Two Heads Are Better Than One: Collaboration-Oriented Multiagent Exploration System

Autonomous exploration in unknown environments is a complex and formidable challenge that requires effective collaboration among multiple agents under partially observable conditions. Due to limited observations and inefficient collaboration, multiagent exploration often suffers from excessively long exploration paths. To address this issue, this article proposes a collaboration-oriented multiagent exploration system (COMAE). To effectively understand and leverage the interagent relationships, this article introduces collaboration-oriented observation (COO). In addition to the basic connectivity graph, the COO further constructs collaboration-oriented node features and an interaction graph to enhance the overall strategic understanding of multiagent. To improve collaboration among agents, this article designs an attention-based sequential network (ASN) to predict strategic actions. Additionally, a novel collaborative exploration reward (CER) is proposed to further prevent noncollaborative behaviors during the exploration process. Extensive experiments demonstrate that the proposed method enhances collaboration among agents and significantly reduces exploration distances.