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.
Keywords for this software
References in zbMATH (referenced in 12 articles )
Showing results 1 to 12 of 12.
- Caballero, William N.; Lunday, Brian J.; Uber, Richard P.: Identifying behaviorally robust strategies for normal form games under varying forms of uncertainty (2021)
- Bard, Nolan; Foerster, Jakob N.; Chandar, Sarath; Burch, Neil; Lanctot, Marc; Song, H. Francis; Parisotto, Emilio; Dumoulin, Vincent; Moitra, Subhodeep; Hughes, Edward; Dunning, Iain; Mourad, Shibl; Larochelle, Hugo; Bellemare, Marc G.; Bowling, Michael: The Hanabi challenge: a new frontier for AI research (2020)
- Čermák, Jiří; Lisý, Viliam; Bošanský, Branislav: Automated construction of bounded-loss imperfect-recall abstractions in extensive-form games (2020)
- Kovařík, Vojtěch; Lisý, Viliam: Analysis of Hannan consistent selection for Monte Carlo tree search in simultaneous move games (2020)
- Kroer, Christian; Sandholm, Tuomas: Limited lookahead in imperfect-information games (2020)
- Kroer, Christian; Waugh, Kevin; Kılınç-Karzan, Fatma; Sandholm, Tuomas: Faster algorithms for extensive-form game solving via improved smoothing functions (2020)
- Mazzolini, Andrea; Celani, Antonio: Generosity, selfishness and exploitation as optimal greedy strategies for resource sharing (2020)
- Brown, Noam; Sandholm, Tuomas: Superhuman AI for multiplayer poker (2019)
- Čermák, Jiří; Bošanský, Branislav; Horák, Karel; Lisý, Viliam; Pěchouček, Michal: Approximating maxmin strategies in imperfect recall games using A-loss recall property (2018)
- Ganzfried, Sam; Nowak, Austin; Pinales, Joannier: Successful Nash equilibrium agent for a three-player imperfect-information game (2018)
- Ganzfried, Sam; Yusuf, Farzana: Computing human-understandable strategies: deducing fundamental rules of poker strategy (2017)
- Moravčík, Matej; Schmid, Martin; Burch, Neil; Lisý, Viliam; Morrill, Dustin; Bard, Nolan; Davis, Trevor; Waugh, Kevin; Johanson, Michael; Bowling, Michael: DeepStack: expert-level artificial intelligence in heads-up no-limit poker (2017)