RLLTE: Long-Term Evolution Project of Reinforcement Learning
Mingqi Yuan Zequn Zhang Yang Xu Shihao Luo Bo Li Xin Jin Wenjun Zeng
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.