Deep Reinforcement Learning Based Weapon-Target Assignment to Support Military Decision-Making

Recently, there has been significant research into technologies to assist commanders in military decision-making through the weapon-target assignment (WTA) problem. As the modern battlefield has evolved, WTA problems consider realistic and complex environments, where deep reinforcement learning methods can be utilized for dynamic decision-making. This paper proposes a Markov decision process (MDP) model for efficient decision-making in WTA problems, leveraging deep reinforcement learning to optimize the actions of friendly units. As a result, we confirmed that the reinforcement learning model based on the proposed MDP improved the commander's objective achievement by 27.17% and ammo efficiency by 38.61%, while reducing ammo usage cost by 11.98% compared to the heuristic approaches.

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Cite this article

[IEEE Style]

J. Lee, C. Eom, K. Kim, H. Kang, M. Kwon, "Deep Reinforcement Learning Based Weapon-Target Assignment to Support Military Decision-Making," The Journal of Korean Institute of Communications and Information Sciences, vol. 50, no. 6, pp. 884-895, 2025. DOI: 10.7840/kics.2025.50.6.884.

[ACM Style]

Jaehwi Lee, Chanin Eom, Kyeongsoo Kim, Hyunsu Kang, and Minhae Kwon. 2025. Deep Reinforcement Learning Based Weapon-Target Assignment to Support Military Decision-Making. The Journal of Korean Institute of Communications and Information Sciences, 50, 6, (2025), 884-895. DOI: 10.7840/kics.2025.50.6.884.

[KICS Style]

Jaehwi Lee, Chanin Eom, Kyeongsoo Kim, Hyunsu Kang, Minhae Kwon, "Deep Reinforcement Learning Based Weapon-Target Assignment to Support Military Decision-Making," The Journal of Korean Institute of Communications and Information Sciences, vol. 50, no. 6, pp. 884-895, 6. 2025. (https://doi.org/10.7840/kics.2025.50.6.884)

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Deep Reinforcement Learning Based Weapon-Target Assignment to Support Military Decision-Making

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