Deep Reinforcement Learning, 1st ed. 2020 Fundamentals, Research and Applications

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 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.

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.