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Deep Reinforcement Learning

Fundamentals, Research and Applications

A Springer Nature Book

Amazon Springer

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About the book

Deep reinforcement learning (DRL) relies on the intersection of reinforcement learning (RL) and deep learning (DL). It has been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine and famously contributed to the success of AlphaGo. Furthermore, it opens up numerous new applications in domains such as healthcare, robotics, smart grids, and finance.

Divided into three main parts, this book provides a comprehensive and self-contained introduction to DRL. The first part introduces the foundations of DL, RL and widely used DRL methods and discusses their implementation. The second part covers selected DRL research topics, which are useful for those wanting to specialize in DRL research. To help readers gain a deep understanding of DRL and quickly apply the techniques in practice, the third part presents mass applications, such as the intelligent transportation system and learning to run, with detailed explanations.

The book is intended for computer science students, both undergraduate and postgraduate, who would like to learn DRL from scratch, practice its implementation, and explore the research topics. This book also appeals to engineers and practitioners who do not have strong machine learning background, but want to quickly understand how DRL works and use the techniques in their applications.

Editors

  • Hao Dong - Peking University
  • Zihan Ding - Princeton University
  • Shanghang Zhang - University of California, Berkeley

Authors

  • Hao Dong - Peking University
  • Zihan Ding - Princeton University
  • Shanghang Zhang - University of California, Berkeley
  • Hang Yuan - Oxford University
  • Hongming Zhang - Peking University
  • Jingqing Zhang - Imperial College London
  • Yanhua Huang - Xiaohongshu Technology Co.
  • Tianyang Yu - Nanchang University
  • Huaqing Zhang - Google
  • Ruitong Huang - Borealis AI

News

  • 03-25-2020: The book is set to publish in July, 2020. Stay tuned!

Community

  • Join Us on Slack for open discussions.
  • Create a Github issue for a technial query.

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    Resources

    FQA

    Can I get a PDF of the book?

    If your institute bought Springer subscriptions, you are free to download the whole PDF from Springer Website under the WIFI of your institute Alternatively, you can purchase the e-book at Springer Website or other book dealers.

    How to contribute?

    If you find any typos or have suggestions for improving the book, do not hesitate to contact us by email at: hao.dong[at]pku.edu.cn

    If you find any bug or error in the code released together with the book, please report them through creating an issue in the corresponding repository.

    Citing the book

    To cite this book, please use this bibtex entry:

    @book{deepRL-2020,
     title={Deep Reinforcement Learning: Fundamentals, Research, and Applications},
     editor={Hao Dong, Zihan Ding, Shanghang Zhang},
     author={Hao Dong, Zihan Ding, Shanghang Zhang, Hang Yuan, Hongming Zhang, Jingqing Zhang, Yanhua Huang, Tianyang Yu, Huaqing Zhang, Ruitong Huang},
     publisher={Springer Nature},
     note={\url{http://www.deepreinforcementlearningbook.org}},
     year={2020}
    }
    

    Alternatively, use this formatted citation:

    Hao Dong, Zihan Ding, Shanghang Zhang, Hang Yuan, Hongming Zhang, Jingqing Zhang, Yanhua Huang, Tianyang Yu, Huaqing Zhang, Ruitong Huang. (2020) Deep Reinforcement Learning: Fundamentals, Research, and Applications. Springer.