Reinforcement Learning

State-of-the-Art

Author: Marco Wiering,Martijn van Otterlo

Publisher: Springer Science & Business Media

ISBN: 3642276458

Category: Computers

Page: 638

View: 5447

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.
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ECAI 2016

22nd European Conference on Artificial Intelligence, 29 August - 2 September 2016, The Hague, The Netherlands - Including Prestigious Applications of Artificial Intelligence (PAIS 2016)

Author: G.A. Kaminka,M. Fox,P. Bouquet

Publisher: IOS Press

ISBN: 1614996725

Category: Computers

Page: 1860

View: 639

Artificial Intelligence continues to be one of the most exciting and fast-developing fields of computer science. This book presents the 177 long papers and 123 short papers accepted for ECAI 2016, the latest edition of the biennial European Conference on Artificial Intelligence, Europe’s premier venue for presenting scientific results in AI. The conference was held in The Hague, the Netherlands, from August 29 to September 2, 2016. ECAI 2016 also incorporated the conference on Prestigious Applications of Intelligent Systems (PAIS) 2016, and the Starting AI Researcher Symposium (STAIRS). The papers from PAIS are included in this volume; the papers from STAIRS are published in a separate volume in the Frontiers in Artificial Intelligence and Applications (FAIA) series. Organized by the European Association for Artificial Intelligence (EurAI) and the Benelux Association for Artificial Intelligence (BNVKI), the ECAI conference provides an opportunity for researchers to present and hear about the very best research in contemporary AI. This proceedings will be of interest to all those seeking an overview of the very latest innovations and developments in this field.
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Machine Learning mit Python

Das Praxis-Handbuch für Data Science, Predictive Analytics und Deep Learning

Author: Sebastian Raschka

Publisher: MITP-Verlags GmbH & Co. KG

ISBN: 3958454240

Category: Computers

Page: 424

View: 6481

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Statistical Reinforcement Learning

Modern Machine Learning Approaches

Author: Masashi Sugiyama

Publisher: CRC Press

ISBN: 1439856907

Category: Business & Economics

Page: 206

View: 5220

Reinforcement learning is a mathematical framework for developing computer agents that can learn an optimal behavior by relating generic reward signals with its past actions. With numerous successful applications in business intelligence, plant control, and gaming, the RL framework is ideal for decision making in unknown environments with large amounts of data. Supplying an up-to-date and accessible introduction to the field, Statistical Reinforcement Learning: Modern Machine Learning Approaches presents fundamental concepts and practical algorithms of statistical reinforcement learning from the modern machine learning viewpoint. It covers various types of RL approaches, including model-based and model-free approaches, policy iteration, and policy search methods. Covers the range of reinforcement learning algorithms from a modern perspective Lays out the associated optimization problems for each reinforcement learning scenario covered Provides thought-provoking statistical treatment of reinforcement learning algorithms The book covers approaches recently introduced in the data mining and machine learning fields to provide a systematic bridge between RL and data mining/machine learning researchers. It presents state-of-the-art results, including dimensionality reduction in RL and risk-sensitive RL. Numerous illustrative examples are included to help readers understand the intuition and usefulness of reinforcement learning techniques. This book is an ideal resource for graduate-level students in computer science and applied statistics programs, as well as researchers and engineers in related fields.
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Reinforcement Learning and Dynamic Programming Using Function Approximators

Author: Lucian Busoniu,Robert Babuska,Bart De Schutter,Damien Ernst

Publisher: CRC Press

ISBN: 9781439821091

Category: Computers

Page: 280

View: 6977

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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Maschinelles Lernen

Author: Ethem Alpaydın

Publisher: Oldenbourg Verlag

ISBN: 9783486581140

Category:

Page: 440

View: 8876

Unter maschinellem Lernen versteht man die kunstliche Generierung von Wissen aus Erfahrung. Das vorliegende Buch diskutiert Methoden aus den Bereichen Statistik, Mustererkennung etc. und versucht, die unterschiedlichen Ansatze zu kombinieren, um moglichst effiziente Losungen zu finden."
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Computational Intelligence and Intelligent Systems

9th International Symposium, ISICA 2017, Guangzhou, China, November 18–19, 2017, Revised Selected Papers

Author: Kangshun Li,Wei Li,Zhangxing Chen,Yong Liu

Publisher: Springer

ISBN: 9811316481

Category: Computers

Page: 603

View: 9938

This two-volume set (CCIS 873 and CCIS 874) constitutes the thoroughly refereed proceedings of the 9th International Symposium, ISICA 2017, held in Guangzhou, China, in November 2017.The 101 full papers presented in both volumes were carefully reviewed and selected from 181 submissions. This first volume is organized in topical sections on neural networks and statistical learning: neural architecture search, transfer of knowledge; evolutionary multi-objective and dynamic optimization: optimal control and design, hybrid methods; data mining: association rule learning, data management platforms; Cloud computing and multiagent systems: service models, Cloud engineering; everywhere connectivity: IoT solutions, wireless sensor networks.
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Anticipatory Learning Classifier Systems

Author: Martin V. Butz

Publisher: Springer Science & Business Media

ISBN: 1461508916

Category: Computers

Page: 172

View: 4135

Anticipatory Learning Classifier Systems describes the state of the art of anticipatory learning classifier systems-adaptive rule learning systems that autonomously build anticipatory environmental models. An anticipatory model specifies all possible action-effects in an environment with respect to given situations. It can be used to simulate anticipatory adaptive behavior. Anticipatory Learning Classifier Systems highlights how anticipations influence cognitive systems and illustrates the use of anticipations for (1) faster reactivity, (2) adaptive behavior beyond reinforcement learning, (3) attentional mechanisms, (4) simulation of other agents and (5) the implementation of a motivational module. The book focuses on a particular evolutionary model learning mechanism, a combination of a directed specializing mechanism and a genetic generalizing mechanism. Experiments show that anticipatory adaptive behavior can be simulated by exploiting the evolving anticipatory model for even faster model learning, planning applications, and adaptive behavior beyond reinforcement learning. Anticipatory Learning Classifier Systems gives a detailed algorithmic description as well as a program documentation of a C++ implementation of the system.
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Recent Advances in Reinforcement Learning

8th European Workshop, EWRL 2008, Villeneuve D'Ascq, France, June 30-July 3, 2008, Revised and Selected Papers

Author: Manuel Loth,Rémi Munos

Publisher: Springer Science & Business Media

ISBN: 3540897216

Category: Computers

Page: 283

View: 8249

This book constitutes revised and selected papers of the 8th European Workshop on Reinforcement Learning, EWRL 2008, which took place in Villeneuve d'Ascq, France, during June 30 - July 3, 2008. The 21 papers presented were carefully reviewed and selected from 61 submissions. They are dedicated to the field of and current researches in reinforcement learning.
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Neuronale Netze selbst programmieren

Ein verständlicher Einstieg mit Python

Author: Tariq Rashid

Publisher: O'Reilly

ISBN: 3960101031

Category: Computers

Page: 232

View: 7715

Neuronale Netze sind Schlüsselelemente des Deep Learning und der Künstlichen Intelligenz, die heute zu Erstaunlichem in der Lage sind. Sie sind Grundlage vieler Anwendungen im Alltag wie beispielsweise Spracherkennung, Gesichtserkennung auf Fotos oder die Umwandlung von Sprache in Text. Dennoch verstehen nur wenige, wie neuronale Netze tatsächlich funktionieren. Dieses Buch nimmt Sie mit auf eine unterhaltsame Reise, die mit ganz einfachen Ideen beginnt und Ihnen Schritt für Schritt zeigt, wie neuronale Netze arbeiten: - Zunächst lernen Sie die mathematischen Konzepte kennen, die den neuronalen Netzen zugrunde liegen. Dafür brauchen Sie keine tieferen Mathematikkenntnisse, denn alle mathematischen Ideen werden behutsam und mit vielen Illustrationen und Beispielen erläutert. Eine Kurzeinführung in die Analysis unterstützt Sie dabei. - Dann geht es in die Praxis: Nach einer Einführung in die populäre und leicht zu lernende Programmiersprache Python bauen Sie allmählich Ihr eigenes neuronales Netz mit Python auf. Sie bringen ihm bei, handgeschriebene Zahlen zu erkennen, bis es eine Performance wie ein professionell entwickeltes Netz erreicht. - Im nächsten Schritt tunen Sie die Leistung Ihres neuronalen Netzes so weit, dass es eine Zahlenerkennung von 98 % erreicht – nur mit einfachen Ideen und simplem Code. Sie testen das Netz mit Ihrer eigenen Handschrift und werfen noch einen Blick in das mysteriöse Innere eines neuronalen Netzes. - Zum Schluss lassen Sie das neuronale Netz auf einem Raspberry Pi Zero laufen. Tariq Rashid erklärt diese schwierige Materie außergewöhnlich klar und verständlich, dadurch werden neuronale Netze für jeden Interessierten zugänglich und praktisch nachvollziehbar.
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Das Geheimnis des menschlichen Denkens

Einblicke in das Reverse Engineering des Gehirns

Author: Ray Kurzweil

Publisher: Lola Books

ISBN: 394420316X

Category: Science

Page: 352

View: 9709

Der Wettlauf um das Gehirn hat begonnen. Sowohl die EU als auch die USA haben gewaltige Forschungsprojekte ins Leben gerufen um das Geheimnis des menschlichen Denkens zu entschlüsseln. 2023 soll es dann soweit sein: Das menschliche Gehirn kann vollständig simuliert werden. In "Das Geheimnis des menschlichen Denkens" gewährt Googles Chefingenieur Ray Kurzweil einen spannenden Einblick in das Reverse Engineering des Gehirns. Er legt dar, wie mithilfe der Mustererkennungstheorie des Geistes der ungeheuren Komplexität des Gehirns beizukommen ist und wirft einen ebenso präzisen wie überraschenden Blick auf die am Horizont sich bereits abzeichnende Zukunft. Ist das menschliche Gehirn erst einmal simuliert, wird künstliche Intelligenz die Fähigkeiten des Menschen schon bald übertreffen. Ein Ereignis, das Kurzweil aufgrund der bereits in "Menschheit 2.0" entworfenen exponentiellen Wachstumskurve der Informationstechnologien bereits für das Jahr 2029 prognostiziert. Aber was dann? Kurzweil ist zuversichtlich, dass die Vorteile künstlicher Intelligenz mögliche Bedrohungsszenarien überwiegen und sie uns entscheidend dabei hilft, uns weiterzuentwickeln und die Herausforderungen der Zukunft zu meistern.
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Deep Learning with TensorFlow

Author: Giancarlo Zaccone,Md. Rezaul Karim,Ahmed Menshawy

Publisher: Packt Publishing Ltd

ISBN: 1786460122

Category: Computers

Page: 320

View: 3938

Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of this comprehensive TensorFlow guide About This Book Learn how to implement advanced techniques in deep learning with Google's brainchild, TensorFlow Explore deep neural networks and layers of data abstraction with the help of this comprehensive guide Real-world contextualization through some deep learning problems concerning research and application Who This Book Is For The book is intended for a general audience of people interested in machine learning and machine intelligence. A rudimentary level of programming in one language is assumed, as is a basic familiarity with computer science techniques and technologies, including a basic awareness of computer hardware and algorithms. Some competence in mathematics is needed to the level of elementary linear algebra and calculus. What You Will Learn Learn about machine learning landscapes along with the historical development and progress of deep learning Learn about deep machine intelligence and GPU computing with the latest TensorFlow 1.x Access public datasets and utilize them using TensorFlow to load, process, and transform data Use TensorFlow on real-world datasets, including images, text, and more Learn how to evaluate the performance of your deep learning models Using deep learning for scalable object detection and mobile computing Train machines quickly to learn from data by exploring reinforcement learning techniques Explore active areas of deep learning research and applications In Detail Deep learning is the step that comes after machine learning, and has more advanced implementations. Machine learning is not just for academics anymore, but is becoming a mainstream practice through wide adoption, and deep learning has taken the front seat. As a data scientist, if you want to explore data abstraction layers, this book will be your guide. This book shows how this can be exploited in the real world with complex raw data using TensorFlow 1.x. Throughout the book, you'll learn how to implement deep learning algorithms for machine learning systems and integrate them into your product offerings, including search, image recognition, and language processing. Additionally, you'll learn how to analyze and improve the performance of deep learning models. This can be done by comparing algorithms against benchmarks, along with machine intelligence, to learn from the information and determine ideal behaviors within a specific context. After finishing the book, you will be familiar with machine learning techniques, in particular the use of TensorFlow for deep learning, and will be ready to apply your knowledge to research or commercial projects. Style and approach This step-by-step guide will explore common, and not so common, deep neural networks and show how these can be exploited in the real world with complex raw data. With the help of practical examples, you will learn how to implement different types of neural nets to build smart applications related to text, speech, and image data processing.
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Methods and Models in Artificial and Natural Computation. A Homage to Professor Mira's Scientific Legacy

Third International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2009, Santiago de Compostela, Spain, June 22-26, 2009, Proceedings

Author: Jose Mira,José M. Ferrández

Publisher: Springer Science & Business Media

ISBN: 3642022634

Category: Computers

Page: 530

View: 3180

The two-volume set LNCS 5601 and LNCS 5602 constitutes the refereed proceedings of the Third International Work-Conference on the Interplay between Natural and Artificial Computation, IWINAC 2009, held in Santiago de Compostela, Spain, in June 2009. The 108 revised papers presented are thematically divided into two volumes. The first volume includes papers relating the most recent collaborations with Professor Mira and contributions mainly related with theoretical, conceptual and methodological aspects linking AI and knowledge engineering with neurophysiology, clinics and cognition. The second volume contains all the contributions connected with biologically inspired methods and techniques for solving AI and knowledge engineering problems in different application domains.
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Growing Adaptive Machines

Combining Development and Learning in Artificial Neural Networks

Author: Taras Kowaliw,Nicolas Bredeche,René Doursat

Publisher: Springer

ISBN: 3642553370

Category: Computers

Page: 261

View: 2899

The pursuit of artificial intelligence has been a highly active domain of research for decades, yielding exciting scientific insights and productive new technologies. In terms of generating intelligence, however, this pursuit has yielded only limited success. This book explores the hypothesis that adaptive growth is a means of moving forward. By emulating the biological process of development, we can incorporate desirable characteristics of natural neural systems into engineered designs and thus move closer towards the creation of brain-like systems. The particular focus is on how to design artificial neural networks for engineering tasks. The book consists of contributions from 18 researchers, ranging from detailed reviews of recent domains by senior scientists, to exciting new contributions representing the state of the art in machine learning research. The book begins with broad overviews of artificial neurogenesis and bio-inspired machine learning, suitable both as an introduction to the domains and as a reference for experts. Several contributions provide perspectives and future hypotheses on recent highly successful trains of research, including deep learning, the Hyper NEAT model of developmental neural network design, and a simulation of the visual cortex. Other contributions cover recent advances in the design of bio-inspired artificial neural networks, including the creation of machines for classification, the behavioural control of virtual agents, the desi gn of virtual multi-component robots and morphologies and the creation of flexible intelligence. Throughout, the contributors share their vast expertise on the means and benefits of creating brain-like machines. This book is appropriate for advanced students and practitioners of artificial intelligence and machine learning.
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Grundlagen zur Neuroinformatik und Neurobiologie

The Computational Brain in deutscher Sprache

Author: Patricia S. Churchland,Terrence J. Sejnowski

Publisher: Springer-Verlag

ISBN: 3322868214

Category: Technology & Engineering

Page: 702

View: 1314

The Computational Brain, das außergewöhnliche Buch über vergleichende Forschung in den Bereichen von menschlichem Gehirn und neuesten Möglichkeiten der Computertechnologie, liegt hiermit erstmals in deutscher Sprache vor. Geschrieben von einem führenden Forscherteam in den USA, ist es eine Fundgrube für alle, die wissen wollen, was der Stand der Wissenschaft auf diesem Gebiet ist. Die Autoren führen die Bereiche der Neuroinformatik und Neurobiologie mit gut ausgesuchten Beispielen und der gebotenen Hintergrundinformation gekonnt zusammen. Das Buch wird somit nicht nur dem Fachwissenschaftler sondern auch dem interdisziplinären Interesse des Informatikers und des Biologen auf eine hervorragende Weise gerecht. Übersetzt wurde das Buch von Prof. Dr. Steffen Hölldobler und Dipl.-Biol. Claudia Hölldobler, einem Informatiker und einer Biologin. Rezension in Spektrum der Wissenschaft nr. 10, S. 122 f. im Oktober 1997 (...) Die 1992 erschienene amerikanische Originalausgabe des vorliegenden Werkes ist so erfolgreich, daß man bereits von einem Klassiker reden kann. (...) (...) ....ist das Buch sehr zu empfehlen. In Verbindung von Neurobiologie und Neuroinformatik konkurrenzlos, vermittelt es einiges von der Faszination theoretischer Hirnforschung, die auch in Deutschland zunehmend mehr Wissenschaftler in ihren Bann schlägt. Rezension erschienen in: Computer Spektrum 3/1997, S. 2 (...)Das Buch wird somit nicht nur dem Fachwissenschaftler, sondern auch den interdisziplinären Interesse des Informatikers und des Biologen auf eine hervorragende Weise gerecht(...)
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Control of Complex Systems

Theory and Applications

Author: Kyriakos Vamvoudakis,Sarangapani Jagannathan

Publisher: Butterworth-Heinemann

ISBN: 0128054379

Category: Technology & Engineering

Page: 762

View: 5934

In the era of cyber-physical systems, the area of control of complex systems has grown to be one of the hardest in terms of algorithmic design techniques and analytical tools. The 23 chapters, written by international specialists in the field, cover a variety of interests within the broader field of learning, adaptation, optimization and networked control. The editors have grouped these into the following 5 sections: “Introduction and Background on Control Theory”, “Adaptive Control and Neuroscience”, “Adaptive Learning Algorithms”, “Cyber-Physical Systems and Cooperative Control”, “Applications”. The diversity of the research presented gives the reader a unique opportunity to explore a comprehensive overview of a field of great interest to control and system theorists. This book is intended for researchers and control engineers in machine learning, adaptive control, optimization and automatic control systems, including Electrical Engineers, Computer Science Engineers, Mechanical Engineers, Aerospace/Automotive Engineers, and Industrial Engineers. It could be used as a text or reference for advanced courses in complex control systems. • Collection of chapters from several well-known professors and researchers that will showcase their recent work • Presents different state-of-the-art control approaches and theory for complex systems • Gives algorithms that take into consideration the presence of modelling uncertainties, the unavailability of the model, the possibility of cooperative/non-cooperative goals and malicious attacks compromising the security of networked teams • Real system examples and figures throughout, make ideas concrete Includes chapters from several well-known professors and researchers that showcases their recent work Presents different state-of-the-art control approaches and theory for complex systems Explores the presence of modelling uncertainties, the unavailability of the model, the possibility of cooperative/non-cooperative goals, and malicious attacks compromising the security of networked teams Serves as a helpful reference for researchers and control engineers working with machine learning, adaptive control, and automatic control systems
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Reinforcement Learning and Approximate Dynamic Programming for Feedback Control

Author: Frank L. Lewis,Derong Liu

Publisher: John Wiley & Sons

ISBN: 1118453972

Category: Technology & Engineering

Page: 648

View: 2236

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.
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Cognitive Networks

Towards Self-Aware Networks

Author: Qusay Mahmoud

Publisher: John Wiley & Sons

ISBN: 9780470515150

Category: Technology & Engineering

Page: 368

View: 2146

Cognitive networks can dynamically adapt their operational parameters in response to user needs or changing environmental conditions. They can learn from these adaptations and exploit knowledge to make future decisions. Cognitive networks are the future, and they are needed simply because they enable users to focus on things other than configuring and managing networks. Without cognitive networks, the pervasive computing vision calls for every consumer to be a network technician. The applications of cognitive networks enable the vision of pervasive computing, seamless mobility, ad-hoc networks, and dynamic spectrum allocation, among others. In detail, the authors describe the main features of cognitive networks clearly indicating that cognitive network design can be applied to any type of network, being fixed or wireless. They explain why cognitive networks promise better protection against security attacks and network intruders and how such networks will benefit the service operator as well as the consumer. Cognitive Networks Explores the state-of-the-art in cognitive networks, compiling a roadmap to future research. Covers the topic of cognitive radio including semantic aspects. Presents hot topics such as biologically-inspired networking, autonomic networking, and adaptive networking. Introduces the applications of machine learning and distributed reasoning to cognitive networks. Addresses cross-layer design and optimization. Discusses security and intrusion detection in cognitive networks. Cognitive Networks is essential reading for advanced students, researchers, as well as practitioners interested in cognitive & wireless networks, pervasive computing, distributed learning, seamless mobility, and self-governed networks. With forewords by Joseph Mitola III as well as Sudhir Dixit.
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