This decommissioned ERA site remains active temporarily to support our final migration steps to https://ualberta.scholaris.ca, ERA's new home. All new collections and items, including Spring 2025 theses, are at that site. For assistance, please contact erahelp@ualberta.ca.
Search
Skip to Search Results- 2Game theory
- 1Computer Games
- 1Extensive form game
- 1Extensive games
- 1Regret minimalization
- 1Regret minimization
-
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...
-
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...