Convergence results on stochastic adaptive learning

We investigate an adaptive learning model which nests several existing learning models such as payoff assessment learning, valuation learning, stochastic fictitious play learning, experience-weighted attraction learning and delta learning with foregone payoff information in normal form games. In particular, we consider adaptive players each of whom assigns payoff assessments to his own actions, chooses the action which has the highest assessment with some perturbations and updates the assessments using observed payoffs, which may include payoffs from unchosen actions. Then, we provide conditions under which the learning process converges to a quantal response equilibrium in normal form games.