{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T16:24:39Z","timestamp":1754151879389,"version":"3.41.2"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["SOCS"],"abstract":"<jats:p>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.\nWe 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.<\/jats:p>","DOI":"10.1609\/socs.v18i1.36015","type":"journal-article","created":{"date-parts":[[2025,7,21]],"date-time":"2025-07-21T06:05:45Z","timestamp":1753077945000},"page":"273-274","source":"Crossref","is-referenced-by-count":0,"title":["RAILGUN: A Unified Convolutional Policy for Multi-Agent Path Finding Across Different Environments and Tasks (Extended Abstract)"],"prefix":"10.1609","volume":"18","author":[{"given":"Yimin","family":"Tang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiao","family":"Xiong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingyi","family":"Xi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaoyang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Erdem","family":"B\u0131y\u0131k","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sven","family":"Koenig","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"9382","published-online":{"date-parts":[[2025,7,20]]},"container-title":["Proceedings of the International Symposium on Combinatorial Search"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/SOCS\/article\/download\/36015\/38170","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/SOCS\/article\/download\/36015\/38170","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,21]],"date-time":"2025-07-21T06:59:26Z","timestamp":1753081166000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/SOCS\/article\/view\/36015"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,20]]},"references-count":0,"URL":"https:\/\/doi.org\/10.1609\/socs.v18i1.36015","relation":{},"ISSN":["2832-9163","2832-9171"],"issn-type":[{"type":"electronic","value":"2832-9163"},{"type":"print","value":"2832-9171"}],"subject":[],"published":{"date-parts":[[2025,7,20]]}}}