DeepStack: Expert-Level Artificial Intelligence in Heads-Up No-Limit Poker

Matej Moravčík, Martin Schmid, Neil Burch, Viliam Lisý, Dustin Morrill, Nolan Bard, Trevor Davis, Kevin Waugh, Michael Johanson, Michael Bowling.
In Science, March 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 is the quintessential game of imperfect information, and a longstanding 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 more difficult to exploit strategies than prior approaches.


@Article{ 2017science-deepstack,
  Title = "DeepStack: Expert-Level Artificial Intelligence in Heads-Up No-Limit Poker",
  Author = "Matej Morav\u{c}\'{i}k and Martin Schmid and Neil Burch and Viliam Lis\'{y} and Dustin Morrill and Nolan Bard and Trevor Davis and Kevin Waugh and Michael Johanson and Michael Bowling",
  Journal = "Science",
  Month = "March",
  Year = "2017",