RLLTE: Long-Term Evolution Project of Reinforcement Learning
This framework aims to set standards for RL engineering practice, potentially stimulating advancements in both industry and academia.
The authors introduced RLLTE, an open-source reinforcement learning framework designed to decouple algorithms from exploration-exploitation trade-offs, providing modular components to accelerate development and including features like training, evaluation, deployment, benchmarks, and an LLM-powered copilot.
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.