SLM Lab

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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.
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 SLM Lab is also the companion library of the book Foundations of Deep Reinforcement Learning. The book's website and errata is here.
✨ 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:
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@misc{kenggraesser2017slmlab,
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    author = {Keng, Wah Loon and Graesser, Laura},
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    title = {SLM Lab},
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    year = {2017},
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    publisher = {GitHub},
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    journal = {GitHub repository},
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    howpublished = {\url{https://github.com/kengz/SLM-Lab}},
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}
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License
This project is licensed under the MIT License.

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