Multi-agent teaming achieves better performance when there is communication
among participating agents allowing them to coordinate their actions for
maximizing shared utility. However, when collaborating a team of agents with
different action and observation spaces, information sharing is not
straightforward and requires customized communication protocols, depending on
sender and receiver types. Without properly modeling such heterogeneity in
agents, communication becomes less helpful and could even deteriorate the
multi-agent cooperation performance. We propose heterogeneous graph attention
networks, called HetNet, to learn efficient and diverse communication models
for coordinating heterogeneous agents towards accomplishing tasks that are of
collaborative nature. We propose a Multi-Agent Heterogeneous Actor-Critic
(MAHAC) learning paradigm to obtain collaborative per-class policies and
effective communication protocols for composite robot teams. Our proposed
framework is evaluated against multiple baselines in a complex environment in
which agents of different types must communicate and cooperate to satisfy the
objectives. Experimental results show that HetNet outperforms the baselines in
learning sophisticated multi-agent communication protocols by achieving
$\sim$10\% improvements in performance metrics.

该研究提出了一种名为 HetNet 的异构网络注意力思想，用于协调不同类型的机器人团队完成合作任务，并评估 HetNet 在解决信息交互问题中的性能提升，实验结果表明其相比基准方法，在学习复杂多智能体协作的通信协议方面，表现更好。