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 …

Direct policy gradient methods for reinforcement learning and continuous control problems
are a popular approach for a variety of reasons: 1) they are easy to implement without …

The linear quadratic regulator (LQR) problem has reemerged as an important theoretical
benchmark for reinforcement learning-based control of complex dynamical systems with …

This monograph presents a new framework for learning-based control synthesis of
continuous-time dynamical systems with unknown dynamics. The new design paradigm …

Model-free reinforcement learning attempts to find an optimal control action for an unknown
dynamical system by directly searching over the parameter space of controllers. The …

This article surveys reinforcement learning from the perspective of optimization and control,
with a focus on continuous control applications. It reviews the general formulation …

Direct policy search serves as one of the workhorses in modern reinforcement learning (RL),
and its applications in continuous control tasks have recently attracted increasing attention …

This paper revisits the classical Linear Quadratic Gaussian (LQG) control from a modern
optimization perspective. We analyze two aspects of the optimization landscape of the LQG …

Many control policies used in applications compute the input or action by solving a convex
optimization problem that depends on the current state and some parameters. Common …

Making the best possible decision according to some desired set of criteria is always difficult.
Such decisions are even more difficult when there are time constraints and can be …