Q-Table learning in OpenAI grid world.

Q-Table Learning-Clean.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Q-Table Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gym\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "env = gym.make('FrozenLake-v0')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implement Q-Table learning algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Initialize table with all zeros\n",
    "Q = np.zeros([env.observation_space.n,env.action_space.n])\n",
    "\n",
    "# Set learning parameters\n",
    "lr = .8\n",
    "gamma = .95\n",
    "num_episodes = 2000\n",
    "max_steps = 99\n",
    "\n",
    "# Create lists to contain total rewards and steps per episode\n",
    "rewards_list = []\n",
    "for i in range(num_episodes):\n",
    "    # Reset environment and get first new observation\n",
    "    state = env.reset()\n",
    "    total_reward = 0\n",
    "    done = False\n",
    "    step = 0\n",
    "    \n",
    "    # The Q-Table learning algorithm\n",
    "    while step < max_steps:\n",
    "        step += 1\n",
    "        \n",
    "        # Choose an action by greedily (with noise) picking from Q table\n",
    "        action = np.argmax(Q[state,:] + np.random.randn(1, env.action_space.n) * (1./(i+1)))\n",
    "        \n",
    "        # Get new state and reward from environment\n",
    "        new_state, reward, done, _ = env.step(action)\n",
    "        \n",
    "        # Update Q-Table with new knowledge\n",
    "        Q[state, action] = Q[state, action] + lr * (reward + gamma * np.max(Q[new_state,:]) - Q[state, action])\n",
    "        total_reward += reward\n",
    "        state = new_state\n",
    "        \n",
    "        if done == True:\n",
    "            break\n",
    "\n",
    "        rewards_list.append(total_reward)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score over time: 0.0\n",
      "Final Q-Table Values\n",
      "[[  1.73606976e-01   8.09353670e-03   5.37655073e-03   7.57670043e-03]\n",
      " [  8.56387621e-04   2.40744772e-05   8.14224075e-04   1.60722899e-01]\n",
      " [  8.83784512e-04   7.39989148e-04   4.10694044e-03   1.91931139e-01]\n",
      " [  9.15500710e-04   1.27182348e-04   9.00477296e-04   7.90341515e-02]\n",
      " [  1.56972694e-01   1.41182122e-03   5.59359958e-03   7.14299174e-04]\n",
      " [  0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00]\n",
      " [  8.90677852e-02   8.89296303e-05   6.39010325e-04   2.34099326e-08]\n",
      " [  0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00]\n",
      " [  7.00541641e-04   1.83801419e-04   5.54609322e-04   1.63727386e-01]\n",
      " [  5.12418126e-04   3.02610162e-01   2.87072750e-04   8.52808810e-04]\n",
      " [  4.75554318e-01   3.22479170e-04   1.51289856e-04   1.97135557e-04]\n",
      " [  0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00]\n",
      " [  0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00]\n",
      " [  1.10336815e-03   1.45180020e-03   6.05380068e-01   1.93977187e-04]\n",
      " [  0.00000000e+00   0.00000000e+00   9.55785422e-01   0.00000000e+00]\n",
      " [  0.00000000e+00   0.00000000e+00   0.00000000e+00   0.00000000e+00]]\n"
     ]
    }
   ],
   "source": [
    "print(\"Score over time: \" +  str(sum(rewards_list)/num_episodes))\n",
    "print(\"Final Q-Table Values\")\n",
    "print(Q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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