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.