  Solving complex optimal control problems have confronted computational
challenges for a long time. Recent advances in machine learning have provided
us with new opportunities to address these challenges. This paper takes model
predictive control, a popular optimal control method, as the primary example to
survey recent progress that leverages machine learning techniques to empower
optimal control solvers. We also discuss some of the main challenges
encountered when applying machine learning to develop more robust optimal
control algorithms.
