The Mathematical Foundation of Reinforcement Learning is a textbook that provides a mathematical introduction to reinforcement learning concepts, problems, and algorithms.
The book addresses the gap between procedural understanding and theoretical comprehension by teaching reinforcement learning from first principles. Rather than simply presenting algorithms, it explains the mathematical reasoning behind their design and effectiveness. The approach carefully calibrates mathematical depth to remain accessible, with optional advanced material presented in gray boxes for interested readers. Concrete examples grounded in a grid world task illustrate concepts throughout, and the curriculum separates core algorithmic ideas from complicating details to aid comprehension.
The book is structured in two parts covering basic mathematical tools and then algorithms across ten coherently organized chapters that build sequentially. It targets senior undergraduate students, graduate students, researchers, and practitioners new to reinforcement learning, though it assumes familiarity with probability theory and linear algebra, with mathematical foundations included in the appendix. The material originated from graduate-level course lectures and does not require prior reinforcement learning background.
The project maintains a measured development pace with the author noting significant delays in responding to reader feedback due to competing commitments. The repository includes LaTeX source code for slides that instructors may request directly, indicating selective sharing of materials rather than open distribution of all teaching resources.
