Reinforcement Learning: An Introduction, Second Edition (Richard S. Sutton, et al)

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Reinforcement Learning: An Introduction, Second Edition (Richard S. Sutton, et al)

In Reinforcement Learning (RL), one of the most active study topics in artificial intelligence, an agent interacts with a complex, uncertain environment while attempting to maximize the overall number of rewards it receives.

The main concepts and algorithms of the area are outlined in Reinforcement Learning by Richard Sutton and Andrew Barto. This second version has been greatly enlarged and revised, adding new themes and revising the treatment of existing ones.

The more mathematical content is highlighted in shaded boxes in this second edition, which, like the first, concentrates on fundamental online learning methods. All readers in related subjects should be able to understand the treatment.

Without moving beyond the tabular situation for which exact answers may be obtained, Part I covers as much reinforcement learning as it can. In the second edition, several of the algorithms discussed in this section are brand-new, such as UCB, Expected Sarsa, and Double Learning.

Part II expands these concepts to function approximation, provides extensive coverage of off-policy learning and policy-gradient techniques, and adds new sections on topics like artificial neural networks and the Fourier basis.

Part III includes brand-new chapters on the connections between psychology and neuroscience and reinforcement learning, as well as an updated case-study chapter that includes playing AlphaGo and AlphaGo Zero, playing Atari games, and IBM Watson's betting strategy. The last chapter explores how reinforcement learning will affect society in the future.

This book is considered to be the holy grail of reinforcement learning, and the release of the new edition coincides perfectly with the field's exploding activity. No student, researcher, practitioner, or inquisitive nonspecialist who is interested in the issue of learning to act should be without it.

Ebook Details

About the Authors
  • Richard S. Sutton is a Canadian computer scientist who holds the position of Distinguished Research Scientist at DeepMind as well as Professor of Computing Science and AITF Chair in Reinforcement Learning and Artificial Intelligence at the University of Alberta.
  • Professor of computer science at the University of Massachusetts Amherst and department chair since January 2007, Andrew G. Barto Reinforcement learning is his primary field of study.
Published Date / Year
2018; eBook (Creative Commons Licensed)
522 pages
eBook Format
PDF (548 pages)

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