Libratus Poker Ai
An artificial intelligence called Libratus beat four top professional poker players in No-Limit Texas Hold’em by breaking the game into smaller, more manageable parts and adjusting its strategy as play progressed during the competition, researchers report.
In a new paper in Science, Tuomas Sandholm, professor of computer science at Carnegie Mellon University, and Noam Brown, a PhD student in the computer science department, detail how their AI achieved superhuman performance in a game with more decision points than atoms in the universe.
AI programs have defeated top humans in checkers, chess, and Go—all challenging games, but ones in which both players know the exact state of the game at all times. Poker players, by contrast, contend with hidden information: what cards their opponents hold and whether an opponent is bluffing.
Imperfect information
In a 20-day competition involving 120,000 hands this past January at Pittsburgh’s Rivers Casino, Libratus became the first AI to defeat top human players at Head’s-Up, No-Limit Texas Hold’em—the primary benchmark and longstanding challenge problem for imperfect-information game-solving by AIs.
Libratus beat each of the players individually in the two-player game and collectively amassed more than $1.8 million in chips. Measured in milli-big blinds per hand (mbb/hand), a standard used by imperfect-information game AI researchers, Libratus decisively defeated the humans by 147 mmb/hand. In poker lingo, this is 14.7 big blinds per game.
Analysis Machines have triumphed again. Libratus, a powerful computer program, has crushed its human opponents at a heads-up no-limit Texas hold’em poker tournament held at Rivers Casino in Pittsburgh, Pennsylvania, winning $1,776,250 over 120,000 hands. “Poker has been a benchmark in the field of AI for a long time,” says Noam Brown, a computer science PhD student at CMU who developed the code for Libratus. Libratus, an artificial intelligence developed by Carnegie Mellon University, made history by defeating four of the world’s best professional poker players in a marathon 20-day poker competition, called “Brains Vs. Artificial Intelligence: Upping the Ante” at Rivers Casino in Pittsburgh.
“The techniques in Libratus do not use expert domain knowledge or human data and are not specific to poker,” Sandholm and Brown write in the paper. “Thus, they apply to a host of imperfect-information games.”
Such hidden information is ubiquitous in real-world strategic interactions, they note, including business negotiation, cybersecurity, finance, strategic pricing, and military applications.
Three modules
Libratus includes three main modules, the first of which computes an abstraction of the game that is smaller and easier to solve than by considering all 10161 (the number 1 followed by 161 zeroes) possible decision points in the game. It then creates its own detailed strategy for the early rounds of Texas Hold’em and a coarse strategy for the later rounds. This strategy is called the blueprint strategy.
One example of these abstractions in poker is grouping similar hands together and treating them identically.
“Intuitively, there is little difference between a king-high flush and a queen-high flush,” Brown says. “Treating those hands as identical reduces the complexity of the game and, thus, makes it computationally easier.” In the same vein, similar bet sizes also can be grouped together.
In the final rounds of the game, however, a second module constructs a new, finer-grained abstraction based on the state of play. It also computes a strategy for this subgame in real-time that balances strategies across different subgames using the blueprint strategy for guidance—something that needs to be done to achieve safe subgame solving. During the January competition, Libratus performed this computation using the Pittsburgh Supercomputing Center’s Bridges computer.
When an opponent makes a move that is not in the abstraction, the module computes a solution to this subgame that includes the opponent’s move. Sandholm and Brown call this “nested subgame solving.” DeepStack, an AI created by the University of Alberta to play Heads-Up, No-Limit Texas Hold’em, also includes a similar algorithm, called continual re-solving. DeepStack has yet to be tested against top professional players, however.
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The third module is designed to improve the blueprint strategy as competition proceeds. Typically, Sandholm says, AIs use machine learning to find mistakes in the opponent’s strategy and exploit them. But that also opens the AI to exploitation if the opponent shifts strategy. Instead, Libratus’ self-improver module analyzes opponents’ bet sizes to detect potential holes in Libratus’ blueprint strategy. Libratus then adds these missing decision branches, computes strategies for them, and adds them to the blueprint.
AI vs. AI
In addition to beating the human pros, researchers evaluated Libratus against the best prior poker AIs. These included Baby Tartanian8, a bot developed by Sandholm and Brown that won the 2016 Annual Computer Poker Competition held in conjunction with the Association for the Advancement of Artificial Intelligence Annual Conference.
Whereas Baby Tartanian8 beat the next two strongest AIs in the competition by 12 (plus/minus 10) mbb/hand and 24 (plus/minus 20) mbb/hand, Libratus bested Baby Tartanian8 by 63 (plus/minus 28) mbb/hand. DeepStack has not been tested against other AIs, the authors note.
“The techniques that we developed are largely domain independent and can thus be applied to other strategic imperfect-information interactions, including nonrecreational applications,” Sandholm and Brown conclude. “Due to the ubiquity of hidden information in real-world strategic interactions, we believe the paradigm introduced in Libratus will be critical to the future growth and widespread application of AI.”
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The technology has been exclusively licensed to Strategic Machine Inc., a company Sandholm founded to apply strategic reasoning technologies to many different applications.
The National Science Foundation and the Army Research Office supported this research.
Source: Carnegie Mellon University
Libratus is an artificial intelligence computer program designed to play poker, specifically heads up no-limitTexas hold 'em. Libratus' creators intend for it to be generalisable to other, non-Poker-specific applications. It was developed at Carnegie Mellon University, Pittsburgh.
Background[edit]
While Libratus was written from scratch, it is the nominal successor of Claudico. Like its predecessor, its name is a Latin expression and means 'balanced'.
Libratus was built with more than 15 million core hours of computation as compared to 2-3 million for Claudico. The computations were carried out on the new 'Bridges' supercomputer at the Pittsburgh Supercomputing Center. According to one of Libratus' creators, Professor Tuomas Sandholm, Libratus does not have a fixed built-in strategy, but an algorithm that computes the strategy. The technique involved is a new variant of counterfactual regret minimization,[1] namely the CFR+ method introduced in 2014 by Oskari Tammelin.[2] On top of CFR+, Libratus used a new technique that Sandholm and his PhD student, Noam Brown, developed for the problem of endgame solving. Their new method gets rid of the prior de facto standard in Poker programming, called 'action mapping'.
As Libratus plays only against one other human or computer player, the special 'heads up' rules for two-player Texas hold 'em are enforced.
2017 humans versus AI match[edit]
From January 11 to 31, 2017, Libratus was pitted in a tournament against four top-class human poker players,[3] namely Jason Les, Dong Kim, Daniel McAulay and Jimmy Chou. In order to gain results of more statistical significance, 120,000 hands were to be played, a 50% increase compared to the previous tournament that Claudico played in 2015. To manage the extra volume, the duration of the tournament was increased from 13 to 20 days.
The four players were grouped into two subteams of two players each. One of the subteams was playing in the open, while the other subteam was located in a separate room nicknamed 'The Dungeon' where no mobile phones or other external communications were allowed. The Dungeon subteam got the same sequence of cards as was being dealt in the open, except that the sides were switched: The Dungeon humans got the cards that the AI got in the open and vice versa. This setup was intended to nullify the effect of card luck.
The prize money of $200,000 was shared exclusively between the human players. Each player received a minimum of $20,000, with the rest distributed in relation to their success playing against the AI. As written in the tournament rules in advance, the AI itself did not receive prize money even though it won the tournament against the human team.
During the tournament, Libratus was competing against the players during the days. Overnight it was perfecting its strategy on its own by analysing the prior gameplay and results of the day, particularly its losses. Therefore, it was able to continuously straighten out the imperfections that the human team had discovered in their extensive analysis, resulting in a permanent arms race between the humans and Libratus. It used another 4 million core hours on the Bridges supercomputer for the competition's purposes.
Strength of the AI[edit]
Libratus had been leading against the human players from day one of the tournament. The player Dong Kim was quoted on the AI's strength as follows: 'I didn’t realize how good it was until today. I felt like I was playing against someone who was cheating, like it could see my cards. I’m not accusing it of cheating. It was just that good.'[4]
At the 16th day of the competition, Libratus broke through the $1,000,000 barrier for the first time. At the end of that day, it was ahead $1,194,402 in chips against the human team. At the end of the competition, Libratus was ahead $1,766,250 in chips and thus won resoundingly. As the big blind in the matches was set to $100, Libratus winrate is equivalent to 14.7 big blinds per 100 hands. This is considered an exceptionally high winrate in poker and is highly statistically significant.[5]
Of the human players, Dong Kim came first, MacAulay second, Jimmy Chou third, and Jason Les fourth.
| Name | Rank | Results (in chips) |
|---|---|---|
| Dong Kim | 1 | -$85,649 |
| Daniel MacAulay | 2 | -$277,657 |
| Jimmy Chou | 3 | -$522,857 |
| Jason Les | 4 | -$880,087 |
| Total: | -$1,766,250 |
Other possible applications[edit]
While Libratus' first application was to play poker, its designers have a much broader mission in mind for the AI.[6] The investigators designed the AI to be able to learn any game or situation in which incomplete information is available and 'opponents' may be hiding information or even engaging in deception. Because of this Sandholm and his colleagues are proposing to apply the system to other, real-world problems as well, including cybersecurity, business negotiations, or medical planning.[7]
See also[edit]
References[edit]
- ^Hsu, Jeremy (10 January 2017). 'Meet the New AI Challenging Human Poker Pros'. IEEE Spectrum. Retrieved 2017-01-15.
- ^Brown, Noam; Sandholm, Tuomas (2017). 'Safe and Nested Endgame Solving for Imperfect-Information Games'(PDF). Proceedings of the AAAI workshop on Computer Poker and Imperfect Information Games.
- ^Spice, Byron; Allen, Garrett (January 4, 2017). 'Upping the Ante: Top Poker Pros Face Off vs. Artificial Intelligence'. Carnegie Mellon University. Retrieved 2017-01-12.
- ^Metz, Cade (24 January 2017). 'Artificial Intelligence Is About to Conquer Poker—But Not Without Human Help'. Wired. Retrieved 2017-01-24.
- ^'Libratus Poker AI Beats Humans for $1.76m; Is End Near?'. PokerListings. 30 January 2017. Retrieved 2018-03-16.
- ^Knight, Will (January 23, 2017). 'Why it's a big deal that AI knows how to bluff in poker'. MIT Technology Review.
- ^'Artificial Intelligence Wins $800,000 Against 4 Poker Masters'. Interesting Engineering. 27 January 2017.
Libratus Poker Air 2
External links[edit]
Libratus Poker Air Compressor
- Brains versus Artificial Intelligence official website at the Rivers Casino