RLLTE: Long-Term Evolution Project of Reinforcement Learning

Mingqi Yuan, Zequn Zhang, Yang Xu, Jake Shihao Luo, Bo Li, Xin Jin, Wenjun Zeng

Primary Area: reinforcement learning

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Keywords: reinforcement learning, framework, benchmark, open-source, library, intrinsic reward, data augmentation, copilot, LLM

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TL;DR: A promising framework for the long-term evolution of reinforcement learning.

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 prototypes to accelerate algorithm development and evolution. In particular, RLLTE is the first RL framework to build a complete and luxuriant 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.

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Submission Number: 1867