Heuristic Action-aware and Priority Communication for Multi-agent Path Finding
Dongming Zhou, Zhengbin Pang · 2024
Large-scale multi-agent path finding (MAPF ) has the problem of shared features, which leads to a large amount of bandwidth and additional overhead. Therefore, this paper proposes a multi-agent path finding method that combines heuristic action-aware network and dual Q network(HADQ). First, we use a convolutional neural network to encoder observation input at field of view scope. On this basis, we combine self-attention network and graph neural network to query the priority of agent communication to reduce collisions. we improves the timeliness of agents timely communication by reducing the size of shared features. Then, we embedding the agent shortest path into the training process as a heuristic guide. Finally, this paper uses the deep Q network to map the observation values into the actions of the agent, thereby constructing a dynamic communication topology network. Compared with the state-of-the-art MAPF method, the method proposed in this paper has certain improvements in solution quality and success rate. Experimental results show that even in unseen data sets. HADQ can also show good generalization ability and robustness.