annotated_deep_learning_paper_implementations/labml_nn/rl/ppo/experiment.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AYV_dMVDxyc2"
   },
   "source": [
    "[![Github](https://img.shields.io/github/stars/labmlai/annotated_deep_learning_paper_implementations?style=social)](https://github.com/labmlai/annotated_deep_learning_paper_implementations)\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/rl/ppo/experiment.ipynb)                    \n",
    "\n",
    "## Proximal Policy Optimization - PPO\n",
    "\n",
    "This is an experiment training an agent to play Atari Breakout game using  Proximal Policy Optimization - PPO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "AahG_i2y5tY9"
   },
   "source": [
    "Install the `labml-nn` package"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZCzmCrAIVg0L",
    "outputId": "028e759e-0c9f-472e-b4b8-fdcf3e4604ee"
   },
   "source": [
    "!pip install labml-nn"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add Atari ROMs (Doesn't work without this in Google Colab)"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "source": [
    "! wget http://www.atarimania.com/roms/Roms.rar\n",
    "! mkdir /content/ROM/\n",
    "! unrar e /content/Roms.rar /content/ROM/\n",
    "! python -m atari_py.import_roms /content/ROM/"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SE2VUQ6L5zxI"
   },
   "source": [
    "Imports"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "0hJXx_g0wS2C"
   },
   "source": [
    "from labml import experiment\n",
    "from labml.configs import FloatDynamicHyperParam, IntDynamicHyperParam\n",
    "from labml_nn.rl.ppo.experiment import Trainer"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Lpggo0wM6qb-"
   },
   "source": [
    "Create an experiment"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "bFcr9k-l4cAg"
   },
   "source": [
    "experiment.create(name=\"ppo\")"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-OnHLi626tJt"
   },
   "source": [
    "### Configurations\n",
    "\n",
    "`IntDynamicHyperParam` and `FloatDynamicHyperParam` are dynamic hyper parameters\n",
    "that you can change while the experiment is running."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "Piz0c5f44hRo"
   },
   "source": [
    "configs = {\n",
    "    # number of updates\n",
    "    'updates': 10000,\n",
    "    # number of epochs to train the model with sampled data\n",
    "    'epochs': IntDynamicHyperParam(8),\n",
    "    # number of worker processes\n",
    "    'n_workers': 8,\n",
    "    # number of steps to run on each process for a single update\n",
    "    'worker_steps': 128,\n",
    "    # number of mini batches\n",
    "    'batches': 4,\n",
    "    # Value loss coefficient\n",
    "    'value_loss_coef': FloatDynamicHyperParam(0.5),\n",
    "    # Entropy bonus coefficient\n",
    "    'entropy_bonus_coef': FloatDynamicHyperParam(0.01),\n",
    "    # Clip range\n",
    "    'clip_range': FloatDynamicHyperParam(0.1),\n",
    "    # Learning rate\n",
    "    'learning_rate': FloatDynamicHyperParam(2.5e-4, (0, 1e-3)),\n",
    "}"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wwMzCqpD6vkL"
   },
   "source": [
    "Set experiment configurations"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 17
    },
    "id": "e6hmQhTw4nks",
    "outputId": "0e978879-5dcd-4140-ec53-24a3fbd547de"
   },
   "source": [
    "experiment.configs(configs)"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qYQCFt_JYsjd"
   },
   "source": [
    "Create trainer"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "8LB7XVViYuPG"
   },
   "source": [
    "trainer = Trainer(**configs)"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KJZRf8527GxL"
   },
   "source": [
    "Start the experiment and run the training loop."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "aIAWo7Fw5DR8"
   },
   "source": [
    "with experiment.start():\n",
    "    trainer.run_training_loop()"
   ],
   "outputs": [],
   "execution_count": null
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "Proximal Policy Optimization - PPO",
   "provenance": []
  },
  "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": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}