We consider the problem of distributed convergence to efficient outcomes in
coordination games through dynamics based on aspiration learning. Under
aspiration learning, a player continues to play an action as long as the
rewards received exceed a specified aspiration level. Here, the aspiration
level is a fading memory average of past rewards, and these levels also are
subject to occasional random perturbations.