Adaptive Missile Avoidance Algorithm for UAV Based on Multi-Head Attention Mechanism and Dual Population Confrontation Game - Cheng Zhang; Junhao Song; Chengyang Tao; Zitao Su; Zhiqiang Xu; Weijia Feng; Zhaoxiang Zhang; Yuelei Xu (Drones)
In recent years, UAVs have faced increasingly severe and diversified missile threats. To address the challenge that reinforcement learning-based missile evasion algorithms struggle to adapt to various unknown missile types, we introduce a risk-sensitive PPO algorithm and propose a training framework incorporating multi-head attention mechanisms and dual-population adversarial training. The multi-head attention mechanism enables the policy network to extract latent features such as missile guidance laws from state sequences, while the dual-population adversarial approach ensures policy diversity and robustness. Compared to conventional self-play methods and GRU-based evasion strategies, our method demonstrates superior training efficiency and generates evasion policies with better adaptability to different missile types.