Abstract
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.
基金项目
National Natural Science Foundation of China(62192731)
National Natural Science Foundation of China(62192730)
National Natural Science Foundation of China(62190200)
NETL
NSTL
万方数据