基于 AM-SAC 的无人机自主空战决策

To address the autonomous decision-making problem of unmanned aerial vehicles (UAV) in modern air combats, a maneuvering decision algorithm based on AM-SAC algorithm is proposed by combining the Attention Mechanism (AM) with Soft Actor Critic (SAC) in deep reinforcement learning. Focusing on 1V1 combat scenarios, the UAV three degree of freedom maneuvering model and the UAV close-range air combat model are established, and the missile attack zone model is built based on the relative distance and relative azimuth angle between both sides in a combat. The attention mechanism is introduced into SAC algorithm to construct the weight network, so as to realize the dynamic adjustment of the weight distribution of reward function during the training process. The simulation experiments are also designed. By comparing with SAC algorithm and testing in multiple environments with different initial situations, it is verified that the UAV air combat decision algorithm based on the AM-SAC algorithm has higher convergence speed and maneuvering stability, as well as better performance in air combat across various initial environments.