Reinforcement Learning 2nd Ed: Introduction by Sutton & Barto ML Textbook

Description

Master the fundamentals of reinforcement learning with the definitive textbook by renowned experts Richard S. Sutton and Andrew G. Barto. This comprehensive 552-page second edition covers the most active research area in artificial intelligence, providing clear explanations of key algorithms and concepts for computational learning through environmental interaction.

This extensively updated edition features new topics including UCB algorithms, Expected Sarsa, Double Learning, artificial neural networks, and policy-gradient methods. Part I covers tabular methods with exact solutions, Part II extends to function approximation with expanded coverage of off-policy learning, and Part III explores connections to psychology, neuroscience, and cutting-edge applications like AlphaGo, Atari game playing, and IBM Watson.

• Definitive RL Textbook – The standard reference by leading experts Sutton (University of Alberta) and Barto, trusted by AI researchers worldwide

• Comprehensive 2nd Edition Updates – New algorithms including UCB, Expected Sarsa, Double Learning, neural networks, and policy-gradient methods

• Three-Part Structure – Tabular methods, function approximation, and real-world applications from AlphaGo to IBM Watson

• Mathematical Clarity – Advanced material in shaded boxes allows flexible reading for different expertise levels

• Practical Applications – Case studies in game playing, robotics, and AI systems with societal impact discussions

• Research Foundation – Essential for understanding modern deep reinforcement learning and AI agent development

• Multi-Disciplinary Approach – Connects RL to psychology, neuroscience, control theory, and optimal decision making