Computer code based on continual problem re-solving beats human professional poker players at a two-player variant of poker. Artificial intelligence masters poker Computers can beat humans at games as complex as chess or go. In these and similar games, both players have access to the same information, as displayed on the board. Although computers have the ultimate poker face, it has been tricky to teach them to be good at poker, where players cannot see their opponents' cards. Moravčík et al. built a code dubbed DeepStack that managed to beat professional poker players at a two-player poker variant called heads-up no-limit Texas hold'em. Instead of devising its strategy beforehand, DeepStack recalculated it at each step, taking into account the current state of the game. The principles behind DeepStack may enable advances in solving real-world problems that involve information asymmetry. Science, this issue p. 508 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.

中文翻译：

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DeepStack：单挑无限注扑克中的专家级人工智能

基于不断重新解决问题的计算机代码在两人扑克变体中击败了人类职业扑克玩家。人工智能大师扑克计算机可以在像国际象棋或围棋这样复杂的游戏中击败人类。在这些和类似的游戏中，两个玩家都可以访问显示在板上的相同信息。尽管计算机拥有终极扑克脸，但要教它们擅长扑克却是一件棘手的事情，因为在这种情况下，玩家看不到对手的牌。莫拉夫奇克等人。构建了一个名为 DeepStack 的代码，它成功地在称为单挑无限德州扑克的两人扑克变体中击败了职业扑克玩家。DeepStack 并没有事先设计其策略，而是在每一步都重新计算它，并考虑到游戏的当前状态。DeepStack 背后的原则可能有助于解决涉及信息不对称的现实问题。科学，这个问题 p。508 人工智能近年来取得了多项突破，游戏往往成为里程碑。这些游戏的一个共同特点是玩家拥有完美的信息。扑克是不完美信息的典型游戏，是人工智能领域长期存在的挑战问题。我们介绍了 DeepStack，一种用于不完美信息设置的算法。它结合了递归推理来处理信息不对称、分解以将计算集中在相关决策上，以及一种使用深度学习从自我对弈中自动学习的直觉形式。在一项涉及 44,000 手扑克的研究中，DeepStack 击败了具有统计意义的 单挑无限注德州扑克中的职业扑克玩家。该方法在理论上是合理的，并且被证明可以产生比先前方法更难以利用的策略。