Poker-AI.orgPoker AI and Botting Discussion Forum2013-03-27T14:36:48+00:00https://poker-ai.org/phpbb/feed.php?f=25&t=24212013-03-27T14:36:48+00:002013-03-27T14:36:48+00:00https://poker-ai.org/phpbb/viewtopic.php?t=2421&p=3525#p3525The 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
]]>2013-03-27T12:04:25+00:002013-03-27T12:04:25+00:00https://poker-ai.org/phpbb/viewtopic.php?t=2421&p=3523#p3523 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
]]>2013-03-22T19:05:31+00:002013-03-22T19:05:31+00:00https://poker-ai.org/phpbb/viewtopic.php?t=2421&p=3455#p3455Statistics: Posted by proud2bBot — Fri Mar 22, 2013 7:05 pm
]]>2013-03-21T01:20:40+00:002013-03-21T01:20:40+00:00https://poker-ai.org/phpbb/viewtopic.php?t=2421&p=3443#p3443Statistics: Posted by cantina — Thu Mar 21, 2013 1:20 am
]]>2013-03-21T01:13:36+00:002013-03-21T01:13:36+00:00https://poker-ai.org/phpbb/viewtopic.php?t=2421&p=3442#p3442Statistics: Posted by proud2bBot — Thu Mar 21, 2013 1:13 am
]]>2013-03-21T16:21:13+00:002013-03-21T01:12:08+00:00https://poker-ai.org/phpbb/viewtopic.php?t=2421&p=3441#p3441Strategy 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