Download or read book Adaptive Representations for Reinforcement Learning written by Shimon Whiteson and published by Springer. This book was released on 2010-07-10 with total page 127 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents new algorithms for reinforcement learning, a form of machine learning in which an autonomous agent seeks a control policy for a sequential decision task. Since current methods typically rely on manually designed solution representations, agents that automatically adapt their own representations have the potential to dramatically improve performance. This book introduces two novel approaches for automatically discovering high-performing representations. The first approach synthesizes temporal difference methods, the traditional approach to reinforcement learning, with evolutionary methods, which can learn representations for a broad class of optimization problems. This synthesis is accomplished by customizing evolutionary methods to the on-line nature of reinforcement learning and using them to evolve representations for value function approximators. The second approach automatically learns representations based on piecewise-constant approximations of value functions. It begins with coarse representations and gradually refines them during learning, analyzing the current policy and value function to deduce the best refinements. This book also introduces a novel method for devising input representations. This method addresses the feature selection problem by extending an algorithm that evolves the topology and weights of neural networks such that it evolves their inputs too. In addition to introducing these new methods, this book presents extensive empirical results in multiple domains demonstrating that these techniques can substantially improve performance over methods with manual representations.
Download or read book Adaptive Representations for Reinforcement Learning written by Shimon Azariah Whiteson and published by . This book was released on 2007 with total page 177 pages. Available in PDF, EPUB and Kindle. Book excerpt: In addition to introducing these new methods, this thesis presents extensive empirical results in multiple domains demonstrating that these techniques can substantially improve performance over methods with manual representations.
Download or read book Adaptive Representations for Reinforcement Learning written by Simon Whiteson and published by Springer Science & Business Media. This book was released on 2010-10-05 with total page 127 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents new algorithms for reinforcement learning, a form of machine learning in which an autonomous agent seeks a control policy for a sequential decision task. Since current methods typically rely on manually designed solution representations, agents that automatically adapt their own representations have the potential to dramatically improve performance. This book introduces two novel approaches for automatically discovering high-performing representations. The first approach synthesizes temporal difference methods, the traditional approach to reinforcement learning, with evolutionary methods, which can learn representations for a broad class of optimization problems. This synthesis is accomplished by customizing evolutionary methods to the on-line nature of reinforcement learning and using them to evolve representations for value function approximators. The second approach automatically learns representations based on piecewise-constant approximations of value functions. It begins with coarse representations and gradually refines them during learning, analyzing the current policy and value function to deduce the best refinements. This book also introduces a novel method for devising input representations. This method addresses the feature selection problem by extending an algorithm that evolves the topology and weights of neural networks such that it evolves their inputs too. In addition to introducing these new methods, this book presents extensive empirical results in multiple domains demonstrating that these techniques can substantially improve performance over methods with manual representations.
Download or read book Adaptive Representation for Policy Gradient written by Ujjwal Das Gupta and published by . This book was released on 2015 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt: Much of the focus on finding good representations in reinforcement learning has been on learning complex non-linear predictors of value. Methods like policy gradient, that do not learn a value function and instead directly represent policy, often need fewer parameters to learn good policies. However, they typically employ a fixed parametric representation that may not be sufficient for complex domains. This thesis introduces two algorithms which can learn an adaptive representation of policy: the Policy Tree algorithm, which learns a decision tree over different instantiations of a base policy, and the Policy Conjunction algorithm, which adds conjunctive features to any base policy that uses a linear feature representation. In both of these algorithms, policy gradient is used to grow the representation in a way that enables the maximum local increase in the expected return of the policy. Experiments show that these algorithms can choose genuinely helpful splits or features, and significantly improve upon the commonly used linear Gibbs softmax policy, which is chosen as the base policy.
Download or read book The Logic of Adaptive Behavior written by Martijn van Otterlo and published by IOS Press. This book was released on 2009 with total page 508 pages. Available in PDF, EPUB and Kindle. Book excerpt: Markov decision processes have become the de facto standard in modeling and solving sequential decision making problems under uncertainty. This book studies lifting Markov decision processes, reinforcement learning and dynamic programming to the first-order (or, relational) setting.
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 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 Theoretical and Practical Advances in Computer based Educational Measurement written by Bernard P. Veldkamp and published by Springer. This book was released on 2019-07-05 with total page 399 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book presents a large number of innovations in the world of operational testing. It brings together different but related areas and provides insight in their possibilities, their advantages and drawbacks. The book not only addresses improvements in the quality of educational measurement, innovations in (inter)national large scale assessments, but also several advances in psychometrics and improvements in computerized adaptive testing, and it also offers examples on the impact of new technology in assessment. Due to its nature, the book will appeal to a broad audience within the educational measurement community. It contributes to both theoretical knowledge and also pays attention to practical implementation of innovations in testing technology.
Download or read book Adaptive Learning Agents written by Matthew E. Taylor and published by Springer Science & Business Media. This book was released on 2010-03-24 with total page 149 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume constitutes the thoroughly refereed post-conference proceedings of the Second Workshop on Adaptive and Learning Agents, ALA 2009, held as part of the AAMAS 2009 conference in Budapest, Hungary, in May 2009. The 8 revised full papers presented were carefully reviewed and selected from numerous submissions. They cover a variety of themes: single and multi-agent reinforcement learning, the evolution and emergence of cooperation in agent systems, sensor networks and coordination in multi-resource job scheduling.
Download or read book Anticipatory Behavior in Adaptive Learning Systems written by Giovanni Pezzulo and published by Springer. This book was released on 2009-06-18 with total page 345 pages. Available in PDF, EPUB and Kindle. Book excerpt: Anticipatory behavior in adaptive learning systems continues attracting attention of researchers in many areas, including cognitive systems, neuroscience, psychology, and machine learning. This book constitutes the thoroughly refereed post-workshop proceedings of the 4th International Workshop on Anticipatory Behavior in Adaptive Learning Systems, ABiALS 2008, held in Munich, Germany, in June 2008, in collaboration with the six-monthly Meeting of euCognition 'The Role of Anticipation in Cognition'. The 18 revised full papers presented were carefully selected during two rounds of reviewing and improvement for inclusion in the book. The introductory chapter of this state-of-the-art survey not only provides an overview of the contributions included in this volume but also revisits the current available terminology on anticipatory behavior and relates it to the available system approaches. The papers are organized in topical sections on anticipation in psychology with focus on the ideomotor view, conceptualizations, anticipation and dynamical systems, computational modeling of psychological processes in the individual and social domains, behavioral and cognitive capabilities based on anticipation, and computational frameworks and algorithms for anticipation, and their evaluation.
Download or read book Deep Learning written by Ian Goodfellow and published by MIT Press. This book was released on 2016-11-10 with total page 801 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
Download or read book Adaptivity and Learning written by Reimer Kühn and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: Adaptivity and learning have in recent decades become a common concern of scientific disciplines. These issues have arisen in mathematics, physics, biology, informatics, economics, and other fields more or less simultaneously. The aim of this publication is the interdisciplinary discourse on the phenomenon of learning and adaptivity. Different perspectives are presented and compared to find fruitful concepts for the disciplines involved. The authors select problems showing representative traits concerning the frame up, the methods and the achievements rather than to present extended overviews.
Download or read book Adaptive Learning and Pattern Recognition Systems theory and applications written by Mendel and published by Academic Press. This book was released on 1970-02-28 with total page 461 pages. Available in PDF, EPUB and Kindle. Book excerpt: Adaptive, Learning, and Pattern Recognition Systems; theory and applications
Download or read book Anticipatory Behavior in Adaptive Learning Systems written by Martin V. Butz and published by Springer. This book was released on 2004-01-21 with total page 313 pages. Available in PDF, EPUB and Kindle. Book excerpt: The interdisciplinary topic of anticipation, attracting attention fromnbsp;computer scientists, psychologists, philosophers, neuroscientists, and biologists is a rather new and often misunderstood matter of research. This book attempts to establish anticipation as a research topic and encourage further research and development work. First, the book presents philosophical thoughts and concepts to stimulate the reader's concern about the topic. Fundamental cognitive psychology experiments then confirm the existence of anticipatory behavior in animals and humans and outline a first framework of anticipatory learning and behavior. Next, several distinctions and frameworks of anticipatory processes are discussed, including first implementations of these concepts. Finally, several anticipatory systems and studies on anticipatory behavior are presented.
Download or read book Soft Computing for Recognition Based on Biometrics written by Patricia Melin and published by Springer Science & Business Media. This book was released on 2010-09-20 with total page 449 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes bio-inspired models and applications of hybrid intelligent systems using soft computing techniques for image analysis and pattern recognition based on biometrics and other sources. Each section groups papers on a similar subject.
Download or read book Software Engineering Artificial Intelligence Networking and Parallel Distributed Computing 2010 written by Roger Lee and published by Springer Science & Business Media. This book was released on 2010-10-01 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt: th The purpose of the 11 Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD 2010) held on June 9 – 11, 2010 in London, United Kingdom was to bring together researchers and scientists, businessmen and entrepreneurs, teachers and students to discuss the numerous fields of computer science, and to share ideas and information in a meaningful way. Our conference officers selected the best 15 papers from those papers accepted for presentation at the conference in order to publish them in this volume. The papers were chosen based on review scores submitted by members of the program committee, and underwent further rounds of rigorous review. In Chapter 1, Cai Luyuan et al. Present a new method of shape decomposition based on a refined morphological shape decomposition process. In Chapter 2, Kazunori Iwata et al. propose a method for reducing the margin of error in effort and error prediction models for embedded software development projects using artificial neural networks (ANNs). In Chapter 3, Viliam Šimko et al. describe a model-driven tool that allows system code to be generated from use-cases in plain English. In Chapter 4, Abir Smiti and Zied Elouedi propose a Case Base Maintenance (CBM) method that uses machine learning techniques to preserve the maximum competence of a system. In Chapter 5, Shagufta Henna and Thomas Erlebach provide a simulation based analysis of some widely used broadcasting schemes within mobile ad hoc networks (MANETs) and propose adaptive extensions to an existing broadcasting algorithm.
Download or read book Computational Intelligence and Informatics written by Imre J. Rudas and published by Springer. This book was released on 2010-10-08 with total page 347 pages. Available in PDF, EPUB and Kindle. Book excerpt: The International Symposium of Hungarian Researchers on Computational Intel- th gence and Informatics celebrated its 10 edition in 2009. This volume contains a careful selection of papers that are based on and are extensions of corresponding l- tures presented at the jubilee conference. This annual Symposium was launched by Budapest Tech (previously Budapest Polytechnic) and by the Hungarian Fuzzy Association in 2000, with the aim to bring together Hungarian speaking researchers working on computational intelligence and related topics from all over the world, but with special emphasis on the Central Eu- pean Region. th The Symposium of the 10 jubilee anniversary contained 70 reviewed papers. The growing interests, the enthusiasm of the participants have proved that the Symposium has become an internationally recognized scientific event providing a good platform for the annual meeting of Hungarian researchers. The main subject area called Computational Intelligence includes diverse topics. Therefore, we offer snapshots rather than a full coverage of a small particular subject to the interested reader. This principle is also supported by the common national root of the authors. The book begins with Information Systems and Communication. This part contains papers on graphs of grammars, software and hardware solution for Mojette transf- mation, statistical intrusion detection, congestion forecast, and 3D-based internet communication and control.
