  We study N-player finite games with costs perturbed due to time-varying
disturbances in the underlying system and to that end we propose the concept of
Robust Correlated Equilibrium that generalizes the definition of Correlated
Equilibrium. Conditions under which the Robust Correlated Equilibrium exists
are specified and a decentralized algorithm for learning strategies that are
optimal in the sense of Robust Correlated Equilibrium is proposed. The primary
contribution of the paper is the convergence analysis of the algorithm and to
that end, we propose an extension of the celebrated Blackwell's Approachability
theorem to games with costs that are not just time-average as in the original
Blackwell's Approachability Theorem but also include time-average of previous
algorithm iterates. The designed algorithm is applied to a practical water
distribution network with pumps being the controllers and their costs being
perturbed by uncertain consumption by consumers. Simulation results show that
each controller achieves no regret and empirical distributions converge to the
Robust Correlated Equilibrium.

Systems and Control;Multiagent Systems;Systems and Control;Machine Learning