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deepmind-research/rl_unplugged/atari_dqn.ipynb
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Sergio Gomez 99aaa6930a Move to dopamine-rl version 3.1.2 in RL Unplugged 
PiperOrigin-RevId: 324071731
2020-08-03 09:16:16 +00:00

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "KDiJzbb8KFvP"
},
"source": [
"Copyright 2020 DeepMind Technologies Limited.\n",
"\n",
"Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use\n",
"this file except in compliance with the License. You may obtain a copy of the\n",
"License at\n",
"\n",
"[https://www.apache.org/licenses/LICENSE-2.0](https://www.apache.org/licenses/LICENSE-2.0)\n",
"\n",
"Unless required by applicable law or agreed to in writing, software distributed\n",
"under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR\n",
"CONDITIONS OF ANY KIND, either express or implied. See the License for the\n",
"specific language governing permissions and limitations under the License."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "ULdrhOaVbsdO"
},
"source": [
"# RL Unplugged: Offline DQN - Atari\n",
"## Guide to training an Acme DQN agent on Atari data.\n",
"# \u003ca href=\"https://colab.research.google.com/github/deepmind/deepmind_research/blob/master/rl_unplugged/atari_dqn.ipynb\" target=\"_parent\"\u003e\u003cimg src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/\u003e\u003c/a\u003e\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "xaJxoatMhJ71"
},
"source": [
"## Installation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "both",
"colab": {},
"colab_type": "code",
"id": "KH3O0zcXUeun"
},
"outputs": [],
"source": [
"!pip install dm-acme\n",
"!pip install dm-acme[reverb]\n",
"!pip install dm-acme[tf]\n",
"!pip install dm-sonnet\n",
"!pip install dopamine-rl==3.1.2\n",
"!pip install atari-py\n",
"!git clone https://github.com/deepmind/deepmind-research.git\n",
"%cd deepmind-research"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "c-H2d6UZi7Sf"
},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "both",
"colab": {},
"colab_type": "code",
"id": "HJ74Id-8MERq"
},
"outputs": [],
"source": [
"import copy\n",
"\n",
"import acme\n",
"from acme.agents.tf import actors\n",
"from acme.agents.tf.dqn import learning as dqn\n",
"from acme.tf import utils as acme_utils\n",
"from acme.utils import loggers\n",
"from rl_unplugged import atari\n",
"import sonnet as snt\n",
"import tensorflow as tf"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "JrOSnoWiY4Xl"
},
"source": [
"## Data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "Vi3_H_h1zy_0"
},
"outputs": [],
"source": [
"game = 'Pong' #@param\n",
"run = 1 #@param\n",
"\n",
"tmp_path = '/tmp/atari'\n",
"gs_path = 'gs://rl_unplugged/atari'\n",
"\n",
"!mkdir -p {tmp_path}/{game}\n",
"\n",
"src = f'{gs_path}/{game}/run_{run}-00000-of-00100'\n",
"dest = f'{tmp_path}/{game}/run_{run}-00000-of-00001'\n",
"!gsutil cp {src} {dest}"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "a9vF7LtYvLzy"
},
"source": [
"## Dataset and environment"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "01AHHNd9cEX2"
},
"outputs": [],
"source": [
"batch_size = 10 #@param\n",
"\n",
"def discard_extras(sample):\n",
" return sample._replace(data=sample.data[:5])\n",
"\n",
"dataset = atari.dataset(path=tmp_path, game='Pong', run=1, num_shards=1)\n",
"# Small batch size, experiments in the paper were run with batch size 256.\n",
"dataset = dataset.map(discard_extras).batch(batch_size)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "KoYBhjPtI_N6"
},
"source": [
""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "4b4_rHwCmQg-"
},
"outputs": [],
"source": [
"environment = atari.environment(game='Pong')"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "BukOfOsmtSQn"
},
"source": [
"## DQN learner"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"height": 34
},
"colab_type": "code",
"executionInfo": {
"elapsed": 83,
"status": "ok",
"timestamp": 1593614657342,
"user": {
"displayName": "",
"photoUrl": "",
"userId": ""
},
"user_tz": -60
},
"id": "3Jcjk1w6oHVX",
"outputId": "1746b0bb-5a5c-45dd-b5a1-c77852545e12"
},
"outputs": [
{
"data": {
"text/plain": [
"TensorSpec(shape=(6,), dtype=tf.float32, name=None)"
]
},
"execution_count": 20,
"metadata": {
"tags": []
},
"output_type": "execute_result"
}
],
"source": [
"# Get total number of actions.\n",
"num_actions = environment.action_spec().num_values\n",
"\n",
"# Create the Q network.\n",
"network = snt.Sequential([\n",
" lambda x: tf.image.convert_image_dtype(x, tf.float32),\n",
" snt.Conv2D(32, [8, 8], [4, 4]),\n",
" tf.nn.relu,\n",
" snt.Conv2D(64, [4, 4], [2, 2]),\n",
" tf.nn.relu,\n",
" snt.Conv2D(64, [3, 3], [1, 1]),\n",
" tf.nn.relu,\n",
" snt.Flatten(),\n",
" snt.nets.MLP([512, num_actions])\n",
"])\n",
"acme_utils.create_variables(network, [environment.observation_spec()])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "9CD2sNK-oA9S"
},
"outputs": [],
"source": [
"# Create a logger.\n",
"logger = loggers.TerminalLogger(label='learner', time_delta=1.)\n",
"\n",
"# Create the DQN learner.\n",
"learner = dqn.DQNLearner(\n",
" network=network,\n",
" target_network=copy.deepcopy(network),\n",
" discount=0.99,\n",
" learning_rate=3e-4,\n",
" importance_sampling_exponent=0.2,\n",
" target_update_period=2500,\n",
" dataset=dataset,\n",
" logger=logger)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "oKeGQxzitXYC"
},
"source": [
"## Training loop"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"height": 51
},
"colab_type": "code",
"executionInfo": {
"elapsed": 4694,
"status": "ok",
"timestamp": 1593614662237,
"user": {
"displayName": "",
"photoUrl": "",
"userId": ""
},
"user_tz": -60
},
"id": "VWZd5N-Qoz82",
"outputId": "5ee2ce7c-b3fe-483b-8893-5a6e13519f48"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Learner] Loss = 0.003 | Steps = 1 | Walltime = 0\n",
"[Learner] Loss = 0.004 | Steps = 54 | Walltime = 1.126\n"
]
}
],
"source": [
"for _ in range(100):\n",
" learner.step()"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "qFQDrp0CgIzU"
},
"source": [
"## Evaluation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"height": 102
},
"colab_type": "code",
"executionInfo": {
"elapsed": 15099,
"status": "ok",
"timestamp": 1593614677360,
"user": {
"displayName": "",
"photoUrl": "",
"userId": ""
},
"user_tz": -60
},
"id": "DWYHBalygIDF",
"outputId": "4ec412c3-810a-4208-b521-919a8ece40df"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[Evaluation] Episode Length = 842 | Episode Return = -20.000 | Episodes = 1 | Steps = 842 | Steps Per Second = 265.850\n",
"[Evaluation] Episode Length = 792 | Episode Return = -21.000 | Episodes = 2 | Steps = 1634 | Steps Per Second = 270.043\n",
"[Evaluation] Episode Length = 812 | Episode Return = -21.000 | Episodes = 3 | Steps = 2446 | Steps Per Second = 274.792\n",
"[Evaluation] Episode Length = 812 | Episode Return = -21.000 | Episodes = 4 | Steps = 3258 | Steps Per Second = 270.967\n",
"[Evaluation] Episode Length = 812 | Episode Return = -21.000 | Episodes = 5 | Steps = 4070 | Steps Per Second = 274.253\n"
]
}
],
"source": [
"# Create a logger.\n",
"logger = loggers.TerminalLogger(label='evaluation', time_delta=1.)\n",
"\n",
"# Create an environment loop.\n",
"policy_network = snt.Sequential([\n",
" network,\n",
" lambda q: tf.argmax(q, axis=-1),\n",
"])\n",
"loop = acme.EnvironmentLoop(\n",
" environment=environment,\n",
" actor=actors.FeedForwardActor(policy_network=policy_network),\n",
" logger=logger)\n",
"\n",
"loop.run(5)"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"last_runtime": {
"build_target": "//learning/deepmind/dm_python:dm_notebook3",
"kind": "private"
},
"name": "RL Unplugged: Offline DQN - Atari",
"provenance": [
{
"file_id": "1g9yTbTuk9aeERxWflOWqUGpx2M3osx0l",
"timestamp": 1593685504110
}
]
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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