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2024 | OriginalPaper | Buchkapitel

Vision Transformers for Computer Go

verfasst von : Amani Sagri, Tristan Cazenave, Jérôme Arjonilla, Abdallah Saffidine

Erschienen in: Applications of Evolutionary Computation

Verlag: Springer Nature Switzerland

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Abstract

Motivated by transformers’ success in diverse fields like language understanding and image analysis, our investigation explores their potential in the game of Go. Specifically, we focus on analyzing Transformers in Vision. Through a comprehensive examination of factors like prediction accuracy, win rates, memory, speed, size, and learning rate, we underscore the significant impact transformers can make in the game of Go. Notably, our findings reveal that transformers outperform the previous state-of-the-art models, demonstrating superior performance metrics. This comparative study was conducted against conventional Residual Networks.

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Vorheriges Kapitel Using Evolution and Deep Learning to Generate Diverse Intelligent Agents

Nächstes Kapitel Integrating Bayesian and Evolutionary Approaches for Multi-objective Optimisation

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Titel: Vision Transformers for Computer Go
verfasst von: Amani Sagri
Tristan Cazenave
Jérôme Arjonilla
Abdallah Saffidine
Verlag: Springer Nature Switzerland
Buch: Applications of Evolutionary Computation
Print ISBN: 978-3-031-56854-1

Electronic ISBN: 978-3-031-56855-8

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-3-031-56855-8_23

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