RobustRLlib unifies and benchmarks robust reinforcement learning methods
RobustRLlib: A Unified Library and Benchmark for Robust Reinforcement Learning Algorithms
Machine LearningArtificial IntelligenceRobotics
Summary
Reinforcement learning (RL) can struggle when faced with unexpected changes or disruptions in real-world use. The authors created RobustRLlib, a software library that gathers together many different RL methods designed to be reliable in such situations. This tool lets people easily compare and test these methods across many scenarios, helping to see which ones work well and where improvements are needed. It’s open source and designed to be easy to use for building more dependable RL systems.
What this means in practice
- •For robotics engineers: Test and compare robust reinforcement learning algorithms for reliable robot control under varied real-world disturbances.
- •For game ai developers: Evaluate and improve AI behavior in games by applying robust RL methods to handle unexpected environment changes.
Authors
Xi Chen, Fengqi Hu, Zeyu Shen, Jiaying Xu, Shangding Gu, Pan Xu, Anqi Liu, Laixi Shi
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.