Reinforcement Learning for decision support in defense and security: A systematic review
Maarten P. D. Schadd, David S. Berman, C.M. Chen, Mika Cohen, John Dorsch, ALEXANDER E. GEGOV, Maximilian Moll, Oliver Rose, Anna Rösner, Kalle Saastamoinen, Thomas Schiller, Andreas Strand, Lauri Vasankari, Mark Rempel · Annals of Operations Research · 2026
Sequential decision making with imperfect knowledge of the environment and adversary lies at the heart of defense and security. As such, contemporary reinforcement learning methods which are well adapted to these sorts of problems have the potential to provide the key tools to support decision makers in a military context. This paper presents a literature review of reinforcement learning for decision support in a defense context with a focus on recent work produced by NATO member states. To aid the reader, we provide the prerequisite background in military decision making; decision support systems in general; reinforcement learning basics; simulation; and explainability. Through a systematic review of the literature and using tools such as UMAP, we construct the research landscape and study the emergent trends and research gaps that are present. This allows us to examine the challenges for exploiting this work in reality and finally, we look to the future and how these challenges can be overcome.