RSL-RL is a GPU-accelerated, lightweight learning library for robotics research. Its 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.
- 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.
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)
Before installing RSL-RL, ensure that Python 3.9+ is available. It is recommended to install the library in a virtual environment (e.g. using venv or conda), which is often already created by the used environment library (e.g. Isaac Lab). If so, make sure to activate it before installing RSL-RL.
pip install rsl-rl-libgit clone https://github.com/leggedrobotics/rsl_rl cd rsl_rl pip install -e .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} }