Inbunden, Engelska, 2027

Reinforcement Learning Explained

Av Rodrigue Rizk, Srikanth Baride, KC Santosh

1487 kr

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Beskrivning

This book provides a rigorous yet accessible foundation in reinforcement learning (RL), focusing on the mathematical principles and practical methods that underpin modern AI. It explains how RL differs from supervised and unsupervised learning, and why its feedback driven structure makes it uniquely suited to interactive environments.Reinforcement Learning Explained introduces the agent–environment interaction loop, the formalism of Markov Decision Processes (MDPs), value functions, and Bellman equations. It then develops classical solution methods, including dynamic programming, Monte Carlo techniques, and temporal-difference learning, leading to SARSA and Qlearning. Each concept is reinforced through intuitive explanations, step-by-step derivations, numerical examples, diagrams, and Python implementations. Readers will not only learn the theory but also practice it. Each chapter is supported with exercises, complete solutions, and a companion Python codebase that allows for hands-on experimentation.This book is designed as a self-contained textbook and reference. It may serve as the foundation for graduate-level RL courses, as a supplement in AI and machine learning curricula, or as a structured guide for self-study. Beyond academia, the book will appeal to industry professionals in fields such as robotics, autonomous systems, finance, and healthcare, where reinforcement learning is increasingly applied.

Produktinformation

Utgivningsdatum: 2027-03-15
Mått: 156 x 234 x undefined mm
Format: Inbunden
Språk: Engelska
Antal sidor: 344
Förlag: Taylor & Francis Ltd
ISBN: 9781041252993

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Mer om författaren

Prof. KC “Casey” Santosh is an AI expert who served as Computer Science Chair (2020–2026), Graduate Program Director (2017–2024), and Founding AI Research Director (2015–2026) at the University of South Dakota. Previously, he was an NIH fellow and LORIA postdoctoral scientist who collaborated with ITESOFT, France. He earned a PhD in Computer Science (Artificial Intelligence) from INRIA Nancy Grand Est. With approximately $10 million in funding, he has delivered 100+ keynotes, including TEDx, authored 13 books and 280+ articles, and serves as an associate editor, conference chair, and NSF/Mitacs review-panel leader. He is a member of NIST’s AI Standards and Innovation initiative and the U.S. Speaker Program for AI education.Dr. Srikanth Baride is a Postdoctoral Researcher in the Department of Computer Science at the University of South Dakota, where he also recently served as a Visiting Assistant Professor. He earned his Ph.D. in Computer Science with a specialization in artificial intelligence. His work explores the areas of data mining, reinforcement learning, and AI, with results published in international journals and conferences. Alongside research, he enjoys teaching and mentoring students in courses on data mining, artificial intelligence, and reinforcement learning.Dr. Rodrigue Rizk is a professor and Graduate Program Director at the University of South Dakota, Vice Director of Engineering with USD Artificial Intelligence Research, and Chair of IEEE Siouxland. He earned his B.E. in Computer Engineering, summa cum laude and as valedictorian, from Notre Dame University, and his M.S. and Ph.D. in Computer Engineering from the University of Louisiana at Lafayette. His research interests span artificial intelligence, deep learning, reinforcement learning, mechanistic interpretability, quantum computing, neuromorphic and high-performance computing, AI hardware acceleration, agentic AI, and healthcare technologies. He is the author of Cracking the Machine Learning Code (Springer, 2024).

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