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G_x}S}&ik_f literal 0 HcmV?d00001 diff --git a/HW2_Policy_Graident.ipynb b/HW2_Policy_Graident.ipynb index 3b51aa3..02d6ad2 100644 --- a/HW2_Policy_Graident.ipynb +++ b/HW2_Policy_Graident.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "collapsed": false }, @@ -26,11 +26,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[2016-10-15 21:57:09,076] Making new env: CartPole-v0\n" + ] + } + ], "source": [ "import gym\n", "import tensorflow as tf\n", @@ -38,6 +46,7 @@ "from policy_gradient import util\n", "from policy_gradient.policy import CategoricalPolicy\n", "from policy_gradient.baselines.linear_feature_baseline import LinearFeatureBaseline\n", + "import matplotlib.pyplot as plt\n", "\n", "np.random.seed(0)\n", "tf.set_random_seed(0)\n", @@ -95,11 +104,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/gradients.py:90: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n", + " \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n" + ] + } + ], "source": [ "sess = tf.Session()\n", "\n", @@ -138,6 +156,30 @@ "2. Implement the commented function." ] }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 2.9701, 1.99 , 1. ])" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rewards = np.array([1, 1, 1])\n", + "discount_rate = 0.99\n", + "util.discount_cumsum(rewards, discount_rate)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -151,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "collapsed": true }, @@ -211,11 +253,13 @@ " \"\"\"\n", " # YOUR CODE HERE >>>>>>\n", " # a = ???\n", + " a = r-b\n", " # <<<<<<<<\n", "\n", " p[\"returns\"] = r\n", " p[\"baselines\"] = b\n", " p[\"advantages\"] = (a - a.mean()) / (a.std() + 1e-8) # normalize\n", + " #p[\"advantages\"] = a\n", "\n", " obs = np.concatenate([ p[\"observations\"] for p in paths ])\n", " actions = np.concatenate([ p[\"actions\"] for p in paths ])\n", @@ -237,7 +281,11 @@ " data = self.process_paths(paths)\n", " loss = self.policy.train(data[\"observations\"], data[\"actions\"], data[\"advantages\"])\n", " avg_return = np.mean([sum(p[\"rewards\"]) for p in paths])\n", - " print(\"Iteration {}: Average Return = {}\".format(i, avg_return))\n", + " std_num=np.std([data[\"advantages\"]])\n", + " print(\"Iteration {}: Average Return = {}, standard deviation = {}\".format(i, avg_return, std_num))\n", + " iter_num.append(i)\n", + " avg.append(avg_return)\n", + " std.append(std_num)\n", " \n", " # CartPole-v0 defines \"solving\" as getting average reward of 195.0 over 100 consecutive trials.\n", " if avg_return >= 195:\n", @@ -250,23 +298,149 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 1: Average Return = 16.3, standard deviation = 0.999999997688\n", + "Iteration 2: Average Return = 19.24, standard deviation = 0.999999998014\n", + "Iteration 3: Average Return = 18.1, standard deviation = 0.9999999979\n", + "Iteration 4: Average Return = 19.02, standard deviation = 0.999999997993\n", + "Iteration 5: Average Return = 20.41, standard deviation = 0.999999998116\n", + "Iteration 6: Average Return = 22.27, standard deviation = 0.999999998257\n", + "Iteration 7: Average Return = 21.07, standard deviation = 0.999999998171\n", + "Iteration 8: Average Return = 27.45, standard deviation = 0.999999998551\n", + "Iteration 9: Average Return = 26.68, standard deviation = 0.999999998514\n", + "Iteration 10: Average Return = 28.76, standard deviation = 0.999999998608\n", + "Iteration 11: Average Return = 27.66, standard deviation = 0.99999999856\n", + "Iteration 12: Average Return = 29.33, standard deviation = 0.999999998632\n", + "Iteration 13: Average Return = 36.47, standard deviation = 0.99999999886\n", + "Iteration 14: Average Return = 34.94, standard deviation = 0.999999998819\n", + "Iteration 15: Average Return = 31.86, standard deviation = 0.999999998725\n", + "Iteration 16: Average Return = 39.61, standard deviation = 0.999999998934\n", + "Iteration 17: Average Return = 39.86, standard deviation = 0.99999999894\n", + "Iteration 18: Average Return = 39.95, standard deviation = 0.999999998942\n", + "Iteration 19: Average Return = 42.42, standard deviation = 0.999999998992\n", + "Iteration 20: Average Return = 42.63, standard deviation = 0.999999998996\n", + "Iteration 21: Average Return = 43.88, standard deviation = 0.999999999019\n", + "Iteration 22: Average Return = 43.58, standard deviation = 0.999999999014\n", + "Iteration 23: Average Return = 43.2, standard deviation = 0.999999999007\n", + "Iteration 24: Average Return = 49.09, standard deviation = 0.9999999991\n", + "Iteration 25: Average Return = 46.21, standard deviation = 0.999999999057\n", + "Iteration 26: Average Return = 51.08, standard deviation = 0.999999999127\n", + "Iteration 27: Average Return = 47.83, standard deviation = 0.999999999083\n", + "Iteration 28: Average Return = 53.05, standard deviation = 0.999999999152\n", + "Iteration 29: Average Return = 49.96, standard deviation = 0.999999999113\n", + "Iteration 30: Average Return = 51.9, standard deviation = 0.999999999139\n", + "Iteration 31: Average Return = 49.43, standard deviation = 0.999999999106\n", + "Iteration 32: Average Return = 59.27, standard deviation = 0.999999999218\n", + "Iteration 33: Average Return = 56.96, standard deviation = 0.999999999195\n", + "Iteration 34: Average Return = 54.57, standard deviation = 0.99999999917\n", + "Iteration 35: Average Return = 56.0, standard deviation = 0.999999999186\n", + "Iteration 36: Average Return = 54.73, standard deviation = 0.999999999172\n", + "Iteration 37: Average Return = 58.02, standard deviation = 0.999999999207\n", + "Iteration 38: Average Return = 59.11, standard deviation = 0.999999999217\n", + "Iteration 39: Average Return = 61.79, standard deviation = 0.999999999241\n", + "Iteration 40: Average Return = 58.5, standard deviation = 0.999999999212\n", + "Iteration 41: Average Return = 57.43, standard deviation = 0.999999999201\n", + "Iteration 42: Average Return = 55.71, standard deviation = 0.999999999183\n", + "Iteration 43: Average Return = 62.84, standard deviation = 0.999999999251\n", + "Iteration 44: Average Return = 63.14, standard deviation = 0.999999999254\n", + "Iteration 45: Average Return = 61.87, standard deviation = 0.999999999243\n", + "Iteration 46: Average Return = 60.83, standard deviation = 0.999999999232\n", + "Iteration 47: Average Return = 63.69, standard deviation = 0.999999999259\n", + "Iteration 48: Average Return = 68.8, standard deviation = 0.999999999295\n", + "Iteration 49: Average Return = 70.23, standard deviation = 0.999999999306\n", + "Iteration 50: Average Return = 67.17, standard deviation = 0.999999999285\n", + "Iteration 51: Average Return = 63.81, standard deviation = 0.999999999259\n", + "Iteration 52: Average Return = 73.18, standard deviation = 0.999999999325\n", + "Iteration 53: Average Return = 67.63, standard deviation = 0.999999999288\n", + "Iteration 54: Average Return = 75.02, standard deviation = 0.999999999335\n", + "Iteration 55: Average Return = 77.8, standard deviation = 0.99999999935\n", + "Iteration 56: Average Return = 77.98, standard deviation = 0.999999999352\n", + "Iteration 57: Average Return = 74.18, standard deviation = 0.999999999331\n", + "Iteration 58: Average Return = 80.69, standard deviation = 0.999999999365\n", + "Iteration 59: Average Return = 80.67, standard deviation = 0.999999999366\n", + "Iteration 60: Average Return = 77.94, standard deviation = 0.999999999352\n", + "Iteration 61: Average Return = 83.69, standard deviation = 0.99999999938\n", + "Iteration 62: Average Return = 76.7, standard deviation = 0.999999999346\n", + "Iteration 63: Average Return = 86.81, standard deviation = 0.999999999394\n", + "Iteration 64: Average Return = 88.94, standard deviation = 0.999999999402\n", + "Iteration 65: Average Return = 95.26, standard deviation = 0.999999999427\n", + "Iteration 66: Average Return = 98.84, standard deviation = 0.999999999437\n", + "Iteration 67: Average Return = 108.2, standard deviation = 0.999999999465\n", + "Iteration 68: Average Return = 111.73, standard deviation = 0.999999999473\n", + "Iteration 69: Average Return = 119.74, standard deviation = 0.999999999492\n", + "Iteration 70: Average Return = 134.98, standard deviation = 0.99999999952\n", + "Iteration 71: Average Return = 142.51, standard deviation = 0.99999999953\n", + "Iteration 72: Average Return = 159.91, standard deviation = 0.999999999551\n", + "Iteration 73: Average Return = 166.55, standard deviation = 0.999999999559\n", + "Iteration 74: Average Return = 173.07, standard deviation = 0.999999999565\n", + "Iteration 75: Average Return = 177.68, standard deviation = 0.999999999569\n", + "Iteration 76: Average Return = 176.37, standard deviation = 0.999999999567\n", + "Iteration 77: Average Return = 179.27, standard deviation = 0.99999999957\n", + "Iteration 78: Average Return = 182.31, standard deviation = 0.999999999573\n", + "Iteration 79: Average Return = 180.76, standard deviation = 0.999999999571\n", + "Iteration 80: Average Return = 184.5, standard deviation = 0.999999999574\n", + "Iteration 81: Average Return = 181.82, standard deviation = 0.999999999571\n", + "Iteration 82: Average Return = 187.33, standard deviation = 0.999999999576\n", + "Iteration 83: Average Return = 192.66, standard deviation = 0.99999999958\n", + "Iteration 84: Average Return = 191.16, standard deviation = 0.999999999579\n", + "Iteration 85: Average Return = 192.81, standard deviation = 0.99999999958\n", + "Iteration 86: Average Return = 195.87, standard deviation = 0.999999999582\n", + "Solve at 86 iterations, which equals 8600 episodes.\n" + ] + } + ], "source": [ "n_iter = 200\n", "n_episode = 100\n", "path_length = 200\n", "discount_rate = 0.99\n", - "baseline = LinearFeatureBaseline(env.spec)\n", + "#baseline = LinearFeatureBaseline(env.spec)\n", + "baseline = None\n", "\n", "po = PolicyOptimizer(env, policy, baseline, n_iter, n_episode, path_length,\n", " discount_rate)\n", "\n", + "# Store result for plot purpose\n", + "iter_num = []\n", + "avg = []\n", + "std = []\n", "# Train the policy optimizer\n", - "po.train()" + "po.train()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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//S+63aNH6uKQrSeRZKQz8Bfn3NRkByMiIhKbjKi/SM2QSAfWb/D9RURERJKq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\n1q0L29w7AVBYCHPmlNO9e3kcoYuIiGSdipF9oH/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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Display figures to visualize average return and standard deviation\n", + "plt.title('Average Return with baseline')\n", + "plt.xlabel('Iteration')\n", + "plt.ylabel('Average Return')\n", + "plt.errorbar(iter_num, avg, yerr=std, color='b', linewidth=2, ecolor='k', elinewidth=0.5)\n", + "plt.show()" ] }, { @@ -297,6 +471,60 @@ "Then, write a report about your findings. (with figures is better)" ] }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "===========================================\n", + "The following two images are pre-trained policy: \n", + "===========================================\n" + ] + }, + { + "data": { + "image/png": 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XoEVQtAyoZmZ1YmpHGgFFzunXq1cv6tSpU6BMVYMiIqUjdtZVSX+xtV0A2dmx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\nR7R/j8zMzKwlaqYYAXoBOwBvli1/E9ir/btjZmZ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Yj6qIQMoYw1IzWwjsGRxaCtQ2s0YxtSPNgcklvVb//v1p1KhRkWP6EIuIVLzo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\nK4BpEXFHRCyJiCuAFcBFBbfLzMzM2tEwxYikPkA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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.display import display, Image\n", + "print(\"===========================================\")\n", + "print(\"The following two images are pre-trained policy: \")\n", + "print(\"===========================================\")\n", + "display(Image(filename=\"Average_return_with_baseline.png\"), Image(filename=\"Average_return_without_baseline.png\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "上兩圖中的藍色線為Average Return值,而黑色線則表示Stardard Deviation範圍。\n", + "\n", + "上圖是有在Policy Gradient中加入Baseline,而下圖則無,可以看到Standar Deviation值具有明顯差異,換算則Variance的話,有加入Baseline大約可以減少Variance約300~400左右,而在Variance值減少的情況下,原本可預期減少iteration數,在實驗中也曾測到Iteration數減少約10~20,但是因為每次執行的結果都不同,所以這裡給的數值只是大概值。" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -309,6 +537,13 @@ "\n", "Include the answer in your report." ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "針對Advantage進行Normalization的話能夠穩定Rewards中的variance大小的影響,進一步讓Iteration數趨於穩定,原預期此一步驟可讓Gradient趨於穩定,然而經多次實驗後卻發現Iteration數量不減反增,加入Normalization僅能讓加入Baseline的因素影響減少而已,因此判定Normalization可讓Training過程穩定。" + ] } ], "metadata": { @@ -327,7 +562,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.11" + "version": "2.7.6" } }, "nbformat": 4, diff --git a/policy_gradient/policy.py b/policy_gradient/policy.py index 70b8cdc..d3f0368 100644 --- a/policy_gradient/policy.py +++ b/policy_gradient/policy.py @@ -27,7 +27,9 @@ def __init__(self, in_dim, out_dim, hidden_dim, optimizer, session): Sample solution is about 2~4 lines. """ # YOUR CODE HERE >>>>>> - # probs = ??? + hidden_layer = tf.contrib.layers.fully_connected(self._observations, hidden_dim, tf.tanh) + probs = tf.contrib.layers.fully_connected(hidden_layer, out_dim, tf.nn.softmax) + #probs = ??? # <<<<<<<< # -------------------------------------------------- @@ -69,6 +71,8 @@ def __init__(self, in_dim, out_dim, hidden_dim, optimizer, session): Sample solution is about 1~3 lines. """ # YOUR CODE HERE >>>>>> + #surr_loss = tf.reduce_mean(log_prob*self._advantages) + surr_loss = -tf.reduce_mean(log_prob*self._advantages) # surr_loss = ??? # <<<<<<<< diff --git a/policy_gradient/util.py b/policy_gradient/util.py index 4c57674..3235d49 100644 --- a/policy_gradient/util.py +++ b/policy_gradient/util.py @@ -19,7 +19,14 @@ def flatten_space(space): Sample solution is about 1~7 lines. """ -# def discount_cumsum(x, discount_rate): +#def discount_cumsum(x, discount_rate): # YOUR CODE HERE >>>>>> # return ??? - # <<<<<<<< \ No newline at end of file + # <<<<<<<< + +def discount_cumsum(x, discount_rate): + result=x.astype(float) + for index in range(1,len(x)): + result[index]=result[index-1]+x[index]*pow(discount_rate,index) + + return result[::-1] \ No newline at end of file From 98e39cdbd081300bc957d7c25afdde7dba9c56d8 Mon Sep 17 00:00:00 2001 From: Timforce Date: Sat, 15 Oct 2016 22:21:24 +0800 Subject: [PATCH 2/3] Update README.md --- README.md | 67 ++++++++++++++++--------------------------------------- 1 file changed, 19 insertions(+), 48 deletions(-) diff --git a/README.md b/README.md index 8da7390..539e527 100644 --- a/README.md +++ b/README.md @@ -11,52 +11,23 @@ CartPole is an environment which contains a pendulum attached by an un-actuated and the goal is to prevent it from falling over. You can apply a force of +1 or -1 to the cart. A reward of +1 is provided for every timestep that the pendulum remains upright. -# Setup -* OpenAI gym -* TensorFlow -* Numpy -* Scipy -* IPython Notebook - -**If you already have some of above libraries installed, try to manage the dependencies by yourself.** - -If you are using a new environment (may be virtual), the preferred approach for installing above dependencies is to use [Anaconda](https://www.continuum.io/downloads), which is a Python distribution that includes many of the most popular Python packages for science, math, engineering and data analysis. - -1. **Install Anaconda**: Follow the instructions on the [Anaconda download site](https://www.continuum.io/downloads). -2. **Install TensorFlow**: See [anaconda section](https://www.tensorflow.org/versions/r0.11/get_started/os_setup.html#anaconda-installation) of TensorFlow installation page. -3. **Install OpenAI gym**: Follow the official installation documents [here](https://gym.openai.com/docs). - -# Prerequisites -If you are unfamiliar with Numpy or IPython, you should read materials from [CS231n](http://cs231n.github.io/): -* [Numpy tutorial](http://cs231n.github.io/python-numpy-tutorial/) -* [IPython tutorial](http://cs231n.github.io/ipython-tutorial/) - -Also, knowing the basics of TensorFlow is required to complete this assignment. - -For introductory material on TensorFlow, see -* [MNIST For ML Beginners](https://www.tensorflow.org/versions/r0.11/tutorials/mnist/beginners/index.html) from official site -* [Tutorial Video](https://www.youtube.com/watch?v=l6K-MFgIEjc&t=3334s) from [Stanford CS224D](http://cs224d.stanford.edu/syllabus.html) - -Feel free to skip these materials if you are already familiar with these libraries. - -# How to Start -1. **Start IPython**: After you clone this repository and install all the dependencies, you should start the IPython notebook server from the home directory -2. **Open the assignment**: Open ``HW2_Policy_Graident.ipynb``, and it will walk you through completing the assignment. - -# To-Do -* [**+20**] Construct a 2-layer neural network to represent policy -* [**+30**] Compute the surrogate loss -* [**+20**] Compute the accumulated discounted rewards at each timestep -* [**+10**] Use baseline to reduce the variance -* [**+10**] Modify the code and write a report to compare the variance and performance before and after adding baseline (with figures is better) -* [**+10**] In function `process_paths` of class `PolicyOptimizer`, why we need to normalize the advantages? - i.e., what's the usage of this line: - - `p["advantages"] = (a - a.mean()) / (a.std() + 1e-8)` - - Include the answer in your report - -# Other -* Office hour 2-3 pm in 資電館 with [YenChen Lin](http://yclin.me/). -* Due on Oct. 17 before class. +# Our report of this paper +We present our report as a pdf file. +# Problem 5 +### Average_return_with_baseline.png +![with B](Average_return_with_baseline.png "Average return with baseline") +### Average_return_without_baseline.png +![without B](Average_return_without_baseline.png "Average return without baseline") + + 上兩圖中的藍色線為Average Return值,而黑色線則表示Stardard Deviation範圍,上圖是有在Policy Gradient中加入Baseline,而下圖則無,可以看到Standar Deviation值具有明顯差異,換算則Variance的話,有加入Baseline大約可以減少Variance約300~400左右,而在Variance值減少的情況下,原本可預期減少iteration數,在實驗中也曾測到Iteration數減少約10~20,但是因為每次執行的結果都不同,所以這裡給的數值只是大概值。 + +# Problem 6 + 針對Advantage進行Normalization的話能夠穩定Rewards中的variance大小的影響,進一步讓Iteration數趨於穩定,原預期此一步驟可讓Gradient趨於穩定,然而經多次實驗後卻發現Iteration數量不減反增,加入Normalization僅能讓加入Baseline的因素影響減少而已,因此判定Normalization可讓Training過程穩定。 + +# Team members and contribution +- 姓名:李冠毅 學號:104064510
+負責內容:數據圖,實驗討論,環境設置 + +- 姓名:李季紘 學號:(交大)0556083
+負責內容:程式設計,實驗討論,資料收集 From ac93cc27756aa560f857256192ae5dd4c47211ec Mon Sep 17 00:00:00 2001 From: Timforce Date: Sat, 15 Oct 2016 22:22:33 +0800 Subject: [PATCH 3/3] Update README.md --- README.md | 11 +---------- 1 file changed, 1 insertion(+), 10 deletions(-) diff --git a/README.md b/README.md index 539e527..4efef99 100644 --- a/README.md +++ b/README.md @@ -2,16 +2,7 @@ Please complete each homework for each team, and
mention who contributed which parts in your report. -# Introduction -In this assignment, we will solve the classic control problem - CartPole. - - - -CartPole is an environment which contains a pendulum attached by an un-actuated joint to a cart, -and the goal is to prevent it from falling over. You can apply a force of +1 or -1 to the cart. -A reward of +1 is provided for every timestep that the pendulum remains upright. - -# Our report of this paper +# Our report of this assignment We present our report as a pdf file. # Problem 5