RSL-RL: A Learning Library for Robotics Research

Clemens Schwarke, Mayank Mittal, Nikita Rudin, David Hoeller, Marco Hutter
9/13/2025
cs.ROcs.LG

Official code: leggedrobotics/rsl_rl (2,805 stars, last commit 2026-04-23).

Abstract

RSL-RL is an open-source Reinforcement Learning library tailored to the specific needs of the robotics community. Unlike broad general-purpose frameworks, its design philosophy prioritizes a compact and easily modifiable codebase, allowing researchers to adapt and extend algorithms with minimal overhead. The library focuses on algorithms most widely adopted in robotics, together with auxiliary techniques that address robotics-specific challenges. Optimized for GPU-only training, RSL-RL achieves high-throughput performance in large-scale simulation environments. Its effectiveness has been validated in both simulation benchmarks and in real-world robotic experiments, demonstrating its utility as a lightweight, extensible, and practical framework to develop learning-based robotic controllers. The library is open-sourced at: https://github.com/leggedrobotics/rsl_rl.

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Code Implementations(1)

leggedrobotics/rsl_rlOfficial100%

A fast and simple implementation of learning algorithms for robotics.

2,805633PythonOct 18, 20216 months agoNOASSERTION

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Cite this paper

@article{schwarke2025rslrl,
  title  = {RSL-RL: A Learning Library for Robotics Research},
  author = {Clemens Schwarke and Mayank Mittal and Nikita Rudin and David Hoeller and Marco Hutter},
  year   = {2025},
  eprint = {2509.10771},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2509.10771v1}
}

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