Perception field based imitation learning for unlabeled multi-agent pathfinding  

作　　者：Wenjie CHU Ailun YU Wei ZHANG Haiyan ZHAO Zhi JIN 

机构地区：[1]School of Computer Science,Peking University,Beijing 100871,China [2]Key Laboratory of High-Confidence Software Technologies(Peking University),Ministry of Education,Beijing 100871,China

出　　处：《Science China(Information Sciences)》2024年第5期111-131,共21页中国科学（信息科学）（英文版）

基　　金：supported by National Natural Science Foundation of China(Grant Nos.62192731,62192730,62190200).

摘　　要：This paper proposes an imitation learning method to learn a universal agent policy for unlabeled multi-agent pathfinding(unlabeled MAPF)in grid environments.The method transforms the unlabeled MAPF problem into a series of temporal-independent homogeneous classification problems for each agent.Based on this transformation,a neural network is designed to imitate a distance-optimal expert algorithm.The neural network consists of two successive modules:perception field learner and field integrating classifier.The former refines and encodes the current system state into a perception field for each agent by combining a set of learnable field-generating functions.The latter takes an agent’s perception field as input and decides the agent’s next action based on a triplet cross-attention mechanism.We evaluate our method on a diverse set of unlabeled MAPF tasks.Compared with state-of-the-art counterparts,the experimental results manifest the superiority of the proposed method in both generalization ability and scalability.

关 键 词：unlabeled multi-agent pathfinding perception field triplet cross-attention multi-agent imitation learning learning-based planning 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]