Distributionally Robust Markov Decision Processes and their Connection to Risk Measures
Nicole Bäuerle, Alexander Glauner
TL;DR
The paper develops a distributionally robust Markov decision process framework with Borel spaces and finite horizon, framing distributional ambiguity as a Stackelberg game against nature. It establishes robust cost and value iteration schemes, proves the existence of deterministic optimal policies, and analyzes when minimax interchange is valid via Sion's theorem, including a counterexample illustrating limitations. It connects robust optimization to coherent and spectral risk measures through special ambiguity sets and demonstrates applications to robust LQ control and wind-storage energy management. The results advance dynamic decision-making under model uncertainty in continuous spaces and offer practical tools for engineering and energy systems.
Abstract
We consider robust Markov Decision Processes with Borel state and action spaces, unbounded cost and finite time horizon. Our formulation leads to a Stackelberg game against nature. Under integrability, continuity and compactness assumptions we derive a robust cost iteration for a fixed policy of the decision maker and a value iteration for the robust optimization problem. Moreover, we show the existence of deterministic optimal policies for both players. This is in contrast to classical zero-sum games. In case the state space is the real line we show under some convexity assumptions that the interchange of supremum and infimum is possible with the help of Sion's minimax Theorem. Further, we consider the problem with special ambiguity sets. In particular we are able to derive some cases where the robust optimization problem coincides with the minimization of a coherent risk measure. In the final section we discuss two applications: A robust LQ problem and a robust problem for managing regenerative energy.