Distributionally Robust Optimization Techniques for Stochastic Optimal Control

Distributionally robust optimal control is a relatively new field of robust control that tries to address the issue of safety by hedging against the worst-cast distributions. However, because probability distributions are infinite-dimensional, this problem is in general computationally intractable. This thesis provides an overview of applications of distributionally robust optimization for stochastic optimal control. In particular, we look at existing and potentially new computationally tractable methods for performing distributionally robust optimal control using the Wasserstein metric.Undergraduat