Selecting a threat to attack is one of the most important decisions on the battlefield. The decision problem is represented as a Weapon-Target Assignment problem (WTA) problem. In the previous studies, dynamic programming, linear programming, metaheuristics, and heuristic methods have been applied to solve this problem. However, previous studies have been limited by oversimplified-model, computational burden, lack of adaptability to disruptive events, and recalculation when the problem size changes. To overcome these limitations, this study aims to solve WTA by using reinforcement learning and graph neural networks. The proposed method has high practicality by reflecting the real-world
decision-making framework, OODA-loop (Observe-Orient-Decide). Experiments are conducted in various environments, and the effectiveness of the proposed method is demonstrated by comparing it with existing heuristic and meta-heuristic methodologies. The proposed method introduces a groundbreaking methodology for intelligent decision-making in tactical command and control traditionally considered exclusive to human-expert.