RSL-RL

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.

Additional algorithms, models, and more, mainly contributed by the community, can be found on the extras branch. This allows us to keep the core library on main minimal and easy to start with. To see a list of the additional features, please check the Extras Documentation.

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)

Installation

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.

Installing RSL-RL as a dependency

pip install rsl-rl-lib

Installing RSL-RL for development

git clone https://github.com/leggedrobotics/rsl_rl
cd rsl_rl
pip install -e .

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}
}