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AlphaZero from DeepMind rapidly caught the fancy of the AI research community when it was released back in There have also been models designed to play other games such as poker.
However, there has been no generalised AI algorithm so designed that could champion both chess and poker. For humans, we recognise these two as just different games in the broadest sense.
So while AlphaZero performs well for chess, it breaks down when used for imperfect information games. It is built on previous models such as AlphaZero but now comes with an additional capability to play games like poker, where it assesses the chances of the opponent player having a particular card, for example, a pair of aces.
Author: Dane Mitrev. Leave A Reply Cancel Reply. This has sparked a new business with individuals profitable and marketing poker chips to other gamers.
If you are truly excellent at poker you could make oneself some income by marketing your very own chips. So why do folks shell out true cash for virtual Facebook poker chips?
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In one experiment, Pluribus played 10, hands of poker against five human players selected randomly from the pool. On the top chart, the solid lines show the win rate plus or minus the standard error.
The bottom chart shows the number of chips won over the course of the games. Pluribus represents one of the major breakthroughs in modern AI systems.
Even though Pluribus was initially implemented for poker, the general techniques can be applied to many other multi-agent systems that require both AI and human skills.
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