Adaptive play in Texas Hold’em Poker.
Raphael Maitrepierre, Jérémie Mary and Rémi Munos
In: European Conference on Artificial Intelligence(2008).
Abstract. We present a Texas Hold’em poker player for limit heads-
up games. Our bot is designed to adapt automatically to the strategy
of the opponent and is not based on Nash equilibrium computation.
The main idea is to design a bot that builds beliefs on his opponent’s
hand. A forest of game trees is generated according to those beliefs
and the solutions of the trees are combined to make the best decision.
The beliefs are updated during the game according to several meth-
ods, each of which corresponding to a basic strategy. We then use
an exploration-exploitation bandit algorithm, namely the UCB (Up-
per Conﬁdence Bound), to select a strategy to follow. This results
in a global play that takes into account the opponent’s strategy, and
which turns out to be rather unpredictable. Indeed, if a given strategy
is exploited by an opponent, the UCB algorithm will detect it using
change point detection, and will choose another one.
The initial resulting program , called Brennus, participated to the
AAAI’07 Computer Poker Competition in both online and equilib-
rium competition and ranked eight out of seventeen competitors.