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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/18244

Title: Analyses of Tabular AlphaZero on NoGo
Authors: Hsueh, Chu-Hsuan
Ikeda, Kokolo
Nam, Sang-Gyu
Wu, I-Chen
Issue Date: 2021-03
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Magazine name: The 2020 Conference on Technologies and Applications of Artificial Intelligence (TAAI 2020)
DOI: 10.1109/TAAI51410.2020.00054
Abstract: The AlphaZero algorithm has been shown to achieve superhuman levels of plays in chess, shogi, and Go. This paper presents analytic investigations of the algorithm on NoGo, a variant of Go that players cannot capture the opponents’ stones. More specifically, lookup tables are employed for learning instead of deep neural networks, referred to as tabular AlphaZero. One goal of this work is to investigate how the algorithm is influenced by hyper-parameters. Another goal is to investigate whether the optimal plays and theoretical values can be learned. One of the hyper-parameters is thoroughly analyzed in the experiments. The results show that the tabular AlphaZero can learn the theoretical values and optimal plays in many settings of the hyper-parameter. Also, NoGo on different board sizes is compared, and the learning difficulty is shown to relate to the game complexity.
Rights: This is the author's version of the work. Copyright (C) 2021 IEEE. 2020 International Conference on Technologies and Applications of Artificial Intelligence (TAAI). DOI: 10.1109/TAAI51410.2020.00054. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
URI: http://hdl.handle.net/10119/18244
Material Type: author
Appears in Collections:d11-1. 会議発表論文 (Conference Papers)

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