Handbook of Reinforcement Learning and Control

Chapter 1 What May Lie Ahead in Reinforcement Learning

Chapter 2 Reinforcement Learning for Distributed Control and Multi-player Games

Chapter 3 From Reinforcement Learning to Optimal Control: A Unified Framework for Sequential Decisions

Chapter 4 Fundamental Design Principles for Reinforcement Learning Algorithms

Chapter 5 Mixed Density Methods for Approximate Dynamic Programming

Chapter 6 Model-Free Linear Quadratic Regulator

Chapter 7 Adaptive Dynamic Programming in the Hamiltonian-Driven Framework

Chapter 8 Reinforcement Learning for Optimal Adaptive Control of Time Delay Systems

Chapter 9 Optimal Adaptive Control of Partially Uncertain Linear Continuous-Time Systems with State Delay

Chapter 10 Dissipativity-Based Verification for Autonomous Systems in Adversarial Environments

Chapter 11 Reinforcement Learning-Based Model Reduction for Partial Differential Equations: Application to the Burgers Equation

Chapter 12 Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms

Chapter 13 Computational Intelligence in Uncertainty Quantification for Learning Control and Differential Games

Chapter 14 A Top-Down Approach to Attain Decentralized Multi-agents

Chapter 15 Modeling and Mitigating Link-Flooding Distributed Denial-of-Service Attacks via Learning in Stackelberg Games

Chapter 16 Bounded Rationality in Differential Games: A Reinforcement Learning-Based Approach

Chapter 17 Bounded Rationality in Learning, Perception, Decision-Making, and Stochastic Games

Chapter 18 Fairness in Learning-Based Sequential Decision Algorithms: A Survey

Chapter 19 Trading Utility and Uncertainty: Applying the Value of Information to Resolve the Exploration–Exploitation Dilemma in Reinforcement Learning

Chapter 21 Reinforcement Learning: An Industrial Perspective

Chapter 22 Robust Autonomous Driving with Human in the Loop

Chapter 23 Decision-Making for Complex Systems Subjected to Uncertainties—A Probability Density Function Control Approach

Chapter 24 A Hybrid Dynamical Systems Perspective on Reinforcement Learning for Cyber-Physical Systems: Vistas, Open Problems, and Challenges

Chapter 25 The Role of Systems Biology, Neuroscience, and Thermodynamics in Network Control and Learning

Chapter 26 Quantum Amplitude Amplification for Reinforcement Learning