  In this note, we consider repeated play of a finite game using learning rules
whose period-by-period behavior probabilities or empirical distributions
converge to some notion of equilibria of the stage game. Our primary focus is
on uncoupled and completely uncoupled learning rules. While the former relies
on players being aware of only their own payoff functions and able to monitor
the action taken by the others, the latter assumes that players only know their
own past realized payoffs. We highlight the border between possible and
impossible results using these rules. We also overview several uncoupled and
completely uncoupled learning rules, most of which leverage notions of regret
as the solution concept to seek payoff-improving action profiles.
