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.

翻译：我们提出RLLTE：一个面向强化学习（RL）研究与应用的长期演进、高度模块化、开源的框架。除了提供顶级的算法实现，RLLTE还作为算法开发的工具包。具体而言，RLLTE从利用-探索视角完全解耦强化学习算法，提供大量组件以加速算法开发与演进。值得注意的是，RLLTE是首个构建完整且丰富生态系统的强化学习框架，涵盖模型训练、评估、部署、基准测试中心和大语言模型（LLM）赋能的辅助系统。RLLTE有望为强化学习工程实践设立标准，并对工业界和学术界产生显著推动作用。