AI Books

Reinforcement Learning, second edition: An Introduction (Adaptive Computation and Machine Learning series)

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This textbook, part of the Adaptive Computation and Machine Learning series, provides an extensive and updated account of reinforcement learning (RL), a core area of machine learning focused on how agents ought to take actions in an environment to maximize cumulative reward.

The second edition expands upon the original work by integrating advances in algorithmic development and theoretical understanding, while maintaining a structured presentation conducive to both instructional use and self-study.

The book is organized into clear, progressive chapters that systematically build the foundation for understanding RL. It begins by establishing the motivation and scope of RL, contrasting it with other machine learning paradigms such as supervised and unsupervised learning. The authors introduce the key RL elements including policies, rewards, value functions, and the exploration-exploitation trade-off, laying the groundwork for deeper treatments of algorithmic approaches.