SLM Lab
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SLM Lab
Modular Deep Reinforcement Learning framework in PyTorch.
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

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:

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.
Last modified 9mo ago
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✨ Features
Algorithms
Environments
Citation
License