Analyses of Tabular AlphaZero on Strongly-Solved Stochastic Games

The AlphaZero algorithm achieved superhuman levels of play in chess, shogi, and Go by learning without domain-specific knowledge except for game rules. This paper targets stochastic games and investigates whether AlphaZero can learn theoretical values and optimal play. Since the theoretical values o...

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Veröffentlicht in:	IEEE access 2023-01, Vol.11, p.18157-18182
Hauptverfasser:	Hsueh, Chu-Hsuan, Ikeda, Kokolo, Wu, I-Chen, Chen, Jr-Chang, Hsu, Tsan-Sheng
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms AlphaZero Approximation algorithms Artificial neural networks board games Chess Chinese dark chess EinStein würfelt nicht Games Lookup tables Machine learning Neural networks Optimization Parameters Reinforcement learning stochastic games Stochastic processes tabular
Online-Zugang:	Volltext
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