Artificial Intelligence in Combat Decision-Making: Weapon Target Assignment via Reinforcement Learning and Graph Neural Networks

Selecting targets to attack and assigning weapons are among the most critical decisions on the battlefield. The decision problem is represented as a dynamic weapon-target assignment (DWTA) problem. While deep reinforcement learning (DRL) is the state-of-the-art approach for DWTA, previous studies have limitations in three key aspects: 1) representing topological relationships on the battlefield; 2) scalability to increased problem sizes; and 3) performance metric relevance. To overcome these limitations, this study aims to solve the DWTA problem by leveraging DRL and graph neural networks (GNNs), with a novel partially observable Markov decision process (POMDP) design, including graph-based action representation, observation features, and reward design. Experiments are conducted across multiple military domains, including naval and ground combat, comparing the proposed approach with existing heuristic and meta-heuristic methodologies. The effectiveness of the GNN and decision-making pattern is extensively analyzed through comprehensive experimental validation.