RL-Baselines3-Zoo

Using RL-Baselines3-Zoo at BOINC AI

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:

Copied

# 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 BOINC AI 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.

Copied

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 BOINC AI username.

• -f: The folder where the model is saved.

Additional resources

• RL-Baselines3-Zoo official trained models
• RL-Baselines3-Zoo documentation