Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has recently been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine.
Deep RL opens up many new applications in domains such as healthcare, robotics, smart grids, finance, and many more. This book provides the reader with a starting point for understanding the topic. Although written at a research level it provides a comprehensive and accessible introduction to deep reinforcement learning models, algorithms and techniques. Particular focus is on the aspects related to generalization and how deep RL can be used for practical applications.
Written by recognized experts, this book is an important introduction to Deep Reinforcement Learning for practitioners, researchers and students alike.
Conditions of Use
This book is licensed under a Creative Commons License (CC BY-NC-SA). You can download the ebook An Introduction to Deep Reinforcement Learning for free.
- Title
- An Introduction to Deep Reinforcement Learning
- Publisher
- Now Publishers Inc
- Author(s)
- Peter Henderson, Riashat Islam, Vincent François-Lavet
- Published
- 2019-03-31
- Edition
- 1
- Format
- eBook (pdf, epub, mobi)
- Pages
- 156
- Language
- English
- ISBN-10
- 1680835386
- ISBN-13
- 9781680835380
- License
- CC BY-NC-SA
- Book Homepage
- Free eBook, Errata, Code, Solutions, etc.
1 Introduction 2 Machine learning and deep learning 3 Introduction to reinforcement learning 4 Value-based methods for deep RL 5 Policy gradient methods for deep RL 6 Model-based methods for deep RL 7 The concept of generalization 8 Particular challenges in the online setting 9 Benchmarking Deep RL 10 Deep reinforcement learning beyond MDPs 11 Perspectives on deep reinforcement learning 12 Conclusion Appendices
