AUTHOR=Liu Jia-yi , Wang Gang , Guo Xiang-ke , Wang Si-yuan , Fu Qiang 

TITLE=Intelligent air defense task assignment based on hierarchical reinforcement learning

JOURNAL=Frontiers in Neurorobotics

VOLUME=16

YEAR=2022

URL=https://www.frontiersin.org/journals/neurorobotics/articles/10.3389/fnbot.2022.1072887

DOI=10.3389/fnbot.2022.1072887

ISSN=1662-5218

ABSTRACT=<p>Modern air defense 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) and an algorithm combining model predictive control with proximal policy optimization (MPC-PPO), which effectively combines the advantages of centralized and distributed approaches. To improve training efficiency while ensuring the quality of the final decision. In a large-scale area air defense 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 defense task assignment in terms of quality and speed.</p>