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RLLTE: Long-Term Evolution Project of Reinforcement Learning

Inspired by the long-term evolution (LTE) standard project in telecommunications, aiming to provide development components and standards for advancing RL research and applications. Beyond delivering top-notch algorithm implementations, RLLTE also serves as a toolkit for developing algorithms.


An introduction to RLLTE.

Why RLLTE?

  • 🧬 Long-term evolution for providing latest algorithms and tricks;
  • 🏞️ Complete ecosystem for task design, model training, evaluation, and deployment (TensorRT, CANN, ...);
  • 🧱 Module-oriented design for complete decoupling of RL algorithms;
  • 🚀 Optimized workflow for full hardware acceleration;
  • ⚙️ Support custom environments and modules;
  • 🖥️ Support multiple computing devices like GPU and NPU;
  • 💾 Large number of reusable benchmarks (RLLTE Hub);
  • 👨‍✈️ Large language model-empowered copilot (RLLTE Copilot).

A PyTorch for RL

RLLTE decouples RL algorithms into minimum primitives and provide standard modules for development.

See Fast Algorithm Development for detailed examples.

Project Evolution

RLLTE selects RL algorithms based on the following tenet:

  • Generality is the most important;
  • Improvements in sample efficiency or generalization ability;
  • Excellent performance on recognized benchmarks;
  • Promising tools for RL.

Cite Us

If you use RLLTE in your research, please cite this project like this: 

@article{yuan2023rllte,
  title={RLLTE: Long-Term Evolution Project of Reinforcement Learning}, 
  author={Mingqi Yuan and Zequn Zhang and Yang Xu and Shihao Luo and Bo Li and Xin Jin and Wenjun Zeng},
  year={2023},
  journal={arXiv preprint arXiv:2309.16382}
}