Reinforcement learning for cyber-physical systems with cybersecurity case studies/ Chong Li, Meikang Qiu.

Includes bibliographical references and index.

Section I Introduction -- Chapter 1 Overview of Reinforcement Learning -- Chapter 2 Overview of Cyber Physical Systems and Cyber security -- Section II Reinforcement Learning for Cyber-Physical Systems -- Chapter 3 Reinforcement Learning Problems -- Chapter 4 Model based Reinforcement Learning -- Chapter 5 Model free Reinforcement Learning -- Chapter 6 Deep Reinforcement Learning -- Section III Case Studies -- Chapter 7 Reinforcement Learning for Cyber security -- Chapter 8 Case Study: Online Cyber Attack Detection in Smart Grid -- Chapter 9 Case Study: Defeat Maninthemiddle Attack

The book focuses recent developments in the fields of reinforcement learning (RL) and cyber-physical systems (CPSs). Rooted in behavioral psychology, RL is one of the primary strands of machine learning. Different from other machine learning algorithms, such as supervised learning and unsupervised learning, the key feature of RL is its unique learning paradigm, i.e., trial-and-error. Combined with the deep neural networks, deep RL become so powerful that many complicated systems can be automatically managed by AI agents at a superhuman level. On the other hand, CPSs are envisioned to revolutionize our society in the near future. Such examples include the emerging smart buildings, intelligent transportation, and electric grids.
However, the conventional hand-programming controller in CPSs could neither handle the increasing complexity of the system, nor automatically adapt itself to new situations that it has never encountered before. The problem of how to apply the existing deep RL algorithms, or develop new RL algorithms to enable the real-time adaptive CPSs, remains open. This book aims to establish a linkage between the two domains by systematically introducing RL foundations and algorithms, each supported by one or a few state-of-the-art CPS examples to help readers understand the intuition and usefulness of RL techniques.