  Recent work in the multi-agent domain has shown the promise of Graph Neural
Networks (GNNs) to learn complex coordination strategies. However, most current
approaches use minor variants of a Graph Convolutional Network (GCN), which
applies a convolution to the communication graph formed by the multi-agent
system. In this paper, we investigate whether the performance and
generalization of GCNs can be improved upon. We introduce ModGNN, a
decentralized framework which serves as a generalization of GCNs, providing
more flexibility. To test our hypothesis, we evaluate an implementation of
ModGNN against several baselines in the multi-agent flocking problem. We
perform an ablation analysis to show that the most important component of our
framework is one that does not exist in a GCN. By varying the number of agents,
we also demonstrate that an application-agnostic implementation of ModGNN
possesses an improved ability to generalize to new environments.

Machine Learning;Multiagent Systems;Robotics