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Inspired by recent advances in agent communication with graph neuralnetworks, this work proposes the representation of multi-agent communicationcapabilities as a directed labeled heterogeneous agent graph, in which nodelabels denote agent classes and edge labels, the communication type between twoclasses of agents. We also introduce a neural network architecture thatspecializes communication in fully cooperative heterogeneous multi-agent tasksby learning individual transformations to the exchanged messages between eachpair of agent classes. By also employing encoding and action selection moduleswith parameter sharing for environments with heterogeneous agents, wedemonstrate comparable or superior performance in environments where a largernumber of agent classes operates.

Specializing Inter-Agent Communication in Heterogeneous Multi-Agent Reinforcement Learning using Agent Class Information