Paper Info
Title
Algorithms for Evolving No-Limit Texas Hold'em Poker Playing Agents
Abstract
Computers have difficulty learning how to play Texas Hold'em Poker. The game contains a high degree of stochasticity, hidden information, and opponents that are deliberately trying to mis-represent their current state. Poker has a much larger game space than classic parlour 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. In this paper, we present several algorithms for teaching agents how to play No-Limit Texas Hold'em Poker using a hybrid method known as evolving neural networks. Furthermore, we adapt heuristics such as halls of fame and co-evolution to be able to handle populations of Poker agents, which can sometimes contain several hundred opponents, instead of a single opponent. Our agents were evaluated against several benchmark agents. Experimental results show the overall best performance was obtained by an agent evolved from a single population (i.e., with no co-evolution) using a large hall of fame. These results demonstrate the effectiveness of our algorithms in creating competitive No-Limit Texas Hold'em Poker agents.
Year
Venue
Keywords
2010
ICEC 2010: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON EVOLUTIONARY COMPUTATION
Poker,Evolutionary algorithms,Evolutionary neural networks
Field
DocType
Citations 
Evolutionary acquisition of neural topologies,Population,Texas hold 'em,Computer science,Algorithm,Heuristics,Artificial intelligence,Artificial neural network,Parlour,Difficulty learning,Machine learning
Conference
0
PageRank 
References 
Authors
0.34
11
2
Name
Order
Citations
PageRank
Garrett Nicolai12511.34
Robert J. Hilderman227029.86