@article{MDCD4E325,
title = "AI Fire Support Officer: Military Decision Support System  Based on Reward Adaptive Reinforcement Learning",
journal = "The Journal of Korean Institute of Communications and Information Sciences",
year = "2026",
issn = "1226-4717",
doi = "10.7840/kics.2026.51.1.209",
author = "Jaehwi Lee, Chanin Eom, Chan Kim, Kyeongsoo Kim,
Hyeongdo Lee, Hyunsu Kang, Minhae Kwon",
keywords = "Military decision-making, Command decision support, Weapon-target assignment,
Reinforcement learning",
abstract = "Recent studies in military decision support have actively explored deep reinforcement learning (RL)
approaches to automate complex battlefield decision-making processes. This paper proposes a reward-adaptive
RL-based firepower operation system designed to support command decisions in dynamic combat environments.
The proposed system perceives battlefield situations through a perception module and makes decisions to
achieve the commander’s desired effects. The decision-making module integrates both pre-collected and online
interaction data while employing a reward-adaptive selective imitation mechanism to enhance sample efficiency
and stability simultaneously. Through simulated battlefield scenarios, the proposed system demonstrated an
average 29% improvement in mission achievement compared to conventional RL and heuristic-based methods,
while effectively satisfying given operational constraints."
}