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Adaptive Computation and Machine Learnin Reinforcement Learning, Second Edition: An Introduction, (Hardcover)

Adaptive Computation and Machine Learnin Reinforcement Learning, Second Edition: An Introduction, (Hardcover)

The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence.

Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field’s key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics.

Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning’s relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson’s wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.

Original price was: $80.00.Current price is: $70.00.
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Master the Fundamentals of Reinforcement Learning

Reinforcement Learning, Second Edition: An Introduction is the definitive guide for anyone looking to deepen their understanding of adaptive computation and machine learning. Authored by renowned experts in the field, this hardcover edition combines foundational principles with practical insights to help both beginners and seasoned professionals enhance their knowledge of reinforcement learning algorithms, techniques, and applications.

Cutting-Edge Insights and Applications

This book masterfully blends theoretical concepts with real-world applications, making it an invaluable resource across industries. Whether you’re exploring robotics, game AI development, financial modeling, or personalized recommendations, the text offers clear explanations, mathematical rigor, and step-by-step approaches to implement reinforcement learning models. From policy gradient methods to value-based learning and beyond, this second edition updates you on the latest advancements shaping the world of artificial intelligence.

Why Choose the Hardcover Edition?

Invest in the hardcover format to enjoy durability and clarity, ensuring the book stands the test of time as a cornerstone in your academic or professional library. Thoughtfully designed with a structured layout, navigable chapters, and detailed graphical illustrations, this edition is crafted for readers seeking reliability and a premium reading experience. Additionally, it serves as a great long-term reference for students, researchers, and developers eager to push the boundaries of machine learning and adaptive computation.

Whether you’re a computer science enthusiast, a data scientist, or an AI practitioner, this second edition of Reinforcement Learning introduces you to an essential knowledge base with practical examples to inspire innovative solutions to complex challenges.

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