_:n62a0251cce17434fab2843caf5fa4858xb47
<http://schema.org/description>
"Multi-agent teaming achieves better performance when there is communication\namong participating agents allowing them to coordinate their actions for\nmaximizing shared utility. However, when collaborating a team of agents with\ndifferent action and observation spaces, information sharing is not\nstraightforward and requires customized communication protocols, depending on\nsender and receiver types. Without properly modeling such heterogeneity in\nagents, communication becomes less helpful and could even deteriorate the\nmulti-agent cooperation performance. We propose heterogeneous graph attention\nnetworks, called HetNet, to learn efficient and diverse communication models\nfor coordinating heterogeneous agents towards accomplishing tasks that are of\ncollaborative nature. We propose a Multi-Agent Heterogeneous Actor-Critic\n(MAHAC) learning paradigm to obtain collaborative per-class policies and\neffective communication protocols for composite robot teams. Our proposed\nframework is evaluated against multiple baselines in a complex environment in\nwhich agents of different types must communicate and cooperate to satisfy the\nobjectives. Experimental results show that HetNet outperforms the baselines in\nlearning sophisticated multi-agent communication protocols by achieving\n∼10% improvements in performance metrics."
<https://deepai.org/profile/esmaeil-seraj>