365 lines
88 KiB
Plaintext
365 lines
88 KiB
Plaintext
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{
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"nbformat": 4,
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"nbformat_minor": 0,
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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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"name": "python3",
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"display_name": "Python 3"
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},
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"language_info": {
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"name": "python"
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}
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},
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"cells": [
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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": "AKoInktmr-3t"
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},
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"outputs": [],
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"source": [
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"# Remember to install tianshou first\n",
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"!pip install tianshou==0.4.8\n",
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"!pip install gym"
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]
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},
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{
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"cell_type": "markdown",
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"source": [
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"# Overview\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 specificly, 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=\"data:image/svg+xml;base64,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"</center>\n",
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"\n"
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],
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"metadata": {
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"id": "M98bqxdMsTXK"
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}
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},
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{
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"cell_type": "markdown",
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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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"metadata": {
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"id": "OX5cayLv4Ziu"
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}
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},
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{
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"cell_type": "markdown",
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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 initialise a Collector with an (vectorized) environment and a given policy (agent)."
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],
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"metadata": {
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"id": "Z6XKbj28u8Ze"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"import gym\n",
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"import numpy as np\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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"\n",
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"import warnings\n",
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"warnings.filterwarnings('ignore')\n",
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"\n",
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"env = gym.make(\"CartPole-v0\")\n",
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"test_envs = DummyVectorEnv([lambda: gym.make(\"CartPole-v0\") for _ in range(2)])\n",
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"\n",
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"# model\n",
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"net = Net(env.observation_space.shape, hidden_sizes=[16,])\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(actor, optim, dist_fn=torch.distributions.Categorical)\n",
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"test_collector = Collector(policy, test_envs)"
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],
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"metadata": {
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"id": "w8t9ubO7u69J"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [
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"Now we would like to collect 9 episodes of data to test how our initialised Policy performs."
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],
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"metadata": {
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"id": "wmt8vuwpzQdR"
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}
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},
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{
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"cell_type": "code",
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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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"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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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"{'n/ep': 9, 'n/st': 85, 'rews': array([ 9., 9., 10., 10., 9., 9., 10., 10., 9.]), 'lens': array([ 9, 9, 10, 10, 9, 9, 10, 10, 9]), 'idxs': array([0, 1, 0, 1, 0, 1, 0, 1, 1]), 'rew': 9.444444444444445, 'len': 9.444444444444445, 'rew_std': 0.49690399499995325, 'len_std': 0.49690399499995325}\n",
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"Rewards of 9 episodes are [ 9. 9. 10. 10. 9. 9. 10. 10. 9.]\n",
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"Average episode reward is 9.444444444444445.\n",
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"Average episode length is 9.444444444444445.\n"
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]
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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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"source": [
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"Now we wonder what is the performance of a random policy."
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],
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"metadata": {
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"id": "zX9AQY0M0R3C"
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}
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},
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{
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"cell_type": "code",
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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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"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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"execution_count": null,
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"{'n/ep': 9, 'n/st': 187, 'rews': array([13., 14., 11., 44., 35., 14., 19., 17., 20.]), 'lens': array([13, 14, 11, 44, 35, 14, 19, 17, 20]), 'idxs': array([1, 0, 0, 1, 0, 0, 1, 0, 1]), 'rew': 20.77777777777778, 'len': 20.77777777777778, 'rew_std': 10.580671872993257, 'len_std': 10.580671872993257}\n",
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"Rewards of 9 episodes are [13. 14. 11. 44. 35. 14. 19. 17. 20.]\n",
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"Average episode reward is 20.77777777777778.\n",
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"Average episode length is 20.77777777777778.\n"
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]
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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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"source": [
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"Seems that an initialised policy performs even worse than a random policy without any training."
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],
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"metadata": {
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"id": "sKQRTiG10ljU"
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}
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},
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{
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"cell_type": "markdown",
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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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"metadata": {
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"id": "8RKmHIoG1A1k"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"from tianshou.data import VectorReplayBuffer\n",
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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-v0\") 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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"metadata": {
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"id": "CB9XB9bF1YPC"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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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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],
|
||
|
|
"metadata": {
|
||
|
|
"id": "rWKDazA42IUQ"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"source": [
|
||
|
|
"print(len(replaybuffer))\n",
|
||
|
|
"collect_result = train_collector.collect(n_step=50)\n",
|
||
|
|
"print(len(replaybuffer))\n",
|
||
|
|
"print(collect_result)"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"colab": {
|
||
|
|
"base_uri": "https://localhost:8080/"
|
||
|
|
},
|
||
|
|
"id": "-fUtQOnM2Yi1",
|
||
|
|
"outputId": "dceee987-433e-4b75-ed9e-823c20a9e1c2"
|
||
|
|
},
|
||
|
|
"execution_count": null,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"output_type": "stream",
|
||
|
|
"name": "stdout",
|
||
|
|
"text": [
|
||
|
|
"0\n",
|
||
|
|
"52\n",
|
||
|
|
"{'n/ep': 4, 'n/st': 52, 'rews': array([ 8., 10., 10., 10.]), 'lens': array([ 8, 10, 10, 10]), 'idxs': array([25, 0, 50, 75]), 'rew': 9.5, 'len': 9.5, 'rew_std': 0.8660254037844386, 'len_std': 0.8660254037844386}\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"source": [
|
||
|
|
"for i in range(13):\n",
|
||
|
|
" print(i, replaybuffer.next(i))"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"colab": {
|
||
|
|
"base_uri": "https://localhost:8080/"
|
||
|
|
},
|
||
|
|
"id": "EWO4A7plefwM",
|
||
|
|
"outputId": "9a6f36d1-2b84-49b0-a03d-a8ebe8acadbf"
|
||
|
|
},
|
||
|
|
"execution_count": null,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"output_type": "stream",
|
||
|
|
"name": "stdout",
|
||
|
|
"text": [
|
||
|
|
"0 1\n",
|
||
|
|
"1 2\n",
|
||
|
|
"2 3\n",
|
||
|
|
"3 4\n",
|
||
|
|
"4 5\n",
|
||
|
|
"5 6\n",
|
||
|
|
"6 7\n",
|
||
|
|
"7 8\n",
|
||
|
|
"8 9\n",
|
||
|
|
"9 9\n",
|
||
|
|
"10 11\n",
|
||
|
|
"11 12\n",
|
||
|
|
"12 12\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"source": [
|
||
|
|
"replaybuffer.sample(10)"
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"colab": {
|
||
|
|
"base_uri": "https://localhost:8080/"
|
||
|
|
},
|
||
|
|
"id": "HW8PpWH9fLCo",
|
||
|
|
"outputId": "7ca70c50-23b9-4405-9e42-2e5771cd9c78"
|
||
|
|
},
|
||
|
|
"execution_count": null,
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"output_type": "execute_result",
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"(Batch(\n",
|
||
|
|
" obs: array([[-6.36541036e-02, -1.15297838e+00, 7.85138179e-02,\n",
|
||
|
|
" 1.77225357e+00],\n",
|
||
|
|
" [-1.05090645e-02, -3.71521519e-01, -2.96323181e-03,\n",
|
||
|
|
" 5.76793524e-01],\n",
|
||
|
|
" [-2.45282997e-02, 4.77850180e-02, 2.21142716e-02,\n",
|
||
|
|
" 4.96743371e-02],\n",
|
||
|
|
" [ 1.68433453e-03, 4.47272356e-02, -1.72360346e-02,\n",
|
||
|
|
" -7.74977680e-03],\n",
|
||
|
|
" [ 4.29854159e-02, -3.95380051e-01, 3.91005958e-02,\n",
|
||
|
|
" 6.41183774e-01],\n",
|
||
|
|
" [ 3.50778149e-02, -5.91024637e-01, 5.19242712e-02,\n",
|
||
|
|
" 9.45918993e-01],\n",
|
||
|
|
" [-1.19358173e-01, -1.38179912e+00, 1.04318690e-01,\n",
|
||
|
|
" 2.10185768e+00],\n",
|
||
|
|
" [-3.48454722e-02, 1.61145106e-03, -2.49944951e-02,\n",
|
||
|
|
" -3.82213155e-02],\n",
|
||
|
|
" [-3.48454722e-02, 1.61145106e-03, -2.49944951e-02,\n",
|
||
|
|
" -3.82213155e-02],\n",
|
||
|
|
" [-4.64338716e-02, -5.82710950e-01, -1.02110827e-02,\n",
|
||
|
|
" 8.16967413e-01]]),\n",
|
||
|
|
" act: array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0]),\n",
|
||
|
|
" rew: array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]),\n",
|
||
|
|
" done: array([False, False, False, False, False, False, False, False, False,\n",
|
||
|
|
" False]),\n",
|
||
|
|
" obs_next: array([[-0.08671367, -1.34889318, 0.11395889, 2.08827982],\n",
|
||
|
|
" [-0.01793949, -0.56660181, 0.00857264, 0.86854149],\n",
|
||
|
|
" [-0.0235726 , -0.14764694, 0.02310776, 0.34925166],\n",
|
||
|
|
" [ 0.00257888, -0.15014334, -0.01739103, 0.27944553],\n",
|
||
|
|
" [ 0.03507781, -0.59102464, 0.05192427, 0.94591899],\n",
|
||
|
|
" [ 0.02325732, -0.78680603, 0.07084265, 1.25445415],\n",
|
||
|
|
" [-0.14699416, -1.57780202, 0.14635584, 2.42487783],\n",
|
||
|
|
" [-0.03481324, -0.19314333, -0.02575892, 0.24647199],\n",
|
||
|
|
" [-0.03481324, -0.19314333, -0.02575892, 0.24647199],\n",
|
||
|
|
" [-0.05808809, -0.77769163, 0.00612827, 1.10642118]]),\n",
|
||
|
|
" info: Batch(\n",
|
||
|
|
" env_id: array([0, 0, 0, 1, 1, 1, 2, 3, 3, 3]),\n",
|
||
|
|
" ),\n",
|
||
|
|
" policy: Batch(),\n",
|
||
|
|
" ), array([ 6, 2, 10, 33, 27, 28, 57, 75, 75, 78]))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"execution_count": 10
|
||
|
|
}
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"source": [
|
||
|
|
"# Further Reading\n",
|
||
|
|
"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."
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"id": "8NP7lOBU3-VS"
|
||
|
|
}
|
||
|
|
}
|
||
|
|
]
|
||
|
|
}
|