Deep Reinforcement Learning

Fundamentals, Research and Applications

A Springer Nature Book

Amazon (English) Springer (English)

京东（中文版） 繁体（中文版）

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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.

关于本书

深度强化学习结合深度学习与强化学习算法各自的优势解决复杂的决策任务。得益于DeepMind AlphaGo和OpenAI Five等成功的案例，深度强化学习受到大量的关注，相关技术广泛应用于不同的领域。

本书分为三大部分，覆盖深度强化学习的全部内容。第一部分介绍深度学习和强化学习的入门知识、一些非常基础的深度强化学习算法及其实现细节，包括第 1～6 章。第二部分是一些精选的深度强化学习研究题目，这些内容对准备开展深度强化学习研究的读者非常有用，包括第 7～12 章。第三部分提供了丰富的应用案例，包括 AlphaZero、让机器人学习跑步等，包括第 13～18 章。

本书是为计算机科学专业背景、希望从零开始学习深度强化学习并开展研究课题和实践项目的学生准备的。本书也适合没有很强的机器学习背景、但是希望快速学习深度强化学习并将其应用到具体产品中的软件工程师阅读。

[中文版PDF现已可以在网页免费获取] 黑白中文版PDF 彩色中文版PDF [百度云] [Google Drive]

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
• Peiyuan Liao (Contributed in Chinese version) - Carnegie Mellon University

News

• 02-05-2022: The e-book of Chinese version is publicly available (for free)！ 中文电子版(PDF)可在网页免费获取!
• 06-18-2021: The book (Chinese version) is published!
• 06-22-2020: The book (English version) is published via Springer Nature!
• 03-25-2020: The book is set to publish in July, 2020. Stay tuned!

Community

@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}
}