Vision Transformers for Computer Go
This is an incremental improvement for computer Go players, as it adapts an existing method to a new domain.
The study tackled applying vision transformers to the game of Go, comparing them to residual networks and finding that transformers can play a substantial role based on metrics like prediction accuracy and win rates.
Motivated by the success of transformers in various fields, such as language understanding and image analysis, this investigation explores their application in the context of the game of Go. In particular, our study focuses on the analysis of the Transformer in Vision. Through a detailed analysis of numerous points such as prediction accuracy, win rates, memory, speed, size, or even learning rate, we have been able to highlight the substantial role that transformers can play in the game of Go. This study was carried out by comparing them to the usual Residual Networks.