Robustness of Optimal Controlled Diffusions with Near-Brownian Noise via Rough Paths Theory

Abstract

In this article we show a robustness theorem for controlled stochastic differential equations driven by approximations of Brownian motion. Often, Brownian motion is used as an idealized model of a diffusion where approximations such as Wong-Zakai, Karhnen-Lo\`eve or fractional Brownian motion are often seen as more physical. However, there has been extensive literature on solving control problems driven by Brownian motion and little on control problems driven by more realistic models that are only approximately Brownian. The question of robustness naturally arises from such approximations. We show robustness using rough paths theory, which allows for a pathwise theory of stochastic differential equations. To this end, in particular, we show that within the class of Lipschitz continuous control policies, an optimal solution for the Brownian idealized model is near optimal for a true system driven by a non-Brownian (but near-Brownian) noise.