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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 12: Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms
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Chapter title
Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms
Chapter number 12
Book title
Handbook of Reinforcement Learning and Control
Published by
Springer, Cham, June 2021
DOI 10.1007/978-3-030-60990-0_12
Book ISBNs
978-3-03-060989-4, 978-3-03-060990-0
Authors

Zhang, Kaiqing, Yang, Zhuoran, Başar, Tamer

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 1,212 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1212 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 251 21%
Student > Master 187 15%
Student > Bachelor 86 7%
Researcher 83 7%
Student > Doctoral Student 46 4%
Other 98 8%
Unknown 461 38%
Readers by discipline Count As %
Computer Science 327 27%
Engineering 245 20%
Mathematics 32 3%
Physics and Astronomy 13 1%
Unspecified 13 1%
Other 89 7%
Unknown 493 41%