During the development of AlphaGo, its many hyper-parameters were tuned with
Bayesian optimization multiple times. This automatic tuning process resulted in
substantial improvements in playing strength. For example, prior to the match
with Lee Sedol, we tuned the latest AlphaGo agent and this improved its
win-rate from 50% to 66.5% in self-play games. This tuned version was deployed
in the final match. Of course, since we tuned AlphaGo many times during its
development cycle, the compounded contribution was even higher than this
percentage. It is our hope that this brief case study will be of interest to Go
fans, and also provide Bayesian optimization practitioners with some insights
and inspiration.

通过贝叶斯优化，不断调节 AlphaGo 的众多超参数，从而在自我博弈过程中显著提高了其胜率，为 Go 爱好者提供了一些启示和灵感。

AlphaGo 中的贝叶斯优化

Bayesian Optimization in AlphaGo

Achieving superhuman playing level by AlphaGo corroborated the capabilities
of convolutional neural architectures (CNNs) for capturing complex spatial
patterns. This result was to a great extent due to several analogies between Go
board states and 2D images CNNs have been designed for, in particular
translational invariance and a relatively large board. In this paper, we verify
whether CNN-based move predictors prove effective for Othello, a game with
significantly different characteristics, including a much smaller board size
and complete lack of translational invariance. We compare several CNN
architectures and board encodings, augment them with state-of-the-art
extensions, train on an extensive database of experts' moves, and examine them
with respect to move prediction accuracy and playing strength. The empirical
evaluation confirms high capabilities of neural move predictors and suggests a
strong correlation between prediction accuracy and playing strength. The best
CNNs not only surpass all other 1-ply Othello players proposed to date but
defeat (2-ply) Edax, the best open-source Othello player.

本研究通过使用卷积神经网络对黑白棋游戏（Othello）中的移动预测器进行测试，证实了神经运动预测器具有非常高的能力和与游戏强度之间的强相关性，并在强度和预测准确度上超过了以前的最佳算法和现有的最强开源算法 Edax。