This is a decommissioned version of ERA which is running to enable completion of migration processes. All new collections and items and all edits to existing items should go to our new ERA instance at https://ualberta.scholaris.ca - Please contact us at erahelp@ualberta.ca for assistance!
Search
Skip to Search Results- 3Game theory
- 3Regret minimization
- 1Artificial intelligence
- 1Extensive form game
- 1General-sum games
- 1Machine learning
-
Fall 2023
Self-play is a technique for machine learning in multi-agent systems where a learning algorithm learns by interacting with copies of itself. Self-play is useful for generating large quantities of data for learning, but has the drawback that agents the learner will face post-training may have...
-
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...
-
Regret Minimization in Games and the Development of Champion Multiplayer Computer Poker-Playing Agents
DownloadSpring 2014
Recently, poker has emerged as a popular domain for investigating decision problems under conditions of uncertainty. Unlike traditional games such as checkers and chess, poker exhibits imperfect information, varying utilities, and stochastic events. Because of these complications, decisions at...