  Inspired by recent advances in agent communication with graph neural
networks, this work proposes the representation of multi-agent communication
capabilities as a directed labeled heterogeneous agent graph, in which node
labels denote agent classes and edge labels, the communication type between two
classes of agents. We also introduce a neural network architecture that
specializes communication in fully cooperative heterogeneous multi-agent tasks
by learning individual transformations to the exchanged messages between each
pair of agent classes. By also employing encoding and action selection modules
with parameter sharing for environments with heterogeneous agents, we
demonstrate comparable or superior performance in environments where a larger
number of agent classes operates.
