Using RL-Baselines3-Zoo at Hugging Face (original) (raw)
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:
- An automatically generated model card with a description, a training configuration, and more.
- Metadata tags that help for discoverability.
- Evaluation results to compare with other models.
- 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 enjoy.py --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
- RL-Baselines3-Zoo official trained models
- RL-Baselines3-Zoo documentation