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- 2Game theory
- 1Computer Games
- 1Extensive form game
- 1Extensive games
- 1Regret minimalization
- 1Regret minimization
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Monte Carlo Sampling and Regret Minimization for Equilibrium Computation and Decision-Making in Large Extensive Form Games
DownloadSpring 2013
In this thesis, we investigate the problem of decision-making in large two-player zero-sum games using Monte Carlo sampling and regret minimization methods. We demonstrate four major contributions. The first is Monte Carlo Counterfactual Regret Minimization (MCCFR): a generic family of...
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2009
Bowling, Michael, Zinkevich, Martin, Waugh, Kevin, Lanctot, Marc
Technical report TR09-15. Sequential decision-making with multiple agents and imperfect information is commonly modeled as an extensive game. One efficient method for computing Nash equilibria in large, zero-sum, imperfect information games is counterfactual regret minimization (CFR). In the...
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