RobustRLlib 发布：统一接口整合 17 种鲁棒强化学习算法
RobustRLlib: A Unified Library and Benchmark for Robust Reinforcement Learning Algorithms
做强化学习的可以看看，17 种鲁棒 RL 算法统一到一个库里，还带 273 种扰动配置的基准测试，省得自己一个个复现。
RobustRLlib 是一个面向鲁棒强化学习的开源库与基准测试平台，将 17 种代表性的在线、离线及安全 RL 算法整合到统一接口下，提升算法复用性。平台覆盖多种真实世界扰动场景，并加入人形机器人和视觉-语言-动作任务。团队在 273 种扰动配置上做了系统评估，绘制出当前鲁棒 RL 算法的能力边界图，标明了哪些场景仍然有效、哪些方面鲁棒性缺口明显。
RobustRLlib: A Unified Library and Benchmark for Robust Reinforcement Learning Algorithms
Robust reinforcement learning (RL) is critical for reliable real-world deployment, where policies may fail under diverse shifts and disruptions. Existing robust RL algorithms offer a rich set of mechanisms and principles, yet two major barriers limit their broader potential: limited algorithm reusability and an unclear empirical capability boundary. We introduce RobustRLlib, an algorithm-centric library and benchmark that addresses both challenges. It integrates 17 representative robust online, offline, and safe RL algorithms under a unified interface for substantially improved reusability, together with an enhanced evaluation platform spanning diverse real-world shifts and advanced humanoid and vision-language-action tasks. Systematic evaluation over 273 perturbation configurations provides an up-to-date capability map of current robust RL algorithms, revealing where they remain effective and where substantial robustness gaps persist. As an open-source, user-friendly platform, RobustRLlib offers a reproducible and extensible codebase for applying existing methods to new problems and developing new robust RL algorithms.