Q-Learning .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gym\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "env = gym.envs.make('FrozenLake8x8-v0')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "q_table = np.random.randn(64,env.action_space.n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "learning_rate = 0.1\n",
    "discount_factor = 0.999 \n",
    "max_step = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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     "output_type": "stream",
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      "\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
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     ]
    },
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     ]
    },
    {
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     "output_type": "stream",
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     ]
    },
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     "output_type": "stream",
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    },
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    },
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     ]
    },
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      "\n",
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    },
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    },
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    },
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    },
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    },
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    },
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    },
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     ]
    }
   ],
   "source": [
    "rewards = []\n",
    "import random\n",
    "for i in tqdm(range(300000)):\n",
    "    #print(i)\n",
    "    state = env.reset()\n",
    "    steps = 0\n",
    "    beta = discount_factor\n",
    "    terminal = False\n",
    "    total_rewards = 0\n",
    "    while terminal==False:\n",
    "        \n",
    "        action =  q_table[state].argmax() if random.random()>0.3 else np.random.randint(4,size=1)[0]\n",
    "        next_state,reward,terminal,_= env.step(action)\n",
    "        \n",
    "        q_table[state][action] = q_table[state][action] + learning_rate*(reward*1000 + beta*q_table[next_state].max()-q_table[state][action])\n",
    "        \n",
    "        state = next_state\n",
    "        beta*=discount_factor\n",
    "        total_rewards+=reward\n",
    "        steps+=1\n",
    "    if i%100 == 0:\n",
    "        #print(total_rewards,reward)\n",
    "        rewards.append(total_rewards)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "policy = {}\n",
    "value_fn = {}\n",
    "for i in range(64):\n",
    "    policy[i]=q_table[i].argmax(axis=0)\n",
    "    value_fn[i] = q_table[i].max(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "state_value = np.zeros(shape=(8,8))\n",
    "state_action = np.zeros(shape=(8,8))\n",
    "#mat[0][1]\n",
    "for i in range(env.observation_space.n):\n",
    "    row = i//8\n",
    "    col = i%8\n",
    "    #print(row,col)\n",
    "    state_value[row][col] = value_fn[i]\n",
    "    state_action"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f96069708d0>"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.16651277e+03  2.96709525e+02  1.12848628e+02  8.31582378e+01\n",
      "   3.16162016e+02  4.67292331e+03  2.88816969e+01  9.48794943e-04]\n",
      " [ 6.77663401e+03  1.48000590e+03  3.63254530e+02  4.82825974e+02\n",
      "   1.39590739e+03  1.91111729e+03  3.85383562e+02  2.26398847e+01]\n",
      " [ 5.51265508e+03  5.30285436e+03  1.29858124e+03  2.28881093e+04\n",
      "   1.34400284e+04  1.26918042e+04  2.64867998e+02  5.15992804e+01]\n",
      " [ 1.04953010e+04  7.31546671e+03  1.26877047e+04  4.93923809e+03\n",
      "   1.50763285e+03  1.78761234e+04  2.71099052e+03  8.03643286e+00]\n",
      " [ 9.09479178e+03  7.22986151e+03  7.34665955e+03  1.76189065e+04\n",
      "   5.90428682e+03  4.51209198e+03  6.56927645e+03  3.27227078e+02]\n",
      " [ 1.46716488e+04  1.63764505e+04  7.37538577e+03  9.61288529e+03\n",
      "   6.79142211e+03  6.27116885e+03  8.66010852e+03  4.17739299e+02]\n",
      " [ 1.17771355e+03 -4.28362041e+03  5.67146940e+03  1.31350724e+04\n",
      "   8.28842036e+03  3.17622076e+03  1.06947424e+03  1.00489442e+04]\n",
      " [ 1.26244392e+02  2.49355346e+02  6.10178762e+02  1.56728956e+04\n",
      "   1.00690596e+04  3.79708829e+03  7.54750346e+02  1.32787952e+03]]\n"
     ]
    }
   ],
   "source": [
    "print(mat*10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}