general
fact
neutral
Alternating learning dynamics achieve O(log d / T) convergence to Nash equilibria in two-player zero-sum games and O(log d / T) convergence to coarse correlated equilibria in two-player general-sum games
As a result, we obtain alternating learning dynamics with $O(\log d /T)$ convergence to Nash equilibria in two-player zero-sum games and $O(\log d /T)$ convergence to coarse correlated equilibria in two-player general-sum games.
Machine Learning (Statistics)28 Aug 2026