gym_q_learning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "#from IPython.display import clear_output\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "!python3 -m pip install --upgrade Pillow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gym\n",
    "import gym_robotmimic\n",
    "\n",
    "env = gym.make(\"RobotMimic-v0\")\n",
    "env.render()\n",
    "env.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "q_table = np.zeros([37, env.action_space.n])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hyperparameters\n",
    "alpha = 0.1\n",
    "gamma = 0.6\n",
    "epsilon = 0.1\n",
    "\n",
    "# For plotting metrics\n",
    "all_epochs = []\n",
    "all_penalties = []\n",
    "\n",
    "for i in range(1, 100001):\n",
    "    state = env.reset()\n",
    "\n",
    "    epochs, penalties, reward, = 0, 0, 0\n",
    "    done = False\n",
    "    \n",
    "    for j in range(1, 251):\n",
    "        if random.uniform(0, 1) < epsilon:\n",
    "            action = env.action_space.sample() # Explore action space\n",
    "            print(\"1:{}\".format(action))\n",
    "        else:\n",
    "            action = np.argmax(q_table[state]) # Exploit learned values\n",
    "            print(\"2:{}\".format(action))\n",
    "\n",
    "        next_state, reward, done, info = env.step(action) \n",
    "        env.render()\n",
    "        \n",
    "        old_value = q_table[state, action]\n",
    "        next_max = np.max(q_table[next_state])\n",
    "        \n",
    "        new_value = (1 - alpha) * old_value + alpha * (reward + gamma * next_max)\n",
    "        q_table[state, action] = new_value\n",
    "\n",
    "\n",
    "        state = next_state\n",
    "        epochs += 1\n",
    "        \n",
    "    if i % 100 == 0:\n",
    "        clear_output(wait=True)\n",
    "        print(f\"Episode: {i}\")\n",
    "\n",
    "print(\"Training finished.\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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