This library standardizes robust RL algorithm implementation and reveals empirical capability boundaries—showing practitioners which robustness techniques handle which types of real-world disruptions, with reproducible benchmarks for developing and testing new methods.
RobustRLlib is a unified library and benchmark for robust reinforcement learning that integrates 17 algorithms (online, offline, and safe RL) under one interface. It provides systematic evaluation across 273 perturbation configurations and diverse tasks to map where current robust RL methods work well and where gaps remain, enabling better real-world deployment.