What's the Next Move? Learning Player Strategies in Zoom Poker Games
Murillo Guimarães Carneiro, Gabriel A. De Lisboa · 2018
In this article, we address the problem of modeling the actions of a human player in order to learn his strategies from his past game logs in Zoom Texas Hold'em poker variant. Although Texas Hold'em is a very popular game, Zoom is yet a very recent format of game in which, instead of playing in a specific table against a specific set of opponents, a player is placed in a large pool of players in which their opponents change every hand. Pros and cons of Zoom include respectively bigger effective time playing (and possibly getting money) and scarcity of data to get reads from the opponents. To deal with this problem, our model consists of a simple and generic set of features designed to fulfill each one of four proposed categories (hand quality, position insights, aggressiveness and current situation) in order to be able to capture a wide range of player strategies in each stage of the game. As a consequence of our modeling, we generate five data sets which were further evaluated by machine learning techniques. The results show that much of the player strategies were effectively learned, especially by non-linear techniques. Moreover, our data sets are available online as a test-bed for machine learning research in poker games.