An Investigation into Tournament Poker Strategy using Evolutionary Algorithms

Abstract

Poker has become the subject of an increasing amount of study in the computational intelligence community. The element of imperfect information presents new and greater challenges than those previously posed by games such as checkers and chess. Advances in computer poker have great potential, since reasoning under conditions of uncertainty is typical of many real world problems. To date the focus of computer poker research has centred on the development of ring game players for limit Texas hold’em. For a computer to compete in the most prestigious poker events, however, it will be required to play in a tournament setting with a no-limit betting structure. This thesis is the first academic attempt to investigate the underlying dynamics of successful no-limit tournament poker play. Professional players have proffered advice in the non-academic poker literature on correct strategies for tournament poker play. This study seeks to empirically validate their suggestions on a simplified no-limit Texas hold’em tournament framework. Starting by using exhaustive simulations, we first assess the hypothesis that a strategy including information related to game-specific factors performs better than one founded on hand strength knowledge alone. Specifically, we demonstrate that the use of information pertaining to one’s seating position, the opponents’ prior actions, the stage of the tournament, and one’s chip stack size all contribute towards a statistically significant improvement in the number of tournaments won. In extending the research to combine all factors we explain the limitations of the exhaustive simulation approach, and introduce evolutionary algorithms as a method of searching the strategy space. We then test the hypothesis that a strategy which combines information from all the aforementioned factors performs better than one which employs only a single factor. We show that an evolutionary algorithm is successfully able to resolve conflicting signals from the specified factors, and that the resulting strategies are statistically stronger than those previously discovered. Our research continues with an analysis of the results, as we interpret them in the context of poker strategy. We compare our findings to poker authors’ recommendations, and conclude with a discussion on the many possible extensions to this work.