融合深度强化学习与图神经网络的动态武器目标分配优化

This paper proposes a dynamic sensor-weapon-target assignment (SWTA) method based on deep reinforcement learning (DRL) and graph neural network (GNN), aimed at addressing the complex and dynamic decision-making requirements on modern battlefields. Traditional static methods are inefficient and lack adaptability in real-time changing 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 assignment 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 protection of key locations. Experiments demonstrate that this method performs excellently in various scenarios, significantly enhancing resource utilization and operational outcomes.