  We consider multi-robot applications, where a team of robots can ask for the
intervention of a human operator to handle difficult situations. As the number
of requests grows, team members will have to wait for the operator attention,
hence the operator becomes a bottleneck for the system. Our aim in this context
is to make the robots learn cooperative strategies to decrease the time spent
waiting for the operator. In particular, we consider a queuing model where
robots decide whether or not to join the queue and use multi-robot learning to
estimate the best cooperative policy. In more detail, we formalize the problem
as Decentralized Markov Decision Process and provide a suitable state
representation, so to apply an independent learners approach. We evaluate the
proposed method in a robotic water monitoring simulation and empirically show
that our approach can significantly improve the team performance, while being
computationally tractable.
