AI RESEARCH
Learning to Communicate Locally for Large-Scale Multi-Agent Pathfinding
arXiv CS.AI
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ArXi:2605.07637v1 Announce Type: new Multi-agent pathfinding (MAPF) is a widely used abstraction for multi-robot trajectory planning problems, where multiple homogeneous agents move simultaneously within a shared environment. Although solving MAPF optimally is NP-hard, scalable and efficient solvers are critical for real-world applications such as logistics and search-and-rescue. To this end, the research community has proposed various decentralized suboptimal MAPF solvers that leverage machine learning.