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.
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}
}
2 maintainers
Metadata
2 maintainers
Release files for rsl-rl-lib 5.5.0
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|---|---|---|---|---|
| rsl_rl_lib-5.5.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 167.1 kB
Release files / rsl_rl_lib-5.5.0.tar.gz
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