Download simulation based optimization parametric optimization techniques and reinforcement learning operations research computer science interfaces series in pdf or read simulation based optimization parametric optimization techniques and reinforcement learning operations research computer science interfaces series in pdf online books in PDF, EPUB and Mobi Format. Click Download or Read Online button to get simulation based optimization parametric optimization techniques and reinforcement learning operations research computer science interfaces series in pdf book now. This site is like a library, Use search box in the widget to get ebook that you want.



Simulation Based Optimization

Author: Abhijit Gosavi
Publisher: Springer
ISBN: 1489974911
Size: 75.85 MB
Format: PDF, ePub, Mobi
View: 7734
Download and Read
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning introduce the evolving area of static and dynamic simulation-based optimization. Covered in detail are model-free optimization techniques – especially designed for those discrete-event, stochastic systems which can be simulated but whose analytical models are difficult to find in closed mathematical forms. Key features of this revised and improved Second Edition include: · Extensive coverage, via step-by-step recipes, of powerful new algorithms for static simulation optimization, including simultaneous perturbation, backtracking adaptive search and nested partitions, in addition to traditional methods, such as response surfaces, Nelder-Mead search and meta-heuristics (simulated annealing, tabu search, and genetic algorithms) · Detailed coverage of the Bellman equation framework for Markov Decision Processes (MDPs), along with dynamic programming (value and policy iteration) for discounted, average, and total reward performance metrics · An in-depth consideration of dynamic simulation optimization via temporal differences and Reinforcement Learning: Q-Learning, SARSA, and R-SMART algorithms, and policy search, via API, Q-P-Learning, actor-critics, and learning automata · A special examination of neural-network-based function approximation for Reinforcement Learning, semi-Markov decision processes (SMDPs), finite-horizon problems, two time scales, case studies for industrial tasks, computer codes (placed online) and convergence proofs, via Banach fixed point theory and Ordinary Differential Equations Themed around three areas in separate sets of chapters – Static Simulation Optimization, Reinforcement Learning and Convergence Analysis – this book is written for researchers and students in the fields of engineering (industrial, systems, electrical and computer), operations research, computer science and applied mathematics.

Algorithms For Reinforcement Learning

Author: Csaba Szepesvari
Publisher: Morgan & Claypool Publishers
ISBN: 1608454924
Size: 63.96 MB
Format: PDF, Kindle
View: 1900
Download and Read
Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms' merits and limitations. Reinforcement learning is of great interest because of the large number of practical applications that it can be used to address, ranging from problems in artificial intelligence to operations research or control engineering. In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming.We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations.

Emerging Artificial Intelligence Applications In Computer Engineering

Author: Ilias G. Maglogiannis
Publisher: IOS Press
ISBN: 1586037803
Size: 68.66 MB
Format: PDF, ePub
View: 3065
Download and Read
"The ever expanding abundance of information and computing power enables researchers and users to tackle highly interesting issues for the first time, such as applications providing personalized access and interactivity to multimodal information based on user preferences and semantic concepts or human-machine interface systems utilizing information on the affective state of the user. The purpose of this book is to provide insights on how todays computer engineers can implement AI in real world applications. Overall, the field of artificial intelligence is extremely broad. In essence, AI has found applications, in one way or another, in every aspect of computing and in most aspects of modern life. Consequently, it is not possible to provide a complete review of the field in the framework of a single book, unless if the review is broad rather than deep. In this book we have chosen to present selected current and emerging practical applications of AI, thus allowing for a more detailed presentation of topics. The book is organized in four parts; General Purpose Applications of AI; Intelligent Human-Computer Interaction; Intelligent Applications in Signal Processing and eHealth; and Real world AI applications in Computer Engineering."

Perspectives In Operations Research

Author: Frank B. Alt
Publisher: Springer Science & Business Media
ISBN: 0387399348
Size: 59.80 MB
Format: PDF, Mobi
View: 6131
Download and Read
A Symposium was held on February 25, 2006 in honor of the 80th birthday of Saul I. Gass and his major contributions to the field of operations research over 50 years. This volume includes articles from each of the Symposium speakers plus 16 other articles from friends, colleagues, and former students. Each contributor offers a forward-looking perspective on the future development of the field.

Reinforcement Learning

Author: Richard S. Sutton
Publisher: Springer Science & Business Media
ISBN: 1461536189
Size: 60.90 MB
Format: PDF, ePub, Mobi
View: 110
Download and Read
Reinforcement learning is the learning of a mapping from situations to actions so as to maximize a scalar reward or reinforcement signal. The learner is not told which action to take, as in most forms of machine learning, but instead must discover which actions yield the highest reward by trying them. In the most interesting and challenging cases, actions may affect not only the immediate reward, but also the next situation, and through that all subsequent rewards. These two characteristics -- trial-and-error search and delayed reward -- are the most important distinguishing features of reinforcement learning. Reinforcement learning is both a new and a very old topic in AI. The term appears to have been coined by Minsk (1961), and independently in control theory by Walz and Fu (1965). The earliest machine learning research now viewed as directly relevant was Samuel's (1959) checker player, which used temporal-difference learning to manage delayed reward much as it is used today. Of course learning and reinforcement have been studied in psychology for almost a century, and that work has had a very strong impact on the AI/engineering work. One could in fact consider all of reinforcement learning to be simply the reverse engineering of certain psychological learning processes (e.g. operant conditioning and secondary reinforcement). Reinforcement Learning is an edited volume of original research, comprising seven invited contributions by leading researchers.

Introduction To Derivative Free Optimization

Author: Andrew R. Conn
Publisher: SIAM
ISBN: 0898716683
Size: 51.85 MB
Format: PDF
View: 503
Download and Read
The first contemporary comprehensive treatment of optimization without derivatives. This text explains how sampling and model techniques are used in derivative-free methods and how they are designed to solve optimization problems. It is designed to be readily accessible to both researchers and those with a modest background in computational mathematics.

Handbook Of Learning And Approximate Dynamic Programming

Author: Jennie Si
Publisher: John Wiley & Sons
ISBN: 9780471660545
Size: 19.35 MB
Format: PDF, Docs
View: 4211
Download and Read
Updated to cover all the latest features and capabilities of Access 2007, this resource provides new and inexperienced Access users with eight task-oriented minibooks that cover begininning to advanced-level material Each minibook covers a specific aspect of Access, such as database design, tables, queries, forms, reports, and macros Shows how to accomplish specific tasks such as database housekeeping, security data, and using Access with the Web Access is the world's leading desktop database solution and is used by millions of people to store, organize, view, analyze, and share data, as well as to build powerful, custom database solutions that integrate with the Web and enterprise data sources

Introduction To Stochastic Search And Optimization

Author: James C. Spall
Publisher: John Wiley & Sons
ISBN: 0471441902
Size: 53.79 MB
Format: PDF, ePub
View: 5879
Download and Read
A unique interdisciplinary foundation for real-world problem solving Stochastic search and optimization techniques are used in a vast number of areas, including aerospace, medicine, transportation, and finance, to name but a few. Whether the goal is refining the design of a missile or aircraft, determining the effectiveness of a new drug, developing the most efficient timing strategies for traffic signals, or making investment decisions in order to increase profits, stochastic algorithms can help researchers and practitioners devise optimal solutions to countless real-world problems. Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control is a graduate-level introduction to the principles, algorithms, and practical aspects of stochastic optimization, including applications drawn from engineering, statistics, and computer science. The treatment is both rigorous and broadly accessible, distinguishing this text from much of the current literature and providing students, researchers, and practitioners with a strong foundation for the often-daunting task of solving real-world problems. The text covers a broad range of today’s most widely used stochastic algorithms, including: Random search Recursive linear estimation Stochastic approximation Simulated annealing Genetic and evolutionary methods Machine (reinforcement) learning Model selection Simulation-based optimization Markov chain Monte Carlo Optimal experimental design The book includes over 130 examples, Web links to software and data sets, more than 250 exercises for the reader, and an extensive list of references. These features help make the text an invaluable resource for those interested in the theory or practice of stochastic search and optimization.

Intelligent Production Machines And Systems 2nd I Proms Virtual International Conference 3 14 July 2006

Author: Duc T. Pham
Publisher: Elsevier
ISBN: 0080556345
Size: 27.38 MB
Format: PDF, Mobi
View: 2036
Download and Read
I*PROMS 2005 is an online web-based conference. It provides a platform for presenting, discussing, and disseminating research results contributed by scientists and industrial practitioners active in the area of intelligent systems and soft computing techniques (such as fuzzy logic, neural networks, evolutionary algorithms, and knowledge-based systems) and their application in different areas of manufacturing. Comprised of 100 peer-reviewed articles, this important resource provides tools to help enterprises achieve goals critical to the future of manufacturing. I*PROMS is an European Union-funded network that involves 30 partner organizations and more than 130 researchers from universities, research organizations, and corporations. * State-of-the-art research results * Leading European researchers and industrial practitioners * Comprehensive collection of indexed and peer-reviewed articles in book format supported by a user-friendly full-text CD-ROM with search functionality