RSL-RL Documentation ==================== .. toctree:: :maxdepth: 1 :hidden: :caption: Guide guide/overview guide/installation guide/configuration guide/contribution .. toctree:: :maxdepth: 1 :hidden: :caption: API Reference api/algorithms api/env api/extensions api/models api/modules api/runners api/storage api/utils .. toctree:: :maxdepth: 1 :hidden: :caption: Project Links GitHub Repository PyPI Package (Core Library) Main Documentation **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 supports multi-GPU training and features common algorithms for robot learning. The core library, without the additional features of this branch, is also available via `PyPI `_. Additional Features ------------------- This is the documentation for the ``extras`` branch of RSL-RL, which contains additional features that are not part of the core library. These features, mostly contributed by the community, are listed below and described in the :ref:`overview `. - No additional features yet :( 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 `_: .. code-block:: text @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} }