A Profitable Online No-Limit Poker Playing Agent

The No-Limit Texas Hold´em variant of Poker is the game that is most frequently used to assess new developments in incomplete information problems, through the development of game playing agents. For this particular game, current state-of-the-art techniques consist in the pre-computation of a set of strategies that are in a Nash-Equilibrium state. However, due to the game´s decision tree size, current algorithms only work in an abstracted version of No-Limit Poker. Moreover, since these strategies are static, they ignore the opponents´ playing style thus being unable to maximize profit against certain kinds of opponents. This makes these strategies unusable when playing in an online environment against human players. In this paper we present a rule-based strategy approach for a No-Limit Poker agent that was developed to play online, against human players and in online multiplayer matches. This strategy is based on a popular technique used by human players - short stack playing - which consists of playing in tables with up to 6 players and low initial resources. Using domain specific opponent modeling techniques and limiting the decisions to the first round of the game, the agent was able to make a good profit margin of 11.5% per game when playing against human players. The significance of our results resides in the fact that, for the first time in the Computer Poker literature, we present a game playing agent that can match human players in multiplayer games.