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

More information can be access at https://deep-reinforcement-learning-book.github.io.