AI Fire Support Officer: Military Decision Support System Based on Reward Adaptive Reinforcement Learning 


Vol. 51,  No. 1, pp. 209-222, Jan.  2026
10.7840/kics.2026.51.1.209


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  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.

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[IEEE Style]

J. Lee, C. Eom, C. Kim, K. Kim, H. Lee, H. Kang, M. Kwon, "AI Fire Support Officer: Military Decision Support System Based on Reward Adaptive Reinforcement Learning," The Journal of Korean Institute of Communications and Information Sciences, vol. 51, no. 1, pp. 209-222, 2026. DOI: 10.7840/kics.2026.51.1.209.

[ACM Style]

Jaehwi Lee, Chanin Eom, Chan Kim, Kyeongsoo Kim, Hyeongdo Lee, Hyunsu Kang, and Minhae Kwon. 2026. AI Fire Support Officer: Military Decision Support System Based on Reward Adaptive Reinforcement Learning. The Journal of Korean Institute of Communications and Information Sciences, 51, 1, (2026), 209-222. DOI: 10.7840/kics.2026.51.1.209.

[KICS Style]

Jaehwi Lee, Chanin Eom, Chan Kim, Kyeongsoo Kim, Hyeongdo Lee, Hyunsu Kang, Minhae Kwon, "AI Fire Support Officer: Military Decision Support System Based on Reward Adaptive Reinforcement Learning," The Journal of Korean Institute of Communications and Information Sciences, vol. 51, no. 1, pp. 209-222, 1. 2026. (https://doi.org/10.7840/kics.2026.51.1.209)
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pISSN : 1226 - 4717
eISSN : 2287 - 3880