Melioration learning in games with constant and frequency-dependent pay-offs

The paper explores the implications of melioration learning—an empirically significant variant of reinforcement learning—for game theory. We show that in games with invariable pay-offs melioration learning converges to Nash equilibria in a way similar to the replicator dynamics. Since melioration learning is known to deviate from optimizing behavior when an action’s rewards decrease with increasing relative frequency of that action, we also investigate an example of a game with frequency-dependent pay-offs. Interactive melioration learning is then still appropriately described by the replicator dynamics, but it indeed deviates from rational choice behavior in such a game.