非对称机动能力多无人机智能协同攻防对抗

The attack-defense game is an important combat scenario of future military Unmanned Aerial Vehicles (UAVs). This paper studies an attack-defense game between groups of UAVs with different maneuverability, establishing a multi-UAV cooperative attack and defense evolution model. Based on the multi-agent reinforcement learning theory, the autonomous decision-making method of multi-UAV cooperative attack-defense game is studied, and a centralized critic and distributed actor algorithm structure is proposed based on the actor-critic algorithm, guaranteeing the convergence of the algorithm and improving the efficiency of decision-making. The critic module of UAVs uses the global information to evaluate the decision-making quality during training, while the actor module only needs to rely on the local perception information to make autonomous decisions during execution, hence improving the effectiveness of the multi-UAV attack-defense game. The simulation results show that the proposed multi-UAV reinforcement learning method has a strong self-evolution property, endowing the UAV certain intelligence, that is, the stable autonomous learning ability. Through continuous training, the UAVs can autonomously learn cooperative attack or defense policies to improve the effectiveness of decision-making.