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

Learning algorithms have become an integral component to modern engineering solutions.
Examples range from self-driving cars and recommender systems to finance and even …

We study the control of linear dynamical systems with adversarial disturbances, as opposed
to statistical noise. We present an efficient algorithm that achieves nearly-tight regret bounds …

We consider the problem of online adaptive control of the linear quadratic regulator, where
the true system parameters are unknown. We prove new upper and lower bounds …

We study the performance of the certainty equivalent controller on Linear Quadratic (LQ)
control problems with unknown transition dynamics. We show that for both the fully and …

We study the sample complexity of model-based reinforcement learning (henceforth RL) in
general contextual decision processes that require strategic exploration to find a near …

We study derivative-free methods for policy optimization over the class of linear policies. We
focus on characterizing the convergence rate of these methods when applied to linear …

We present the first computationally-efficient algorithm with $\widetilde {O}(\sqrt {T}) $ regret
for learning in Linear Quadratic Control systems with unknown dynamics. By that, we resolve …

This work studies the problem of sequential control in an unknown, nonlinear dynamical
system, where we model the underlying system dynamics as an unknown function in a …

This article surveys the System Level Synthesis framework, which presents a novel
perspective on constrained robust and optimal controller synthesis for linear systems. We …