Intelligent Pursuit–Evasion Game Based on Deep Reinforcement Learning for Hypersonic Vehicles
Mengjing Gao, Tian Yan, Quancheng Li, Wenxing Fu, Jin Zhang
Abstract
As defense technology develops, it is essential to study the pursuit–evasion (PE) game problem in hypersonic vehicles, especially in the situation where a head-on scenario is created. Under a head-on situation, the hypersonic vehicle’s speed advantage is offset. This paper, therefore, establishes the scenario and model for the two sides of attack and defense, using the twin delayed deep deterministic (TD3) gradient strategy, which has a faster convergence speed and reduces over-estimation. In view of the flight state–action value function, the decision framework for escape control based on the actor–critic method is constructed, and the solution method for a deep reinforcement learning model based on the TD3 gradient network is presented. Simulation results show that the proposed strategy enables the hypersonic vehicle to evade successfully, even under an adverse head-on scene. Moreover, the programmed maneuver strategy of the hypersonic vehicle is improved, transforming it into an intelligent maneuver strategy.