6 papers · 1 filter
Lipschitz Regularity in Wasserstein Robust Stochastic Optimal Control
Shengbo Wang, Jose Blanchet
Robust Markov decision processes provide a principled framework for protecting sequential decision-making against transition-law misspecification and have attracted substantial rec…
Fast Convergence of Policy Regret in Learning Stochastic Optimal Control
Shengbo Wang, Jose Blanchet, Peter Glynn
Policy learning in modern operations environments faces a fundamental tension between limited operational data and the large, often continuous, state and action spaces over which g…
Non-Rectangular Average-Reward Robust MDPs: Optimal Policies and Their Transient Values
Shengbo Wang, Nian Si
We study non-rectangular robust Markov decision processes under the average-reward criterion, where the ambiguity set couples transition probabilities across states and the adversa…
Bellman Optimality of Average-Reward Robust Markov Decision Processes with a Constant Gain
Shengbo Wang, Nian Si
Learning and optimal control under robust Markov decision processes (MDPs) have received increasing attention, yet most existing theory, algorithms, and applications focus on finit…
Tractable Robust Markov Decision Processes
Julien Grand-Clément, Nian Si, Shengbo Wang
In this paper we investigate the tractability of robust Markov Decision Processes (RMDPs) under various structural assumptions on the uncertainty set. Surprisingly, we show that in…
An Efficient High-Dimensional Gradient Estimator for Stochastic Differential Equations
Shengbo Wang, Jose Blanchet, Peter Glynn
Overparameterized stochastic differential equation (SDE) models have achieved remarkable success in various complex environments, such as PDE-constrained optimization, stochastic c…