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Match Recap: DeepStack plays Taylor von Kriegenbergh. 1/5. Info. Shopping. Tap to unmute. If playback doesn't begin shortly, try restarting your device. You're signed out. Videos you watch may be added to the TV's watch history and influence TV recommendations.MEDIA CONTACT
For media inquiries, please contact Spencer Murray at theAlberta Machine Intelligence Institute. www.amii.ca. DEEPSTACKDOWNLOADS & VIDEOSFIGURESMEDIA CONTACT DeepStack 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. At the heart of DeepStack is continual re-solving, a sound local strategy computation that only considers situations as they arise during play. FIGURES — DEEPSTACK Figures. Figures. Fig. 1 – Poker Game Tree. Fig. 2 – DeepStack Operation. Fig. 3 – DeepStack Neural Networks. Fig. 4 – Poker Match Results. Table S1 – DeepStack Human results Table.DOWNLOADS & VIDEOS
Match Recap: DeepStack plays Taylor von Kriegenbergh. 1/5. Info. Shopping. Tap to unmute. If playback doesn't begin shortly, try restarting your device. You're signed out. Videos you watch may be added to the TV's watch history and influence TV recommendations.MEDIA CONTACT
For media inquiries, please contact Spencer Murray at theAlberta Machine Intelligence Institute. www.amii.ca.DEEPSTACK
DeepStack 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. At the heart of DeepStack is continual re-solving, a sound local strategy computation that only considers situations as they arise during play. FIGURES — DEEPSTACK Figures. Figures. Fig. 1 – Poker Game Tree. Fig. 2 – DeepStack Operation. Fig. 3 – DeepStack Neural Networks. Fig. 4 – Poker Match Results. Table S1 – DeepStack Human results Table.DOWNLOADS & VIDEOS
Match Recap: DeepStack plays Taylor von Kriegenbergh. 1/5. Info. Shopping. Tap to unmute. If playback doesn't begin shortly, try restarting your device. You're signed out. Videos you watch may be added to the TV's watch history and influence TV recommendations.MEDIA CONTACT
Acknowledgements. The researchers would like to thank the professional players who committed valuable time to play DeepStack as well as our many reviewers and our families & friends. DEEPSTACKDOWNLOADS & VIDEOSFIGURESMEDIA CONTACT DeepStack 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. At the heart of DeepStack is continual re-solving, a sound local strategy computation that only considers situations as they arise during play. FIGURES — DEEPSTACK Figures. Figures. Fig. 1 – Poker Game Tree. Fig. 2 – DeepStack Operation. Fig. 3 – DeepStack Neural Networks. Fig. 4 – Poker Match Results. Table S1 – DeepStack Human results Table.DOWNLOADS & VIDEOS
Match Recap: DeepStack plays Taylor von Kriegenbergh. 1/5. Info. Shopping. Tap to unmute. If playback doesn't begin shortly, try restarting your device. You're signed out. Videos you watch may be added to the TV's watch history and influence TV recommendations.MEDIA CONTACT
For media inquiries, please contact Spencer Murray at theAlberta Machine Intelligence Institute. www.amii.ca. DEEPSTACKDOWNLOADS & VIDEOSFIGURESMEDIA CONTACT DeepStack 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. At the heart of DeepStack is continual re-solving, a sound local strategy computation that only considers situations as they arise during play. FIGURES — DEEPSTACK Figures. Figures. Fig. 1 – Poker Game Tree. Fig. 2 – DeepStack Operation. Fig. 3 – DeepStack Neural Networks. Fig. 4 – Poker Match Results. Table S1 – DeepStack Human results Table.DOWNLOADS & VIDEOS
Match Recap: DeepStack plays Taylor von Kriegenbergh. 1/5. Info. Shopping. Tap to unmute. If playback doesn't begin shortly, try restarting your device. You're signed out. Videos you watch may be added to the TV's watch history and influence TV recommendations.MEDIA CONTACT
For media inquiries, please contact Spencer Murray at theAlberta Machine Intelligence Institute. www.amii.ca.DEEPSTACK
DeepStack 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. At the heart of DeepStack is continual re-solving, a sound local strategy computation that only considers situations as they arise during play. FIGURES — DEEPSTACK Figures. Figures. Fig. 1 – Poker Game Tree. Fig. 2 – DeepStack Operation. Fig. 3 – DeepStack Neural Networks. Fig. 4 – Poker Match Results. Table S1 – DeepStack Human results Table.DOWNLOADS & VIDEOS
Match Recap: DeepStack plays Taylor von Kriegenbergh. 1/5. Info. Shopping. Tap to unmute. If playback doesn't begin shortly, try restarting your device. You're signed out. Videos you watch may be added to the TV's watch history and influence TV recommendations.MEDIA CONTACT
Acknowledgements. The researchers would like to thank the professional players who committed valuable time to play DeepStack as well as our many reviewers and our families & friends.DeepStack
DEEPSTACK
Expert-Level Artificial Intelligence in Heads-Up No-Limit PokerLINKS
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with each decision.
With a study completed in December 2016 and published in _Science_in
March 2017, DeepStack became the first AI capable of beating professional poker players at heads-up no-limit Texas hold'em poker. ------------------------- CONTINUAL RE-SOLVING DeepStack computes a strategy based on the current state of the game for only the remainder of the hand, not maintaining one for the full game, which leads to lower overall exploitability. "INTUITIVE" LOCAL SEARCH DeepStack avoids reasoning about the full remaining game by substituting computation beyond a certain depth with a fast-approximate estimate. Automatically trained with deep learning, DeepStack's “intuition” gives a gut feeling of the value of holding any cards in any situation. SPARSE LOOKAHEAD TREES DeepStack considers a reduced number of actions, allowing it to play at conventional human speeds. The system re-solves games in under five seconds using a simple gaming laptop with an Nvidia GPU. -------------------------ABOUT THE ALGORITHM
THE FIRST COMPUTER PROGRAM TO OUTPLAY HUMAN PROFESSIONALS AT HEADS-UP NO-LIMIT 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. 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. GAMES ARE SERIOUS BUSINESS Don’t let the name fool you, “games” of imperfect information provide a general mathematical model that describes how decision-makers interact. 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. Poker is the quintessential game of imperfect information, where you and your opponent hold information that each other doesn't have (yourcards).
Until now, competitive AI approaches in imperfect information games have typically reasoned about the entire game, producing a complete strategy prior to 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. A FUNDAMENTALLY DIFFERENT APPROACH DeepStack 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. At the heart of DeepStack is continual re-solving, a sound local strategy computation that only considers situations as they arise during play. This lets DeepStack avoid computing a complete strategy in advance, skirting the need for explicit abstraction. During re-solving, DeepStack doesn’t need to reason about the entire remainder of the game because it substitutes computation beyond a certain depth with a fast approximate estimate, DeepStack’s "intuition" – a gut feeling of the value of holding any possible private cards in any possible poker situation. Finally, DeepStack’s intuition, much like human intuition, needs to be trained. We train it with deep learning using examples generated from random poker situations. DeepStack is theoretically sound, produces strategies substantially more difficult to exploit than abstraction-based techniques and defeats professional poker players at heads-up no-limit poker with statistical significance.EXPLORE DEEPSTACK
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DeepStack Implementation for Leduc Hold'emPAPER & SUPPLEMENTS
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Paper (.pdf)
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Figures
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Supplement (.pdf)
HAND HISTORIES
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DeepStack vs. IFP Pros*
DeepStack vs. LBR
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DeepStack vs. Twitch Streamers (Season 1)RESEARCH TEAM
MEMBERS (FRONT-BACK) Michael Bowling, Dustin Morrill, Nolan Bard, Trevor Davis, Kevin Waugh, Michael Johanson, Viliam Lisý, Martin Schmid, Matej Moravčík, Neil Burch ------------------------- STACKING UP DEEPSTACK LOW-VARIANCE EVALUATION The performance of DeepStack and its opponents was evaluated using AIVAT , a provably unbiased low-variance technique based on carefully constructed control variates. Thanks to this technique, which gives an unbiased performance estimate with 85% reduction in standard deviation, we can show statistical significance in matches with as few as 3,000 games. ABSTRACTION-BASED APPROACHES Despite using ideas from abstraction, DeepStack is fundamentally different from abstraction-based approaches, which compute and store a strategy prior to play. While DeepStack restricts the number of actions in its lookahead trees, it has no need for explicit abstraction as each re-solve starts from the actual public state, meaning DeepStack always perfectly understands the current situation. PROFESSIONAL MATCHES We evaluated DeepStack by playing it against a pool of professional poker players recruited by the International Federation of Poker. 44,852 games were played by 33 players from 17 countries. Eleven players completed the requested 3,000 games with DeepStack beating all but one by a statistically-significant margin. Over all games played, DeepStack outperformed players by over four standard deviations fromzero.
HEURISTIC SEARCH
At a conceptual level, DeepStack’s continual re-solving, “intuitive” local search and sparse lookahead trees describe heuristic search, which is responsible for many AI successes in perfect information games. Until DeepStack, no theoretically sound application of heuristic search was known in imperfect informationgames.
-------------------------DEEPSTACK IN ACTION
TWITCH HIGHLIGHTS
TWITCH RECAPS
FULL TWITCH MATCHES
","resolveObject":"","resolvedBy":"manual","resolved":true}" data-block-type="22" id="block-yui_3_17_2_2_1492695980568_593079"> ","resolvedBy":"manual","resolved":true}" data-block-type="22" id="block-yui_3_17_2_2_1492695980568_1105797"> ","resolveObject":"","resolvedBy":"manual","resolved":true}" data-block-type="22" id="block-yui_3_17_2_2_1492695980568_1112010"> ------------------------- > “Real life consists of bluffing, of little tactics of deception, > of asking yourself what is the other man going to think I mean to> do.”
— John von Neumann, Founder of Game Theory -------------------------LINKS
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ACKNOWLEDGEMENTS
The researchers would like to thank the professional players who committed valuable time to play DeepStack as well as our many reviewers and our families & friends. Our research is supported by the International Federation of Poker, IBM , the Alberta
Machine Intelligence Institute , the Natural Sciences and Engineering Research Council of Canadaand the Charles
University Grant Agency . DeepStack was possible thanks to computing resources provided by Compute Canada and Calcul Québec.
_DeepStack was jointly developed by an international team: _ Charles University _(Prague, CzechRepublic)_
The Czech Technical University _(Prague,Czech Republic)_
The University of Alberta _(Edmonton,Canada)_
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