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Reinforcement Learning, second edition: An Introduction (Adaptive Computation and Machine Learning series),New
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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 offpolicy learning and policygradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated casestudies 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.
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- Q: What are the main topics covered in 'Reinforcement Learning, second edition'? A: The book covers core online learning algorithms, function approximation, artificial neural networks, off-policy learning, policy-gradient methods, and relationships between reinforcement learning, psychology, and neuroscience.
- Q: Who are the authors of this book? A: The authors of 'Reinforcement Learning, second edition' are Richard S. Sutton and Andrew Barto.
- Q: What is the publication date of this edition? A: This edition was published on November 13, 2018.
- Q: How many pages does the book have? A: The book contains 552 pages.
- Q: What is the binding type of this book? A: The book is available in a hardcover binding.
- Q: Is this book suitable for beginners in reinforcement learning? A: Yes, the book provides a clear and simple account of key ideas and algorithms, making it suitable for both beginners and experienced readers.
- Q: Does this edition include new algorithms? A: Yes, this edition includes new algorithms such as UCB, Expected Sarsa, and Double Learning.
- Q: Are there case studies included in the book? A: Yes, the book includes updated case studies featuring AlphaGo, AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy.
- Q: What is the focus of Part I in the book? A: Part I focuses on core reinforcement learning concepts without going beyond the tabular case, where exact solutions can be found.
- Q: What additional topics are explored in this second edition? A: This edition explores the future societal impacts of reinforcement learning and offers expanded treatment of various topics in the field.