> For the complete documentation index, see [llms.txt](https://slm-lab.gitbook.io/slm-lab/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://slm-lab.gitbook.io/slm-lab/master/master.md).

# SLM Lab

[![CircleCI](https://circleci.com/gh/kengz/SLM-Lab.svg?style=shield)](https://circleci.com/gh/kengz/SLM-Lab)[![Maintainability](https://api.codeclimate.com/v1/badges/20c6a124c468b4d3e967/maintainability)](https://codeclimate.com/github/kengz/SLM-Lab/maintainability)[![Test Coverage](https://api.codeclimate.com/v1/badges/20c6a124c468b4d3e967/test_coverage)](https://codeclimate.com/github/kengz/SLM-Lab/test_coverage)

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](https://www.amazon.com/dp/0135172381).

## ✨ Features

* [Modular design](/slm-lab/master/development/modular-lab-components.md) for building deep RL algorithms
* [Reproducibility](/slm-lab/master/using-slm-lab/lab-organization.md#reproducibility-design) using spec file and git SHA
* [Experiment framework](/slm-lab/master/using-slm-lab/lab-organization.md#session-trial-and-experiment) with [automatic analysis](/slm-lab/master/analyzing-results/analytics.md)
* [Extensive benchmark results](/slm-lab/master/benchmark-results/public-benchmark-data.md)
* Well-tuned algorithm implementations
* Multiple RL environment offerings

### Algorithms

SLM Lab implements most of the [canonical RL algorithms](/slm-lab/master/development/modular-lab-components/algorithm-taxonomy.md):

* 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](https://github.com/openai/gym)
* [OpenAI Roboschool](https://github.com/openai/roboschool)
* [VizDoom](https://github.com/mwydmuch/ViZDoom#documentation) (credit: joelouismarino)
* [Unity environments](https://github.com/Unity-Technologies/ml-agents) 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](https://github.com/kengz/SLM-Lab/blob/master/LICENSE).


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