7.1_ReinforcementLearning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7.1 Reinforcement Learning in Sports\n",
    "\n",
    "Module: Reinforcement Learning\n",
    "\n",
    "For book, references and training materials, please check this project website [http://activefitness.ai/ai-in-sports-with-python](http://activefitness.ai/ai-in-sports-with-python).\n",
    "\n",
    "Reference: Chapter 10, [Applied Machine Learning for Health and Fitness](https://www.apress.com/us/book/9781484257715)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Box(1,)\n",
      "Box(2,)\n"
     ]
    }
   ],
   "source": [
    "import gym\n",
    "env = gym.make('MountainCarContinuous-v0')\n",
    "print(env.action_space)\n",
    "print(env.observation_space)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 2700x2700 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from utils.gym import gymplot\n",
    "\n",
    "env.reset()\n",
    "img = gymplot.plot_init(env)\n",
    "for _ in range(100):\n",
    "    gymplot.plot_next(img, env)\n",
    "    action = env.action_space.sample()\n",
    "    env.step(action)\n",
    "env.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "P3.6 (tf11)",
   "language": "python",
   "name": "tf11"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}