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Jon Krohn
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add gym to Dockerfiles
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-27
lines changed

4 files changed

+772
-27
lines changed

‎Dockerfile

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@@ -14,3 +14,6 @@ RUN pip install keras==2.0.8
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# install NLP packages:
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RUN pip install nltk==3.2.4
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RUN pip install gensim==2.3.0
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# install Reinforcement Learning packages:
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RUN pip install gym==0.9.4

‎Dockerfile-gpu

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@@ -181,3 +181,6 @@ RUN pip install keras==2.0.8
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## Install NLP packages
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RUN pip install nltk==3.2.4
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RUN pip install gensim==2.3.0
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# install Reinforcement Learning packages:
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RUN pip install gym==0.9.4

‎notebooks/experimental/JSAnimate_cartpole.ipynb

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@@ -0,0 +1,181 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"from JSAnimation.IPython_display import display_animation\n",
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"from matplotlib import animation\n",
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"import matplotlib.pyplot as plt\n",
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"from IPython.display import display"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import gym"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"def display_frames_as_gif(frames):\n",
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" \"\"\"\n",
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" Displays a list of frames as a gif, with controls\n",
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" \"\"\"\n",
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" plt.figure(figsize=(frames[0].shape[1] / 72.0, frames[0].shape[0] / 72.0), dpi = 72)\n",
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" patch = plt.imshow(frames[0])\n",
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" plt.axis('off')\n",
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"\n",
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" def animate(i):\n",
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" patch.set_data(frames[i])\n",
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"\n",
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" anim = animation.FuncAnimation(plt.gcf(), animate, frames = len(frames), interval=50)\n",
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" display(display_animation(anim, default_mode='loop'))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\r",
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"Episode 0/40 finished after 13 timesteps\r",
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"Episode 1/40 finished after 15 timesteps\r",
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"Episode 2/40 finished after 23 timesteps\r",
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"Episode 3/40 finished after 13 timesteps\r",
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"Episode 4/40 finished after 12 timesteps\r",
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"Episode 5/40 finished after 22 timesteps\r",
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"Episode 6/40 finished after 21 timesteps\r",
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"Episode 7/40 finished after 14 timesteps\r",
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"Episode 8/40 finished after 10 timesteps\r",
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"Episode 9/40 finished after 18 timesteps\r",
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"Episode 10/40 finished after 22 timesteps\r",
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"Episode 11/40 finished after 13 timesteps\r",
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"Episode 12/40 finished after 20 timesteps\r",
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"Episode 13/40 finished after 43 timesteps\r",
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"Episode 14/40 finished after 34 timesteps\r",
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"Episode 15/40 finished after 32 timesteps\r",
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"Episode 16/40 finished after 14 timesteps\r",
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"Episode 17/40 finished after 14 timesteps\r",
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"Episode 18/40 finished after 16 timesteps\r",
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"Episode 19/40 finished after 14 timesteps\r",
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"Episode 20/40 finished after 17 timesteps\r",
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"Episode 21/40 finished after 20 timesteps\r",
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"Episode 22/40 finished after 13 timesteps\r",
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"Episode 23/40 finished after 21 timesteps\r",
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"Episode 24/40 finished after 12 timesteps\r",
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"Episode 25/40 finished after 17 timesteps\r",
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"Episode 26/40 finished after 23 timesteps\r",
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"Episode 27/40 finished after 30 timesteps\r",
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"Episode 28/40 finished after 43 timesteps\r",
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"Episode 29/40 finished after 17 timesteps\r",
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"Episode 30/40 finished after 28 timesteps\r",
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"Episode 31/40 finished after 26 timesteps\r",
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"Episode 32/40 finished after 19 timesteps\r",
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"Episode 33/40 finished after 15 timesteps\r",
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"Episode 34/40 finished after 44 timesteps\r",
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"Episode 35/40 finished after 22 timesteps\r",
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"Episode 36/40 finished after 70 timesteps\r",
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"Episode 37/40 finished after 24 timesteps\r",
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"Episode 38/40 finished after 23 timesteps\r",
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"Episode 39/40 finished after 15 timesteps"
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]
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}
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],
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"source": [
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"env = gym.make('CartPole-v0')\n",
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"cum_reward = 0\n",
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"frames = []\n",
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"num_episodes=40\n",
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"for i_episode in range(num_episodes):\n",
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" observation = env.reset()\n",
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" for t in range(500):\n",
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" # Render into buffer. \n",
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" frames.append(env.render(mode = 'rgb_array', close=True))\n",
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" action = env.action_space.sample() # random action\n",
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" observation, reward, done, info = env.step(action)\n",
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" if done:\n",
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" print(\"\\rEpisode {}/{} finished after {} timesteps\".format(i_episode, num_episodes, t+1), end=\"\")\n",
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" break"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"ename": "AttributeError",
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"evalue": "'NoneType' object has no attribute 'shape'",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m<ipython-input-6-d67b09ec9573>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdisplay_frames_as_gif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
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"\u001b[0;32m<ipython-input-4-9da36626cf94>\u001b[0m in \u001b[0;36mdisplay_frames_as_gif\u001b[0;34m(frames)\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mDisplays\u001b[0m \u001b[0ma\u001b[0m \u001b[0mlist\u001b[0m \u001b[0mof\u001b[0m \u001b[0mframes\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0ma\u001b[0m \u001b[0mgif\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mcontrols\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \"\"\"\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m72.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mframes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m72.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdpi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m72\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m \u001b[0mpatch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mframes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'off'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'shape'"
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]
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}
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],
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"source": [
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"display_frames_as_gif(frames)"
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]
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}
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],
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"metadata": {
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"anaconda-cloud": {},
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"kernelspec": {
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"display_name": "Python [conda root]",
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"language": "python",
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"name": "conda-root-py"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.5.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}

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