Untitled.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bf7a6910-341e-4528-8b44-ced88b5940d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import time\n",
    "\n",
    "from tensorflow.python.keras import Sequential\n",
    "from tensorflow.python.keras.layers import Dense\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "\n",
    "from DQN import Agent\n",
    "import gym\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "528dc988-1ae3-45f9-9190-a1d0c3a1ea0a",
   "metadata": {},
   "source": [
    "### Load the lunar lander environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6547e48e-ccec-4772-b872-03d64ed53dca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of elements in state space vector :  8\n",
      "Number of actions there :  4\n"
     ]
    }
   ],
   "source": [
    "env = gym.make(\"LunarLander-v2\")\n",
    "print(\"Number of elements in state space vector : \", env.observation_space.shape[0])\n",
    "print(\"Number of actions there : \", env.action_space.n)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "058042fa-cfeb-4479-9382-7def30517253",
   "metadata": {},
   "source": [
    "In Lunar environment state space vector contains 8 elements, they are :\n",
    "* 0 - Lander horizontal coordinate\n",
    "* 1 - Lander vertical coordinate\n",
    "* 2 - Lander horizontal speed\n",
    "* 3 - Lander vertical speed\n",
    "* 4 - Lander angle\n",
    "* 5 - Lander angular speed\n",
    "* 6 - Bool: 1 if first leg has contact, else 0\n",
    "* 7 - Bool: 1 if second leg has contact, else 0\n",
    "\n",
    "There are four actions avaialbe in lunar lander environment, they are:\n",
    "* 0 - No action\n",
    "* 1 - Fire left engine\n",
    "* 2 - Fire main engine\n",
    "* 3 - Fire right engine"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9eb95c9-8c60-4c30-8d79-69f3ee152843",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Define the model parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ebf43eaa-bda8-4416-b311-d7b7ad9e6779",
   "metadata": {},
   "outputs": [],
   "source": [
    "discount_factor = 0.99\n",
    "learning_rate = 0.00075\n",
    "number_of_epochs = 500\n",
    "mini_batch_size = 32\n",
    "initial_epsilon_value = 1.0\n",
    "epsilon_decay_value = 0.001\n",
    "min_epsilon_value = 0.1\n",
    "copy_rate = 120 # copy the main network to target network after every update rate number of iterations\n",
    "number_of_actions = env.action_space.n\n",
    "input_dim = env.observation_space.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0085ccd7-9c36-41a9-9f9f-e4429127eb4e",
   "metadata": {},
   "source": [
    "Now let's create agent and train the DQN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4e7c89ca-ef42-48bc-a8da-dc9b87d173e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-12-17 12:17:47.704139: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set\n",
      "2021-12-17 12:17:47.704402: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "dqn_agent = Agent(lr=learning_rate, discount_factor=discount_factor, \n",
    "                  num_actions=number_of_actions, epsilon=initial_epsilon_value, \n",
    "                  batch_size=mini_batch_size, input_dims=input_dim, \n",
    "                  update_rate=copy_rate, epsilon_decay=epsilon_decay_value, epsilon_final=min_epsilon_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bd5799c3-1356-4ace-9f4e-0923e7e9950c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-12-16 18:31:14.876126: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Episode 0/5000, Score: -322.7501496124823, AVG Score: -322.7501496124823\n",
      "Episode 1/5000, Score: -296.92122887610714, AVG Score: -309.8356892442947\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-12-16 19:11:31.018991: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: saved_networks/dqn_model0/assets\n",
      "Network saved\n",
      "CPU times: user 40min 38s, sys: 2min 38s, total: 43min 16s\n",
      "Wall time: 40min 16s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "episodes, scores, avg_scores, obj = dqn_agent.train_model(env, 5000, graph=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fc0ea264-1ebc-41e9-a035-511be1e93758",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "df = pd.DataFrame({'x': episodes, 'Score': scores, 'Average Score': avg_scores, 'Solved Requirement': obj})\n",
    "\n",
    "plt.plot('x', 'Score', data=df, marker='', color='blue', linewidth=2, label='Score')\n",
    "plt.plot('x', 'Average Score', data=df, marker='', color='orange', linewidth=2, linestyle='dashed',\n",
    "         label='AverageScore')\n",
    "plt.plot('x', 'Solved Requirement', data=df, marker='', color='red', linewidth=2, linestyle='dashed',\n",
    "         label='Solved Requirement')\n",
    "plt.legend()\n",
    "plt.savefig('LunarLander_Train.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ed19e8a4-58f1-4dd8-bfae-8426728e22ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fcefec6c110>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(scores)\n",
    "plt.plot(avg_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4bb9c2c9-3841-4066-9482-ec38d76ff649",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-12-17 12:21:24.032 python[28238:3917919] Warning: Expected min height of view: (<NSPopoverTouchBarItemButton: 0x7fefd86c4570>) to be less than or equal to 30 but got a height of 32.000000. This error will be logged once per view in violation.\n",
      "2021-12-17 12:21:24.032 python[28238:3917919] Warning: Expected min height of view: (<NSButton: 0x7fefd86c92f0>) to be less than or equal to 30 but got a height of 32.000000. This error will be logged once per view in violation.\n",
      "2021-12-17 12:21:24.035 python[28238:3917919] Warning: Expected min height of view: (<NSPopoverTouchBarItemButton: 0x7fefd86c95a0>) to be less than or equal to 30 but got a height of 32.000000. This error will be logged once per view in violation.\n",
      "2021-12-17 12:21:24.037 python[28238:3917919] Warning: Expected min height of view: (<NSPopoverTouchBarItemButton: 0x7fefd9808310>) to be less than or equal to 30 but got a height of 32.000000. This error will be logged once per view in violation.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "file_type = 'tf'\n",
    "file = 'saved_networks/dqn_model0'\n",
    "dqn_agent.test(env, 10, file_type, file, True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e77caa37-07a0-4b4a-8065-e8f02deace7d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}