Poker Ai Libratus

The blueprints for Libratus – the poker AI bot that crushed professional players in a Texas hold ’em tournament earlier this year – were published on Monday in a research paper. The software's victory over humans sparked a lot of headlines as it demonstrated a computer mastering an imperfect information game. Jul 17, 2020 “Poker is the main benchmark and challenge program for games of imperfect information,” Sandholm told me on a warm spring afternoon in 2018, when we met in his offices in Pittsburgh. The game, it turns out, has become the gold standard for developing artificial intelligence. Dec 19, 2017 Back in January, the Libratus poker AI defeated four top heads-up no limit hold’em specialists, marking the first time that a computer showed it could defeat high-level professional players. Dec 17, 2017 Libratus, an artificial intelligence that defeated four top professional poker playersin no-limit Texas Hold'em earlier this year, uses a three-pronged approach to master a game with more decision points than atoms in the universe, researchers at Carnegie Mellon University report.

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.

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]

1. ^Hsu, Jeremy (10 January 2017). 'Meet the New AI Challenging Human Poker Pros'. IEEE Spectrum. Retrieved 2017-01-15.
2. ^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.
3. ^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.
4. ^Metz, Cade (24 January 2017). 'Artificial Intelligence Is About to Conquer Poker—But Not Without Human Help'. Wired. Retrieved 2017-01-24.
5. ^'Libratus Poker AI Beats Humans for $1.76m; Is End Near?'. PokerListings. 30 January 2017. Retrieved 2018-03-16.
6. ^Knight, Will (January 23, 2017). 'Why it's a big deal that AI knows how to bluff in poker'. MIT Technology Review.
7. ^'Artificial Intelligence Wins $800,000 Against 4 Poker Masters'. Interesting Engineering. 27 January 2017.

Poker Ai Program

External links[edit]

• Brains versus Artificial Intelligence official website at the Rivers Casino

The blueprints for Libratus – the poker AI bot that crushed professional players in a Texas hold ’em tournament earlier this year – were published on Monday in a research paper.

The software's victory over humans sparked a lot of headlines as it demonstrated a computer mastering an imperfect information game. Unlike chess or Go where players can see all the board pieces at all times, poker players have to come up with a strategy based more on probabilities since they do not know their opponent’s cards.

Libratus emerged as the clear victor after playing more than 120,000 hands in a heads-up no-limit Texas hold ’em poker tournament back in February. The machine crushed its meatbag opponents by 14.7 big blinds per game, drawing in $1,776,250 in prize money.

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Now, a paper published in Science reveals how Libratus was programmed. The approach taken by its creators Noam Brown, a PhD student, and Tuomas Sandholm, a professor of computer science, both at Carnegie Mellon University in the US, employed three algorithms.

“Our game-theoretic approach features application independent techniques: an algorithm for computing a blueprint for the overall strategy, an algorithm that fleshes out the details of the strategy for subgames that are reached during play, and a self-improver algorithm that fixes potential weaknesses that opponents have identified in the blueprint strategy,” the pair's paper stated.

The first algorithm was briefly discussed after the competition as “counterfactual regret minimization.” It modeled a simpler version of poker – heads-up pot-limit Texas hold 'em – using a precomputed decision tree containing about 10¹³ nodes – much smaller than the 10¹⁶¹ nodes needed to cover all possible unique decisions in a no-limit game – and gradually learned to pick the best moves from the tree by playing simulated match after simulated match.

Flushed away

Similar hands were grouped together, Brown explained this week: 'Intuitively, there is little difference between a King-high flush and a Queen-high flush. Treating those hands as identical reduces the complexity of the game and thus makes it computationally easier.” Also, betting, say, $100 or $101 is basically the same, so again, the betting decisions could be simplified.

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So, essentially, Libratus started off with a fairly simple weighted decision tree from which to select its moves depending on its hole cards and those on the board.

Next, to elevate the software to superhuman level, it would whip out a more advanced strategy in the latter betting rounds during a hand. Once play had reached that point, a more detailed, fine-grained abstraction model of Texas hold 'em would be produced in real time to best win the hand. This algorithm was dubbed 'nested subgame solving.'

Dong Kim, one of Libratus’ opponents, previously said the competition was “extremely tough as the AI keeps getting better.” This is where the third algorithm came in: Libratus wasn't just trained offline once and used inference to make decisions in real-time during hands – it had a “self-improver” module to refine its decision-making processes.

It used machine learning to fill in missing branches of the overall 'blueprint' decision model based on its opponents' moves. “In principle, one could conduct all such computations in advance, but the game tree is way too large for that to be feasible,” the paper stated.

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By watching how its human rivals played, Libratus fleshed out the relatively simple 'blueprint' decision tree with extra nodes to help it win hands against those opponents. It would analyze the frequency of its opponents' bet sizes, and update itself overnight, improving throughout the competition.

Felted! AI poker bot Libratus cleans out pros in grueling tournament, smugly trousers $1.8m

Libratus is computationally expensive, and was powered by the Bridges system, a high-performance computer at the Pittsburgh Supercomputer Center. It could achieve, at maximum, 1.35 PFLOPS – or more than a quadrillion floating-point math calculations per second. Libratus burned through approximately 19 million core hours of computing throughout the tournament.

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'The techniques that we developed are largely domain independent and can thus be applied to other strategic imperfect-information interactions, including non-recreational applications,' the paper concluded.

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This is a high-level overview of the system, of course, and the paper goes into some more detail. The code, however, will not be released publicly as the technology behind Libratus has been exclusively licensed to Strategic Machine, a startup founded by Sandholm in March this year.

At the Neural Processing Information Systems conference (NIPS) this year, during a demonstration of Libratus, Sandholm told The Register that the AI could be used for calculating strategic decisions in the real world, such as in finance and information security.

Sandholm said it could be deployed to help organizations thwart hackers exploiting zero-day vulnerabilities, where bugs in software are unknown to the folks trying to defend against such attacks. Meanwhile, Noam and Sandholm's research on nested subgame solving [PDF] won best paper at NIPS 2017. ®

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