原始文本：
huggingface.co/learn/deep-rl-course/unit8/hands-on-cleanrl
现在我们已经研究了 PPO 背后的理论，理解它的最佳方法是从头开始实现它。
从头开始实现架构是理解它的最佳方法，也是一个好习惯。我们已经为基于价值的 Q 学习方法和基于策略的 Reinforce 方法做过了。
因此，为了能够编写代码，我们将使用两个资源：
-
由Costa Huang制作的教程。Costa 是CleanRL背后的人，这是一个提供高质量单文件实现和研究友好功能的深度强化学习库。
-
除了教程外，为了更深入地了解，您可以阅读 13 个核心实现细节：
iclr-blog-track.github.io/2022/03/25/ppo-implementation-details/
然后，为了测试其稳健性，我们将在以下进行训练：
<assets/63_deep_rl_intro/lunarlander.mp4>
最后，我们将把训练好的模型推送到 Hub，以评估和可视化您的代理游戏。
LunarLander-v2 是您开始本课程时使用的第一个环境。那时，您不知道它是如何工作的，现在您可以从头开始编写代码并进行训练。这是多么令人难以置信的 🤩。
让我们开始吧！🚀
colab 笔记本：
在本笔记本中，您将学习使用 PyTorch 从头开始编写您的 PPO 代理，使用 CleanRL 实现作为模型。
为了测试其稳健性，我们将在以下进行训练：
我们不断努力改进我们的教程，所以如果您在本笔记本中发现问题，请在 GitHub Repo 上提出问题。
在笔记本的末尾，您将：
-
能够使用 PyTorch 从头开始编写您的 PPO 代理。
-
能够将您训练的代理和代码推送到 Hub，并附带一个漂亮的视频回放和评估分数🔥。
在深入研究笔记本之前，您需要：
🔲 📚 学习PPO，阅读第 8 单元 🤗
为了验证这个实践过程的认证过程，您需要推送一个模型，我们不要求最小结果，但我们建议您尝试不同的超参数设置以获得更好的结果。
如果找不到您的模型，请转到页面底部并单击刷新按钮
有关认证过程的更多信息，请查看此部分👉huggingface.co/deep-rl-course/en/unit0/introduction#certification-process
- 为了加速代理的训练，我们将使用 GPU。要做到这一点，请转到
运行时 > 更改运行时类型
硬件加速器 > GPU
在笔记本中，我们需要生成一个重播视频。为此，在 colab 中，我们需要有一个虚拟屏幕来渲染环境（从而记录帧）。
因此，以下单元格将安装库并创建并运行一个虚拟屏幕 🖥
apt install python-opengl
apt install ffmpeg
apt install xvfb
pip install pyglet==1.5
pip install pyvirtualdisplay# Virtual display
from pyvirtualdisplay import Display
virtual_display = Display(visible=0, size=(1400, 900))
virtual_display.start()对于这个练习，我们使用gym==0.21，因为视频是用 Gym 录制的。
pip install gym==0.22
pip install imageio-ffmpeg
pip install huggingface_hub
pip install gym[box2d]==0.22-
对于 PPO 的核心实现，我们将使用优秀的Costa Huang教程。
-
除了教程，要深入了解，您可以阅读 37 个核心实现细节：
iclr-blog-track.github.io/2022/03/25/ppo-implementation-details/
👉 视频教程：youtu.be/MEt6rrxH8W4
from IPython.display import HTML
HTML(
'<iframe width="560" height="315" src="https://www.youtube.com/embed/MEt6rrxH8W4" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>'
)-
为了将我们的模型推送到 Hub，我们需要定义一个
package_to_hub函数 -
添加我们需要将模型推送到 Hub 的依赖项
from huggingface_hub import HfApi, upload_folder
from huggingface_hub.repocard import metadata_eval_result, metadata_save
from pathlib import Path
import datetime
import tempfile
import json
import shutil
import imageio
from wasabi import Printer
msg = Printer()- 在
parse_args()函数中添加新参数，以定义我们要推送模型的 repo-id。
# Adding HuggingFace argument
parser.add_argument(
"--repo-id",
type=str,
default="ThomasSimonini/ppo-CartPole-v1",
help="id of the model repository from the Hugging Face Hub {username/repo_name}",
)-
接下来，我们添加推送模型到 Hub 所需的方法
-
这些方法将：
-
_evalutate_agent(): 评估代理。 -
_generate_model_card(): 生成您的代理的模型卡片。 -
_record_video(): 记录您的代理的视频。
-
def package_to_hub( repo_id,
model,
hyperparameters,
eval_env,
video_fps=30,
commit_message="Push agent to the Hub",
token=None,
logs=None, ):
"""
Evaluate, Generate a video and Upload a model to Hugging Face Hub.
This method does the complete pipeline:
- It evaluates the model
- It generates the model card
- It generates a replay video of the agent
- It pushes everything to the hub
:param repo_id: id of the model repository from the Hugging Face Hub
:param model: trained model
:param eval_env: environment used to evaluate the agent
:param fps: number of fps for rendering the video
:param commit_message: commit message
:param logs: directory on local machine of tensorboard logs you'd like to upload
"""
msg.info(
"This function will save, evaluate, generate a video of your agent, "
"create a model card and push everything to the hub. "
"It might take up to 1min. \n "
"This is a work in progress: if you encounter a bug, please open an issue."
)
# Step 1: Clone or create the repo
repo_url = HfApi().create_repo(
repo_id=repo_id,
token=token,
private=False,
exist_ok=True,
)
with tempfile.TemporaryDirectory() as tmpdirname:
tmpdirname = Path(tmpdirname)
# Step 2: Save the model
torch.save(model.state_dict(), tmpdirname / "model.pt")
# Step 3: Evaluate the model and build JSON
mean_reward, std_reward = _evaluate_agent(eval_env, 10, model)
# First get datetime
eval_datetime = datetime.datetime.now()
eval_form_datetime = eval_datetime.isoformat()
evaluate_data = {
"env_id": hyperparameters.env_id,
"mean_reward": mean_reward,
"std_reward": std_reward,
"n_evaluation_episodes": 10,
"eval_datetime": eval_form_datetime,
}
# Write a JSON file
with open(tmpdirname / "results.json", "w") as outfile:
json.dump(evaluate_data, outfile)
# Step 4: Generate a video
video_path = tmpdirname / "replay.mp4"
record_video(eval_env, model, video_path, video_fps)
# Step 5: Generate the model card
generated_model_card, metadata = _generate_model_card(
"PPO", hyperparameters.env_id, mean_reward, std_reward, hyperparameters
)
_save_model_card(tmpdirname, generated_model_card, metadata)
# Step 6: Add logs if needed
if logs:
_add_logdir(tmpdirname, Path(logs))
msg.info(f"Pushing repo {repo_id} to the Hugging Face Hub")
repo_url = upload_folder(
repo_id=repo_id,
folder_path=tmpdirname,
path_in_repo="",
commit_message=commit_message,
token=token,
)
msg.info(f"Your model is pushed to the Hub. You can view your model here: {repo_url}")
return repo_url
def _evaluate_agent(env, n_eval_episodes, policy):
"""
Evaluate the agent for ``n_eval_episodes`` episodes and returns average reward and std of reward.
:param env: The evaluation environment
:param n_eval_episodes: Number of episode to evaluate the agent
:param policy: The agent
"""
episode_rewards = []
for episode in range(n_eval_episodes):
state = env.reset()
step = 0
done = False
total_rewards_ep = 0
while done is False:
state = torch.Tensor(state).to(device)
action, _, _, _ = policy.get_action_and_value(state)
new_state, reward, done, info = env.step(action.cpu().numpy())
total_rewards_ep += reward
if done:
break
state = new_state
episode_rewards.append(total_rewards_ep)
mean_reward = np.mean(episode_rewards)
std_reward = np.std(episode_rewards)
return mean_reward, std_reward
def record_video(env, policy, out_directory, fps=30):
images = []
done = False
state = env.reset()
img = env.render(mode="rgb_array")
images.append(img)
while not done:
state = torch.Tensor(state).to(device)
# Take the action (index) that have the maximum expected future reward given that state
action, _, _, _ = policy.get_action_and_value(state)
state, reward, done, info = env.step(
action.cpu().numpy()
) # We directly put next_state = state for recording logic
img = env.render(mode="rgb_array")
images.append(img)
imageio.mimsave(out_directory, [np.array(img) for i, img in enumerate(images)], fps=fps)
def _generate_model_card(model_name, env_id, mean_reward, std_reward, hyperparameters):
"""
Generate the model card for the Hub
:param model_name: name of the model
:env_id: name of the environment
:mean_reward: mean reward of the agent
:std_reward: standard deviation of the mean reward of the agent
:hyperparameters: training arguments
"""
# Step 1: Select the tags
metadata = generate_metadata(model_name, env_id, mean_reward, std_reward)
# Transform the hyperparams namespace to string
converted_dict = vars(hyperparameters)
converted_str = str(converted_dict)
converted_str = converted_str.split(", ")
converted_str = "\n".join(converted_str)
# Step 2: Generate the model card
model_card = f"""
# PPO Agent Playing {env_id}
This is a trained model of a PPO agent playing {env_id}.
# Hyperparameters
"""
return model_card, metadata
def generate_metadata(model_name, env_id, mean_reward, std_reward):
"""
Define the tags for the model card
:param model_name: name of the model
:param env_id: name of the environment
:mean_reward: mean reward of the agent
:std_reward: standard deviation of the mean reward of the agent
"""
metadata = {}
metadata["tags"] = [
env_id,
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
]
# Add metrics
eval = metadata_eval_result(
model_pretty_name=model_name,
task_pretty_name="reinforcement-learning",
task_id="reinforcement-learning",
metrics_pretty_name="mean_reward",
metrics_id="mean_reward",
metrics_value=f"{mean_reward:.2f} +/- {std_reward:.2f}",
dataset_pretty_name=env_id,
dataset_id=env_id,
)
# Merges both dictionaries
metadata = {**metadata, **eval}
return metadata
def _save_model_card(local_path, generated_model_card, metadata):
"""Saves a model card for the repository.
:param local_path: repository directory
:param generated_model_card: model card generated by _generate_model_card()
:param metadata: metadata
"""
readme_path = local_path / "README.md"
readme = ""
if readme_path.exists():
with readme_path.open("r", encoding="utf8") as f:
readme = f.read()
else:
readme = generated_model_card
with readme_path.open("w", encoding="utf-8") as f:
f.write(readme)
# Save our metrics to Readme metadata
metadata_save(readme_path, metadata)
def _add_logdir(local_path: Path, logdir: Path):
"""Adds a logdir to the repository.
:param local_path: repository directory
:param logdir: logdir directory
"""
if logdir.exists() and logdir.is_dir():
# Add the logdir to the repository under new dir called logs
repo_logdir = local_path / "logs"
# Delete current logs if they exist
if repo_logdir.exists():
shutil.rmtree(repo_logdir)
# Copy logdir into repo logdir
shutil.copytree(logdir, repo_logdir)- 最后，在 PPO 训练结束时调用这个函数
# Create the evaluation environment
eval_env = gym.make(args.env_id)
package_to_hub(
repo_id=args.repo_id,
model=agent, # The model we want to save
hyperparameters=args,
eval_env=gym.make(args.env_id),
logs=f"runs/{run_name}",
)- 这是最终 ppo.py 文件的样子：
# docs and experiment results can be found at https://docs.cleanrl.dev/rl-algorithms/ppo/#ppopy
import argparse
import os
import random
import time
from distutils.util import strtobool
import gym
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.distributions.categorical import Categorical
from torch.utils.tensorboard import SummaryWriter
from huggingface_hub import HfApi, upload_folder
from huggingface_hub.repocard import metadata_eval_result, metadata_save
from pathlib import Path
import datetime
import tempfile
import json
import shutil
import imageio
from wasabi import Printer
msg = Printer()
def parse_args():
# fmt: off
parser = argparse.ArgumentParser()
parser.add_argument("--exp-name", type=str, default=os.path.basename(__file__).rstrip(".py"),
help="the name of this experiment")
parser.add_argument("--seed", type=int, default=1,
help="seed of the experiment")
parser.add_argument("--torch-deterministic", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
help="if toggled, `torch.backends.cudnn.deterministic=False`")
parser.add_argument("--cuda", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
help="if toggled, cuda will be enabled by default")
parser.add_argument("--track", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
help="if toggled, this experiment will be tracked with Weights and Biases")
parser.add_argument("--wandb-project-name", type=str, default="cleanRL",
help="the wandb's project name")
parser.add_argument("--wandb-entity", type=str, default=None,
help="the entity (team) of wandb's project")
parser.add_argument("--capture-video", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
help="weather to capture videos of the agent performances (check out `videos` folder)")
# Algorithm specific arguments
parser.add_argument("--env-id", type=str, default="CartPole-v1",
help="the id of the environment")
parser.add_argument("--total-timesteps", type=int, default=50000,
help="total timesteps of the experiments")
parser.add_argument("--learning-rate", type=float, default=2.5e-4,
help="the learning rate of the optimizer")
parser.add_argument("--num-envs", type=int, default=4,
help="the number of parallel game environments")
parser.add_argument("--num-steps", type=int, default=128,
help="the number of steps to run in each environment per policy rollout")
parser.add_argument("--anneal-lr", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
help="Toggle learning rate annealing for policy and value networks")
parser.add_argument("--gae", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
help="Use GAE for advantage computation")
parser.add_argument("--gamma", type=float, default=0.99,
help="the discount factor gamma")
parser.add_argument("--gae-lambda", type=float, default=0.95,
help="the lambda for the general advantage estimation")
parser.add_argument("--num-minibatches", type=int, default=4,
help="the number of mini-batches")
parser.add_argument("--update-epochs", type=int, default=4,
help="the K epochs to update the policy")
parser.add_argument("--norm-adv", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
help="Toggles advantages normalization")
parser.add_argument("--clip-coef", type=float, default=0.2,
help="the surrogate clipping coefficient")
parser.add_argument("--clip-vloss", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
help="Toggles whether or not to use a clipped loss for the value function, as per the paper.")
parser.add_argument("--ent-coef", type=float, default=0.01,
help="coefficient of the entropy")
parser.add_argument("--vf-coef", type=float, default=0.5,
help="coefficient of the value function")
parser.add_argument("--max-grad-norm", type=float, default=0.5,
help="the maximum norm for the gradient clipping")
parser.add_argument("--target-kl", type=float, default=None,
help="the target KL divergence threshold")
# Adding HuggingFace argument
parser.add_argument("--repo-id", type=str, default="ThomasSimonini/ppo-CartPole-v1", help="id of the model repository from the Hugging Face Hub {username/repo_name}")
args = parser.parse_args()
args.batch_size = int(args.num_envs * args.num_steps)
args.minibatch_size = int(args.batch_size // args.num_minibatches)
# fmt: on
return args
def package_to_hub( repo_id,
model,
hyperparameters,
eval_env,
video_fps=30,
commit_message="Push agent to the Hub",
token=None,
logs=None, ):
"""
Evaluate, Generate a video and Upload a model to Hugging Face Hub.
This method does the complete pipeline:
- It evaluates the model
- It generates the model card
- It generates a replay video of the agent
- It pushes everything to the hub
:param repo_id: id of the model repository from the Hugging Face Hub
:param model: trained model
:param eval_env: environment used to evaluate the agent
:param fps: number of fps for rendering the video
:param commit_message: commit message
:param logs: directory on local machine of tensorboard logs you'd like to upload
"""
msg.info(
"This function will save, evaluate, generate a video of your agent, "
"create a model card and push everything to the hub. "
"It might take up to 1min. \n "
"This is a work in progress: if you encounter a bug, please open an issue."
)
# Step 1: Clone or create the repo
repo_url = HfApi().create_repo(
repo_id=repo_id,
token=token,
private=False,
exist_ok=True,
)
with tempfile.TemporaryDirectory() as tmpdirname:
tmpdirname = Path(tmpdirname)
# Step 2: Save the model
torch.save(model.state_dict(), tmpdirname / "model.pt")
# Step 3: Evaluate the model and build JSON
mean_reward, std_reward = _evaluate_agent(eval_env, 10, model)
# First get datetime
eval_datetime = datetime.datetime.now()
eval_form_datetime = eval_datetime.isoformat()
evaluate_data = {
"env_id": hyperparameters.env_id,
"mean_reward": mean_reward,
"std_reward": std_reward,
"n_evaluation_episodes": 10,
"eval_datetime": eval_form_datetime,
}
# Write a JSON file
with open(tmpdirname / "results.json", "w") as outfile:
json.dump(evaluate_data, outfile)
# Step 4: Generate a video
video_path = tmpdirname / "replay.mp4"
record_video(eval_env, model, video_path, video_fps)
# Step 5: Generate the model card
generated_model_card, metadata = _generate_model_card(
"PPO", hyperparameters.env_id, mean_reward, std_reward, hyperparameters
)
_save_model_card(tmpdirname, generated_model_card, metadata)
# Step 6: Add logs if needed
if logs:
_add_logdir(tmpdirname, Path(logs))
msg.info(f"Pushing repo {repo_id} to the Hugging Face Hub")
repo_url = upload_folder(
repo_id=repo_id,
folder_path=tmpdirname,
path_in_repo="",
commit_message=commit_message,
token=token,
)
msg.info(f"Your model is pushed to the Hub. You can view your model here: {repo_url}")
return repo_url
def _evaluate_agent(env, n_eval_episodes, policy):
"""
Evaluate the agent for ``n_eval_episodes`` episodes and returns average reward and std of reward.
:param env: The evaluation environment
:param n_eval_episodes: Number of episode to evaluate the agent
:param policy: The agent
"""
episode_rewards = []
for episode in range(n_eval_episodes):
state = env.reset()
step = 0
done = False
total_rewards_ep = 0
while done is False:
state = torch.Tensor(state).to(device)
action, _, _, _ = policy.get_action_and_value(state)
new_state, reward, done, info = env.step(action.cpu().numpy())
total_rewards_ep += reward
if done:
break
state = new_state
episode_rewards.append(total_rewards_ep)
mean_reward = np.mean(episode_rewards)
std_reward = np.std(episode_rewards)
return mean_reward, std_reward
def record_video(env, policy, out_directory, fps=30):
images = []
done = False
state = env.reset()
img = env.render(mode="rgb_array")
images.append(img)
while not done:
state = torch.Tensor(state).to(device)
# Take the action (index) that have the maximum expected future reward given that state
action, _, _, _ = policy.get_action_and_value(state)
state, reward, done, info = env.step(
action.cpu().numpy()
) # We directly put next_state = state for recording logic
img = env.render(mode="rgb_array")
images.append(img)
imageio.mimsave(out_directory, [np.array(img) for i, img in enumerate(images)], fps=fps)
def _generate_model_card(model_name, env_id, mean_reward, std_reward, hyperparameters):
"""
Generate the model card for the Hub
:param model_name: name of the model
:env_id: name of the environment
:mean_reward: mean reward of the agent
:std_reward: standard deviation of the mean reward of the agent
:hyperparameters: training arguments
"""
# Step 1: Select the tags
metadata = generate_metadata(model_name, env_id, mean_reward, std_reward)
# Transform the hyperparams namespace to string
converted_dict = vars(hyperparameters)
converted_str = str(converted_dict)
converted_str = converted_str.split(", ")
converted_str = "\n".join(converted_str)
# Step 2: Generate the model card
model_card = f"""
# PPO Agent Playing {env_id}
This is a trained model of a PPO agent playing {env_id}.
# Hyperparameters
"""
return model_card, metadata
def generate_metadata(model_name, env_id, mean_reward, std_reward):
"""
Define the tags for the model card
:param model_name: name of the model
:param env_id: name of the environment
:mean_reward: mean reward of the agent
:std_reward: standard deviation of the mean reward of the agent
"""
metadata = {}
metadata["tags"] = [
env_id,
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
]
# Add metrics
eval = metadata_eval_result(
model_pretty_name=model_name,
task_pretty_name="reinforcement-learning",
task_id="reinforcement-learning",
metrics_pretty_name="mean_reward",
metrics_id="mean_reward",
metrics_value=f"{mean_reward:.2f} +/- {std_reward:.2f}",
dataset_pretty_name=env_id,
dataset_id=env_id,
)
# Merges both dictionaries
metadata = {**metadata, **eval}
return metadata
def _save_model_card(local_path, generated_model_card, metadata):
"""Saves a model card for the repository.
:param local_path: repository directory
:param generated_model_card: model card generated by _generate_model_card()
:param metadata: metadata
"""
readme_path = local_path / "README.md"
readme = ""
if readme_path.exists():
with readme_path.open("r", encoding="utf8") as f:
readme = f.read()
else:
readme = generated_model_card
with readme_path.open("w", encoding="utf-8") as f:
f.write(readme)
# Save our metrics to Readme metadata
metadata_save(readme_path, metadata)
def _add_logdir(local_path: Path, logdir: Path):
"""Adds a logdir to the repository.
:param local_path: repository directory
:param logdir: logdir directory
"""
if logdir.exists() and logdir.is_dir():
# Add the logdir to the repository under new dir called logs
repo_logdir = local_path / "logs"
# Delete current logs if they exist
if repo_logdir.exists():
shutil.rmtree(repo_logdir)
# Copy logdir into repo logdir
shutil.copytree(logdir, repo_logdir)
def make_env(env_id, seed, idx, capture_video, run_name):
def thunk():
env = gym.make(env_id)
env = gym.wrappers.RecordEpisodeStatistics(env)
if capture_video:
if idx == 0:
env = gym.wrappers.RecordVideo(env, f"videos/{run_name}")
env.seed(seed)
env.action_space.seed(seed)
env.observation_space.seed(seed)
return env
return thunk
def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
torch.nn.init.orthogonal_(layer.weight, std)
torch.nn.init.constant_(layer.bias, bias_const)
return layer
class Agent(nn.Module):
def __init__(self, envs):
super().__init__()
self.critic = nn.Sequential(
layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(), 64)),
nn.Tanh(),
layer_init(nn.Linear(64, 64)),
nn.Tanh(),
layer_init(nn.Linear(64, 1), std=1.0),
)
self.actor = nn.Sequential(
layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(), 64)),
nn.Tanh(),
layer_init(nn.Linear(64, 64)),
nn.Tanh(),
layer_init(nn.Linear(64, envs.single_action_space.n), std=0.01),
)
def get_value(self, x):
return self.critic(x)
def get_action_and_value(self, x, action=None):
logits = self.actor(x)
probs = Categorical(logits=logits)
if action is None:
action = probs.sample()
return action, probs.log_prob(action), probs.entropy(), self.critic(x)
if __name__ == "__main__":
args = parse_args()
run_name = f"{args.env_id}__{args.exp_name}__{args.seed}__{int(time.time())}"
if args.track:
import wandb
wandb.init(
project=args.wandb_project_name,
entity=args.wandb_entity,
sync_tensorboard=True,
config=vars(args),
name=run_name,
monitor_gym=True,
save_code=True,
)
writer = SummaryWriter(f"runs/{run_name}")
writer.add_text(
"hyperparameters",
"|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key, value in vars(args).items()])),
)
# TRY NOT TO MODIFY: seeding
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.backends.cudnn.deterministic = args.torch_deterministic
device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
# env setup
envs = gym.vector.SyncVectorEnv(
[make_env(args.env_id, args.seed + i, i, args.capture_video, run_name) for i in range(args.num_envs)]
)
assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"
agent = Agent(envs).to(device)
optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5)
# ALGO Logic: Storage setup
obs = torch.zeros((args.num_steps, args.num_envs) + envs.single_observation_space.shape).to(device)
actions = torch.zeros((args.num_steps, args.num_envs) + envs.single_action_space.shape).to(device)
logprobs = torch.zeros((args.num_steps, args.num_envs)).to(device)
rewards = torch.zeros((args.num_steps, args.num_envs)).to(device)
dones = torch.zeros((args.num_steps, args.num_envs)).to(device)
values = torch.zeros((args.num_steps, args.num_envs)).to(device)
# TRY NOT TO MODIFY: start the game
global_step = 0
start_time = time.time()
next_obs = torch.Tensor(envs.reset()).to(device)
next_done = torch.zeros(args.num_envs).to(device)
num_updates = args.total_timesteps // args.batch_size
for update in range(1, num_updates + 1):
# Annealing the rate if instructed to do so.
if args.anneal_lr:
frac = 1.0 - (update - 1.0) / num_updates
lrnow = frac * args.learning_rate
optimizer.param_groups[0]["lr"] = lrnow
for step in range(0, args.num_steps):
global_step += 1 * args.num_envs
obs[step] = next_obs
dones[step] = next_done
# ALGO LOGIC: action logic
with torch.no_grad():
action, logprob, _, value = agent.get_action_and_value(next_obs)
values[step] = value.flatten()
actions[step] = action
logprobs[step] = logprob
# TRY NOT TO MODIFY: execute the game and log data.
next_obs, reward, done, info = envs.step(action.cpu().numpy())
rewards[step] = torch.tensor(reward).to(device).view(-1)
next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(done).to(device)
for item in info:
if "episode" in item.keys():
print(f"global_step={global_step}, episodic_return={item['episode']['r']}")
writer.add_scalar("charts/episodic_return", item["episode"]["r"], global_step)
writer.add_scalar("charts/episodic_length", item["episode"]["l"], global_step)
break
# bootstrap value if not done
with torch.no_grad():
next_value = agent.get_value(next_obs).reshape(1, -1)
if args.gae:
advantages = torch.zeros_like(rewards).to(device)
lastgaelam = 0
for t in reversed(range(args.num_steps)):
if t == args.num_steps - 1:
nextnonterminal = 1.0 - next_done
nextvalues = next_value
else:
nextnonterminal = 1.0 - dones[t + 1]
nextvalues = values[t + 1]
delta = rewards[t] + args.gamma * nextvalues * nextnonterminal - values[t]
advantages[t] = lastgaelam = delta + args.gamma * args.gae_lambda * nextnonterminal * lastgaelam
returns = advantages + values
else:
returns = torch.zeros_like(rewards).to(device)
for t in reversed(range(args.num_steps)):
if t == args.num_steps - 1:
nextnonterminal = 1.0 - next_done
next_return = next_value
else:
nextnonterminal = 1.0 - dones[t + 1]
next_return = returns[t + 1]
returns[t] = rewards[t] + args.gamma * nextnonterminal * next_return
advantages = returns - values
# flatten the batch
b_obs = obs.reshape((-1,) + envs.single_observation_space.shape)
b_logprobs = logprobs.reshape(-1)
b_actions = actions.reshape((-1,) + envs.single_action_space.shape)
b_advantages = advantages.reshape(-1)
b_returns = returns.reshape(-1)
b_values = values.reshape(-1)
# Optimizing the policy and value network
b_inds = np.arange(args.batch_size)
clipfracs = []
for epoch in range(args.update_epochs):
np.random.shuffle(b_inds)
for start in range(0, args.batch_size, args.minibatch_size):
end = start + args.minibatch_size
mb_inds = b_inds[start:end]
_, newlogprob, entropy, newvalue = agent.get_action_and_value(
b_obs[mb_inds], b_actions.long()[mb_inds]
)
logratio = newlogprob - b_logprobs[mb_inds]
ratio = logratio.exp()
with torch.no_grad():
# calculate approx_kl http://joschu.net/blog/kl-approx.html
old_approx_kl = (-logratio).mean()
approx_kl = ((ratio - 1) - logratio).mean()
clipfracs += [((ratio - 1.0).abs() > args.clip_coef).float().mean().item()]
mb_advantages = b_advantages[mb_inds]
if args.norm_adv:
mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-8)
# Policy loss
pg_loss1 = -mb_advantages * ratio
pg_loss2 = -mb_advantages * torch.clamp(ratio, 1 - args.clip_coef, 1 + args.clip_coef)
pg_loss = torch.max(pg_loss1, pg_loss2).mean()
# Value loss
newvalue = newvalue.view(-1)
if args.clip_vloss:
v_loss_unclipped = (newvalue - b_returns[mb_inds]) ** 2
v_clipped = b_values[mb_inds] + torch.clamp(
newvalue - b_values[mb_inds],
-args.clip_coef,
args.clip_coef,
)
v_loss_clipped = (v_clipped - b_returns[mb_inds]) ** 2
v_loss_max = torch.max(v_loss_unclipped, v_loss_clipped)
v_loss = 0.5 * v_loss_max.mean()
else:
v_loss = 0.5 * ((newvalue - b_returns[mb_inds]) ** 2).mean()
entropy_loss = entropy.mean()
loss = pg_loss - args.ent_coef * entropy_loss + v_loss * args.vf_coef
optimizer.zero_grad()
loss.backward()
nn.utils.clip_grad_norm_(agent.parameters(), args.max_grad_norm)
optimizer.step()
if args.target_kl is not None:
if approx_kl > args.target_kl:
break
y_pred, y_true = b_values.cpu().numpy(), b_returns.cpu().numpy()
var_y = np.var(y_true)
explained_var = np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y
# TRY NOT TO MODIFY: record rewards for plotting purposes
writer.add_scalar("charts/learning_rate", optimizer.param_groups[0]["lr"], global_step)
writer.add_scalar("losses/value_loss", v_loss.item(), global_step)
writer.add_scalar("losses/policy_loss", pg_loss.item(), global_step)
writer.add_scalar("losses/entropy", entropy_loss.item(), global_step)
writer.add_scalar("losses/old_approx_kl", old_approx_kl.item(), global_step)
writer.add_scalar("losses/approx_kl", approx_kl.item(), global_step)
writer.add_scalar("losses/clipfrac", np.mean(clipfracs), global_step)
writer.add_scalar("losses/explained_variance", explained_var, global_step)
print("SPS:", int(global_step / (time.time() - start_time)))
writer.add_scalar("charts/SPS", int(global_step / (time.time() - start_time)), global_step)
envs.close()
writer.close()
# Create the evaluation environment
eval_env = gym.make(args.env_id)
package_to_hub(
repo_id=args.repo_id,
model=agent, # The model we want to save
hyperparameters=args,
eval_env=gym.make(args.env_id),
logs=f"runs/{run_name}",
)为了能够与社区分享您的模型，还有三个步骤要遵循：
1️⃣ (如果尚未完成) 创建一个 HF 帐户 ➡ huggingface.co/join
2️⃣ 登录并从 Hugging Face 网站获取您的身份验证令牌。
- 创建一个新的令牌(
huggingface.co/settings/tokens) 具有写入权限
-
复制令牌
-
运行下面的单元格并粘贴令牌
from huggingface_hub import notebook_login
notebook_login()
!git config --global credential.helper store如果您不想使用 Google Colab 或 Jupyter Notebook，您需要使用这个命令：huggingface-cli login
-
现在您已经从头开始编写了 PPO 并添加了 Hugging Face 集成，我们准备开始训练 🔥
-
首先，您需要将所有代码复制到一个名为
ppo.py的文件中
-
现在我们只需要使用
python <name-of-python-script>.py运行这个 python 脚本，使用我们定义的额外参数argparse -
您应该修改更多超参数，否则训练将不会很稳定。
!python ppo.py --env-id="LunarLander-v2" --repo-id="YOUR_REPO_ID" --total-timesteps=50000学习的最佳方式是自己尝试！为什么不尝试另一个环境？或者为什么不尝试修改实现以适用于体育馆？
我们在第 8 单元的第 2 部分见，那里我们将训练代理玩毁灭 🔥