Strategy purification and thresholding: effective non-equilibrium approaches for playing large games

There has been significant recent interest in computing effective strategies for playing large imperfect-information games. Much prior work involves computing an approximate equilibrium strategy in a smaller abstract game, then playing this strategy in the full game (with the hope that it also well approximates an equilibrium in the full game). In this paper, we present a family of modifications to this approach that work by constructing non-equilibrium strategies in the abstract game, which are then played in the full game. Our new procedures, called purification and thresholding, modify the action probabilities of an abstract equilibrium by preferring the higher-probability actions. Using a variety of domains, we show that these approaches lead to significantly stronger play than the standard equilibrium approach. As one example, our program that uses purification came in first place in the two-player no-limit Texas Hold'em total bankroll division of the 2010 Annual Computer Poker Competition. Surprisingly, we also show that purification significantly improves performance (against the full equilibrium strategy) in random 4 x 4 matrix games using random 3 x 3 abstractions. We present several additional results (both theoretical and empirical). Overall, one can view these approaches as ways of achieving robustness against overfitting one's strategy to one's lossy abstraction. Perhaps surprisingly, the performance gains do not necessarily come at the expense of worst-case exploitability.