Superhuman AI for multiplayer poker. Computer programs have shown superiority over humans in two-player games such as chess, Go, and heads-up, no-limit Texas hold’em poker. However, poker games usually include six players – a much trickier challenge for artificial intelligence than the two-player variant. Brown and Sandholm developed a program, dubbed Pluribus, that learned how to play six-player no-limit Texas hold’em by playing against five copies of itself (see the Perspective by Blair and Saffidine). When pitted against five elite professional poker players, or with five copies of Pluribus playing against one professional, the computer performed significantly better than humans over the course of 10,000 hands of poker.
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References in zbMATH (referenced in 6 articles )
Showing results 1 to 6 of 6.
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- Ganzfried, Sam: Algorithm for computing approximate Nash equilibrium in continuous games with application to continuous blotto (2021)
- Garnier, Paul; Viquerat, Jonathan; Rabault, Jean; Larcher, Aurélien; Kuhnle, Alexander; Hachem, Elie: A review on deep reinforcement learning for fluid mechanics (2021)
- Kroer, Christian; Sandholm, Tuomas: Limited lookahead in imperfect-information games (2020)
- Brown, Noam; Sandholm, Tuomas: Superhuman AI for multiplayer poker (2019)