RAILGUN: A Unified Convolutional Policy for Multi-Agent Path Finding Across Different Environments and Tasks

• URL: http://arxiv.org/abs/2503.02992v1
• Date: Tue, 04 Mar 2025 20:35:20 GMT
• Title: RAILGUN: A Unified Convolutional Policy for Multi-Agent Path Finding Across Different Environments and Tasks
• Authors: Yimin Tang, Xiao Xiong, Jingyi Xi, Jiaoyang Li, Erdem Bıyık, Sven Koenig,
• Abstract summary: Multi-Agent Path Finding (MAPF) is crucial for applications ranging from aerial swarms to warehouse automation.<n>We have developed the first centralized learning-based policy for MAPF problem called RAILGUN.<n>By leveraging a CNN-based architecture, RAILGUN can generalize across different maps and handle any number of agents.
• Score: 17.17370365888357
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Multi-Agent Path Finding (MAPF), which focuses on finding collision-free paths for multiple robots, is crucial for applications ranging from aerial swarms to warehouse automation. Solving MAPF is NP-hard so learning-based approaches for MAPF have gained attention, particularly those leveraging deep neural networks. Nonetheless, despite the community's continued efforts, all learning-based MAPF planners still rely on decentralized planning due to variability in the number of agents and map sizes. We have developed the first centralized learning-based policy for MAPF problem called RAILGUN. RAILGUN is not an agent-based policy but a map-based policy. By leveraging a CNN-based architecture, RAILGUN can generalize across different maps and handle any number of agents. We collect trajectories from rule-based methods to train our model in a supervised way. In experiments, RAILGUN outperforms most baseline methods and demonstrates great zero-shot generalization capabilities on various tasks, maps and agent numbers that were not seen in the training dataset.

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