Dynamic weapon-target assignment optimization integrating deep reinforcement learning and graph neural networks
WANG Qing
WANG Yu-jue
WANG Hao-ran
XIN Bin
Abstract：This paper proposes a dynamic sensor-weapon-target assignment(SWTA)method based on deep reinforce-ment learning(DRL)and graph neural network(GNN),aimed at addressing the complex and dynamic decision-making requirements on modem battlefields.Traditional static methods are inefficient and lack adaptability in real-time chang-ing battlefield environments.To tackle this issue,DRL is combined with GNN to build an intelligent decision-making framework.This framework leverages environmental interaction and deep learning to optimize decision-making strategies,thereby improving resource allocation efficiency and decision accuracy.Guided by the OODA loop theory,the framework uses GNN to capture the relationships between weapons,targets,and sensors in the battlefield,quickly generating assign-ment solutions.The DRL component then optimizes these strategies,enabling resource allocation optimization in dynamic environments.The optimization process takes into account operational effectiveness,resource consumption,and the pro-tection of key locations.Experiments demonstrate that this method performs excellently in various scenarios,significantly enhancing resource utilization and operational outcomes.
Keywords：dynamic sensor-weapon-target assignmentdeep reinforcement learninggraph neural networkOODA loop
Publication Date：2025-11-30
Online Publishing Date：2025-12-29(First online date of this platform, not the publication date of the document)
Pages：9( 2252-2260 )