  Sequential Model-based Bayesian Optimization has been successful-ly applied
to several application domains, characterized by complex search spaces, such as
Automated Machine Learning and Neural Architecture Search. This paper focuses
on optimal control problems, proposing a Sequential Model-based Bayesian
Optimization framework to learn optimal control strategies. A quite general
formalization of the problem is provided, along with a specific instance
related to optimization of pumping operations in an urban Water Distri-bution
Network. Relevant results on a real-life Water Distribution Network are
reported, comparing different possible choices for the proposed framework.
