Abstract Optimal control and Reinforcement Learning deal both with sequential decision-
making problems, although they use different tools. In this thesis, we have investigated the …

In this paper, we will deal with a Linear Quadratic Optimal Control problem with unknown
dynamics. As a modeling assumption, we will suppose that the knowledge that an agent has …

In this paper, we will deal with a linear quadratic optimal control problem with unknown
dynamics. As a modeling assumption, we will suppose that the knowledge that an agent has …

Reinforcement learning (RL) studies the problem where an agent maximizes its cumulative
reward through sequential interactions with an initially unknown environment, usually …

Reinforcement learning (RL) is a branch of machine learning that deals with making
sequences of decisions. It refers to an agent that interacts with its environment, and receives …

A key barrier to using reinforcement learning (RL) in many real-world applications is the
requirement of a large number of system interactions to learn a good control policy. Off …

Optimal control describes the problem of finding a control to minimize an objective function
for a dynamical system over a period of time. It has been applied in a wide variety of …

We consider a class of sequential decision making problems in the presence of uncertainty,
which belongs to the field of Reinforcement Learning (RL). Specifically, we study discrete …

Reinforcement learning describes how an agent can learn to act in an unknown
environment in order to maximize its reward in the long run. It has its origins in the field of …

Abstract In many Reinforcement Learning tasks, the classical online interaction of the
learning agent with the environment is impractical, either because such interaction is …