ppo_from_scratch_lunar.py

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():
    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)",
    )

    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",
    )

    parser.add_argument(
        "--repo-id",
        type=str,
        default="shahzebnaveed/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)

    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."
    )

    repo_url = HfApi().create_repo(
        repo_id=repo_id,
        token=token,
        private=False,
        exist_ok=True,
    )

    with tempfile.TemporaryDirectory() as tmpdirname:
        tmpdirname = Path(tmpdirname)

        torch.save(model.state_dict(), tmpdirname / "model.pt")

        mean_reward, std_reward = _evaluate_agent(eval_env, 10, model)

        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,
        }

        with open(tmpdirname / "results.json", "w") as outfile:
            json.dump(evaluate_data, outfile)

        video_path = tmpdirname / "replay.mp4"
        record_video(eval_env, model, video_path, video_fps)

        generated_model_card, metadata = _generate_model_card(
            "PPO", hyperparameters.env_id, mean_reward, std_reward, hyperparameters
        )
        _save_model_card(tmpdirname, generated_model_card, metadata)

        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)

        action, _, _, _ = policy.get_action_and_value(state)
        state, reward, done, info = env.step(action.cpu().numpy())
        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
    """

    metadata = generate_metadata(model_name, env_id, mean_reward, std_reward)

    converted_dict = vars(hyperparameters)
    converted_str = str(converted_dict)
    converted_str = converted_str.split(", ")
    converted_str = "\n".join(converted_str)

    model_card = f"""
  

  This is a trained model of a PPO agent playing {env_id}.

  
  ```python
  {converted_str}
  ```
  """
    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",
    ]

    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,
    )

    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)

    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():
        repo_logdir = local_path / "logs"

        if repo_logdir.exists():
            shutil.rmtree(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()])),
    )

    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")

    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)

    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)

    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):
        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

            with torch.no_grad():
                action, logprob, _, value = agent.get_action_and_value(next_obs)
                values[step] = value.flatten()
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