  This paper investigates multi-agent frequencybased patrolling of
intersecting, circle graphs under conditions where graph nodes have non-uniform
visitation requirements and agents have limited ability to communicate. The
task is modeled as a partially observable Markov decision process, and a
reinforcement learning solution is developed. Each agent generates its own
policy from Markov chains, and policies are exchanged only when agents occupy
the same or adjacent nodes. This constraint on policy exchange models sparse
communication conditions over large, unstructured environments. Empirical
results provide perspectives on convergence properties, agent cooperation, and
generalization of learned patrolling policies to new instances of the task. The
emergent behavior indicates learned coordination strategies between
heterogeneous agents for patrolling large, unstructured regions as well as the
ability to generalize to dynamic variation in node visitation requirements.
