Deep Reinforcement Learning from Self-Play in No-limit Texas Hold'em Poker

Deep Reinforcement Learning from Self-Play in No-limit Texas Hold'em Poker

Imperfect information games describe many practical applications found in the real world as the information space is rarely fully available. This particular set of problems is challenging due to the random factor that makes even adaptive methods fail to correctly model the problem and find the best...

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Bibliographic Details
Main Author: Tidor-Vlad PRICOPE
Format: Article
Language:English
Published: Babes-Bolyai University, Cluj-Napoca 2021-12-01
Series:Studia Universitatis Babes-Bolyai: Series Informatica
Subjects:
Artificial Intelligence, Computer Poker, Adaptive Learning, Fictitious Play, Self-Play, Deep Reinforcement Learning, Neural Networks.
Online Access:http://193.231.18.162/index.php/subbinformatica/article/view/1188

Internet

http://193.231.18.162/index.php/subbinformatica/article/view/1188

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