@article{M502FC231,
title = "Deep Reinforcement Learning Based Weapon-Target Assignment to Support Military Decision-Making",
journal = "The Journal of Korean Institute of Communications and Information Sciences",
year = "2025",
issn = "1226-4717",
doi = "10.7840/kics.2025.50.6.884",
author = "Jaehwi Lee, Chanin Eom, Kyeongsoo Kim, Hyunsu Kang, Minhae Kwon",
keywords = "Weapon-target assignment, Deep reinforcement learning, Markov decision process (MDP), Deep deterministic policy gradient (DDPG), Twin delayed DDPG (TD3)",
abstract = "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."
}