{"ID":2852688,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.17382","arxiv_id":"2510.17382","title":"Graph Attention-Guided Search for Dense Multi-Agent Pathfinding","abstract":"Finding near-optimal solutions for dense multi-agent pathfinding (MAPF) problems in real-time remains challenging even for state-of-the-art planners. To this end, we develop a hybrid framework that integrates a learned heuristic derived from MAGAT, a neural MAPF policy with a graph attention scheme, into a leading search-based algorithm, LaCAM. While prior work has explored learning-guided search in MAPF, such methods have historically underperformed. In contrast, our approach, termed LaGAT, outperforms both purely search-based and purely learning-based methods in dense scenarios. This is achieved through an enhanced MAGAT architecture, a pre-train-then-fine-tune strategy on maps of interest, and a deadlock detection scheme to account for imperfect neural guidance. Our results demonstrate that, when carefully designed, hybrid search offers a powerful solution for tightly coupled, challenging multi-agent coordination problems.","short_abstract":"Finding near-optimal solutions for dense multi-agent pathfinding (MAPF) problems in real-time remains challenging even for state-of-the-art planners. To this end, we develop a hybrid framework that integrates a learned heuristic derived from MAGAT, a neural MAPF policy with a graph attention scheme, into a leading sear...","url_abs":"https://arxiv.org/abs/2510.17382","url_pdf":"https://arxiv.org/pdf/2510.17382v1","authors":"[\"Rishabh Jain\",\"Keisuke Okumura\",\"Michael Amir\",\"Amanda Prorok\"]","published":"2025-10-20T10:19:35Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"cs.LG\",\"cs.MA\",\"cs.RO\"]","methods":"[]","has_code":false}