Deep Learning for Robot Perception and Cognition
Deep Learning for Robot Perception and Cognition

Author: Alexandros Iosifidis

Publisher: Academic Press

Published: 2022-02-04

Total Pages: 638

ISBN-13: 0323885721

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Deep Learning for Robot Perception and Cognition introduces a broad range of topics and methods in deep learning for robot perception and cognition together with end-to-end methodologies. The book provides the conceptual and mathematical background needed for approaching a large number of robot perception and cognition tasks from an end-to-end learning point-of-view. The book is suitable for students, university and industry researchers and practitioners in Robotic Vision, Intelligent Control, Mechatronics, Deep Learning, Robotic Perception and Cognition tasks. - Presents deep learning principles and methodologies - Explains the principles of applying end-to-end learning in robotics applications - Presents how to design and train deep learning models - Shows how to apply deep learning in robot vision tasks such as object recognition, image classification, video analysis, and more - Uses robotic simulation environments for training deep learning models - Applies deep learning methods for different tasks ranging from planning and navigation to biosignal analysis

Deep Reinforcement Learning
Deep Reinforcement Learning

Author: Mohit Sewak

Publisher: Springer

Published: 2019-06-27

Total Pages: 215

ISBN-13: 9811382859

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This book starts by presenting the basics of reinforcement learning using highly intuitive and easy-to-understand examples and applications, and then introduces the cutting-edge research advances that make reinforcement learning capable of out-performing most state-of-art systems, and even humans in a number of applications. The book not only equips readers with an understanding of multiple advanced and innovative algorithms, but also prepares them to implement systems such as those created by Google Deep Mind in actual code. This book is intended for readers who want to both understand and apply advanced concepts in a field that combines the best of two worlds – deep learning and reinforcement learning – to tap the potential of ‘advanced artificial intelligence’ for creating real-world applications and game-winning algorithms.

Reinforcement Learning, second edition
Reinforcement Learning, second edition

Author: Richard S. Sutton

Publisher: MIT Press

Published: 2018-11-13

Total Pages: 549

ISBN-13: 0262352702

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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.

Reinforcement Learning Algorithms: Analysis and Applications
Reinforcement Learning Algorithms: Analysis and Applications

Author: Boris Belousov

Publisher: Springer Nature

Published: 2021-01-02

Total Pages: 197

ISBN-13: 3030411885

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This book reviews research developments in diverse areas of reinforcement learning such as model-free actor-critic methods, model-based learning and control, information geometry of policy searches, reward design, and exploration in biology and the behavioral sciences. Special emphasis is placed on advanced ideas, algorithms, methods, and applications. The contributed papers gathered here grew out of a lecture course on reinforcement learning held by Prof. Jan Peters in the winter semester 2018/2019 at Technische Universität Darmstadt. The book is intended for reinforcement learning students and researchers with a firm grasp of linear algebra, statistics, and optimization. Nevertheless, all key concepts are introduced in each chapter, making the content self-contained and accessible to a broader audience.

An Introduction to Deep Reinforcement Learning
An Introduction to Deep Reinforcement Learning

Author: Vincent Francois-Lavet

Publisher: Foundations and Trends (R) in Machine Learning

Published: 2018-12-20

Total Pages: 156

ISBN-13: 9781680835380

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Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has recently been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine. Deep RL opens up many new applications in domains such as healthcare, robotics, smart grids, finance, and many more. This book provides the reader with a starting point for understanding the topic. Although written at a research level it provides a comprehensive and accessible introduction to deep reinforcement learning models, algorithms and techniques. Particular focus is on the aspects related to generalization and how deep RL can be used for practical applications. Written by recognized experts, this book is an important introduction to Deep Reinforcement Learning for practitioners, researchers and students alike.

Reinforcement Learning
Reinforcement Learning

Author: Marco Wiering

Publisher: Springer Science & Business Media

Published: 2012-03-05

Total Pages: 653

ISBN-13: 3642276458

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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.

Mathematics for Machine Learning
Mathematics for Machine Learning

Author: Marc Peter Deisenroth

Publisher: Cambridge University Press

Published: 2020-04-23

Total Pages: 392

ISBN-13: 1108569323

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The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.

Legged Robots that Balance
Legged Robots that Balance

Author: Marc H. Raibert

Publisher: MIT Press

Published: 1986

Total Pages: 254

ISBN-13: 9780262181174

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This book, by a leading authority on legged locomotion, presents exciting engineering and science, along with fascinating implications for theories of human motor control. It lays fundamental groundwork in legged locomotion, one of the least developed areas of robotics, addressing the possibility of building useful legged robots that run and balance. The book describes the study of physical machines that run and balance on just one leg, including analysis, computer simulation, and laboratory experiments. Contrary to expectations, it reveals that control of such machines is not particularly difficult. It describes how the principles of locomotion discovered with one leg can be extended to systems with several legs and reports preliminary experiments with a quadruped machine that runs using these principles. Raibert's work is unique in its emphasis on dynamics and active balance, aspects of the problem that have played a minor role in most previous work. His studies focus on the central issues of balance and dynamic control, while avoiding several problems that have dominated previous research on legged machines. Marc Raibert is Associate Professor of Computer Science and Robotics at Carnegie-Mellon University and on the editorial board of The MIT Press journal, Robotics Research. Legged Robots That Balanceis fifteenth in the Artificial Intelligence Series, edited by Patrick Winston and Michael Brady.

International Conference on Intelligent Emerging Methods of Artificial Intelligence & Cloud Computing
International Conference on Intelligent Emerging Methods of Artificial Intelligence & Cloud Computing

Author: Fausto Pedro García Márquez

Publisher: Springer Nature

Published: 2022-05-04

Total Pages: 582

ISBN-13: 3030929051

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This book consists of different accepted papers of the conference. Firstly, the artificial intelligence and its application-related topics are provided. Secondly, cloud computing and related topics are also provided. The book has been designed to help research organisations and business leaders from across industries to transform their organisations into AI-driven disruptors. The utility of the technology in the face of massive globally interconnected complexity is explored. The significant characteristics of IEMAICLOUD are the promotion of inevitable dialogue between scientists, researchers, engineers, corporate’s and scholar’s students to mitigate the gap between academia, industry and governmental ethics which has been fostered through keynote speeches, workshops, panel discussion and oral presentations by eminent researchers in relevant field. The industry personnel depict cutting-edge researches in artificial intelligence and cloud computing to convey academia regarding real-time scenario and practical findings. Conference has been well equipped with talks by industry experts on the state of the art in computer science, lectures by eminent scientists designed to inspire and inform presentations by innovative researchers coming from 20+ countries from Europe and abroad. There has been discussion-oriented sessions and networking breaks to enable collaborations. Papers consist abstract, result, discussions and conclusions by the help of different tables and diagrams.