A survey paper titled "A Survey of Explainable Reinforcement Learning: Targets, Methods and Needs" reviews over 250 studies in the sub‑domain of eXplainable AI focused on reinforcement learning (XRL). The authors introduce a taxonomy based on the questions "What" (the target of explanation) and "How" (the method of explanation) and use it to organize the state‑of‑the‑art literature. They also highlight related domains that warrant further attention and outline key needs for advancing the field of XRL.
By L\'eo Sauli\`eres
arXiv:2606. 04167v1 Announce Type: cross Abstract: We tackle the Metro Network Expansion Problem (MNEP), a subset of the Transport Network Design Problem (TNDP), which focuses on expanding metro systems to satisfy travel demand.
By Dimitris Michailidis, Sennay Ghebreab, Fernando P. Santos
arXiv:2608. 14620v1 Announce Type: cross Abstract: Reinforcement Learning (RL) has been extensively used in autonomous and self-* systems, but RL policies, especially deep RL ones relying on neural networks, lack transparency and are difficult to understand.
By Jasmina Gajcin, Juan C. Rosero, Ivana Dusparic
arXiv:2608. 14963v1 Announce Type: cross Abstract: Pareto Conditioned Networks learn multiple multi-objective reinforcement learning behaviours by conditioning a single policy on a desired return command.
By Joanikij Chulev, Hendrik Baier
arXiv:2502. 00684v2 Announce Type: replace-cross Abstract: Deep reinforcement learning (DRL) has successfully addressed many complex control problems.
By Zeyu Jiang, Hai Huang, Xingquan Zuo
arXiv:2606. 10129v1 Announce Type: new Abstract: While deep Reinforcement Learning (deep-RL) has been increasingly applied to parameter control in evolutionary algorithms, rigorous theoretical analysis of parameter control remains largely restricted to single-parameter settings, owing to the difficulty of deriving effective, interpretable multi-parameter policies amenable to formal study.
By Tai Nguyen, Phong Le, Carola Doerr, Nguyen Dang