Sequence Pathfinder for Multi-Agent Pickup and Delivery in the Warehouse

• URL: http://arxiv.org/abs/2509.23778v2
• Date: Tue, 30 Sep 2025 12:39:02 GMT
• Title: Sequence Pathfinder for Multi-Agent Pickup and Delivery in the Warehouse
• Authors: Zeyuan Zhao, Chaoran Li, Shao Zhang, Ying Wen,
• Abstract summary: Multi-Agent Pickup and Delivery (MAPD) is a challenging extension of Multi-Agent Path Finding (MAPF)<n> Communication learning can alleviate the lack of global information but introduce high computational complexity due to point-to-point communication.<n>We propose the Sequential Pathfinder (SePar) to achieve implicit information exchange, reducing decision-making complexity from exponential to linear.
• Score: 10.576983033957953
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-Agent Pickup and Delivery (MAPD) is a challenging extension of Multi-Agent Path Finding (MAPF), where agents are required to sequentially complete tasks with fixed-location pickup and delivery demands. Although learning-based methods have made progress in MAPD, they often perform poorly in warehouse-like environments with narrow pathways and long corridors when relying only on local observations for distributed decision-making. Communication learning can alleviate the lack of global information but introduce high computational complexity due to point-to-point communication. To address this challenge, we formulate MAPF as a sequence modeling problem and prove that path-finding policies under sequence modeling possess order-invariant optimality, ensuring its effectiveness in MAPD. Building on this, we propose the Sequential Pathfinder (SePar), which leverages the Transformer paradigm to achieve implicit information exchange, reducing decision-making complexity from exponential to linear while maintaining efficiency and global awareness. Experiments demonstrate that SePar consistently outperforms existing learning-based methods across various MAPF tasks and their variants, and generalizes well to unseen environments. Furthermore, we highlight the necessity of integrating imitation learning in complex maps like warehouses.

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