Other AI strategies based on reinforcement learning have been able to master multi-player games Dota 2 Five and Quake III. All those games are constrained to only two players and are zero-sum games (meaning that whatever one player wins, the other player loses). After all, in recent years AI researchers have made tremendous progress across different complex games such as checkers, chess, Go, two-player poker, StarCraft 2, and Dota 2. The reasons why Pluribus represents a major breakthrough in AI systems might result confusing to many readers. Last week, a team of AI researchers from Facebook in collaboration with Carnegie Mellon University achieved a major milestone in the conquest of Poker by creating Pluribus, an AI agent that beat elite human professional players in the most popular and widely played poker format in the world: six-player no-limit Texas Hold’em poker. In recent years, AI has made some progress in poker environments with systems such as Libratus, defeating human pros in two-player no-limit Hold’em in 2017. From the game theory-creator John Von Neumann writing about poker in his 1928 essay “Theory of Parlor Games, to Edward Thorp masterful book “Beat the Dealer” to the MIT Blackjack Team, poker strategies has been an obsession to mathematicians for decades. Poker has remained as one of the most challenging games to master in the fields of artificial intelligence(AI) and game theory.

Smith poker play in machine learning