Description: 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.
Brief description: 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.
Review Quotes: '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