Poker-AI.org Poker AI and Botting Discussion Forum 2013-03-27T14:36:48+00:00 https://poker-ai.org/phpbb/feed.php?f=25&t=2421 2013-03-27T14:36:48+00:00 2013-03-27T14:36:48+00:00 https://poker-ai.org/phpbb/viewtopic.php?t=2421&p=3525#p3525 <![CDATA[Re: Strategy Purification and Thresholding]]> The results are:
threshold-0.05: +0.1bb/100
threshold-0.1: +1.29
purify: +4.0bb/100

Statistics: Posted by proud2bBot — Wed Mar 27, 2013 2:36 pm


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2013-03-27T12:04:25+00:00 2013-03-27T12:04:25+00:00 https://poker-ai.org/phpbb/viewtopic.php?t=2421&p=3523#p3523 <![CDATA[Re: Strategy Purification and Thresholding]]>
One such idea:
Using something like NEAT supplied with a more complex picture of the hand value and board texture to evolve a situationally-aware purification strategy.

Statistics: Posted by cantina — Wed Mar 27, 2013 12:04 pm


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2013-03-22T19:05:31+00:00 2013-03-22T19:05:31+00:00 https://poker-ai.org/phpbb/viewtopic.php?t=2421&p=3455#p3455 <![CDATA[Re: Strategy Purification and Thresholding]]> Statistics: Posted by proud2bBot — Fri Mar 22, 2013 7:05 pm


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2013-03-21T01:20:40+00:00 2013-03-21T01:20:40+00:00 https://poker-ai.org/phpbb/viewtopic.php?t=2421&p=3443#p3443 <![CDATA[Re: Strategy Purification and Thresholding]]> Statistics: Posted by cantina — Thu Mar 21, 2013 1:20 am


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2013-03-21T01:13:36+00:00 2013-03-21T01:13:36+00:00 https://poker-ai.org/phpbb/viewtopic.php?t=2421&p=3442#p3442 <![CDATA[Re: Strategy Purification and Thresholding]]> Statistics: Posted by proud2bBot — Thu Mar 21, 2013 1:13 am


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2013-03-21T16:21:13+00:00 2013-03-21T01:12:08+00:00 https://poker-ai.org/phpbb/viewtopic.php?t=2421&p=3441#p3441 <![CDATA[Strategy Purification and Thresholding]]> Strategy Purification and Thresholding: Effective Non-Equilibrium Approaches for Playing Large Games
by: Sam Ganzfried, Tuomas Sandholm, and Kevin Waugh

Abstract
There has been signicant recent interest in computing effective strategies for playing large imperfect-information games. Much prior work involves computing an approximate equilibrium strategy in a smaller abstract game, then playing this strategy in the full game (with the hope that it also well approximates an equilibrium in the full game). In this paper, we present a family of modications to this approach that work by constructing non-equilibrium strategies in the abstract game, which are then played in the full game. Our new procedures, called purication and thresholding, modify the action probabilities of an abstract equilibrium by preferring the higher-probability actions. Using a variety of domains, we show that these approaches lead to significantly stronger play than the standard equilibrium approach. As one example, our program that uses purication came in first place in the two-player no-limit Texas Hold'em total bankroll division of the 2010 Annual Computer Poker Competition. Surprisingly, we also show that purication significantly improves performance (against the full equilibrium strategy) in random 4x4 matrix games using random 3x3 abstractions. We present several additional results (both theoretical and empirical). Overall, one can view these approaches as ways of achieving robustness against overfitting one's strategy to one's lossy abstraction. Perhaps surprisingly, the performance gains do not necessarily come at the expense of worst-case exploitability

http://www.cs.cmu.edu/~sandholm/StrategyPurification_AAMAS2012_camera_ready_2.pdf

Statistics: Posted by proud2bBot — Thu Mar 21, 2013 1:12 am


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