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SLM Lab is a software framework for reproducible reinforcement learning (RL) research. It enables easy development of RL algorithms using modular components and file-based configuration. It also enables flexible experimentation completed with hyperparameter search, result analysis and benchmark results.

SLM Lab is also the companion library of the book Foundations of Deep Reinforcement Learning.

✨ Features

• ​Modular design for building deep RL algorithms

• ​Reproducibility using spec file and git SHA

• ​Experiment framework with automatic analysis​

• ​Extensive benchmark results​

• Well-tuned algorithm implementations

• Multiple RL environment offerings

Algorithms

SLM Lab implements most of the canonical RL algorithms:

• SARSA

• DQN (Deep Q-Network)

• Double-DQN, Dueling-DQN, PER (Prioritized Experience Replay)

• REINFORCE

• A2C (Advantage Actor-Critic) with GAE & n-step

• PPO (Proximal Policy Optimization)

• SAC (Soft Actor-Critic)

• SIL (Self Imitation Learning)

• Asynchronous version of all the above

They are implemented in a modular way such that differences in algorithm performance can be confidently ascribed to differences between algorithms, not between implementations.

Environments

SLM Lab currently includes the following environment offerings:

• ​OpenAI gym​

• ​OpenAI Roboschool​

• ​VizDoom (credit: joelouismarino)

• ​Unity environments with prebuilt binaries

Citation

If you use SLM Lab in your publication, please cite below:

@misc{kenggraesser2017slmlab,
    author = {Keng, Wah Loon and Graesser, Laura},
    title = {SLM Lab},
    year = {2017},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\url{https://github.com/kengz/SLM-Lab}},
}

License

This project is licensed under the MIT License.