#
Using Stable-Baselines3 at Hugging Face

`stable-baselines3` is a set of reliable implementations of reinforcement learning algorithms in PyTorch.

##
Exploring Stable-Baselines3 in the Hub

You can find Stable-Baselines3 models by filtering at the left of the models page.

All models on the Hub come up with useful features:

1. An automatically generated model card with a description, a training configuration, and more.
2. Metadata tags that help for discoverability.
3. Evaluation results to compare with other models.
4. A video widget where you can watch your agent performing.

##
Install the library

To install the `stable-baselines3` library, you need to install two packages:

- `stable-baselines3`: Stable-Baselines3 library.
- `huggingface-sb3`: additional code to load and upload Stable-baselines3 models from the Hub.

    pip install stable-baselines3
    pip install huggingface-sb3

##
Using existing models

You can simply download a model from the Hub using the `load_from_hub` function

    checkpoint = load_from_hub(
        repo_id="sb3/demo-hf-CartPole-v1",
        filename="ppo-CartPole-v1.zip",
    )

You need to define two parameters:

- `--repo-id`: the name of the Hugging Face repo you want to download.
- `--filename`: the file you want to download.

##
Sharing your models

You can easily upload your models using two different functions:

1. `package_to_hub()`: save the model, evaluate it, generate a model card and record a replay video of your agent before pushing the complete repo to the Hub.

    package_to_hub(model=model,
                   model_name="ppo-LunarLander-v2",
                   model_architecture="PPO",
                   env_id=env_id,
                   eval_env=eval_env,
                   repo_id="ThomasSimonini/ppo-LunarLander-v2",
                   commit_message="Test commit")

You need to define seven parameters:

- `--model`: your trained model.
- `--model_architecture`: name of the architecture of your model (DQN, PPO, A2C, SAC…).
- `--env_id`: name of the environment.
- `--eval_env`: environment used to evaluate the agent.
- `--repo-id`: the name of the Hugging Face repo you want to create or update. It’s `<your huggingface username>/<the repo name>`.
- `--commit-message`.
- `--filename`: the file you want to push to the Hub.

2. `push_to_hub()`: simply push a file to the Hub

    push_to_hub(
        repo_id="ThomasSimonini/ppo-LunarLander-v2",
        filename="ppo-LunarLander-v2.zip",
        commit_message="Added LunarLander-v2 model trained with PPO",
    )

You need to define three parameters:

- `--repo-id`: the name of the Hugging Face repo you want to create or update. It’s `<your huggingface username>/<the repo name>`.
- `--filename`: the file you want to push to the Hub.
- `--commit-message`.

##
Additional resources

- Hugging Face Stable-Baselines3 documentation
- Stable-Baselines3 documentation

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