Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import absolute_import, division, print_function, unicode_literals\n",
    "\n",
    "from agents.DuelingDDQNAgent import DuelingDDQNAgent\n",
    "from utils import *\n",
    "import os\n",
    "import numpy as np\n",
    "import time\n",
    "import gym\n",
    "from gym.wrappers import AtariPreprocessing, FrameStack\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# env = gym.make('SpaceInvadersNoFrameskip-v4')\n",
    "# env = gym.make('BreakoutNoFrameskip-v4')\n",
    "env = gym.make('PongNoFrameskip-v4')\n",
    "env = AtariPreprocessing(env)\n",
    "env = FrameStack(env, num_stack=4)\n",
    "\n",
    "agent = DuelingDDQNAgent(input_shape=(env.observation_space.shape), n_actions=env.action_space.n)\n",
    "agent.eps = 0.00\n",
    "\n",
    "load_agent(agent, os.path.join('results', env.game, 'latest_model'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Game 0 Score 19.0 Best Score 19.0 ε 0.00\n",
      "Time taken for game 11.06 sec\n",
      "\n",
      "Game 1 Score 21.0 Best Score 21.0 ε 0.00\n",
      "Time taken for game 8.71 sec\n",
      "\n",
      "Game 2 Score 19.0 Best Score 21.0 ε 0.00\n",
      "Time taken for game 12.72 sec\n",
      "\n",
      "Game 3 Score 19.0 Best Score 21.0 ε 0.00\n",
      "Time taken for game 12.90 sec\n",
      "\n",
      "Game 4 Score 19.0 Best Score 21.0 ε 0.00\n",
      "Time taken for game 11.36 sec\n",
      "\n",
      "Game 5 Score 21.0 Best Score 21.0 ε 0.00\n",
      "Time taken for game 8.94 sec\n",
      "\n",
      "Game 6 Score 19.0 Best Score 21.0 ε 0.00\n",
      "Time taken for game 13.40 sec\n",
      "\n",
      "Game 7 Score 20.0 Best Score 21.0 ε 0.00\n",
      "Time taken for game 8.99 sec\n",
      "\n",
      "Game 8 Score 20.0 Best Score 21.0 ε 0.00\n",
      "Time taken for game 8.88 sec\n",
      "\n",
      "Game 9 Score 19.0 Best Score 21.0 ε 0.00\n",
      "Time taken for game 12.81 sec\n",
      "\n",
      "Game 10 Score 19.0 Best Score 21.0 ε 0.00\n",
      "Time taken for game 11.11 sec\n",
      "\n",
      "Game 11 Score 20.0 Best Score 21.0 ε 0.00\n",
      "Time taken for game 9.05 sec\n",
      "\n",
      "Game 12 Score 19.0 Best Score 21.0 ε 0.00\n",
      "Time taken for game 11.42 sec\n",
      "\n",
      "Game 13 Score 21.0 Best Score 21.0 ε 0.00\n",
      "Time taken for game 9.60 sec\n",
      "\n",
      "Game 14 Score 19.0 Best Score 21.0 ε 0.00\n",
      "Time taken for game 11.53 sec\n",
      "\n",
      "Game 15 Score 19.0 Best Score 21.0 ε 0.00\n",
      "Time taken for game 11.40 sec\n",
      "\n",
      "Game 16 Score 19.0 Best Score 21.0 ε 0.00\n",
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      "Time taken for game 9.10 sec\n",
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      "Game 20 Score 21.0 Best Score 21.0 ε 0.00\n",
      "Time taken for game 9.19 sec\n",
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      "Game 21 Score 19.0 Best Score 21.0 ε 0.00\n",
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      "Time taken for game 9.08 sec\n",
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      "Time taken for game 8.91 sec\n",
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      "Game 24 Score 19.0 Best Score 21.0 ε 0.00\n",
      "Time taken for game 13.28 sec\n",
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      "Game 25 Score 20.0 Best Score 21.0 ε 0.00\n",
      "Time taken for game 8.85 sec\n",
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      "Game 26 Score 21.0 Best Score 21.0 ε 0.00\n",
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      "Game 38 Score 19.0 Best Score 21.0 ε 0.00\n",
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      "\n",
      "Game 39 Score 19.0 Best Score 21.0 ε 0.00\n",
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      "\n",
      "Game 40 Score 20.0 Best Score 21.0 ε 0.00\n",
      "Time taken for game 8.84 sec\n",
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      "Game 64 Score 19.0 Best Score 21.0 ε 0.00\n",
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      "\n",
      "Game 67 Score 21.0 Best Score 21.0 ε 0.00\n",
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      "Game 68 Score 20.0 Best Score 21.0 ε 0.00\n",
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      "Game 69 Score 19.0 Best Score 21.0 ε 0.00\n",
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      "\n",
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      "\n",
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      "Game 76 Score 19.0 Best Score 21.0 ε 0.00\n",
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      "Game 77 Score 19.0 Best Score 21.0 ε 0.00\n",
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      "\n",
      "Game 78 Score 20.0 Best Score 21.0 ε 0.00\n",
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      "\n",
      "Game 79 Score 19.0 Best Score 21.0 ε 0.00\n",
      "Time taken for game 11.00 sec\n",
      "\n",
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      "Time taken for game 12.84 sec\n",
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      "Game 85 Score 20.0 Best Score 21.0 ε 0.00\n",
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      "Game 86 Score 21.0 Best Score 21.0 ε 0.00\n",
      "Time taken for game 9.10 sec\n",
      "\n",
      "Game 87 Score 19.0 Best Score 21.0 ε 0.00\n",
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      "\n",
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      "\n",
      "Game 90 Score 19.0 Best Score 21.0 ε 0.00\n",
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      "\n",
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      "Game 99 Score 19.0 Best Score 21.0 ε 0.00\n",
      "Time taken for game 13.06 sec\n",
      "\n",
      "------------------------------\n",
      "Average 19.77  Best Score 21.0 Worst Score 19.0\n",
      "------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "epochs = 100\n",
    "scores = []\n",
    "best_score = -np.inf\n",
    "\n",
    "for i in range(epochs):\n",
    "    done = False\n",
    "    observation = env.reset()\n",
    "\n",
    "    score = 0\n",
    "    start_time = time.time()\n",
    "    while not done:\n",
    "        action = agent.choose_action(observation)\n",
    "        next_observation, reward, done, info = env.step(action)\n",
    "        score += reward\n",
    "        \n",
    "        if score > best_score:\n",
    "            best_score = score\n",
    "        observation = next_observation\n",
    "\n",
    "    scores.append(score)\n",
    "\n",
    "    print('Game {} Score {} Best Score {} ε {:.2f}'.format(\n",
    "        i, score , best_score, agent.eps\n",
    "    ))\n",
    "    print ('Time taken for game {:.2f} sec\\n'.format(time.time() - start_time))\n",
    "    \n",
    "print(\"------------------------------\")\n",
    "print('Average {}  Best Score {} Worst Score {}'.format(sum(scores) / len(scores), max(scores), min(scores)))\n",
    "print(\"------------------------------\\n\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(scores)\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Score')\n",
    "plt.title('Scores Plot')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The system cannot find the path specified.\n",
      "The system cannot find the path specified.\n"
     ]
    }
   ],
   "source": [
    "!pip install gym pyvirtualdisplay > /dev/null 2>&1\n",
    "!apt-get install -y xvfb python-opengl ffmpeg > /dev/null 2>&1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "ename": "DependencyNotInstalled",
     "evalue": "Found neither the ffmpeg nor avconv executables. On OS X, you can install ffmpeg via `brew install ffmpeg`. On most Ubuntu variants, `sudo apt-get install ffmpeg` should do it. On Ubuntu 14.04, however, you'll need to install avconv with `sudo apt-get install libav-tools`.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mDependencyNotInstalled\u001b[0m                    Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-27-0779a9161579>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0menv\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mwrappers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMonitor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0menv\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"./gym-results\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mforce\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0menv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1000\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0maction\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0magent\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchoose_action\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobservation\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mobservation\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreward\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minfo\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0menv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maction\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\stefan\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\gym\\wrappers\\monitor.py\u001b[0m in \u001b[0;36mreset\u001b[1;34m(self, **kwargs)\u001b[0m\n\u001b[0;32m     36\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mreset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     37\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_before_reset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 38\u001b[1;33m         \u001b[0mobservation\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0menv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     39\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_after_reset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobservation\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     40\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\stefan\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\gym\\wrappers\\monitor.py\u001b[0m in \u001b[0;36mreset\u001b[1;34m(self, **kwargs)\u001b[0m\n\u001b[0;32m     37\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_before_reset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     38\u001b[0m         \u001b[0mobservation\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0menv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 39\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_after_reset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobservation\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     40\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     41\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mobservation\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\stefan\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\gym\\wrappers\\monitor.py\u001b[0m in \u001b[0;36m_after_reset\u001b[1;34m(self, observation)\u001b[0m\n\u001b[0;32m    183\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstats_recorder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mafter_reset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobservation\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    184\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 185\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreset_video_recorder\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    186\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    187\u001b[0m         \u001b[1;31m# Bump *after* all reset activity has finished\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\stefan\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\gym\\wrappers\\monitor.py\u001b[0m in \u001b[0;36mreset_video_recorder\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    204\u001b[0m             \u001b[0menabled\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_video_enabled\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    205\u001b[0m         )\n\u001b[1;32m--> 206\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvideo_recorder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcapture_frame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    207\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    208\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_close_video_recorder\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\stefan\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\gym\\wrappers\\monitoring\\video_recorder.py\u001b[0m in \u001b[0;36mcapture_frame\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    114\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_encode_ansi_frame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mframe\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    115\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 116\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_encode_image_frame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mframe\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    117\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    118\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\stefan\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\gym\\wrappers\\monitoring\\video_recorder.py\u001b[0m in \u001b[0;36m_encode_image_frame\u001b[1;34m(self, frame)\u001b[0m\n\u001b[0;32m    160\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_encode_image_frame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mframe\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    161\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mencoder\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 162\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mencoder\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImageEncoder\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mframe\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mframes_per_sec\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutput_frames_per_sec\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    163\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmetadata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'encoder_version'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mencoder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mversion_info\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    164\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\stefan\\appdata\\local\\programs\\python\\python37\\lib\\site-packages\\gym\\wrappers\\monitoring\\video_recorder.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, output_path, frame_shape, frames_per_sec, output_frames_per_sec)\u001b[0m\n\u001b[0;32m    253\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbackend\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'ffmpeg'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    254\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 255\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0merror\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDependencyNotInstalled\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"\"\"Found neither the ffmpeg nor avconv executables. On OS X, you can install ffmpeg via `brew install ffmpeg`. On most Ubuntu variants, `sudo apt-get install ffmpeg` should do it. On Ubuntu 14.04, however, you'll need to install avconv with `sudo apt-get install libav-tools`.\"\"\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    256\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    257\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mDependencyNotInstalled\u001b[0m: Found neither the ffmpeg nor avconv executables. On OS X, you can install ffmpeg via `brew install ffmpeg`. On most Ubuntu variants, `sudo apt-get install ffmpeg` should do it. On Ubuntu 14.04, however, you'll need to install avconv with `sudo apt-get install libav-tools`."
     ]
    }
   ],
   "source": [
    "env = wrappers.Monitor(env, \"./gym-results\", force=True)\n",
    "env.reset()\n",
    "for _ in range(1000):\n",
    "    action = agent.choose_action(observation)\n",
    "    observation, reward, done, info = env.step(action)\n",
    "    if done: break\n",
    "env.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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