288 lines
7.3 KiB
Plaintext
288 lines
7.3 KiB
Plaintext
{
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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": "M98bqxdMsTXK"
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},
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"source": [
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"# Collector\n",
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"From its literal meaning, we can easily know that the Collector in Tianshou is used to collect training data. More specifically, the Collector controls the interaction between Policy (agent) and the environment. It also helps save the interaction data into the ReplayBuffer and returns episode statistics.\n",
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"\n",
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"<center>\n",
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"<img src=../_static/images/structure.svg></img>\n",
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"</center>\n",
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"\n"
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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": "OX5cayLv4Ziu"
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},
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"source": [
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"## Usages\n",
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"Collector can be used both for training (data collecting) and evaluation in Tianshou."
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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": "Z6XKbj28u8Ze"
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},
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"source": [
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"### Policy evaluation\n",
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"We need to evaluate our trained policy from time to time in DRL experiments. Collector can help us with this.\n",
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"\n",
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"First we have to initialize a Collector with an (vectorized) environment and a given policy (agent)."
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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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"editable": true,
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"id": "w8t9ubO7u69J",
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"slideshow": {
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"slide_type": ""
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},
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"tags": [
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"hide-cell",
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"remove-output"
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]
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},
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"outputs": [],
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"source": [
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"import gymnasium as gym\n",
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"import torch\n",
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"\n",
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"from tianshou.data import Collector\n",
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"from tianshou.env import DummyVectorEnv\n",
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"from tianshou.policy import PGPolicy\n",
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"from tianshou.utils.net.common import Net\n",
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"from tianshou.utils.net.discrete import Actor\n",
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"from tianshou.data import VectorReplayBuffer"
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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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"env = gym.make(\"CartPole-v1\")\n",
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"test_envs = DummyVectorEnv([lambda: gym.make(\"CartPole-v1\") for _ in range(2)])\n",
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"\n",
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"# model\n",
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"net = Net(\n",
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" env.observation_space.shape,\n",
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" hidden_sizes=[\n",
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" 16,\n",
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" ],\n",
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")\n",
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"actor = Actor(net, env.action_space.shape)\n",
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"optim = torch.optim.Adam(actor.parameters(), lr=0.0003)\n",
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"\n",
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"policy = PGPolicy(\n",
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" actor=actor,\n",
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" optim=optim,\n",
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" dist_fn=torch.distributions.Categorical,\n",
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" action_space=env.action_space,\n",
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" action_scaling=False,\n",
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")\n",
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"test_collector = Collector(policy, test_envs)"
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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": "wmt8vuwpzQdR"
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},
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"source": [
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"Now we would like to collect 9 episodes of data to test how our initialized Policy performs."
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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": "9SuT6MClyjyH",
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"outputId": "1e48f13b-c1fe-4fc2-ca1b-669485efdcae"
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},
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"outputs": [],
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"source": [
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"collect_result = test_collector.collect(n_episode=9)\n",
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"print(collect_result)\n",
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"print(\"Rewards of 9 episodes are {}\".format(collect_result[\"rews\"]))\n",
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"print(\"Average episode reward is {}.\".format(collect_result[\"rew\"]))\n",
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"print(\"Average episode length is {}.\".format(collect_result[\"len\"]))"
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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": "zX9AQY0M0R3C"
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},
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"source": [
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"Now we wonder what is the performance of a random policy."
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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": "UEcs8P8P0RLt",
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"outputId": "85f02f9d-b79b-48b2-99c6-36a1602f0884"
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},
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"outputs": [],
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"source": [
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"# Reset the collector\n",
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"test_collector.reset()\n",
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"collect_result = test_collector.collect(n_episode=9, random=True)\n",
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"print(collect_result)\n",
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"print(\"Rewards of 9 episodes are {}\".format(collect_result[\"rews\"]))\n",
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"print(\"Average episode reward is {}.\".format(collect_result[\"rew\"]))\n",
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"print(\"Average episode length is {}.\".format(collect_result[\"len\"]))"
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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": "sKQRTiG10ljU"
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},
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"source": [
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"Seems that an initialized policy performs even worse than a random policy without any training."
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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": "8RKmHIoG1A1k"
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},
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"source": [
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"### Data Collecting\n",
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"Data collecting is mostly used during training, when we need to store the collected data in a ReplayBuffer."
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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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"editable": true,
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"id": "CB9XB9bF1YPC",
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"slideshow": {
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"slide_type": ""
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"train_env_num = 4\n",
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"buffer_size = 100\n",
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"train_envs = DummyVectorEnv([lambda: gym.make(\"CartPole-v1\") for _ in range(train_env_num)])\n",
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"replaybuffer = VectorReplayBuffer(buffer_size, train_env_num)\n",
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"\n",
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"train_collector = Collector(policy, train_envs, replaybuffer)"
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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": "rWKDazA42IUQ"
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},
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"source": [
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"Now we can collect 50 steps of data, which will be automatically saved in the replay buffer. You can still choose to collect a certain number of episodes rather than steps. Try it yourself."
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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": "-fUtQOnM2Yi1",
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"outputId": "dceee987-433e-4b75-ed9e-823c20a9e1c2"
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},
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"outputs": [],
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"source": [
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"print(len(replaybuffer))\n",
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"collect_result = train_collector.collect(n_step=50)\n",
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"print(len(replaybuffer))\n",
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"print(collect_result)"
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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": "EWO4A7plefwM",
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"outputId": "9a6f36d1-2b84-49b0-a03d-a8ebe8acadbf"
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},
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"outputs": [],
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"source": [
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"for i in range(13):\n",
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" print(i, replaybuffer.next(i))"
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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": "HW8PpWH9fLCo",
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"outputId": "7ca70c50-23b9-4405-9e42-2e5771cd9c78"
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},
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"outputs": [],
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"source": [
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"replaybuffer.sample(10)"
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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": "8NP7lOBU3-VS"
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},
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"source": [
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"## Further Reading\n",
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"The above collector actually collects 52 data at a time because 52 % 4 = 0. There is one asynchronous collector which allows you collect exactly 50 steps. Check the [documentation](https://tianshou.readthedocs.io/en/master/api/tianshou.data.html#asynccollector) for details."
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]
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}
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],
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"metadata": {
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"colab": {
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"provenance": []
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},
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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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": "3.11.5"
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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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