RL Baselines Zoo: A Comprehensive Resource for Reinforcement Learning Enthusiasts
Overview
The RL Baselines Zoo is a fascinating collection of pre-trained reinforcement learning agents, developed using Stable Baselines. This repository is a valuable resource for anyone interested in reinforcement learning, offering a multitude of agents with fine-tuned hyperparameters optimized for various environments. The project is useful for both beginners and advanced users looking to train, evaluate, or benchmark RL algorithms.
Important Note: This repository is no longer maintained. For the most up-to-date version, users are directed to the RL-Baselines3 Zoo, which is powered by Stable-Baselines3.
Project Goals
The RL Baselines Zoo serves several primary purposes:
- Simple Interface: It provides a straightforward interface to train and enjoy RL agents, making it accessible even to novices.
- Benchmarking: It offers a platform to benchmark different RL algorithms, enabling comparisons across various metrics.
- Tuned Hyperparameters: The repository includes meticulously tuned hyperparameters for each environment and RL algorithm, ensuring optimal performance.
- Enjoyment and Learning: Lastly, it allows users to have fun and learn from the trained agents.
Using a Trained Agent
With RL Baselines Zoo, embracing the capabilities of a trained agent is simple. Users can view agents in action by executing a command with the specific algorithm and environment they are interested in. Agents can be tested for a defined number of timesteps, providing insights into their performance.
Training Your Agent
The repository also supports training new agents. Each environment's hyperparameters are defined in YAML files, tailored for optimal training outcomes. Users can employ these hyperparameters to initiate training sessions and can evaluate and save progress periodically, ensuring comprehensive experimentation and continuous improvement.
Hyperparameter Optimization
For users keen on experimenting with hyperparameters, the RL Baselines Zoo leverages Optuna for optimization. Though this feature is not implemented for some algorithms like ACER and DQN, it offers robust support for others, allowing users to conduct experiments with a significant number of trials and steps.
Environment Customization
Users can enhance their environments with custom wrappers, adding versatility and extending their application range. This feature enables a more refined control over the training environments, facilitating the execution of more complex tasks.
Recording and Evaluation
The project allows users to record performance videos, making it easy to analyze and share an agent’s progress. Performance metrics and scores are documented comprehensively, assisting in benchmarking and comparisons.
Supported Environments
The RL Baselines Zoo boasts support for a wide variety of environments, including Atari games, classic control scenarios, Box2D simulations, and more. It provides detailed tables indicating the availability of algorithms across these environments, showcasing its extensive applicability.
Trying It Online and Installation
For those looking to experiment without a complex setup, an online Colab notebook is available. Users can also set up the project locally using a Docker image for streamlined and consistent deployment across different systems.
Testing and Contributing
The repository supports robust testing using pytest and encourages contributions from the community. Trained agents not currently part of the collection can be added through pull requests. The contributions extend the zoo’s breadth and improve its relevance to a wider audience.
Conclusion
The RL Baselines Zoo is a dynamic, feature-rich repository perfect for exploring reinforcement learning's frontiers. Although no longer maintained, it stands as a testament to collaborative growth, learning, and shared knowledge in the exciting field of artificial intelligence.