Paper Info
Title
No-limit texas hold'em poker agents created with evolutionary neural networks
Abstract
In order for computer Poker agents to play the game well, they must analyse their current quality despite imperfect information, predict the likelihood of future game states dependent upon random outcomes, model opponents who are deliberately trying to mislead them, and manage finances to improve their current condition. This leads to a game space that is large compared to other classic games such as Chess and Backgammon. Evolutionary methods have been shown to find relatively good results in large state spaces, and neural networks have been shown to be able to find solutions to non-linear search problems such as Poker. In this paper, we develop No-Limit Texas Hold'em Poker agents using a hybrid method known as evolving neural networks. We also investigate the appropriateness of evolving these agents using evolutionary heuristics such as co-evolution and halls of fame. Our agents were experimentally evaluated against several benchmark agents as well as agents previously developed in other work. Experimental results show the overall best performance was obtained by an agent evolved from a single population (i.e., no co-evolution) using a large hall of fame. These results demonstrate an effective use of evolving neural networks to create competitive No-Limit Texas Hold'em Poker agents.
Year
DOI
Venue
2009
10.1109/CIG.2009.5286485
Milano
Keywords
Field
DocType
neural network,classic game,game space,em poker agent,large hall,no-limit texas,evolutionary neural network,computer poker agent,competitive no-limit texas,future game state,large state space,no-limit texas hold,next generation networking,multi agent systems,data mining,neural nets,state space,evolutionary computation,imperfect information,games,pediatrics,artificial neural networks
Population,Next-generation network,Texas hold 'em,Computer science,Simulation,Evolutionary computation,Multi-agent system,Heuristics,Artificial intelligence,Perfect information,Artificial neural network,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-4244-4815-9
2
0.37
References 
Authors
11
2
Name
Order
Citations
PageRank
Garrett Nicolai12511.34
Robert J. Hilderman227029.86