  The majority of distributed learning literature focuses on convergence to
Nash equilibria. Correlated equilibria, on the other hand, can often
characterize more efficient collective behavior than even the best Nash
equilibrium. However, there are no existing distributed learning algorithms
that converge to specific correlated equilibria. In this paper, we provide one
such algorithm which guarantees that the agents' collective joint strategy will
constitute an efficient correlated equilibrium with high probability. The key
to attaining efficient correlated behavior through distributed learning
involves incorporating a common random signal into the learning environment.
