The growing demand for robots able to act autonomously in complex scenarios has widely
accelerated the introduction of Reinforcement Learning (RL) in robots control applications …

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 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 …

Temporal difference learning (TD) is a simple iterative algorithm used to estimate the value
function corresponding to a given policy in a Markov decision process. Although TD is one of …

This paper considers the creation of parametric surrogate models for applications in science
and engineering where the goal is to predict high-dimensional output quantities of interest …

A common belief in model-free reinforcement learning is that methods based on random
search in the parameter space of policies exhibit significantly worse sample complexity than …

Abstract Model-free reinforcement learning aims to offer off-the-shelf solutions for controlling
dynamical systems without requiring models of the system dynamics. We introduce a model …

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 consider adaptive control of the Linear Quadratic Regulator (LQR), where an unknown
linear system is controlled subject to quadratic costs. Leveraging recent developments in the …

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 …