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Deep Reinforcement Learning

Frontiers of Artificial Intelligence

  • Mohit Sewak

Table of contents

  1. Front Matter
    Pages i-xvii
  2. Mohit Sewak
    Pages 29-49
  3. Mohit Sewak
    Pages 65-74
  4. Mohit Sewak
    Pages 75-88
  5. Mohit Sewak
    Pages 89-94
  6. Mohit Sewak
    Pages 109-126
  7. Mohit Sewak
    Pages 141-152
  8. Mohit Sewak
    Pages 153-172
  9. Mohit Sewak
    Pages 173-184
  10. Mohit Sewak
    Pages 185-191
  11. Back Matter
    Pages 193-203

About this book

Introduction

This book starts by presenting the basics of reinforcement learning using highly intuitive and easy-to-understand examples and applications, and then introduces the cutting-edge research advances that make reinforcement learning capable of out-performing most state-of-art systems, and even humans in a number of applications. The book not only equips readers with an understanding of multiple advanced and innovative algorithms, but also prepares them to implement systems such as those created by Google Deep Mind in actual code.

 This book is intended for readers who want to both understand and apply advanced concepts in a field that combines the best of two worlds – deep learning and reinforcement learning – to tap the potential of ‘advanced artificial intelligence’ for creating real-world applications and game-winning algorithms.


Keywords

Reinforcement Learning Deep Learning Artificial Intelligence Deep Q Learning A3C Actor-Critic Deep Mind AI Agents Alpha-Go Attention Mechanism Temporal Difference Learning TD Lambda SARSA Hard Attention Recurrent Attention Model Dynamic Programming Monte Carlo

Authors and affiliations

  • Mohit Sewak
    • 1
  1. 1.PuneIndia

Bibliographic information

  • DOI https://doi.org/10.1007/978-981-13-8285-7
  • Copyright Information Springer Nature Singapore Pte Ltd. 2019
  • Publisher Name Springer, Singapore
  • eBook Packages Computer Science
  • Print ISBN 978-981-13-8284-0
  • Online ISBN 978-981-13-8285-7
  • Buy this book on publisher's site