1st Edition
Reinforcement Learning in Action From Foundations to Frontier AI
List of Figures. List of Tables. Foreword. Preface. Author Bios. Contributors. Notation. Chapter 1: Introduction to Reinforcement Learning. Section I: Basics of Reinforcement Learning. Chapter 2: Fundamentals of Reinforcement Learning. Section II: Classical Reinforcement Learning. Chapter 3: Classical Reinforcement Learning Algorithms. Section III: Deep Reinforcement Learning. Chapter 4: Scaling Reinforcement Learning: Function Approximation and Deep Methods. Section IV: LLMs and Reinforcement Learning. Chapter 5: Preference-Based Alignment: Reward Modeling and Reinforcement Learning for LLMs. Chapter 6: Reinforcement Learning for Reasoning Models. Section V: Agentic and Reinforcement Learning. Chapter 7: Reinforcement Learning Enabled Agentic AI. Appendix A: Mathematical Proofs and Derivations. Bibliography. Index.
Biography
Uday Kamath has over 25 years of experience in AI product development with a Ph.D. in scalable machine learning. His significant contributions span numerous journals, conferences, books, and patents. Notable books include Large Language Models: A Deep Dive, Applied Causal Inference, Explainable Artificial Intelligence, Transformers for Machine Learning, Deep Learning for NLP and Speech Recognition, Mastering Java Machine Learning, and Machine Learning: End-to-End Guide for Java Developers. Currently serving as the Chief Analytics Officer at Smarsh, he spearheads data science and research in communications AI for regulated industries. He is also an active member of the Board of Advisors for entities, including commercial companies and academic institutions.
Vedant Vajre is an aspiring AI researcher with a strong interest in reinforcement learning and intelligent decision-making systems. He is graduating from Penn State University and intends to pursue doctoral research in artificial intelligence. He has authored two peer-reviewed research publications with IEEE and continues to pursue active research in machine learning. Having worked at organizations including NASA, IBM, and early-stage startups, he has gained experience applying machine learning and AI in both research and production settings. Outside of research, he loves spending time with his two Shih Tzus (Pinot and Buzz), playing tennis, and solving Sudoku puzzles.
