4 papers
Central Limit Theorems for Sample Average Approximations in Stochastic Optimal Control
Johannes Milz, Alexander Shapiro
We establish central limit theorems for the Sample Average Approximation (SAA) method in discrete-time, finite-horizon stochastic optimal control. Our analysis is based on an abstr…
Stochastic Optimal Control with Side Information and Bayesian Learning
Johannes Milz, Alexander Shapiro, Enlu Zhou
We study infinite-horizon stochastic optimal control problems with observable side information: a Markov chain that modulates an unknown context-conditional randomness distribution…
Risk-averse formulations of Stochastic Optimal Control and Markov Decision Processes
Alexander Shapiro, Yan Li
The aim of this paper is to investigate risk-averse and distributionally robust modeling of Stochastic Optimal Control (SOC) and Markov Decision Process (MDP). We discuss construct…
Distributionally robust stochastic optimal control
Alexander Shapiro, Yan Li
The main goal of this paper is to discuss the construction of distributionally robust counterparts of stochastic optimal control problems. Randomized and non-randomized policies ar…