Stable-Baselines3

Using Stable-Baselines3 at BOINC AI

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.

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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

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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 BOINC AI 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.

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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 BOINC AI 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.

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

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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 BOINC AI 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

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