  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.
