Libratus’ Succesful Approach To Poker Revealed By Researchers

Martin Derbyshire December 22, 2017 1606 Reads
Libratus Poker

Carnegie Mellon University’s Libratus, an artificial intelligence computer program designed to play poker, started the year by proving it could beat four human poker pros. Now, a pair of university researchers behind the program are ending the year by telling the world exactly how the AI program managed to do it.

Libratus beat pros Jason Les, Dong Kim, Daniel McCauley and Jimmy Chou in a 20-day competition held in January at Rivers Casino in Pittsburgh, Pennsylvania. In fact, it beat each of the players at heads-up no-limit hold’em. Over 120,000 total hands, Libratus managed to end the sessions up more than $1.8 million in chips.

This week, Carnegie Mellon’s professor of computer science Tuomas Sandholm and Ph.D. student Noam Brown published an article in the research journal Science, detailing how it managed to do all that.

According to the article, Libratus was programmed to use a three-pronged approach to the game of poker. Plus, it included more decision points than there are atoms in the universe.

Libratus adjusted on the fly

Poker involves bluffing. So, the researchers said the program was designed to recognize and understand the tactic. It really went deeper than just taking a simple black and white approach to the game.

Sandholm and Brown claim Libratus was able to break poker down into computationally manageable parts. That way it could fix weaknesses in its strategy based on its opponents’ play. Essentially, Libratus did what every good poker player has done for decades: It adjusted to the strategies employed by its opponents on the fly.

Libratus’ three-pronged approach to the game included:

  • Creating an abstract version of the game which was easier to solve
  • Creating a more detailed plan-of-action based on how the game was playing out
  • Improving on that plan in real time by detecting mistakes in its opponent’s strategy and exploiting them

Simply put, Libratus began with a basic strategy designed by looking at a simplified version of the game. That strategy became more complex as it learned how its opponents were playing. Finally, it adjusted the strategy even further to exploit weakness shown by its opponents.

If an opponent were to switch to a different strategy, Libratus also avoided opening itself up to exploitation by detecting potential holes in its own game.

Should bet sizing change, Libratus would add the missing decision branches and compute strategies for them. Then it would add those strategies to its plan going forward.

Libratus demoralizes opponents

After losing in January, Les described playing Libratus as a slightly demoralizing experience:

“Libratus turned out to be way better than we imagined. It’s slightly demoralizing. If you play a human and lose, you can stop, take a break. Here we have to show up to take a beating every day for 11 hours a day. It’s a real different emotional experience when you’re not used to losing that often.”

There may even be further reaching implications of Libratus’ success. Several bot rings employing AI have been discovered on online poker sites, including PokerStars. The success of Libratus could lead to an increase in the prevalence of bots online. However, this specific technology has yet to be tested in full-ring games.

The future of AI

In the end, they built an artificial intelligence computer program that can beat the pros at poker. However, Sandholm and Brown say they are hoping the AI can ultimately do a lot more:

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

The technology behind Libratus has now been licensed to Sandholm’s company Strategic Machine. The company aims to apply strategic reasoning technologies to many different applications.