Reinforcement Learning: Theory Algorithms and Applications

<p>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.</p><p>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.</p>