Drift Detection in Episodic Data: Detect When Your Agent Starts Faltering

Ido Greenberg, Shie Mannor

Keywords: Reinforcement learning reliability, Reinforcement learning stability, Drift detection, Degradation test, Bootstrapping

Abstract: Detection of deterioration of agent performance in dynamic environments is challenging due to the non-i.i.d nature of the observed performance. We consider an episodic framework, where the objective is to detect when an agent begins to falter. We devise a hypothesis testing procedure for non-i.i.d rewards, which is optimal under certain conditions. To apply the procedure sequentially in an online manner, we also suggest a novel Bootstrap mechanism for False Alarm Rate control (BFAR). We demonstrate our procedure in problems where the rewards are not independent, nor identically-distributed, nor normally-distributed. The statistical power of the new testing procedure is shown to outperform alternative tests - often by orders of magnitude - for a variety of environment modifications (which cause deterioration in agent performance). Our detection method is entirely external to the agent, and in particular does not require model-based learning. Furthermore, it can be applied to detect changes or drifts in any episodic signal.

One-sentence Summary: Optimal test for change detection in non-i.i.d settings, with applications to identifying deterioration in agent performance in episodic RL.

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