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arXiv 2402.01446cs.MAcs.AIcs.RO
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为终身多智能体路径寻找设计的引导图优化

Guidance Graph Optimization for Lifelong Multi-Agent Path Finding

Yulun Zhang, He Jiang, Varun Bhatt, Stefanos Nikolaidis, Jiaoyang Li

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AI总结：

本文提出引导图优化方法，用于自动为终身多智能体路径寻找生成高效引导，提升算法吞吐量。

摘要：

我们研究如何利用引导来提高终身多智能体路径寻找（MAPF）的吞吐量。先前的研究表明，尽管结合引导，如高速公路，可以加速MAPF算法，但往往会导致解决方案质量的权衡。此外，如何自动生成良好的引导仍 largely 未被探索，当前的方法在超越人工设计的引导方面表现不足。在本工作中，我们引入引导图作为终身MAPF引导的通用表示，将引导图优化定义为优化其边权重的任务。我们提出了两种GGO算法，以自动为任意的终身MAPF算法和地图生成引导。第一种方法直接优化边权重，而第二种方法优化一个能够生成边权重的更新模型。实证地，我们显示（1）我们的引导图在八个基准地图上提高了三种代表性的终身MAPF算法的吞吐量，（2）我们的更新模型可以为高达93×91的地图和多达3000个智能体生成引导图。我们包含源代码在：https://github.com/lunjohnzhang/ggo_public。所有优化的引导图都可在：https://yulunzhang.net/publication/zhang2024ggo 上获得。

英文摘要：

We study how to use guidance to improve the throughput of lifelong Multi-Agent Path Finding (MAPF). Previous studies have demonstrated that, while incorporating guidance, such as highways, can accelerate MAPF algorithms, this often results in a trade-off with solution quality. In addition, how to generate good guidance automatically remains largely unexplored, with current methods falling short of surpassing manually designed ones. In this work, we introduce the guidance graph as a versatile representation of guidance for lifelong MAPF, framing Guidance Graph Optimization as the task of optimizing its edge weights. We present two GGO algorithms to automatically generate guidance for arbitrary lifelong MAPF algorithms and maps. The first method directly optimizes edge weights, while the second method optimizes an update model capable of generating edge weights. Empirically, we show that (1) our guidance graphs improve the throughput of three representative lifelong MAPF algorithms in eight benchmark maps, and (2) our update model can generate guidance graphs for as large as $93 \times 91$ maps and as many as 3,000 agents. We include the source code at: \url{https://github.com/lunjohnzhang/ggo_public}. All optimized guidance graphs are available online at: \url{https://yulunzhang.net/publication/zhang2024ggo}.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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