UID:
almahu_9949083913402882
Format:
XXIV, 833 p. 159 illus., 145 illus. in color.
,
online resource.
Edition:
1st ed. 2021.
ISBN:
9783030609900
Series Statement:
Studies in Systems, Decision and Control, 325
Content:
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. .
Note:
The Cognitive Dialogue: A New Architecture for Perception and Cognition -- Rooftop-Aware Emergency Landing Planning for Small Unmanned Aircraft Systems -- Quantum Reinforcement Learning in Changing Environment -- The Role of Thermodynamics in the Future Research Directions in Control and Learning -- Mixed Density Reinforcement Learning Methods for Approximate Dynamic Programming -- Analyzing and Mitigating Link-Flooding DoS Attacks Using Stackelberg Games and Adaptive Learning -- Learning and Decision Making for Complex Systems Subjected to Uncertainties: A Stochastic Distribution Control Approach -- Optimal Adaptive Control of Partially Unknown Linear Continuous-time Systems with Input and State Delay -- Gradient Methods Solve the Linear Quadratic Regulator Problem Exponentially Fast -- Architectures, Data Representations and Learning Algorithms: New Directions at the Confluence of Control and Learning -- Reinforcement Learning for Optimal Feedback Control and Multiplayer Games -- Fundamental Principles of Design for Reinforcement Learning Algorithms Course Titles -- Long-Term Impacts of Fair Machine Learning -- Learning-based Model Reduction for Partial Differential Equations with Applications to Thermo-Fluid Models' Identification, State Estimation, and Stabilization -- CESMA: Centralized Expert Supervises Multi-Agents, for Decentralization -- A Unified Framework for Reinforcement Learning and Sequential Decision Analytics -- Trading Utility and Uncertainty: Applying the Value of Information to Resolve the Exploration-Exploitation Dilemma in Reinforcement Learning -- Multi-Agent Reinforcement Learning: Recent Advances, Challenges, and Applications -- Reinforcement Learning Applications, An Industrial Perspective -- A Hybrid Dynamical Systems Perspective of Reinforcement Learning -- Bounded Rationality and Computability Issues in Learning, Perception, Decision-Making, and Games Panagiotis Tsiotras -- Mixed Modality Learning -- Computational Intelligence in Uncertainty Quantification for Learning Control and Games -- Reinforcement Learning Based Optimal Stabilization of Unknown Time Delay Systems Using State and Output Feedback -- Robust Autonomous Driving with Humans in the Loop -- Boundedly Rational Reinforcement Learning for Secure Control.
In:
Springer Nature eBook
Additional Edition:
Printed edition: ISBN 9783030609894
Additional Edition:
Printed edition: ISBN 9783030609917
Additional Edition:
Printed edition: ISBN 9783030609924
Language:
English
DOI:
10.1007/978-3-030-60990-0
URL:
https://doi.org/10.1007/978-3-030-60990-0
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