  Many real-world problems require the coordination of multiple autonomous
agents. Recent work has shown the promise of Graph Neural Networks (GNNs) to
learn explicit communication strategies that enable complex multi-agent
coordination. These works use models of cooperative multi-agent systems whereby
agents strive to achieve a shared global goal. When considering agents with
self-interested local objectives, the standard design choice is to model these
as separate learning systems (albeit sharing the same environment). Such a
design choice, however, precludes the existence of a single, differentiable
communication channel, and consequently prohibits the learning of inter-agent
communication strategies. In this work, we address this gap by presenting a
learning model that accommodates individual non-shared rewards and a
differentiable communication channel that is common among all agents. We focus
on the case where agents have self-interested objectives, and develop a
learning algorithm that elicits the emergence of adversarial communications. We
perform experiments on multi-agent coverage and path planning problems, and
employ a post-hoc interpretability technique to visualize the messages that
agents communicate to each other. We show how a single self-interested agent is
capable of learning highly manipulative communication strategies that allows it
to significantly outperform a cooperative team of agents.
