Reinforcement Learning

This is a rigorous non-fiction reference in reinforcement learning, designed for researchers and practitioners in artificial intelligence and machine learning. The central aim is to explain how an agent learns to maximize rewards in complex, uncertain environments, from core ideas to modern algorithms. The intended reader is a graduate student, researcher, or engineer seeking a clear, structured guide to essential concepts and recent advances. The tone is precise, insightful, and encouraging, inviting careful study and practical application. The content is presented as a balanced blend of theory and practice, with core online learning algorithms explained in depth and the more mathematical material set off in shaded boxes for quick reference. The reading experience is designed to be approachable for those building a solid foundation while still challenging experienced readers to refine their understanding. The second edition expands coverage with fresh topics and updated insights to reflect the field’s rapid evolution. The new edition introduces important developments and clarifications, including algorithms like UCB, Expected Sarsa, and Double Learning, while extending discussion to function approximation with neural networks and the Fourier basis. It deepens treatment of off-policy learning and policy-gradient methods, and adds new chapters linking reinforcement learning to p