Contributed by: Oliver Jackson, oliver.jackson@springer.com
Title: Handbook of Reinforcement Learning and Control
Editors: Kyriakos G. Vamvoudakis, Yan Wan, Frank L. Lewis and Derya Cansever
Publisher: Springer
ISBN: 978-3-030-60989-4 (Hardcover); 978-3-030-60990-0 (e-book)
Extent: 858 pages.
Month of Publication: June 2021
Price: $249.99/199.99 Euros (Hardcover); $189.00/149.99 Euros (e-book) (local taxes may apply)
Available from: https://www.springer.com/gb/book/9783030609894
This handbook presents state-of-the-art research in reinforcement learning, focusing on its applications in the control and game theory of dynamic systems and future directions for related research and technology.
The contributions gathered in this book deal with challenges faced when using learning and adaptation methods to solve academic and industrial problems, such as optimization in dynamic environments with single and multiple agents, convergence and performance analysis, and online implementation. They explore means by which these difficulties can be solved, and cover a wide range of related topics including:
- deep learning;
- artificial intelligence;
- applications of game theory;
- mixed modality learning; and
- multi-agent reinforcement learning.
Practicing engineers and scholars in the field of machine learning, game theory, and autonomous control will find the Handbook of Reinforcement Learning and Control to be thought-provoking, instructive and informative.
Contents:
- What May Lie Ahead in Reinforcement Learning (Derya Cansever)
- Reinforcement Learning for Distributed Control and Multi-player Games (Bahare Kiumarsi, Hamidreza Modares, Frank Lewis)
- From Reinforcement Learning to Optimal Control: A Unified Framework for Sequential Decisions (Warren B. Powell)
- Fundamental Design Principles for Reinforcement Learning Algorithms (Adithya M. Devraj, Ana Bušić, Sean Meyn)
- Mixed Density Methods for Approximate Dynamic Programming (Max L. Greene, Patryk Deptula, Rushikesh Kamalapurkar, Warren E. Dixon)
- Model-Free Linear Quadratic Regulator (Hesameddin Mohammadi, Mahdi Soltanolkotabi, Mihailo R. Jovanović)
- Adaptive Dynamic Programming in the Hamiltonian-Driven Framework (Yongliang Yang, Donald C. Wunsch II, Yixin Yin)
- Reinforcement Learning for Optimal Adaptive Control of Time Delay Systems (Syed Ali Asad Rizvi, Yusheng Wei, Zongli Lin)
- Optimal Adaptive Control of Partially Uncertain Linear Continuous-Time Systems with State Delay (Rohollah Moghadam, S. Jagannathan, Vignesh Narayanan, Krishnan Raghavan)
- Dissipativity-Based Verification for Autonomous Systems in Adversarial Environments (Aris Kanellopoulos, Kyriakos G. Vamvoudakis, Vijay Gupta, Panos Antsaklis)
- Reinforcement Learning-Based Model Reduction for Partial Differential Equations: Application to the Burgers Equation (Mouhacine Benosman, Ankush Chakrabarty, Jeff Borggaard)
- Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms (Kaiqing Zhang, Zhuoran Yang, Tamer Başar)
- Computational Intelligence in Uncertainty Quantification for Learning Control and Differential Games (Mushuang Liu, Yan Wan, Zongli Lin, Frank L. Lewis, Junfei Xie, Brian A. Jalaian)
- A Top-Down Approach to Attain Decentralized Multi-agents (Alex Tong Lin, Guido Montúfar, Stanley J. Osher)
- Modeling and Mitigating Link-Flooding Distributed Denial-of-Service Attacks via Learning in Stackelberg Games (Guosong Yang, João P. Hespanha)
- Bounded Rationality in Differential Games: A Reinforcement Learning-Based Approach (Nick-Marios T. Kokolakis, Aris Kanellopoulos, Kyriakos G. Vamvoudakis)
- Bounded Rationality in Learning, Perception, Decision-Making, and Stochastic Games (Panagiotis Tsiotras)
- Fairness in Learning-Based Sequential Decision Algorithms: A Survey (Xueru Zhang, Mingyan Liu)
- Trading Utility and Uncertainty: Applying the Value of Information to Resolve the Exploration–Exploitation Dilemma in Reinforcement Learning (Isaac J. Sledge, José C. Príncipe)
- Map-Based Planning for Small Unmanned Aircraft Rooftop Landing (J. Castagno, E. Atkins)
- Reinforcement Learning: An Industrial Perspective (Amit Surana)
- Robust Autonomous Driving with Human in the Loop (Mengzhe Huang, Zhong-Ping Jiang, Michael Malisoff, Leilei Cui)
- Decision-Making for Complex Systems Subjected to Uncertainties—A Probability Density Function Control Approach (Aiping Wang, Hong Wang)
- A Hybrid Dynamical Systems Perspective on Reinforcement Learning for Cyber-Physical Systems: Vistas, Open Problems, and Challenges (Jorge I. Poveda, Andrew R. Teel)
- The Role of Systems Biology, Neuroscience, and Thermodynamics in Network Control and Learning (Wassim M. Haddad)
- Quantum Amplitude Amplification for Reinforcement Learning (K. Rajagopal, Q. Zhang, S. N. Balakrishnan, P. Fakhari, J. R. Busemeyer)
