AAAI Publications, Workshops at the Twenty-Seventh AAAI Conference on Artificial Intelligence

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Speeding-up Poker Game Abstraction Computation: Average Rank Strength
Luís Filipe Guimarães Teófilo, Luís Paulo Reis, Henrique Lopes Cardoso

Last modified: 2013-06-29

Abstract


Some of the most successful Poker agents that participate in the Annual Computer Poker Competition (ACPC) use an almost zero regret strategy: a strategy that approximates a Nash Equilibrium. However, it is still unfeasible to efficiently compute a Nash Equilibrium without some sort of information set abstraction due to the size of Poker’s search tree. One popular technique for abstracting Poker information sets is to group hands with similar Expected Hand Strength (E[HS]) and thus play them in the same way. For large Poker variants, algorithms like CFR might need to calculate E[HS] billions of times, when the game abstraction is so large that it cannot be pre-computed, implying that E[HS] must be determined online. This way, improving the efficiency of this method would certainly reduce the computation time needed by CFR for these cases. In this paper we describe Average Rank Strength; a technique based on a pre-computed lookup table that speeds up E[HS] computation. Ours results demonstrate speed improvements of about three orders of magnitude and negligible results difference, when compared to the original E[HS].

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