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Download the fantastic book titled Reinforcement Learning second edition written by Richard S. Sutton, available in its entirety in both PDF and EPUB formats for online reading. This page includes a concise summary, a preview of the book cover, and detailed information about "Reinforcement Learning second edition", which was released on 13 November 2018. We suggest perusing the summary before initiating your download. This book is a top selection for enthusiasts of the Computers genre.

Summary of Reinforcement Learning second edition by Richard S. Sutton PDF

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.

Reinforcement Learning second edition

Detail About Reinforcement Learning second edition PDF

  • Author : Richard S. Sutton
  • Publisher : MIT Press
  • Genre : Computers
  • Total Pages : 549 pages
  • ISBN : 0262352702
  • PDF File Size : 32,8 Mb
  • Language : English
  • Rating : 4/5 from 21 reviews

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Reinforcement Learning, second edition

Reinforcement Learning, second edition
  • Publisher : MIT Press
  • File Size : 49,7 Mb
  • Release Date : 13 November 2018
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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

Reinforcement Learning

Reinforcement Learning
  • Publisher : "O'Reilly Media, Inc."
  • File Size : 26,8 Mb
  • Release Date : 06 November 2020
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Reinforcement learning (RL) will deliver one of the biggest breakthroughs in AI over the next decade, enabling algorithms to learn from their environment to achieve arbitrary goals. This exciting development

Reinforcement Learning

Reinforcement Learning
  • Publisher : Springer Science & Business Media
  • File Size : 22,8 Mb
  • Release Date : 05 March 2012
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Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncertain environments and a computational methodology for finding optimal behaviors for challenging problems in control, optimization and

Foundations of Deep Reinforcement Learning

Foundations of Deep Reinforcement Learning
  • Publisher : Addison-Wesley Professional
  • File Size : 35,7 Mb
  • Release Date : 20 November 2019
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The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve

An Introduction to Deep Reinforcement Learning

An Introduction to Deep Reinforcement Learning
  • Publisher : Foundations and Trends (R) in Machine Learning
  • File Size : 43,8 Mb
  • Release Date : 20 December 2018
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Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has recently been able to solve a wide range of complex decision-making tasks

Deep Reinforcement Learning in Action

Deep Reinforcement Learning in Action
  • Publisher : Manning Publications
  • File Size : 41,5 Mb
  • Release Date : 28 April 2020
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Summary Humans learn best from feedback—we are encouraged to take actions that lead to positive results while deterred by decisions with negative consequences. This reinforcement process can be applied

Reinforcement Learning

Reinforcement Learning
  • Publisher : Apress
  • File Size : 40,9 Mb
  • Release Date : 07 December 2017
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Master reinforcement learning, a popular area of machine learning, starting with the basics: discover how agents and the environment evolve and then gain a clear picture of how they are

Grokking Deep Reinforcement Learning

Grokking Deep Reinforcement Learning
  • Publisher : Manning Publications
  • File Size : 42,8 Mb
  • Release Date : 10 November 2020
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Grokking Deep Reinforcement Learning uses engaging exercises to teach you how to build deep learning systems. This book combines annotated Python code with intuitive explanations to explore DRL techniques. You’

Algorithms for Reinforcement Learning

Algorithms for Reinforcement Learning
  • Publisher : Springer Nature
  • File Size : 41,7 Mb
  • Release Date : 31 May 2022
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Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning

Hands-On Reinforcement Learning for Games

Hands-On Reinforcement Learning for Games
  • Publisher : Packt Publishing Ltd
  • File Size : 23,9 Mb
  • Release Date : 03 January 2020
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Explore reinforcement learning (RL) techniques to build cutting-edge games using Python libraries such as PyTorch, OpenAI Gym, and TensorFlow Key FeaturesGet to grips with the different reinforcement and DRL algorithms