Language : English
Published : 2010-04-23
Pages : 286
Reinforcement Learning and Dynamic Programming Using Function Approximators
From household appliances to applications in robotics, engineered systems involving complex dynamics can only be as effective as the algorithms that control them. While Dynamic Programming (DP) has provided researchers with a way to optimally solve decision and control problems involving complex dynamic systems, its practical value was limited by algorithms that lacked the capacity to scale up to realistic problems. However, in recent years, dramatic developments in Reinforcement Learning (RL), the model-free counterpart of DP, changed our understanding of what is possible. Those developments led to the creation of reliable methods that can be applied even when a mathematical model of the system is unavailable, allowing researchers to solve challenging control problems in engineering, as well as in a variety of other disciplines, including economics, medicine, and artificial intelligence. Reinforcement Learning and Dynamic Programming Using Function Approximators provides a comprehensive and unparalleled exploration of the field of RL and DP. With a focus on continuous-variable problems, this seminal text details essential developments that have substantially altered the field over the past decade. In its pages, pioneering experts provide a concise introduction to classical RL and DP, followed by an extensive presentation of the state-of-the-art and novel methods in RL and DP with approximation. Combining algorithm development with theoretical guarantees, they elaborate on their work with illustrative examples and insightful comparisons. Three individual chapters are dedicated to representative algorithms from each of the major classes of techniques: value iteration, policy iteration, and policy search. The features and performance of these algorithms are highlighted in extensive experimental studies on a range of control applications. The recent development of applications involving complex systems has led to a surge of interest in RL and DP methods and the subsequent need for a quality resource on the subject. For graduate students and others new to the field, this book offers a thorough introduction to both the basics and emerging methods. And for those researchers and practitioners working in the fields of optimal and adaptive control, machine learning, artificial intelligence, and operations research, this resource offers a combination of practical algorithms, theoretical analysis, and comprehensive examples that they will be able to adapt and apply to their own work. Access the authors’ website at www.dcsc.tudelft.nl/rlbook/ for additional material, including computer code used in the studies and information concerning new developments.
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A major objective of this book is to fill the gap between traditional logic design principles and logic design/optimization techniques used in practice. Over the last two decades several techniques for computer-aided design and optimization of logic circuits have been developed. However, underlying theories of these techniques are inadequately covered or not covered at all in undergraduate text books. This book covers not only the “classical” material found in current text books but also selected materials that modern logic designers need to be familiar with.
About the Author
Parag K. Lala, PhD, DSc(Eng), is the Cary and Lois Patterson Chair of Electrical Engineering at Texas A&M University-Texarkana. Dr. Lala is the author of five books, including Fault-Tolerant and Fault-Testable Hardware Design and Practical Digital Logic Design and Testing. Dr. Lala was named a Fellow of the IEEE for “contributions to the development of self-checking logic and associated checker design.” He is also a Fellow of the Institution of Engineering and Technology, United Kingdom.
Nelson Systems Engineering VCE Units 1-4 has been thoroughly updated and produced in full colour for better student learning. This comprehensive and useful resource book has two new chapters on digital manufacturing and control systems, and many more photos throughout.
Table of Contents
Introduction The systems engineering process Syllabus outcomes guide 1 Understanding systems Case study 1: Water treatment and recycling system 2 Technology systems in society Case study 2: Manufacturing and technology 3 Energy systems Case study 3: Solar house 4 Mechanical systems 5 Electrotechnology 6 Digital manufacturing 7 Control systems Case study 4: Remote control systems 8 Testing engineering systems Case study 5: Testing flight data systems 9 The systems engineering process Case study 6: Vehicle style and design 10 Production: Equipment, safety and materials Revision tasks A – Z Systems terminology Index
The Seventh Edition of Statistical Quality Control provides a comprehensive treatment of the major aspects of using statistical methodology for quality control and improvement. Both traditional and modern methods are presented, including state-of-the-art techniques for statistical process monitoring and control and statistically designed experiments for process characterization, optimization, and process robustness studies. The seventh edition continues to focus on DMAIC (define, measure, analyze, improve, and control–the problem-solving strategy of six sigma) including a chapter on the implementation process. Additionally, the text includes new examples, exercises, problems, and techniques. Statistical Quality Control is best suited for students in engineering, statistics, business and management science or students in undergraduate courses.
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