Q-Table learning in OpenAI grid world.

Q-Table Learning-Clean.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q-Table Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import gym\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
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   "outputs": [],
   "source": [
    "env = gym.make('FrozenLake-v0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implement Q-Table learning algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Initialize table with all zeros\n",
    "Q = np.zeros([env.observation_space.n,env.action_space.n])\n",
    "# Set learning parameters\n",
    "lr = .8\n",
    "y = .95\n",
    "num_episodes = 2000\n",
    "#create lists to contain total rewards and steps per episode\n",
    "#jList = []\n",
    "rList = []\n",
    "for i in range(num_episodes):\n",
    "    #Reset environment and get first new observation\n",
    "    s = env.reset()\n",
    "    rAll = 0\n",
    "    d = False\n",
    "    j = 0\n",
    "    #The Q-Table learning algorithm\n",
    "    while j < 99:\n",
    "        j+=1\n",
    "        #Choose an action by greedily (with noise) picking from Q table\n",
    "        a = np.argmax(Q[s,:] + np.random.randn(1,env.action_space.n)*(1./(i+1)))\n",
    "        #Get new state and reward from environment\n",
    "        s1,r,d,_ = env.step(a)\n",
    "        #Update Q-Table with new knowledge\n",
    "        Q[s,a] = Q[s,a] + lr*(r + y*np.max(Q[s1,:]) - Q[s,a])\n",
    "        rAll += r\n",
    "        s = s1\n",
    "        if d == True:\n",
    "            break\n",
    "    #jList.append(j)\n",
    "    rList.append(rAll)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "print \"Score over time: \" +  str(sum(rList)/num_episodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "print \"Final Q-Table Values\"\n",
    "print Q"
   ]
  }
 ],
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