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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Veröffentlicht in:	Studia Universitatis Babes-Bolyai: Series Informatica Jg. 66; H. 2; S. 51
1. Verfasser:	Pricope, T.-V.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Babes-Bolyai University, Cluj-Napoca 15.12.2021
Schlagworte:	Artificial Intelligence, Computer Poker, Adaptive Learning, Fictitious Play, Self-Play, Deep Reinforcement Learning, Neural Networks
ISSN:	1224-869X, 2065-9601
Online-Zugang:	Volltext
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