Fundamentals of Reinforcement Learning

ISBN-13: 9783031373442 / Angielski / Twarda / 2023 / 136 str.

Rafael Ris-Ala

Spis treści

Chapter. 1. Introduction

Chapter. 2. Concepts

Chapter. 3. Q-Learning algorithm

Chapter. 4. Development tools

Chapter. 5. Practice with code

Chapter. 6. Recent applications and future research

Index.

O autorze

Rafael Ris-Ala José Jardim is a professor and researcher in Machine Learning and Research Methodology at the Federal University of Rio de Janeiro (UFRJ) and at Faculdade XP Educação (XPE). He holds a master's degree in Data Science from UFRJ and is currently pursuing his Ph.D. in Artificial Intelligence at the same institution.

He is the author of several articles on Software Engineering and has supervised more than 50 academic papers. He is a recognized journal reviewer for Elsevier and Clarivate and participates in reviewing IEEE scientific papers.

He served as Infrastructure Project Manager at the Pontifical Catholic University of Rio de Janeiro (PUC-Rio) and was responsible for creating a Data Center. He has more than 10 years of experience in Software Development in the Brazilian Navy.

Blurb

Artificial intelligence (AI) applications bring agility and modernity to our lives, and the reinforcement learning technique is at the forefront of this technology. It can outperform human competitors in strategy games, creative compositing, and autonomous movement. Moreover, it is just starting to transform our civilization.

This book provides an introduction to AI, specifies machine learning techniques, and explores various aspects of reinforcement learning, approaching the latest concepts in a didactic and illustrated manner. It is aimed at students who want to be part of technological advances and professors engaged in the development of innovative applications, helping with academic and industrial challenges.

Understanding the Fundamentals of Reinforcement Learning will allow you to:

• Understand essential AI concepts
• Gain professional experience
• Interpret sequential decision problems and solve them with reinforcement learning
• Learn how the Q-Learning algorithm works
• Practice with commented Python code
• Find advantageous directions