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RobustRLlib released: unifies 17 robust reinforcement learning algorithms and provides evaluation across 273 perturbation configurations

RobustRLlib is an algorithm-centric library and benchmark for robust reinforcement learning that integrates 17 representative online, offline, and safe reinforcement learning algorithms under a unified interface. Its evaluation platform covers a variety of real-world shifts as well as humanoid robot and vision-language-action tasks, and it presents the capability distribution of current robust reinforcement learning algorithms across 273 perturbation configurations, identifying which scenarios remain effective and where substantial robustness gaps persist. The project provides open-source code, a project page, and tutorials to facilitate reproduction and extension.

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