无人机集群对抗决策算法研究综述OA

UAV swarm air-combat has become the development trend of future warfare,and the selection of UAV swarm air-combat decision-making algorithms is crucial for improving the UAV swarm combat ability.This paper delve into three types of UAV swarm air-combat decision-making algorithms based on rules,game theory,and neural networks,and comprehensively analyze and summarize their advantages and limitations.On this basis,this paper propose to apply the multi-agent reinforcement learning based credit assignment model and role-based malti-agent reinforcement learning model and design for UAV swarm air-combat.Finally,it emphasize the importance of selecting appropriate decision algorithms to improve the combat effectiveness of UAV clusters,and provide useful suggestions for the development of UAV countermeasures decision-making in the future,providing in-depth insights for research and application in related fields.