Reinforcement Learning: Theory, Algorithms, and Applications

This book is a comprehensive guide to one of the most exciting fields in artificial intelligence. This book blends foundational theory with cutting-edge algorithms and real-world applications, offering readers a practical and intuitive understanding of how intelligent agents learn through interaction.

Designed for students, researchers, and professionals, the book covers key concepts such as Markov decision processes, dynamic programming, Monte Carlo methods, temporal difference learning, deep reinforcement learning, and multi-agent systems. With a balance of mathematical rigor and accessible explanations, it serves as both a learning resource and a reference guide for applying RL to solve complex problem across domains.