Discrete Distributionally Robust Optimal Control with Explicitly Constrained Optimization

TL;DR

This paper introduces a reformulation technique for discrete distributionally robust optimal control problems, making them more tractable by converting them into smooth convex programs with minimal inequalities, demonstrated on a patrol-agent scenario.

Contribution

It proposes a novel reformulation method for discrete DROC problems that simplifies the optimization into a single-layer convex program, improving tractability.

Findings

Reformulation reduces inequalities to trivial ones, enhancing computational efficiency.

Applied method successfully to a patrol-agent control problem.

Demonstrates improved tractability over previous approaches.

Abstract

Distributionally robust optimal control (DROC) is gaining interest. This study presents a reformulation method for discrete DROC (DDROC) problems to design optimal control policies under a worst-case distributional uncertainty. The reformulation of DDROC problems impacts both the utility of tractable improvements in continuous DROC problems and the inherent discretization modeling of DROC problems. DROC is believed to have tractability issues; namely, infinite inequalities emerge over the distribution space. Therefore, investigating tractable reformulation methods for these DROC problems is crucial. One such method utilizes the strong dualities of the worst-case expectations. However, previous studies demonstrated that certain non-trivial inequalities remain after the reformulation. To enhance the tractability of DDROC, the proposed method reformulates DDROC problems into one-layer smooth convex programming with only a few trivial inequalities. The proposed method is applied to a DDROC version of a patrol-agent design problem.