Reinforcement Learning Foundations

Bridging the gap between introductory texts and the specialized research literature, this is one of the first truly rigorous yet accessible treatments of modern reinforcement learning. Written by three leading researchers with over a decade of teaching experience, the book uniquely combines mathematical precision with practical insights. It progresses naturally from planning (dynamic programming, MDPs, value and policy iteration) to learning (model-based and model-free algorithms, function approximation, policy gradients, and regret minimization). Each concept is developed from first principles with complete proofs, making the material self-contained. The modular chapter organization enables flexible course design. The book's website offers battle-tested exercises refined through years of classroom use. Combining mathematical rigor with practical applications, this definitive text is ideal for advanced undergraduate and graduate students as well as practitioners seeking a deep understanding of sequential decision-making and intelligent agent design.

Table of Contents:
1. Introduction and overview; 2. Preface to the planning chapters; 3. Deterministic decision processes; 4. Markov chains; 5. Markov decision processes and finite horizon dynamic programming; 6. Discounted Markov decision processes; 7. Episodic Markov decision processes; 8. Linear programming solutions; 9. Preface to the learning chapters; 10. Reinforcement learning: model based; 11. Reinforcement learning: model free; 12. Large state spaces: value function approximation; 13. Large state space: policy gradient methods; 14. Regret minimization; A. Dynamic programming; B. Ordinary differential equations; References; Index.

About the Author :
Shie Mannor is a professor at Technion's Electrical and Computer Engineering faculty, Chief Scientist and co-founder of Jether Energy Research, Distinguished Scientist at Nvidia, and an IEEE Fellow. A pioneer in reinforcement learning, planning, and control, he bridges theory and practice with over 330 papers and 35,000 citations. Yishay Mansour is a professor at the Blavatnik School of Computer Science, Tel Aviv University, and is an ACM Fellow. An early pioneer in machine learning theory, reinforcement learning, algorithmic game theory, and theory of computing at large, he has authored over 300 papers with over 40,000 citations on those topics. Aviv Tamar is Associate Professor of Electrical and Computer Engineering at the Technion. He studies how machines learn to act and perceive. His research in reinforcement learning, representation learning, and robotics has led to over 70 publications, 17,000 citations, and multiple best-paper awards and distinctions.