Intelligent air defense task assignment based on hierarchical reinforcement learning - Liu, Jia-yi; Wang, Gang; Guo, Xiang-ke; Wang, Si-yuan; Fu, Qiang (Frontiers in Neurorobotics, 2022)
Modern air defence battlefield situations are complex and varied, requiring high-speed computing capabilities and real-time situational processing for task assignment. Current methods struggle to balance the quality and speed of assignment strategies. This paper proposes a hierarchical reinforcement learning architecture for ground-to-air confrontation (HRL-GC), which divides the task assignment problem into two levels: scheduling and execution. The architecture effectively combines the advantages of centralized and distributed approaches. To improve the executive agent's inefficiency in the initial training phase, this paper proposes an algorithm combining model predictive control with proximal policy optimization (MPC-PPO), which improves training efficiency while ensuring the quality of the final decision. In a large-scale area air defence scenario, this paper validates the effectiveness and superiority of the HRL-GC architecture and MPC-PPO algorithm, proving that the method can meet the needs of large-scale air defence task assignment in terms of quality and speed.