RLLTE: Long-Term Evolution Project of Reinforcement Learning
Mingqi Yuan, Zequn Zhang, Yang Xu, Shihao Luo, Bo Li, Xin Jin, Wenjun Zeng
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.
BibTeX
@article{Yuan_Zhang_Xu_Luo_Li_Jin_Zeng_2025, title={RLLTE: Long-Term Evolution Project of Reinforcement Learning}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/35378}, DOI={10.1609/aaai.v39i28.35378}, abstractNote={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.}, number={28}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Yuan, Mingqi and Zhang, Zequn and Xu, Yang and Luo, Shihao and Li, Bo and Jin, Xin and Zeng, Wenjun}, year={2025}, month={Apr.}, pages={29718-29720} }