PACT: Predictive Action Coordination with Traffic Awareness for Lifelong MAPF

Learning-based policies offer scalable decentralized multi-agent path finding (MAPF) but struggle to maintain throughput in crowded lifelong settings. Two information gaps cause this: uncertainty about neighbors’ concurrent decisions and a lack of traffic awareness beyond the local view. Relying solely on local conflict resolution or global traffic guidance fails to simultaneously resolve incompatible actions and prevent bottlenecks. We propose PACT (Predictive Action Coordination with Traffic Awareness) to bridge both gaps. PACT avoids exchanging action proposals by using the Neighbor Action Predictor (NAP) to infer neighboring agents’ decisions from partial observations for local coordination. Complementarily, coarse sector-level occupancy information provides long-range awareness of congestion beyond each agent’s local field of view. This enables agents to coordinate immediate interactions and bypass emerging bottlenecks without peer-to-peer communication. Across diverse MAPF benchmarks, PACT achieves over a 13× macro-average per-map throughput ratio relative to MAPF-GPT in highly congested settings. Furthermore, it reaches over 90% of the throughput of the centralized RHCR-PBS planner. These results demonstrate that combining inferred intentions with lightweight global traffic awareness substantially narrows the performance gap between learned policies and centralized planning.