Search
Skip to Search Results- 4Computer Games
- 3Game theory
- 3Regret minimization
- 3Reinforcement Learning
- 2Artificial Intelligence
- 2Extensive games
-
2007
Johanson, Michael, Bowling, Michael, Zinkevich, Martin
Technical report TR07-15. Adaptation to other initially unknown agents often requires computing an effective counter-strategy. In the Bayesian paradigm, one must find a good counter-strategy to the inferred posterior of the other agents' behavior. In the experts paradigm, one may want to choose...
-
2008
Lizotte, Daniel, Wang, Tao, Bowling, Michael, Schuurmans, Dale
Technical report TR08-16. We propose a dual approach to dynamic programming and reinforcement learning based on maintaining an explicit representation of visit distributions as opposed to value functions. An advantage of working in the dual is that it allows one to exploit techniques for...
-
2007
Wang, Tao, Bowling, Michael, Lizotte, Daniel, Schuurmans, Dale
Technical report TR07-10. We propose to use a new dual approach to dynamic programming. The idea is to maintain an explicit representation of stationary distributions as opposed to value functions. A significant advantage of the dual approach is that it allows one to exploit well developed...
-
2006
Wang, Tao, Schuurmans, Dale, Bowling, Michael
Technical report TR06-26. We investigate the dual approach to dynamic programming and reinforcement learning, based on maintaining an explicit representation of stationary distributions as opposed to value functions. A significant advantage of the dual approach is that it allows one to exploit...
-
2012
Lanctot, Marc, Gibson, Richard, Burch, Neil, Szafron, Duane
In large extensive form games with imperfect information, Counterfactual Regret Minimization (CFR) is a popular, iterative algorithm for computing approximate Nash equilibria. While the base algorithm performs a full tree traversal on each iteration, Monte Carlo CFR (MCCFR) reduces the per...
-
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...
-
2012
Bowling, Michael, Zinkevich, Martin
Online learning aims to perform nearly as well as the best hypothesis in hindsight. For some hypothesis classes, though, even finding the best hypothesis offline is challenging. In such offline cases, local search techniques are often employed and only local optimality guaranteed. For online...
-
2007
Bowling, Michael, Johanson, Michael, Zinkevich, Martin, Piccione, Carmelo
Technical report TR07-14. Extensive games are a powerful model of multiagent decision-making scenarios with incomplete information. Finding a Nash equilibrium for very large instances of these games has received a great deal of recent attention. In this paper, we describe a new technique for...