Details

Autor(en) / Beteiligte

• Liu, Jia-Yi
• Wang, Gang
• Guo, Xiang-Ke
• Wang, Si-Yuan
• Fu, Qiang

Titel

Intelligent air defense task assignment based on hierarchical reinforcement learning

Ist Teil von

• Frontiers in neurorobotics, 2022-12, Vol.16, p.1072887-1072887

Ort / Verlag

Switzerland: Frontiers Research Foundation

Erscheinungsjahr

2022

Link zum Volltext

Quelle

EZB Electronic Journals Library

Beschreibungen/Notizen

• 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.