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<p>In multi-agent path finding, coordinates reflect the positions of agents in the environment and play a crucial role in preventing collisions between them. A key issue that needs to be addressed is how to establish efficient communication and learning strategies between agents to share this coordinate information. However, current methods face problems such as information fusion loss and redundant shared information. Therefore, this paper proposes CoMP-LG, a multi-agent path finding method based on learnable communication graphs. First, the SCDCN network is used to extract feature information from the agents’ observations, and SCA is applied to enhance the coordinate information within these observation features, reducing redundancy in the spatial dimensions while improving the representational ability of the coordinate features. Next, the communication problem between agents is conceptualized as a learnable graph, and the transformer is used to optimize this graph to reduce communication redundancy. Finally, this communication graph is utilized to enable information sharing between agents, thereby reducing uncertainty in local decisions during multi-agent path finding. Empirical results demonstrate that CoMP-LG achieves strong performance in both accuracy and FlowTime across maps of varying sizes and numbers of agents.</p>

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CoMP-LG: coordinate-aware multi-agent pathfinding via learnable communication graphs

  • 期刊论文

  • 发表日期：

    2025年5月24日

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  • Qingling Zhang,
  • Peng Wang,
  • Cui Ni,
  • Wen Rong,
  • Chongguang Ren,
  • Xianchang Liu,
  • Haoyuan Shan

摘要

In multi-agent path finding, coordinates reflect the positions of agents in the environment and play a crucial role in preventing collisions between them. A key issue that needs to be addressed is how to establish efficient communication and learning strategies between agents to share this coordinate information. However, current methods face problems such as information fusion loss and redundant shared information. Therefore, this paper proposes CoMP-LG, a multi-agent path finding method based on learnable communication graphs. First, the SCDCN network is used to extract feature information from the agents’ observations, and SCA is applied to enhance the coordinate information within these observation features, reducing redundancy in the spatial dimensions while improving the representational ability of the coordinate features. Next, the communication problem between agents is conceptualized as a learnable graph, and the transformer is used to optimize this graph to reduce communication redundancy. Finally, this communication graph is utilized to enable information sharing between agents, thereby reducing uncertainty in local decisions during multi-agent path finding. Empirical results demonstrate that CoMP-LG achieves strong performance in both accuracy and FlowTime across maps of varying sizes and numbers of agents.