Abstract: Multi-agent Path Finding(MAPF) is an essential component of large-scale robotic systems.Traditional planners based on conflict search are limited in scalability due to computation time,whereas multi-agent reinforcement learning strategies based on communication mechanisms significantly alleviate this issue.As task complexity and scale increase,how to effectively communicate and avoid congestion becomes significant obstacles for learning-based methods.To overcome these challenges,this paper proposes a decentralized planning method called Congestion-Aware and Cached Communication for Multi-agent Pathfinding(C3MAP),which features cache-based communication and congestion-awareness capabilities.Specifically,agents broadcast communications to neighbors only when the current environmental information significantly differs from the previous communication or when receiving request signals from other agents.Additionally,congestion information is incorporated as locally observable information to guide agents in avoiding congested areas.Experimental results on benchmarks indicate that C3MAP achieves a solution success rate of over 90% in structured scenarios,significantly outperforming existing learning-based methods.Additionally,experiments in large-scale environments confirm the greater stability of the caching communication mechanism and the effectiveness of the congestion awareness strategy.

Key words: Multi-agent system, Path planning, Deep reinforcement learning, Congestion aware, Cached communication