Deep reinforcement learning : fundamentals, research and applications / Hao Dong, Zihan Ding, Shanghang Zhang, editors.
Dong, Hao.; Ding, Zihan.; Zhang, Shanghang.
2020
Q325.6
Available Online

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Title Deep reinforcement learning : fundamentals, research and applications / Hao Dong, Zihan Ding, Shanghang Zhang, editors.
ISBN 9789811540950 (electronic book)
9811540950 (electronic book)
9811540942
9789811540943
DOI https://doi.org/10.1007/978-981-15-4
Publication Details Singapore : Springer, 2020.
Language English
Description 1 online resource (526 pages)
Item Number 10.1007/978-981-15-4
Call Number Q325.6
Dewey Decimal Classification 006.3/1
Summary Deep reinforcement learning (DRL) is the combination of reinforcement learning (RL) and deep learning. 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 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 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. 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.
Access Note Access limited to authorized users.
Source of Description Description based on print version record.
Added Author Dong, Hao.
Ding, Zihan.
Zhang, Shanghang.
Available in Other Form Deep Reinforcement Learning : Fundamentals, Research and Applications
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Preface
Contributors
Acknowledgements
Mathematical Notation
Acronyms
Introduction
Part 1: Foundamentals
Chapter 1: Introduction to Deep Learning
Chapter 2: Introduction to Reinforcement Learning
Chapter 3: Taxonomy of Reinforcement Learning Algorithms
Chapter 4: Deep Q-Networks
Chapter 5: Policy Gradient
Chapter 6: Combine Deep Q-Networks with Actor-Critic
Part II: Research
Chapter 7: Challenges of Reinforcement Learning
Chapter 8: Imitation Learning
Chapter 9: Integrating Learning and Planning
Chapter 10: Hierarchical Reinforcement Learning
Chapter 11: Multi-Agent Reinforcement Learning
Chapter 12: Parallel Computing
Part III: Applications
Chapter 13: Learning to Run
Chapter 14: Robust Image Enhancement
Chapter 15: AlphaZero
Chapter 16: Robot Learning in Simulation
Chapter 17: Arena Platform for Multi-Agent Reinforcement Learning
Chapter 18: Tricks of Implementation
Part IV: Summary
Chapter 19: Algorithm Table
Chapter 20: Algorithm Cheatsheet.

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