  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.
