用图神经网络生成追逃博弈的最优初始位置，提升追捕效率。
Fast and the Furious: Hot Starts in Pursuit-Evasion Games
- 将追捕者布局建模为图，通过多目标优化找最优配置。
- 生成的热启动配置使逃逸者存活率下降更快，追捕者行程更短。
- 适合需要快速部署策略的多智能体追逃场景。
在不知晓逃逸者位置的情况下，有效部署追捕者仍是重大挑战。本文提出一种结合博弈论控制与图神经网络的新方法：将追捕者布局视为战略组合，并以图形式表示，通过多目标优化构建图特征空间，识别帕累托最优配置；再用图卷积网络（GCN）在这些最优图上训练，生成具有战略优势的初始布局，称为“热启动”。实证表明，相比随机布局，该方法显著加速逃逸者存活率下降，减少追捕者移动距离，提升围捕效果，尤其在多追捕者与多逃逸者场景中表现突出。
原文摘要 · Abstract (English)
Effectively positioning pursuers in pursuit-evasion games without prior knowledge of evader locations remains a significant challenge. A novel approach that combines game-theoretic control theory with Graph Neural Networks is introduced in this work. By conceptualizing pursuer configurations as strategic arrangements and representing them as graphs, a Graph Characteristic Space is constructed via multi-objective optimization to identify Pareto-optimal configurations. A Graph Convolutional Network (GCN) is trained on these Pareto-optimal graphs to generate strategically effective initial configurations, termed "hot starts". Empirical evaluations demonstrate that the GCN-generated hot starts provide a significant advantage over random configurations. In scenarios considering multiple pursuers and evaders, this method hastens the decline in evader survival rates, reduces pursuer travel distances, and enhances containment, showcasing clear strategic benefits.
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