Online Poker Research Paper

Online Poker Research Paper-27
There were several plays that humans simply are not making at all, especially relating to its bet sizing.All AI breakthroughs in previous benchmark games have been limited to those with only two players or two teams facing off in a zero-sum competition (for example, .

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(This is also true for two-player general-sum games.) Moreover, in a game with more than two players, it is possible to lose even when playing an exact Nash equilibrium strategy.

One such example is the , in which each player simultaneously picks a point on a ring and wants to be as far away as possible from any other player.

These results are considered a decisive margin of victory by poker professionals.

This is the first time an AI bot has proven capable of defeating top professionals in any major benchmark game that has more than two players (or two teams).

Pluribus also uses new, faster self-play algorithms for games with hidden information.

Combined, these advances made it possible to train Pluribus using very little processing power and memory — the equivalent of less than 0 worth of cloud computing resources.Attempting to respond to nonlinear open ranges was a fun challenge that differs from human games." —Seth Davies, professional poker player“I was really excited to get to play against the bot and saw it as a unique learning experience.I thought the bot played a very solid, fundamentally sound game.It’s really hard to pin him down on any kind of hand.He’s also very good at making thin value bets on the river.As humans I think we tend to oversimplify the game for ourselves, making strategies easier to adopt and remember.The bot doesn't take any of these shortcuts and has an immensely complicated/balanced game tree for every decision.” —Jimmy Chou, professional poker player “It was incredibly fascinating getting to play against the poker bot and seeing some of the strategies it chose.It did a very good job of putting me to tough decisions when I didn’t have a strong hand and getting value when it had the best hand.It was a fun challenge and I’d enjoy the opportunity to compete with it again.” —Trevor Savage, professional poker player “Pluribus is a very hard opponent to play against.In two-player and two-team zero-sum games, playing an exact Nash equilibrium makes it impossible to lose no matter what the opponent does.(For example, the Nash equilibrium strategy for rock-paper-scissors is to randomly pick rock, paper, or scissors with equal probability.) Although a Nash equilibrium is guaranteed to exist in any finite game, it is not generally possible to efficiently compute a Nash equilibrium in a game with three or more players.

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