  We consider the problem of classifying a map using a team of communicating
robots. It is assumed that all robots have localized visual sensing
capabilities and can exchange their information with neighboring robots. Using
a graph decomposition technique, we proposed an offline learning structure that
makes every robot capable of communicating with and fusing information from its
neighbors to plan its next move towards the most informative parts of the
environment for map classification purposes. The main idea is to decompose a
given undirected graph into a union of directed star graphs and train robots
w.r.t a bounded number of star graphs. This will significantly reduce the
computational cost of offline training and makes learning scalable (independent
of the number of robots). Our approach is particularly useful for fast map
classification in large environments using a large number of communicating
robots. We validate the usefulness of our proposed methodology through
extensive simulations.
