  In this paper, we examine the Nash equilibrium convergence properties of
no-regret learning in general N-player games. For concreteness, we focus on the
archetypal follow the regularized leader (FTRL) family of algorithms, and we
consider the full spectrum of uncertainty that the players may encounter - from
noisy, oracle-based feedback, to bandit, payoff-based information. In this
general context, we establish a comprehensive equivalence between the stability
of a Nash equilibrium and its support: a Nash equilibrium is stable and
attracting with arbitrarily high probability if and only if it is strict (i.e.,
each equilibrium strategy has a unique best response). This equivalence extends
existing continuous-time versions of the folk theorem of evolutionary game
theory to a bona fide algorithmic learning setting, and it provides a clear
refinement criterion for the prediction of the day-to-day behavior of no-regret
learning in games
