{"type":"ScholarlyArticle","title":"Guidance Graph Optimization for Lifelong Multi-Agent Path Finding","authors":[{"name":"Yulun Zhang","slug":"yulun-zhang","given":"Yulun","family":"Zhang","affiliation":"Robotics Institute , Carnegie Mellon University"},{"name":"He Jiang","slug":"he-jiang","given":"He","family":"Jiang","affiliation":"Robotics Institute , Carnegie Mellon University"},{"name":"Varun Bhatt","slug":"varun-bhatt","given":"Varun","family":"Bhatt","affiliation":"Thomas Lord Department of Computer Science , University of Southern California"},{"name":"Stefanos Nikolaidis","slug":"stefanos-nikolaidis","given":"Stefanos","family":"Nikolaidis","affiliation":"Thomas Lord Department of Computer Science , University of Southern California"},{"name":"Jiaoyang Li","slug":"jiaoyang-li","given":"Jiaoyang","family":"Li","affiliation":"Robotics Institute , Carnegie Mellon University"}],"abstract":"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 x 91 maps and as many as 3,000 agents. We include the source code at: https://github.com/lunjohnzhang/ggo_public. All optimized guidance graphs are available online at: https://yulunzhang.net/publication/zhang2024ggo.","doi":"10.24963/ijcai.2024/35","url":"https://ijcai.org/proceedings/2024/35","pdf":"https://ijcai.org/proceedings/2024/0035.pdf","markdown":"https://ijcai.org/proceedings/2024/35.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/35","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Agent-based and Multi-agent Systems","track":"Main Track","number":35,"pages":{"first":311,"last":320},"keywords":["Robotics: ROB: Multi-robot systems","Agent-based and Multi-agent Systems: MAS: Multi-agent planning","Planning and Scheduling: PS: Applications","Search: S: Evolutionary computation"],"videos":[],"references":[{"position":1,"raw":"[Barer et al., 2014] Max Barer, Guni Sharon, Roni Stern, and Ariel Felner. 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