  We consider the problem of finding decentralized strategies for multi-agent
perimeter defense games. In this work, we design a graph neural network-based
learning framework to learn a mapping from defenders' local perceptions and the
communication graph to defenders' actions such that the learned actions are
close to that generated by a centralized expert algorithm. We demonstrate that
our proposed networks stay closer to the expert policy and are superior to
other baseline algorithms by capturing more intruders. Our GNN-based networks
are trained at a small scale and can generalize to large scales. To validate
our results, we run perimeter defense games in scenarios with different team
sizes and initial configurations to evaluate the performance of the learned
networks.
