RAILGUN: A Unified Convolutional Policy for Multi-Agent Path Finding Across Different Environments and Tasks
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866913976804179968 |
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| author | Tang, Yimin Xiong, Xiao Xi, Jingyi Li, Jiaoyang Bıyık, Erdem Koenig, Sven |
| author_facet | Tang, Yimin Xiong, Xiao Xi, Jingyi Li, Jiaoyang Bıyık, Erdem Koenig, Sven |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_02992 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RAILGUN: A Unified Convolutional Policy for Multi-Agent Path Finding Across Different Environments and Tasks Tang, Yimin Xiong, Xiao Xi, Jingyi Li, Jiaoyang Bıyık, Erdem Koenig, Sven Robotics Artificial Intelligence 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. |
| title | RAILGUN: A Unified Convolutional Policy for Multi-Agent Path Finding Across Different Environments and Tasks |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2503.02992 |