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Jonathan Jones
Jonathan Jones

Expert Heads Up No Limit Hold'Em _VERIFIED_



Artificial intelligence has seen several breakthroughs in recent years, with games often serving as milestones. A common feature of these games is that players have perfect information. Poker, the quintessential game of imperfect information, is a long-standing challenge problem in artificial intelligence. We introduce DeepStack, an algorithm for imperfect-information settings. It combines recursive reasoning to handle information asymmetry, decomposition to focus computation on the relevant decision, and a form of intuition that is automatically learned from self-play using deep learning. In a study involving 44,000 hands of poker, DeepStack defeated, with statistical significance, professional poker players in heads-up no-limit Texas hold'em. The approach is theoretically sound and is shown to produce strategies that are more difficult to exploit than prior approaches.




Expert Heads Up No Limit Hold'Em



Almost all authors agree that where a player sits in the order of play (known as position) is an important element of Texas hold 'em strategy, particularly in no-limit hold'em.[1] Players who act later have more information than players who act earlier. As a result, players typically play fewer hands from early positions than later positions. 041b061a72


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