AAV Swarm Intelligent Interception Driven by Mission-Empowered Mean-Field Gate Recurrent Reinforcement Learning

Intercepting a saturation attack from a loitering munition (LM) swarm with a swarm of unmanned aerial vehicles (UAVs) is a complex challenge. In this study, we developed a specific intercept mission simulation environment and proposed an approach based on the Gate Recurrent Unit Mean-Field Deep Deterministic Policy Gradient (GRU-MFDDPG) algorithm to tackle this issue. Based on the battle situation of both sides, we constructed a target assignment model for the UAV swarm based on the extensible Hungarian algorithm. To address the operational characteristics of large-scale UAV swarm interception missions, a partially observable mean-field game theory was integrated to modify the DDPG algorithm, with a Gate Recurrent Unit (GRU) incorporated to predict the movements of incoming loitering munitions accurately. The algorithm was trained under a “centralized training, distributed execution” framework. Simulation results demonstrate that this approach significantly enhances the interception success rate of the UAV swarm against large-scale loitering munitions.