Graph Attention-Guided Search for Dense Multi-Agent Pathfinding

Rishabh Jain; Keisuke Okumura; Michael Amir; Amanda Prorok

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arXiv · 2510.17382

Graph Attention-Guided Search for Dense Multi-Agent Pathfinding

Rishabh Jain·Keisuke Okumura·Michael Amir·Amanda Prorok

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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.

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Rishabh Jain, Keisuke Okumura, Michael Amir, Amanda Prorok. 2025-10-20. Graph Attention-Guided Search for Dense Multi-Agent Pathfinding. https://arxiv.org/abs/2510.17382

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