  The problem of decentralized multi-robot target tracking asks for jointly
selecting actions, e.g., motion primitives, for the robots to maximize target
tracking performance with local communications. One major challenge for
practical implementations is to make target tracking approaches scalable for
large-scale problem instances. In this work, we propose a general-purpose
learning architecture toward collaborative target tracking at scale, with
decentralized communications. Particularly, our learning architecture leverages
a graph neural network (GNN) to capture local interactions of the robots and
learns decentralized decision-making for the robots. We train the learning
model by imitating an expert solution and implement the resulting model for
decentralized action selection involving local observations and communications
only. We demonstrate the performance of our GNN-based learning approach in a
scenario of active target tracking with large networks of robots. The
simulation results show our approach nearly matches the tracking performance of
the expert algorithm, and yet runs several orders faster with up to 100 robots.
Moreover, it slightly outperforms a decentralized greedy algorithm but runs
faster (especially with more than 20 robots). The results also exhibit our
approach's generalization capability in previously unseen scenarios, e.g.,
larger environments and larger networks of robots.
