An adaptive learning model for simplified poker using evolutionary algorithms

Evolution is the process of adapting to a potentially dynamic environment. By utilising the implicit learning characteristic of evolution in our algorithms, we can create computer programs that learn, and evolve, in uncertain environments. We propose to use evolutionary algorithms to learn to play games of imperfect information-in particular, the game of poker. We describe a new adaptive learning model using evolutionary algorithms that is suitable for designing adaptive computer poker players. We identify several important principles of poker play and use these as the basis for a hypercube of evolving populations in our model. We report experiments using this model to learn a simplified version of poker; results indicate that our new approach demonstrates emergent adaptive behaviour in evolving computer poker players. In particular, we show that our evolving poker players develop different techniques to counteract the variety of strategies employed by their opponents in order to maximise winnings. We compare the strategies evolved by our evolved poker players with a competent static player to demonstrate the importance of adaptation to achieve this end. Comparison with our existing evolutionary poker model highlights the improved performance of this approach