# Hands-on

      

Now that we studied the theory behind PPO, the best way to understand how it works **is to implement it from scratch.**

Implementing an architecture from scratch is the best way to understand it, and it's a good habit. We have already done it for a value-based method with Q-Learning and a Policy-based method with Reinforce.

So, to be able to code it, we're going to use two resources:
- A tutorial made by [Costa Huang](https://github.com/vwxyzjn). Costa is behind [CleanRL](https://github.com/vwxyzjn/cleanrl), a Deep Reinforcement Learning library that provides high-quality single-file implementation with research-friendly features.
- In addition to the tutorial, to go deeper, you can read the 13 core implementation details: [https://iclr-blog-track.github.io/2022/03/25/ppo-implementation-details/](https://iclr-blog-track.github.io/2022/03/25/ppo-implementation-details/)

Then, to test its robustness, we're going to train it in:
- [LunarLander-v2](https://www.gymlibrary.ml/environments/box2d/lunar_lander/)

    
      
  

And finally, we will push the trained model to the Hub to evaluate and visualize your agent playing.

LunarLander-v2 is the first environment you used when you started this course. At that time, you didn't know how it worked, and now you can code it from scratch and train it. **How incredible is that 🤩.**

via GIPHY

Let's get started! 🚀

The colab notebook: 

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/huggingface/deep-rl-class/blob/master/notebooks/unit8/unit8_part1.ipynb)

# Unit 8: Proximal Policy Gradient (PPO) with PyTorch 🤖

In this notebook, you'll learn to **code your PPO agent from scratch with PyTorch using CleanRL implementation as model**.

To test its robustness, we're going to train it in:

- [LunarLander-v2 🚀](https://www.gymlibrary.dev/environments/box2d/lunar_lander/)

We're constantly trying to improve our tutorials, so **if you find some issues in this notebook**, please [open an issue on the GitHub Repo](https://github.com/huggingface/deep-rl-class/issues).

## Objectives of this notebook 🏆

At the end of the notebook, you will:

- Be able to **code your PPO agent from scratch using PyTorch**.
- Be able to **push your trained agent and the code to the Hub** with a nice video replay and an evaluation score 🔥.

## Prerequisites 🏗️

Before diving into the notebook, you need to:

🔲 📚 Study [PPO by reading Unit 8](https://huggingface.co/deep-rl-course/unit8/introduction) 🤗

To validate this hands-on for the [certification process](https://huggingface.co/deep-rl-course/en/unit0/introduction#certification-process), you need to push one model, we don't ask for a minimal result but we **advise you to try different hyperparameters settings to get better results**.

If you don't find your model, **go to the bottom of the page and click on the refresh button**

For more information about the certification process, check this section 👉 https://huggingface.co/deep-rl-course/en/unit0/introduction#certification-process

## Set the GPU 💪

- To **accelerate the agent's training, we'll use a GPU**. To do that, go to `Runtime > Change Runtime type`

- `Hardware Accelerator > GPU`

## Create a virtual display 🔽

During the notebook, we'll need to generate a replay video. To do so, with colab, **we need to have a virtual screen to be able to render the environment** (and thus record the frames).

Hence the following cell will install the librairies and create and run a virtual screen 🖥

```python
apt install python-opengl
apt install ffmpeg
apt install xvfb
pip install pyglet==1.5
pip install pyvirtualdisplay
```

```python
# Virtual display
from pyvirtualdisplay import Display

virtual_display = Display(visible=0, size=(1400, 900))
virtual_display.start()
```

## Install dependencies 🔽
For this exercise, we use `gym==0.21` because the video was recorded with Gym.

```python
pip install gym==0.22
pip install imageio-ffmpeg
pip install huggingface_hub
pip install gym[box2d]==0.22
```

## Let's code PPO from scratch with Costa Huang's tutorial
- For the core implementation of PPO we're going to use the excellent [Costa Huang](https://costa.sh/) tutorial.
- In addition to the tutorial, to go deeper you can read the 37 core implementation details: https://iclr-blog-track.github.io/2022/03/25/ppo-implementation-details/

👉 The video tutorial: https://youtu.be/MEt6rrxH8W4

```python
from IPython.display import HTML

HTML(
    ''
)
```

## Add the Hugging Face Integration 🤗
- In order to push our model to the Hub, we need to define a function `package_to_hub`

- Add dependencies we need to push our model to the Hub

```python
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()
```

- Add new argument in `parse_args()` function to define the repo-id where we want to push the model.

```python
# 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}",
)
```

- Next, we add the methods needed to push the model to the Hub

- These methods will:
  - `_evalutate_agent()`: evaluate the agent.
  - `_generate_model_card()`: generate the model card of your agent.
  - `_record_video()`: record a video of your agent.

```python
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)
```

- Finally, we call this function at the end of the PPO training

```python
# 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}",
)
```

- Here's what the final ppo.py file looks like:

```python
# 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}",
    )
```

To be able to share your model with the community there are three more steps to follow:

1️⃣ (If it's not already done) create an account to HF ➡ https://huggingface.co/join

2️⃣ Sign in and get your authentication token from the Hugging Face website.
- Create a new token (https://huggingface.co/settings/tokens) **with write role**

- Copy the token
- Run the cell below and paste the token

```python
from huggingface_hub import notebook_login
notebook_login()
!git config --global credential.helper store
```

If you don't want to use Google Colab or a Jupyter Notebook, you need to use this command instead: `huggingface-cli login`

## Let's start the training 🔥

⚠️ ⚠️ ⚠️  Don't use **the same repo id with the one you used for the Unit 1**

- Now that you've coded PPO from scratch and added the Hugging Face Integration, we're ready to start the training 🔥

- First, you need to copy all your code to a file you create called `ppo.py`

- Now we just need to run this python script using `python .py` with the additional parameters we defined using `argparse`

- You should modify more hyperparameters otherwise the training will not be super stable.

```python
!python ppo.py --env-id="LunarLander-v2" --repo-id="YOUR_REPO_ID" --total-timesteps=50000
```

## Some additional challenges 🏆

The best way to learn **is to try things on your own**! Why not try another environment? Or why not trying to modify the implementation to work with Gymnasium?

See you in Unit 8, part 2 where we're going to train agents to play Doom 🔥

## Keep learning, stay awesome 🤗

