texts/about.md

The Open RL Leaderboard is a community-driven benchmark for reinforcement learning models.

🔌 How to have your agent evaluated?

The Open RL Leaderboard constantly scans the 🤗 Hub to detect new models to be evaluated. For your model to be evaluated, it must meet the following criteria.

1. The model must be public on the 🤗 Hub
2. The model must contain an agent.pt file.
3. The model must be tagged reinforcement-learning
4. The model must be tagged with the name of the environment you want to evaluate (for example MountainCar-v0)

Once your model meets these criteria, it will be automatically evaluated on the Open RL Leaderboard. It usually takes a few minutes for the evaluation to be completed. That's it!

🕵 How are the models evaluated?

The evaluation is done by running the agent on the environment for 50 episodes. You can get the raw evaluation scores in the Leaderboard dataset.

For further information, please refer to the Open RL Leaderboard evaluation script.

The particular case of Atari environments

Atari environments are evaluated on the NoFrameskip-v4 version of the environment. For example, to evaluate an agent on the Pong environment, you must tag your model with PongNoFrameskip-v4. The environment is then wrapped to match the standard Atari preprocessing pipeline.

• No-op reset with a maximum of 30 no-ops
• Max and skip with a skip of 4
• Episodic life (although the reported score is for the full episode, not the life)
• Fire reset
• Clip reward (although the reported score is not clipped)
• Resize observation to 84x84
• Grayscale observation
• Frame stack of 4

🚑 Troubleshooting

If you encounter any issue, please open an issue on the Open RL Leaderboard repository.

🏃 Next steps

We are working on adding more environments and metrics to the Open RL Leaderboard. If you have any suggestions, please open an discussion on the Open RL Leaderboard repository.

📜 Citation

@misc{open-rl-leaderboard,
  author = {Quentin Gallouédec and TODO},
  title = {Open RL Leaderboard},
  year = {2024},
  publisher = {Hugging Face},
  howpublished = "\url{https://huggingface.co/spaces/open-rl-leaderboard/leaderboard}",
}