AI 中文总结
多智能体追逃博弈计算纳什均衡策略有挑战，本文提出原始引导树搜索框架PGTS，结合离线精确纳什均衡计算与在线树搜索，实验表明其显著优于现有基线，在多种图拓扑上对对手保持稳健性能。
AI 中文摘要
在多智能体追逃博弈（PEG）中计算纳什均衡策略具有挑战性，因为联合状态和动作空间会随智能体数量呈指数增长。现有方法要么依赖离线均衡近似，执行时可能缺乏适应性；要么依赖在线规划方法，存在大分支因子问题。本文提出原始引导树搜索（PGTS），这是一个将离线精确纳什均衡计算与在线树搜索相结合的混合框架。PGTS先离线求解一系列较小的可处理子博弈，在部署时，在每个时间步进行在线树搜索，用最优子博弈策略和值函数指导树扩展并估计叶节点值。在包括真实网络在内的各种图拓扑上的大量实验表明，PGTS显著优于现有学习和启发式基线，同时对对手保持稳健性能。
英文摘要
Computing Nash equilibrium policies in multi-agent Pursuit-Evasion games (PEG) is challenging due to the exponential growth of the joint state and action spaces with the number of agents. Existing approaches either rely on offline equilibrium approximations, which may lack adaptability during execution, or online planning methods, which suffer from large branching factors. In this work, we propose Primitive-Guided Tree Search (PGTS), a hybrid framework that integrates offline exact Nash equilibrium computation with online tree search: PGTS first solves a collection of smaller, tractable sub-games offline; at deployment, PGTS performs online tree search at each time step, using the optimal sub-game policies and value functions to guide tree expansion and estimate leaf-node values. Extensive experiments on varied graph topologies, including real-world networks, demonstrate that PGTS significantly outperforms state-of-the-art learning and heuristic baselines, while maintaining robust performance against adversaries.
