = None, **kwargs: Union[List, int, bool, None]) -> UnityAECEnv Creates the environment with env_id from unity's default_registry and wraps it in a UnityToPettingZooWrapper Arguments : seed : The seed for the action spaces of the agents. kwargs : Any argument accepted by UnityEnvironment class except file_name mlagents_envs.envs.unity_aec_env UnityAECEnv Objects class UnityAECEnv(UnityPettingzooBaseEnv, AECEnv) Unity AEC (PettingZoo) environment wrapper. __init__ | __init__(env: BaseEnv, seed: Optional[int] = None) Initializes a Unity AEC environment wrapper. Arguments : env : The UnityEnvironment that is being wrapped. seed : The seed for the action spaces of the agents. step | step(action: Any) -> None Sets the action of the active agent and get the observation, reward, done and info of the next agent. Arguments : action : The action for the active agent observe | observe(agent_id) Returns the observation an agent currently can make. last() calls this function. last | last(observe=True) returns observation, cumulative reward, done, info for the current agent (specified by self.agent_selection) mlagents_envs.envs.unity_parallel_env UnityParallelEnv Objects class UnityParallelEnv(UnityPettingzooBaseEnv, ParallelEnv) Unity Parallel (PettingZoo) environment wrapper. __init__ | __init__(env: BaseEnv, seed: Optional[int] = None) Initializes a Unity Parallel environment wrapper. Arguments : env : The UnityEnvironment that is being wrapped. seed : The seed for the action spaces of the agents. reset | reset() -> Dict[str, Any] Resets the environment. mlagents_envs.envs.unity_pettingzoo_base_env UnityPettingzooBaseEnv Objects class UnityPettingzooBaseEnv() Unity Petting Zoo base environment. observation_spaces | @property | observation_spaces() -> Dict[str, spaces.Space] Return the observation spaces of all the agents. observation_space | observation_space(agent: str) -> Optional[spaces.Space] The observation space of the current agent. action_spaces | @property | action_spaces() -> Dict[str, spaces.Space] Return the action spaces of all the agents. action_space | action_space(agent: str) -> Optional[spaces.Space] The action space of the current agent. side_channel | @property | side_channel() -> Dict[str, Any] The side channels of the environment. You can access the side channels of an environment with env.side_channel[<name-of-channel>] . reset | reset() Resets the environment. seed | seed(seed=None) Reseeds the environment (making the resulting environment deterministic). reset() must be called after seed() , and before step() . render | render(mode=\"human\") NOT SUPPORTED. Displays a rendered frame from the environment, if supported. Alternate render modes in the default environments are 'rgb_array' which returns a numpy array and is supported by all environments outside of classic, and 'ansi' which returns the strings printed (specific to classic environments). close | close() -> None Close the environment.","title":"Petting Zoo Documentation"},{"location":"Python-PettingZoo-API-Documentation/#table-of-contents","text":"mlagents_envs.envs.pettingzoo_env_factory PettingZooEnvFactory env mlagents_envs.envs.unity_aec_env UnityAECEnv __init__ step observe last mlagents_envs.envs.unity_parallel_env UnityParallelEnv __init__ reset mlagents_envs.envs.unity_pettingzoo_base_env UnityPettingzooBaseEnv observation_spaces observation_space action_spaces action_space side_channel reset seed render close","title":"Table of Contents"},{"location":"Python-PettingZoo-API-Documentation/#mlagents_envsenvspettingzoo_env_factory","text":"","title":"mlagents_envs.envs.pettingzoo_env_factory"},{"location":"Python-PettingZoo-API-Documentation/#pettingzooenvfactory-objects","text":"class PettingZooEnvFactory()","title":"PettingZooEnvFactory Objects"},{"location":"Python-PettingZoo-API-Documentation/#env","text":"| env(seed: Optional[int] = None, **kwargs: Union[List, int, bool, None]) -> UnityAECEnv Creates the environment with env_id from unity's default_registry and wraps it in a UnityToPettingZooWrapper Arguments : seed : The seed for the action spaces of the agents. kwargs : Any argument accepted by UnityEnvironment class except file_name","title":"env"},{"location":"Python-PettingZoo-API-Documentation/#mlagents_envsenvsunity_aec_env","text":"","title":"mlagents_envs.envs.unity_aec_env"},{"location":"Python-PettingZoo-API-Documentation/#unityaecenv-objects","text":"class UnityAECEnv(UnityPettingzooBaseEnv, AECEnv) Unity AEC (PettingZoo) environment wrapper.","title":"UnityAECEnv Objects"},{"location":"Python-PettingZoo-API-Documentation/#__init__","text":"| __init__(env: BaseEnv, seed: Optional[int] = None) Initializes a Unity AEC environment wrapper. Arguments : env : The UnityEnvironment that is being wrapped. seed : The seed for the action spaces of the agents.","title":"__init__"},{"location":"Python-PettingZoo-API-Documentation/#step","text":"| step(action: Any) -> None Sets the action of the active agent and get the observation, reward, done and info of the next agent. Arguments : action : The action for the active agent","title":"step"},{"location":"Python-PettingZoo-API-Documentation/#observe","text":"| observe(agent_id) Returns the observation an agent currently can make. last() calls this function.","title":"observe"},{"location":"Python-PettingZoo-API-Documentation/#last","text":"| last(observe=True) returns observation, cumulative reward, done, info for the current agent (specified by self.agent_selection)","title":"last"},{"location":"Python-PettingZoo-API-Documentation/#mlagents_envsenvsunity_parallel_env","text":"","title":"mlagents_envs.envs.unity_parallel_env"},{"location":"Python-PettingZoo-API-Documentation/#unityparallelenv-objects","text":"class UnityParallelEnv(UnityPettingzooBaseEnv, ParallelEnv) Unity Parallel (PettingZoo) environment wrapper.","title":"UnityParallelEnv Objects"},{"location":"Python-PettingZoo-API-Documentation/#__init___1","text":"| __init__(env: BaseEnv, seed: Optional[int] = None) Initializes a Unity Parallel environment wrapper. Arguments : env : The UnityEnvironment that is being wrapped. seed : The seed for the action spaces of the agents.","title":"__init__"},{"location":"Python-PettingZoo-API-Documentation/#reset","text":"| reset() -> Dict[str, Any] Resets the environment.","title":"reset"},{"location":"Python-PettingZoo-API-Documentation/#mlagents_envsenvsunity_pettingzoo_base_env","text":"","title":"mlagents_envs.envs.unity_pettingzoo_base_env"},{"location":"Python-PettingZoo-API-Documentation/#unitypettingzoobaseenv-objects","text":"class UnityPettingzooBaseEnv() Unity Petting Zoo base environment.","title":"UnityPettingzooBaseEnv Objects"},{"location":"Python-PettingZoo-API-Documentation/#observation_spaces","text":"| @property | observation_spaces() -> Dict[str, spaces.Space] Return the observation spaces of all the agents.","title":"observation_spaces"},{"location":"Python-PettingZoo-API-Documentation/#observation_space","text":"| observation_space(agent: str) -> Optional[spaces.Space] The observation space of the current agent.","title":"observation_space"},{"location":"Python-PettingZoo-API-Documentation/#action_spaces","text":"| @property | action_spaces() -> Dict[str, spaces.Space] Return the action spaces of all the agents.","title":"action_spaces"},{"location":"Python-PettingZoo-API-Documentation/#action_space","text":"| action_space(agent: str) -> Optional[spaces.Space] The action space of the current agent.","title":"action_space"},{"location":"Python-PettingZoo-API-Documentation/#side_channel","text":"| @property | side_channel() -> Dict[str, Any] The side channels of the environment. You can access the side channels of an environment with env.side_channel[<name-of-channel>] .","title":"side_channel"},{"location":"Python-PettingZoo-API-Documentation/#reset_1","text":"| reset() Resets the environment.","title":"reset"},{"location":"Python-PettingZoo-API-Documentation/#seed","text":"| seed(seed=None) Reseeds the environment (making the resulting environment deterministic). reset() must be called after seed() , and before step() .","title":"seed"},{"location":"Python-PettingZoo-API-Documentation/#render","text":"| render(mode=\"human\") NOT SUPPORTED. Displays a rendered frame from the environment, if supported. Alternate render modes in the default environments are 'rgb_array' which returns a numpy array and is supported by all environments outside of classic, and 'ansi' which returns the strings printed (specific to classic environments).","title":"render"},{"location":"Python-PettingZoo-API-Documentation/#close","text":"| close() -> None Close the environment.","title":"close"},{"location":"Python-PettingZoo-API/","text":"Unity ML-Agents PettingZoo Wrapper With the increasing interest in multi-agent training with a gym-like API, we provide a PettingZoo Wrapper around the Petting Zoo API . Our wrapper provides interfaces on top of our UnityEnvironment class, which is the default way of interfacing with a Unity environment via Python. Installation and Examples The PettingZoo wrapper is part of the mlgents_envs package. Please refer to the mlagents_envs installation instructions . [Colab] PettingZoo Wrapper Example This colab notebook demonstrates the example usage of the wrapper, including installation, basic usages, and an example with our Striker vs Goalie environment which is a multi-agents environment with multiple different behavior names. API interface This wrapper is compatible with PettingZoo API. Please check out PettingZoo API page for more details. Here's an example of interacting with wrapped environment: from mlagents_envs.environment import UnityEnvironment from mlagents_envs.envs import UnityToPettingZooWrapper unity_env = UnityEnvironment(\"StrikersVsGoalie\") env = UnityToPettingZooWrapper(unity_env) env.reset() for agent in env.agent_iter(): observation, reward, done, info = env.last() action = policy(observation, agent) env.step(action) Notes There is support for both AEC and Parallel PettingZoo APIs. The AEC wrapper is compatible with PettingZoo (PZ) API interface but works in a slightly different way under the hood. For the AEC API, Instead of stepping the environment in every env.step(action) , the PZ wrapper will store the action, and will only perform environment stepping when all the agents requesting for actions in the current step have been assigned an action. This is for performance, considering that the communication between Unity and python is more efficient when data are sent in batches. Since the actions for the AEC wrapper are stored without applying them to the environment until all the actions are queued, some components of the API might behave in unexpected way. For example, a call to env.reward should return the instantaneous reward for that particular step, but the true reward would only be available when an actual environment step is performed. It's recommended that you follow the API definition for training (access rewards from env.last() instead of env.reward ) and the underlying mechanism shouldn't affect training results. The environments will automatically reset when it's done, so env.agent_iter(max_step) will keep going on until the specified max step is reached (default: 2**63 ). There is no need to call env.reset() except for the very beginning of instantiating an environment.","title":"Getting started with the PettingZoo API"},{"location":"Python-PettingZoo-API/#unity-ml-agents-pettingzoo-wrapper","text":"With the increasing interest in multi-agent training with a gym-like API, we provide a PettingZoo Wrapper around the Petting Zoo API . Our wrapper provides interfaces on top of our UnityEnvironment class, which is the default way of interfacing with a Unity environment via Python.","title":"Unity ML-Agents PettingZoo Wrapper"},{"location":"Python-PettingZoo-API/#installation-and-examples","text":"The PettingZoo wrapper is part of the mlgents_envs package. Please refer to the mlagents_envs installation instructions . [Colab] PettingZoo Wrapper Example This colab notebook demonstrates the example usage of the wrapper, including installation, basic usages, and an example with our Striker vs Goalie environment which is a multi-agents environment with multiple different behavior names.","title":"Installation and Examples"},{"location":"Python-PettingZoo-API/#api-interface","text":"This wrapper is compatible with PettingZoo API. Please check out PettingZoo API page for more details. Here's an example of interacting with wrapped environment: from mlagents_envs.environment import UnityEnvironment from mlagents_envs.envs import UnityToPettingZooWrapper unity_env = UnityEnvironment(\"StrikersVsGoalie\") env = UnityToPettingZooWrapper(unity_env) env.reset() for agent in env.agent_iter(): observation, reward, done, info = env.last() action = policy(observation, agent) env.step(action)","title":"API interface"},{"location":"Python-PettingZoo-API/#notes","text":"There is support for both AEC and Parallel PettingZoo APIs. The AEC wrapper is compatible with PettingZoo (PZ) API interface but works in a slightly different way under the hood. For the AEC API, Instead of stepping the environment in every env.step(action) , the PZ wrapper will store the action, and will only perform environment stepping when all the agents requesting for actions in the current step have been assigned an action. This is for performance, considering that the communication between Unity and python is more efficient when data are sent in batches. Since the actions for the AEC wrapper are stored without applying them to the environment until all the actions are queued, some components of the API might behave in unexpected way. For example, a call to env.reward should return the instantaneous reward for that particular step, but the true reward would only be available when an actual environment step is performed. It's recommended that you follow the API definition for training (access rewards from env.last() instead of env.reward ) and the underlying mechanism shouldn't affect training results. The environments will automatically reset when it's done, so env.agent_iter(max_step) will keep going on until the specified max step is reached (default: 2**63 ). There is no need to call env.reset() except for the very beginning of instantiating an environment.","title":"Notes"},{"location":"Readme/","text":"Unity ML-Agents Toolkit ( latest release ) ( all releases ) The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents. We provide implementations (based on PyTorch) of state-of-the-art algorithms to enable game developers and hobbyists to easily train intelligent agents for 2D, 3D and VR/AR games. Researchers can also use the provided simple-to-use Python API to train Agents using reinforcement learning, imitation learning, neuroevolution, or any other methods. These trained agents can be used for multiple purposes, including controlling NPC behavior (in a variety of settings such as multi-agent and adversarial), automated testing of game builds and evaluating different game design decisions pre-release. The ML-Agents Toolkit is mutually beneficial for both game developers and AI researchers as it provides a central platform where advances in AI can be evaluated on Unity\u2019s rich environments and then made accessible to the wider research and game developer communities. Features 17+ example Unity environments Support for multiple environment configurations and training scenarios Flexible Unity SDK that can be integrated into your game or custom Unity scene Support for training single-agent, multi-agent cooperative, and multi-agent competitive scenarios via several Deep Reinforcement Learning algorithms (PPO, SAC, MA-POCA, self-play). Support for learning from demonstrations through two Imitation Learning algorithms (BC and GAIL). Quickly and easily add your own custom training algorithm and/or components. Easily definable Curriculum Learning scenarios for complex tasks Train robust agents using environment randomization Flexible agent control with On Demand Decision Making Train using multiple concurrent Unity environment instances Utilizes the Unity Inference Engine to provide native cross-platform support Unity environment control from Python Wrap Unity learning environments as a gym environment Wrap Unity learning environments as a PettingZoo environment See our ML-Agents Overview page for detailed descriptions of all these features. Or go straight to our web docs . Releases & Documentation Our latest, stable release is Release 20 . Click here to get started with the latest release of ML-Agents. You can also check out our new web docs ! The table below lists all our releases, including our main branch which is under active development and may be unstable. A few helpful guidelines: - The Versioning page overviews how we manage our GitHub releases and the versioning process for each of the ML-Agents components. - The Releases page contains details of the changes between releases. - The Migration page contains details on how to upgrade from earlier releases of the ML-Agents Toolkit. - The Documentation links in the table below include installation and usage instructions specific to each release. Remember to always use the documentation that corresponds to the release version you're using. - The com.unity.ml-agents package is verified for Unity 2020.1 and later. Verified packages releases are numbered 1.0.x. Version Release Date Source Documentation Download Python Package Unity Package Release 20 November 21, 2022 source docs download 0.30.0 2.3.0 main (unstable) -- source docs download -- -- Verified Package 1.0.8 May 26, 2021 source docs download 0.16.1 1.0.8 If you are a researcher interested in a discussion of Unity as an AI platform, see a pre-print of our reference paper on Unity and the ML-Agents Toolkit . If you use Unity or the ML-Agents Toolkit to conduct research, we ask that you cite the following paper as a reference: @article{juliani2020, title={Unity: A general platform for intelligent agents}, author={Juliani, Arthur and Berges, Vincent-Pierre and Teng, Ervin and Cohen, Andrew and Harper, Jonathan and Elion, Chris and Goy, Chris and Gao, Yuan and Henry, Hunter and Mattar, Marwan and Lange, Danny}, journal={arXiv preprint arXiv:1809.02627}, url={https://arxiv.org/pdf/1809.02627.pdf}, year={2020} } Additionally, if you use the MA-POCA trainer in your research, we ask that you cite the following paper as a reference: @article{cohen2022, title={On the Use and Misuse of Absorbing States in Multi-agent Reinforcement Learning}, author={Cohen, Andrew and Teng, Ervin and Berges, Vincent-Pierre and Dong, Ruo-Ping and Henry, Hunter and Mattar, Marwan and Zook, Alexander and Ganguly, Sujoy}, journal={RL in Games Workshop AAAI 2022}, url={http://aaai-rlg.mlanctot.info/papers/AAAI22-RLG_paper_32.pdf}, year={2022} } Additional Resources We have a Unity Learn course, ML-Agents: Hummingbirds , that provides a gentle introduction to Unity and the ML-Agents Toolkit. We've also partnered with CodeMonkeyUnity to create a series of tutorial videos on how to implement and use the ML-Agents Toolkit. We have also published a series of blog posts that are relevant for ML-Agents: (July 12, 2021) ML-Agents plays Dodgeball (May 5, 2021) ML-Agents v2.0 release: Now supports training complex cooperative behaviors (December 28, 2020) Happy holidays from the Unity ML-Agents team! (November 20, 2020) How Eidos-Montr\u00e9al created Grid Sensors to improve observations for training agents (November 11, 2020) 2020 AI@Unity interns shoutout (May 12, 2020) Announcing ML-Agents Unity Package v1.0! (February 28, 2020) Training intelligent adversaries using self-play with ML-Agents (November 11, 2019) Training your agents 7 times faster with ML-Agents (October 21, 2019) The AI@Unity interns help shape the world (April 15, 2019) Unity ML-Agents Toolkit v0.8: Faster training on real games (March 1, 2019) Unity ML-Agents Toolkit v0.7: A leap towards cross-platform inference (December 17, 2018) ML-Agents Toolkit v0.6: Improved usability of Brains and Imitation Learning (October 2, 2018) Puppo, The Corgi: Cuteness Overload with the Unity ML-Agents Toolkit (September 11, 2018) ML-Agents Toolkit v0.5, new resources for AI researchers available now (June 26, 2018) Solving sparse-reward tasks with Curiosity (June 19, 2018) Unity ML-Agents Toolkit v0.4 and Udacity Deep Reinforcement Learning Nanodegree (May 24, 2018) Imitation Learning in Unity: The Workflow (March 15, 2018) ML-Agents Toolkit v0.3 Beta released: Imitation Learning, feedback-driven features, and more (December 11, 2017) Using Machine Learning Agents in a real game: a beginner\u2019s guide (December 8, 2017) Introducing ML-Agents Toolkit v0.2: Curriculum Learning, new environments, and more (September 19, 2017) Introducing: Unity Machine Learning Agents Toolkit Overviewing reinforcement learning concepts ( multi-armed bandit and Q-learning ) More from Unity Unity Robotics Unity Computer Vision Community and Feedback The ML-Agents Toolkit is an open-source project and we encourage and welcome contributions. If you wish to contribute, be sure to review our contribution guidelines and code of conduct . For problems with the installation and setup of the ML-Agents Toolkit, or discussions about how to best setup or train your agents, please create a new thread on the Unity ML-Agents forum and make sure to include as much detail as possible. If you run into any other problems using the ML-Agents Toolkit or have a specific feature request, please submit a GitHub issue . Please tell us which samples you would like to see shipped with the ML-Agents Unity package by replying to this forum thread . Your opinion matters a great deal to us. Only by hearing your thoughts on the Unity ML-Agents Toolkit can we continue to improve and grow. Please take a few minutes to let us know about it . For any other questions or feedback, connect directly with the ML-Agents team at ml-agents@unity3d.com. Privacy In order to improve the developer experience for Unity ML-Agents Toolkit, we have added in-editor analytics. Please refer to \"Information that is passively collected by Unity\" in the Unity Privacy Policy .","title":"Unity ML-Agents Toolkit"},{"location":"Readme/#unity-ml-agents-toolkit","text":"( latest release ) ( all releases ) The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents. We provide implementations (based on PyTorch) of state-of-the-art algorithms to enable game developers and hobbyists to easily train intelligent agents for 2D, 3D and VR/AR games. Researchers can also use the provided simple-to-use Python API to train Agents using reinforcement learning, imitation learning, neuroevolution, or any other methods. These trained agents can be used for multiple purposes, including controlling NPC behavior (in a variety of settings such as multi-agent and adversarial), automated testing of game builds and evaluating different game design decisions pre-release. The ML-Agents Toolkit is mutually beneficial for both game developers and AI researchers as it provides a central platform where advances in AI can be evaluated on Unity\u2019s rich environments and then made accessible to the wider research and game developer communities.","title":"Unity ML-Agents Toolkit"},{"location":"Readme/#features","text":"17+ example Unity environments Support for multiple environment configurations and training scenarios Flexible Unity SDK that can be integrated into your game or custom Unity scene Support for training single-agent, multi-agent cooperative, and multi-agent competitive scenarios via several Deep Reinforcement Learning algorithms (PPO, SAC, MA-POCA, self-play). Support for learning from demonstrations through two Imitation Learning algorithms (BC and GAIL). Quickly and easily add your own custom training algorithm and/or components. Easily definable Curriculum Learning scenarios for complex tasks Train robust agents using environment randomization Flexible agent control with On Demand Decision Making Train using multiple concurrent Unity environment instances Utilizes the Unity Inference Engine to provide native cross-platform support Unity environment control from Python Wrap Unity learning environments as a gym environment Wrap Unity learning environments as a PettingZoo environment See our ML-Agents Overview page for detailed descriptions of all these features. Or go straight to our web docs .","title":"Features"},{"location":"Readme/#releases-documentation","text":"Our latest, stable release is Release 20 . Click here to get started with the latest release of ML-Agents. You can also check out our new web docs ! The table below lists all our releases, including our main branch which is under active development and may be unstable. A few helpful guidelines: - The Versioning page overviews how we manage our GitHub releases and the versioning process for each of the ML-Agents components. - The Releases page contains details of the changes between releases. - The Migration page contains details on how to upgrade from earlier releases of the ML-Agents Toolkit. - The Documentation links in the table below include installation and usage instructions specific to each release. Remember to always use the documentation that corresponds to the release version you're using. - The com.unity.ml-agents package is verified for Unity 2020.1 and later. Verified packages releases are numbered 1.0.x. Version Release Date Source Documentation Download Python Package Unity Package Release 20 November 21, 2022 source docs download 0.30.0 2.3.0 main (unstable) -- source docs download -- -- Verified Package 1.0.8 May 26, 2021 source docs download 0.16.1 1.0.8 If you are a researcher interested in a discussion of Unity as an AI platform, see a pre-print of our reference paper on Unity and the ML-Agents Toolkit . If you use Unity or the ML-Agents Toolkit to conduct research, we ask that you cite the following paper as a reference: @article{juliani2020, title={Unity: A general platform for intelligent agents}, author={Juliani, Arthur and Berges, Vincent-Pierre and Teng, Ervin and Cohen, Andrew and Harper, Jonathan and Elion, Chris and Goy, Chris and Gao, Yuan and Henry, Hunter and Mattar, Marwan and Lange, Danny}, journal={arXiv preprint arXiv:1809.02627}, url={https://arxiv.org/pdf/1809.02627.pdf}, year={2020} } Additionally, if you use the MA-POCA trainer in your research, we ask that you cite the following paper as a reference: @article{cohen2022, title={On the Use and Misuse of Absorbing States in Multi-agent Reinforcement Learning}, author={Cohen, Andrew and Teng, Ervin and Berges, Vincent-Pierre and Dong, Ruo-Ping and Henry, Hunter and Mattar, Marwan and Zook, Alexander and Ganguly, Sujoy}, journal={RL in Games Workshop AAAI 2022}, url={http://aaai-rlg.mlanctot.info/papers/AAAI22-RLG_paper_32.pdf}, year={2022} }","title":"Releases &amp; Documentation"},{"location":"Readme/#additional-resources","text":"We have a Unity Learn course, ML-Agents: Hummingbirds , that provides a gentle introduction to Unity and the ML-Agents Toolkit. We've also partnered with CodeMonkeyUnity to create a series of tutorial videos on how to implement and use the ML-Agents Toolkit. We have also published a series of blog posts that are relevant for ML-Agents: (July 12, 2021) ML-Agents plays Dodgeball (May 5, 2021) ML-Agents v2.0 release: Now supports training complex cooperative behaviors (December 28, 2020) Happy holidays from the Unity ML-Agents team! (November 20, 2020) How Eidos-Montr\u00e9al created Grid Sensors to improve observations for training agents (November 11, 2020) 2020 AI@Unity interns shoutout (May 12, 2020) Announcing ML-Agents Unity Package v1.0! (February 28, 2020) Training intelligent adversaries using self-play with ML-Agents (November 11, 2019) Training your agents 7 times faster with ML-Agents (October 21, 2019) The AI@Unity interns help shape the world (April 15, 2019) Unity ML-Agents Toolkit v0.8: Faster training on real games (March 1, 2019) Unity ML-Agents Toolkit v0.7: A leap towards cross-platform inference (December 17, 2018) ML-Agents Toolkit v0.6: Improved usability of Brains and Imitation Learning (October 2, 2018) Puppo, The Corgi: Cuteness Overload with the Unity ML-Agents Toolkit (September 11, 2018) ML-Agents Toolkit v0.5, new resources for AI researchers available now (June 26, 2018) Solving sparse-reward tasks with Curiosity (June 19, 2018) Unity ML-Agents Toolkit v0.4 and Udacity Deep Reinforcement Learning Nanodegree (May 24, 2018) Imitation Learning in Unity: The Workflow (March 15, 2018) ML-Agents Toolkit v0.3 Beta released: Imitation Learning, feedback-driven features, and more (December 11, 2017) Using Machine Learning Agents in a real game: a beginner\u2019s guide (December 8, 2017) Introducing ML-Agents Toolkit v0.2: Curriculum Learning, new environments, and more (September 19, 2017) Introducing: Unity Machine Learning Agents Toolkit Overviewing reinforcement learning concepts ( multi-armed bandit and Q-learning )","title":"Additional Resources"},{"location":"Readme/#more-from-unity","text":"Unity Robotics Unity Computer Vision","title":"More from Unity"},{"location":"Readme/#community-and-feedback","text":"The ML-Agents Toolkit is an open-source project and we encourage and welcome contributions. If you wish to contribute, be sure to review our contribution guidelines and code of conduct . For problems with the installation and setup of the ML-Agents Toolkit, or discussions about how to best setup or train your agents, please create a new thread on the Unity ML-Agents forum and make sure to include as much detail as possible. If you run into any other problems using the ML-Agents Toolkit or have a specific feature request, please submit a GitHub issue . Please tell us which samples you would like to see shipped with the ML-Agents Unity package by replying to this forum thread . Your opinion matters a great deal to us. Only by hearing your thoughts on the Unity ML-Agents Toolkit can we continue to improve and grow. Please take a few minutes to let us know about it . For any other questions or feedback, connect directly with the ML-Agents team at ml-agents@unity3d.com.","title":"Community and Feedback"},{"location":"Readme/#privacy","text":"In order to improve the developer experience for Unity ML-Agents Toolkit, we have added in-editor analytics. Please refer to \"Information that is passively collected by Unity\" in the Unity Privacy Policy .","title":"Privacy"},{"location":"Training-Configuration-File/","text":"Training Configuration File Table of Contents Common Trainer Configurations Trainer-specific Configurations PPO-specific Configurations SAC-specific Configurations Reward Signals Extrinsic Rewards Curiosity Intrinsic Reward GAIL Intrinsic Reward RND Intrinsic Reward Reward Signal Settings for SAC Behavioral Cloning Memory-enhanced Agents using Recurrent Neural Networks Self-Play Note on Reward Signals Note on Swap Steps Common Trainer Configurations One of the first decisions you need to make regarding your training run is which trainer to use: PPO, SAC, or POCA. There are some training configurations that are common to both trainers (which we review now) and others that depend on the choice of the trainer (which we review on subsequent sections). Setting Description trainer_type (default = ppo ) The type of trainer to use: ppo , sac , or poca . summary_freq (default = 50000 ) Number of experiences that needs to be collected before generating and displaying training statistics. This determines the granularity of the graphs in Tensorboard. time_horizon (default = 64 ) How many steps of experience to collect per-agent before adding it to the experience buffer. When this limit is reached before the end of an episode, a value estimate is used to predict the overall expected reward from the agent's current state. As such, this parameter trades off between a less biased, but higher variance estimate (long time horizon) and more biased, but less varied estimate (short time horizon). In cases where there are frequent rewards within an episode, or episodes are prohibitively large, a smaller number can be more ideal. This number should be large enough to capture all the important behavior within a sequence of an agent's actions. Typical range: 32 - 2048 max_steps (default = 500000 ) Total number of steps (i.e., observation collected and action taken) that must be taken in the environment (or across all environments if using multiple in parallel) before ending the training process. If you have multiple agents with the same behavior name within your environment, all steps taken by those agents will contribute to the same max_steps count. Typical range: 5e5 - 1e7 keep_checkpoints (default = 5 ) The maximum number of model checkpoints to keep. Checkpoints are saved after the number of steps specified by the checkpoint_interval option. Once the maximum number of checkpoints has been reached, the oldest checkpoint is deleted when saving a new checkpoint. even_checkpoints (default = false ) If set to true, ignores checkpoint_interval and evenly distributes checkpoints throughout training based on keep_checkpoints and max_steps , i.e. checkpoint_interval = max_steps / keep_checkpoints . Useful for cataloging agent behavior throughout training. checkpoint_interval (default = 500000 ) The number of experiences collected between each checkpoint by the trainer. A maximum of keep_checkpoints checkpoints are saved before old ones are deleted. Each checkpoint saves the .onnx files in results/ folder. init_path (default = None) Initialize trainer from a previously saved model. Note that the prior run should have used the same trainer configurations as the current run, and have been saved with the same version of ML-Agents. You can provide either the file name or the full path to the checkpoint, e.g. {checkpoint_name.pt} or ./models/{run-id}/{behavior_name}/{checkpoint_name.pt} . This option is provided in case you want to initialize different behaviors from different runs or initialize from an older checkpoint; in most cases, it is sufficient to use the --initialize-from CLI parameter to initialize all models from the same run. threaded (default = false ) Allow environments to step while updating the model. This might result in a training speedup, especially when using SAC. For best performance, leave setting to false when using self-play. hyperparameters -> learning_rate (default = 3e-4 ) Initial learning rate for gradient descent. Corresponds to the strength of each gradient descent update step. This should typically be decreased if training is unstable, and the reward does not consistently increase. Typical range: 1e-5 - 1e-3 hyperparameters -> batch_size Number of experiences in each iteration of gradient descent. This should always be multiple times smaller than buffer_size . If you are using continuous actions, this value should be large (on the order of 1000s). If you are using only discrete actions, this value should be smaller (on the order of 10s). Typical range: (Continuous - PPO): 512 - 5120 ; (Continuous - SAC): 128 - 1024 ; (Discrete, PPO & SAC): 32 - 512 . hyperparameters -> buffer_size (default = 10240 for PPO and 50000 for SAC) PPO: Number of experiences to collect before updating the policy model. Corresponds to how many experiences should be collected before we do any learning or updating of the model. This should be multiple times larger than batch_size . Typically a larger buffer_size corresponds to more stable training updates. SAC: The max size of the experience buffer - on the order of thousands of times longer than your episodes, so that SAC can learn from old as well as new experiences. Typical range: PPO: 2048 - 409600 ; SAC: 50000 - 1000000 hyperparameters -> learning_rate_schedule (default = linear for PPO and constant for SAC) Determines how learning rate changes over time. For PPO, we recommend decaying learning rate until max_steps so learning converges more stably. However, for some cases (e.g. training for an unknown amount of time) this feature can be disabled. For SAC, we recommend holding learning rate constant so that the agent can continue to learn until its Q function converges naturally. linear decays the learning_rate linearly, reaching 0 at max_steps, while constant keeps the learning rate constant for the entire training run. network_settings -> hidden_units (default = 128 ) Number of units in the hidden layers of the neural network. Correspond to how many units are in each fully connected layer of the neural network. For simple problems where the correct action is a straightforward combination of the observation inputs, this should be small. For problems where the action is a very complex interaction between the observation variables, this should be larger. Typical range: 32 - 512 network_settings -> num_layers (default = 2 ) The number of hidden layers in the neural network. Corresponds to how many hidden layers are present after the observation input, or after the CNN encoding of the visual observation. For simple problems, fewer layers are likely to train faster and more efficiently. More layers may be necessary for more complex control problems. Typical range: 1 - 3 network_settings -> normalize (default = false ) Whether normalization is applied to the vector observation inputs. This normalization is based on the running average and variance of the vector observation. Normalization can be helpful in cases with complex continuous control problems, but may be harmful with simpler discrete control problems. network_settings -> vis_encode_type (default = simple ) Encoder type for encoding visual observations. simple (default) uses a simple encoder which consists of two convolutional layers, nature_cnn uses the CNN implementation proposed by Mnih et al. , consisting of three convolutional layers, and resnet uses the IMPALA Resnet consisting of three stacked layers, each with two residual blocks, making a much larger network than the other two. match3 is a smaller CNN ( Gudmundsoon et al. ) that can capture more granular spatial relationships and is optimized for board games. fully_connected uses a single fully connected dense layer as encoder without any convolutional layers. Due to the size of convolution kernel, there is a minimum observation size limitation that each encoder type can handle - simple : 20x20, nature_cnn : 36x36, resnet : 15 x 15, match3 : 5x5. fully_connected doesn't have convolutional layers and thus no size limits, but since it has less representation power it should be reserved for very small inputs. Note that using the match3 CNN with very large visual input might result in a huge observation encoding and thus potentially slow down training or cause memory issues. network_settings -> conditioning_type (default = hyper ) Conditioning type for the policy using goal observations. none treats the goal observations as regular observations, hyper (default) uses a HyperNetwork with goal observations as input to generate some of the weights of the policy. Note that when using hyper the number of parameters of the network increases greatly. Therefore, it is recommended to reduce the number of hidden_units when using this conditioning_type Trainer-specific Configurations Depending on your choice of a trainer, there are additional trainer-specific configurations. We present them below in two separate tables, but keep in mind that you only need to include the configurations for the trainer selected (i.e. the trainer setting above). PPO-specific Configurations Setting Description hyperparameters -> beta (default = 5.0e-3 ) Strength of the entropy regularization, which makes the policy \"more random.\" This ensures that agents properly explore the action space during training. Increasing this will ensure more random actions are taken. This should be adjusted such that the entropy (measurable from TensorBoard) slowly decreases alongside increases in reward. If entropy drops too quickly, increase beta. If entropy drops too slowly, decrease beta . Typical range: 1e-4 - 1e-2 hyperparameters -> epsilon (default = 0.2 ) Influences how rapidly the policy can evolve during training. Corresponds to the acceptable threshold of divergence between the old and new policies during gradient descent updating. Setting this value small will result in more stable updates, but will also slow the training process. Typical range: 0.1 - 0.3 hyperparameters -> beta_schedule (default = learning_rate_schedule ) Determines how beta changes over time. linear decays beta linearly, reaching 0 at max_steps, while constant keeps beta constant for the entire training run. If not explicitly set, the default beta schedule will be set to hyperparameters -> learning_rate_schedule . hyperparameters -> epsilon_schedule (default = learning_rate_schedule ) Determines how epsilon changes over time (PPO only). linear decays epsilon linearly, reaching 0 at max_steps, while constant keeps the epsilon constant for the entire training run. If not explicitly set, the default epsilon schedule will be set to hyperparameters -> learning_rate_schedule . hyperparameters -> lambd (default = 0.95 ) Regularization parameter (lambda) used when calculating the Generalized Advantage Estimate ( GAE ). This can be thought of as how much the agent relies on its current value estimate when calculating an updated value estimate. Low values correspond to relying more on the current value estimate (which can be high bias), and high values correspond to relying more on the actual rewards received in the environment (which can be high variance). The parameter provides a trade-off between the two, and the right value can lead to a more stable training process. Typical range: 0.9 - 0.95 hyperparameters -> num_epoch (default = 3 ) Number of passes to make through the experience buffer when performing gradient descent optimization.The larger the batch_size, the larger it is acceptable to make this. Decreasing this will ensure more stable updates, at the cost of slower learning. Typical range: 3 - 10 hyperparameters -> shared_critic (default = False ) Whether or not the policy and value function networks share a backbone. It may be useful to use a shared backbone when learning from image observations. SAC-specific Configurations Setting Description hyperparameters -> buffer_init_steps (default = 0 ) Number of experiences to collect into the buffer before updating the policy model. As the untrained policy is fairly random, pre-filling the buffer with random actions is useful for exploration. Typically, at least several episodes of experiences should be pre-filled. Typical range: 1000 - 10000 hyperparameters -> init_entcoef (default = 1.0 ) How much the agent should explore in the beginning of training. Corresponds to the initial entropy coefficient set at the beginning of training. In SAC, the agent is incentivized to make its actions entropic to facilitate better exploration. The entropy coefficient weighs the true reward with a bonus entropy reward. The entropy coefficient is automatically adjusted to a preset target entropy, so the init_entcoef only corresponds to the starting value of the entropy bonus. Increase init_entcoef to explore more in the beginning, decrease to converge to a solution faster. Typical range: (Continuous): 0.5 - 1.0 ; (Discrete): 0.05 - 0.5 hyperparameters -> save_replay_buffer (default = false ) Whether to save and load the experience replay buffer as well as the model when quitting and re-starting training. This may help resumes go more smoothly, as the experiences collected won't be wiped. Note that replay buffers can be very large, and will take up a considerable amount of disk space. For that reason, we disable this feature by default. hyperparameters -> tau (default = 0.005 ) How aggressively to update the target network used for bootstrapping value estimation in SAC. Corresponds to the magnitude of the target Q update during the SAC model update. In SAC, there are two neural networks: the target and the policy. The target network is used to bootstrap the policy's estimate of the future rewards at a given state, and is fixed while the policy is being updated. This target is then slowly updated according to tau. Typically, this value should be left at 0.005. For simple problems, increasing tau to 0.01 might reduce the time it takes to learn, at the cost of stability. Typical range: 0.005 - 0.01 hyperparameters -> steps_per_update (default = 1 ) Average ratio of agent steps (actions) taken to updates made of the agent's policy. In SAC, a single \"update\" corresponds to grabbing a batch of size batch_size from the experience replay buffer, and using this mini batch to update the models. Note that it is not guaranteed that after exactly steps_per_update steps an update will be made, only that the ratio will hold true over many steps. Typically, steps_per_update should be greater than or equal to 1. Note that setting steps_per_update lower will improve sample efficiency (reduce the number of steps required to train) but increase the CPU time spent performing updates. For most environments where steps are fairly fast (e.g. our example environments) steps_per_update equal to the number of agents in the scene is a good balance. For slow environments (steps take 0.1 seconds or more) reducing steps_per_update may improve training speed. We can also change steps_per_update to lower than 1 to update more often than once per step, though this will usually result in a slowdown unless the environment is very slow. Typical range: 1 - 20 hyperparameters -> reward_signal_num_update (default = steps_per_update ) Number of steps per mini batch sampled and used for updating the reward signals. By default, we update the reward signals once every time the main policy is updated. However, to imitate the training procedure in certain imitation learning papers (e.g. Kostrikov et. al , Blond\u00e9 et. al ), we may want to update the reward signal (GAIL) M times for every update of the policy. We can change steps_per_update of SAC to N, as well as reward_signal_steps_per_update under reward_signals to N / M to accomplish this. By default, reward_signal_steps_per_update is set to steps_per_update . MA-POCA-specific Configurations MA-POCA uses the same configurations as PPO, and there are no additional POCA-specific parameters. NOTE : Reward signals other than Extrinsic Rewards have not been extensively tested with MA-POCA, though they can still be added and used for training on a your-mileage-may-vary basis. Reward Signals The reward_signals section enables the specification of settings for both extrinsic (i.e. environment-based) and intrinsic reward signals (e.g. curiosity and GAIL). Each reward signal should define at least two parameters, strength and gamma , in addition to any class-specific hyperparameters. Note that to remove a reward signal, you should delete its entry entirely from reward_signals . At least one reward signal should be left defined at all times. Provide the following configurations to design the reward signal for your training run. Extrinsic Rewards Enable these settings to ensure that your training run incorporates your environment-based reward signal: Setting Description extrinsic -> strength (default = 1.0 ) Factor by which to multiply the reward given by the environment. Typical ranges will vary depending on the reward signal. Typical range: 1.00 extrinsic -> gamma (default = 0.99 ) Discount factor for future rewards coming from the environment. This can be thought of as how far into the future the agent should care about possible rewards. In situations when the agent should be acting in the present in order to prepare for rewards in the distant future, this value should be large. In cases when rewards are more immediate, it can be smaller. Must be strictly smaller than 1. Typical range: 0.8 - 0.995 Curiosity Intrinsic Reward To enable curiosity, provide these settings: Setting Description curiosity -> strength (default = 1.0 ) Magnitude of the curiosity reward generated by the intrinsic curiosity module. This should be scaled in order to ensure it is large enough to not be overwhelmed by extrinsic reward signals in the environment. Likewise it should not be too large to overwhelm the extrinsic reward signal. Typical range: 0.001 - 0.1 curiosity -> gamma (default = 0.99 ) Discount factor for future rewards. Typical range: 0.8 - 0.995 curiosity -> network_settings Please see the documentation for network_settings under Common Trainer Configurations . The network specs used by the intrinsic curiosity model. The value should of hidden_units should be small enough to encourage the ICM to compress the original observation, but also not too small to prevent it from learning to differentiate between expected and actual observations. Typical range: 64 - 256 curiosity -> learning_rate (default = 3e-4 ) Learning rate used to update the intrinsic curiosity module. This should typically be decreased if training is unstable, and the curiosity loss is unstable. Typical range: 1e-5 - 1e-3 GAIL Intrinsic Reward To enable GAIL (assuming you have recorded demonstrations), provide these settings: Setting Description gail -> strength (default = 1.0 ) Factor by which to multiply the raw reward. Note that when using GAIL with an Extrinsic Signal, this value should be set lower if your demonstrations are suboptimal (e.g. from a human), so that a trained agent will focus on receiving extrinsic rewards instead of exactly copying the demonstrations. Keep the strength below about 0.1 in those cases. Typical range: 0.01 - 1.0 gail -> gamma (default = 0.99 ) Discount factor for future rewards. Typical range: 0.8 - 0.9 gail -> demo_path (Required, no default) The path to your .demo file or directory of .demo files. gail -> network_settings Please see the documentation for network_settings under Common Trainer Configurations . The network specs for the GAIL discriminator. The value of hidden_units should be small enough to encourage the discriminator to compress the original observation, but also not too small to prevent it from learning to differentiate between demonstrated and actual behavior. Dramatically increasing this size will also negatively affect training times. Typical range: 64 - 256 gail -> learning_rate (Optional, default = 3e-4 ) Learning rate used to update the discriminator. This should typically be decreased if training is unstable, and the GAIL loss is unstable. Typical range: 1e-5 - 1e-3 gail -> use_actions (default = false ) Determines whether the discriminator should discriminate based on both observations and actions, or just observations. Set to True if you want the agent to mimic the actions from the demonstrations, and False if you'd rather have the agent visit the same states as in the demonstrations but with possibly different actions. Setting to False is more likely to be stable, especially with imperfect demonstrations, but may learn slower. gail -> use_vail (default = false ) Enables a variational bottleneck within the GAIL discriminator. This forces the discriminator to learn a more general representation and reduces its tendency to be \"too good\" at discriminating, making learning more stable. However, it does increase training time. Enable this if you notice your imitation learning is unstable, or unable to learn the task at hand. RND Intrinsic Reward Random Network Distillation (RND) is only available for the PyTorch trainers. To enable RND, provide these settings: Setting Description rnd -> strength (default = 1.0 ) Magnitude of the curiosity reward generated by the intrinsic rnd module. This should be scaled in order to ensure it is large enough to not be overwhelmed by extrinsic reward signals in the environment. Likewise it should not be too large to overwhelm the extrinsic reward signal. Typical range: 0.001 - 0.01 rnd -> gamma (default = 0.99 ) Discount factor for future rewards. Typical range: 0.8 - 0.995 rnd -> network_settings Please see the documentation for network_settings under Common Trainer Configurations . The network specs for the RND model. curiosity -> learning_rate (default = 3e-4 ) Learning rate used to update the RND module. This should be large enough for the RND module to quickly learn the state representation, but small enough to allow for stable learning. Typical range: 1e-5 - 1e-3 Behavioral Cloning To enable Behavioral Cloning as a pre-training option (assuming you have recorded demonstrations), provide the following configurations under the behavioral_cloning section: Setting Description demo_path (Required, no default) The path to your .demo file or directory of .demo files. strength (default = 1.0 ) Learning rate of the imitation relative to the learning rate of PPO, and roughly corresponds to how strongly we allow BC to influence the policy. Typical range: 0.1 - 0.5 steps (default = 0 ) During BC, it is often desirable to stop using demonstrations after the agent has \"seen\" rewards, and allow it to optimize past the available demonstrations and/or generalize outside of the provided demonstrations. steps corresponds to the training steps over which BC is active. The learning rate of BC will anneal over the steps. Set the steps to 0 for constant imitation over the entire training run. batch_size (default = batch_size of trainer) Number of demonstration experiences used for one iteration of a gradient descent update. If not specified, it will default to the batch_size of the trainer. Typical range: (Continuous): 512 - 5120 ; (Discrete): 32 - 512 num_epoch (default = num_epoch of trainer) Number of passes through the experience buffer during gradient descent. If not specified, it will default to the number of epochs set for PPO. Typical range: 3 - 10 samples_per_update (default = 0 ) Maximum number of samples to use during each imitation update. You may want to lower this if your demonstration dataset is very large to avoid overfitting the policy on demonstrations. Set to 0 to train over all of the demonstrations at each update step. Typical range: buffer_size Memory-enhanced Agents using Recurrent Neural Networks You can enable your agents to use memory by adding a memory section under network_settings , and setting memory_size and sequence_length : Setting Description network_settings -> memory -> memory_size (default = 128 ) Size of the memory an agent must keep. In order to use a LSTM, training requires a sequence of experiences instead of single experiences. Corresponds to the size of the array of floating point numbers used to store the hidden state of the recurrent neural network of the policy. This value must be a multiple of 2, and should scale with the amount of information you expect the agent will need to remember in order to successfully complete the task. Typical range: 32 - 256 network_settings -> memory -> sequence_length (default = 64 ) Defines how long the sequences of experiences must be while training. Note that if this number is too small, the agent will not be able to remember things over longer periods of time. If this number is too large, the neural network will take longer to train. Typical range: 4 - 128 A few considerations when deciding to use memory: LSTM does not work well with continuous actions. Please use discrete actions for better results. Adding a recurrent layer increases the complexity of the neural network, it is recommended to decrease num_layers when using recurrent. It is required that memory_size be divisible by 2. Self-Play Training with self-play adds additional confounding factors to the usual issues faced by reinforcement learning. In general, the tradeoff is between the skill level and generality of the final policy and the stability of learning. Training against a set of slowly or unchanging adversaries with low diversity results in a more stable learning process than training against a set of quickly changing adversaries with high diversity. With this context, this guide discusses the exposed self-play hyperparameters and intuitions for tuning them. If your environment contains multiple agents that are divided into teams, you can leverage our self-play training option by providing these configurations for each Behavior: Setting Description save_steps (default = 20000 ) Number of trainer steps between snapshots. For example, if save_steps=10000 then a snapshot of the current policy will be saved every 10000 trainer steps. Note, trainer steps are counted per agent. For more information, please see the migration doc after v0.13. A larger value of save_steps will yield a set of opponents that cover a wider range of skill levels and possibly play styles since the policy receives more training. As a result, the agent trains against a wider variety of opponents. Learning a policy to defeat more diverse opponents is a harder problem and so may require more overall training steps but also may lead to more general and robust policy at the end of training. This value is also dependent on how intrinsically difficult the environment is for the agent. Typical range: 10000 - 100000 team_change (default = 5 * save_steps ) Number of trainer_steps between switching the learning team. This is the number of trainer steps the teams associated with a specific ghost trainer will train before a different team becomes the new learning team. It is possible that, in asymmetric games, opposing teams require fewer trainer steps to make similar performance gains. This enables users to train a more complicated team of agents for more trainer steps than a simpler team of agents per team switch. A larger value of team-change will allow the agent to train longer against it's opponents. The longer an agent trains against the same set of opponents the more able it will be to defeat them. However, training against them for too long may result in overfitting to the particular opponent strategies and so the agent may fail against the next batch of opponents. The value of team-change will determine how many snapshots of the agent's policy are saved to be used as opponents for the other team. So, we recommend setting this value as a function of the save_steps parameter discussed previously. Typical range: 4x-10x where x= save_steps swap_steps (default = 10000 ) Number of ghost steps (not trainer steps) between swapping the opponents policy with a different snapshot. A 'ghost step' refers to a step taken by an agent that is following a fixed policy and not learning . The reason for this distinction is that in asymmetric games, we may have teams with an unequal number of agents e.g. a 2v1 scenario like our Strikers Vs Goalie example environment. The team with two agents collects twice as many agent steps per environment step as the team with one agent. Thus, these two values will need to be distinct to ensure that the same number of trainer steps corresponds to the same number of opponent swaps for each team. The formula for swap_steps if a user desires x swaps of a team with num_agents agents against an opponent team with num_opponent_agents agents during team-change total steps is: (num_agents / num_opponent_agents) * (team_change / x) Typical range: 10000 - 100000 play_against_latest_model_ratio (default = 0.5 ) Probability an agent will play against the latest opponent policy. With probability 1 - play_against_latest_model_ratio , the agent will play against a snapshot of its opponent from a past iteration. A larger value of play_against_latest_model_ratio indicates that an agent will be playing against the current opponent more often. Since the agent is updating it's policy, the opponent will be different from iteration to iteration. This can lead to an unstable learning environment, but poses the agent with an auto-curricula of more increasingly challenging situations which may lead to a stronger final policy. Typical range: 0.0 - 1.0 window (default = 10 ) Size of the sliding window of past snapshots from which the agent's opponents are sampled. For example, a window size of 5 will save the last 5 snapshots taken. Each time a new snapshot is taken, the oldest is discarded. A larger value of window means that an agent's pool of opponents will contain a larger diversity of behaviors since it will contain policies from earlier in the training run. Like in the save_steps hyperparameter, the agent trains against a wider variety of opponents. Learning a policy to defeat more diverse opponents is a harder problem and so may require more overall training steps but also may lead to more general and robust policy at the end of training. Typical range: 5 - 30 Note on Reward Signals We make the assumption that the final reward in a trajectory corresponds to the outcome of an episode. A final reward of +1 indicates winning, -1 indicates losing and 0 indicates a draw. The ELO calculation (discussed below) depends on this final reward being either +1, 0, -1. The reward signal should still be used as described in the documentation for the other trainers. However, we encourage users to be a bit more conservative when shaping reward functions due to the instability and non-stationarity of learning in adversarial games. Specifically, we encourage users to begin with the simplest possible reward function (+1 winning, -1 losing) and to allow for more iterations of training to compensate for the sparsity of reward. Note on Swap Steps As an example, in a 2v1 scenario, if we want the swap to occur x=4 times during team-change=200000 steps, the swap_steps for the team of one agent is: swap_steps = (1 / 2) * (200000 / 4) = 25000 The swap_steps for the team of two agents is: swap_steps = (2 / 1) * (200000 / 4) = 100000 Note, with equal team sizes, the first term is equal to 1 and swap_steps can be calculated by just dividing the total steps by the desired number of swaps. A larger value of swap_steps means that an agent will play against the same fixed opponent for a longer number of training iterations. This results in a more stable training scenario, but leaves the agent open to the risk of overfitting it's behavior for this particular opponent. Thus, when a new opponent is swapped, the agent may lose more often than expected.","title":"Training Configuration File"},{"location":"Training-Configuration-File/#training-configuration-file","text":"Table of Contents Common Trainer Configurations Trainer-specific Configurations PPO-specific Configurations SAC-specific Configurations Reward Signals Extrinsic Rewards Curiosity Intrinsic Reward GAIL Intrinsic Reward RND Intrinsic Reward Reward Signal Settings for SAC Behavioral Cloning Memory-enhanced Agents using Recurrent Neural Networks Self-Play Note on Reward Signals Note on Swap Steps","title":"Training Configuration File"},{"location":"Training-Configuration-File/#common-trainer-configurations","text":"One of the first decisions you need to make regarding your training run is which trainer to use: PPO, SAC, or POCA. There are some training configurations that are common to both trainers (which we review now) and others that depend on the choice of the trainer (which we review on subsequent sections). Setting Description trainer_type (default = ppo ) The type of trainer to use: ppo , sac , or poca . summary_freq (default = 50000 ) Number of experiences that needs to be collected before generating and displaying training statistics. This determines the granularity of the graphs in Tensorboard. time_horizon (default = 64 ) How many steps of experience to collect per-agent before adding it to the experience buffer. When this limit is reached before the end of an episode, a value estimate is used to predict the overall expected reward from the agent's current state. As such, this parameter trades off between a less biased, but higher variance estimate (long time horizon) and more biased, but less varied estimate (short time horizon). In cases where there are frequent rewards within an episode, or episodes are prohibitively large, a smaller number can be more ideal. This number should be large enough to capture all the important behavior within a sequence of an agent's actions. Typical range: 32 - 2048 max_steps (default = 500000 ) Total number of steps (i.e., observation collected and action taken) that must be taken in the environment (or across all environments if using multiple in parallel) before ending the training process. If you have multiple agents with the same behavior name within your environment, all steps taken by those agents will contribute to the same max_steps count. Typical range: 5e5 - 1e7 keep_checkpoints (default = 5 ) The maximum number of model checkpoints to keep. Checkpoints are saved after the number of steps specified by the checkpoint_interval option. Once the maximum number of checkpoints has been reached, the oldest checkpoint is deleted when saving a new checkpoint. even_checkpoints (default = false ) If set to true, ignores checkpoint_interval and evenly distributes checkpoints throughout training based on keep_checkpoints and max_steps , i.e. checkpoint_interval = max_steps / keep_checkpoints . Useful for cataloging agent behavior throughout training. checkpoint_interval (default = 500000 ) The number of experiences collected between each checkpoint by the trainer. A maximum of keep_checkpoints checkpoints are saved before old ones are deleted. Each checkpoint saves the .onnx files in results/ folder. init_path (default = None) Initialize trainer from a previously saved model. Note that the prior run should have used the same trainer configurations as the current run, and have been saved with the same version of ML-Agents. You can provide either the file name or the full path to the checkpoint, e.g. {checkpoint_name.pt} or ./models/{run-id}/{behavior_name}/{checkpoint_name.pt} . This option is provided in case you want to initialize different behaviors from different runs or initialize from an older checkpoint; in most cases, it is sufficient to use the --initialize-from CLI parameter to initialize all models from the same run. threaded (default = false ) Allow environments to step while updating the model. This might result in a training speedup, especially when using SAC. For best performance, leave setting to false when using self-play. hyperparameters -> learning_rate (default = 3e-4 ) Initial learning rate for gradient descent. Corresponds to the strength of each gradient descent update step. This should typically be decreased if training is unstable, and the reward does not consistently increase. Typical range: 1e-5 - 1e-3 hyperparameters -> batch_size Number of experiences in each iteration of gradient descent. This should always be multiple times smaller than buffer_size . If you are using continuous actions, this value should be large (on the order of 1000s). If you are using only discrete actions, this value should be smaller (on the order of 10s). Typical range: (Continuous - PPO): 512 - 5120 ; (Continuous - SAC): 128 - 1024 ; (Discrete, PPO & SAC): 32 - 512 . hyperparameters -> buffer_size (default = 10240 for PPO and 50000 for SAC) PPO: Number of experiences to collect before updating the policy model. Corresponds to how many experiences should be collected before we do any learning or updating of the model. This should be multiple times larger than batch_size . Typically a larger buffer_size corresponds to more stable training updates. SAC: The max size of the experience buffer - on the order of thousands of times longer than your episodes, so that SAC can learn from old as well as new experiences. Typical range: PPO: 2048 - 409600 ; SAC: 50000 - 1000000 hyperparameters -> learning_rate_schedule (default = linear for PPO and constant for SAC) Determines how learning rate changes over time. For PPO, we recommend decaying learning rate until max_steps so learning converges more stably. However, for some cases (e.g. training for an unknown amount of time) this feature can be disabled. For SAC, we recommend holding learning rate constant so that the agent can continue to learn until its Q function converges naturally. linear decays the learning_rate linearly, reaching 0 at max_steps, while constant keeps the learning rate constant for the entire training run. network_settings -> hidden_units (default = 128 ) Number of units in the hidden layers of the neural network. Correspond to how many units are in each fully connected layer of the neural network. For simple problems where the correct action is a straightforward combination of the observation inputs, this should be small. For problems where the action is a very complex interaction between the observation variables, this should be larger. Typical range: 32 - 512 network_settings -> num_layers (default = 2 ) The number of hidden layers in the neural network. Corresponds to how many hidden layers are present after the observation input, or after the CNN encoding of the visual observation. For simple problems, fewer layers are likely to train faster and more efficiently. More layers may be necessary for more complex control problems. Typical range: 1 - 3 network_settings -> normalize (default = false ) Whether normalization is applied to the vector observation inputs. This normalization is based on the running average and variance of the vector observation. Normalization can be helpful in cases with complex continuous control problems, but may be harmful with simpler discrete control problems. network_settings -> vis_encode_type (default = simple ) Encoder type for encoding visual observations. simple (default) uses a simple encoder which consists of two convolutional layers, nature_cnn uses the CNN implementation proposed by Mnih et al. , consisting of three convolutional layers, and resnet uses the IMPALA Resnet consisting of three stacked layers, each with two residual blocks, making a much larger network than the other two. match3 is a smaller CNN ( Gudmundsoon et al. ) that can capture more granular spatial relationships and is optimized for board games. fully_connected uses a single fully connected dense layer as encoder without any convolutional layers. Due to the size of convolution kernel, there is a minimum observation size limitation that each encoder type can handle - simple : 20x20, nature_cnn : 36x36, resnet : 15 x 15, match3 : 5x5. fully_connected doesn't have convolutional layers and thus no size limits, but since it has less representation power it should be reserved for very small inputs. Note that using the match3 CNN with very large visual input might result in a huge observation encoding and thus potentially slow down training or cause memory issues. network_settings -> conditioning_type (default = hyper ) Conditioning type for the policy using goal observations. none treats the goal observations as regular observations, hyper (default) uses a HyperNetwork with goal observations as input to generate some of the weights of the policy. Note that when using hyper the number of parameters of the network increases greatly. Therefore, it is recommended to reduce the number of hidden_units when using this conditioning_type","title":"Common Trainer Configurations"},{"location":"Training-Configuration-File/#trainer-specific-configurations","text":"Depending on your choice of a trainer, there are additional trainer-specific configurations. We present them below in two separate tables, but keep in mind that you only need to include the configurations for the trainer selected (i.e. the trainer setting above).","title":"Trainer-specific Configurations"},{"location":"Training-Configuration-File/#ppo-specific-configurations","text":"Setting Description hyperparameters -> beta (default = 5.0e-3 ) Strength of the entropy regularization, which makes the policy \"more random.\" This ensures that agents properly explore the action space during training. Increasing this will ensure more random actions are taken. This should be adjusted such that the entropy (measurable from TensorBoard) slowly decreases alongside increases in reward. If entropy drops too quickly, increase beta. If entropy drops too slowly, decrease beta . Typical range: 1e-4 - 1e-2 hyperparameters -> epsilon (default = 0.2 ) Influences how rapidly the policy can evolve during training. Corresponds to the acceptable threshold of divergence between the old and new policies during gradient descent updating. Setting this value small will result in more stable updates, but will also slow the training process. Typical range: 0.1 - 0.3 hyperparameters -> beta_schedule (default = learning_rate_schedule ) Determines how beta changes over time. linear decays beta linearly, reaching 0 at max_steps, while constant keeps beta constant for the entire training run. If not explicitly set, the default beta schedule will be set to hyperparameters -> learning_rate_schedule . hyperparameters -> epsilon_schedule (default = learning_rate_schedule ) Determines how epsilon changes over time (PPO only). linear decays epsilon linearly, reaching 0 at max_steps, while constant keeps the epsilon constant for the entire training run. If not explicitly set, the default epsilon schedule will be set to hyperparameters -> learning_rate_schedule . hyperparameters -> lambd (default = 0.95 ) Regularization parameter (lambda) used when calculating the Generalized Advantage Estimate ( GAE ). This can be thought of as how much the agent relies on its current value estimate when calculating an updated value estimate. Low values correspond to relying more on the current value estimate (which can be high bias), and high values correspond to relying more on the actual rewards received in the environment (which can be high variance). The parameter provides a trade-off between the two, and the right value can lead to a more stable training process. Typical range: 0.9 - 0.95 hyperparameters -> num_epoch (default = 3 ) Number of passes to make through the experience buffer when performing gradient descent optimization.The larger the batch_size, the larger it is acceptable to make this. Decreasing this will ensure more stable updates, at the cost of slower learning. Typical range: 3 - 10 hyperparameters -> shared_critic (default = False ) Whether or not the policy and value function networks share a backbone. It may be useful to use a shared backbone when learning from image observations.","title":"PPO-specific Configurations"},{"location":"Training-Configuration-File/#sac-specific-configurations","text":"Setting Description hyperparameters -> buffer_init_steps (default = 0 ) Number of experiences to collect into the buffer before updating the policy model. As the untrained policy is fairly random, pre-filling the buffer with random actions is useful for exploration. Typically, at least several episodes of experiences should be pre-filled. Typical range: 1000 - 10000 hyperparameters -> init_entcoef (default = 1.0 ) How much the agent should explore in the beginning of training. Corresponds to the initial entropy coefficient set at the beginning of training. In SAC, the agent is incentivized to make its actions entropic to facilitate better exploration. The entropy coefficient weighs the true reward with a bonus entropy reward. The entropy coefficient is automatically adjusted to a preset target entropy, so the init_entcoef only corresponds to the starting value of the entropy bonus. Increase init_entcoef to explore more in the beginning, decrease to converge to a solution faster. Typical range: (Continuous): 0.5 - 1.0 ; (Discrete): 0.05 - 0.5 hyperparameters -> save_replay_buffer (default = false ) Whether to save and load the experience replay buffer as well as the model when quitting and re-starting training. This may help resumes go more smoothly, as the experiences collected won't be wiped. Note that replay buffers can be very large, and will take up a considerable amount of disk space. For that reason, we disable this feature by default. hyperparameters -> tau (default = 0.005 ) How aggressively to update the target network used for bootstrapping value estimation in SAC. Corresponds to the magnitude of the target Q update during the SAC model update. In SAC, there are two neural networks: the target and the policy. The target network is used to bootstrap the policy's estimate of the future rewards at a given state, and is fixed while the policy is being updated. This target is then slowly updated according to tau. Typically, this value should be left at 0.005. For simple problems, increasing tau to 0.01 might reduce the time it takes to learn, at the cost of stability. Typical range: 0.005 - 0.01 hyperparameters -> steps_per_update (default = 1 ) Average ratio of agent steps (actions) taken to updates made of the agent's policy. In SAC, a single \"update\" corresponds to grabbing a batch of size batch_size from the experience replay buffer, and using this mini batch to update the models. Note that it is not guaranteed that after exactly steps_per_update steps an update will be made, only that the ratio will hold true over many steps. Typically, steps_per_update should be greater than or equal to 1. Note that setting steps_per_update lower will improve sample efficiency (reduce the number of steps required to train) but increase the CPU time spent performing updates. For most environments where steps are fairly fast (e.g. our example environments) steps_per_update equal to the number of agents in the scene is a good balance. For slow environments (steps take 0.1 seconds or more) reducing steps_per_update may improve training speed. We can also change steps_per_update to lower than 1 to update more often than once per step, though this will usually result in a slowdown unless the environment is very slow. Typical range: 1 - 20 hyperparameters -> reward_signal_num_update (default = steps_per_update ) Number of steps per mini batch sampled and used for updating the reward signals. By default, we update the reward signals once every time the main policy is updated. However, to imitate the training procedure in certain imitation learning papers (e.g. Kostrikov et. al , Blond\u00e9 et. al ), we may want to update the reward signal (GAIL) M times for every update of the policy. We can change steps_per_update of SAC to N, as well as reward_signal_steps_per_update under reward_signals to N / M to accomplish this. By default, reward_signal_steps_per_update is set to steps_per_update .","title":"SAC-specific Configurations"},{"location":"Training-Configuration-File/#ma-poca-specific-configurations","text":"MA-POCA uses the same configurations as PPO, and there are no additional POCA-specific parameters. NOTE : Reward signals other than Extrinsic Rewards have not been extensively tested with MA-POCA, though they can still be added and used for training on a your-mileage-may-vary basis.","title":"MA-POCA-specific Configurations"},{"location":"Training-Configuration-File/#reward-signals","text":"The reward_signals section enables the specification of settings for both extrinsic (i.e. environment-based) and intrinsic reward signals (e.g. curiosity and GAIL). Each reward signal should define at least two parameters, strength and gamma , in addition to any class-specific hyperparameters. Note that to remove a reward signal, you should delete its entry entirely from reward_signals . At least one reward signal should be left defined at all times. Provide the following configurations to design the reward signal for your training run.","title":"Reward Signals"},{"location":"Training-Configuration-File/#extrinsic-rewards","text":"Enable these settings to ensure that your training run incorporates your environment-based reward signal: Setting Description extrinsic -> strength (default = 1.0 ) Factor by which to multiply the reward given by the environment. Typical ranges will vary depending on the reward signal. Typical range: 1.00 extrinsic -> gamma (default = 0.99 ) Discount factor for future rewards coming from the environment. This can be thought of as how far into the future the agent should care about possible rewards. In situations when the agent should be acting in the present in order to prepare for rewards in the distant future, this value should be large. In cases when rewards are more immediate, it can be smaller. Must be strictly smaller than 1. Typical range: 0.8 - 0.995","title":"Extrinsic Rewards"},{"location":"Training-Configuration-File/#curiosity-intrinsic-reward","text":"To enable curiosity, provide these settings: Setting Description curiosity -> strength (default = 1.0 ) Magnitude of the curiosity reward generated by the intrinsic curiosity module. This should be scaled in order to ensure it is large enough to not be overwhelmed by extrinsic reward signals in the environment. Likewise it should not be too large to overwhelm the extrinsic reward signal. Typical range: 0.001 - 0.1 curiosity -> gamma (default = 0.99 ) Discount factor for future rewards. Typical range: 0.8 - 0.995 curiosity -> network_settings Please see the documentation for network_settings under Common Trainer Configurations . The network specs used by the intrinsic curiosity model. The value should of hidden_units should be small enough to encourage the ICM to compress the original observation, but also not too small to prevent it from learning to differentiate between expected and actual observations. Typical range: 64 - 256 curiosity -> learning_rate (default = 3e-4 ) Learning rate used to update the intrinsic curiosity module. This should typically be decreased if training is unstable, and the curiosity loss is unstable. Typical range: 1e-5 - 1e-3","title":"Curiosity Intrinsic Reward"},{"location":"Training-Configuration-File/#gail-intrinsic-reward","text":"To enable GAIL (assuming you have recorded demonstrations), provide these settings: Setting Description gail -> strength (default = 1.0 ) Factor by which to multiply the raw reward. Note that when using GAIL with an Extrinsic Signal, this value should be set lower if your demonstrations are suboptimal (e.g. from a human), so that a trained agent will focus on receiving extrinsic rewards instead of exactly copying the demonstrations. Keep the strength below about 0.1 in those cases. Typical range: 0.01 - 1.0 gail -> gamma (default = 0.99 ) Discount factor for future rewards. Typical range: 0.8 - 0.9 gail -> demo_path (Required, no default) The path to your .demo file or directory of .demo files. gail -> network_settings Please see the documentation for network_settings under Common Trainer Configurations . The network specs for the GAIL discriminator. The value of hidden_units should be small enough to encourage the discriminator to compress the original observation, but also not too small to prevent it from learning to differentiate between demonstrated and actual behavior. Dramatically increasing this size will also negatively affect training times. Typical range: 64 - 256 gail -> learning_rate (Optional, default = 3e-4 ) Learning rate used to update the discriminator. This should typically be decreased if training is unstable, and the GAIL loss is unstable. Typical range: 1e-5 - 1e-3 gail -> use_actions (default = false ) Determines whether the discriminator should discriminate based on both observations and actions, or just observations. Set to True if you want the agent to mimic the actions from the demonstrations, and False if you'd rather have the agent visit the same states as in the demonstrations but with possibly different actions. Setting to False is more likely to be stable, especially with imperfect demonstrations, but may learn slower. gail -> use_vail (default = false ) Enables a variational bottleneck within the GAIL discriminator. This forces the discriminator to learn a more general representation and reduces its tendency to be \"too good\" at discriminating, making learning more stable. However, it does increase training time. Enable this if you notice your imitation learning is unstable, or unable to learn the task at hand.","title":"GAIL Intrinsic Reward"},{"location":"Training-Configuration-File/#rnd-intrinsic-reward","text":"Random Network Distillation (RND) is only available for the PyTorch trainers. To enable RND, provide these settings: Setting Description rnd -> strength (default = 1.0 ) Magnitude of the curiosity reward generated by the intrinsic rnd module. This should be scaled in order to ensure it is large enough to not be overwhelmed by extrinsic reward signals in the environment. Likewise it should not be too large to overwhelm the extrinsic reward signal. Typical range: 0.001 - 0.01 rnd -> gamma (default = 0.99 ) Discount factor for future rewards. Typical range: 0.8 - 0.995 rnd -> network_settings Please see the documentation for network_settings under Common Trainer Configurations . The network specs for the RND model. curiosity -> learning_rate (default = 3e-4 ) Learning rate used to update the RND module. This should be large enough for the RND module to quickly learn the state representation, but small enough to allow for stable learning. Typical range: 1e-5 - 1e-3","title":"RND Intrinsic Reward"},{"location":"Training-Configuration-File/#behavioral-cloning","text":"To enable Behavioral Cloning as a pre-training option (assuming you have recorded demonstrations), provide the following configurations under the behavioral_cloning section: Setting Description demo_path (Required, no default) The path to your .demo file or directory of .demo files. strength (default = 1.0 ) Learning rate of the imitation relative to the learning rate of PPO, and roughly corresponds to how strongly we allow BC to influence the policy. Typical range: 0.1 - 0.5 steps (default = 0 ) During BC, it is often desirable to stop using demonstrations after the agent has \"seen\" rewards, and allow it to optimize past the available demonstrations and/or generalize outside of the provided demonstrations. steps corresponds to the training steps over which BC is active. The learning rate of BC will anneal over the steps. Set the steps to 0 for constant imitation over the entire training run. batch_size (default = batch_size of trainer) Number of demonstration experiences used for one iteration of a gradient descent update. If not specified, it will default to the batch_size of the trainer. Typical range: (Continuous): 512 - 5120 ; (Discrete): 32 - 512 num_epoch (default = num_epoch of trainer) Number of passes through the experience buffer during gradient descent. If not specified, it will default to the number of epochs set for PPO. Typical range: 3 - 10 samples_per_update (default = 0 ) Maximum number of samples to use during each imitation update. You may want to lower this if your demonstration dataset is very large to avoid overfitting the policy on demonstrations. Set to 0 to train over all of the demonstrations at each update step. Typical range: buffer_size","title":"Behavioral Cloning"},{"location":"Training-Configuration-File/#memory-enhanced-agents-using-recurrent-neural-networks","text":"You can enable your agents to use memory by adding a memory section under network_settings , and setting memory_size and sequence_length : Setting Description network_settings -> memory -> memory_size (default = 128 ) Size of the memory an agent must keep. In order to use a LSTM, training requires a sequence of experiences instead of single experiences. Corresponds to the size of the array of floating point numbers used to store the hidden state of the recurrent neural network of the policy. This value must be a multiple of 2, and should scale with the amount of information you expect the agent will need to remember in order to successfully complete the task. Typical range: 32 - 256 network_settings -> memory -> sequence_length (default = 64 ) Defines how long the sequences of experiences must be while training. Note that if this number is too small, the agent will not be able to remember things over longer periods of time. If this number is too large, the neural network will take longer to train. Typical range: 4 - 128 A few considerations when deciding to use memory: LSTM does not work well with continuous actions. Please use discrete actions for better results. Adding a recurrent layer increases the complexity of the neural network, it is recommended to decrease num_layers when using recurrent. It is required that memory_size be divisible by 2.","title":"Memory-enhanced Agents using Recurrent Neural Networks"},{"location":"Training-Configuration-File/#self-play","text":"Training with self-play adds additional confounding factors to the usual issues faced by reinforcement learning. In general, the tradeoff is between the skill level and generality of the final policy and the stability of learning. Training against a set of slowly or unchanging adversaries with low diversity results in a more stable learning process than training against a set of quickly changing adversaries with high diversity. With this context, this guide discusses the exposed self-play hyperparameters and intuitions for tuning them. If your environment contains multiple agents that are divided into teams, you can leverage our self-play training option by providing these configurations for each Behavior: Setting Description save_steps (default = 20000 ) Number of trainer steps between snapshots. For example, if save_steps=10000 then a snapshot of the current policy will be saved every 10000 trainer steps. Note, trainer steps are counted per agent. For more information, please see the migration doc after v0.13. A larger value of save_steps will yield a set of opponents that cover a wider range of skill levels and possibly play styles since the policy receives more training. As a result, the agent trains against a wider variety of opponents. Learning a policy to defeat more diverse opponents is a harder problem and so may require more overall training steps but also may lead to more general and robust policy at the end of training. This value is also dependent on how intrinsically difficult the environment is for the agent. Typical range: 10000 - 100000 team_change (default = 5 * save_steps ) Number of trainer_steps between switching the learning team. This is the number of trainer steps the teams associated with a specific ghost trainer will train before a different team becomes the new learning team. It is possible that, in asymmetric games, opposing teams require fewer trainer steps to make similar performance gains. This enables users to train a more complicated team of agents for more trainer steps than a simpler team of agents per team switch. A larger value of team-change will allow the agent to train longer against it's opponents. The longer an agent trains against the same set of opponents the more able it will be to defeat them. However, training against them for too long may result in overfitting to the particular opponent strategies and so the agent may fail against the next batch of opponents. The value of team-change will determine how many snapshots of the agent's policy are saved to be used as opponents for the other team. So, we recommend setting this value as a function of the save_steps parameter discussed previously. Typical range: 4x-10x where x= save_steps swap_steps (default = 10000 ) Number of ghost steps (not trainer steps) between swapping the opponents policy with a different snapshot. A 'ghost step' refers to a step taken by an agent that is following a fixed policy and not learning . The reason for this distinction is that in asymmetric games, we may have teams with an unequal number of agents e.g. a 2v1 scenario like our Strikers Vs Goalie example environment. The team with two agents collects twice as many agent steps per environment step as the team with one agent. Thus, these two values will need to be distinct to ensure that the same number of trainer steps corresponds to the same number of opponent swaps for each team. The formula for swap_steps if a user desires x swaps of a team with num_agents agents against an opponent team with num_opponent_agents agents during team-change total steps is: (num_agents / num_opponent_agents) * (team_change / x) Typical range: 10000 - 100000 play_against_latest_model_ratio (default = 0.5 ) Probability an agent will play against the latest opponent policy. With probability 1 - play_against_latest_model_ratio , the agent will play against a snapshot of its opponent from a past iteration. A larger value of play_against_latest_model_ratio indicates that an agent will be playing against the current opponent more often. Since the agent is updating it's policy, the opponent will be different from iteration to iteration. This can lead to an unstable learning environment, but poses the agent with an auto-curricula of more increasingly challenging situations which may lead to a stronger final policy. Typical range: 0.0 - 1.0 window (default = 10 ) Size of the sliding window of past snapshots from which the agent's opponents are sampled. For example, a window size of 5 will save the last 5 snapshots taken. Each time a new snapshot is taken, the oldest is discarded. A larger value of window means that an agent's pool of opponents will contain a larger diversity of behaviors since it will contain policies from earlier in the training run. Like in the save_steps hyperparameter, the agent trains against a wider variety of opponents. Learning a policy to defeat more diverse opponents is a harder problem and so may require more overall training steps but also may lead to more general and robust policy at the end of training. Typical range: 5 - 30","title":"Self-Play"},{"location":"Training-Configuration-File/#note-on-reward-signals","text":"We make the assumption that the final reward in a trajectory corresponds to the outcome of an episode. A final reward of +1 indicates winning, -1 indicates losing and 0 indicates a draw. The ELO calculation (discussed below) depends on this final reward being either +1, 0, -1. The reward signal should still be used as described in the documentation for the other trainers. However, we encourage users to be a bit more conservative when shaping reward functions due to the instability and non-stationarity of learning in adversarial games. Specifically, we encourage users to begin with the simplest possible reward function (+1 winning, -1 losing) and to allow for more iterations of training to compensate for the sparsity of reward.","title":"Note on Reward Signals"},{"location":"Training-Configuration-File/#note-on-swap-steps","text":"As an example, in a 2v1 scenario, if we want the swap to occur x=4 times during team-change=200000 steps, the swap_steps for the team of one agent is: swap_steps = (1 / 2) * (200000 / 4) = 25000 The swap_steps for the team of two agents is: swap_steps = (2 / 1) * (200000 / 4) = 100000 Note, with equal team sizes, the first term is equal to 1 and swap_steps can be calculated by just dividing the total steps by the desired number of swaps. A larger value of swap_steps means that an agent will play against the same fixed opponent for a longer number of training iterations. This results in a more stable training scenario, but leaves the agent open to the risk of overfitting it's behavior for this particular opponent. Thus, when a new opponent is swapped, the agent may lose more often than expected.","title":"Note on Swap Steps"},{"location":"Training-ML-Agents/","text":"Training ML-Agents Table of Contents Training ML-Agents Training with mlagents-learn Starting Training Observing Training Stopping and Resuming Training Loading an Existing Model Training Configurations Adding CLI Arguments to the Training Configuration file Environment settings Engine settings Checkpoint settings Torch settings: Behavior Configurations Default Behavior Settings Environment Parameters Environment Parameter Randomization Supported Sampler Types Training with Environment Parameter Randomization Curriculum Training with a Curriculum Training Using Concurrent Unity Instances For a broad overview of reinforcement learning, imitation learning and all the training scenarios, methods and options within the ML-Agents Toolkit, see ML-Agents Toolkit Overview . Once your learning environment has been created and is ready for training, the next step is to initiate a training run. Training in the ML-Agents Toolkit is powered by a dedicated Python package, mlagents . This package exposes a command mlagents-learn that is the single entry point for all training workflows (e.g. reinforcement leaning, imitation learning, curriculum learning). Its implementation can be found at ml-agents/mlagents/trainers/learn.py . Training with mlagents-learn Starting Training mlagents-learn is the main training utility provided by the ML-Agents Toolkit. It accepts a number of CLI options in addition to a YAML configuration file that contains all the configurations and hyperparameters to be used during training. The set of configurations and hyperparameters to include in this file depend on the agents in your environment and the specific training method you wish to utilize. Keep in mind that the hyperparameter values can have a big impact on the training performance (i.e. your agent's ability to learn a policy that solves the task). In this page, we will review all the hyperparameters for all training methods and provide guidelines and advice on their values. To view a description of all the CLI options accepted by mlagents-learn , use the --help : mlagents-learn --help The basic command for training is: mlagents-learn <trainer-config-file> --env=<env_name> --run-id=<run-identifier> where <trainer-config-file> is the file path of the trainer configuration YAML. This contains all the hyperparameter values. We offer a detailed guide on the structure of this file and the meaning of the hyperparameters (and advice on how to set them) in the dedicated Training Configurations section below. <env_name> (Optional) is the name (including path) of your Unity executable containing the agents to be trained. If <env_name> is not passed, the training will happen in the Editor. Press the Play button in Unity when the message \"Start training by pressing the Play button in the Unity Editor\" is displayed on the screen. <run-identifier> is a unique name you can use to identify the results of your training runs. See the Getting Started Guide for a sample execution of the mlagents-learn command. Observing Training Regardless of which training methods, configurations or hyperparameters you provide, the training process will always generate three artifacts, all found in the results/<run-identifier> folder: Summaries: these are training metrics that are updated throughout the training process. They are helpful to monitor your training performance and may help inform how to update your hyperparameter values. See Using TensorBoard for more details on how to visualize the training metrics. Models: these contain the model checkpoints that are updated throughout training and the final model file ( .onnx ). This final model file is generated once either when training completes or is interrupted. Timers file (under results/<run-identifier>/run_logs ): this contains aggregated metrics on your training process, including time spent on specific code blocks. See Profiling in Python for more information on the timers generated. These artifacts are updated throughout the training process and finalized when training is completed or is interrupted. Stopping and Resuming Training To interrupt training and save the current progress, hit Ctrl+C once and wait for the model(s) to be saved out. To resume a previously interrupted or completed training run, use the --resume flag and make sure to specify the previously used run ID. If you would like to re-run a previously interrupted or completed training run and re-use the same run ID (in this case, overwriting the previously generated artifacts), then use the --force flag. Loading an Existing Model You can also use this mode to run inference of an already-trained model in Python by using both the --resume and --inference flags. Note that if you want to run inference in Unity, you should use the Unity Inference Engine . Additionally, if the network architecture changes, you may still load an existing model, but ML-Agents will only load the parts of the model it can load and ignore all others. For instance, if you add a new reward signal, the existing model will load but the new reward signal will be initialized from scratch. If you have a model with a visual encoder (CNN) but change the hidden_units , the CNN will be loaded but the body of the network will be initialized from scratch. Alternatively, you might want to start a new training run but initialize it using an already-trained model. You may want to do this, for instance, if your environment changed and you want a new model, but the old behavior is still better than random. You can do this by specifying --initialize-from=<run-identifier> , where <run-identifier> is the old run ID. Training Configurations The Unity ML-Agents Toolkit provides a wide range of training scenarios, methods and options. As such, specific training runs may require different training configurations and may generate different artifacts and TensorBoard statistics. This section offers a detailed guide into how to manage the different training set-ups withing the toolkit. More specifically, this section offers a detailed guide on the command-line flags for mlagents-learn that control the training configurations: <trainer-config-file> : defines the training hyperparameters for each Behavior in the scene, and the set-ups for the environment parameters (Curriculum Learning and Environment Parameter Randomization) It is important to highlight that successfully training a Behavior in the ML-Agents Toolkit involves tuning the training hyperparameters and configuration. This guide contains some best practices for tuning the training process when the default parameters don't seem to be giving the level of performance you would like. We provide sample configuration files for our example environments in the config/ directory. The config/ppo/3DBall.yaml was used to train the 3D Balance Ball in the Getting Started guide. That configuration file uses the PPO trainer, but we also have configuration files for SAC and GAIL. Additionally, the set of configurations you provide depend on the training functionalities you use (see ML-Agents Toolkit Overview for a description of all the training functionalities). Each functionality you add typically has its own training configurations. For instance: Use PPO or SAC? Use Recurrent Neural Networks for adding memory to your agents? Use the intrinsic curiosity module? Ignore the environment reward signal? Pre-train using behavioral cloning? (Assuming you have recorded demonstrations.) Include the GAIL intrinsic reward signals? (Assuming you have recorded demonstrations.) Use self-play? (Assuming your environment includes multiple agents.) The trainer config file, <trainer-config-file> , determines the features you will use during training, and the answers to the above questions will dictate its contents. The rest of this guide breaks down the different sub-sections of the trainer config file and explains the possible settings for each. If you need a list of all the trainer configurations, please see Training Configuration File . NOTE: The configuration file format has been changed between 0.17.0 and 0.18.0 and between 0.18.0 and onwards. To convert an old set of configuration files (trainer config, curriculum, and sampler files) to the new format, a script has been provided. Run python -m mlagents.trainers.upgrade_config -h in your console to see the script's usage. Adding CLI Arguments to the Training Configuration file Additionally, within the training configuration YAML file, you can also add the CLI arguments (such as --num-envs ). Reminder that a detailed description of all the CLI arguments can be found by using the help utility: mlagents-learn --help These additional CLI arguments are grouped into environment, engine, checkpoint and torch. The available settings and example values are shown below. Environment settings env_settings: env_path: FoodCollector env_args: null base_port: 5005 num_envs: 1 seed: -1 max_lifetime_restarts: 10 restarts_rate_limit_n: 1 restarts_rate_limit_period_s: 60 Engine settings engine_settings: width: 84 height: 84 quality_level: 5 time_scale: 20 target_frame_rate: -1 capture_frame_rate: 60 no_graphics: false Checkpoint settings checkpoint_settings: run_id: foodtorch initialize_from: null load_model: false resume: false force: true train_model: false inference: false Torch settings: torch_settings: device: cpu Behavior Configurations The primary section of the trainer config file is a set of configurations for each Behavior in your scene. These are defined under the sub-section behaviors in your trainer config file. Some of the configurations are required while others are optional. To help us get started, below is a sample file that includes all the possible settings if we're using a PPO trainer with all the possible training functionalities enabled (memory, behavioral cloning, curiosity, GAIL and self-play). You will notice that curriculum and environment parameter randomization settings are not part of the behaviors configuration, but in their own section called environment_parameters . behaviors: BehaviorPPO: trainer_type: ppo hyperparameters: # Hyperparameters common to PPO and SAC batch_size: 1024 buffer_size: 10240 learning_rate: 3.0e-4 learning_rate_schedule: linear # PPO-specific hyperparameters beta: 5.0e-3 beta_schedule: constant epsilon: 0.2 epsilon_schedule: linear lambd: 0.95 num_epoch: 3 shared_critic: False # Configuration of the neural network (common to PPO/SAC) network_settings: vis_encode_type: simple normalize: false hidden_units: 128 num_layers: 2 # memory memory: sequence_length: 64 memory_size: 256 # Trainer configurations common to all trainers max_steps: 5.0e5 time_horizon: 64 summary_freq: 10000 keep_checkpoints: 5 checkpoint_interval: 50000 threaded: false init_path: null # behavior cloning behavioral_cloning: demo_path: Project/Assets/ML-Agents/Examples/Pyramids/Demos/ExpertPyramid.demo strength: 0.5 steps: 150000 batch_size: 512 num_epoch: 3 samples_per_update: 0 reward_signals: # environment reward (default) extrinsic: strength: 1.0 gamma: 0.99 # curiosity module curiosity: strength: 0.02 gamma: 0.99 encoding_size: 256 learning_rate: 3.0e-4 # GAIL gail: strength: 0.01 gamma: 0.99 encoding_size: 128 demo_path: Project/Assets/ML-Agents/Examples/Pyramids/Demos/ExpertPyramid.demo learning_rate: 3.0e-4 use_actions: false use_vail: false # self-play self_play: window: 10 play_against_latest_model_ratio: 0.5 save_steps: 50000 swap_steps: 2000 team_change: 100000 Here is an equivalent file if we use an SAC trainer instead. Notice that the configurations for the additional functionalities (memory, behavioral cloning, curiosity and self-play) remain unchanged. behaviors: BehaviorSAC: trainer_type: sac # Trainer configs common to PPO/SAC (excluding reward signals) # same as PPO config # SAC-specific configs (replaces the hyperparameters section above) hyperparameters: # Hyperparameters common to PPO and SAC # Same as PPO config # SAC-specific hyperparameters # Replaces the \"PPO-specific hyperparameters\" section above buffer_init_steps: 0 tau: 0.005 steps_per_update: 10.0 save_replay_buffer: false init_entcoef: 0.5 reward_signal_steps_per_update: 10.0 # Configuration of the neural network (common to PPO/SAC) network_settings: # Same as PPO config # Trainer configurations common to all trainers # <Same as PPO config> # pre-training using behavior cloning behavioral_cloning: # same as PPO config reward_signals: # environment reward extrinsic: # same as PPO config # curiosity module curiosity: # same as PPO config # GAIL gail: # same as PPO config # self-play self_play: # same as PPO config We now break apart the components of the configuration file and describe what each of these parameters mean and provide guidelines on how to set them. See Training Configuration File for a detailed description of all the configurations listed above, along with their defaults. Unless otherwise specified, omitting a configuration will revert it to its default. Default Behavior Settings In some cases, you may want to specify a set of default configurations for your Behaviors. This may be useful, for instance, if your Behavior names are generated procedurally by the environment and not known before runtime, or if you have many Behaviors with very similar settings. To specify a default configuraton, insert a default_settings section in your YAML. This section should be formatted exactly like a configuration for a Behavior. default_settings: # < Same as Behavior configuration > behaviors: # < Same as above > Behaviors found in the environment that aren't specified in the YAML will now use the default_settings , and unspecified settings in behavior configurations will default to the values in default_settings if specified there. Environment Parameters In order to control the EnvironmentParameters in the Unity simulation during training, you need to add a section called environment_parameters . For example you can set the value of an EnvironmentParameter called my_environment_parameter to 3.0 with the following code : behaviors: BehaviorY: # < Same as above > # Add this section environment_parameters: my_environment_parameter: 3.0 Inside the Unity simulation, you can access your Environment Parameters by doing : Academy.Instance.EnvironmentParameters.GetWithDefault(\"my_environment_parameter\", 0.0f); Environment Parameter Randomization To enable environment parameter randomization, you need to edit the environment_parameters section of your training configuration yaml file. Instead of providing a single float value for your environment parameter, you can specify a sampler instead. Here is an example with three environment parameters called mass , length and scale : behaviors: BehaviorY: # < Same as above > # Add this section environment_parameters: mass: sampler_type: uniform sampler_parameters: min_value: 0.5 max_value: 10 length: sampler_type: multirangeuniform sampler_parameters: intervals: [[7, 10], [15, 20]] scale: sampler_type: gaussian sampler_parameters: mean: 2 st_dev: .3 Setting Description sampler_type A string identifier for the type of sampler to use for this Environment Parameter . sampler_parameters The parameters for a given sampler_type . Samplers of different types can have different sampler_parameters Supported Sampler Types Below is a list of the sampler_type values supported by the toolkit. uniform - Uniform sampler Uniformly samples a single float value from a range with a given minimum and maximum value (inclusive). parameters - min_value , max_value gaussian - Gaussian sampler Samples a single float value from a normal distribution with a given mean and standard deviation. parameters - mean , st_dev multirange_uniform - Multirange uniform sampler First, samples an interval from a set of intervals in proportion to relative length of the intervals. Then, uniformly samples a single float value from the sampled interval (inclusive). This sampler can take an arbitrary number of intervals in a list in the following format: [[ interval_1_min , interval_1_max ], [ interval_2_min , interval_2_max ], ...] parameters - intervals The implementation of the samplers can be found in the Samplers.cs file . Training with Environment Parameter Randomization After the sampler configuration is defined, we proceed by launching mlagents-learn and specify trainer configuration with parameter randomization enabled. For example, if we wanted to train the 3D ball agent with parameter randomization, we would run mlagents-learn config/ppo/3DBall_randomize.yaml --run-id=3D-Ball-randomize We can observe progress and metrics via TensorBoard. Curriculum To enable curriculum learning, you need to add a curriculum sub-section to your environment parameter. Here is one example with the environment parameter my_environment_parameter : behaviors: BehaviorY: # < Same as above > # Add this section environment_parameters: my_environment_parameter: curriculum: - name: MyFirstLesson # The '-' is important as this is a list completion_criteria: measure: progress behavior: my_behavior signal_smoothing: true min_lesson_length: 100 threshold: 0.2 value: 0.0 - name: MySecondLesson # This is the start of the second lesson completion_criteria: measure: progress behavior: my_behavior signal_smoothing: true min_lesson_length: 100 threshold: 0.6 require_reset: true value: sampler_type: uniform sampler_parameters: min_value: 4.0 max_value: 7.0 - name: MyLastLesson value: 8.0 Note that this curriculum only applies to my_environment_parameter . The curriculum section contains a list of Lessons . In the example, the lessons are named MyFirstLesson , MySecondLesson and MyLastLesson . Each Lesson has 3 fields : name which is a user defined name for the lesson (The name of the lesson will be displayed in the console when the lesson changes) completion_criteria which determines what needs to happen in the simulation before the lesson can be considered complete. When that condition is met, the curriculum moves on to the next Lesson . Note that you do not need to specify a completion_criteria for the last Lesson value which is the value the environment parameter will take during the lesson. Note that this can be a float or a sampler. There are the different settings of the completion_criteria : Setting Description measure What to measure learning progress, and advancement in lessons by. reward uses a measure of received reward, progress uses the ratio of steps/max_steps, while Elo is available only for self-play situations and uses Elo score as a curriculum completion measure. behavior Specifies which behavior is being tracked. There can be multiple behaviors with different names, each at different points of training. This setting allows the curriculum to track only one of them. threshold Determines at what point in value of measure the lesson should be increased. min_lesson_length The minimum number of episodes that should be completed before the lesson can change. If measure is set to reward , the average cumulative reward of the last min_lesson_length episodes will be used to determine if the lesson should change. Must be nonnegative. Important : the average reward that is compared to the thresholds is different than the mean reward that is logged to the console. For example, if min_lesson_length is 100 , the lesson will increment after the average cumulative reward of the last 100 episodes exceeds the current threshold. The mean reward logged to the console is dictated by the summary_freq parameter defined above. signal_smoothing Whether to weight the current progress measure by previous values. require_reset Whether changing lesson requires the environment to reset (default: false) ##### Training with a Curriculum Once we have specified our metacurriculum and curricula, we can launch mlagents-learn to point to the config file containing our curricula and PPO will train using Curriculum Learning. For example, to train agents in the Wall Jump environment with curriculum learning, we can run: mlagents-learn config/ppo/WallJump_curriculum.yaml --run-id=wall-jump-curriculum We can then keep track of the current lessons and progresses via TensorBoard. If you've terminated the run, you can resume it using --resume and lesson progress will start off where it ended. Training Using Concurrent Unity Instances In order to run concurrent Unity instances during training, set the number of environment instances using the command line option --num-envs=<n> when you invoke mlagents-learn . Optionally, you can also set the --base-port , which is the starting port used for the concurrent Unity instances. Some considerations: Buffer Size - If you are having trouble getting an agent to train, even with multiple concurrent Unity instances, you could increase buffer_size in the trainer config file. A common practice is to multiply buffer_size by num-envs . Resource Constraints - Invoking concurrent Unity instances is constrained by the resources on the machine. Please use discretion when setting --num-envs=<n> . Result Variation Using Concurrent Unity Instances - If you keep all the hyperparameters the same, but change --num-envs=<n> , the results and model would likely change.","title":"Training ML-Agents"},{"location":"Training-ML-Agents/#training-ml-agents","text":"Table of Contents Training ML-Agents Training with mlagents-learn Starting Training Observing Training Stopping and Resuming Training Loading an Existing Model Training Configurations Adding CLI Arguments to the Training Configuration file Environment settings Engine settings Checkpoint settings Torch settings: Behavior Configurations Default Behavior Settings Environment Parameters Environment Parameter Randomization Supported Sampler Types Training with Environment Parameter Randomization Curriculum Training with a Curriculum Training Using Concurrent Unity Instances For a broad overview of reinforcement learning, imitation learning and all the training scenarios, methods and options within the ML-Agents Toolkit, see ML-Agents Toolkit Overview . Once your learning environment has been created and is ready for training, the next step is to initiate a training run. Training in the ML-Agents Toolkit is powered by a dedicated Python package, mlagents . This package exposes a command mlagents-learn that is the single entry point for all training workflows (e.g. reinforcement leaning, imitation learning, curriculum learning). Its implementation can be found at ml-agents/mlagents/trainers/learn.py .","title":"Training ML-Agents"},{"location":"Training-ML-Agents/#training-with-mlagents-learn","text":"","title":"Training with mlagents-learn"},{"location":"Training-ML-Agents/#starting-training","text":"mlagents-learn is the main training utility provided by the ML-Agents Toolkit. It accepts a number of CLI options in addition to a YAML configuration file that contains all the configurations and hyperparameters to be used during training. The set of configurations and hyperparameters to include in this file depend on the agents in your environment and the specific training method you wish to utilize. Keep in mind that the hyperparameter values can have a big impact on the training performance (i.e. your agent's ability to learn a policy that solves the task). In this page, we will review all the hyperparameters for all training methods and provide guidelines and advice on their values. To view a description of all the CLI options accepted by mlagents-learn , use the --help : mlagents-learn --help The basic command for training is: mlagents-learn <trainer-config-file> --env=<env_name> --run-id=<run-identifier> where <trainer-config-file> is the file path of the trainer configuration YAML. This contains all the hyperparameter values. We offer a detailed guide on the structure of this file and the meaning of the hyperparameters (and advice on how to set them) in the dedicated Training Configurations section below. <env_name> (Optional) is the name (including path) of your Unity executable containing the agents to be trained. If <env_name> is not passed, the training will happen in the Editor. Press the Play button in Unity when the message \"Start training by pressing the Play button in the Unity Editor\" is displayed on the screen. <run-identifier> is a unique name you can use to identify the results of your training runs. See the Getting Started Guide for a sample execution of the mlagents-learn command.","title":"Starting Training"},{"location":"Training-ML-Agents/#observing-training","text":"Regardless of which training methods, configurations or hyperparameters you provide, the training process will always generate three artifacts, all found in the results/<run-identifier> folder: Summaries: these are training metrics that are updated throughout the training process. They are helpful to monitor your training performance and may help inform how to update your hyperparameter values. See Using TensorBoard for more details on how to visualize the training metrics. Models: these contain the model checkpoints that are updated throughout training and the final model file ( .onnx ). This final model file is generated once either when training completes or is interrupted. Timers file (under results/<run-identifier>/run_logs ): this contains aggregated metrics on your training process, including time spent on specific code blocks. See Profiling in Python for more information on the timers generated. These artifacts are updated throughout the training process and finalized when training is completed or is interrupted.","title":"Observing Training"},{"location":"Training-ML-Agents/#stopping-and-resuming-training","text":"To interrupt training and save the current progress, hit Ctrl+C once and wait for the model(s) to be saved out. To resume a previously interrupted or completed training run, use the --resume flag and make sure to specify the previously used run ID. If you would like to re-run a previously interrupted or completed training run and re-use the same run ID (in this case, overwriting the previously generated artifacts), then use the --force flag.","title":"Stopping and Resuming Training"},{"location":"Training-ML-Agents/#loading-an-existing-model","text":"You can also use this mode to run inference of an already-trained model in Python by using both the --resume and --inference flags. Note that if you want to run inference in Unity, you should use the Unity Inference Engine . Additionally, if the network architecture changes, you may still load an existing model, but ML-Agents will only load the parts of the model it can load and ignore all others. For instance, if you add a new reward signal, the existing model will load but the new reward signal will be initialized from scratch. If you have a model with a visual encoder (CNN) but change the hidden_units , the CNN will be loaded but the body of the network will be initialized from scratch. Alternatively, you might want to start a new training run but initialize it using an already-trained model. You may want to do this, for instance, if your environment changed and you want a new model, but the old behavior is still better than random. You can do this by specifying --initialize-from=<run-identifier> , where <run-identifier> is the old run ID.","title":"Loading an Existing Model"},{"location":"Training-ML-Agents/#training-configurations","text":"The Unity ML-Agents Toolkit provides a wide range of training scenarios, methods and options. As such, specific training runs may require different training configurations and may generate different artifacts and TensorBoard statistics. This section offers a detailed guide into how to manage the different training set-ups withing the toolkit. More specifically, this section offers a detailed guide on the command-line flags for mlagents-learn that control the training configurations: <trainer-config-file> : defines the training hyperparameters for each Behavior in the scene, and the set-ups for the environment parameters (Curriculum Learning and Environment Parameter Randomization) It is important to highlight that successfully training a Behavior in the ML-Agents Toolkit involves tuning the training hyperparameters and configuration. This guide contains some best practices for tuning the training process when the default parameters don't seem to be giving the level of performance you would like. We provide sample configuration files for our example environments in the config/ directory. The config/ppo/3DBall.yaml was used to train the 3D Balance Ball in the Getting Started guide. That configuration file uses the PPO trainer, but we also have configuration files for SAC and GAIL. Additionally, the set of configurations you provide depend on the training functionalities you use (see ML-Agents Toolkit Overview for a description of all the training functionalities). Each functionality you add typically has its own training configurations. For instance: Use PPO or SAC? Use Recurrent Neural Networks for adding memory to your agents? Use the intrinsic curiosity module? Ignore the environment reward signal? Pre-train using behavioral cloning? (Assuming you have recorded demonstrations.) Include the GAIL intrinsic reward signals? (Assuming you have recorded demonstrations.) Use self-play? (Assuming your environment includes multiple agents.) The trainer config file, <trainer-config-file> , determines the features you will use during training, and the answers to the above questions will dictate its contents. The rest of this guide breaks down the different sub-sections of the trainer config file and explains the possible settings for each. If you need a list of all the trainer configurations, please see Training Configuration File . NOTE: The configuration file format has been changed between 0.17.0 and 0.18.0 and between 0.18.0 and onwards. To convert an old set of configuration files (trainer config, curriculum, and sampler files) to the new format, a script has been provided. Run python -m mlagents.trainers.upgrade_config -h in your console to see the script's usage.","title":"Training Configurations"},{"location":"Training-ML-Agents/#adding-cli-arguments-to-the-training-configuration-file","text":"Additionally, within the training configuration YAML file, you can also add the CLI arguments (such as --num-envs ). Reminder that a detailed description of all the CLI arguments can be found by using the help utility: mlagents-learn --help These additional CLI arguments are grouped into environment, engine, checkpoint and torch. The available settings and example values are shown below.","title":"Adding CLI Arguments to the Training Configuration file"},{"location":"Training-ML-Agents/#environment-settings","text":"env_settings: env_path: FoodCollector env_args: null base_port: 5005 num_envs: 1 seed: -1 max_lifetime_restarts: 10 restarts_rate_limit_n: 1 restarts_rate_limit_period_s: 60","title":"Environment settings"},{"location":"Training-ML-Agents/#engine-settings","text":"engine_settings: width: 84 height: 84 quality_level: 5 time_scale: 20 target_frame_rate: -1 capture_frame_rate: 60 no_graphics: false","title":"Engine settings"},{"location":"Training-ML-Agents/#checkpoint-settings","text":"checkpoint_settings: run_id: foodtorch initialize_from: null load_model: false resume: false force: true train_model: false inference: false","title":"Checkpoint settings"},{"location":"Training-ML-Agents/#torch-settings","text":"torch_settings: device: cpu","title":"Torch settings:"},{"location":"Training-ML-Agents/#behavior-configurations","text":"The primary section of the trainer config file is a set of configurations for each Behavior in your scene. These are defined under the sub-section behaviors in your trainer config file. Some of the configurations are required while others are optional. To help us get started, below is a sample file that includes all the possible settings if we're using a PPO trainer with all the possible training functionalities enabled (memory, behavioral cloning, curiosity, GAIL and self-play). You will notice that curriculum and environment parameter randomization settings are not part of the behaviors configuration, but in their own section called environment_parameters . behaviors: BehaviorPPO: trainer_type: ppo hyperparameters: # Hyperparameters common to PPO and SAC batch_size: 1024 buffer_size: 10240 learning_rate: 3.0e-4 learning_rate_schedule: linear # PPO-specific hyperparameters beta: 5.0e-3 beta_schedule: constant epsilon: 0.2 epsilon_schedule: linear lambd: 0.95 num_epoch: 3 shared_critic: False # Configuration of the neural network (common to PPO/SAC) network_settings: vis_encode_type: simple normalize: false hidden_units: 128 num_layers: 2 # memory memory: sequence_length: 64 memory_size: 256 # Trainer configurations common to all trainers max_steps: 5.0e5 time_horizon: 64 summary_freq: 10000 keep_checkpoints: 5 checkpoint_interval: 50000 threaded: false init_path: null # behavior cloning behavioral_cloning: demo_path: Project/Assets/ML-Agents/Examples/Pyramids/Demos/ExpertPyramid.demo strength: 0.5 steps: 150000 batch_size: 512 num_epoch: 3 samples_per_update: 0 reward_signals: # environment reward (default) extrinsic: strength: 1.0 gamma: 0.99 # curiosity module curiosity: strength: 0.02 gamma: 0.99 encoding_size: 256 learning_rate: 3.0e-4 # GAIL gail: strength: 0.01 gamma: 0.99 encoding_size: 128 demo_path: Project/Assets/ML-Agents/Examples/Pyramids/Demos/ExpertPyramid.demo learning_rate: 3.0e-4 use_actions: false use_vail: false # self-play self_play: window: 10 play_against_latest_model_ratio: 0.5 save_steps: 50000 swap_steps: 2000 team_change: 100000 Here is an equivalent file if we use an SAC trainer instead. Notice that the configurations for the additional functionalities (memory, behavioral cloning, curiosity and self-play) remain unchanged. behaviors: BehaviorSAC: trainer_type: sac # Trainer configs common to PPO/SAC (excluding reward signals) # same as PPO config # SAC-specific configs (replaces the hyperparameters section above) hyperparameters: # Hyperparameters common to PPO and SAC # Same as PPO config # SAC-specific hyperparameters # Replaces the \"PPO-specific hyperparameters\" section above buffer_init_steps: 0 tau: 0.005 steps_per_update: 10.0 save_replay_buffer: false init_entcoef: 0.5 reward_signal_steps_per_update: 10.0 # Configuration of the neural network (common to PPO/SAC) network_settings: # Same as PPO config # Trainer configurations common to all trainers # <Same as PPO config> # pre-training using behavior cloning behavioral_cloning: # same as PPO config reward_signals: # environment reward extrinsic: # same as PPO config # curiosity module curiosity: # same as PPO config # GAIL gail: # same as PPO config # self-play self_play: # same as PPO config We now break apart the components of the configuration file and describe what each of these parameters mean and provide guidelines on how to set them. See Training Configuration File for a detailed description of all the configurations listed above, along with their defaults. Unless otherwise specified, omitting a configuration will revert it to its default.","title":"Behavior Configurations"},{"location":"Training-ML-Agents/#default-behavior-settings","text":"In some cases, you may want to specify a set of default configurations for your Behaviors. This may be useful, for instance, if your Behavior names are generated procedurally by the environment and not known before runtime, or if you have many Behaviors with very similar settings. To specify a default configuraton, insert a default_settings section in your YAML. This section should be formatted exactly like a configuration for a Behavior. default_settings: # < Same as Behavior configuration > behaviors: # < Same as above > Behaviors found in the environment that aren't specified in the YAML will now use the default_settings , and unspecified settings in behavior configurations will default to the values in default_settings if specified there.","title":"Default Behavior Settings"},{"location":"Training-ML-Agents/#environment-parameters","text":"In order to control the EnvironmentParameters in the Unity simulation during training, you need to add a section called environment_parameters . For example you can set the value of an EnvironmentParameter called my_environment_parameter to 3.0 with the following code : behaviors: BehaviorY: # < Same as above > # Add this section environment_parameters: my_environment_parameter: 3.0 Inside the Unity simulation, you can access your Environment Parameters by doing : Academy.Instance.EnvironmentParameters.GetWithDefault(\"my_environment_parameter\", 0.0f);","title":"Environment Parameters"},{"location":"Training-ML-Agents/#environment-parameter-randomization","text":"To enable environment parameter randomization, you need to edit the environment_parameters section of your training configuration yaml file. Instead of providing a single float value for your environment parameter, you can specify a sampler instead. Here is an example with three environment parameters called mass , length and scale : behaviors: BehaviorY: # < Same as above > # Add this section environment_parameters: mass: sampler_type: uniform sampler_parameters: min_value: 0.5 max_value: 10 length: sampler_type: multirangeuniform sampler_parameters: intervals: [[7, 10], [15, 20]] scale: sampler_type: gaussian sampler_parameters: mean: 2 st_dev: .3 Setting Description sampler_type A string identifier for the type of sampler to use for this Environment Parameter . sampler_parameters The parameters for a given sampler_type . Samplers of different types can have different sampler_parameters","title":"Environment Parameter Randomization"},{"location":"Training-ML-Agents/#supported-sampler-types","text":"Below is a list of the sampler_type values supported by the toolkit. uniform - Uniform sampler Uniformly samples a single float value from a range with a given minimum and maximum value (inclusive). parameters - min_value , max_value gaussian - Gaussian sampler Samples a single float value from a normal distribution with a given mean and standard deviation. parameters - mean , st_dev multirange_uniform - Multirange uniform sampler First, samples an interval from a set of intervals in proportion to relative length of the intervals. Then, uniformly samples a single float value from the sampled interval (inclusive). This sampler can take an arbitrary number of intervals in a list in the following format: [[ interval_1_min , interval_1_max ], [ interval_2_min , interval_2_max ], ...] parameters - intervals The implementation of the samplers can be found in the Samplers.cs file .","title":"Supported Sampler Types"},{"location":"Training-ML-Agents/#training-with-environment-parameter-randomization","text":"After the sampler configuration is defined, we proceed by launching mlagents-learn and specify trainer configuration with parameter randomization enabled. For example, if we wanted to train the 3D ball agent with parameter randomization, we would run mlagents-learn config/ppo/3DBall_randomize.yaml --run-id=3D-Ball-randomize We can observe progress and metrics via TensorBoard.","title":"Training with Environment Parameter Randomization"},{"location":"Training-ML-Agents/#curriculum","text":"To enable curriculum learning, you need to add a curriculum sub-section to your environment parameter. Here is one example with the environment parameter my_environment_parameter : behaviors: BehaviorY: # < Same as above > # Add this section environment_parameters: my_environment_parameter: curriculum: - name: MyFirstLesson # The '-' is important as this is a list completion_criteria: measure: progress behavior: my_behavior signal_smoothing: true min_lesson_length: 100 threshold: 0.2 value: 0.0 - name: MySecondLesson # This is the start of the second lesson completion_criteria: measure: progress behavior: my_behavior signal_smoothing: true min_lesson_length: 100 threshold: 0.6 require_reset: true value: sampler_type: uniform sampler_parameters: min_value: 4.0 max_value: 7.0 - name: MyLastLesson value: 8.0 Note that this curriculum only applies to my_environment_parameter . The curriculum section contains a list of Lessons . In the example, the lessons are named MyFirstLesson , MySecondLesson and MyLastLesson . Each Lesson has 3 fields : name which is a user defined name for the lesson (The name of the lesson will be displayed in the console when the lesson changes) completion_criteria which determines what needs to happen in the simulation before the lesson can be considered complete. When that condition is met, the curriculum moves on to the next Lesson . Note that you do not need to specify a completion_criteria for the last Lesson value which is the value the environment parameter will take during the lesson. Note that this can be a float or a sampler. There are the different settings of the completion_criteria : Setting Description measure What to measure learning progress, and advancement in lessons by. reward uses a measure of received reward, progress uses the ratio of steps/max_steps, while Elo is available only for self-play situations and uses Elo score as a curriculum completion measure. behavior Specifies which behavior is being tracked. There can be multiple behaviors with different names, each at different points of training. This setting allows the curriculum to track only one of them. threshold Determines at what point in value of measure the lesson should be increased. min_lesson_length The minimum number of episodes that should be completed before the lesson can change. If measure is set to reward , the average cumulative reward of the last min_lesson_length episodes will be used to determine if the lesson should change. Must be nonnegative. Important : the average reward that is compared to the thresholds is different than the mean reward that is logged to the console. For example, if min_lesson_length is 100 , the lesson will increment after the average cumulative reward of the last 100 episodes exceeds the current threshold. The mean reward logged to the console is dictated by the summary_freq parameter defined above. signal_smoothing Whether to weight the current progress measure by previous values. require_reset Whether changing lesson requires the environment to reset (default: false) ##### Training with a Curriculum Once we have specified our metacurriculum and curricula, we can launch mlagents-learn to point to the config file containing our curricula and PPO will train using Curriculum Learning. For example, to train agents in the Wall Jump environment with curriculum learning, we can run: mlagents-learn config/ppo/WallJump_curriculum.yaml --run-id=wall-jump-curriculum We can then keep track of the current lessons and progresses via TensorBoard. If you've terminated the run, you can resume it using --resume and lesson progress will start off where it ended.","title":"Curriculum"},{"location":"Training-ML-Agents/#training-using-concurrent-unity-instances","text":"In order to run concurrent Unity instances during training, set the number of environment instances using the command line option --num-envs=<n> when you invoke mlagents-learn . Optionally, you can also set the --base-port , which is the starting port used for the concurrent Unity instances. Some considerations: Buffer Size - If you are having trouble getting an agent to train, even with multiple concurrent Unity instances, you could increase buffer_size in the trainer config file. A common practice is to multiply buffer_size by num-envs . Resource Constraints - Invoking concurrent Unity instances is constrained by the resources on the machine. Please use discretion when setting --num-envs=<n> . Result Variation Using Concurrent Unity Instances - If you keep all the hyperparameters the same, but change --num-envs=<n> , the results and model would likely change.","title":"Training Using Concurrent Unity Instances"},{"location":"Training-Plugins/","text":"Customizing Training via Plugins ML-Agents provides support for running your own python implementations of specific interfaces during the training process. These interfaces are currently fairly limited, but will be expanded in the future. Note: Plugin interfaces should currently be considered \"in beta\", and they may change in future releases. How to Write Your Own Plugin This video explains the basics of how to create a plugin system using setuptools, and is the same approach that ML-Agents' plugin system is based on. The ml-agents-plugin-examples directory contains a reference implementation of each plugin interface, so it's a good starting point. setup.py If you don't already have a setup.py file for your python code, you'll need to add one. ml-agents-plugin-examples has a minimal example of this. In the call to setup() , you'll need to add to the entry_points dictionary for each plugin interface that you implement. The form of this is {entry point name}={plugin module}:{plugin function} . For example, in ml-agents-plugin-examples : entry_points={ ML_AGENTS_STATS_WRITER: [ \"example=mlagents_plugin_examples.example_stats_writer:get_example_stats_writer\" ] } ML_AGENTS_STATS_WRITER (which is a string constant, mlagents.stats_writer ) is the name of the plugin interface. This must be one of the provided interfaces ( see below ). example is the plugin implementation name. This can be anything. mlagents_plugin_examples.example_stats_writer is the plugin module. This points to the module where the plugin registration function is defined. get_example_stats_writer is the plugin registration function. This is called when running mlagents-learn . The arguments and expected return type for this are different for each plugin interface. Local Installation Once you've defined entry_points in your setup.py , you will need to run pip install -e [path to your plugin code] in the same python virtual environment that you have mlagents installed. Plugin Interfaces StatsWriter The StatsWriter class receives various information from the training process, such as the average Agent reward in each summary period. By default, we log this information to the console and write it to TensorBoard . Interface The StatsWriter.write_stats() method must be implemented in any derived classes. It takes a \"category\" parameter, which typically is the behavior name of the Agents being trained, and a dictionary of StatSummary values with string keys. Additionally, StatsWriter.on_add_stat() may be extended to register a callback handler for each stat emission. Registration The StatsWriter registration function takes a RunOptions argument and returns a list of StatsWriter s. An example implementation is provided in mlagents_plugin_examples","title":"Customizing Training via Plugins"},{"location":"Training-Plugins/#customizing-training-via-plugins","text":"ML-Agents provides support for running your own python implementations of specific interfaces during the training process. These interfaces are currently fairly limited, but will be expanded in the future. Note: Plugin interfaces should currently be considered \"in beta\", and they may change in future releases.","title":"Customizing Training via Plugins"},{"location":"Training-Plugins/#how-to-write-your-own-plugin","text":"This video explains the basics of how to create a plugin system using setuptools, and is the same approach that ML-Agents' plugin system is based on. The ml-agents-plugin-examples directory contains a reference implementation of each plugin interface, so it's a good starting point.","title":"How to Write Your Own Plugin"},{"location":"Training-Plugins/#setuppy","text":"If you don't already have a setup.py file for your python code, you'll need to add one. ml-agents-plugin-examples has a minimal example of this. In the call to setup() , you'll need to add to the entry_points dictionary for each plugin interface that you implement. The form of this is {entry point name}={plugin module}:{plugin function} . For example, in ml-agents-plugin-examples : entry_points={ ML_AGENTS_STATS_WRITER: [ \"example=mlagents_plugin_examples.example_stats_writer:get_example_stats_writer\" ] } ML_AGENTS_STATS_WRITER (which is a string constant, mlagents.stats_writer ) is the name of the plugin interface. This must be one of the provided interfaces ( see below ). example is the plugin implementation name. This can be anything. mlagents_plugin_examples.example_stats_writer is the plugin module. This points to the module where the plugin registration function is defined. get_example_stats_writer is the plugin registration function. This is called when running mlagents-learn . The arguments and expected return type for this are different for each plugin interface.","title":"setup.py"},{"location":"Training-Plugins/#local-installation","text":"Once you've defined entry_points in your setup.py , you will need to run pip install -e [path to your plugin code] in the same python virtual environment that you have mlagents installed.","title":"Local Installation"},{"location":"Training-Plugins/#plugin-interfaces","text":"","title":"Plugin Interfaces"},{"location":"Training-Plugins/#statswriter","text":"The StatsWriter class receives various information from the training process, such as the average Agent reward in each summary period. By default, we log this information to the console and write it to TensorBoard .","title":"StatsWriter"},{"location":"Training-Plugins/#interface","text":"The StatsWriter.write_stats() method must be implemented in any derived classes. It takes a \"category\" parameter, which typically is the behavior name of the Agents being trained, and a dictionary of StatSummary values with string keys. Additionally, StatsWriter.on_add_stat() may be extended to register a callback handler for each stat emission.","title":"Interface"},{"location":"Training-Plugins/#registration","text":"The StatsWriter registration function takes a RunOptions argument and returns a list of StatsWriter s. An example implementation is provided in mlagents_plugin_examples","title":"Registration"},{"location":"Training-on-Amazon-Web-Service/","text":"Training on Amazon Web Service :warning: Note: We no longer use this guide ourselves and so it may not work correctly. We've decided to keep it up just in case it is helpful to you. This page contains instructions for setting up an EC2 instance on Amazon Web Service for training ML-Agents environments. Pre-configured AMI We've prepared a pre-configured AMI for you with the ID: ami-016ff5559334f8619 in the us-east-1 region. It was created as a modification of Deep Learning AMI (Ubuntu) . The AMI has been tested with p2.xlarge instance. Furthermore, if you want to train without headless mode, you need to enable X Server. After launching your EC2 instance using the ami and ssh into it, run the following commands to enable it: # Start the X Server, press Enter to come to the command line $ sudo /usr/bin/X :0 & # Check if Xorg process is running # You will have a list of processes running on the GPU, Xorg should be in the # list, as shown below $ nvidia-smi # Thu Jun 14 20:27:26 2018 # +-----------------------------------------------------------------------------+ # | NVIDIA-SMI 390.67 Driver Version: 390.67 | # |-------------------------------+----------------------+----------------------+ # | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | # | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | # |===============================+======================+======================| # | 0 Tesla K80 On | 00000000:00:1E.0 Off | 0 | # | N/A 35C P8 31W / 149W | 9MiB / 11441MiB | 0% Default | # +-------------------------------+----------------------+----------------------+ # # +-----------------------------------------------------------------------------+ # | Processes: GPU Memory | # | GPU PID Type Process name Usage | # |=============================================================================| # | 0 2331 G /usr/lib/xorg/Xorg 8MiB | # +-----------------------------------------------------------------------------+ # Make the ubuntu use X Server for display $ export DISPLAY=:0 Configuring your own instance You could also choose to configure your own instance. To begin with, you will need an EC2 instance which contains the latest Nvidia drivers, CUDA9, and cuDNN. In this tutorial we used the Deep Learning AMI (Ubuntu) listed under AWS Marketplace with a p2.xlarge instance. Installing the ML-Agents Toolkit on the instance After launching your EC2 instance using the ami and ssh into it: Activate the python3 environment sh source activate python3 Clone the ML-Agents repo and install the required Python packages sh git clone --branch release_20 https://github.com/Unity-Technologies/ml-agents.git cd ml-agents/ml-agents/ pip3 install -e . Setting up X Server (optional) X Server setup is only necessary if you want to do training that requires visual observation input. Instructions here are adapted from this Medium post on running general Unity applications in the cloud. Current limitations of the Unity Engine require that a screen be available to render to when using visual observations. In order to make this possible when training on a remote server, a virtual screen is required. We can do this by installing Xorg and creating a virtual screen. Once installed and created, we can display the Unity environment in the virtual environment, and train as we would on a local machine. Ensure that headless mode is disabled when building linux executables which use visual observations. Install and setup Xorg: ```sh # Install Xorg $ sudo apt-get update $ sudo apt-get install -y xserver-xorg mesa-utils $ sudo nvidia-xconfig -a --use-display-device=None --virtual=1280x1024 # Get the BusID information $ nvidia-xconfig --query-gpu-info # Add the BusID information to your /etc/X11/xorg.conf file $ sudo sed -i 's/ BoardName \"Tesla K80\"/ BoardName \"Tesla K80\"\\n BusID \"0:30:0\"/g' /etc/X11/xorg.conf # Remove the Section \"Files\" from the /etc/X11/xorg.conf file # And remove two lines that contain Section \"Files\" and EndSection $ sudo vim /etc/X11/xorg.conf ``` Update and setup Nvidia driver: ```sh # Download and install the latest Nvidia driver for ubuntu # Please refer to http://download.nvidia.com/XFree86/Linux-#x86_64/latest.txt $ wget http://download.nvidia.com/XFree86/Linux-x86_64/390.87/NVIDIA-Linux-x86_64-390.87.run $ sudo /bin/bash ./NVIDIA-Linux-x86_64-390.87.run --accept-license --no-questions --ui=none # Disable Nouveau as it will clash with the Nvidia driver $ sudo echo 'blacklist nouveau' | sudo tee -a /etc/modprobe.d/blacklist.conf $ sudo echo 'options nouveau modeset=0' | sudo tee -a /etc/modprobe.d/blacklist.conf $ sudo echo options nouveau modeset=0 | sudo tee -a /etc/modprobe.d/nouveau-kms.conf $ sudo update-initramfs -u ``` Restart the EC2 instance: ```sh sudo reboot now ``` Make sure there are no Xorg processes running: # Kill any possible running Xorg processes # Note that you might have to run this command multiple times depending on # how Xorg is configured. $ sudo killall Xorg # Check if there is any Xorg process left # You will have a list of processes running on the GPU, Xorg should not be in # the list, as shown below. $ nvidia-smi # Thu Jun 14 20:21:11 2018 # +-----------------------------------------------------------------------------+ # | NVIDIA-SMI 390.67 Driver Version: 390.67 | # |-------------------------------+----------------------+----------------------+ # | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | # | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | # |===============================+======================+======================| # | 0 Tesla K80 On | 00000000:00:1E.0 Off | 0 | # | N/A 37C P8 31W / 149W | 0MiB / 11441MiB | 0% Default | # +-------------------------------+----------------------+----------------------+ # # +-----------------------------------------------------------------------------+ # | Processes: GPU Memory | # | GPU PID Type Process name Usage | # |=============================================================================| # | No running processes found | # +-----------------------------------------------------------------------------+ Start X Server and make the ubuntu use X Server for display: ```console # Start the X Server, press Enter to come back to the command line $ sudo /usr/bin/X :0 & # Check if Xorg process is running # You will have a list of processes running on the GPU, Xorg should be in the list. $ nvidia-smi # Make the ubuntu use X Server for display $ export DISPLAY=:0 ``` Ensure the Xorg is correctly configured: ```sh # For more information on glxgears, see ftp://www.x.org/pub/X11R6.8.1/doc/glxgears.1.html. $ glxgears # If Xorg is configured correctly, you should see the following message # Running synchronized to the vertical refresh. The framerate should be # approximately the same as the monitor refresh rate. # 137296 frames in 5.0 seconds = 27459.053 FPS # 141674 frames in 5.0 seconds = 28334.779 FPS # 141490 frames in 5.0 seconds = 28297.875 FPS ``` Training on EC2 instance In the Unity Editor, load a project containing an ML-Agents environment (you can use one of the example environments if you have not created your own). Open the Build Settings window (menu: File > Build Settings). Select Linux as the Target Platform, and x86_64 as the target architecture (the default x86 currently does not work). Check Headless Mode if you have not setup the X Server. (If you do not use Headless Mode, you have to setup the X Server to enable training.) Click Build to build the Unity environment executable. Upload the executable to your EC2 instance within ml-agents folder. Change the permissions of the executable. sh chmod +x <your_env>.x86_64 (Without Headless Mode) Start X Server and use it for display: ```sh # Start the X Server, press Enter to come back to the command line $ sudo /usr/bin/X :0 & # Check if Xorg process is running # You will have a list of processes running on the GPU, Xorg should be in the list. $ nvidia-smi # Make the ubuntu use X Server for display $ export DISPLAY=:0 ``` Test the instance setup from Python using: ```python from mlagents_envs.environment import UnityEnvironment env = UnityEnvironment( ) ``` Where <your_env> corresponds to the path to your environment executable. You should receive a message confirming that the environment was loaded successfully. Train your models console mlagents-learn <trainer-config-file> --env=<your_env> --train FAQ The _Data folder hasn't been copied cover If you've built your Linux executable, but forget to copy over the corresponding _Data folder, you will see error message like the following: Set current directory to /home/ubuntu/ml-agents/ml-agents Found path: /home/ubuntu/ml-agents/ml-agents/3dball_linux.x86_64 no boot config - using default values (Filename: Line: 403) There is no data folder Unity Environment not responding If you didn't setup X Server or hasn't launched it properly, or your environment somehow crashes, or you haven't chmod +x your Unity Environment, all of these will cause connection between Unity and Python to fail. Then you will see something like this: Logging to /home/ubuntu/.config/unity3d/<Some_Path>/Player.log Traceback (most recent call last): File \"<stdin>\", line 1, in <module> File \"/home/ubuntu/ml-agents/ml-agents/mlagents_envs/environment.py\", line 63, in __init__ aca_params = self.send_academy_parameters(rl_init_parameters_in) File \"/home/ubuntu/ml-agents/ml-agents/mlagents_envs/environment.py\", line 489, in send_academy_parameters return self.communicator.initialize(inputs).rl_initialization_output File \"/home/ubuntu/ml-agents/ml-agents/mlagents_envs/rpc_communicator.py\", line 60, in initialize mlagents_envs.exception.UnityTimeOutException: The Unity environment took too long to respond. Make sure that : The environment does not need user interaction to launch The environment and the Python interface have compatible versions. It would be also really helpful to check your /home/ubuntu/.config/unity3d/ /Player.log to see what happens with your Unity environment. Could not launch X Server When you execute: sudo /usr/bin/X :0 & You might see something like: X.Org X Server 1.18.4 ... (==) Log file: \"/var/log/Xorg.0.log\", Time: Thu Oct 11 21:10:38 2018 (==) Using config file: \"/etc/X11/xorg.conf\" (==) Using system config directory \"/usr/share/X11/xorg.conf.d\" (EE) Fatal server error: (EE) no screens found(EE) (EE) Please consult the X.Org Foundation support at http://wiki.x.org for help. (EE) Please also check the log file at \"/var/log/Xorg.0.log\" for additional information. (EE) (EE) Server terminated with error (1). Closing log file. And when you execute: nvidia-smi You might see something like: NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running. This means the NVIDIA's driver needs to be updated. Refer to this section for more information.","title":"Training on Amazon Web Service"},{"location":"Training-on-Amazon-Web-Service/#training-on-amazon-web-service","text":":warning: Note: We no longer use this guide ourselves and so it may not work correctly. We've decided to keep it up just in case it is helpful to you. This page contains instructions for setting up an EC2 instance on Amazon Web Service for training ML-Agents environments.","title":"Training on Amazon Web Service"},{"location":"Training-on-Amazon-Web-Service/#pre-configured-ami","text":"We've prepared a pre-configured AMI for you with the ID: ami-016ff5559334f8619 in the us-east-1 region. It was created as a modification of Deep Learning AMI (Ubuntu) . The AMI has been tested with p2.xlarge instance. Furthermore, if you want to train without headless mode, you need to enable X Server. After launching your EC2 instance using the ami and ssh into it, run the following commands to enable it: # Start the X Server, press Enter to come to the command line $ sudo /usr/bin/X :0 & # Check if Xorg process is running # You will have a list of processes running on the GPU, Xorg should be in the # list, as shown below $ nvidia-smi # Thu Jun 14 20:27:26 2018 # +-----------------------------------------------------------------------------+ # | NVIDIA-SMI 390.67 Driver Version: 390.67 | # |-------------------------------+----------------------+----------------------+ # | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | # | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | # |===============================+======================+======================| # | 0 Tesla K80 On | 00000000:00:1E.0 Off | 0 | # | N/A 35C P8 31W / 149W | 9MiB / 11441MiB | 0% Default | # +-------------------------------+----------------------+----------------------+ # # +-----------------------------------------------------------------------------+ # | Processes: GPU Memory | # | GPU PID Type Process name Usage | # |=============================================================================| # | 0 2331 G /usr/lib/xorg/Xorg 8MiB | # +-----------------------------------------------------------------------------+ # Make the ubuntu use X Server for display $ export DISPLAY=:0","title":"Pre-configured AMI"},{"location":"Training-on-Amazon-Web-Service/#configuring-your-own-instance","text":"You could also choose to configure your own instance. To begin with, you will need an EC2 instance which contains the latest Nvidia drivers, CUDA9, and cuDNN. In this tutorial we used the Deep Learning AMI (Ubuntu) listed under AWS Marketplace with a p2.xlarge instance.","title":"Configuring your own instance"},{"location":"Training-on-Amazon-Web-Service/#installing-the-ml-agents-toolkit-on-the-instance","text":"After launching your EC2 instance using the ami and ssh into it: Activate the python3 environment sh source activate python3 Clone the ML-Agents repo and install the required Python packages sh git clone --branch release_20 https://github.com/Unity-Technologies/ml-agents.git cd ml-agents/ml-agents/ pip3 install -e .","title":"Installing the ML-Agents Toolkit on the instance"},{"location":"Training-on-Amazon-Web-Service/#setting-up-x-server-optional","text":"X Server setup is only necessary if you want to do training that requires visual observation input. Instructions here are adapted from this Medium post on running general Unity applications in the cloud. Current limitations of the Unity Engine require that a screen be available to render to when using visual observations. In order to make this possible when training on a remote server, a virtual screen is required. We can do this by installing Xorg and creating a virtual screen. Once installed and created, we can display the Unity environment in the virtual environment, and train as we would on a local machine. Ensure that headless mode is disabled when building linux executables which use visual observations.","title":"Setting up X Server (optional)"},{"location":"Training-on-Amazon-Web-Service/#install-and-setup-xorg","text":"```sh # Install Xorg $ sudo apt-get update $ sudo apt-get install -y xserver-xorg mesa-utils $ sudo nvidia-xconfig -a --use-display-device=None --virtual=1280x1024 # Get the BusID information $ nvidia-xconfig --query-gpu-info # Add the BusID information to your /etc/X11/xorg.conf file $ sudo sed -i 's/ BoardName \"Tesla K80\"/ BoardName \"Tesla K80\"\\n BusID \"0:30:0\"/g' /etc/X11/xorg.conf # Remove the Section \"Files\" from the /etc/X11/xorg.conf file # And remove two lines that contain Section \"Files\" and EndSection $ sudo vim /etc/X11/xorg.conf ```","title":"Install and setup Xorg:"},{"location":"Training-on-Amazon-Web-Service/#update-and-setup-nvidia-driver","text":"```sh # Download and install the latest Nvidia driver for ubuntu # Please refer to http://download.nvidia.com/XFree86/Linux-#x86_64/latest.txt $ wget http://download.nvidia.com/XFree86/Linux-x86_64/390.87/NVIDIA-Linux-x86_64-390.87.run $ sudo /bin/bash ./NVIDIA-Linux-x86_64-390.87.run --accept-license --no-questions --ui=none # Disable Nouveau as it will clash with the Nvidia driver $ sudo echo 'blacklist nouveau' | sudo tee -a /etc/modprobe.d/blacklist.conf $ sudo echo 'options nouveau modeset=0' | sudo tee -a /etc/modprobe.d/blacklist.conf $ sudo echo options nouveau modeset=0 | sudo tee -a /etc/modprobe.d/nouveau-kms.conf $ sudo update-initramfs -u ```","title":"Update and setup Nvidia driver:"},{"location":"Training-on-Amazon-Web-Service/#restart-the-ec2-instance","text":"```sh sudo reboot now ```","title":"Restart the EC2 instance:"},{"location":"Training-on-Amazon-Web-Service/#make-sure-there-are-no-xorg-processes-running","text":"# Kill any possible running Xorg processes # Note that you might have to run this command multiple times depending on # how Xorg is configured. $ sudo killall Xorg # Check if there is any Xorg process left # You will have a list of processes running on the GPU, Xorg should not be in # the list, as shown below. $ nvidia-smi # Thu Jun 14 20:21:11 2018 # +-----------------------------------------------------------------------------+ # | NVIDIA-SMI 390.67 Driver Version: 390.67 | # |-------------------------------+----------------------+----------------------+ # | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | # | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | # |===============================+======================+======================| # | 0 Tesla K80 On | 00000000:00:1E.0 Off | 0 | # | N/A 37C P8 31W / 149W | 0MiB / 11441MiB | 0% Default | # +-------------------------------+----------------------+----------------------+ # # +-----------------------------------------------------------------------------+ # | Processes: GPU Memory | # | GPU PID Type Process name Usage | # |=============================================================================| # | No running processes found | # +-----------------------------------------------------------------------------+","title":"Make sure there are no Xorg processes running:"},{"location":"Training-on-Amazon-Web-Service/#start-x-server-and-make-the-ubuntu-use-x-server-for-display","text":"```console # Start the X Server, press Enter to come back to the command line $ sudo /usr/bin/X :0 & # Check if Xorg process is running # You will have a list of processes running on the GPU, Xorg should be in the list. $ nvidia-smi # Make the ubuntu use X Server for display $ export DISPLAY=:0 ```","title":"Start X Server and make the ubuntu use X Server for display:"},{"location":"Training-on-Amazon-Web-Service/#ensure-the-xorg-is-correctly-configured","text":"```sh # For more information on glxgears, see ftp://www.x.org/pub/X11R6.8.1/doc/glxgears.1.html. $ glxgears # If Xorg is configured correctly, you should see the following message # Running synchronized to the vertical refresh. The framerate should be # approximately the same as the monitor refresh rate. # 137296 frames in 5.0 seconds = 27459.053 FPS # 141674 frames in 5.0 seconds = 28334.779 FPS # 141490 frames in 5.0 seconds = 28297.875 FPS ```","title":"Ensure the Xorg is correctly configured:"},{"location":"Training-on-Amazon-Web-Service/#training-on-ec2-instance","text":"In the Unity Editor, load a project containing an ML-Agents environment (you can use one of the example environments if you have not created your own). Open the Build Settings window (menu: File > Build Settings). Select Linux as the Target Platform, and x86_64 as the target architecture (the default x86 currently does not work). Check Headless Mode if you have not setup the X Server. (If you do not use Headless Mode, you have to setup the X Server to enable training.) Click Build to build the Unity environment executable. Upload the executable to your EC2 instance within ml-agents folder. Change the permissions of the executable. sh chmod +x <your_env>.x86_64 (Without Headless Mode) Start X Server and use it for display: ```sh # Start the X Server, press Enter to come back to the command line $ sudo /usr/bin/X :0 & # Check if Xorg process is running # You will have a list of processes running on the GPU, Xorg should be in the list. $ nvidia-smi # Make the ubuntu use X Server for display $ export DISPLAY=:0 ``` Test the instance setup from Python using: ```python from mlagents_envs.environment import UnityEnvironment env = UnityEnvironment( ) ``` Where <your_env> corresponds to the path to your environment executable. You should receive a message confirming that the environment was loaded successfully. Train your models console mlagents-learn <trainer-config-file> --env=<your_env> --train","title":"Training on EC2 instance"},{"location":"Training-on-Amazon-Web-Service/#faq","text":"","title":"FAQ"},{"location":"Training-on-Amazon-Web-Service/#the-_data-folder-hasnt-been-copied-cover","text":"If you've built your Linux executable, but forget to copy over the corresponding _Data folder, you will see error message like the following: Set current directory to /home/ubuntu/ml-agents/ml-agents Found path: /home/ubuntu/ml-agents/ml-agents/3dball_linux.x86_64 no boot config - using default values (Filename: Line: 403) There is no data folder","title":"The _Data folder hasn't been copied cover"},{"location":"Training-on-Amazon-Web-Service/#unity-environment-not-responding","text":"If you didn't setup X Server or hasn't launched it properly, or your environment somehow crashes, or you haven't chmod +x your Unity Environment, all of these will cause connection between Unity and Python to fail. Then you will see something like this: Logging to /home/ubuntu/.config/unity3d/<Some_Path>/Player.log Traceback (most recent call last): File \"<stdin>\", line 1, in <module> File \"/home/ubuntu/ml-agents/ml-agents/mlagents_envs/environment.py\", line 63, in __init__ aca_params = self.send_academy_parameters(rl_init_parameters_in) File \"/home/ubuntu/ml-agents/ml-agents/mlagents_envs/environment.py\", line 489, in send_academy_parameters return self.communicator.initialize(inputs).rl_initialization_output File \"/home/ubuntu/ml-agents/ml-agents/mlagents_envs/rpc_communicator.py\", line 60, in initialize mlagents_envs.exception.UnityTimeOutException: The Unity environment took too long to respond. Make sure that : The environment does not need user interaction to launch The environment and the Python interface have compatible versions. It would be also really helpful to check your /home/ubuntu/.config/unity3d/ /Player.log to see what happens with your Unity environment.","title":"Unity Environment not responding"},{"location":"Training-on-Amazon-Web-Service/#could-not-launch-x-server","text":"When you execute: sudo /usr/bin/X :0 & You might see something like: X.Org X Server 1.18.4 ... (==) Log file: \"/var/log/Xorg.0.log\", Time: Thu Oct 11 21:10:38 2018 (==) Using config file: \"/etc/X11/xorg.conf\" (==) Using system config directory \"/usr/share/X11/xorg.conf.d\" (EE) Fatal server error: (EE) no screens found(EE) (EE) Please consult the X.Org Foundation support at http://wiki.x.org for help. (EE) Please also check the log file at \"/var/log/Xorg.0.log\" for additional information. (EE) (EE) Server terminated with error (1). Closing log file. And when you execute: nvidia-smi You might see something like: NVIDIA-SMI has failed because it couldn't communicate with the NVIDIA driver. Make sure that the latest NVIDIA driver is installed and running. This means the NVIDIA's driver needs to be updated. Refer to this section for more information.","title":"Could not launch X Server"},{"location":"Training-on-Microsoft-Azure/","text":"Training on Microsoft Azure (works with ML-Agents Toolkit v0.3) :warning: Note: We no longer use this guide ourselves and so it may not work correctly. We've decided to keep it up just in case it is helpful to you. This page contains instructions for setting up training on Microsoft Azure through either Azure Container Instances or Virtual Machines. Non \"headless\" training has not yet been tested to verify support. Pre-Configured Azure Virtual Machine A pre-configured virtual machine image is available in the Azure Marketplace and is nearly completely ready for training. You can start by deploying the Data Science Virtual Machine for Linux (Ubuntu) into your Azure subscription. Note that, if you choose to deploy the image to an N-Series GPU optimized VM , training will, by default, run on the GPU. If you choose any other type of VM, training will run on the CPU. Configuring your own Instance Setting up your own instance requires a number of package installations. Please view the documentation for doing so here . Installing ML-Agents Move the ml-agents sub-folder of this ml-agents repo to the remote Azure instance, and set it as the working directory. Install the required packages: Torch: pip3 install torch==1.7.0 -f https://download.pytorch.org/whl/torch_stable.html and MLAgents: python -m pip install mlagents==0.30.0 Testing To verify that all steps worked correctly: In the Unity Editor, load a project containing an ML-Agents environment (you can use one of the example environments if you have not created your own). Open the Build Settings window (menu: File > Build Settings). Select Linux as the Target Platform, and x86_64 as the target architecture. Check Headless Mode. Click Build to build the Unity environment executable. Upload the resulting files to your Azure instance. Test the instance setup from Python using: from mlagents_envs.environment import UnityEnvironment env = UnityEnvironment(file_name=\"<your_env>\", seed=1, side_channels=[]) Where <your_env> corresponds to the path to your environment executable (i.e. /home/UserName/Build/yourFile ). You should receive a message confirming that the environment was loaded successfully. Note: When running your environment in headless mode, you must append --no-graphics to your mlagents-learn command, as it won't train otherwise. You can test this simply by aborting a training and check if it says \"Model Saved\" or \"Aborted\", or see if it generated the .onnx in the result folder. Running Training on your Virtual Machine To run your training on the VM: Move your built Unity application to your Virtual Machine. Set the directory where the ML-Agents Toolkit was installed to your working directory. Run the following command: mlagents-learn <trainer_config> --env=<your_app> --run-id=<run_id> --train Where <your_app> is the path to your app (i.e. ~/unity-volume/3DBallHeadless ) and <run_id> is an identifier you would like to identify your training run with. If you've selected to run on a N-Series VM with GPU support, you can verify that the GPU is being used by running nvidia-smi from the command line. Monitoring your Training Run with TensorBoard Once you have started training, you can use TensorBoard to observe the training . Start by opening the appropriate port for web traffic to connect to your VM . Note that you don't need to generate a new Network Security Group but instead, go to the Networking tab under Settings for your VM. As an example, you could use the following settings to open the Port with the following Inbound Rule settings: Source: Any Source Port Ranges: * Destination: Any Destination Port Ranges: 6006 Protocol: Any Action: Allow Priority: (Leave as default) Unless you started the training as a background process, connect to your VM from another terminal instance. Run the following command from your terminal tensorboard --logdir results --host 0.0.0.0 You should now be able to open a browser and navigate to <Your_VM_IP_Address>:6060 to view the TensorBoard report. Running on Azure Container Instances Azure Container Instances allow you to spin up a container, on demand, that will run your training and then be shut down. This ensures you aren't leaving a billable VM running when it isn't needed. Using ACI enables you to offload training of your models without needing to install Python and TensorFlow on your own computer. Custom Instances This page contains instructions for setting up a custom Virtual Machine on Microsoft Azure so you can running ML-Agents training in the cloud. Start by deploying an Azure VM with Ubuntu Linux (tests were done with 16.04 LTS). To use GPU support, use a N-Series VM. SSH into your VM. Start with the following commands to install the Nvidia driver: ```sh wget http://us.download.nvidia.com/tesla/375.66/nvidia-diag-driver-local-repo-ubuntu1604_375.66-1_amd64.deb sudo dpkg -i nvidia-diag-driver-local-repo-ubuntu1604_375.66-1_amd64.deb sudo apt-get update sudo apt-get install cuda-drivers sudo reboot ``` After a minute you should be able to reconnect to your VM and install the CUDA toolkit: ```sh wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_8.0.61-1_amd64.deb sudo dpkg -i cuda-repo-ubuntu1604_8.0.61-1_amd64.deb sudo apt-get update sudo apt-get install cuda-8-0 ``` You'll next need to download cuDNN from the Nvidia developer site. This requires a registered account. Navigate to http://developer.nvidia.com and create an account and verify it. Download (to your own computer) cuDNN from this url . Copy the deb package to your VM: sh scp libcudnn6_6.0.21-1+cuda8.0_amd64.deb <VMUserName>@<VMIPAddress>:libcudnn6_6.0.21-1+cuda8.0_amd64.deb SSH back to your VM and execute the following: ```console sudo dpkg -i libcudnn6_6.0.21-1+cuda8.0_amd64.deb export LD_LIBRARY_PATH=/usr/local/cuda/lib64/:/usr/lib/x86_64-linux-gnu/:$LD_LIBRARY_PATH . ~/.profile sudo reboot ``` After a minute, you should be able to SSH back into your VM. After doing so, run the following: sh sudo apt install python-pip sudo apt install python3-pip At this point, you need to install TensorFlow. The version you install should be tied to if you are using GPU to train: sh pip3 install tensorflow-gpu==1.4.0 keras==2.0.6 Or CPU to train: sh pip3 install tensorflow==1.4.0 keras==2.0.6 You'll then need to install additional dependencies: sh pip3 install pillow pip3 install numpy","title":"Training on Microsoft Azure (works with ML-Agents Toolkit v0.3)"},{"location":"Training-on-Microsoft-Azure/#training-on-microsoft-azure-works-with-ml-agents-toolkit-v03","text":":warning: Note: We no longer use this guide ourselves and so it may not work correctly. We've decided to keep it up just in case it is helpful to you. This page contains instructions for setting up training on Microsoft Azure through either Azure Container Instances or Virtual Machines. Non \"headless\" training has not yet been tested to verify support.","title":"Training on Microsoft Azure (works with ML-Agents Toolkit v0.3)"},{"location":"Training-on-Microsoft-Azure/#pre-configured-azure-virtual-machine","text":"A pre-configured virtual machine image is available in the Azure Marketplace and is nearly completely ready for training. You can start by deploying the Data Science Virtual Machine for Linux (Ubuntu) into your Azure subscription. Note that, if you choose to deploy the image to an N-Series GPU optimized VM , training will, by default, run on the GPU. If you choose any other type of VM, training will run on the CPU.","title":"Pre-Configured Azure Virtual Machine"},{"location":"Training-on-Microsoft-Azure/#configuring-your-own-instance","text":"Setting up your own instance requires a number of package installations. Please view the documentation for doing so here .","title":"Configuring your own Instance"},{"location":"Training-on-Microsoft-Azure/#installing-ml-agents","text":"Move the ml-agents sub-folder of this ml-agents repo to the remote Azure instance, and set it as the working directory. Install the required packages: Torch: pip3 install torch==1.7.0 -f https://download.pytorch.org/whl/torch_stable.html and MLAgents: python -m pip install mlagents==0.30.0","title":"Installing ML-Agents"},{"location":"Training-on-Microsoft-Azure/#testing","text":"To verify that all steps worked correctly: In the Unity Editor, load a project containing an ML-Agents environment (you can use one of the example environments if you have not created your own). Open the Build Settings window (menu: File > Build Settings). Select Linux as the Target Platform, and x86_64 as the target architecture. Check Headless Mode. Click Build to build the Unity environment executable. Upload the resulting files to your Azure instance. Test the instance setup from Python using: from mlagents_envs.environment import UnityEnvironment env = UnityEnvironment(file_name=\"<your_env>\", seed=1, side_channels=[]) Where <your_env> corresponds to the path to your environment executable (i.e. /home/UserName/Build/yourFile ). You should receive a message confirming that the environment was loaded successfully. Note: When running your environment in headless mode, you must append --no-graphics to your mlagents-learn command, as it won't train otherwise. You can test this simply by aborting a training and check if it says \"Model Saved\" or \"Aborted\", or see if it generated the .onnx in the result folder.","title":"Testing"},{"location":"Training-on-Microsoft-Azure/#running-training-on-your-virtual-machine","text":"To run your training on the VM: Move your built Unity application to your Virtual Machine. Set the directory where the ML-Agents Toolkit was installed to your working directory. Run the following command: mlagents-learn <trainer_config> --env=<your_app> --run-id=<run_id> --train Where <your_app> is the path to your app (i.e. ~/unity-volume/3DBallHeadless ) and <run_id> is an identifier you would like to identify your training run with. If you've selected to run on a N-Series VM with GPU support, you can verify that the GPU is being used by running nvidia-smi from the command line.","title":"Running Training on your Virtual Machine"},{"location":"Training-on-Microsoft-Azure/#monitoring-your-training-run-with-tensorboard","text":"Once you have started training, you can use TensorBoard to observe the training . Start by opening the appropriate port for web traffic to connect to your VM . Note that you don't need to generate a new Network Security Group but instead, go to the Networking tab under Settings for your VM. As an example, you could use the following settings to open the Port with the following Inbound Rule settings: Source: Any Source Port Ranges: * Destination: Any Destination Port Ranges: 6006 Protocol: Any Action: Allow Priority: (Leave as default) Unless you started the training as a background process, connect to your VM from another terminal instance. Run the following command from your terminal tensorboard --logdir results --host 0.0.0.0 You should now be able to open a browser and navigate to <Your_VM_IP_Address>:6060 to view the TensorBoard report.","title":"Monitoring your Training Run with TensorBoard"},{"location":"Training-on-Microsoft-Azure/#running-on-azure-container-instances","text":"Azure Container Instances allow you to spin up a container, on demand, that will run your training and then be shut down. This ensures you aren't leaving a billable VM running when it isn't needed. Using ACI enables you to offload training of your models without needing to install Python and TensorFlow on your own computer.","title":"Running on Azure Container Instances"},{"location":"Training-on-Microsoft-Azure/#custom-instances","text":"This page contains instructions for setting up a custom Virtual Machine on Microsoft Azure so you can running ML-Agents training in the cloud. Start by deploying an Azure VM with Ubuntu Linux (tests were done with 16.04 LTS). To use GPU support, use a N-Series VM. SSH into your VM. Start with the following commands to install the Nvidia driver: ```sh wget http://us.download.nvidia.com/tesla/375.66/nvidia-diag-driver-local-repo-ubuntu1604_375.66-1_amd64.deb sudo dpkg -i nvidia-diag-driver-local-repo-ubuntu1604_375.66-1_amd64.deb sudo apt-get update sudo apt-get install cuda-drivers sudo reboot ``` After a minute you should be able to reconnect to your VM and install the CUDA toolkit: ```sh wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/cuda-repo-ubuntu1604_8.0.61-1_amd64.deb sudo dpkg -i cuda-repo-ubuntu1604_8.0.61-1_amd64.deb sudo apt-get update sudo apt-get install cuda-8-0 ``` You'll next need to download cuDNN from the Nvidia developer site. This requires a registered account. Navigate to http://developer.nvidia.com and create an account and verify it. Download (to your own computer) cuDNN from this url . Copy the deb package to your VM: sh scp libcudnn6_6.0.21-1+cuda8.0_amd64.deb <VMUserName>@<VMIPAddress>:libcudnn6_6.0.21-1+cuda8.0_amd64.deb SSH back to your VM and execute the following: ```console sudo dpkg -i libcudnn6_6.0.21-1+cuda8.0_amd64.deb export LD_LIBRARY_PATH=/usr/local/cuda/lib64/:/usr/lib/x86_64-linux-gnu/:$LD_LIBRARY_PATH . ~/.profile sudo reboot ``` After a minute, you should be able to SSH back into your VM. After doing so, run the following: sh sudo apt install python-pip sudo apt install python3-pip At this point, you need to install TensorFlow. The version you install should be tied to if you are using GPU to train: sh pip3 install tensorflow-gpu==1.4.0 keras==2.0.6 Or CPU to train: sh pip3 install tensorflow==1.4.0 keras==2.0.6 You'll then need to install additional dependencies: sh pip3 install pillow pip3 install numpy","title":"Custom Instances"},{"location":"Tutorial-Custom-Trainer-Plugin/","text":"Step 1: Write your custom trainer class Before you start writing your code, make sure to use your favorite environment management tool(e.g. venv or conda ) to create and activate a Python virtual environment. The following command uses conda , but other tools work similarly: conda create -n trainer-env python=3.8.13 conda activate trainer-env Users of the plug-in system are responsible for implementing the trainer class subject to the API standard. Let us follow an example by implementing a custom trainer named \"YourCustomTrainer\". You can either extend OnPolicyTrainer or OffPolicyTrainer classes depending on the training strategies you choose. Please refer to the internal PPO implementation for a complete code example. We will not provide a workable code in the document. The purpose of the tutorial is to introduce you to the core components and interfaces of our plugin framework. We use code snippets and patterns to demonstrate the control and data flow. Your custom trainers are responsible for collecting experiences and training the models. Your custom trainer class acts like a co-ordinator to the policy and optimizer. To start implementing methods in the class, create a policy class objects from method create_policy : def create_policy( self, parsed_behavior_id: BehaviorIdentifiers, behavior_spec: BehaviorSpec ) -> TorchPolicy: actor_cls: Union[Type[SimpleActor], Type[SharedActorCritic]] = SimpleActor actor_kwargs: Dict[str, Any] = { \"conditional_sigma\": False, \"tanh_squash\": False, } if self.shared_critic: reward_signal_configs = self.trainer_settings.reward_signals reward_signal_names = [ key.value for key, _ in reward_signal_configs.items() ] actor_cls = SharedActorCritic actor_kwargs.update({\"stream_names\": reward_signal_names}) policy = TorchPolicy( self.seed, behavior_spec, self.trainer_settings.network_settings, actor_cls, actor_kwargs, ) return policy Depending on whether you use shared or separate network architecture for your policy, we provide SimpleActor and SharedActorCritic from mlagents.trainers.torch_entities.networks that you can choose from. In our example above, we use a SimpleActor . Next, create an optimizer class object from create_optimizer method and connect it to the policy object you created above: def create_optimizer(self) -> TorchOptimizer: return TorchPPOOptimizer( # type: ignore cast(TorchPolicy, self.policy), self.trainer_settings # type: ignore ) # type: ignore There are a couple of abstract methods( _process_trajectory and _update_policy ) inherited from RLTrainer that you need to implement in your custom trainer class. _process_trajectory takes a trajectory and processes it, putting it into the update buffer. Processing involves calculating value and advantage targets for the model updating step. Given input trajectory: Trajectory , users are responsible for processing the data in the trajectory and append agent_buffer_trajectory to the back of the update buffer by calling self._append_to_update_buffer(agent_buffer_trajectory) , whose output will be used in updating the model in optimizer class. A typical _process_trajectory function(incomplete) will convert a trajectory object to an agent buffer then get all value estimates from the trajectory by calling self.optimizer.get_trajectory_value_estimates . From the returned dictionary of value estimates we extract reward signals keyed by their names: def _process_trajectory(self, trajectory: Trajectory) -> None: super()._process_trajectory(trajectory) agent_id = trajectory.agent_id # All the agents should have the same ID agent_buffer_trajectory = trajectory.to_agentbuffer() # Get all value estimates ( value_estimates, value_next, value_memories, ) = self.optimizer.get_trajectory_value_estimates( agent_buffer_trajectory, trajectory.next_obs, trajectory.done_reached and not trajectory.interrupted, ) for name, v in value_estimates.items(): agent_buffer_trajectory[RewardSignalUtil.value_estimates_key(name)].extend( v ) self._stats_reporter.add_stat( f\"Policy/{self.optimizer.reward_signals[name].name.capitalize()} Value Estimate\", np.mean(v), ) # Evaluate all reward functions self.collected_rewards[\"environment\"][agent_id] += np.sum( agent_buffer_trajectory[BufferKey.ENVIRONMENT_REWARDS] ) for name, reward_signal in self.optimizer.reward_signals.items(): evaluate_result = ( reward_signal.evaluate(agent_buffer_trajectory) * reward_signal.strength ) agent_buffer_trajectory[RewardSignalUtil.rewards_key(name)].extend( evaluate_result ) # Report the reward signals self.collected_rewards[name][agent_id] += np.sum(evaluate_result) self._append_to_update_buffer(agent_buffer_trajectory) A trajectory will be a list of dictionaries of strings mapped to Anything . When calling forward on a policy, the argument will include an \u201cexperience\u201d dictionary from the last step. The forward method will generate an action and the next \u201cexperience\u201d dictionary. Examples of fields in the \u201cexperience\u201d dictionary include observation, action, reward, done status, group_reward, LSTM memory state, etc. Step 2: implement your custom optimizer for the trainer. We will show you an example we implemented - class TorchPPOOptimizer(TorchOptimizer) , which takes a Policy and a Dict of trainer parameters and creates an Optimizer that connects to the policy. Your optimizer should include a value estimator and a loss function in the update method. Before writing your optimizer class, first define setting class class PPOSettings(OnPolicyHyperparamSettings) for your custom optimizer: class PPOSettings(OnPolicyHyperparamSettings): beta: float = 5.0e-3 epsilon: float = 0.2 lambd: float = 0.95 num_epoch: int = 3 shared_critic: bool = False learning_rate_schedule: ScheduleType = ScheduleType.LINEAR beta_schedule: ScheduleType = ScheduleType.LINEAR epsilon_schedule: ScheduleType = ScheduleType.LINEAR You should implement update function following interface: def update(self, batch: AgentBuffer, num_sequences: int) -> Dict[str, float]: In which losses and other metrics are calculated from an AgentBuffer that is generated from your trainer class, depending on which model you choose to implement the loss functions will be different. In our case we calculate value loss from critic and trust region policy loss. A typical pattern(incomplete) of the calculations will look like the following: run_out = self.policy.actor.get_stats( current_obs, actions, masks=act_masks, memories=memories, sequence_length=self.policy.sequence_length, ) log_probs = run_out[\"log_probs\"] entropy = run_out[\"entropy\"] values, _ = self.critic.critic_pass( current_obs, memories=value_memories, sequence_length=self.policy.sequence_length, ) policy_loss = ModelUtils.trust_region_policy_loss( ModelUtils.list_to_tensor(batch[BufferKey.ADVANTAGES]), log_probs, old_log_probs, loss_masks, decay_eps, ) loss = ( policy_loss + 0.5 * value_loss - decay_bet * ModelUtils.masked_mean(entropy, loss_masks) ) Finally update the model and return the a dictionary including calculated losses and updated decay learning rate: ModelUtils.update_learning_rate(self.optimizer, decay_lr) self.optimizer.zero_grad() loss.backward() self.optimizer.step() update_stats = { \"Losses/Policy Loss\": torch.abs(policy_loss).item(), \"Losses/Value Loss\": value_loss.item(), \"Policy/Learning Rate\": decay_lr, \"Policy/Epsilon\": decay_eps, \"Policy/Beta\": decay_bet, } Step 3: Integrate your custom trainer into the plugin system By integrating a custom trainer into the plugin system, a user can use their published packages which have their implementations. To do that, you need to add a setup.py file. In the call to setup(), you'll need to add to the entry_points dictionary for each plugin interface that you implement. The form of this is {entry point name}={plugin module}:{plugin function}. For example: entry_points={ ML_AGENTS_TRAINER_TYPE: [ \"your_trainer_type=your_package.your_custom_trainer:get_type_and_setting\" ] }, Some key elements in the code: ML_AGENTS_TRAINER_TYPE: a string constant for trainer type your_trainer_type: name your trainer type, used in configuration file your_package: your pip installable package containing custom trainer implementation Also define get_type_and_setting method in YourCustomTrainer class: def get_type_and_setting(): return {YourCustomTrainer.get_trainer_name(): YourCustomTrainer}, { YourCustomTrainer.get_trainer_name(): YourCustomSetting } Finally, specify trainer type in the config file: behaviors: 3DBall: trainer_type: your_trainer_type ... Step 4: Install your custom trainer and run training: Before installing your custom trainer package, make sure you have ml-agents-env and ml-agents installed pip3 install -e ./ml-agents-envs && pip3 install -e ./ml-agents Install your cutom trainer package(if your package is pip installable): pip3 install your_custom_package Or follow our internal implementations: pip3 install -e ./ml-agents-trainer-plugin Following the previous installations your package is added as an entrypoint and you can use a config file with new trainers: mlagents-learn ml-agents-trainer-plugin/mlagents_trainer_plugin/a2c/a2c_3DBall.yaml --run-id <run-id-name> --env <env-executable> Validate your implementations: Create a clean Python environment with Python 3.8+ and activate it before you start, if you haven't done so already: conda create -n trainer-env python=3.8.13 conda activate trainer-env Make sure you follow previous steps and install all required packages. We are testing internal implementations in this tutorial, but ML-Agents users can run similar validations once they have their own implementations installed: pip3 install -e ./ml-agents-envs && pip3 install -e ./ml-agents pip3 install -e ./ml-agents-trainer-plugin Once your package is added as an entrypoint , you can add to the config file the new trainer type. Check if trainer type is specified in the config file a2c_3DBall.yaml : trainer_type: a2c Test if custom trainer package is installed by running: mlagents-learn ml-agents-trainer-plugin/mlagents_trainer_plugin/a2c/a2c_3DBall.yaml --run-id test-trainer You can also list all trainers installed in the registry. Type python in your shell to open a REPL session. Run the python code below, you should be able to see all trainer types currently installed: >>> import pkg_resources >>> for entry in pkg_resources.iter_entry_points('mlagents.trainer_type'): ... print(entry) ... default = mlagents.plugins.trainer_type:get_default_trainer_types a2c = mlagents_trainer_plugin.a2c.a2c_trainer:get_type_and_setting dqn = mlagents_trainer_plugin.dqn.dqn_trainer:get_type_and_setting If it is properly installed, you will see Unity logo and message indicating training will start: [INFO] Listening on port 5004. Start training by pressing the Play button in the Unity Editor. If you see the following error message, it could be due to trainer type is wrong or the trainer type specified is not installed: mlagents.trainers.exception.TrainerConfigError: Invalid trainer type a2c was found","title":"Custom Trainer Plugin"},{"location":"Tutorial-Custom-Trainer-Plugin/#step-1-write-your-custom-trainer-class","text":"Before you start writing your code, make sure to use your favorite environment management tool(e.g. venv or conda ) to create and activate a Python virtual environment. The following command uses conda , but other tools work similarly: conda create -n trainer-env python=3.8.13 conda activate trainer-env Users of the plug-in system are responsible for implementing the trainer class subject to the API standard. Let us follow an example by implementing a custom trainer named \"YourCustomTrainer\". You can either extend OnPolicyTrainer or OffPolicyTrainer classes depending on the training strategies you choose. Please refer to the internal PPO implementation for a complete code example. We will not provide a workable code in the document. The purpose of the tutorial is to introduce you to the core components and interfaces of our plugin framework. We use code snippets and patterns to demonstrate the control and data flow. Your custom trainers are responsible for collecting experiences and training the models. Your custom trainer class acts like a co-ordinator to the policy and optimizer. To start implementing methods in the class, create a policy class objects from method create_policy : def create_policy( self, parsed_behavior_id: BehaviorIdentifiers, behavior_spec: BehaviorSpec ) -> TorchPolicy: actor_cls: Union[Type[SimpleActor], Type[SharedActorCritic]] = SimpleActor actor_kwargs: Dict[str, Any] = { \"conditional_sigma\": False, \"tanh_squash\": False, } if self.shared_critic: reward_signal_configs = self.trainer_settings.reward_signals reward_signal_names = [ key.value for key, _ in reward_signal_configs.items() ] actor_cls = SharedActorCritic actor_kwargs.update({\"stream_names\": reward_signal_names}) policy = TorchPolicy( self.seed, behavior_spec, self.trainer_settings.network_settings, actor_cls, actor_kwargs, ) return policy Depending on whether you use shared or separate network architecture for your policy, we provide SimpleActor and SharedActorCritic from mlagents.trainers.torch_entities.networks that you can choose from. In our example above, we use a SimpleActor . Next, create an optimizer class object from create_optimizer method and connect it to the policy object you created above: def create_optimizer(self) -> TorchOptimizer: return TorchPPOOptimizer( # type: ignore cast(TorchPolicy, self.policy), self.trainer_settings # type: ignore ) # type: ignore There are a couple of abstract methods( _process_trajectory and _update_policy ) inherited from RLTrainer that you need to implement in your custom trainer class. _process_trajectory takes a trajectory and processes it, putting it into the update buffer. Processing involves calculating value and advantage targets for the model updating step. Given input trajectory: Trajectory , users are responsible for processing the data in the trajectory and append agent_buffer_trajectory to the back of the update buffer by calling self._append_to_update_buffer(agent_buffer_trajectory) , whose output will be used in updating the model in optimizer class. A typical _process_trajectory function(incomplete) will convert a trajectory object to an agent buffer then get all value estimates from the trajectory by calling self.optimizer.get_trajectory_value_estimates . From the returned dictionary of value estimates we extract reward signals keyed by their names: def _process_trajectory(self, trajectory: Trajectory) -> None: super()._process_trajectory(trajectory) agent_id = trajectory.agent_id # All the agents should have the same ID agent_buffer_trajectory = trajectory.to_agentbuffer() # Get all value estimates ( value_estimates, value_next, value_memories, ) = self.optimizer.get_trajectory_value_estimates( agent_buffer_trajectory, trajectory.next_obs, trajectory.done_reached and not trajectory.interrupted, ) for name, v in value_estimates.items(): agent_buffer_trajectory[RewardSignalUtil.value_estimates_key(name)].extend( v ) self._stats_reporter.add_stat( f\"Policy/{self.optimizer.reward_signals[name].name.capitalize()} Value Estimate\", np.mean(v), ) # Evaluate all reward functions self.collected_rewards[\"environment\"][agent_id] += np.sum( agent_buffer_trajectory[BufferKey.ENVIRONMENT_REWARDS] ) for name, reward_signal in self.optimizer.reward_signals.items(): evaluate_result = ( reward_signal.evaluate(agent_buffer_trajectory) * reward_signal.strength ) agent_buffer_trajectory[RewardSignalUtil.rewards_key(name)].extend( evaluate_result ) # Report the reward signals self.collected_rewards[name][agent_id] += np.sum(evaluate_result) self._append_to_update_buffer(agent_buffer_trajectory) A trajectory will be a list of dictionaries of strings mapped to Anything . When calling forward on a policy, the argument will include an \u201cexperience\u201d dictionary from the last step. The forward method will generate an action and the next \u201cexperience\u201d dictionary. Examples of fields in the \u201cexperience\u201d dictionary include observation, action, reward, done status, group_reward, LSTM memory state, etc.","title":"Step 1: Write your custom trainer class"},{"location":"Tutorial-Custom-Trainer-Plugin/#step-2-implement-your-custom-optimizer-for-the-trainer","text":"We will show you an example we implemented - class TorchPPOOptimizer(TorchOptimizer) , which takes a Policy and a Dict of trainer parameters and creates an Optimizer that connects to the policy. Your optimizer should include a value estimator and a loss function in the update method. Before writing your optimizer class, first define setting class class PPOSettings(OnPolicyHyperparamSettings) for your custom optimizer: class PPOSettings(OnPolicyHyperparamSettings): beta: float = 5.0e-3 epsilon: float = 0.2 lambd: float = 0.95 num_epoch: int = 3 shared_critic: bool = False learning_rate_schedule: ScheduleType = ScheduleType.LINEAR beta_schedule: ScheduleType = ScheduleType.LINEAR epsilon_schedule: ScheduleType = ScheduleType.LINEAR You should implement update function following interface: def update(self, batch: AgentBuffer, num_sequences: int) -> Dict[str, float]: In which losses and other metrics are calculated from an AgentBuffer that is generated from your trainer class, depending on which model you choose to implement the loss functions will be different. In our case we calculate value loss from critic and trust region policy loss. A typical pattern(incomplete) of the calculations will look like the following: run_out = self.policy.actor.get_stats( current_obs, actions, masks=act_masks, memories=memories, sequence_length=self.policy.sequence_length, ) log_probs = run_out[\"log_probs\"] entropy = run_out[\"entropy\"] values, _ = self.critic.critic_pass( current_obs, memories=value_memories, sequence_length=self.policy.sequence_length, ) policy_loss = ModelUtils.trust_region_policy_loss( ModelUtils.list_to_tensor(batch[BufferKey.ADVANTAGES]), log_probs, old_log_probs, loss_masks, decay_eps, ) loss = ( policy_loss + 0.5 * value_loss - decay_bet * ModelUtils.masked_mean(entropy, loss_masks) ) Finally update the model and return the a dictionary including calculated losses and updated decay learning rate: ModelUtils.update_learning_rate(self.optimizer, decay_lr) self.optimizer.zero_grad() loss.backward() self.optimizer.step() update_stats = { \"Losses/Policy Loss\": torch.abs(policy_loss).item(), \"Losses/Value Loss\": value_loss.item(), \"Policy/Learning Rate\": decay_lr, \"Policy/Epsilon\": decay_eps, \"Policy/Beta\": decay_bet, }","title":"Step 2: implement your custom optimizer for the trainer."},{"location":"Tutorial-Custom-Trainer-Plugin/#step-3-integrate-your-custom-trainer-into-the-plugin-system","text":"By integrating a custom trainer into the plugin system, a user can use their published packages which have their implementations. To do that, you need to add a setup.py file. In the call to setup(), you'll need to add to the entry_points dictionary for each plugin interface that you implement. The form of this is {entry point name}={plugin module}:{plugin function}. For example: entry_points={ ML_AGENTS_TRAINER_TYPE: [ \"your_trainer_type=your_package.your_custom_trainer:get_type_and_setting\" ] }, Some key elements in the code: ML_AGENTS_TRAINER_TYPE: a string constant for trainer type your_trainer_type: name your trainer type, used in configuration file your_package: your pip installable package containing custom trainer implementation Also define get_type_and_setting method in YourCustomTrainer class: def get_type_and_setting(): return {YourCustomTrainer.get_trainer_name(): YourCustomTrainer}, { YourCustomTrainer.get_trainer_name(): YourCustomSetting } Finally, specify trainer type in the config file: behaviors: 3DBall: trainer_type: your_trainer_type ...","title":"Step 3: Integrate your custom trainer into the plugin system"},{"location":"Tutorial-Custom-Trainer-Plugin/#step-4-install-your-custom-trainer-and-run-training","text":"Before installing your custom trainer package, make sure you have ml-agents-env and ml-agents installed pip3 install -e ./ml-agents-envs && pip3 install -e ./ml-agents Install your cutom trainer package(if your package is pip installable): pip3 install your_custom_package Or follow our internal implementations: pip3 install -e ./ml-agents-trainer-plugin Following the previous installations your package is added as an entrypoint and you can use a config file with new trainers: mlagents-learn ml-agents-trainer-plugin/mlagents_trainer_plugin/a2c/a2c_3DBall.yaml --run-id <run-id-name> --env <env-executable>","title":"Step 4: Install your custom trainer and run training:"},{"location":"Tutorial-Custom-Trainer-Plugin/#validate-your-implementations","text":"Create a clean Python environment with Python 3.8+ and activate it before you start, if you haven't done so already: conda create -n trainer-env python=3.8.13 conda activate trainer-env Make sure you follow previous steps and install all required packages. We are testing internal implementations in this tutorial, but ML-Agents users can run similar validations once they have their own implementations installed: pip3 install -e ./ml-agents-envs && pip3 install -e ./ml-agents pip3 install -e ./ml-agents-trainer-plugin Once your package is added as an entrypoint , you can add to the config file the new trainer type. Check if trainer type is specified in the config file a2c_3DBall.yaml : trainer_type: a2c Test if custom trainer package is installed by running: mlagents-learn ml-agents-trainer-plugin/mlagents_trainer_plugin/a2c/a2c_3DBall.yaml --run-id test-trainer You can also list all trainers installed in the registry. Type python in your shell to open a REPL session. Run the python code below, you should be able to see all trainer types currently installed: >>> import pkg_resources >>> for entry in pkg_resources.iter_entry_points('mlagents.trainer_type'): ... print(entry) ... default = mlagents.plugins.trainer_type:get_default_trainer_types a2c = mlagents_trainer_plugin.a2c.a2c_trainer:get_type_and_setting dqn = mlagents_trainer_plugin.dqn.dqn_trainer:get_type_and_setting If it is properly installed, you will see Unity logo and message indicating training will start: [INFO] Listening on port 5004. Start training by pressing the Play button in the Unity Editor. If you see the following error message, it could be due to trainer type is wrong or the trainer type specified is not installed: mlagents.trainers.exception.TrainerConfigError: Invalid trainer type a2c was found","title":"Validate your implementations:"},{"location":"Unity-Environment-Registry/","text":"Unity Environment Registry [Experimental] The Unity Environment Registry is a database of pre-built Unity environments that can be easily used without having to install the Unity Editor. It is a great way to get started with our UnityEnvironment API . Loading an Environment from the Registry To get started, you can access the default registry we provide with our Example Environments . The Unity Environment Registry implements a Mapping , therefore, you can access an entry with its identifier with the square brackets [ ] . Use the following code to list all of the environment identifiers present in the default registry: from mlagents_envs.registry import default_registry environment_names = list(default_registry.keys()) for name in environment_names: print(name) The make() method on a registry value will return a UnityEnvironment ready to be used. All arguments passed to the make method will be passed to the constructor of the UnityEnvironment as well. Refer to the documentation on the Python-API for more information about the arguments of the UnityEnvironment constructor. For example, the following code will create the environment under the identifier \"my-env\" , reset it, perform a few steps and finally close it: from mlagents_envs.registry import default_registry env = default_registry[\"my-env\"].make() env.reset() for _ in range(10): env.step() env.close() Create and share your own registry In order to share the UnityEnvironemnt you created, you must : - Create a Unity executable of your environment for each platform (Linux, OSX and/or Windows) - Place each executable in a zip compressed folder - Upload each zip file online to your preferred hosting platform - Create a yaml file that will contain the description and path to your environment - Upload the yaml file online The yaml file must have the following format : environments: - <environment-identifier>: expected_reward: <expected-reward-float> description: <description-of-the-environment> linux_url: <url-to-the-linux-zip-folder> darwin_url: <url-to-the-osx-zip-folder> win_url: <url-to-the-windows-zip-folder> additional_args: - <an-optional-list-of-command-line-arguments-for-the-executable> - ... Your users can now use your environment with the following code : from mlagents_envs.registry import UnityEnvRegistry registry = UnityEnvRegistry() registry.register_from_yaml(\"url-or-path-to-your-yaml-file\") Note : The \"url-or-path-to-your-yaml-file\" can be either a url or a local path.","title":"Unity Environment Registry [Experimental]"},{"location":"Unity-Environment-Registry/#unity-environment-registry-experimental","text":"The Unity Environment Registry is a database of pre-built Unity environments that can be easily used without having to install the Unity Editor. It is a great way to get started with our UnityEnvironment API .","title":"Unity Environment Registry [Experimental]"},{"location":"Unity-Environment-Registry/#loading-an-environment-from-the-registry","text":"To get started, you can access the default registry we provide with our Example Environments . The Unity Environment Registry implements a Mapping , therefore, you can access an entry with its identifier with the square brackets [ ] . Use the following code to list all of the environment identifiers present in the default registry: from mlagents_envs.registry import default_registry environment_names = list(default_registry.keys()) for name in environment_names: print(name) The make() method on a registry value will return a UnityEnvironment ready to be used. All arguments passed to the make method will be passed to the constructor of the UnityEnvironment as well. Refer to the documentation on the Python-API for more information about the arguments of the UnityEnvironment constructor. For example, the following code will create the environment under the identifier \"my-env\" , reset it, perform a few steps and finally close it: from mlagents_envs.registry import default_registry env = default_registry[\"my-env\"].make() env.reset() for _ in range(10): env.step() env.close()","title":"Loading an Environment from the Registry"},{"location":"Unity-Environment-Registry/#create-and-share-your-own-registry","text":"In order to share the UnityEnvironemnt you created, you must : - Create a Unity executable of your environment for each platform (Linux, OSX and/or Windows) - Place each executable in a zip compressed folder - Upload each zip file online to your preferred hosting platform - Create a yaml file that will contain the description and path to your environment - Upload the yaml file online The yaml file must have the following format : environments: - <environment-identifier>: expected_reward: <expected-reward-float> description: <description-of-the-environment> linux_url: <url-to-the-linux-zip-folder> darwin_url: <url-to-the-osx-zip-folder> win_url: <url-to-the-windows-zip-folder> additional_args: - <an-optional-list-of-command-line-arguments-for-the-executable> - ... Your users can now use your environment with the following code : from mlagents_envs.registry import UnityEnvRegistry registry = UnityEnvRegistry() registry.register_from_yaml(\"url-or-path-to-your-yaml-file\") Note : The \"url-or-path-to-your-yaml-file\" can be either a url or a local path.","title":"Create and share your own registry"},{"location":"Unity-Inference-Engine/","text":"Unity Inference Engine The ML-Agents Toolkit allows you to use pre-trained neural network models inside your Unity games. This support is possible thanks to the Unity Inference Engine (codenamed Barracuda). The Unity Inference Engine uses compute shaders to run the neural network within Unity. Supported devices See the Unity Inference Engine documentation for a list of the supported platforms . Scripting Backends : The Unity Inference Engine is generally faster with IL2CPP than with Mono for Standalone builds. In the Editor, It is not possible to use the Unity Inference Engine with GPU device selected when Editor Graphics Emulation is set to OpenGL(ES) 3.0 or 2.0 emulation . Also there might be non-fatal build time errors when target platform includes Graphics API that does not support Unity Compute Shaders . Using the Unity Inference Engine When using a model, drag the model file into the Model field in the Inspector of the Agent. Select the Inference Device : CPU or GPU you want to use for Inference. Note: For most of the models generated with the ML-Agents Toolkit, CPU will be faster than GPU. You should use the GPU only if you use the ResNet visual encoder or have a large number of agents with visual observations. Unsupported use cases Externally trained models The ML-Agents Toolkit only supports the models created with our trainers. Model loading expects certain conventions for constants and tensor names. While it is possible to construct a model that follows these conventions, we don't provide any additional help for this. More details can be found in TensorNames.cs and BarracudaModelParamLoader.cs . If you wish to run inference on an externally trained model, you should use Barracuda directly, instead of trying to run it through ML-Agents. Model inference outside of Unity We do not provide support for inference anywhere outside of Unity. The .onnx files produced by training use the open format ONNX; if you wish to convert a .onnx file to another format or run inference with them, refer to their documentation.","title":"Unity Inference Engine"},{"location":"Unity-Inference-Engine/#unity-inference-engine","text":"The ML-Agents Toolkit allows you to use pre-trained neural network models inside your Unity games. This support is possible thanks to the Unity Inference Engine (codenamed Barracuda). The Unity Inference Engine uses compute shaders to run the neural network within Unity.","title":"Unity Inference Engine"},{"location":"Unity-Inference-Engine/#supported-devices","text":"See the Unity Inference Engine documentation for a list of the supported platforms . Scripting Backends : The Unity Inference Engine is generally faster with IL2CPP than with Mono for Standalone builds. In the Editor, It is not possible to use the Unity Inference Engine with GPU device selected when Editor Graphics Emulation is set to OpenGL(ES) 3.0 or 2.0 emulation . Also there might be non-fatal build time errors when target platform includes Graphics API that does not support Unity Compute Shaders .","title":"Supported devices"},{"location":"Unity-Inference-Engine/#using-the-unity-inference-engine","text":"When using a model, drag the model file into the Model field in the Inspector of the Agent. Select the Inference Device : CPU or GPU you want to use for Inference. Note: For most of the models generated with the ML-Agents Toolkit, CPU will be faster than GPU. You should use the GPU only if you use the ResNet visual encoder or have a large number of agents with visual observations.","title":"Using the Unity Inference Engine"},{"location":"Unity-Inference-Engine/#unsupported-use-cases","text":"","title":"Unsupported use cases"},{"location":"Unity-Inference-Engine/#externally-trained-models","text":"The ML-Agents Toolkit only supports the models created with our trainers. Model loading expects certain conventions for constants and tensor names. While it is possible to construct a model that follows these conventions, we don't provide any additional help for this. More details can be found in TensorNames.cs and BarracudaModelParamLoader.cs . If you wish to run inference on an externally trained model, you should use Barracuda directly, instead of trying to run it through ML-Agents.","title":"Externally trained models"},{"location":"Unity-Inference-Engine/#model-inference-outside-of-unity","text":"We do not provide support for inference anywhere outside of Unity. The .onnx files produced by training use the open format ONNX; if you wish to convert a .onnx file to another format or run inference with them, refer to their documentation.","title":"Model inference outside of Unity"},{"location":"Using-Docker/","text":"Using Docker For ML-Agents (Deprecated) :warning: Note: We no longer use this guide ourselves and so it may not work correctly. We've decided to keep it up just in case it is helpful to you. We currently offer a solution for Windows and Mac users who would like to do training or inference using Docker. This option may be appealing to those who would like to avoid installing Python and TensorFlow themselves. The current setup forces both TensorFlow and Unity to only rely on the CPU for computations. Consequently, our Docker simulation does not use a GPU and uses Xvfb to do visual rendering. Xvfb is a utility that enables ML-Agents (or any other application) to do rendering virtually i.e. it does not assume that the machine running ML-Agents has a GPU or a display attached to it. This means that rich environments which involve agents using camera-based visual observations might be slower. Requirements Docker Unity Linux Build Support Component. Make sure to select the Linux Build Support component when installing Unity. Setup Download the Unity Installer and add the Linux Build Support Component Download and install Docker if you don't have it setup on your machine. Since Docker runs a container in an environment that is isolated from the host machine, a mounted directory in your host machine is used to share data, e.g. the trainer configuration file, Unity executable and TensorFlow graph. For convenience, we created an empty unity-volume directory at the root of the repository for this purpose, but feel free to use any other directory. The remainder of this guide assumes that the unity-volume directory is the one used. Usage Using Docker for ML-Agents involves three steps: building the Unity environment with specific flags, building a Docker container and, finally, running the container. If you are not familiar with building a Unity environment for ML-Agents, please read through our Getting Started with the 3D Balance Ball Example guide first. Build the Environment (Optional) If you want to used the Editor to perform training, you can skip this step. Since Docker typically runs a container sharing a (linux) kernel with the host machine, the Unity environment has to be built for the linux platform . When building a Unity environment, please select the following options from the the Build Settings window: Set the Target Platform to Linux Set the Architecture to x86_64 Then click Build , pick an environment name (e.g. 3DBall ) and set the output directory to unity-volume . After building, ensure that the file <environment-name>.x86_64 and subdirectory <environment-name>_Data/ are created under unity-volume . Build the Docker Container First, make sure the Docker engine is running on your machine. Then build the Docker container by calling the following command at the top-level of the repository: docker build -t <image-name> . Replace <image-name> with a name for the Docker image, e.g. balance.ball.v0.1 . Run the Docker Container Run the Docker container by calling the following command at the top-level of the repository: docker run -it --name <container-name> \\ --mount type=bind,source=\"$(pwd)\"/unity-volume,target=/unity-volume \\ -p 5005:5005 \\ -p 6006:6006 \\ <image-name>:latest \\ <trainer-config-file> \\ --env=<environment-name> \\ --train \\ --run-id=<run-id> Notes on argument values: <container-name> is used to identify the container (in case you want to interrupt and terminate it). This is optional and Docker will generate a random name if this is not set. Note that this must be unique for every run of a Docker image. <image-name> references the image name used when building the container. <environment-name> (Optional) : If you are training with a linux executable, this is the name of the executable. If you are training in the Editor, do not pass a <environment-name> argument and press the Play button in Unity when the message \"Start training by pressing the Play button in the Unity Editor\" is displayed on the screen. source : Reference to the path in your host OS where you will store the Unity executable. target : Tells Docker to mount the source path as a disk with this name. trainer-config-file , train , run-id : ML-Agents arguments passed to mlagents-learn . trainer-config-file is the filename of the trainer config file, train trains the algorithm, and run-id is used to tag each experiment with a unique identifier. We recommend placing the trainer-config file inside unity-volume so that the container has access to the file. To train with a 3DBall environment executable, the command would be: docker run -it --name 3DBallContainer.first.trial \\ --mount type=bind,source=\"$(pwd)\"/unity-volume,target=/unity-volume \\ -p 5005:5005 \\ -p 6006:6006 \\ balance.ball.v0.1:latest 3DBall \\ /unity-volume/trainer_config.yaml \\ --env=/unity-volume/3DBall \\ --train \\ --run-id=3dball_first_trial For more detail on Docker mounts, check out these docs from Docker. NOTE If you are training using docker for environments that use visual observations, you may need to increase the default memory that Docker allocates for the container. For example, see here for instructions for Docker for Mac. Running Tensorboard You can run Tensorboard to monitor your training instance on http://localhost:6006: docker exec -it <container-name> tensorboard --logdir /unity-volume/results --host 0.0.0.0 With our previous 3DBall example, this command would look like this: docker exec -it 3DBallContainer.first.trial tensorboard --logdir /unity-volume/results --host 0.0.0.0 For more details on Tensorboard, check out the documentation about Using Tensorboard . Stopping Container and Saving State If you are satisfied with the training progress, you can stop the Docker container while saving state by either using Ctrl+C or \u2318+C (Mac) or by using the following command: docker kill --signal=SIGINT <container-name> <container-name> is the name of the container specified in the earlier docker run command. If you didn't specify one, you can find the randomly generated identifier by running docker container ls .","title":"Using Docker For ML-Agents (Deprecated)"},{"location":"Using-Docker/#using-docker-for-ml-agents-deprecated","text":":warning: Note: We no longer use this guide ourselves and so it may not work correctly. We've decided to keep it up just in case it is helpful to you. We currently offer a solution for Windows and Mac users who would like to do training or inference using Docker. This option may be appealing to those who would like to avoid installing Python and TensorFlow themselves. The current setup forces both TensorFlow and Unity to only rely on the CPU for computations. Consequently, our Docker simulation does not use a GPU and uses Xvfb to do visual rendering. Xvfb is a utility that enables ML-Agents (or any other application) to do rendering virtually i.e. it does not assume that the machine running ML-Agents has a GPU or a display attached to it. This means that rich environments which involve agents using camera-based visual observations might be slower.","title":"Using Docker For ML-Agents (Deprecated)"},{"location":"Using-Docker/#requirements","text":"Docker Unity Linux Build Support Component. Make sure to select the Linux Build Support component when installing Unity.","title":"Requirements"},{"location":"Using-Docker/#setup","text":"Download the Unity Installer and add the Linux Build Support Component Download and install Docker if you don't have it setup on your machine. Since Docker runs a container in an environment that is isolated from the host machine, a mounted directory in your host machine is used to share data, e.g. the trainer configuration file, Unity executable and TensorFlow graph. For convenience, we created an empty unity-volume directory at the root of the repository for this purpose, but feel free to use any other directory. The remainder of this guide assumes that the unity-volume directory is the one used.","title":"Setup"},{"location":"Using-Docker/#usage","text":"Using Docker for ML-Agents involves three steps: building the Unity environment with specific flags, building a Docker container and, finally, running the container. If you are not familiar with building a Unity environment for ML-Agents, please read through our Getting Started with the 3D Balance Ball Example guide first.","title":"Usage"},{"location":"Using-Docker/#build-the-environment-optional","text":"If you want to used the Editor to perform training, you can skip this step. Since Docker typically runs a container sharing a (linux) kernel with the host machine, the Unity environment has to be built for the linux platform . When building a Unity environment, please select the following options from the the Build Settings window: Set the Target Platform to Linux Set the Architecture to x86_64 Then click Build , pick an environment name (e.g. 3DBall ) and set the output directory to unity-volume . After building, ensure that the file <environment-name>.x86_64 and subdirectory <environment-name>_Data/ are created under unity-volume .","title":"Build the Environment (Optional)"},{"location":"Using-Docker/#build-the-docker-container","text":"First, make sure the Docker engine is running on your machine. Then build the Docker container by calling the following command at the top-level of the repository: docker build -t <image-name> . Replace <image-name> with a name for the Docker image, e.g. balance.ball.v0.1 .","title":"Build the Docker Container"},{"location":"Using-Docker/#run-the-docker-container","text":"Run the Docker container by calling the following command at the top-level of the repository: docker run -it --name <container-name> \\ --mount type=bind,source=\"$(pwd)\"/unity-volume,target=/unity-volume \\ -p 5005:5005 \\ -p 6006:6006 \\ <image-name>:latest \\ <trainer-config-file> \\ --env=<environment-name> \\ --train \\ --run-id=<run-id> Notes on argument values: <container-name> is used to identify the container (in case you want to interrupt and terminate it). This is optional and Docker will generate a random name if this is not set. Note that this must be unique for every run of a Docker image. <image-name> references the image name used when building the container. <environment-name> (Optional) : If you are training with a linux executable, this is the name of the executable. If you are training in the Editor, do not pass a <environment-name> argument and press the Play button in Unity when the message \"Start training by pressing the Play button in the Unity Editor\" is displayed on the screen. source : Reference to the path in your host OS where you will store the Unity executable. target : Tells Docker to mount the source path as a disk with this name. trainer-config-file , train , run-id : ML-Agents arguments passed to mlagents-learn . trainer-config-file is the filename of the trainer config file, train trains the algorithm, and run-id is used to tag each experiment with a unique identifier. We recommend placing the trainer-config file inside unity-volume so that the container has access to the file. To train with a 3DBall environment executable, the command would be: docker run -it --name 3DBallContainer.first.trial \\ --mount type=bind,source=\"$(pwd)\"/unity-volume,target=/unity-volume \\ -p 5005:5005 \\ -p 6006:6006 \\ balance.ball.v0.1:latest 3DBall \\ /unity-volume/trainer_config.yaml \\ --env=/unity-volume/3DBall \\ --train \\ --run-id=3dball_first_trial For more detail on Docker mounts, check out these docs from Docker. NOTE If you are training using docker for environments that use visual observations, you may need to increase the default memory that Docker allocates for the container. For example, see here for instructions for Docker for Mac.","title":"Run the Docker Container"},{"location":"Using-Docker/#running-tensorboard","text":"You can run Tensorboard to monitor your training instance on http://localhost:6006: docker exec -it <container-name> tensorboard --logdir /unity-volume/results --host 0.0.0.0 With our previous 3DBall example, this command would look like this: docker exec -it 3DBallContainer.first.trial tensorboard --logdir /unity-volume/results --host 0.0.0.0 For more details on Tensorboard, check out the documentation about Using Tensorboard .","title":"Running Tensorboard"},{"location":"Using-Docker/#stopping-container-and-saving-state","text":"If you are satisfied with the training progress, you can stop the Docker container while saving state by either using Ctrl+C or \u2318+C (Mac) or by using the following command: docker kill --signal=SIGINT <container-name> <container-name> is the name of the container specified in the earlier docker run command. If you didn't specify one, you can find the randomly generated identifier by running docker container ls .","title":"Stopping Container and Saving State"},{"location":"Using-Tensorboard/","text":"Using TensorBoard to Observe Training The ML-Agents Toolkit saves statistics during learning session that you can view with a TensorFlow utility named, TensorBoard . The mlagents-learn command saves training statistics to a folder named results , organized by the run-id value you assign to a training session. In order to observe the training process, either during training or afterward, start TensorBoard: Open a terminal or console window: Navigate to the directory where the ML-Agents Toolkit is installed. From the command line run: tensorboard --logdir results --port 6006 Open a browser window and navigate to localhost:6006 . Note: The default port TensorBoard uses is 6006. If there is an existing session running on port 6006 a new session can be launched on an open port using the --port option. Note: If you don't assign a run-id identifier, mlagents-learn uses the default string, \"ppo\". You can delete the folders under the results directory to clear out old statistics. On the left side of the TensorBoard window, you can select which of the training runs you want to display. You can select multiple run-ids to compare statistics. The TensorBoard window also provides options for how to display and smooth graphs. The ML-Agents Toolkit training statistics The ML-Agents training program saves the following statistics: Environment Statistics Environment/Lesson - Plots the progress from lesson to lesson. Only interesting when performing curriculum training. Environment/Cumulative Reward - The mean cumulative episode reward over all agents. Should increase during a successful training session. Environment/Episode Length - The mean length of each episode in the environment for all agents. Is Training Is Training - A boolean indicating if the agent is updating its model. Policy Statistics Policy/Entropy (PPO; SAC) - How random the decisions of the model are. Should slowly decrease during a successful training process. If it decreases too quickly, the beta hyperparameter should be increased. Policy/Learning Rate (PPO; SAC) - How large a step the training algorithm takes as it searches for the optimal policy. Should decrease over time. Policy/Entropy Coefficient (SAC) - Determines the relative importance of the entropy term. This value is adjusted automatically so that the agent retains some amount of randomness during training. Policy/Extrinsic Reward (PPO; SAC) - This corresponds to the mean cumulative reward received from the environment per-episode. Policy/Value Estimate (PPO; SAC) - The mean value estimate for all states visited by the agent. Should increase during a successful training session. Policy/Curiosity Reward (PPO/SAC+Curiosity) - This corresponds to the mean cumulative intrinsic reward generated per-episode. Policy/Curiosity Value Estimate (PPO/SAC+Curiosity) - The agent's value estimate for the curiosity reward. Policy/GAIL Reward (PPO/SAC+GAIL) - This corresponds to the mean cumulative discriminator-based reward generated per-episode. Policy/GAIL Value Estimate (PPO/SAC+GAIL) - The agent's value estimate for the GAIL reward. Policy/GAIL Policy Estimate (PPO/SAC+GAIL) - The discriminator's estimate for states and actions generated by the policy. Policy/GAIL Expert Estimate (PPO/SAC+GAIL) - The discriminator's estimate for states and actions drawn from expert demonstrations. Learning Loss Functions Losses/Policy Loss (PPO; SAC) - The mean magnitude of policy loss function. Correlates to how much the policy (process for deciding actions) is changing. The magnitude of this should decrease during a successful training session. Losses/Value Loss (PPO; SAC) - The mean loss of the value function update. Correlates to how well the model is able to predict the value of each state. This should increase while the agent is learning, and then decrease once the reward stabilizes. Losses/Forward Loss (PPO/SAC+Curiosity) - The mean magnitude of the forward model loss function. Corresponds to how well the model is able to predict the new observation encoding. Losses/Inverse Loss (PPO/SAC+Curiosity) - The mean magnitude of the inverse model loss function. Corresponds to how well the model is able to predict the action taken between two observations. Losses/Pretraining Loss (BC) - The mean magnitude of the behavioral cloning loss. Corresponds to how well the model imitates the demonstration data. Losses/GAIL Loss (GAIL) - The mean magnitude of the GAIL discriminator loss. Corresponds to how well the model imitates the demonstration data. Self-Play Self-Play/ELO (Self-Play) - ELO measures the relative skill level between two players. In a proper training run, the ELO of the agent should steadily increase. Exporting Data from TensorBoard To export timeseries data in CSV or JSON format, check the \"Show data download links\" in the upper left. This will enable download links below each chart. Custom Metrics from Unity To get custom metrics from a C# environment into TensorBoard, you can use the StatsRecorder : var statsRecorder = Academy.Instance.StatsRecorder; statsRecorder.Add(\"MyMetric\", 1.0);","title":"Using TensorBoard to Observe Training"},{"location":"Using-Tensorboard/#using-tensorboard-to-observe-training","text":"The ML-Agents Toolkit saves statistics during learning session that you can view with a TensorFlow utility named, TensorBoard . The mlagents-learn command saves training statistics to a folder named results , organized by the run-id value you assign to a training session. In order to observe the training process, either during training or afterward, start TensorBoard: Open a terminal or console window: Navigate to the directory where the ML-Agents Toolkit is installed. From the command line run: tensorboard --logdir results --port 6006 Open a browser window and navigate to localhost:6006 . Note: The default port TensorBoard uses is 6006. If there is an existing session running on port 6006 a new session can be launched on an open port using the --port option. Note: If you don't assign a run-id identifier, mlagents-learn uses the default string, \"ppo\". You can delete the folders under the results directory to clear out old statistics. On the left side of the TensorBoard window, you can select which of the training runs you want to display. You can select multiple run-ids to compare statistics. The TensorBoard window also provides options for how to display and smooth graphs.","title":"Using TensorBoard to Observe Training"},{"location":"Using-Tensorboard/#the-ml-agents-toolkit-training-statistics","text":"The ML-Agents training program saves the following statistics:","title":"The ML-Agents Toolkit training statistics"},{"location":"Using-Tensorboard/#environment-statistics","text":"Environment/Lesson - Plots the progress from lesson to lesson. Only interesting when performing curriculum training. Environment/Cumulative Reward - The mean cumulative episode reward over all agents. Should increase during a successful training session. Environment/Episode Length - The mean length of each episode in the environment for all agents.","title":"Environment Statistics"},{"location":"Using-Tensorboard/#is-training","text":"Is Training - A boolean indicating if the agent is updating its model.","title":"Is Training"},{"location":"Using-Tensorboard/#policy-statistics","text":"Policy/Entropy (PPO; SAC) - How random the decisions of the model are. Should slowly decrease during a successful training process. If it decreases too quickly, the beta hyperparameter should be increased. Policy/Learning Rate (PPO; SAC) - How large a step the training algorithm takes as it searches for the optimal policy. Should decrease over time. Policy/Entropy Coefficient (SAC) - Determines the relative importance of the entropy term. This value is adjusted automatically so that the agent retains some amount of randomness during training. Policy/Extrinsic Reward (PPO; SAC) - This corresponds to the mean cumulative reward received from the environment per-episode. Policy/Value Estimate (PPO; SAC) - The mean value estimate for all states visited by the agent. Should increase during a successful training session. Policy/Curiosity Reward (PPO/SAC+Curiosity) - This corresponds to the mean cumulative intrinsic reward generated per-episode. Policy/Curiosity Value Estimate (PPO/SAC+Curiosity) - The agent's value estimate for the curiosity reward. Policy/GAIL Reward (PPO/SAC+GAIL) - This corresponds to the mean cumulative discriminator-based reward generated per-episode. Policy/GAIL Value Estimate (PPO/SAC+GAIL) - The agent's value estimate for the GAIL reward. Policy/GAIL Policy Estimate (PPO/SAC+GAIL) - The discriminator's estimate for states and actions generated by the policy. Policy/GAIL Expert Estimate (PPO/SAC+GAIL) - The discriminator's estimate for states and actions drawn from expert demonstrations.","title":"Policy Statistics"},{"location":"Using-Tensorboard/#learning-loss-functions","text":"Losses/Policy Loss (PPO; SAC) - The mean magnitude of policy loss function. Correlates to how much the policy (process for deciding actions) is changing. The magnitude of this should decrease during a successful training session. Losses/Value Loss (PPO; SAC) - The mean loss of the value function update. Correlates to how well the model is able to predict the value of each state. This should increase while the agent is learning, and then decrease once the reward stabilizes. Losses/Forward Loss (PPO/SAC+Curiosity) - The mean magnitude of the forward model loss function. Corresponds to how well the model is able to predict the new observation encoding. Losses/Inverse Loss (PPO/SAC+Curiosity) - The mean magnitude of the inverse model loss function. Corresponds to how well the model is able to predict the action taken between two observations. Losses/Pretraining Loss (BC) - The mean magnitude of the behavioral cloning loss. Corresponds to how well the model imitates the demonstration data. Losses/GAIL Loss (GAIL) - The mean magnitude of the GAIL discriminator loss. Corresponds to how well the model imitates the demonstration data.","title":"Learning Loss Functions"},{"location":"Using-Tensorboard/#self-play","text":"Self-Play/ELO (Self-Play) - ELO measures the relative skill level between two players. In a proper training run, the ELO of the agent should steadily increase.","title":"Self-Play"},{"location":"Using-Tensorboard/#exporting-data-from-tensorboard","text":"To export timeseries data in CSV or JSON format, check the \"Show data download links\" in the upper left. This will enable download links below each chart.","title":"Exporting Data from TensorBoard"},{"location":"Using-Tensorboard/#custom-metrics-from-unity","text":"To get custom metrics from a C# environment into TensorBoard, you can use the StatsRecorder : var statsRecorder = Academy.Instance.StatsRecorder; statsRecorder.Add(\"MyMetric\", 1.0);","title":"Custom Metrics from Unity"},{"location":"Using-Virtual-Environment/","text":"Using Virtual Environment What is a Virtual Environment? A Virtual Environment is a self contained directory tree that contains a Python installation for a particular version of Python, plus a number of additional packages. To learn more about Virtual Environments see here . Why should I use a Virtual Environment? A Virtual Environment keeps all dependencies for the Python project separate from dependencies of other projects. This has a few advantages: It makes dependency management for the project easy. It enables using and testing of different library versions by quickly spinning up a new environment and verifying the compatibility of the code with the different version. Python Version Requirement (Required) This guide has been tested with Python 3.8.13 through Python 3.10.x. Newer versions might not have support for the dependent libraries, so are not recommended. Installing Pip (Required) Download the get-pip.py file using the command curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py Run the following python3 get-pip.py Check pip version using pip3 -V Note (for Ubuntu users): If the ModuleNotFoundError: No module named 'distutils.util' error is encountered, then python3-distutils needs to be installed. Install python3-distutils using sudo apt-get install python3-distutils Mac OS X Setup Create a folder where the virtual environments will reside $ mkdir ~/python-envs To create a new environment named sample-env execute $ python3 -m venv ~/python-envs/sample-env To activate the environment execute $ source ~/python-envs/sample-env/bin/activate Upgrade to the latest pip version using $ pip3 install --upgrade pip Upgrade to the latest setuptools version using $ pip3 install --upgrade setuptools To deactivate the environment execute $ deactivate (you can reactivate the environment using the same activate command listed above) Ubuntu Setup Install the python3-venv package using $ sudo apt-get install python3-venv Follow the steps in the Mac OS X installation. Windows Setup Create a folder where the virtual environments will reside md python-envs To create a new environment named sample-env execute python -m venv python-envs\\sample-env To activate the environment execute python-envs\\sample-env\\Scripts\\activate Upgrade to the latest pip version using pip install --upgrade pip To deactivate the environment execute deactivate (you can reactivate the environment using the same activate command listed above) Note: - Verify that you are using a Python version between 3.8.13 and 3.10.x. Launch a command prompt using cmd and execute python --version to verify the version. - Python3 installation may require admin privileges on Windows. - This guide is for Windows 10 using a 64-bit architecture only.","title":"Using Virtual Environment"},{"location":"Using-Virtual-Environment/#using-virtual-environment","text":"","title":"Using Virtual Environment"},{"location":"Using-Virtual-Environment/#what-is-a-virtual-environment","text":"A Virtual Environment is a self contained directory tree that contains a Python installation for a particular version of Python, plus a number of additional packages. To learn more about Virtual Environments see here .","title":"What is a Virtual Environment?"},{"location":"Using-Virtual-Environment/#why-should-i-use-a-virtual-environment","text":"A Virtual Environment keeps all dependencies for the Python project separate from dependencies of other projects. This has a few advantages: It makes dependency management for the project easy. It enables using and testing of different library versions by quickly spinning up a new environment and verifying the compatibility of the code with the different version.","title":"Why should I use a Virtual Environment?"},{"location":"Using-Virtual-Environment/#python-version-requirement-required","text":"This guide has been tested with Python 3.8.13 through Python 3.10.x. Newer versions might not have support for the dependent libraries, so are not recommended.","title":"Python Version Requirement (Required)"},{"location":"Using-Virtual-Environment/#installing-pip-required","text":"Download the get-pip.py file using the command curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py Run the following python3 get-pip.py Check pip version using pip3 -V Note (for Ubuntu users): If the ModuleNotFoundError: No module named 'distutils.util' error is encountered, then python3-distutils needs to be installed. Install python3-distutils using sudo apt-get install python3-distutils","title":"Installing Pip (Required)"},{"location":"Using-Virtual-Environment/#mac-os-x-setup","text":"Create a folder where the virtual environments will reside $ mkdir ~/python-envs To create a new environment named sample-env execute $ python3 -m venv ~/python-envs/sample-env To activate the environment execute $ source ~/python-envs/sample-env/bin/activate Upgrade to the latest pip version using $ pip3 install --upgrade pip Upgrade to the latest setuptools version using $ pip3 install --upgrade setuptools To deactivate the environment execute $ deactivate (you can reactivate the environment using the same activate command listed above)","title":"Mac OS X Setup"},{"location":"Using-Virtual-Environment/#ubuntu-setup","text":"Install the python3-venv package using $ sudo apt-get install python3-venv Follow the steps in the Mac OS X installation.","title":"Ubuntu Setup"},{"location":"Using-Virtual-Environment/#windows-setup","text":"Create a folder where the virtual environments will reside md python-envs To create a new environment named sample-env execute python -m venv python-envs\\sample-env To activate the environment execute python-envs\\sample-env\\Scripts\\activate Upgrade to the latest pip version using pip install --upgrade pip To deactivate the environment execute deactivate (you can reactivate the environment using the same activate command listed above) Note: - Verify that you are using a Python version between 3.8.13 and 3.10.x. Launch a command prompt using cmd and execute python --version to verify the version. - Python3 installation may require admin privileges on Windows. - This guide is for Windows 10 using a 64-bit architecture only.","title":"Windows Setup"},{"location":"Versioning/","text":"ML-Agents Versioning Context As the ML-Agents project evolves into a more mature product, we want to communicate the process we use to version our packages and the data that flows into, through, and out of them clearly. Our project now has four packages (1 Unity, 3 Python) along with artifacts that are produced as well as consumed. This document covers the versioning for these packages and artifacts. GitHub Releases Up until now, all packages were in lockstep in-terms of versioning. As a result, the GitHub releases were tagged with the version of all those packages (e.g. v0.15.0, v0.15.1) and labeled accordingly. With the decoupling of package versions, we now need to revisit our GitHub release tagging. The proposal is that we move towards an integer release numbering for our repo and each such release will call out specific version upgrades of each package. For instance, with the April 30th release , we will have: - GitHub Release 1 (branch name: release_1_branch ) - com.unity.ml-agents release 1.0.0 - ml-agents release 0.16.0 - ml-agents-envs release 0.16.0 - gym-unity release 0.16.0 Our release cadence will not be affected by these versioning changes. We will keep having monthly releases to fix bugs and release new features. Packages All of the software packages, and their generated artifacts will be versioned. Any automation tools will not be versioned. Unity package Package name: com.unity.ml-agents - Versioned following Semantic Versioning Guidelines - This package consumes an artifact of the training process: the .nn file. These files are integer versioned and currently at version 2. The com.unity.ml-agents package will need to support the version of .nn files which existed at its 1.0.0 release. For example, consider that com.unity.ml-agents is at version 1.0.0 and the NN files are at version 2. If the NN files change to version 3, the next release of com.unity.ml-agents at version 1.1.0 guarantees it will be able to read both of these formats. If the NN files were to change to version 4 and com.unity.ml-agents to version 2.0.0, support for NN versions 2 and 3 could be dropped for com.unity.ml-agents version 2.0.0. - This package produces one artifact, the .demo files. These files will have integer versioning. This means their version will increment by 1 at each change. The com.unity.ml-agents package must be backward compatible with version changes that occur between minor versions. - To summarize, the artifacts produced and consumed by com.unity.ml-agents are guaranteed to be supported for 1.x.x versions of com.unity.ml-agents. We intend to provide stability for our users by moving to a 1.0.0 release of com.unity.ml-agents. Python Packages Package names: ml-agents / ml-agents-envs / gym-unity - The python packages remain in \"Beta.\" This means that breaking changes to the public API of the python packages can change without having to have a major version bump. Historically, the python and C# packages were in version lockstep. This is no longer the case. The python packages will remain in lockstep with each other for now, while the C# package will follow its own versioning as is appropriate. However, the python package versions may diverge in the future. - While the python packages will remain in Beta for now, we acknowledge that the most heavily used portion of our python interface is the mlagents-learn CLI and strive to make this part of our API backward compatible. We are actively working on this and expect to have a stable CLI in the next few weeks. Communicator Packages which communicate: com.unity.ml-agents / ml-agents-envs Another entity of the ML-Agents Toolkit that requires versioning is the communication layer between C# and Python, which will follow also semantic versioning. This guarantees a level of backward compatibility between different versions of C# and Python packages which communicate. Any Communicator version 1.x.x of the Unity package should be compatible with any 1.x.x Communicator Version in Python. An RLCapabilities struct keeps track of which features exist. This struct is passed from C# to Python, and another from Python to C#. With this feature level granularity, we can notify users more specifically about feature limitations based on what's available in both C# and Python. These notifications will be logged to the python terminal, or to the Unity Editor Console. Side Channels The communicator is what manages data transfer between Unity and Python for the core training loop. Side Channels are another means of data transfer between Unity and Python. Side Channels are not versioned, but have been designed to support backward compatibility for what they are. As of today, we provide 4 side channels: - FloatProperties: shared float data between Unity - Python (bidirectional) - RawBytes: raw data that can be sent Unity - Python (bidirectional) - EngineConfig: a set of numeric fields in a pre-defined order sent from Python to Unity - Stats: (name, value, agg) messages sent from Unity to Python Aside from the specific implementations of side channels we provide (and use ourselves), the Side Channel interface is made available for users to create their own custom side channels. As such, we guarantee that the built in SideChannel interface between Unity and Python is backward compatible in packages that share the same major version.","title":"Versioning"},{"location":"Versioning/#ml-agents-versioning","text":"","title":"ML-Agents Versioning"},{"location":"Versioning/#context","text":"As the ML-Agents project evolves into a more mature product, we want to communicate the process we use to version our packages and the data that flows into, through, and out of them clearly. Our project now has four packages (1 Unity, 3 Python) along with artifacts that are produced as well as consumed. This document covers the versioning for these packages and artifacts.","title":"Context"},{"location":"Versioning/#github-releases","text":"Up until now, all packages were in lockstep in-terms of versioning. As a result, the GitHub releases were tagged with the version of all those packages (e.g. v0.15.0, v0.15.1) and labeled accordingly. With the decoupling of package versions, we now need to revisit our GitHub release tagging. The proposal is that we move towards an integer release numbering for our repo and each such release will call out specific version upgrades of each package. For instance, with the April 30th release , we will have: - GitHub Release 1 (branch name: release_1_branch ) - com.unity.ml-agents release 1.0.0 - ml-agents release 0.16.0 - ml-agents-envs release 0.16.0 - gym-unity release 0.16.0 Our release cadence will not be affected by these versioning changes. We will keep having monthly releases to fix bugs and release new features.","title":"GitHub Releases"},{"location":"Versioning/#packages","text":"All of the software packages, and their generated artifacts will be versioned. Any automation tools will not be versioned.","title":"Packages"},{"location":"Versioning/#unity-package","text":"Package name: com.unity.ml-agents - Versioned following Semantic Versioning Guidelines - This package consumes an artifact of the training process: the .nn file. These files are integer versioned and currently at version 2. The com.unity.ml-agents package will need to support the version of .nn files which existed at its 1.0.0 release. For example, consider that com.unity.ml-agents is at version 1.0.0 and the NN files are at version 2. If the NN files change to version 3, the next release of com.unity.ml-agents at version 1.1.0 guarantees it will be able to read both of these formats. If the NN files were to change to version 4 and com.unity.ml-agents to version 2.0.0, support for NN versions 2 and 3 could be dropped for com.unity.ml-agents version 2.0.0. - This package produces one artifact, the .demo files. These files will have integer versioning. This means their version will increment by 1 at each change. The com.unity.ml-agents package must be backward compatible with version changes that occur between minor versions. - To summarize, the artifacts produced and consumed by com.unity.ml-agents are guaranteed to be supported for 1.x.x versions of com.unity.ml-agents. We intend to provide stability for our users by moving to a 1.0.0 release of com.unity.ml-agents.","title":"Unity package"},{"location":"Versioning/#python-packages","text":"Package names: ml-agents / ml-agents-envs / gym-unity - The python packages remain in \"Beta.\" This means that breaking changes to the public API of the python packages can change without having to have a major version bump. Historically, the python and C# packages were in version lockstep. This is no longer the case. The python packages will remain in lockstep with each other for now, while the C# package will follow its own versioning as is appropriate. However, the python package versions may diverge in the future. - While the python packages will remain in Beta for now, we acknowledge that the most heavily used portion of our python interface is the mlagents-learn CLI and strive to make this part of our API backward compatible. We are actively working on this and expect to have a stable CLI in the next few weeks.","title":"Python Packages"},{"location":"Versioning/#communicator","text":"Packages which communicate: com.unity.ml-agents / ml-agents-envs Another entity of the ML-Agents Toolkit that requires versioning is the communication layer between C# and Python, which will follow also semantic versioning. This guarantees a level of backward compatibility between different versions of C# and Python packages which communicate. Any Communicator version 1.x.x of the Unity package should be compatible with any 1.x.x Communicator Version in Python. An RLCapabilities struct keeps track of which features exist. This struct is passed from C# to Python, and another from Python to C#. With this feature level granularity, we can notify users more specifically about feature limitations based on what's available in both C# and Python. These notifications will be logged to the python terminal, or to the Unity Editor Console.","title":"Communicator"},{"location":"Versioning/#side-channels","text":"The communicator is what manages data transfer between Unity and Python for the core training loop. Side Channels are another means of data transfer between Unity and Python. Side Channels are not versioned, but have been designed to support backward compatibility for what they are. As of today, we provide 4 side channels: - FloatProperties: shared float data between Unity - Python (bidirectional) - RawBytes: raw data that can be sent Unity - Python (bidirectional) - EngineConfig: a set of numeric fields in a pre-defined order sent from Python to Unity - Stats: (name, value, agg) messages sent from Unity to Python Aside from the specific implementations of side channels we provide (and use ourselves), the Side Channel interface is made available for users to create their own custom side channels. As such, we guarantee that the built in SideChannel interface between Unity and Python is backward compatible in packages that share the same major version.","title":"Side Channels"},{"location":"com.unity.ml-agents.extensions/","text":"About ML-Agents Extensions package ( com.unity.ml-agents.extensions ) The Unity ML-Agents Extensions package contains optional add-ons to the C# SDK for the Unity ML-Agents Toolkit . These extensions are all considered experimental, and their API or behavior may change between versions. Package contents The following table describes the package folder structure: Location Description Documentation~ Contains the documentation for the Unity package. Editor Contains utilities for Editor windows and drawers. Runtime Contains core C# APIs for integrating ML-Agents into your Unity scene. Tests Contains the unit tests for the package. The Runtime directory currently contains these features: * Physics-based sensors * Input System Package Integration * Custom Grid-based Sensors Installation The ML-Agents Extensions package is not currently available in the Package Manager. There are two recommended ways to install the package: Local Installation Clone the repository and follow the Local Installation for Development directions (substituting com.unity.ml-agents.extensions for the package name). Github via Package Manager In Unity 2019.4 or later, open the Package Manager, hit the \"+\" button, and select \"Add package from git URL\". In the dialog that appears, enter ``` git+https://github.com/Unity-Technologies/ml-agents.git?path=com.unity.ml-agents.extensions#release_20 You can also edit your project's `manifest.json` directly and add the following line to the `dependencies` section: \"com.unity.ml-agents.extensions\": \"git+https://github.com/Unity-Technologies/ml-agents.git?path=com.unity.ml-agents.extensions#release_20\", ``` See Git dependencies for more information. Note that this may take several minutes to resolve the packages the first time that you add it. Requirements This version of the Unity ML-Agents package is compatible with the following versions of the Unity Editor: 2019.4 and later If using the InputActuatorComponent - install the com.unity.inputsystem package version 1.1.0-preview.3 or later. Known Limitations For the InputActuatorComponent Limited implementation of InputControls No way to customize the action space of the InputActuatorComponent Need Help? The main README contains links for contacting the team or getting support.","title":"Com.unity.ml agents.extensions"},{"location":"com.unity.ml-agents.extensions/#about-ml-agents-extensions-package-comunityml-agentsextensions","text":"The Unity ML-Agents Extensions package contains optional add-ons to the C# SDK for the Unity ML-Agents Toolkit . These extensions are all considered experimental, and their API or behavior may change between versions.","title":"About ML-Agents Extensions package (com.unity.ml-agents.extensions)"},{"location":"com.unity.ml-agents.extensions/#package-contents","text":"The following table describes the package folder structure: Location Description Documentation~ Contains the documentation for the Unity package. Editor Contains utilities for Editor windows and drawers. Runtime Contains core C# APIs for integrating ML-Agents into your Unity scene. Tests Contains the unit tests for the package. The Runtime directory currently contains these features: * Physics-based sensors * Input System Package Integration * Custom Grid-based Sensors","title":"Package contents"},{"location":"com.unity.ml-agents.extensions/#installation","text":"The ML-Agents Extensions package is not currently available in the Package Manager. There are two recommended ways to install the package:","title":"Installation"},{"location":"com.unity.ml-agents.extensions/#local-installation","text":"Clone the repository and follow the Local Installation for Development directions (substituting com.unity.ml-agents.extensions for the package name).","title":"Local Installation"},{"location":"com.unity.ml-agents.extensions/#github-via-package-manager","text":"In Unity 2019.4 or later, open the Package Manager, hit the \"+\" button, and select \"Add package from git URL\". In the dialog that appears, enter ``` git+https://github.com/Unity-Technologies/ml-agents.git?path=com.unity.ml-agents.extensions#release_20 You can also edit your project's `manifest.json` directly and add the following line to the `dependencies` section: \"com.unity.ml-agents.extensions\": \"git+https://github.com/Unity-Technologies/ml-agents.git?path=com.unity.ml-agents.extensions#release_20\", ``` See Git dependencies for more information. Note that this may take several minutes to resolve the packages the first time that you add it.","title":"Github via Package Manager"},{"location":"com.unity.ml-agents.extensions/#requirements","text":"This version of the Unity ML-Agents package is compatible with the following versions of the Unity Editor: 2019.4 and later If using the InputActuatorComponent - install the com.unity.inputsystem package version 1.1.0-preview.3 or later.","title":"Requirements"},{"location":"com.unity.ml-agents.extensions/#known-limitations","text":"For the InputActuatorComponent Limited implementation of InputControls No way to customize the action space of the InputActuatorComponent","title":"Known Limitations"},{"location":"com.unity.ml-agents.extensions/#need-help","text":"The main README contains links for contacting the team or getting support.","title":"Need Help?"},{"location":"com.unity.ml-agents/","text":"About ML-Agents package ( com.unity.ml-agents ) The ML-Agents package contains the primary C# SDK for the Unity ML-Agents Toolkit . The package allows you to convert any Unity scene into a learning environment and train character behaviors using a variety of machine learning algorithms. Additionally, it allows you to embed these trained behaviors back into Unity scenes to control your characters. More specifically, the package provides the following core functionalities: Define Agents: entities, or characters, whose behavior will be learned. Agents are entities that generate observations (through sensors), take actions, and receive rewards from the environment. Define Behaviors: entities that specify how an agent should act. Multiple agents can share the same Behavior and a scene may have multiple Behaviors. Record demonstrations of an agent within the Editor. You can use demonstrations to help train a behavior for that agent. Embedding a trained behavior into the scene via the Unity Inference Engine . Embedded behaviors allow you to switch an Agent between learning and inference. Note that the ML-Agents package does not contain the machine learning algorithms for training behaviors. The ML-Agents package only supports instrumenting a Unity scene, setting it up for training, and then embedding the trained model back into your Unity scene. The machine learning algorithms that orchestrate training are part of the companion Python package . Note that we also provide an ML-Agents Extensions package ( com.unity.ml-agents.extensions ) that contains early/experimental features that you may find useful. This package is only available from the ML-Agents GitHub repo . Package contents The following table describes the package folder structure: Location Description Documentation~ Contains the documentation for the Unity package. Editor Contains utilities for Editor windows and drawers. Plugins Contains third-party DLLs. Runtime Contains core C# APIs for integrating ML-Agents into your Unity scene. Runtime/Integrations Contains utilities for integrating ML-Agents into specific game genres. Tests Contains the unit tests for the package. Installation To install this ML-Agents package, follow the instructions in the Package Manager documentation . To install the companion Python package to enable training behaviors, follow the installation instructions on our GitHub repository . Advanced Installation With the changes to Unity Package Manager in 2021, experimental packages will not show up in the package list and have to be installed manually. There are two recommended ways to install the package manually: Github via Package Manager In Unity 2019.4 or later, open the Package Manager, hit the \"+\" button, and select \"Add package from git URL\". In the dialog that appears, enter git+https://github.com/Unity-Technologies/ml-agents.git?path=com.unity.ml-agents#release_20 You can also edit your project's manifest.json directly and add the following line to the dependencies section: \"com.unity.ml-agents\": \"git+https://github.com/Unity-Technologies/ml-agents.git?path=com.unity.ml-agents#release_20\", See Git dependencies for more information. Note that this may take several minutes to resolve the packages the first time that you add it. Local Installation for Development Clone the repository and follow the Local Installation for Development directions. Requirements This version of the Unity ML-Agents package is compatible with the following versions of the Unity Editor: 2019.4 and later Known Limitations Training Training is limited to the Unity Editor and Standalone builds on Windows, MacOS, and Linux with the Mono scripting backend. Currently, training does not work with the IL2CPP scripting backend. Your environment will default to inference mode if training is not supported or is not currently running. Inference Inference is executed via the Unity Inference Engine . CPU All platforms supported. GPU All platforms supported except: WebGL and GLES 3/2 on Android / iPhone NOTE: Mobile platform support includes: Vulkan for Android Metal for iOS. Headless Mode If you enable Headless mode, you will not be able to collect visual observations from your agents. Rendering Speed and Synchronization Currently the speed of the game physics can only be increased to 100x real-time. The Academy also moves in time with FixedUpdate() rather than Update(), so game behavior implemented in Update() may be out of sync with the agent decision making. See Execution Order of Event Functions for more information. You can control the frequency of Academy stepping by calling Academy.Instance.DisableAutomaticStepping() , and then calling Academy.Instance.EnvironmentStep() Unity Inference Engine Models Currently, only models created with our trainers are supported for running ML-Agents with a neural network behavior. Helpful links If you are new to the Unity ML-Agents package, or have a question after reading the documentation, you can checkout our GitHub Repository , which also includes a number of ways to connect with us including our ML-Agents Forum . In order to improve the developer experience for Unity ML-Agents Toolkit, we have added in-editor analytics. Please refer to \"Information that is passively collected by Unity\" in the Unity Privacy Policy .","title":"Com.unity.ml agents"},{"location":"com.unity.ml-agents/#about-ml-agents-package-comunityml-agents","text":"The ML-Agents package contains the primary C# SDK for the Unity ML-Agents Toolkit . The package allows you to convert any Unity scene into a learning environment and train character behaviors using a variety of machine learning algorithms. Additionally, it allows you to embed these trained behaviors back into Unity scenes to control your characters. More specifically, the package provides the following core functionalities: Define Agents: entities, or characters, whose behavior will be learned. Agents are entities that generate observations (through sensors), take actions, and receive rewards from the environment. Define Behaviors: entities that specify how an agent should act. Multiple agents can share the same Behavior and a scene may have multiple Behaviors. Record demonstrations of an agent within the Editor. You can use demonstrations to help train a behavior for that agent. Embedding a trained behavior into the scene via the Unity Inference Engine . Embedded behaviors allow you to switch an Agent between learning and inference. Note that the ML-Agents package does not contain the machine learning algorithms for training behaviors. The ML-Agents package only supports instrumenting a Unity scene, setting it up for training, and then embedding the trained model back into your Unity scene. The machine learning algorithms that orchestrate training are part of the companion Python package . Note that we also provide an ML-Agents Extensions package ( com.unity.ml-agents.extensions ) that contains early/experimental features that you may find useful. This package is only available from the ML-Agents GitHub repo .","title":"About ML-Agents package (com.unity.ml-agents)"},{"location":"com.unity.ml-agents/#package-contents","text":"The following table describes the package folder structure: Location Description Documentation~ Contains the documentation for the Unity package. Editor Contains utilities for Editor windows and drawers. Plugins Contains third-party DLLs. Runtime Contains core C# APIs for integrating ML-Agents into your Unity scene. Runtime/Integrations Contains utilities for integrating ML-Agents into specific game genres. Tests Contains the unit tests for the package.","title":"Package contents"},{"location":"com.unity.ml-agents/#installation","text":"To install this ML-Agents package, follow the instructions in the Package Manager documentation . To install the companion Python package to enable training behaviors, follow the installation instructions on our GitHub repository .","title":"Installation"},{"location":"com.unity.ml-agents/#advanced-installation","text":"With the changes to Unity Package Manager in 2021, experimental packages will not show up in the package list and have to be installed manually. There are two recommended ways to install the package manually:","title":"Advanced Installation"},{"location":"com.unity.ml-agents/#github-via-package-manager","text":"In Unity 2019.4 or later, open the Package Manager, hit the \"+\" button, and select \"Add package from git URL\". In the dialog that appears, enter git+https://github.com/Unity-Technologies/ml-agents.git?path=com.unity.ml-agents#release_20 You can also edit your project's manifest.json directly and add the following line to the dependencies section: \"com.unity.ml-agents\": \"git+https://github.com/Unity-Technologies/ml-agents.git?path=com.unity.ml-agents#release_20\", See Git dependencies for more information. Note that this may take several minutes to resolve the packages the first time that you add it.","title":"Github via Package Manager"},{"location":"com.unity.ml-agents/#local-installation-for-development","text":"Clone the repository and follow the Local Installation for Development directions.","title":"Local Installation for Development"},{"location":"com.unity.ml-agents/#requirements","text":"This version of the Unity ML-Agents package is compatible with the following versions of the Unity Editor: 2019.4 and later","title":"Requirements"},{"location":"com.unity.ml-agents/#known-limitations","text":"","title":"Known Limitations"},{"location":"com.unity.ml-agents/#training","text":"Training is limited to the Unity Editor and Standalone builds on Windows, MacOS, and Linux with the Mono scripting backend. Currently, training does not work with the IL2CPP scripting backend. Your environment will default to inference mode if training is not supported or is not currently running.","title":"Training"},{"location":"com.unity.ml-agents/#inference","text":"Inference is executed via the Unity Inference Engine . CPU All platforms supported. GPU All platforms supported except: WebGL and GLES 3/2 on Android / iPhone NOTE: Mobile platform support includes: Vulkan for Android Metal for iOS.","title":"Inference"},{"location":"com.unity.ml-agents/#headless-mode","text":"If you enable Headless mode, you will not be able to collect visual observations from your agents.","title":"Headless Mode"},{"location":"com.unity.ml-agents/#rendering-speed-and-synchronization","text":"Currently the speed of the game physics can only be increased to 100x real-time. The Academy also moves in time with FixedUpdate() rather than Update(), so game behavior implemented in Update() may be out of sync with the agent decision making. See Execution Order of Event Functions for more information. You can control the frequency of Academy stepping by calling Academy.Instance.DisableAutomaticStepping() , and then calling Academy.Instance.EnvironmentStep()","title":"Rendering Speed and Synchronization"},{"location":"com.unity.ml-agents/#unity-inference-engine-models","text":"Currently, only models created with our trainers are supported for running ML-Agents with a neural network behavior.","title":"Unity Inference Engine Models"},{"location":"com.unity.ml-agents/#helpful-links","text":"If you are new to the Unity ML-Agents package, or have a question after reading the documentation, you can checkout our GitHub Repository , which also includes a number of ways to connect with us including our ML-Agents Forum . In order to improve the developer experience for Unity ML-Agents Toolkit, we have added in-editor analytics. Please refer to \"Information that is passively collected by Unity\" in the Unity Privacy Policy .","title":"Helpful links"},{"location":"ml-agents-envs/","text":"Unity ML-Agents Python Interface The mlagents_envs Python package is part of the ML-Agents Toolkit . mlagents_envs provides three Python APIs that allows direct interaction with the Unity game engine: - A single agent API (Gym API) - A gym-like multi-agent API (PettingZoo API) - A low-level API (LLAPI) The LLAPI is used by the trainer implementation in mlagents . mlagents_envs can be used independently of mlagents for Python communication. Installation Install the mlagents_envs package with: python -m pip install mlagents_envs==0.30.0 Usage & More Information See - Gym API Guide - PettingZoo API Guide - Python API Guide for more information on how to use the API to interact with a Unity environment. For more information on the ML-Agents Toolkit and how to instrument a Unity scene with the ML-Agents SDK, check out the main ML-Agents Toolkit documentation . Limitations mlagents_envs uses localhost ports to exchange data between Unity and Python. As such, multiple instances can have their ports collide, leading to errors. Make sure to use a different port if you are using multiple instances of UnityEnvironment . Communication between Unity and the Python UnityEnvironment is not secure. On Linux, ports are not released immediately after the communication closes. As such, you cannot reuse ports right after closing a UnityEnvironment .","title":"Ml agents envs"},{"location":"ml-agents-envs/#unity-ml-agents-python-interface","text":"The mlagents_envs Python package is part of the ML-Agents Toolkit . mlagents_envs provides three Python APIs that allows direct interaction with the Unity game engine: - A single agent API (Gym API) - A gym-like multi-agent API (PettingZoo API) - A low-level API (LLAPI) The LLAPI is used by the trainer implementation in mlagents . mlagents_envs can be used independently of mlagents for Python communication.","title":"Unity ML-Agents Python Interface"},{"location":"ml-agents-envs/#installation","text":"Install the mlagents_envs package with: python -m pip install mlagents_envs==0.30.0","title":"Installation"},{"location":"ml-agents-envs/#usage-more-information","text":"See - Gym API Guide - PettingZoo API Guide - Python API Guide for more information on how to use the API to interact with a Unity environment. For more information on the ML-Agents Toolkit and how to instrument a Unity scene with the ML-Agents SDK, check out the main ML-Agents Toolkit documentation .","title":"Usage &amp; More Information"},{"location":"ml-agents-envs/#limitations","text":"mlagents_envs uses localhost ports to exchange data between Unity and Python. As such, multiple instances can have their ports collide, leading to errors. Make sure to use a different port if you are using multiple instances of UnityEnvironment . Communication between Unity and the Python UnityEnvironment is not secure. On Linux, ports are not released immediately after the communication closes. As such, you cannot reuse ports right after closing a UnityEnvironment .","title":"Limitations"},{"location":"ml-agents/","text":"Unity ML-Agents Trainers The mlagents Python package is part of the ML-Agents Toolkit . mlagents provides a set of reinforcement and imitation learning algorithms designed to be used with Unity environments. The algorithms interface with the Python API provided by the mlagents_envs package. See here for more information on mlagents_envs . The algorithms can be accessed using the: mlagents-learn access point. See here for more information on using this package. Installation Install the mlagents package with: python -m pip install mlagents==0.30.0 Usage & More Information For more information on the ML-Agents Toolkit and how to instrument a Unity scene with the ML-Agents SDK, check out the main ML-Agents Toolkit documentation . Limitations Resuming self-play from a checkpoint resets the reported ELO to the default value.","title":"Ml agents"},{"location":"ml-agents/#unity-ml-agents-trainers","text":"The mlagents Python package is part of the ML-Agents Toolkit . mlagents provides a set of reinforcement and imitation learning algorithms designed to be used with Unity environments. The algorithms interface with the Python API provided by the mlagents_envs package. See here for more information on mlagents_envs . The algorithms can be accessed using the: mlagents-learn access point. See here for more information on using this package.","title":"Unity ML-Agents Trainers"},{"location":"ml-agents/#installation","text":"Install the mlagents package with: python -m pip install mlagents==0.30.0","title":"Installation"},{"location":"ml-agents/#usage-more-information","text":"For more information on the ML-Agents Toolkit and how to instrument a Unity scene with the ML-Agents SDK, check out the main ML-Agents Toolkit documentation .","title":"Usage &amp; More Information"},{"location":"ml-agents/#limitations","text":"Resuming self-play from a checkpoint resets the reported ELO to the default value.","title":"Limitations"},{"location":"doxygen/Readme/","text":"Doxygen files To generate the API reference as HTML files, run: doxygen dox-ml-agents.conf","title":"Doxygen files"},{"location":"doxygen/Readme/#doxygen-files","text":"To generate the API reference as HTML files, run: doxygen dox-ml-agents.conf","title":"Doxygen files"}]}
\ No newline at end of file
diff --git a/sitemap.xml b/sitemap.xml
index 54a1a0b42e..a4fcb24af8 100644
--- a/sitemap.xml
+++ b/sitemap.xml
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     <url>
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     <url>
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     <url>
          <loc>https://unity-technologies.github.io/ml-agents/CONTRIBUTING/</loc>
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     <url>
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          <loc>https://unity-technologies.github.io/ml-agents/Glossary/</loc>
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+    <url>
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     <url>
          <loc>https://unity-technologies.github.io/ml-agents/LICENSE/</loc>
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     <url>
          <loc>https://unity-technologies.github.io/ml-agents/Learning-Environment-Create-New/</loc>
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     <url>
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     <url>
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     <url>
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     <url>
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     <url>
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     <url>
          <loc>https://unity-technologies.github.io/ml-agents/com.unity.ml-agents/</loc>
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          <changefreq>daily</changefreq>
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     <url>
          <loc>https://unity-technologies.github.io/ml-agents/ml-agents-envs/</loc>
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          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://unity-technologies.github.io/ml-agents/ml-agents/</loc>
-         <lastmod>2023-06-05</lastmod>
+         <lastmod>2023-06-27</lastmod>
          <changefreq>daily</changefreq>
     </url>
     <url>
          <loc>https://unity-technologies.github.io/ml-agents/doxygen/Readme/</loc>
-         <lastmod>2023-06-05</lastmod>
+         <lastmod>2023-06-27</lastmod>
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\ No newline at end of file
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