Robustness to incorrect system models in stochastic control

TL;DR

This paper investigates the robustness of stochastic control policies to model inaccuracies, establishing conditions under which optimal costs are continuous and providing bounds on performance loss due to model mismatch.

Contribution

It offers refined robustness results applicable under various convergence criteria, including weak and setwise, extending the understanding of control policy robustness in data-driven settings.

Findings

Optimal cost is robust under total variation convergence.

Continuity of optimal cost can be achieved under weak convergence with additional assumptions.

Provides convergence results and error bounds for model mismatch scenarios.

Abstract

In stochastic control applications, typically only an ideal model (controlled transition kernel) is assumed and the control design is based on the given model, raising the problem of performance loss due to the mismatch between the assumed model and the actual model. Toward this end, we study continuity properties of discrete-time stochastic control problems with respect to system models (i.e., controlled transition kernels) and robustness of optimal control policies designed for incorrect models applied to the true system. We study both fully observed and partially observed setups under an infinite horizon discounted expected cost criterion. We show that continuity and robustness cannot be established under weak and setwise convergences of transition kernels in general, but that the expected induced cost is robust under total variation. By imposing further assumptions on the measurement models and on the kernel itself (such as continuous convergence), we show that the optimal cost can be made continuous under weak convergence of transition kernels as well. Using these continuity properties, we establish convergence results and error bounds due to mismatch that occurs by the application of a control policy which is designed for an incorrectly estimated system model to a true model, thus establishing positive and negative results on robustness.Compared to the existing literature, we obtain strictly refined robustness results that are applicable even when the incorrect models can be investigated under weak convergence and setwise convergence criteria (with respect to a true model), in addition to the total variation criteria. These entail positive implications on empirical learning in (data-driven) stochastic control since often system models are learned through empirical training data where typically weak convergence criterion applies but stronger convergence criteria do not.