专为机器人强化学习设计的轻量开源库，易改易用。
RSL-RL: A Learning Library for Robotics Research
- 专注机器人场景，代码简洁可快速修改。
- 支持GPU高效训练，大规模仿真下性能出色。
- 适合做机器人控制算法开发的研究者使用。
RSL-RL 是一个面向机器人研究领域的开源强化学习库。与通用框架不同，其设计理念强调代码库紧凑且易于修改，使研究人员能以极低开销适配和扩展算法。该库聚焦机器人领域最广泛使用的算法，同时集成解决机器人特有问题的辅助技术。针对仅GPU训练优化，在大规模仿真环境中实现高吞吐性能。其有效性已在仿真基准测试和真实机器人实验中验证，证明其作为轻量、可扩展、实用的基于学习的机器人控制器开发框架的价值。项目已开源：https://github.com/leggedrobotics/rsl_rl。
原文摘要 · Abstract (English)
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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