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About the Algorithm The first computer program to outplay human professionals at heads-up no-limit Hold'em poker In a study completed December 2024 ⭕️ and involving 44,000 hands of poker, DeepStack defeated 11 professional poker players with only one outside the margin of statistical ⭕️ significance. Over all games played, DeepStack won 49 big blinds/100 (always folding would only lose 75 bb/100), over four standard ⭕️ deviations from zero, making it the first computer program to beat professional poker players in heads-up no-limit Texas hold'em poker. Games ⭕️ are serious business Don’t let the name fool you, “games” of imperfect information provide a general mathematical model that describes how ⭕️ decision-makers interact. AI research has a long history of using parlour games to study these models, but attention has been ⭕️ focused primarily on perfect information games, like checkers, chess or go. Poker is the quintessential game of imperfect information, where ⭕️ you and your opponent hold information that each other doesn't have (your cards). Until now, competitive AI approaches in imperfect information ⭕️ games have typically reasoned about the entire game, producing a complete strategy prior to play. However, to make this approach ⭕️ feasible in heads-up no-limit Texas hold’em—a game with vastly more unique situations than there are atoms in the universe—a simplified ⭕️ abstraction of the game is often needed. A fundamentally different approach DeepStack is the first theoretically sound application of heuristic search methods—which ⭕️ have been famously successful in games like checkers, chess, and Go—to imperfect information games. At the heart of DeepStack is continual ⭕️ re-solving, a sound local strategy computation that only considers situations as they arise during play. This lets DeepStack avoid computing ⭕️ a complete strategy in advance, skirting the need for explicit abstraction. During re-solving, DeepStack doesn’t need to reason about the entire ⭕️ remainder of the game because it substitutes computation beyond a certain depth with a fast approximate estimate, DeepStack’s "intuition" – ⭕️ a gut feeling of the value of holding any possible private cards in any possible poker situation. Finally, DeepStack’s intuition, much ⭕️ like human intuition, needs to be trained. We train it with deep learning using examples generated from random poker situations. DeepStack ⭕️ is theoretically sound, produces strategies substantially more difficult to exploit than abstraction-based techniques and defeats professional poker players at heads-up ⭕️ no-limit poker with statistical significance.
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