RLLTE: Long-Term Evolution Project of Reinforcement Learning

• URL: http://arxiv.org/abs/2309.16382v1
• Date: Thu, 28 Sep 2023 12:30:37 GMT
• Title: RLLTE: Long-Term Evolution Project of Reinforcement Learning
• Authors: Mingqi Yuan, Zequn Zhang, Yang Xu, Shihao Luo, Bo Li, Xin Jin, Wenjun Zeng
• Abstract summary: We present RLLTE: a long-term evolution, extremely modular, and open-source framework for reinforcement learning research and application. Beyond delivering top-notch algorithm implementations, RLLTE also serves as a toolkit for developing algorithms. RLLTE is expected to set standards for RL engineering practice and be highly stimulative for industry and academia.
• Score: 48.181733263496746
• License: http://creativecommons.org/publicdomain/zero/1.0/
• 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 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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