RLLTE: Long-Term Evolution Project of Reinforcement Learning

Mingqi Yuan, Zequn Zhang, Yang Xu, Shihao Luo, Bo Li +2 more
9/28/2023
cs.AIcs.LG

Official code: RLE-Foundation/rllte (471 stars, MIT, last commit 2025-07-22).

Abstract

We present RLLTE: a long-term evolution, extremely modular, and open-source framework for reinforcement learning (RL) research and application. Beyond delivering top-notch algorithm implementations, RLLTE also serves as a toolkit for developing algorithms. More specifically, RLLTE decouples the RL algorithms completely from the exploitation-exploration perspective, providing a large number of components to accelerate algorithm development and evolution. In particular, RLLTE is the first RL framework to build a comprehensive ecosystem, which includes model training, evaluation, deployment, benchmark hub, and large language model (LLM)-empowered copilot. RLLTE is expected to set standards for RL engineering practice and be highly stimulative for industry and academia. Our documentation, examples, and source code are available at https://github.com/RLE-Foundation/rllte.

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Code Implementations(1)

RLE-Foundation/rllteOfficial100%
MIT

Long-Term Evolution Project of Reinforcement Learning

47190C, C++Jan 19, 20231 years agoMIT
augmentationbenchmark-frameworkdeep-learningdeepmind-control-suitedrq-v2+11 more

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Cite this paper

@article{yuan2023rllte,
  title  = {RLLTE: Long-Term Evolution Project of Reinforcement Learning},
  author = {Mingqi Yuan and Zequn Zhang and Yang Xu and Shihao Luo and Bo Li and Xin Jin and Wenjun Zeng},
  year   = {2023},
  eprint = {2309.16382},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2309.16382v2}
}

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