Reinforcement Learning for Cyber-Physical Systems by Li, Chong; Qiu, Meikang;

Short description:

This book introduces reinforcement learning, and provides novel ideas and use cases to demonstrate the benefits of using reinforcement learning for Cyber Physical Systems. Two important case studies on applying reinforcement learning to cybersecurity problems are included.

Long description:

Reinforcement Learning for Cyber-Physical Systems: with Cybersecurity Case Studies was inspired by 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.

• Introduces reinforcement learning, including advanced topics in RL











• Applies reinforcement learning to cyber-physical systems and cybersecurity











• Contains state-of-the-art examples and exercises in each chapter











• Provides two cybersecurity case studies

Reinforcement Learning for Cyber-Physical Systems with Cybersecurity Case Studies is an ideal text for graduate students or junior/senior undergraduates in the fields of science, engineering, computer science, or applied mathematics. It would also prove useful to researchers and engineers interested in cybersecurity, RL, and CPS. The only background knowledge required to appreciate the book is a basic knowledge of calculus and probability theory.

Table of Contents:

Section I Introduction

Chapter 1 Overview of Reinforcement Learning

Chapter 2 Overview of CyberPhysical Systems and Cybersecurity

Section II Reinforcement Learning for Cyber-Physical Systems

Chapter 3 Reinforcement Learning Problems

Chapter 4 Modelbased Reinforcement Learning

Chapter 5 Modelfree Reinforcement Learning

Chapter 6 Deep Reinforcement Learning

Section III Case Studies

Chapter 7 Reinforcement Learning for Cybersecurity

Chapter 8 Case Study: Online CyberAttack Detection in Smart Grid

Chapter 9 Case Study: Defeat Maninthemiddle Attack