In plain words: A poker AI that reasons about what the opponent might hold, focuses its thinking on the current hand, and learns intuition by playing itself. It beat professional players over 44,000 hands and is harder to exploit than earlier poker programs.
Abstract
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 is the quintessential game of imperfect information, and a longstanding 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 more difficult to exploit strategies than prior approaches.
Matej Moravčík, Martin Schmid, Neil Burch, Viliam Lisý, Dustin Morrill, Nolan Bard, Trevor Davis, Kevin Waugh, Michael Johanson, Michael Bowling
arXiv:1701.01724 · cs.AI · submitted Jan 6, 2017 · updated Mar 3, 2017
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Whether any others have been made before now is anyone's guess. Botting is a known problem in online poker. If there's a golden goose out there, I'm sure it's being kept under wraps.