Transfer_flipped_4x4.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gym"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "flipped_4X4 = ['FFFS',\n",
    "        'HFHF',\n",
    "        'HFFF',\n",
    "        'GFFH'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "FFF\u001b[41mS\u001b[0m\n",
      "HFHF\n",
      "HFFF\n",
      "GFFH\n"
     ]
    }
   ],
   "source": [
    "env = gym.make('FrozenLakeNotSlippery-v0', is_slippery=False, desc = flipped_4X4)\n",
    "\n",
    "# Instantiate the Environment.\n",
    "# env = gym.make('FrozenLake-v0')\n",
    "\n",
    "# To check all environments present in OpenAI\n",
    "# print(envs.registry.all())\n",
    "\n",
    "# visualize 4x4 frozen lake\n",
    "env.render()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "States =  16\n",
      "Actions =  4\n",
      "Rows =  4\n",
      "Cols =  4\n"
     ]
    }
   ],
   "source": [
    "# Total number of States and Actions\n",
    "n_states = env.observation_space.n\n",
    "n_actions = env.action_space.n\n",
    "n_rows = int(np.sqrt(n_states))\n",
    "n_cols=int(np.sqrt(n_states))\n",
    "print( \"States = \", n_states)\n",
    "print( \"Actions = \", n_actions)\n",
    "print( \"Rows = \", n_rows)\n",
    "print( \"Cols = \", n_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Start with 80% random actions to explore the environment\n",
    "# exploration decays with each timestep by a factor of 0.999 until it hits 10% exploration rate \n",
    "\n",
    "epsilon = 1.0\n",
    "epsilon_final = 0.10\n",
    "epsilon_decay = 0.9999"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "gamma = 0.90 # discount factor\n",
    "learning_rate = 0.90 #how important is the difference between q-val from q-table and what's observed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#Q = np.random.rand(15,4)*(1/100)\n",
    "#Q = np.vstack([Q, np.zeros((1,4))])\n",
    "\n",
    "Q = np.array([[0.        , 0.47827059, 0.59049   , 0. ],\n",
    "       [0.53144098, 0.        , 0.6561    , 0.        ],\n",
    "       [0.5904841 , 0.729     , 0.59048622, 0.        ],\n",
    "       [0.65609993, 0.        , 0.        , 0.        ],\n",
    "       [0.        , 0.        , 0.        , 0.53144047],\n",
    "       [0.        , 0.        , 0.        , 0.        ],\n",
    "       [0.        , 0.81      , 0.        , 0.65609999],\n",
    "       [0.        , 0.        , 0.        , 0.        ],\n",
    "       [0.        , 0.        , 0.        , 0.        ],\n",
    "       [0.        , 0.        , 0.81      , 0.        ],\n",
    "       [0.72899264, 0.9       , 0.        , 0.72899271],\n",
    "       [0.        , 0.        , 0.        , 0.        ],\n",
    "       [0.        , 0.        , 0.        , 0.        ],\n",
    "       [0.        , 0.        , 0.        , 0.72899788],\n",
    "       [0.65601433, 0.        , 1.        , 0.80919   ],\n",
    "       [0.        , 0.        , 0.        , 0.        ]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "##assign index to each state using state-matrix\n",
    "\n",
    "state_matrix = np.arange(0,16).reshape(4,4)\n",
    "state_matrix\n",
    "\n",
    "def rowsandcols(state):\n",
    "    ''' input: state returned by env\n",
    "    output: location of state as (row,col) tuple'''\n",
    "    return int(np.where(state_matrix ==state)[0]), int(np.where(state_matrix ==state)[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "def choose_random_action(state):\n",
    "    ''' input = state returned by env.\n",
    "      output = a random action depending on \n",
    "      whether the state is an edge/ corner or landlocked tile '''\n",
    "\n",
    "   ##corner points    \n",
    "    if rowsandcols(state)==( 0,0):\n",
    "        action = random.choice([1,2])\n",
    "    elif rowsandcols(state) ==(0,n_cols):\n",
    "        action = random.choice([0,1])\n",
    "    elif rowsandcols(state)==(n_rows,0):\n",
    "        action = random.choice([2,3])\n",
    "    elif rowsandcols(state)==(n_rows,n_cols):\n",
    "        action = random.choice([0,3])\n",
    "    ## no action needed to be chosen for (3,3) -- terminal state \n",
    "\n",
    "    ##edge tiles\n",
    "    elif rowsandcols(state)  == (0,1) or rowsandcols(state)  ==  (0,2):\n",
    "        action = random.choice([0,1,2])\n",
    "    elif rowsandcols(state) == (1,0) or rowsandcols(state)  == (2,0):\n",
    "        action = random.choice([1,2,3])\n",
    "    elif rowsandcols(state) == (1,3) or rowsandcols(state) == (2,3):\n",
    "        action = random.choice([0,1,3])\n",
    "    elif rowsandcols(state) == (3,1) or rowsandcols(state) == (3,2) :\n",
    "        action = random.choice([0,2,3])\n",
    "  \n",
    "    else:\n",
    "        action = env.action_space.sample()\n",
    "    \n",
    "    return action"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_episodes = 100000\n",
    "steps_total = [] # store number of steps taken in each episode\n",
    "rewards_total = [] #store reward obtained for each episode\n",
    "epsilon_total = [] #store epsilon obtained at the end of each episode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "for i_episode in range(num_episodes):\n",
    "    \n",
    "    # resets the environment\n",
    "    state = env.reset()\n",
    "    step = 0\n",
    "\n",
    "    while True:\n",
    "        \n",
    "        step += 1\n",
    "        \n",
    "        \n",
    "        ## generating a random num\n",
    "        random_for_epsilon = np.random.rand()\n",
    "        \n",
    "        ## if random num lesser or equal to epsilon, then select random action\n",
    "        if random_for_epsilon <= epsilon:     \n",
    "            action = choose_random_action(state)  ##replace with action = env.action_space.sample() for action without restrictions\n",
    "        \n",
    "        ## else update q-vals for given state with infitismly small random nums and select argmax(a) for Q(s,a)\n",
    "        else: \n",
    "            random_values = Q[state] + np.random.rand(1,n_actions)/100\n",
    "            action =  np.argmax(random_values)\n",
    "        \n",
    "        \n",
    "        ## as epsilon decays with more timesteps, the prob. of selecting a random val < e decays --> more likely to exploit.     \n",
    "        if epsilon > epsilon_final:\n",
    "            epsilon *= epsilon_decay\n",
    "        \n",
    "        ## env gives reward and next state and whether we've reached terminal state upon taking a at current state.. \n",
    "        new_state, reward, done, info = env.step(action)\n",
    "        ##if you want reward penalized at for each timestep\n",
    "        ## reward= reward*(0.9**step) \n",
    "\n",
    "        # filling the Q Table - \n",
    "        Q[state, action] = (1- learning_rate)*Q[state, action] + learning_rate*(reward + gamma * np.max(Q[new_state]))\n",
    "        \n",
    "        # Setting new state for next action\n",
    "        state = new_state\n",
    "        \n",
    "        # env.render()\n",
    "        \n",
    "        if done:\n",
    "            steps_total.append(step)\n",
    "            rewards_total.append(reward)\n",
    "            epsilon_total.append(epsilon)\n",
    "            if i_episode % 10 == 0:\n",
    "                print('Episode: {} Reward: {} Steps Taken: {}'.format(i_episode,reward, step, epsilon))\n",
    "            break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '4x4: restricted actions')"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.axhline(y=6, color='green', linestyle='-')\n",
    "plt.bar(np.arange(0, 100000), steps_total, \n",
    "        width = 0.4, color = 'blue')\n",
    "plt.xlabel('episode index')\n",
    "plt.ylabel('number of steps taken')\n",
    "plt.title('4x4: restricted actions')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00000000e+00, 4.78296165e-01, 6.56093411e-01, 0.00000000e+00],\n",
       "       [5.90271254e-01, 7.29000000e-01, 6.56100000e-01, 0.00000000e+00],\n",
       "       [6.56100000e-01, 7.29000000e-01, 5.90490000e-01, 0.00000000e+00],\n",
       "       [6.56100000e-01, 6.56100000e-01, 5.90490000e-01, 5.90490000e-01],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 5.31440470e-01],\n",
       "       [4.78296418e-01, 8.10000000e-01, 7.29000000e-01, 6.56099993e-01],\n",
       "       [0.00000000e+00, 8.10000000e-01, 0.00000000e+00, 6.56099990e-01],\n",
       "       [7.29000000e-01, 6.56100000e-01, 0.00000000e+00, 5.90490000e-01],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [0.00000000e+00, 9.00000000e-01, 7.28999993e-01, 7.28991404e-01],\n",
       "       [8.10000000e-01, 8.10000000e-01, 6.56099935e-01, 7.29000000e-01],\n",
       "       [7.29000000e-01, 0.00000000e+00, 0.00000000e+00, 6.56100000e-01],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "       [1.00000000e+00, 0.00000000e+00, 8.10000000e-01, 8.10000000e-01],\n",
       "       [9.00000000e-01, 0.00000000e+00, 1.00000000e-06, 7.28999259e-01],\n",
       "       [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00]])"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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