Can you trust your Agent? The Effect of Out-of-Distribution Detection on the Safety of Reinforcement Learning Systems

Tom Haider, Karsten Roscher, Benjamin Herd, Felippe Schmoeller Roza, Simon Burton

Abstract: Deep Reinforcement Learning (RL) has the potential to revolutionize the automation of complex sequential decision-making problems. Although it has been successfully applied to a wide range of tasks, deployment to real-world settings remains challenging and is often limited. One of the main reasons for this is the lack of safety guarantees for conventional RL algorithms, especially in situations that substantially differ from the learning environment. In such situations, state-of-the-art systems will fail silently, producing action sequences without signalizing any uncertainty regarding the current input. Recent works have suggested Out-of-Distribution (OOD) detection as an additional reliability measure when deploying RL in the real world. How these mechanisms benefit the safety of the entire system, however, is not yet fully understood. In this work, we study how OOD detection contributes to the safety of RL systems by describing the challenges involved with detecting unknown situations. We derive several definitions for unknown events and explore potential avenues for a successful safety argumentation, building on recent work for safety assurance of Machine Learning components. In a series of experiments, we compare different OOD detectors and show how difficult it is to distinguish harmless from potentially unsafe OOD events in practice, and how standard evaluation schemes can lead to deceptive conclusions, depending on which definition of unknown is applied.