Frontiers in Neurorobotics (Dec 2022)

Intelligent air defense task assignment based on hierarchical reinforcement learning

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

DOI
https://doi.org/10.3389/fnbot.2022.1072887
Journal volume & issue
Vol. 16

Abstract

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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.

Keywords