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docs/compilation/html/_sources/pages/modules/sinergym.utils.rewards.BaseReward.rst.txt

@@ -0,0 +1,24 @@
+sinergym.utils.rewards.BaseReward
+=================================
+
+.. currentmodule:: sinergym.utils.rewards
+
+.. autoclass:: BaseReward
+   :members:                                                           
+   :undoc-members:               
+
+
+   .. automethod:: __init__
+
+
+   .. rubric:: Methods
+
+   .. autosummary::
+
+      ~BaseReward.__init__
+
+
+
+
+
+

docs/compilation/html/_sources/pages/modules/sinergym.utils.rewards.ExpReward.rst.txt

@@ -16,7 +16,6 @@ sinergym.utils.rewards.ExpReward
    .. autosummary::
 
       ~ExpReward.__init__
-      ~ExpReward.calculate
 
 
 

docs/compilation/html/_sources/pages/modules/sinergym.utils.rewards.HourlyLinearReward.rst.txt

@@ -0,0 +1,24 @@
+sinergym.utils.rewards.HourlyLinearReward
+=========================================
+
+.. currentmodule:: sinergym.utils.rewards
+
+.. autoclass:: HourlyLinearReward
+   :members:                                                           
+   :undoc-members:               
+
+
+   .. automethod:: __init__
+
+
+   .. rubric:: Methods
+
+   .. autosummary::
+
+      ~HourlyLinearReward.__init__
+
+
+
+
+
+

docs/compilation/html/_sources/pages/modules/sinergym.utils.rewards.LinearReward.rst.txt

@@ -16,7 +16,6 @@ sinergym.utils.rewards.LinearReward
    .. autosummary::
 
       ~LinearReward.__init__
-      ~LinearReward.calculate
 
 
 

docs/compilation/html/_sources/pages/modules/sinergym.utils.rewards.rst.txt

@@ -1,4 +1,4 @@
-sinergym.utils.rewards
+sinergym.utils.rewards
 ======================
 
 .. automodule:: sinergym.utils.rewards
@@ -19,7 +19,9 @@ sinergym.utils.rewards
       :toctree:                                          
       :template: custom-class-template.rst               
 
+      BaseReward
       ExpReward
+      HourlyLinearReward
       LinearReward
 
 

docs/compilation/html/_sources/pages/rewards.rst.txt

@@ -2,26 +2,51 @@
 Rewards
 #######
 
-Defining a reward function is one of the most important things in reinforcement learning. Consequently, our team has designed an structure which let you use our
-reward class or defining a new one and integrate in available environments if you want:
+Defining a reward function is one of the most important things in reinforcement learning. Consequently, Sinergym allows you to define your own reward functions or use 
+the ones we have already implemented (see code below).
 
-.. literalinclude:: ../../../sinergym/utils/rewards.py
-    :language: python
+-  ``LinearReward`` implements a linear reward function, where both energy consumption and thermal discomfort are normalized and add together with different weights. The 
+   discomfort is calculated as the absolute difference between current temperature and comfort range (so if the temperature is inside that range, the discomfort would be 0).
+   This is a typically used function where thermal satisfaction of people inside the controlled building has been taken into account.
 
-``LinearReward()`` class implements an evaluation which consists in taking into account **power consumption** and **temperature comfort**. This class is used
-inner environment as an attribute.
+-  ``ExpReward`` is very similar, but in this case the discomfort is calculated using the exponential difference between current temperature and comfort ranges. That means 
+   that the penalty for the discomfort is higher is we are far from the target temperatures.
 
-``ExpReward()`` class is the same than ``LinearReward()`` class, but comfort penalty is exponential instead of lineal.
+-  ``HourlyLinearReward`` is a slight modification of the linear function, but the weight given to the discomfort depends on the hour of the day. If the current hour of the 
+   simulation is in working hours (by default, from 9 AM to 7 PM) both comfort and energy consumption weights equally, but outside those hours only energy is considered.
 
-Reward is always negative. This means that perfect reward would be 0 (perfect power consumption and perfect temperature comfort), we apply penalties in both factors.
-Notice there are two temperature comfort ranges in that class, those ranges are used rely on the specific date on the simulation. Moreover, notice there are
-two weights in the reward function, this allows you to adjust how important each aspect is when making a general evaluation of the environment.
 
-By default, all environments in gym register will use LinearReward() with default parameters. However, this configuration can be overwriting in ``gym.make()``, for example:
+These rewards are always negative, meaning that perfect behavior has a cumulative reward of 0. Notice also that there are two temperature comfort ranges defined, one for the 
+summer period and other for the winter period. The weights of each term in the reward allow to adjust the importance of each aspect when evaluating the environments.
+
+By default, all environments use ``LinearReward`` with default parameters. But you can change this configuration using ``gym.make()`` as follows:
 
 .. code:: python
     
-    env = gym.make('Eplus-discrete-stochastic-mixed-v1', reward=ExpReward(energy_weight=0.5))
+    from sinergym.utils.rewards import ExpReward
+
+    env = gym.make('Eplus-discrete-stochastic-mixed-v1', reward=ExpReward, reward_kwargs={'energy_weight': 0.1})
+
+
+It is also pretty simple to define your own classes. For example, imagine you want a reward signal which returns always -1 (however we do not recommend using it for training agents :)).
+The only requirement is that the calculation is performed using ``__call__`` method, which returns the reward and a dictionary with extra information. The below code implements this.
+
+.. code:: python
 
-.. note:: *Currently, it is only available these classes. However, more reward functions could be designed in the future!*
+    from sinergym.utils.rewards import BaseReward
 
+    class CustomReward(BaseReward):
+        """Naive reward function."""
+        def __init__(self, env):
+            super(CustomReward, self).__init__(env)
+        def __call__(self):
+            return -1.0, {}
+
+    env = gym.make('Eplus-discrete-stochastic-mixed-v1', reward=CustomReward)
+
+
+More reward functions will be included in the future, so stay tuned!
+
+
+.. literalinclude:: ../../../sinergym/utils/rewards.py
+    :language: python

docs/compilation/html/genindex.html

@@ -86,6 +86,7 @@ <h1 id="index">Index</h1>
  | <a href="#D"><strong>D</strong></a>
  | <a href="#E"><strong>E</strong></a>
  | <a href="#G"><strong>G</strong></a>
+ | <a href="#H"><strong>H</strong></a>
  | <a href="#I"><strong>I</strong></a>
  | <a href="#L"><strong>L</strong></a>
  | <a href="#M"><strong>M</strong></a>
@@ -122,8 +123,12 @@ <h2 id="_">_</h2>
         <li><a href="pages/modules/sinergym.utils.controllers.RandomController.html#sinergym.utils.controllers.RandomController.__init__">(sinergym.utils.controllers.RandomController method)</a>
 </li>
         <li><a href="pages/modules/sinergym.utils.controllers.RuleBasedController.html#sinergym.utils.controllers.RuleBasedController.__init__">(sinergym.utils.controllers.RuleBasedController method)</a>
+</li>
+        <li><a href="pages/modules/sinergym.utils.rewards.BaseReward.html#sinergym.utils.rewards.BaseReward.__init__">(sinergym.utils.rewards.BaseReward method)</a>
 </li>
         <li><a href="pages/modules/sinergym.utils.rewards.ExpReward.html#sinergym.utils.rewards.ExpReward.__init__">(sinergym.utils.rewards.ExpReward method)</a>
+</li>
+        <li><a href="pages/modules/sinergym.utils.rewards.HourlyLinearReward.html#sinergym.utils.rewards.HourlyLinearReward.__init__">(sinergym.utils.rewards.HourlyLinearReward method)</a>
 </li>
         <li><a href="pages/modules/sinergym.utils.rewards.LinearReward.html#sinergym.utils.rewards.LinearReward.__init__">(sinergym.utils.rewards.LinearReward method)</a>
 </li>
@@ -163,6 +168,10 @@ <h2 id="A">A</h2>
 
 <h2 id="B">B</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="pages/modules/sinergym.utils.rewards.BaseReward.html#sinergym.utils.rewards.BaseReward">BaseReward (class in sinergym.utils.rewards)</a>
+</li>
+  </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="pages/modules/sinergym.simulators.base.BaseSimulator.html#sinergym.simulators.base.BaseSimulator">BaseSimulator (class in sinergym.simulators.base)</a>
 </li>
@@ -172,12 +181,6 @@ <h2 id="B">B</h2>
 <h2 id="C">C</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="pages/modules/sinergym.utils.rewards.ExpReward.html#sinergym.utils.rewards.ExpReward.calculate">calculate() (sinergym.utils.rewards.ExpReward method)</a>
-
-      <ul>
-        <li><a href="pages/modules/sinergym.utils.rewards.LinearReward.html#sinergym.utils.rewards.LinearReward.calculate">(sinergym.utils.rewards.LinearReward method)</a>
-</li>
-      </ul></li>
       <li><a href="pages/modules/sinergym.envs.eplus_env.EplusEnv.html#sinergym.envs.eplus_env.EplusEnv.close">close() (sinergym.envs.eplus_env.EplusEnv method)</a>
 
       <ul>
@@ -261,6 +264,14 @@ <h2 id="G">G</h2>
   </ul></td>
 </tr></table>
 
+<h2 id="H">H</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="pages/modules/sinergym.utils.rewards.HourlyLinearReward.html#sinergym.utils.rewards.HourlyLinearReward">HourlyLinearReward (class in sinergym.utils.rewards)</a>
+</li>
+  </ul></td>
+</tr></table>
+
 <h2 id="I">I</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>