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Deep learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep

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There seems to be some papers on this topic, like this - [1412.3397] Sequential Labeling with online Deep Learning. I think the learning paradigm itself is not

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Note that both of these systems played Heads-up poker, With the goal of making Deep Learning more accessible, we also got a few frameworks for the web,

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Here is our AAAI'16 paper: Poker-CNN: A Pattern Learning Strategy for Making Draws and Bets in Poker Games, By Nikolai Yakovenko, Liangliang Cao,

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DeepStack bridges the gap between AI techniques for games of perfect information—like checkers, chess and Go—with ones for imperfect information games–like poker–to reason while it plays using “intuition” honed through deep learning to reassess its strategy with each decision.

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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.