  In this work, we study the problem of decentralized multi-agent perimeter
defense that asks for computing actions for defenders with local perceptions
and communications to maximize the capture of intruders. One major challenge
for practical implementations is to make perimeter defense strategies scalable
for large-scale problem instances. To this end, we leverage graph neural
networks (GNNs) to develop an imitation learning framework that learns a
mapping from defenders' local perceptions and their communication graph to
their actions. The proposed GNN-based learning network is trained by imitating
a centralized expert algorithm such that the learned actions are close to that
generated by the expert algorithm. We demonstrate that our proposed network
performs closer to the expert algorithm and is superior to other baseline
algorithms by capturing more intruders. Our GNN-based network is trained at a
small scale and can be generalized to large-scale cases. We run perimeter
defense games in scenarios with different team sizes and configurations to
demonstrate the performance of the learned network.
