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Handbook of Reinforcement Learning and Control

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Cover of 'Handbook of Reinforcement Learning and Control'

Table of Contents

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    Book Overview
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    Chapter 1 What May Lie Ahead in Reinforcement Learning
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    Chapter 2 Reinforcement Learning for Distributed Control and Multi-player Games
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    Chapter 3 From Reinforcement Learning to Optimal Control: A Unified Framework for Sequential Decisions
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    Chapter 4 Fundamental Design Principles for Reinforcement Learning Algorithms
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    Chapter 5 Mixed Density Methods for Approximate Dynamic Programming
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    Chapter 6 Model-Free Linear Quadratic Regulator
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    Chapter 7 Adaptive Dynamic Programming in the Hamiltonian-Driven Framework
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    Chapter 8 Reinforcement Learning for Optimal Adaptive Control of Time Delay Systems
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    Chapter 9 Optimal Adaptive Control of Partially Uncertain Linear Continuous-Time Systems with State Delay
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    Chapter 10 Dissipativity-Based Verification for Autonomous Systems in Adversarial Environments
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    Chapter 11 Reinforcement Learning-Based Model Reduction for Partial Differential Equations: Application to the Burgers Equation
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    Chapter 12 Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms
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    Chapter 13 Computational Intelligence in Uncertainty Quantification for Learning Control and Differential Games
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    Chapter 14 A Top-Down Approach to Attain Decentralized Multi-agents
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    Chapter 15 Modeling and Mitigating Link-Flooding Distributed Denial-of-Service Attacks via Learning in Stackelberg Games
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    Chapter 16 Bounded Rationality in Differential Games: A Reinforcement Learning-Based Approach
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    Chapter 17 Bounded Rationality in Learning, Perception, Decision-Making, and Stochastic Games
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    Chapter 18 Fairness in Learning-Based Sequential Decision Algorithms: A Survey
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    Chapter 19 Trading Utility and Uncertainty: Applying the Value of Information to Resolve the Exploration–Exploitation Dilemma in Reinforcement Learning
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    Chapter 20 Map-Based Planning for Small Unmanned Aircraft Rooftop Landing
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    Chapter 21 Reinforcement Learning: An Industrial Perspective
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    Chapter 22 Robust Autonomous Driving with Human in the Loop
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    Chapter 23 Decision-Making for Complex Systems Subjected to Uncertainties—A Probability Density Function Control Approach
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    Chapter 24 A Hybrid Dynamical Systems Perspective on Reinforcement Learning for Cyber-Physical Systems: Vistas, Open Problems, and Challenges
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    Chapter 25 The Role of Systems Biology, Neuroscience, and Thermodynamics in Network Control and Learning
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    Chapter 26 Quantum Amplitude Amplification for Reinforcement Learning
Attention for Chapter 26: Quantum Amplitude Amplification for Reinforcement Learning
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Citations

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Chapter title
Quantum Amplitude Amplification for Reinforcement Learning
Chapter number 26
Book title
Handbook of Reinforcement Learning and Control
Published by
Springer, Cham, June 2021
DOI 10.1007/978-3-030-60990-0_26
Book ISBNs
978-3-03-060989-4, 978-3-03-060990-0
Authors

Rajagopal, K., Zhang, Q., Balakrishnan, S. N., Fakhari, P., Busemeyer, J. R.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 3 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 1 33%
Student > Ph. D. Student 1 33%
Student > Bachelor 1 33%
Readers by discipline Count As %
Unspecified 1 33%
Mathematics 1 33%
Physics and Astronomy 1 33%