Editors:
- Offers a comprehensive and self-contained introduction to deep reinforcement learning
- Covers deep reinforcement learning from scratch to advanced research topics
- Provides rich example codes (free access through Github) to help readers to practice and implement the methods easily
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Table of contents (20 chapters)
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Front Matter
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Applications
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Front Matter
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About this book
Divided into three main parts, this book provides a comprehensive and self-contained introduction to DRL. The first part introduces the foundations of deep learning, reinforcement learning (RL) and widely used deep RL 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 detailedexplanations.
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. It 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 and Affiliations
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EECS, Peking University, Beijing, China
Hao Dong
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CS, Imperial College London, London, UK
Zihan Ding
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EECS, University of California, Berkeley, Berkeley, USA
Shanghang Zhang
About the editors
Zihan Ding received his M.Sc. degree in Machine Learning with distinction from the Department of Computing, Imperial College London, supervised by Dr. Edward Johns. He holds double Bachelor degrees from the University of Science and Technology of China: in Photoelectric Information Science and Engineering (Physics) and in Computer Science and Technology. His research interests includedeep reinforcement learning, robotics, computer vision, quantum computation and machine learning. He has published papers in ICRA, AAAI, NIPS, IJCAI, and Physical Review. He also contributed to the open-source projects TensorLayer RLzoo, TensorLet and Arena.
Dr. Shanghang Zhang is a postdoctoral research fellow in the Berkeley AI Research (BAIR) Lab, the Department of Electrical Engineering and Computer Sciences, UC Berkeley, USA. She received her Ph.D. from Carnegie Mellon University in 2018. Her research interests cover deep learning, computer vision, and reinforcement learning, as reflected in her numerous publications in top-tier journals and conference proceedings, including NeurIPS, CVPR, ICCV, and AAAI. Her research mainly focuses on machine learning with limited training data, including low-shot learning, domain adaptation, and meta-learning, which enables the learning system to automatically adapt to real-world variations and new environments. She was one of the “2018 Rising Stars in EECS” (a highly selective program launched at MIT in 2012, which has since been hosted at UC Berkeley, Carnegie Mellon, and Stanford annually). She has also been selected for the Adobe Academic Collaboration Fund, Qualcomm Innovation Fellowship (QInF) Finalist Award, and Chiang Chen Overseas Graduate Fellowship.
Bibliographic Information
Book Title: Deep Reinforcement Learning
Book Subtitle: Fundamentals, Research and Applications
Editors: Hao Dong, Zihan Ding, Shanghang Zhang
DOI: https://doi.org/10.1007/978-981-15-4095-0
Publisher: Springer Singapore
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Nature Singapore Pte Ltd. 2020
Hardcover ISBN: 978-981-15-4094-3Published: 30 June 2020
Softcover ISBN: 978-981-15-4097-4Published: 30 June 2021
eBook ISBN: 978-981-15-4095-0Published: 29 June 2020
Edition Number: 1
Number of Pages: XXVII, 514
Number of Illustrations: 281 b/w illustrations, 208 illustrations in colour
Topics: Machine Learning, Data Mining and Knowledge Discovery, Image Processing and Computer Vision, Robotics, Programming Techniques, Natural Language Processing (NLP)