RSL-RL Documentation

RSL-RL is a GPU-accelerated, lightweight learning library for robotics research. It’s compact design allows researchers to prototype and test new ideas without the overhead of modifying large, complex libraries. RSL-RL can also be used out-of-the-box by installing it via PyPI, supports multi-GPU training and features common algorithms for robot learning.

Key Features

• Minimal, readable codebase with clear extension points for rapid prototyping.

• Robotics-first methods including PPO and Student-Teacher Distillation.

• High-throughput training with native Multi-GPU support.

• Proven performance in numerous research publications.

Learning Environments

RSL-RL is currently used by the following robot learning libraries:

• Isaac Lab (built on top of NVIDIA Isaac Sim)

• Legged Gym (built on top of NVIDIA Isaac Gym)

• mjlab (built on top of MuJoCo Warp)

• MuJoCo Playground (built on top of MuJoCo MJX and Warp)

Citation

If you use RSL-RL in your research, please cite the paper:

@article{schwarke2025rslrl,
  title={RSL-RL: A Learning Library for Robotics Research},
  author={Schwarke, Clemens and Mittal, Mayank and Rudin, Nikita and Hoeller, David and Hutter, Marco},
  journal={arXiv preprint arXiv:2509.10771},
  year={2025}
}