Poker-AI.org

Recently, poker has emerged as a popular domain for investigating decision problems under condi-
tions 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
the poker table are more analogous to the decisions faced by humans in everyday life.
In this dissertation, we investigate regret minimization in extensive-form games and apply our
work in developing champion computer poker agents. Counterfactual Regret Minimization (CFR) is
the current state-of-the-art approach to computing capable strategy profiles for large extensive-form
games. Our primary focus is to advance our understanding and application of CFR in domains with
more than two players. We present four major contributions. First, we provide the first set of theo-
retical guarantees for CFR when applied to games that are not two-player zero-sum. We prove that
in such domains, CFR eliminates strictly dominated plays. In addition, we provide a modification
of CFR that is both more efficient and can lead to stronger strategies than were previously possi-
ble. Second, we provide new regret bounds for CFR, present three new CFR sampling variants, and
demonstrate their efficiency in several different domains. Third, we prove the first set of sufficient
conditions that guarantee CFR will minimize regret in games with imperfect recall. Fourth, we gen-
eralize three previous game tree decomposition methods, present a new decomposition method, and
demonstrate their improvement empirically over standard techniques. Finally, we apply the work in
this thesis to construct three-player Texas hold’em agents and enter them into the Annual Computer
Poker Competition. Our agents won six out of the seven three-player events that we entered from
the 2010, 2011, 2012, and 2013 computer poker competitions.