  This paper introduces a novel payoff-based learning scheme for distributed
optimization in repeatedly-played strategic-form games. Standard
reinforcement-based learning exhibits several limitations with respect to their
asymptotic stability. For example, in two-player coordination games,
payoff-dominant (or efficient) Nash equilibria may not be stochastically
stable. In this work, we present an extension of perturbed learning automata,
namely aspiration-based perturbed learning automata (APLA) that overcomes these
limitations. We provide a stochastic stability analysis of APLA in multi-player
coordination games. We further show that payoff-dominant Nash equilibria are
the only stochastically stable states.
