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Reinforcement Learning Explained: A Practical Problem-Solving Approach book cover

1st Edition

Reinforcement Learning Explained A Practical Problem-Solving Approach

By Jonas Hellgren, Johannes Lindgren Copyright 2026
298 Pages 121 B/W Illustrations
by CRC Press

298 Pages 121 B/W Illustrations
by CRC Press

298 Pages 121 B/W Illustrations
by CRC Press
Also available as eBook on:
Reinforcement Learning (RL) is a branch of Artificial Intelligence (AI) where agents learn optimal behavior through interaction with an environment by receiving feedback in the form of reward. 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... Read more
About the Authors. Introduction. Preface. Acknowledgements. Cover. 1 From Rules to Learning. 2 From Markov to Bellman. 3 Reinforcement Learning Concepts. 4 Temporal Difference Learning. 5 Monte Carlo Methods. 6 n-Step Learning. 7 Safe-Action Reinforcement Learning. 8 Non-Episodic Learning. 9 Next-Level Concepts. 10 Policy Gradient Methods. 11 Actor-Critic Methods. 12 Deep Reinforcement Learning. 13 Monte Carlo Tree Search. 14 Combining Learning and Search. 15 Multi-Agent Reinforcement Learning. 16 Outlook. Appendix. Index.

Biography

Jonas Hellgren is a researcher specializing in reinforcement learning, optimization, and electrified vehicle systems. With experience across academia and industry spanning patents, publications, thesis supervision, and industrial projects, 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.

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