본문 바로가기
  • Home

Reinforcement Learning-Based Multi-Target Threat Assessment and Weapon Assignment Algorithm Using Attention and Responsibility-Based Rewards

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2026, 31(1), pp.261~270
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : November 10, 2025
  • Accepted : December 23, 2025
  • Published : January 30, 2026

Woo-Hyeon Moon 1 Seo-Ho Lee 1 Won-Seok Jang 1 Ji-Seok Yoon 1 Hyeon-Mo Kim 1 Ju-Mi Park 1 Jae-Bok Sung 2

1한화시스템
2애자일소다

Accredited

ABSTRACT

In this paper, we propose a reinforcement learning-based decision-making algorithm tailored for multi-weapon and multi-threat combat environments. The proposed method separates actor-critic structures by weapon type and incorporates self-attention and cross-attention mechanisms to enable interaction-aware learning across agents. A responsibility-based reward function is designed to evaluate the contribution of each weapon to combat outcomes, promoting cooperative behavior and preventing policy bias. The algorithm is implemented using the Stable-Baselines3 library and validated through tactical simulations. Experimental results demonstrate that our method achieves superior strategic efficiency and collaboration performance compared to conventional PPO-based models, especially under sparse reward conditions and resource constraints.

Citation status

* References for papers published after 2024 are currently being built.