Reinforcement Learning For Optimal Feedback Control PDF Download
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Author | : Rushikesh Kamalapurkar |
Publisher | : Springer |
Total Pages | : 293 |
Release | : 2018-05-10 |
Genre | : Technology & Engineering |
ISBN | : 331978384X |
Download Reinforcement Learning for Optimal Feedback Control Book in PDF, ePub and Kindle
Reinforcement Learning for Optimal Feedback Control develops model-based and data-driven reinforcement learning methods for solving optimal control problems in nonlinear deterministic dynamical systems. In order to achieve learning under uncertainty, data-driven methods for identifying system models in real-time are also developed. The book illustrates the advantages gained from the use of a model and the use of previous experience in the form of recorded data through simulations and experiments. The book’s focus on deterministic systems allows for an in-depth Lyapunov-based analysis of the performance of the methods described during the learning phase and during execution. To yield an approximate optimal controller, the authors focus on theories and methods that fall under the umbrella of actor–critic methods for machine learning. They concentrate on establishing stability during the learning phase and the execution phase, and adaptive model-based and data-driven reinforcement learning, to assist readers in the learning process, which typically relies on instantaneous input-output measurements. This monograph provides academic researchers with backgrounds in diverse disciplines from aerospace engineering to computer science, who are interested in optimal reinforcement learning functional analysis and functional approximation theory, with a good introduction to the use of model-based methods. The thorough treatment of an advanced treatment to control will also interest practitioners working in the chemical-process and power-supply industry.
Author | : Draguna L. Vrabie |
Publisher | : IET |
Total Pages | : 305 |
Release | : 2013 |
Genre | : Computers |
ISBN | : 1849194890 |
Download Optimal Adaptive Control and Differential Games by Reinforcement Learning Principles Book in PDF, ePub and Kindle
The book reviews developments in the following fields: optimal adaptive control; online differential games; reinforcement learning principles; and dynamic feedback control systems.
Author | : Jinna Li |
Publisher | : |
Total Pages | : 0 |
Release | : 2023 |
Genre | : |
ISBN | : 9783031283963 |
Download Reinforcement Learning Book in PDF, ePub and Kindle
This book offers a thorough introduction to the basics and scientific and technological innovations involved in the modern study of reinforcement-learning-based feedback control. The authors address a wide variety of systems including work on nonlinear, networked, multi-agent and multi-player systems. A concise description of classical reinforcement learning (RL), the basics of optimal control with dynamic programming and network control architectures, and a brief introduction to typical algorithms build the foundation for the remainder of the book. Extensive research on data-driven robust control for nonlinear systems with unknown dynamics and multi-player systems follows. Data-driven optimal control of networked single- and multi-player systems leads readers into the development of novel RL algorithms with increased learning efficiency. The book concludes with a treatment of how these RL algorithms can achieve optimal synchronization policies for multi-agent systems with unknown model parameters and how game RL can solve problems of optimal operation in various process industries. Illustrative numerical examples and complex process control applications emphasize the realistic usefulness of the algorithms discussed. The combination of practical algorithms, theoretical analysis and comprehensive examples presented in Reinforcement Learning will interest researchers and practitioners studying or using optimal and adaptive control, machine learning, artificial intelligence, and operations research, whether advancing the theory or applying it in mineral-process, chemical-process, power-supply or other industries.
Author | : Olivier Sigaud |
Publisher | : Springer Science & Business Media |
Total Pages | : 534 |
Release | : 2010-02-04 |
Genre | : Computers |
ISBN | : 3642051804 |
Download From Motor Learning to Interaction Learning in Robots Book in PDF, ePub and Kindle
From an engineering standpoint, the increasing complexity of robotic systems and the increasing demand for more autonomously learning robots, has become essential. This book is largely based on the successful workshop “From motor to interaction learning in robots” held at the IEEE/RSJ International Conference on Intelligent Robot Systems. The major aim of the book is to give students interested the topics described above a chance to get started faster and researchers a helpful compandium.
Author | : Frank L. Lewis |
Publisher | : John Wiley & Sons |
Total Pages | : 498 |
Release | : 2013-01-28 |
Genre | : Technology & Engineering |
ISBN | : 1118453972 |
Download Reinforcement Learning and Approximate Dynamic Programming for Feedback Control Book in PDF, ePub and Kindle
Reinforcement learning (RL) and adaptive dynamic programming (ADP) has been one of the most critical research fields in science and engineering for modern complex systems. This book describes the latest RL and ADP techniques for decision and control in human engineered systems, covering both single player decision and control and multi-player games. Edited by the pioneers of RL and ADP research, the book brings together ideas and methods from many fields and provides an important and timely guidance on controlling a wide variety of systems, such as robots, industrial processes, and economic decision-making.
Author | : Sean Meyn |
Publisher | : Cambridge University Press |
Total Pages | : 453 |
Release | : 2022-06-09 |
Genre | : Business & Economics |
ISBN | : 1316511960 |
Download Control Systems and Reinforcement Learning Book in PDF, ePub and Kindle
A how-to guide and scientific tutorial covering the universe of reinforcement learning and control theory for online decision making.
Author | : Dimitri P. Bertsekas |
Publisher | : |
Total Pages | : 373 |
Release | : 2020 |
Genre | : Artificial intelligence |
ISBN | : 9787302540328 |
Download Reinforcement Learning and Optimal Control Book in PDF, ePub and Kindle
Author | : Richard S. Sutton |
Publisher | : MIT Press |
Total Pages | : 549 |
Release | : 2018-11-13 |
Genre | : Computers |
ISBN | : 0262352702 |
Download Reinforcement Learning, second edition Book in PDF, ePub and Kindle
The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.
Author | : Kyriakos G. Vamvoudakis |
Publisher | : Springer Nature |
Total Pages | : 833 |
Release | : 2021-06-23 |
Genre | : Technology & Engineering |
ISBN | : 3030609901 |
Download Handbook of Reinforcement Learning and Control Book in PDF, ePub and Kindle
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.
Author | : Syed Ali Asad Rizvi |
Publisher | : Springer Nature |
Total Pages | : 304 |
Release | : 2022-11-29 |
Genre | : Science |
ISBN | : 303115858X |
Download Output Feedback Reinforcement Learning Control for Linear Systems Book in PDF, ePub and Kindle
This monograph explores the analysis and design of model-free optimal control systems based on reinforcement learning (RL) theory, presenting new methods that overcome recent challenges faced by RL. New developments in the design of sensor data efficient RL algorithms are demonstrated that not only reduce the requirement of sensors by means of output feedback, but also ensure optimality and stability guarantees. A variety of practical challenges are considered, including disturbance rejection, control constraints, and communication delays. Ideas from game theory are incorporated to solve output feedback disturbance rejection problems, and the concepts of low gain feedback control are employed to develop RL controllers that achieve global stability under control constraints. Output Feedback Reinforcement Learning Control for Linear Systems will be a valuable reference for graduate students, control theorists working on optimal control systems, engineers, and applied mathematicians.