Aspiration-based Perturbed Learning Automata
Conference object, Preprint
Chasparis, Georgios C.
Computer Science - Computer Science and Game Theory | Learning automata, distributed optimization, coordination games
acm: TheoryofComputation_GENERAL | ComputingMilieux_PERSONALCOMPUTING
arxiv: Computer Science::Computer Science and Game Theory
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 in multi-player coordination games. In the case of two-player coordination games, we show that the payoff-dominant Nash equilibrium is the unique stochastically stable state.