mirror of
https://github.com/labmlai/annotated_deep_learning_paper_implementations.git
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rl colab notebooks
This commit is contained in:
Varuna Jayasiri
243
labml_nn/rl/dqn/experiment.ipynb
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243
labml_nn/rl/dqn/experiment.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "AYV_dMVDxyc2"
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},
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"source": [
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"[](https://github.com/labmlai/annotated_deep_learning_paper_implementations)\n",
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"[](https://colab.research.google.com/github/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/rl/dqn/experiment.ipynb) \n",
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"\n",
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"## Deep Q Networks (DQN)\n",
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"\n",
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"This is an experiment training an agent to play Atari Breakout game using Deep Q Networks (DQN)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "AahG_i2y5tY9"
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},
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"source": [
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"Install the `labml-nn` package"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "ZCzmCrAIVg0L",
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"outputId": "6c416266-1e99-4e60-a665-06ff9fba22a6"
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},
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"outputs": [],
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"source": [
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"!pip install labml-nn"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "3-G5kplRFmsO"
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},
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"source": [
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"Add Atari ROMs (Doesn't work without this in Google Colab)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "SByhklD1FlSj",
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"outputId": "74075a5e-ec1c-43dc-8859-8f7c3b3b8402"
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},
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"outputs": [],
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"source": [
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"! wget http://www.atarimania.com/roms/Roms.rar\n",
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"! mkdir /content/ROM/\n",
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"! unrar e /content/Roms.rar /content/ROM/\n",
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"! python -m atari_py.import_roms /content/ROM/"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "SE2VUQ6L5zxI"
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},
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"source": [
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"Imports"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "0hJXx_g0wS2C"
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},
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"outputs": [],
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"source": [
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"from labml import experiment\n",
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"from labml.configs import FloatDynamicHyperParam\n",
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"from labml_nn.rl.dqn.experiment import Trainer"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "Lpggo0wM6qb-"
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},
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"source": [
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"Create an experiment"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "bFcr9k-l4cAg"
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},
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"outputs": [],
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"source": [
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"experiment.create(name=\"dqn\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "Hw6uVl1_GaPv"
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},
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"source": [
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"### Configurations\n",
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"\n",
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"`FloatDynamicHyperParam` is a dynamic hyper-parameter\n",
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"that you can change while the experiment is running."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 17
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},
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"id": "L8bUtLD6GksC",
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"outputId": "c7d4efe7-490e-4153-e691-ca31df1e1275"
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},
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"outputs": [],
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"source": [
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"configs = {\n",
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" # Number of updates\n",
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" 'updates': 1_000_000,\n",
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" # Number of epochs to train the model with sampled data.\n",
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" 'epochs': 8,\n",
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" # Number of worker processes\n",
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" 'n_workers': 8,\n",
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" # Number of steps to run on each process for a single update\n",
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" 'worker_steps': 4,\n",
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" # Mini batch size\n",
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" 'mini_batch_size': 32,\n",
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" # Target model updating interval\n",
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" 'update_target_model': 250,\n",
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" # Learning rate.\n",
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" 'learning_rate': FloatDynamicHyperParam(2.5e-4, (0, 1e-3)),\n",
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"}"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Set experiment configurations"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"experiment.configs(configs)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "qYQCFt_JYsjd"
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},
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"source": [
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"Create trainer"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "8LB7XVViYuPG"
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},
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"outputs": [],
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"source": [
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"trainer = Trainer(**configs)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "KJZRf8527GxL"
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},
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"source": [
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"Start the experiment and run the training loop."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 520
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},
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"id": "aIAWo7Fw5DR8",
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"outputId": "f2bca844-662d-4bfb-a295-d8529f538eaa"
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},
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"outputs": [],
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"source": [
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"with experiment.start():\n",
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" trainer.run_training_loop()"
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]
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}
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],
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"metadata": {
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"accelerator": "GPU",
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"colab": {
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"collapsed_sections": [],
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"name": "Deep Q Networks (DQN)",
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"provenance": []
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},
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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@ -38,6 +38,25 @@
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"!pip install labml-nn"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Add Atari ROMs (Doesn't work without this in Google Colab)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"! wget http://www.atarimania.com/roms/Roms.rar\n",
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"! mkdir /content/ROM/\n",
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"! unrar e /content/Roms.rar /content/ROM/\n",
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"! python -m atari_py.import_roms /content/ROM/"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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@ -164,17 +183,7 @@
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},
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"outputs": [],
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"source": [
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"trainer = Trainer(\n",
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" updates=configs['updates'],\n",
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" epochs=configs['epochs'],\n",
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" n_workers=configs['n_workers'],\n",
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" worker_steps=configs['worker_steps'],\n",
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" batches=configs['batches'],\n",
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" value_loss_coef=configs['value_loss_coef'],\n",
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" entropy_bonus_coef=configs['entropy_bonus_coef'],\n",
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" clip_range=configs['clip_range'],\n",
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" learning_rate=configs['learning_rate'],\n",
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")"
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"trainer = Trainer(**configs)"
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]
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},
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{
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@ -221,9 +230,9 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.5"
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"version": "3.7.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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}
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