UAV Swarm Rounding Strategy Based on Deep Reinforcement Learning Goal Consistency with Multi-Head Soft Attention Algorithm

Abstract

Aiming at the problem of target rounding by UAV swarms in complex environments, this paper proposes a goal consistency reinforcement learning approach based on multi-head soft attention (GCMSA). Firstly, in order to make the model closer to reality, the reward function when the target is at different positions and the target escape strategy are set, respectively. Then, the Multi-head soft attention module is used to promote the information cognition of the target among the UAVs, so that the UAVs can complete the target roundup more smoothly. Finally, in the training phase, this paper introduces cognitive dissonance loss to improve the sample utilization. Simulation experiments show that GCMSA is able to obtain a higher task success rate and is significantly better than MADDPG in terms of algorithm performance.