This chapter provides a concise introduction to Reinforcement Learning (RL) from a
machine learning perspective. It provides the required background to understand the …

The purpose of this tutorial is to provide an introduction to reinforcement learning (RL) at a
level easily understood by students and researchers in a wide range of disciplines. The …

Abstract Machine Learning is a field of research aimed at constructing intelligent machines
that gain and improve their skills by learning and adaptation. As such, Machine Learning …

This book grew out of a lecture course on reinforcement learning held by Prof. Jan Peters in
the winter semester 2018/2019 at Technische Universität Darmstadt. Student research …

Reinforcement Learning (RL) is a machine learning (ML) technique to learn sequential
decision-making in complex problems. RL is inspired by trial-and-error based human/animal …

Reinforcement learning (RL) is a set of mathematical methods and algorithms that can be
applied to a wide array of problems and plays a central role in machine learning. The aim of …

Reinforcement Learning (RL) has emerged as a strong approach in the field of Artificial
intelligence, specifically, in the field of machine learning, robotic navigation, etc. In this paper …

A fundamental issue in reinforcement learning algorithms is the balance between
exploration of the environment and exploitation of information already obtained by the agent …

This article is related to the research effort of constructing an intelligent agent, ie, a computer
system that is able to sense its environment (world), reason utilizing its internal knowledge …

In this chapter, we introduce and summarize the taxonomy and categories for reinforcement
learning (RL) algorithms. Figure 3.1 presents an overview of the typical and popular …