2025/07/16 by Léo Saulières, Saulières, Léo · 1 voice
Computer Science · Psychology · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Mental Health Research Topics #cs.AI #cs.LG
paper · pdf · doi:10.48550/arxiv.2507.12599
openalex publication_date 2025/07/16 · arxiv published 2025/07/16 · arxiv updated 2025/07/16 · openalex created_date 2025/10/18 · openalex updated_date 2026/07/28
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.