Pursuit-Interception Strategy in Differential Games Based on Q-Learning-Cover Algorithm

Due to the limited difference in maneuverability between the pursuer and the evader in three-dimensional space, it is difficult for a single pursuer to capture the evader. To address this, this paper proposes a strategy where three pursuers intercept one evader and introduces a Q-learning-cover algorithm. Based on the motion models of the pursuers and the evader in three-dimensional space, this paper presents a region coverage scheme based on the Ahlswede ball and analyzes the convergence upper bound of the Q-learning-cover algorithm by designing an appropriate Lyapunov function. Through extensive model training, the successful capture of the evader by the pursuers in a three-on-one scenario was achieved. Finally, numerical simulation experiments and hardware-in-the-loop simulation experiments are presented, both of which demonstrate that the proposed Q-learning-cover algorithm can effectively realize the three-on-one encirclement and interception of the evading target.