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

Ido Greenberg, Shie Mannor

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.
Original languageEnglish
Title of host publicationICLR 2021
Number of pages13
StatePublished - 2021
Event9th International Conference on Learning Representations, ICLR 2021 - Virtual, Online
Duration: 3 May 20217 May 2021

Conference

Conference9th International Conference on Learning Representations, ICLR 2021
CityVirtual, Online
Period3/05/217/05/21

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