Gradient-based methods have been widely used for system design and optimization in
diverse application domains. Recently, there has been a renewed interest in studying …

For discrete-time nonlinear systems, this research is concerned with optimal control
problems (OCPs) with constrained cost, and a novel value iteration with constrained cost …

While the techniques in optimal control theory are often model-based, the policy optimization
(PO) approach directly optimizes the performance metric of interest. Even though it has been …

In this work, we study model-based reinforcement learning (RL) in unknown stabilizable
linear dynamical systems. When learning a dynamical system, one needs to stabilize the …

We consider the problem of controlling an unknown stochastic linear system with quadratic
costs--called the adaptive LQ control problem. We re-examine an approach called``Reward …

Risk-aware control, though with promise to tackle unexpected events, requires a known
exact dynamical model. In this work, we propose a model-free framework to learn a risk …

We propose a new risk-constrained formulation of the classical Linear Quadratic (LQ)
stochastic control problem for general partially-observed systems. Our framework is …

In this article, we consider identification and control for potentially unstable linear systems
with multiagent controllers in the presence of wireless interference channels among the …

We study the problem of computing deterministic optimal policies for constrained Markov
decision processes (MDPs) with continuous state and action spaces, which are widely …

This paper studies the linear quadratic control for non-Gaussian interconnected systems
(ISs). A nonclassical information pattern called delayed sharing pattern is considered. Most …