Download or read book Synchronous Reinforcement Learning Based Control for Cognitive Autonomy written by Kyriakos G. Vamvoudakis and published by . This book was released on 2020-11-12 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph describes the use of principles of reinforcement learning (RL) to design feedback policies for continuous-time dynamical systems that combine features of adaptive control and optimal control. In a control engineering context, RL bridges the gap between traditional optimal control and adaptive control algorithms.The authors give an insightful introduction to reinforcement learning techniques that can address various control problems. In this context, they give a detailed description of techniques such as Game-Theoretic Learning, Q-learning, and Intermittent RL; with each chapter providing a self-contained exposition of the topic and giving the reader suggestions for further reading. Finally, the authors demonstrate the application of the techniques in autonomous vehicles.This review of a topic that is rapidly becoming ubiquitous in many engineering systems enables to reader dip in and out of the topic to quickly understand the essentials and provides the starting point for further research.
Download or read book Industrial Networks and Intelligent Systems written by Nguyen-Son Vo and published by Springer Nature. This book was released on with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Handbook of Reinforcement Learning and Control written by Kyriakos G. Vamvoudakis and published by Springer Nature. This book was released on 2021-06-23 with total page 833 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Reinforcement Learning Based Control Mechanism for Elastic Resource Scheduling in Cognitive Optical Networks written by Konstantinos Banias and published by . This book was released on 2013 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Handbook On Computer Learning And Intelligence In 2 Volumes written by Plamen Parvanov Angelov and published by World Scientific. This book was released on 2022-06-29 with total page 1057 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Handbook on Computer Learning and Intelligence is a second edition which aims to be a one-stop-shop for the various aspects of the broad research area of computer learning and intelligence. This field of research evolved so much in the last five years that it necessitates this new edition of the earlier Handbook on Computational Intelligence.This two-volume handbook is divided into five parts. Volume 1 covers Explainable AI and Supervised Learning. Volume 2 covers three parts: Deep Learning, Intelligent Control, and Evolutionary Computation. The chapters detail the theory, methodology and applications of computer learning and intelligence, and are authored by some of the leading experts in the respective areas. The fifteen core chapters of the previous edition have been written and significantly refreshed by the same authors. Parts of the handbook have evolved to keep pace with the latest developments in computational intelligence in the areas that span across Machine Learning and Artificial Intelligence. The Handbook remains dedicated to applications and engineering-orientated aspects of these areas over abstract theories.Related Link(s)
Download or read book Artificial Cognitive Architecture with Self Learning and Self Optimization Capabilities written by Gerardo Beruvides and published by Springer. This book was released on 2018-12-14 with total page 195 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces three key issues: (i) development of a gradient-free method to enable multi-objective self-optimization; (ii) development of a reinforcement learning strategy to carry out self-learning and finally, (iii) experimental evaluation and validation in two micromachining processes (i.e., micro-milling and micro-drilling). The computational architecture (modular, network and reconfigurable for real-time monitoring and control) takes into account the analysis of different types of sensors, processing strategies and methodologies for extracting behavior patterns from representative process’ signals. The reconfiguration capability and portability of this architecture are supported by two major levels: the cognitive level (core) and the executive level (direct data exchange with the process). At the same time, the architecture includes different operating modes that interact with the process to be monitored and/or controlled. The cognitive level includes three fundamental modes such as modeling, optimization and learning, which are necessary for decision-making (in the form of control signals) and for the real-time experimental characterization of complex processes. In the specific case of the micromachining processes, a series of models based on linear regression, nonlinear regression and artificial intelligence techniques were obtained. On the other hand, the executive level has a constant interaction with the process to be monitored and/or controlled. This level receives the configuration and parameterization from the cognitive level to perform the desired monitoring and control tasks.
Download or read book Reinforcement Learning written by Marco Wiering and published by Springer Science & Business Media. This book was released on 2012-03-05 with total page 653 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncertain environments and a computational methodology for finding optimal behaviors for challenging problems in control, optimization and adaptive behavior of intelligent agents. As a field, reinforcement learning has progressed tremendously in the past decade. The main goal of this book is to present an up-to-date series of survey articles on the main contemporary sub-fields of reinforcement learning. This includes surveys on partially observable environments, hierarchical task decompositions, relational knowledge representation and predictive state representations. Furthermore, topics such as transfer, evolutionary methods and continuous spaces in reinforcement learning are surveyed. In addition, several chapters review reinforcement learning methods in robotics, in games, and in computational neuroscience. In total seventeen different subfields are presented by mostly young experts in those areas, and together they truly represent a state-of-the-art of current reinforcement learning research. Marco Wiering works at the artificial intelligence department of the University of Groningen in the Netherlands. He has published extensively on various reinforcement learning topics. Martijn van Otterlo works in the cognitive artificial intelligence group at the Radboud University Nijmegen in The Netherlands. He has mainly focused on expressive knowledge representation in reinforcement learning settings.
Download or read book Index to IEEE Publications written by Institute of Electrical and Electronics Engineers and published by . This book was released on 1989 with total page 944 pages. Available in PDF, EPUB and Kindle. Book excerpt: Issues for 1973- cover the entire IEEE technical literature.
Download or read book Reinforcement Learning second edition written by Richard S. Sutton and published by MIT Press. This book was released on 2018-11-13 with total page 549 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Robotic Systems Concepts Methodologies Tools and Applications written by Management Association, Information Resources and published by IGI Global. This book was released on 2020-01-03 with total page 2075 pages. Available in PDF, EPUB and Kindle. Book excerpt: Through expanded intelligence, the use of robotics has fundamentally transformed a variety of fields, including manufacturing, aerospace, medicine, social services, and agriculture. Continued research on robotic design is critical to solving various dynamic obstacles individuals, enterprises, and humanity at large face on a daily basis. Robotic Systems: Concepts, Methodologies, Tools, and Applications is a vital reference source that delves into the current issues, methodologies, and trends relating to advanced robotic technology in the modern world. Highlighting a range of topics such as mechatronics, cybernetics, and human-computer interaction, this multi-volume book is ideally designed for robotics engineers, mechanical engineers, robotics technicians, operators, software engineers, designers, programmers, industry professionals, researchers, students, academicians, and computer practitioners seeking current research on developing innovative ideas for intelligent and autonomous robotics systems.
Download or read book Learning Motor Skills written by Jens Kober and published by Springer. This book was released on 2013-11-23 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the state of the art in reinforcement learning applied to robotics both in terms of novel algorithms and applications. It discusses recent approaches that allow robots to learn motor. skills and presents tasks that need to take into account the dynamic behavior of the robot and its environment, where a kinematic movement plan is not sufficient. The book illustrates a method that learns to generalize parameterized motor plans which is obtained by imitation or reinforcement learning, by adapting a small set of global parameters and appropriate kernel-based reinforcement learning algorithms. The presented applications explore highly dynamic tasks and exhibit a very efficient learning process. All proposed approaches have been extensively validated with benchmarks tasks, in simulation and on real robots. These tasks correspond to sports and games but the presented techniques are also applicable to more mundane household tasks. The book is based on the first author’s doctoral thesis, which won the 2013 EURON Georges Giralt PhD Award.
Download or read book Optimal Adaptive Control and Differential Games by Reinforcement Learning Principles written by Draguna L. Vrabie and published by IET. This book was released on 2013 with total page 305 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book reviews developments in the following fields: optimal adaptive control; online differential games; reinforcement learning principles; and dynamic feedback control systems.
Download or read book Artificial Cognitive Systems written by David Vernon and published by MIT Press. This book was released on 2024-08-20 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: A concise introduction to a complex field, bringing together recent work in cognitive science and cognitive robotics to offer a solid grounding on key issues. This book offers a concise and accessible introduction to the emerging field of artificial cognitive systems. Cognition, both natural and artificial, is about anticipating the need for action and developing the capacity to predict the outcome of those actions. Drawing on artificial intelligence, developmental psychology, and cognitive neuroscience, the field of artificial cognitive systems has as its ultimate goal the creation of computer-based systems that can interact with humans and serve society in a variety of ways. This primer brings together recent work in cognitive science and cognitive robotics to offer readers a solid grounding on key issues. The book first develops a working definition of cognitive systems—broad enough to encompass multiple views of the subject and deep enough to help in the formulation of theories and models. It surveys the cognitivist, emergent, and hybrid paradigms of cognitive science and discusses cognitive architectures derived from them. It then turns to the key issues, with chapters devoted to autonomy, embodiment, learning and development, memory and prospection, knowledge and representation, and social cognition. Ideas are introduced in an intuitive, natural order, with an emphasis on the relationships among ideas and building to an overview of the field. The main text is straightforward and succinct; sidenotes drill deeper on specific topics and provide contextual links to further reading.
Download or read book Dissertation Abstracts International written by and published by . This book was released on 2002 with total page 712 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Encyclopedia of Artificial Intelligence written by Juan Ramon Rabunal and published by IGI Global. This book was released on 2009-01-01 with total page 1640 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book is a comprehensive and in-depth reference to the most recent developments in the field covering theoretical developments, techniques, technologies, among others"--Provided by publisher.
Download or read book Ways of Learning written by Alan Pritchard and published by Routledge. This book was released on 2013-12-04 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt: Whilst most teachers are skilled in providing opportunities for the progression of children’s learning, it is often without fully understanding the theory behind it. With greater insight into what is currently known about the processes of learning and about individual learning preferences, teachers are better equipped to provide effective experiences and situations which are more likely to lead to lasting attainment. Now fully updated, Ways of Learning seeks to provide an understanding of the ways in which learning takes place, which teachers can make use of in their planning and teaching, including: An overview of learning Behaviourism and the beginning of theory Cognitive and constructivist learning Multiple intelligences Learning styles Difficulties with learning The influence of neuro-psychology Relating theory to practice The third edition of this book includes developments in areas covered in the first and second editions, as well as expanding on certain topics to bring about a wider perspective; most noticeably a newly updated and fully expanded chapter on the influence of neuro-educational research. The book also reflects changes in government policy and is closely related to new developments in practice. Written for trainee teachers, serving teachers, and others interested in learning for various reasons, Ways of Learning serves as a valuable introduction for students setting out on higher degree work who are in need of an introduction to the topic.
Download or read book Reinforcement Learning for Optimal Feedback Control written by Rushikesh Kamalapurkar and published by Springer. This book was released on 2018-05-10 with total page 305 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
