Reinforcement Learning Explained

A Practical Problem-Solving Approach
1. Auflage 2026
ISBN: 978-1-032-99665-3
Verlag: Taylor & Francis

Buch, Englisch, 432 Seiten, Format (B × H): 178 mm x 254 mm

Reinforcement Learning (RL) is a branch of Artificial Intelligence (AI) that teaches agents to learn optimal behavior through interaction, feedback, and long-term goals. After decades of research, RL has matured into a powerful technology driving real-world innovation; it is now used in areas such as robotics, energy systems, finance, and autonomous vehicles.

Yet, for many, RL feels inaccessible, buried under dense mathematics and complex theory. This book changes that. It is designed to help newcomers start applying RL as quickly as possible through a classical pedagogical approach: many small, focused examples that build intuition and practical skill step by step.

Featuring:

• Essential concepts explained from the ground up

• Code-based examples that reveal how algorithms work in practice

• Worked examples by hand to strengthen intuition, just like in engineering or mathematics

• Language-agnostic guidance, easily followed using Python, Java, or C++

Even readers without coding or university-level mathematics backgrounds will gain valuable insight into the fascinating world of RL - insight that may become a critical differentiator in the age of AI. Whether you are a student or professional, Reinforcement Learning Explained will give you the tools and confidence to explore one of AI’s most exciting frontiers.

Zielgruppe

Academic, Postgraduate, Professional Practice & Development, and Undergraduate Advanced

Autoren/Hrsg.

Hellgren, Jonas

Lindgren, Johannes

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Neuronale Netzwerke

Inhaltsverzeichnis

1 Foreword
2 Scope
3 Reinforcement Learning in a Wider Context
4 Terms, Definitions and Abbreviations
5 Mathematical Foundations
6 Cementing Mathematical Foundations by Hands-on Examples
7 Major Software Components
8 Temporal-Difference Learning
9 Monte Carlo Methods
10 Multi-Step Updating
11 Policy Gradient Methods
12 Actor-Critic Methods
13 Deep Reinforcement Learning
14 Monte Carlo Tree Search
15 Alpha Zero
16 Safe Reinforcement Learning
17 Multi-Agent Reinforcement Learning
18 References
19 Appendix

Über Autor(innen)

Jonas Hellgren is a researcher specializing in reinforcement learning, optimization, and electrified vehicle systems. With experience across academia and industry spanning patents, publications, and thesis supervision, he brings both practical insight and theoretical depth. This book reflects his commitment to making complex ideas accessible.

Johannes Lindgren is a technical consultant specializing in software development, verification, and commissioning across rail, automotive, and maritime applications. Currently at Combine, developing software for the rail sector. Previous roles include simulation and verification at Volvo Autonomous Solutions and system commissioning at Lean Marine, along with research in image segmentation at CPAC Systems.