342 lines
8.9 KiB
Plaintext
342 lines
8.9 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": "_UaXOSRjDUF9"
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},
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"source": [
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"# Experiment\n",
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"Finally, we can assemble building blocks that we have came across in previous tutorials to conduct our first DRL experiment. In this experiment, we will use [PPO](https://arxiv.org/abs/1707.06347) algorithm to solve the classic CartPole task in Gym."
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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": "2QRbCJvDHNAd"
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},
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"source": [
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"## Experiment\n",
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"To conduct this experiment, we need the following building blocks.\n",
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"\n",
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"\n",
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"* Two vectorized environments, one for training and one for evaluation\n",
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"* A PPO agent\n",
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"* A replay buffer to store transition data\n",
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"* Two collectors to manage the data collecting process, one for training and one for evaluation\n",
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"* A trainer to manage the training loop\n",
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"\n",
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"<div align=center>\n",
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"<img src=\"https://tianshou.readthedocs.io/en/master/_images/pipeline.png\">\n",
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"\n",
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"</div>\n",
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"\n",
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"Let us do this step by step."
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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": "-Hh4E6i0Hj0I"
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},
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"source": [
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"## Preparation\n",
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"Firstly, install Tianshou if you haven't installed it before."
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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": "7E4EhiBeHxD5"
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},
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"source": [
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"Import libraries we might need later."
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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": "ao9gWJDiHgG-",
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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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"%%capture\n",
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"\n",
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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, VectorReplayBuffer\n",
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"from tianshou.env import DummyVectorEnv\n",
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"from tianshou.policy import PPOPolicy\n",
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"from tianshou.trainer import OnpolicyTrainer\n",
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"from tianshou.utils.net.common import ActorCritic, Net\n",
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"from tianshou.utils.net.discrete import Actor, Critic\n",
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"\n",
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"device = \"cuda\" if torch.cuda.is_available() else \"cpu\""
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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": "QnRg5y7THRYw"
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},
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"source": [
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"## Environment"
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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": "YZERKCGtH8W1"
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},
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"source": [
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"We create two vectorized environments both for training and testing. Since the execution time of CartPole is extremely short, there is no need to use multi-process wrappers and we simply use DummyVectorEnv."
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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": "Mpuj5PFnDKVS"
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},
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"outputs": [],
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"source": [
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"env = gym.make(\"CartPole-v1\")\n",
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"train_envs = DummyVectorEnv([lambda: gym.make(\"CartPole-v1\") for _ in range(20)])\n",
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"test_envs = DummyVectorEnv([lambda: gym.make(\"CartPole-v1\") for _ in range(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": "BJtt_Ya8DTAh"
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},
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"source": [
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"## Policy\n",
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"Next we need to initialize our PPO policy. PPO is an actor-critic-style on-policy algorithm, so we have to define the actor and the critic in PPO first.\n",
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"\n",
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"The actor is a neural network that shares the same network head with the critic. Both networks' input is the environment observation. The output of the actor is the action and the output of the critic is a single value, representing the value of the current policy.\n",
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"\n",
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"Luckily, Tianshou already provides basic network modules that we can use in this 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": "_Vy8uPWXP4m_"
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},
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"outputs": [],
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"source": [
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"# net is the shared head of the actor and the critic\n",
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"assert env.observation_space.shape is not None # for mypy\n",
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"assert isinstance(env.action_space, gym.spaces.Discrete) # for mypy\n",
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"net = Net(state_shape=env.observation_space.shape, hidden_sizes=[64, 64], device=device)\n",
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"actor = Actor(preprocess_net=net, action_shape=env.action_space.n, device=device).to(device)\n",
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"critic = Critic(preprocess_net=net, device=device).to(device)\n",
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"actor_critic = ActorCritic(actor=actor, critic=critic)\n",
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"\n",
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"# optimizer of the actor and the critic\n",
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"optim = torch.optim.Adam(actor_critic.parameters(), lr=0.0003)"
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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": "Lh2-hwE5Dn9I"
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},
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"source": [
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"Once we have defined the actor, the critic and the optimizer, we can use them to construct our PPO agent. CartPole is a discrete action space problem, so the distribution of our action space can be a categorical distribution."
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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": "OiJ2GkT0Qnbr"
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},
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"outputs": [],
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"source": [
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"dist = torch.distributions.Categorical\n",
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"policy: PPOPolicy = PPOPolicy(\n",
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" actor=actor,\n",
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" critic=critic,\n",
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" optim=optim,\n",
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" dist_fn=dist,\n",
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" action_space=env.action_space,\n",
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" deterministic_eval=True,\n",
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" action_scaling=False,\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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"id": "okxfj6IEQ-r8"
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},
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"source": [
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"`deterministic_eval=True` means that we want to sample actions during training but we would like to always use the best action in evaluation. No randomness included."
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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": "n5XAAbuBZarO"
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},
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"source": [
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"## Collector\n",
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"We can set up the collectors now. Train collector is used to collect and store training data, so an additional replay buffer has to be passed in."
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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": "ezwz0qerZhQM"
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},
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"outputs": [],
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"source": [
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"train_collector = Collector(\n",
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" policy=policy,\n",
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" env=train_envs,\n",
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" buffer=VectorReplayBuffer(20000, len(train_envs)),\n",
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")\n",
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"test_collector = Collector(policy=policy, env=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": "ZaoPxOd2hm0b"
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},
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"source": [
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"We use `VectorReplayBuffer` here because it's more efficient to collaborate with vectorized environments, you can simply consider `VectorReplayBuffer` as a a list of ordinary replay buffers."
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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": "qBoE9pLUiC-8"
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},
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"source": [
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"## Trainer\n",
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"Finally, we can use the trainer to help us set up 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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},
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"editable": true,
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"id": "i45EDnpxQ8gu",
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"outputId": "b1666b88-0bfa-4340-868e-58611872d988",
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"tags": [
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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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"result = OnpolicyTrainer(\n",
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" policy=policy,\n",
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" train_collector=train_collector,\n",
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" test_collector=test_collector,\n",
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" max_epoch=10,\n",
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" step_per_epoch=50000,\n",
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" repeat_per_collect=10,\n",
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" episode_per_test=10,\n",
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" batch_size=256,\n",
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" step_per_collect=2000,\n",
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" stop_fn=lambda mean_reward: mean_reward >= 195,\n",
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").run()"
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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": "ckgINHE2iTFR"
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},
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"source": [
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"## Results\n",
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"Print the training 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": "tJCPgmiyiaaX",
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"outputId": "40123ae3-3365-4782-9563-46c43812f10f",
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"tags": []
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},
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"outputs": [],
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"source": [
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"result.pprint_asdict()"
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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": "A-MJ9avMibxN"
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},
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"source": [
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"We can also test our trained 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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"colab": {
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"base_uri": "https://localhost:8080/"
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},
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"id": "mnMANFcciiAQ",
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"outputId": "6febcc1e-7265-4a75-c9dd-34e29a3e5d21"
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},
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"outputs": [],
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"source": [
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"# Let's watch its performance!\n",
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"policy.eval()\n",
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"result = test_collector.collect(n_episode=1, render=False)\n",
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"print(f\"Final episode reward: {result.returns.mean()}, length: {result.lens.mean()}\")"
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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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"toc_visible": true
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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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