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This is an independent study on the application of current machine learning techniques into Texas Hold'em AI. Setup. TensorFlow documentation. # Ubuntu/​Linux.


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How AI beat the best poker players in the world - Engadget R+D

Worried poker fans may have even greater cause for concern with the success of DeepStack.{/INSERTKEYS}{/PARAGRAPH} Typically researchers would need to have their computer algorithms play a huge number of poker hands to ensure that the results are statistically significant and not simply due to chance. {PARAGRAPH}{INSERTKEYS}An analysis showed that four of the top AI competitors in the Annual Computer Poker Competition were beatable by more than 3, milli-big-blinds per game in poker parlance. Of all the players, 11 poker pros completed the requested 3, games over a period of four weeks from November 7 to December 12, DeepStack handily beat 10 of the 11 with a statistically significant victory margin, and still technically beat the 11th player. That performance is four times worse than if the AI simply folded and gave up the pot at the start of every game. DeepStack takes a very different approach that combines both old and new techniques. Deep learning enables AI to learn from example by filtering huge amounts of data through multiple layers of artificial neural networks. He thought the challenge would prove too tough even for deep learning. But the DeepStack team used a low-variance technique called AIVAT that filters out much of the chance factor and enabled them to come up with statistically significant results with as few as 3, games. This victory margin also amounted to over 20 standard deviations from zero in statistical terms.