Decentralized Multi-Goal Multi-Agent Pathfinding with Spatial Prior and Neighbor Intent Prediction

As a variant of the Multi-Agent Pathfinding (MAPF) problem, Multi-Goal Multi-Agent Pathfinding (MG-MAPF) requires planning conflict-free paths for a team of agents to visit sequences of preassigned goal vertices. Existing search-based methods suffer from poor scalability due to their reliance on centralized control, while decentralized learning-based approaches struggle with long-horizon planning under partial observability. Furthermore, existing paradigms rarely address joint goal ordering across agents, where uncoordinated goal sequences cause multiple agents to simultaneously converge on the same region, increasing congestion and conflicts. To address these challenges, we propose the Intent-aware Multi-Goal Multi-Agent Pathfinding (IMMP), a hierarchical framework for decentralized MG-MAPF that explicitly models spatial congestion at the high level and neighbor intent at the execution level. The high-level planner encodes a spatial prior via spatially-balanced goal ordering and path planning, reducing conflict pressure passed to the low-level policy. The low-level module resolves real-time conflicts via a reinforcement learning policy augmented with a neighbor intent prediction module, trained end-to-end through a prediction-guided collaborative proximal policy optimization. Extensive experiments demonstrate that IMMP achieves strong scalability and generalization, Extensive experiments demonstrate that IMMP achieves strong scalability and generalization, reducing SoC by an average of 19.77\% compared to the best-performing decentralized baseline, while maintaining robust performance. Our code is available at https://anonymous.4open.science/r/IMMP-CB2E/