Open AccessProceedings Article
Approximating game-theoretic optimal strategies for full-scale poker
Darse Billings,Neil Burch,Aaron Davidson,Robert C. Holte,Jonathan Schaeffer,Terence Schauenberg,Duane Szafron +6 more
- pp 661-668
TLDR
The computation of the first complete approximations of game-theoretic optimal strategies for full-scale poker is addressed, and linear programming solutions to the abstracted game are used to create substantially improved poker-playing programs.Abstract:
The computation of the first complete approximations of game-theoretic optimal strategies for full-scale poker is addressed. Several abstraction techniques are combined to represent the game of 2-player Texas Hold'em, having size O(1018), using closely related models each having size O(1O7). Despite the reduction in size by a factor of 100 billion, the resulting models retain the key properties and structure of the real game. Linear programming solutions to the abstracted game are used to create substantially improved poker-playing programs, able to defeat strong human players and be competitive against world-class opponents.read more
Citations
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TL;DR: It is announced that heads-up limit Texas hold’em is now essentially weakly solved, and this computation formally proves the common wisdom that the dealer in the game holds a substantial advantage.
Proceedings Article
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Dissertation
Deep Reinforcement Learning from Self-Play in Imperfect-Information Games
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TL;DR: This paper introduces the first scalable end-to-end approach to learning approximate Nash equilibria without prior domain knowledge, and combines fictitious self-play with deep reinforcement learning.
References
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A Computing Procedure for Quantification Theory
Martin Davis,Hilary Putnam +1 more
TL;DR: In the present paper, a uniform proof procedure for quantification theory is given which is feasible for use with some rather complicated formulas and which does not ordinarily lead to exponentiation.