A Survey of Explainable Reinforcement Learning: Targets, Methods and Needs

Abstract:The success of recent Artificial Intelligence (AI) models has been accompanied by the opacity of their internal mechanisms, due notably to the use of deep neural networks. In order to understand these internal mechanisms and explain the output of these AI models, a set of methods have been proposed, grouped under the domain of eXplainable AI (XAI). This paper focuses on a sub-domain of XAI, called eXplainable Reinforcement Learning (XRL), which aims to explain the actions of an agent that has learned by reinforcement learning. We propose an intuitive taxonomy based on two questions "What" and "How". The first question focuses on the target that the method explains, while the second relates to the way the explanation is provided. We use this taxonomy to provide a state-of-the-art review of over 250 papers. In addition, we present a set of domains close to XRL, which we believe should get attention from the community. Finally, we identify some needs for the field of XRL.

Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

arXiv:2507.12599 [cs.AI]

(or arXiv:2507.12599v2 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2507.12599 Focus to learn more arXiv-issued DOI via DataCite

https://doi.org/10.1613/jair.1.20550 Focus to learn more DOI(s) linking to related resources

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