Learning to imitate expert behavior from demonstrations can be challenging, especially in
environments with high-dimensional, continuous observations and unknown dynamics …

We present an approximate dynamic programming approach for making ambulance
redeployment decisions in an emergency medical service system. The primary decision is …

The management of the urban water cycle (UWC) is a subject of increasing interest because
of its social, economic, and environmental impact. The most important issues include the …

The cross-entropy method is an efficient and general optimization algorithm. However, its
applicability in reinforcement learning (RL) seems to be limited because it often converges …

Inferring intent from observed behavior has been studied extensively within the frameworks
of Bayesian inverse planning and inverse reinforcement learning. These methods infer a …

Abstract The Unit Commitment problem in energy management aims at finding the optimal
productions schedule of a set of generation units while meeting various system-wide …

This paper presents a robust learning-based predictive control strategy for nonlinear
systems subject to both input and output constraints, under the assumption that the model …

A novel approach based on probability and randomization has emerged to synergize with
the standard deterministic methods for control of systems with uncertainty. The main …

This paper addresses a chance-constrained model predictive control (CC-MPC) strategy for
the management of drinking water networks (DWNs) based on a finite horizon stochastic …

We consider a general worst-case robust convex optimization problem, with arbitrary
dependence on the uncertain parameters, which are assumed to lie in some given set of …