分层决策多机空战对抗方法

In air combat research, tactical decision-making aims to improve the gain of game confrontation and then the attack efficiency of one's own fighter aircraft. Most existing tactical decision-making algorithms are designed based on the rule-based approach, which brings difficulty to designing and solving the optimal solution for the complex environment of multi-aircraft air combat. This paper proposes a hierarchical decision-making multi-aircraft air combat method. First, we draw on the existing human expert experience in the initial stage of training to guide model training; second, we design a hierarchical action decision-making network according to the tactical action types to reduce the action decision space dimensions; and finally, we decompose the training-generated adversarial experience in stages to reduce the strategy learning difficulty. Experiments in a multi-aircraft air combat simulation environment demonstrate that the proposed method shows better performance regarding training convergence and decision-making performance compared with common multi-aircraft air combat decision-making methods.