Reinforcement Learning Introduction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import gym\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2017-07-07 16:04:18,920] Making new env: Taxi-v2\n"
     ]
    }
   ],
   "source": [
    "env = gym.make(\"Taxi-v2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "369"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "env.reset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "500"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "env.observation_space.n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------+\n",
      "|R: | : :\u001b[35mG\u001b[0m|\n",
      "| : : : : |\n",
      "| : : : : |\n",
      "| | : |\u001b[43m \u001b[0m: |\n",
      "|\u001b[34;1mY\u001b[0m| : |B: |\n",
      "+---------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "env.render()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "env.action_space.n\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------+\n",
      "|R: | : :G|\n",
      "|\u001b[43m \u001b[0m: : : : |\n",
      "| : : : : |\n",
      "| | : | : |\n",
      "|\u001b[35mY\u001b[0m| : |\u001b[34;1mB\u001b[0m: |\n",
      "+---------+\n",
      "\n"
     ]
    }
   ],
   "source": [
    "env.env.s = 114\n",
    "env.render()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14, -1, False, {'prob': 1.0})"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "env.step(1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+---------+\n",
      "|\u001b[43mR\u001b[0m: | : :G|\n",
      "| : : : : |\n",
      "| : : : : |\n",
      "| | : | : |\n",
      "|\u001b[35mY\u001b[0m| : |\u001b[34;1mB\u001b[0m: |\n",
      "+---------+\n",
      "  (North)\n"
     ]
    }
   ],
   "source": [
    "env.render()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "438\n"
     ]
    }
   ],
   "source": [
    "state = env.reset()\n",
    "counter = 0\n",
    "reward = None\n",
    "while reward != 20:\n",
    "    state, reward, done, info = env.step(env.action_space.sample())\n",
    "    counter += 1\n",
    "\n",
    "print(counter)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q = np.zeros([env.observation_space.n, env.action_space.n])\n",
    "G = 0\n",
    "alpha = 0.618"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Episode 50 Total Reward: -254\n",
      "Episode 100 Total Reward: -66\n",
      "Episode 150 Total Reward: -38\n",
      "Episode 200 Total Reward: 11\n",
      "Episode 250 Total Reward: 12\n",
      "Episode 300 Total Reward: 9\n",
      "Episode 350 Total Reward: 11\n",
      "Episode 400 Total Reward: 5\n",
      "Episode 450 Total Reward: 6\n",
      "Episode 500 Total Reward: 11\n",
      "Episode 550 Total Reward: 10\n",
      "Episode 600 Total Reward: 11\n",
      "Episode 650 Total Reward: 6\n",
      "Episode 700 Total Reward: 6\n",
      "Episode 750 Total Reward: 12\n",
      "Episode 800 Total Reward: 9\n",
      "Episode 850 Total Reward: 4\n",
      "Episode 900 Total Reward: 12\n",
      "Episode 950 Total Reward: 9\n",
      "Episode 1000 Total Reward: 5\n"
     ]
    }
   ],
   "source": [
    "for episode in range(1,1001):\n",
    "    done = False\n",
    "    G, reward = 0,0\n",
    "    state = env.reset()\n",
    "    while done != True:\n",
    "        action = np.argmax(Q[state]) #1\n",
    "        state2, reward, done, info = env.step(action) #2\n",
    "        Q[state,action] += alpha * (reward + np.max(Q[state2]) - Q[state,action]) #3\n",
    "        G += reward\n",
    "        state = state2    \n",
    "    if episode % 50 == 0:\n",
    "        print('Episode {} Total Reward: {}'.format(episode,G))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2017-07-07 16:09:53,634] Making new env: MsPacman-v0\n"
     ]
    }
   ],
   "source": [
    "env = gym.make(\"MsPacman-v0\")\n",
    "state = env.reset()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "env.render()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "env.action_space.n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['NOOP',\n",
       " 'UP',\n",
       " 'RIGHT',\n",
       " 'LEFT',\n",
       " 'DOWN',\n",
       " 'UPRIGHT',\n",
       " 'UPLEFT',\n",
       " 'DOWNRIGHT',\n",
       " 'DOWNLEFT']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "env.env.get_action_meanings()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "state = env.reset()\n",
    "reward, info, done = None, None, None\n",
    "while done != True:\n",
    "    state, reward, done, info = env.step(env.action_space.sample())\n",
    "    env.render()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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