Buch, Englisch, 298 Seiten, Format (B × H): 178 mm x 254 mm
A Practical Problem-Solving Approach
Buch, Englisch, 298 Seiten, Format (B × H): 178 mm x 254 mm
ISBN: 978-1-041-06226-4
Verlag: Taylor & Francis Ltd
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 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 textbooks
- 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.
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 Neuronale Netzwerke
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken
Weitere Infos & Material
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.
