DeepStack: Expert-level artificial intelligence in heads-up no-limit poker.
Matej MoravčíkMartin SchmidNeil BurchViliam LisýDustin MorrillNolan BardTrevor DavisKevin WaughMichael JohansonMichael BowlingPublished in: Science (New York, N.Y.) (2017)
Artificial intelligence has seen several breakthroughs in recent years, with games often serving as milestones. A common feature of these games is that players have perfect information. Poker, the quintessential game of imperfect information, is a long-standing challenge problem in artificial intelligence. We introduce DeepStack, an algorithm for imperfect-information settings. It combines recursive reasoning to handle information asymmetry, decomposition to focus computation on the relevant decision, and a form of intuition that is automatically learned from self-play using deep learning. In a study involving 44,000 hands of poker, DeepStack defeated, with statistical significance, professional poker players in heads-up no-limit Texas hold'em. The approach is theoretically sound and is shown to produce strategies that are more difficult to exploit than prior approaches.