LAUSR

Hannan consistency, or no external regret, is a key concept for learning in games. An action selection algorithm is Hannan consistent (HC) if its performance is eventually as good as selecting the best fixed action in hindsight. If both players in a zero-sum normal form game use a Hannan consistent algorithm, their average behavior converges to a Nash equilibrium of the game. A similar result is known about extensive form games, but the played strategies need to be Hannan consistent with respect to the counterfactual values, which are often difficult to obtain. We study zero-sum extensive form games with simultaneous moves, but otherwise perfect information. These games generalize normal form games and they are a special case of extensive form games. We study whether applying HC algorithms in each decision point of these games directly to the observed payoffs leads to convergence to a Nash equilibrium. This learning process corresponds to a class of Monte Carlo Tree Search algorithms, which are popular for playing simultaneous-move games but do not have any known performance guarantees. We show that using HC algorithms directly on the observed payoffs is not sufficient to guarantee the convergence. With an additional averaging over joint actions, the convergence is guaranteed, but empirically slower. We further define an additional property of HC algorithms, which is sufficient to guarantee the convergence without the averaging and we empirically show that commonly used HC algorithms have this property.