The growing necessity for environmentally benign hybrid propulsion systems has led to the
development of advanced power management control algorithms to maximize fuel economy …

This article explores various uncertain control co-design (UCCD) problem formulations.
While previous work offers formulations that are method-dependent and limited to only a …

Policy gradients methods apply to complex, poorly understood, control problems by
performing stochastic gradient descent over a parameterized class of polices. Unfortunately …

Covering formulation, algorithms, and structural results, and linking theory to real-world
applications in controlled sensing (including social learning, adaptive radars and sequential …

Multi-armed bandit problems (MABPs) are a special type of optimal control problem well
suited to model resource allocation under uncertainty in a wide variety of contexts. Since the …

Satisfying safety constraints almost surely (or with probability one) can be critical for the
deployment of Reinforcement Learning (RL) in real-life applications. For example, plane …

This book provides a unified approach for the study of constrained Markov decision
processes with a finite state space and unbounded costs. Unlike the single controller case …

Reinforcement learning aims to determine an optimal control policy from interaction with a
system or from observations gathered from a system. In batch mode, it can be achieved by …

There is an increasing interest in studying control systems employing multiple sensors and
actuators that are geographically distributed. Communication is an important component of …

Stochastic hybrid systems have received significant attentions as a relevant modeling
framework describing many systems, from engineering to the life sciences: they enable the …