A fundamentally different approachĭeepStack is the first theoretically sound application of heuristic search methods-which have been famously successful in games like checkers, chess, and Go-to imperfect information games.Īt the heart of DeepStack is continual re-solving, a sound local strategy computation that only considers situations as they arise during play. However, to make this approach feasible in heads-up no-limit Texas hold’em-a game with vastly more unique situations than there are atoms in the universe-a simplified abstraction of the game is often needed. Until now, competitive AI approaches in imperfect information games have typically reasoned about the entire game, producing a complete strategy prior to play. Poker is the quintessential game of imperfect information, where you and your opponent hold information that each other doesn't have (your cards). AI research has a long history of using parlour games to study these models, but attention has been focused primarily on perfect information games, like checkers, chess or go. Games are serious businessĭon’t let the name fool you, “games” of imperfect information provide a general mathematical model that describes how decision-makers interact.

Over all games played, DeepStack won 49 big blinds/100 (always folding would only lose 75 bb/100), over four standard deviations from zero, making it the first computer program to beat professional poker players in heads-up no-limit Texas hold'em poker.

In a study completed December 2016 and involving 44,000 hands of poker, DeepStack defeated 11 professional poker players with only one outside the margin of statistical significance. About the Algorithm The first computer program to outplay human professionals at heads-up no-limit Hold'em poker

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