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<p>In cooperative counter-drone defense, dynamic interception task allocation presents a fundamental challenge: targets may appear, vanish, or shift trajectory unpredictably throughout an engagement. Current multi-agent reinforcement learning approaches generally lack explicit mechanisms to capture the changing correspondence between interceptors and targets as the operational topology evolves. We propose DT-GAT-MARL, a hierarchical framework pairing a Dynamic-Topology Graph Attention Network (DT-GAT) at the strategic allocation level with Multi-Agent Proximal Policy Optimization (MAPPO)-based maneuver control at the tactical level. Three design choices define DT-GAT: a masking mechanism that handles node additions and removals without rebuilding the graph, learnable edge-feature biases that embed spatial reachability and interception urgency directly into attention computation, and a Gumbel-Softmax allocation head that enables differentiable discrete assignment while preserving end-to-end gradient flow. A dual-frequency architecture separates the allocation and maneuvering timescales, which helps suppress assignment oscillations. We evaluate the framework across balanced, numerically disadvantaged, and dynamic intrusion scenarios ranging from 4v4 to 12v12. In the dynamic intrusion setting, DT-GAT-MARL exceeds the strongest baseline by 10.3 percentage points, reaches an 87.3% effective reallocation rate, and keeps oscillation at just 9.6%. Ablation results confirm that edge-feature bias is the single most critical component.</p>

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Graph attention network-enhanced multi-agent reinforcement learning for dynamic interception task allocation in counter-drone defense

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月18日

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  • Dianbo Jia,
  • Ganliang Wang,
  • Hongfei Bu,
  • Junwei Wang,
  • Zhengkai Wang

摘要

In cooperative counter-drone defense, dynamic interception task allocation presents a fundamental challenge: targets may appear, vanish, or shift trajectory unpredictably throughout an engagement. Current multi-agent reinforcement learning approaches generally lack explicit mechanisms to capture the changing correspondence between interceptors and targets as the operational topology evolves. We propose DT-GAT-MARL, a hierarchical framework pairing a Dynamic-Topology Graph Attention Network (DT-GAT) at the strategic allocation level with Multi-Agent Proximal Policy Optimization (MAPPO)-based maneuver control at the tactical level. Three design choices define DT-GAT: a masking mechanism that handles node additions and removals without rebuilding the graph, learnable edge-feature biases that embed spatial reachability and interception urgency directly into attention computation, and a Gumbel-Softmax allocation head that enables differentiable discrete assignment while preserving end-to-end gradient flow. A dual-frequency architecture separates the allocation and maneuvering timescales, which helps suppress assignment oscillations. We evaluate the framework across balanced, numerically disadvantaged, and dynamic intrusion scenarios ranging from 4v4 to 12v12. In the dynamic intrusion setting, DT-GAT-MARL exceeds the strongest baseline by 10.3 percentage points, reaches an 87.3% effective reallocation rate, and keeps oscillation at just 9.6%. Ablation results confirm that edge-feature bias is the single most critical component.