Learning Adaptive Communication Ranges for Decentralized Multi-Agent Pathfinding

Communication is central to learned decentralized multi-agent pathfinding, en- abling agents to coordinate decisions beyond their local observations. Yet effec- tive coordination requires communication to match the needs of individual agents, which vary across locations and evolve throughout planning. Fixed-range com- munication cannot accommodate this variation: it may exclude relevant agents when coordination is needed or introduce redundant exchanges when local in- formation is sufficient. We propose a framework for state-adaptive communica- tion that learns to adjust each agent’s communication neighborhood from its local state. The resulting topology supports differentiated information exchange across agents and time. Experiments with three planners spanning distinct communica- tion architectures, across six map families and 21 map–population configurations, demonstrate improved planning success alongside reduced communication and overall runtime. The configuration-averaged success-rate improvement reaches approximately 13 percentage points across the evaluated planners. These findings highlight the value of learning how to organize communication for effective and efficient decentralized multi-agent coordination.