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Using RL-Baselines3-Zoo at Hugging Face

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Using RL-Baselines3-Zoo at Hugging Face

rl-baselines3-zoo is a training framework for Reinforcement Learning using Stable Baselines3.

Exploring RL-Baselines3-Zoo in the Hub

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

The Stable-Baselines3 team is hosting a collection of +150 trained Reinforcement Learning agents with tuned hyperparameters that you can find here.

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.

Using existing models

You can simply download a model from the Hub using load_from_hub:

# Download ppo SpaceInvadersNoFrameskip-v4 model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga sb3
python --algo dqn --env SpaceInvadersNoFrameskip-v4  -f logs/

You can define three parameters:

  • --repo-name: The name of the repo.
  • -orga: A Hugging Face username or organization.
  • -f: The destination folder.

Sharing your models

You can easily upload your models with push_to_hub. That will 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.

python -m rl_zoo3.push_to_hub  --algo dqn  --env SpaceInvadersNoFrameskip-v4  --repo-name dqn-SpaceInvadersNoFrameskip-v4  -orga ThomasSimonini  -f logs/

You can define three parameters:

  • --repo-name: The name of the repo.
  • -orga: Your Hugging Face username.
  • -f: The folder where the model is saved.

Additional resources