Algorithmic Approach to Reinforcement Learning

Algorithmic Approach to Reinforcement Learning offers a clear, structured, and comprehensive introduction to the core ideas and methods that define reinforcement learning (RL). Designed with both clarity and depth in mind, this textbook walks readers through foundational concepts before progressing to advanced algorithms that power intelligent decision-making systems.

The book is divided into two key sections. The first focuses on tabular methods, where solutions can be precisely computed, covering techniques like dynamic programming, Monte Carlo methods, temporal-difference learning, and multi-step algorithms. The second section explores function approximation, including neural networks, gradient methods, and policy gradient approaches—essential for scaling RL to real-world problems.

Each chapter includes summaries, thoughtful questions, and further reading suggestions, making the book ideal for students, researchers, and practitioners. Mathematical notations are kept consistent and intuitive, with a complete reference provided in the appendix.

Whether you’re entering the field or deepening your expertise, this book equips you with the algorithmic tools and theoretical insight needed to understand and apply reinforcement learning in a rigorous yet accessible way.