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Deepstack: Expert-level artificial intelligence in heads-up no-limit poker

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.

Citation

M. Moravcík, M. Schmid, N. Burch, V. Lisy, D. Morrill, N. Bard, T. Davis, K. Waugh, M. Johanson, M. Bowling. "Deepstack: Expert-level artificial intelligence in heads-up no-limit poker". Science, 356(6337), pp 508-513, May 2017.

Keywords:  
Category: In Journal
Web Links: Science

BibTeX

@article{Moravcík+al:17,
  author = {Matej Moravcík and Martin Schmid and Neil Burch and Viliam Lisy
    and Dustin Morrill and Nolan Bard and Trevor Davis and Kevin Waugh and
    Michael Johanson and Michael Bowling},
  title = {Deepstack: Expert-level artificial intelligence in heads-up no-limit
    poker},
  Volume = "356",
  Number = "6337",
  Pages = {508-513},
  journal = {Science},
  year = 2017,
}

Last Updated: July 13, 2020
Submitted by Sabina P

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