一种深度强化学习与模仿学习结合的突防策略

Considering the requirements for penetration and strike after penetration when the fighter encounters the interceptor in the process of attacking the target, an intelligent maneuver penetration for fighter algorithm based on deep reinforcement learning and imitation learning theory is proposed. Firstly, the maneuver penetration of fighter is transformed into a Markov decision process, and a reward function is designed that comprehensively takes into account both penetration and attack by considering the distance between the fighter and the defense missile, the distance between the fighter and the target after penetration, and the velocity deflection angle of the fighter relative to fighter-target line of sight. Then combining Proximal Policy Optimization ( PPO) algorithm and imitation learning theory, the Generative antagonistic imitation learning-proximal policy optimization (GAIL-PPO ) intelligent penetration network is constructed, which is composed of Discrimination network, Actor network and Critic network. Finally, the intelligent penetration network is trained with expert strategy. The simulation results show that the GAIL-PPO penetration strategy can quickly converge by learning the experience of expert strategies in the early stage, and can fully explore in the complex environment in the later stage, obtaining better performance than the expert strategies.