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arXiv · 2610.32971

RobustRLlib: A Unified Library and Benchmark for Robust Reinforcement Learning Algorithms

Xi Chen·Fengqi Hu·Zeyu Shen·Jiaying Xu·Shangding Gu·Pan Xu·Anqi Liu·Laixi Shi
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Abstract

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.

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Xi Chen, Fengqi Hu, Zeyu Shen, Jiaying Xu, Shangding Gu, Pan Xu, Anqi Liu, Laixi Shi. 2026-10-23. RobustRLlib: A Unified Library and Benchmark for Robust Reinforcement Learning Algorithms. https://arxiv.org/abs/2610.32971

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