Cooperative Pursuit-Evasion With Low Altitude Wireless Network: A Hierarchical Reinforcement Learning Approach

作者：Zhengzhi Yang, Yuanhao Cui, Wenbo Du, Fanbiao Li, Yumeng Li · 发表于：IEEE Transactions on Mobile Computing · 年份：2025 · DOI：10.1109/gcwkshps68340.2025.11591106 · 被引用次数：2 · 研究领域：Guidance and Control Systems、UAV Applications and Optimization、Air Traffic Management and Optimization

As an emerging countermeasure, cooperative interception by multiple UAVs offers an effective solution to neutralize rogue drones and safeguard low-altitude airspace operations. Effective coordination among counter-UAVs in encircling intruding drones remains challenging. This paper proposes a Hierarchical Cooperative Deep Reinforcement Learning (HCDRL) algorithm to enhance cooperation and efficiency among UAVs pursuing agile targets. The proposed approach decomposes the multi agent pursuit-evasion scenario into multiple subtasks using a two-layer hierarchical decision-making framework. Specifically, the upper-layer network acts as a meta-strategy, dynamically assessing pursuit scenarios and assigning optimal subtasks. Meanwhile, the lower-layer policy networks of individual agents determine maneuver actions based on local observations and assigned subtasks. Simulation results demonstrate that the proposed algorithm significantly improves multi-agent cooperative encirclement performance, achieving an 11.18% higher success rate and a 9.94% reduction in completion time compared to state-of-the-art methods.