Reinforcement Learning for decision support in defense and security. A systematic review
Authors/Creators
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Schadd, Maarten
(Contact person)1
- Berman, David (Researcher)2, 3
- Chen, Carolyn (Researcher)4
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Cohen, Mika
(Researcher)5
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Dorsch, John
(Researcher)6, 7
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Gegov, Alexander
(Researcher)8
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Moll, Maximilian
(Researcher)9
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Rose, Oliver10
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Rösner, Anna
(Researcher)11
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Saastamoinen, Kalle
(Researcher)12
- et al. Show all 13 authors
-
Schadd, Maarten
(Contact person)1
- Berman, David (Researcher)2, 3
- Chen, Carolyn (Researcher)4
-
Cohen, Mika
(Researcher)5
-
Dorsch, John
(Researcher)6, 7
-
Gegov, Alexander
(Researcher)8
-
Moll, Maximilian
(Researcher)9
-
Rose, Oliver10
-
Rösner, Anna
(Researcher)11
-
Saastamoinen, Kalle
(Researcher)12
- Schiller, Thomas (Researcher)13
-
Vasankari, Lauri
(Researcher)14
-
Rempel, Mark
(Contact person)15
- 1. Intelligence & Decision Support, TNO
- 2. Center for Theoretical Physics, Queen Mary University of London
- 3. Artificial Intelligence Department, Cambridge Consultants
- 4. Centre for Operational Research and Analysis, Defence Research and Development Canada
- 5. Cyber defence and C2 technology, FOI Swedish Defence Research Agency
- 6. Center for Environmental and Technology Ethics - Prague (CETE-P), Institute of Philosophy of the Czech Academy of Sciences
- 7. Faculty of Philosophy, Philosophy of Science and the Study of Religion, Ludwig-Maximilians-Universität München
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8.
University of Portsmouth
- 9. Department of Computer Science, University of the Bundeswehr Munich
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10.
Universität der Bundeswehr München
- 11. School of Computing, University of Portsmouth
- 12. Department of Military Technology, National Defence University
- 13. Department of Operations Research and Modeling & Simulation, Army Concepts and Capabilities Development Centre
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14.
Finnish Defence Forces
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15.
Defence Research and Development Canada
Description
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.
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article_Schadd_Dorsch_et_al_Reinforcement_Learning_for_Decision_support.pdf
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- Is published in
- Journal article: 10.1007/s10479-026-07205-x (DOI)