Download or read book Continuous Homomorphisms and Leveraging Symmetries in Policy Gradient Algorithms for Markov Decision Processes written by Rosie Zhao and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Making meaningful abstractions is key to a reinforcement learning agent's ability to make sense of unstructured streams of data, explore intractable spaces, and generalize to new tasks. Symmetries within the environment can be used to make such abstractions; in this thesis, we focus on state abstractions and establish the theoretical foundations of Markov decision processes (MDPs) containing underlying symmetries in the state-action space. In particular, we focus on one definition of behavioural equivalence between states, and present three existing views of capturing such an equivalence: bisimulation relations, symmetry groups, and MDP homomorphisms. We show equivalence between the three views for countable state spaces. We also turn our attention to continuous MDP homomorphisms, establishing theoretical results for these maps on general continuous state and action spaces which previously have not been properly derived. These results include the value-equivalence property, which states that the value functions given a state-action pair in the original MDP are equal to that of their transformed state-action pair in the abstract MDP. Thus, we can introduce new reinforcement learning algorithms that use existing methods on the abstract MDP and ``lift'' the results to the original MDP. In this vein, we prove a result called the stochastic homomorphic policy gradient (HPG) theorem, which shows that the policy gradient on the original MDP can be calculated using the policy gradient on the abstract MDP under an appropriate lifting procedure. We demonstrate the stochastic HPG theorem's utility by proposing a deep reinforcement learning algorithm leveraging the MDP homomorphism on continuous control tasks"--

Download or read book Learning Representation and Control in Markov Decision Processes written by Sridhar Mahadevan and published by Now Publishers Inc. This book was released on 2009 with total page 185 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides a comprehensive survey of techniques to automatically construct basis functions or features for value function approximation in Markov decision processes and reinforcement learning.

Download or read book Markov Decision Processes written by Martin L. Puterman and published by John Wiley & Sons. This book was released on 2014-08-28 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "This text is unique in bringing together so many results hitherto found only in part in other texts and papers. . . . The text is fairly self-contained, inclusive of some basic mathematical results needed, and provides a rich diet of examples, applications, and exercises. The bibliographical material at the end of each chapter is excellent, not only from a historical perspective, but because it is valuable for researchers in acquiring a good perspective of the MDP research potential." —Zentralblatt fur Mathematik ". . . it is of great value to advanced-level students, researchers, and professional practitioners of this field to have now a complete volume (with more than 600 pages) devoted to this topic. . . . Markov Decision Processes: Discrete Stochastic Dynamic Programming represents an up-to-date, unified, and rigorous treatment of theoretical and computational aspects of discrete-time Markov decision processes." —Journal of the American Statistical Association

Download or read book The Koopman Operator in Systems and Control written by Alexandre Mauroy and published by Springer Nature. This book was released on 2020-02-22 with total page 568 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a broad overview of state-of-the-art research at the intersection of the Koopman operator theory and control theory. It also reviews novel theoretical results obtained and efficient numerical methods developed within the framework of Koopman operator theory. The contributions discuss the latest findings and techniques in several areas of control theory, including model predictive control, optimal control, observer design, systems identification and structural analysis of controlled systems, addressing both theoretical and numerical aspects and presenting open research directions, as well as detailed numerical schemes and data-driven methods. Each contribution addresses a specific problem. After a brief introduction of the Koopman operator framework, including basic notions and definitions, the book explores numerical methods, such as the dynamic mode decomposition (DMD) algorithm and Arnoldi-based methods, which are used to represent the operator in a finite-dimensional basis and to compute its spectral properties from data. The main body of the book is divided into three parts: theoretical results and numerical techniques for observer design, synthesis analysis, stability analysis, parameter estimation, and identification; data-driven techniques based on DMD, which extract the spectral properties of the Koopman operator from data for the structural analysis of controlled systems; and Koopman operator techniques with specific applications in systems and control, which range from heat transfer analysis to robot control. A useful reference resource on the Koopman operator theory for control theorists and practitioners, the book is also of interest to graduate students, researchers, and engineers looking for an introduction to a novel and comprehensive approach to systems and control, from pure theory to data-driven methods.

Download or read book Representation Learning for Natural Language Processing written by Zhiyuan Liu and published by Springer Nature. This book was released on 2020-07-03 with total page 319 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing.

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 Handbook of Research on Machine Learning Applications and Trends Algorithms Methods and Techniques written by Olivas, Emilio Soria and published by IGI Global. This book was released on 2009-08-31 with total page 852 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book investiges machine learning (ML), one of the most fruitful fields of current research, both in the proposal of new techniques and theoretic algorithms and in their application to real-life problems"--Provided by publisher.

Download or read book Geographic Knowledge Graph Summarization written by B. Yan and published by IOS Press. This book was released on 2019-08-08 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt: Geographic knowledge graphs can have an important role in delivering interoperability, accessibility and the demands of conceptualization in geographic information science (GIS). However, the massive amount of accompanying information and the enormous diversity of geographic knowledge graphs limits their applicability and hinders the widespread adoption of this useful structured knowledge. This book, Geographic Knowledge Graph Summarization, focuses on the ways in which geographic knowledge graphs can be digested and summarized. Such a summarization would relieve the burden of information overload for end users and reduce data storage, as well as speeding up queries and eliminating ‘noise’. The book introduces the general concept of geospatial inductive bias and explains the different ways in which this idea can be used in the summarization of geographic knowledge graphs. The book breaks up the task of summarization into separate but related components, and after an introduction and a brief overview of concepts and theories, Chapters 3, 4 and 5 explore hierarchical place type structure, multimedia leaf nodes, and general relation and entity components respectively. Chapter 6 presents a spatial knowledge map interface which illustrates the effectiveness of summarization. The book integrates top-down knowledge engineering and bottom-up knowledge learning methods, and will do much to promote awareness of this fascinating area and related issues.

Download or read book Adaptive and Natural Computing Algorithms written by Andrej Dobnikar and published by Springer. This book was released on 2011-04-09 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set LNCS 6593 and 6594 constitutes the refereed proceedings of the 10th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2010, held in Ljubljana, Slovenia, in April 2010. The 83 revised full papers presented were carefully reviewed and selected from a total of 144 submissions. The first volume includes 42 papers and a plenary lecture and is organized in topical sections on neural networks and evolutionary computation.

Download or read book Knowledge Graphs written by Aidan Hogan and published by Morgan & Claypool Publishers. This book was released on 2021-11-08 with total page 257 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive and accessible introduction to knowledge graphs, which have recently garnered notable attention from both industry and academia. Knowledge graphs are founded on the principle of applying a graph-based abstraction to data, and are now broadly deployed in scenarios that require integrating and extracting value from multiple, diverse sources of data at large scale. The book defines knowledge graphs and provides a high-level overview of how they are used. It presents and contrasts popular graph models that are commonly used to represent data as graphs, and the languages by which they can be queried before describing how the resulting data graph can be enhanced with notions of schema, identity, and context. The book discusses how ontologies and rules can be used to encode knowledge as well as how inductive techniques—based on statistics, graph analytics, machine learning, etc.—can be used to encode and extract knowledge. It covers techniques for the creation, enrichment, assessment, and refinement of knowledge graphs and surveys recent open and enterprise knowledge graphs and the industries or applications within which they have been most widely adopted. The book closes by discussing the current limitations and future directions along which knowledge graphs are likely to evolve. This book is aimed at students, researchers, and practitioners who wish to learn more about knowledge graphs and how they facilitate extracting value from diverse data at large scale. To make the book accessible for newcomers, running examples and graphical notation are used throughout. Formal definitions and extensive references are also provided for those who opt to delve more deeply into specific topics.

Download or read book Machine Learners written by Adrian Mackenzie and published by MIT Press. This book was released on 2017-11-16 with total page 269 pages. Available in PDF, EPUB and Kindle. Book excerpt: If machine learning transforms the nature of knowledge, does it also transform the practice of critical thought? Machine learning—programming computers to learn from data—has spread across scientific disciplines, media, entertainment, and government. Medical research, autonomous vehicles, credit transaction processing, computer gaming, recommendation systems, finance, surveillance, and robotics use machine learning. Machine learning devices (sometimes understood as scientific models, sometimes as operational algorithms) anchor the field of data science. They have also become mundane mechanisms deeply embedded in a variety of systems and gadgets. In contexts from the everyday to the esoteric, machine learning is said to transform the nature of knowledge. In this book, Adrian Mackenzie investigates whether machine learning also transforms the practice of critical thinking. Mackenzie focuses on machine learners—either humans and machines or human-machine relations—situated among settings, data, and devices. The settings range from fMRI to Facebook; the data anything from cat images to DNA sequences; the devices include neural networks, support vector machines, and decision trees. He examines specific learning algorithms—writing code and writing about code—and develops an archaeology of operations that, following Foucault, views machine learning as a form of knowledge production and a strategy of power. Exploring layers of abstraction, data infrastructures, coding practices, diagrams, mathematical formalisms, and the social organization of machine learning, Mackenzie traces the mostly invisible architecture of one of the central zones of contemporary technological cultures. Mackenzie's account of machine learning locates places in which a sense of agency can take root. His archaeology of the operational formation of machine learning does not unearth the footprint of a strategic monolith but reveals the local tributaries of force that feed into the generalization and plurality of the field.

Download or read book Algebra and Geometry written by Alan F. Beardon and published by Cambridge University Press. This book was released on 2005-05-12 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: Describing two cornerstones of mathematics, this basic textbook presents a unified approach to algebra and geometry. It covers the ideas of complex numbers, scalar and vector products, determinants, linear algebra, group theory, permutation groups, symmetry groups and aspects of geometry including groups of isometries, rotations, and spherical geometry. The book emphasises the interactions between topics, and each topic is constantly illustrated by using it to describe and discuss the others. Many ideas are developed gradually, with each aspect presented at a time when its importance becomes clearer. To aid in this, the text is divided into short chapters, each with exercises at the end. The related website features an HTML version of the book, extra text at higher and lower levels, and more exercises and examples. It also links to an electronic maths thesaurus, giving definitions, examples and links both to the book and to external sources.

Download or read book Black Holes in Higher Dimensions written by Gary T. Horowitz and published by Cambridge University Press. This book was released on 2012-04-19 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first book devoted to black holes in more than four dimensions, for graduate students and researchers.

Download or read book From Animals to Animats 3 written by Dave Cliff and published by MIT Press. This book was released on 1994 with total page 526 pages. Available in PDF, EPUB and Kindle. Book excerpt: August 8-12, 1994, Brighton, England From Animals to Animats 3 brings together research intended to advance the fron tier of an exciting new approach to understanding intelligence. The contributors represent a broad range of interests from artificial intelligence and robotics to ethology and the neurosciences. Unifying these approaches is the notion of "animat" -- an artificial animal, either simulated by a computer or embodied in a robot, which must survive and adapt in progressively more challenging environments. The 58 contributions focus particularly on well-defined models, computer simulations, and built robots in order to help characterize and compare various principles and architectures capable of inducing adaptive behavior in real or artificial animals. Topics include: - Individual and collective behavior. - Neural correlates of behavior. - Perception and motor control. - Motivation and emotion. - Action selection and behavioral sequences. - Ontogeny, learning, and evolution. - Internal world models and cognitive processes. - Applied adaptive behavior. - Autonomous robots. - Heirarchical and parallel organizations. - Emergent structures and behaviors. - Problem solving and planning. - Goal-directed behavior. - Neural networks and evolutionary computation. - Characterization of environments. A Bradford Book

Download or read book Numerical Computations Theory and Algorithms written by Yaroslav D. Sergeyev and published by Springer Nature. This book was released on 2020-02-13 with total page 550 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set LNCS 11973 and 11974 constitute revised selected papers from the Third International Conference on Numerical Computations: Theory and Algorithms, NUMTA 2019, held in Crotone, Italy, in June 2019. This volume, LNCS 11974, consists of 19 full and 32 short papers chosen among regular papers presented at the the Conference including also the paper of the winner (Lorenzo Fiaschi, Pisa, Italy) of The Springer Young Researcher Prize for the best NUMTA 2019 presentation made by a young scientist. The papers in part II explore the advanced research developments in such interconnected fields as local and global optimization, machine learning, approximation, and differential equations. A special focus is given to advanced ideas related to methods and applications using emerging computational paradigms.

Download or read book Rate Distortion Theory written by Toby Berger and published by . This book was released on 1971 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Topics at the Frontier of Statistics and Network Analysis written by Eric D. Kolaczyk and published by Cambridge University Press. This book was released on 2017-08-10 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: This snapshot of the current frontier of statistics and network analysis focuses on the foundational topics of modeling, sampling, and design. Primarily for graduate students and researchers in statistics and closely related fields, emphasis is not only on what has been done, but on what remains to be done.