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.

‘Written by world-class experts Mannor, Mansour, and Tamar, Reinforcement Learning: Foundations is a masterclass in the field. It covers essential topics comprehensively and accessibly, while brilliantly conveying the underlying intuition behind complex concepts, proofs, and algorithms. This is an essential, self-contained guide for both students and researchers.’

Mehryar Mohri - Google Research and Courant Institute of Mathematical Sciences

‘Reinforcement Learning Foundations offers a rare combination of rigor, clarity, and insight. It builds a deep understanding of the principles that underlie modern reinforcement learning, making it essential reading not only for students and researchers, but also for practitioners who want to move beyond recipes and gain real conceptual understanding of the field.’

Pieter Abbeel - University of California, Berkeley

‘This book is a clear, rigorous, and remarkably comprehensive treatment of reinforcement learning. Starting from fundamental concepts such as shortest paths on graphs and Markov chains, the authors connect planning, learning, approximation, and regret minimization with exceptional pedagogical skill. It will be invaluable to students, researchers, and instructors seeking a principled introduction to modern RL.’

Nicolò Cesa-Bianchi - University of Milan, Italy