Opponent Modeling in Poker

In recent years much progress has been made on computer gameplay in games of complete information such as chess and go. Computers have surpassed the ability of top chess players and are well on their way to doing so at Go. Games of incomplete information, on the other hand, are far less studied. Despite significant financial incentives, computerized poker players still perform at a level well below that of poker professionals. They have only reached anything near optimality on 2-player poker, which is much simpler than the normal 9 or 10 player version. We believe that the complex games of incomplete information like poker represent an important future application of machine learning. In particular, we believe that online learning techniques are a good fit for many important problems posed by poker. Currently the most popular form of poker is No-Limit holdem. A yearly series of tournaments, called the World Series of Poker, sees the largest prize pools. Its championship and largest tournament is a No-Limit Holdem tournament whose top prize now exceeds $10,000,000. However, you don’t have to win the world series to earn money playing No-Limit Holdem. With the prevalence of online games, just about anything you can predict with a computer more effectively will help you turn a profit. For this reason we have chosen to explore the game of No-Limit holdem and, more specifically to use online expert algorithms to develop opponent player models, a critical component of successful gameplay and a problem area in current poker research.