Action Selection Methods Using Reinforcement Learning

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Action Selection Methods Using Reinforcement Learning

Author : Mark Humphrys
Publisher :
ISBN 13 :
Total Pages : 204 pages
Book Rating : 4.:/5 (321 download)

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Book Synopsis Action Selection Methods Using Reinforcement Learning by : Mark Humphrys

Download or read book Action Selection Methods Using Reinforcement Learning written by Mark Humphrys and published by . This book was released on 1997 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: "The Action Selection problem is the problem of run- time choice between conflicting and heterogenous goals, a central problem in the simulation of whole creatures (as opposed to the solution of isolated uninterrupted tasks). This thesis argues that Reinforcement Learning has been overlooked in the solution of the Action Selection problem. Considering a decentralised model of mind, with internal tension and competition between selfish behaviors, this thesis introduces an algorithm called 'W-learning', whereby different parts of the mind modify their behavior based on whether or not they are succeeding in getting the body to execute their actions. This thesis sets W-learning in context among the different ways of exploiting Reinforcement Learning numbers for the purposes of Action Selection. It is a 'Minimize the Worst Unhappiness' strategy. The different methods are tested and their strengths and weaknesses analysed in an artificial world."

Reinforcement Learning, second edition

Author : Richard S. Sutton
Publisher : MIT Press
ISBN 13 : 0262352702
Total Pages : 549 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis Reinforcement Learning, second edition by : Richard S. Sutton

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.

Modelling Natural Action Selection

Author : Anil K. Seth
Publisher : Cambridge University Press
ISBN 13 : 113950097X
Total Pages : 569 pages
Book Rating : 4.1/5 (395 download)

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Book Synopsis Modelling Natural Action Selection by : Anil K. Seth

Download or read book Modelling Natural Action Selection written by Anil K. Seth and published by Cambridge University Press. This book was released on 2011-11-10 with total page 569 pages. Available in PDF, EPUB and Kindle. Book excerpt: Action selection is the task of doing the right thing at the right time. It requires the assessment of available alternatives, executing those most appropriate, and resolving conflicts among competing goals and possibilities. Using advanced computational modelling, this book explores cutting-edge research into action selection in nature from a wide range of disciplines, from neuroscience to behavioural ecology, and even political science. It delivers new insights into both detailed and systems-level attributes of natural intelligence and demonstrates advances in methodological practice. Contributions from leading researchers cover issues including whether biological action selection is optimal, neural substrates for action selection in the vertebrate brain, perceptual selection in decision making, and interactions between group and individual action selection. This first integrated review of action selection in nature contains a balance of review and original research material, consolidating current knowledge into a valuable reference for researchers while illustrating potential paths for future studies.

From Animals to Animats 4

Author : Pattie Maes
Publisher : MIT Press
ISBN 13 : 9780262631785
Total Pages : 664 pages
Book Rating : 4.6/5 (317 download)

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Book Synopsis From Animals to Animats 4 by : Pattie Maes

Download or read book From Animals to Animats 4 written by Pattie Maes and published by MIT Press. This book was released on 1996 with total page 664 pages. Available in PDF, EPUB and Kindle. Book excerpt: From Animals to Animats 4 brings together the latest research at the frontier of an exciting new approach to understanding intelligence.

Design of Experiments for Reinforcement Learning

Author : Christopher Gatti
Publisher : Springer
ISBN 13 : 3319121979
Total Pages : 196 pages
Book Rating : 4.3/5 (191 download)

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Book Synopsis Design of Experiments for Reinforcement Learning by : Christopher Gatti

Download or read book Design of Experiments for Reinforcement Learning written by Christopher Gatti and published by Springer. This book was released on 2014-11-22 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis takes an empirical approach to understanding of the behavior and interactions between the two main components of reinforcement learning: the learning algorithm and the functional representation of learned knowledge. The author approaches these entities using design of experiments not commonly employed to study machine learning methods. The results outlined in this work provide insight as to what enables and what has an effect on successful reinforcement learning implementations so that this learning method can be applied to more challenging problems.

Bayesian Reinforcement Learning

Author : Mohammad Ghavamzadeh
Publisher :
ISBN 13 : 9781680830880
Total Pages : 146 pages
Book Rating : 4.8/5 (38 download)

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Book Synopsis Bayesian Reinforcement Learning by : Mohammad Ghavamzadeh

Download or read book Bayesian Reinforcement Learning written by Mohammad Ghavamzadeh and published by . This book was released on 2015-11-18 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt: Bayesian methods for machine learning have been widely investigated, yielding principled methods for incorporating prior information into inference algorithms. This monograph provides the reader with an in-depth review of the role of Bayesian methods for the reinforcement learning (RL) paradigm. The major incentives for incorporating Bayesian reasoning in RL are that it provides an elegant approach to action-selection (exploration/exploitation) as a function of the uncertainty in learning, and it provides a machinery to incorporate prior knowledge into the algorithms. Bayesian Reinforcement Learning: A Survey first discusses models and methods for Bayesian inference in the simple single-step Bandit model. It then reviews the extensive recent literature on Bayesian methods for model-based RL, where prior information can be expressed on the parameters of the Markov model. It also presents Bayesian methods for model-free RL, where priors are expressed over the value function or policy class. Bayesian Reinforcement Learning: A Survey is a comprehensive reference for students and researchers with an interest in Bayesian RL algorithms and their theoretical and empirical properties.

From Animals to Animats 7

Author : Bridget Hallam
Publisher : MIT Press
ISBN 13 : 9780262582179
Total Pages : 438 pages
Book Rating : 4.5/5 (821 download)

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Book Synopsis From Animals to Animats 7 by : Bridget Hallam

Download or read book From Animals to Animats 7 written by Bridget Hallam and published by MIT Press. This book was released on 2002 with total page 438 pages. Available in PDF, EPUB and Kindle. Book excerpt: Proceedings of the Seventh International Conference on Simulation of Adaptive Behavior

Neural Information Processing

Author : Bao-Liang Lu
Publisher : Springer
ISBN 13 : 3642249655
Total Pages : 810 pages
Book Rating : 4.6/5 (422 download)

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Book Synopsis Neural Information Processing by : Bao-Liang Lu

Download or read book Neural Information Processing written by Bao-Liang Lu and published by Springer. This book was released on 2011-11-12 with total page 810 pages. Available in PDF, EPUB and Kindle. Book excerpt: The three volume set LNCS 7062, LNCS 7063, and LNCS 7064 constitutes the proceedings of the 18th International Conference on Neural Information Processing, ICONIP 2011, held in Shanghai, China, in November 2011. The 262 regular session papers presented were carefully reviewed and selected from numerous submissions. The papers of part I are organized in topical sections on perception, emotion and development, bioinformatics, biologically inspired vision and recognition, bio-medical data analysis, brain signal processing, brain-computer interfaces, brain-like systems, brain-realistic models for learning, memory and embodied cognition, Clifford algebraic neural networks, combining multiple learners, computational advances in bioinformatics, and computational-intelligent human computer interaction. The second volume is structured in topical sections on cybersecurity and data mining workshop, data mining and knowledge doscovery, evolutionary design and optimisation, graphical models, human-originated data analysis and implementation, information retrieval, integrating multiple nature-inspired approaches, kernel methods and support vector machines, and learning and memory. The third volume contains all the contributions connected with multi-agent systems, natural language processing and intelligent Web information processing, neural encoding and decoding, neural network models, neuromorphic hardware and implementations, object recognition, visual perception modelling, and advances in computational intelligence methods based pattern recognition.

Machine Learning Methods for High-Level Cognitive Capabilities in Robotics

Author : Emre Ugur
Publisher : Frontiers Media SA
ISBN 13 : 288963261X
Total Pages : 149 pages
Book Rating : 4.8/5 (896 download)

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Book Synopsis Machine Learning Methods for High-Level Cognitive Capabilities in Robotics by : Emre Ugur

Download or read book Machine Learning Methods for High-Level Cognitive Capabilities in Robotics written by Emre Ugur and published by Frontiers Media SA. This book was released on 2019-12-24 with total page 149 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Proceedings of 2022 International Conference on Autonomous Unmanned Systems (ICAUS 2022)

Author : Wenxing Fu
Publisher : Springer Nature
ISBN 13 : 981990479X
Total Pages : 3985 pages
Book Rating : 4.8/5 (199 download)

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Book Synopsis Proceedings of 2022 International Conference on Autonomous Unmanned Systems (ICAUS 2022) by : Wenxing Fu

Download or read book Proceedings of 2022 International Conference on Autonomous Unmanned Systems (ICAUS 2022) written by Wenxing Fu and published by Springer Nature. This book was released on 2023-03-10 with total page 3985 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book includes original, peer-reviewed research papers from the ICAUS 2022, which offers a unique and interesting platform for scientists, engineers and practitioners throughout the world to present and share their most recent research and innovative ideas. The aim of the ICAUS 2022 is to stimulate researchers active in the areas pertinent to intelligent unmanned systems. The topics covered include but are not limited to Unmanned Aerial/Ground/Surface/Underwater Systems, Robotic, Autonomous Control/Navigation and Positioning/ Architecture, Energy and Task Planning and Effectiveness Evaluation Technologies, Artificial Intelligence Algorithm/Bionic Technology and Its Application in Unmanned Systems. The papers showcased here share the latest findings on Unmanned Systems, Robotics, Automation, Intelligent Systems, Control Systems, Integrated Networks, Modeling and Simulation. It makes the book a valuable asset for researchers, engineers, and university students alike.

EVOLVING AN INTEGRAL ECOLOGY OF MIND

Author : Chris Lucas
Publisher : Infinite Study
ISBN 13 :
Total Pages : 17 pages
Book Rating : 4./5 ( download)

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Book Synopsis EVOLVING AN INTEGRAL ECOLOGY OF MIND by : Chris Lucas

Download or read book EVOLVING AN INTEGRAL ECOLOGY OF MIND written by Chris Lucas and published by Infinite Study. This book was released on with total page 17 pages. Available in PDF, EPUB and Kindle. Book excerpt: A deliberation upon the possibility of generating a comprehensive view of ‘mind as a whole’ by integrating biology, psychology and sociology, and considering ‘Mind’ as a dynamical interplay between values existing over many levels and scales of complex systems.

Handbook of Reinforcement Learning and Control

Author : Kyriakos G. Vamvoudakis
Publisher : Springer Nature
ISBN 13 : 3030609901
Total Pages : 833 pages
Book Rating : 4.0/5 (36 download)

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Book Synopsis Handbook of Reinforcement Learning and Control by : Kyriakos G. Vamvoudakis

Download or read book Handbook of Reinforcement Learning and Control written by Kyriakos G. Vamvoudakis and published by Springer Nature. This book was released on 2021-06-23 with total page 833 pages. Available in PDF, EPUB and Kindle. Book excerpt: This handbook presents state-of-the-art research in reinforcement learning, focusing on its applications in the control and game theory of dynamic systems and future directions for related research and technology. The contributions gathered in this book deal with challenges faced when using learning and adaptation methods to solve academic and industrial problems, such as optimization in dynamic environments with single and multiple agents, convergence and performance analysis, and online implementation. They explore means by which these difficulties can be solved, and cover a wide range of related topics including: deep learning; artificial intelligence; applications of game theory; mixed modality learning; and multi-agent reinforcement learning. Practicing engineers and scholars in the field of machine learning, game theory, and autonomous control will find the Handbook of Reinforcement Learning and Control to be thought-provoking, instructive and informative.

Rapid Automation: Concepts, Methodologies, Tools, and Applications

Author : Management Association, Information Resources
Publisher : IGI Global
ISBN 13 : 1522580611
Total Pages : 1597 pages
Book Rating : 4.5/5 (225 download)

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Book Synopsis Rapid Automation: Concepts, Methodologies, Tools, and Applications by : Management Association, Information Resources

Download or read book Rapid Automation: Concepts, Methodologies, Tools, and Applications written by Management Association, Information Resources and published by IGI Global. This book was released on 2019-03-01 with total page 1597 pages. Available in PDF, EPUB and Kindle. Book excerpt: Through expanded intelligence, the use of robotics has fundamentally transformed the business industry. Providing successful techniques in robotic design allows for increased autonomous mobility, which leads to a greater productivity and production level. Rapid Automation: Concepts, Methodologies, Tools, and Applications provides innovative insights into the state-of-the-art technologies in the design and development of robotics and their real-world applications in business processes. Highlighting a range of topics such as workflow automation tools, human-computer interaction, and swarm robotics, this multi-volume book is ideally designed for computer engineers, business managers, robotic developers, business and IT professionals, academicians, and researchers.

From Animals to Animats 8

Author : Stefan Schaal
Publisher : MIT Press
ISBN 13 : 9780262693417
Total Pages : 554 pages
Book Rating : 4.6/5 (934 download)

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Book Synopsis From Animals to Animats 8 by : Stefan Schaal

Download or read book From Animals to Animats 8 written by Stefan Schaal and published by MIT Press. This book was released on 2004 with total page 554 pages. Available in PDF, EPUB and Kindle. Book excerpt: New research on the adaptive behavior of natural and synthetic agents.

Advances in Intelligent Signal Processing and Data Mining

Author : Petia Georgieva
Publisher : Springer
ISBN 13 : 3642286968
Total Pages : 359 pages
Book Rating : 4.6/5 (422 download)

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Book Synopsis Advances in Intelligent Signal Processing and Data Mining by : Petia Georgieva

Download or read book Advances in Intelligent Signal Processing and Data Mining written by Petia Georgieva and published by Springer. This book was released on 2012-07-27 with total page 359 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book presents some of the most efficient statistical and deterministic methods for information processing and applications in order to extract targeted information and find hidden patterns. The techniques presented range from Bayesian approaches and their variations such as sequential Monte Carlo methods, Markov Chain Monte Carlo filters, Rao Blackwellization, to the biologically inspired paradigm of Neural Networks and decomposition techniques such as Empirical Mode Decomposition, Independent Component Analysis and Singular Spectrum Analysis. The book is directed to the research students, professors, researchers and practitioners interested in exploring the advanced techniques in intelligent signal processing and data mining paradigms.

Advances in Reinforcement Learning

Author : Abdelhamid Mellouk
Publisher : BoD – Books on Demand
ISBN 13 : 9533073691
Total Pages : 486 pages
Book Rating : 4.5/5 (33 download)

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Book Synopsis Advances in Reinforcement Learning by : Abdelhamid Mellouk

Download or read book Advances in Reinforcement Learning written by Abdelhamid Mellouk and published by BoD – Books on Demand. This book was released on 2011-01-14 with total page 486 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reinforcement Learning (RL) is a very dynamic area in terms of theory and application. This book brings together many different aspects of the current research on several fields associated to RL which has been growing rapidly, producing a wide variety of learning algorithms for different applications. Based on 24 Chapters, it covers a very broad variety of topics in RL and their application in autonomous systems. A set of chapters in this book provide a general overview of RL while other chapters focus mostly on the applications of RL paradigms: Game Theory, Multi-Agent Theory, Robotic, Networking Technologies, Vehicular Navigation, Medicine and Industrial Logistic.

Learning and Robust Control in Quantum Technology

Author : Daoyi Dong
Publisher : Springer Nature
ISBN 13 : 3031202457
Total Pages : 265 pages
Book Rating : 4.0/5 (312 download)

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Book Synopsis Learning and Robust Control in Quantum Technology by : Daoyi Dong

Download or read book Learning and Robust Control in Quantum Technology written by Daoyi Dong and published by Springer Nature. This book was released on 2023-03-24 with total page 265 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph provides a state-of-the-art treatment of learning and robust control in quantum technology. It presents a systematic investigation of control design and algorithm realisation for several classes of quantum systems using control-theoretic tools and machine-learning methods. The approaches rely heavily on examples and the authors cover: sliding mode control of quantum systems; control and classification of inhomogeneous quantum ensembles using sampling-based learning control; robust and optimal control design using machine-learning methods; robust stability of quantum systems; and H∞ and fault-tolerant control of quantum systems. Both theoretical algorithm design and potential practical applications are considered. Methods for enhancing robustness of performance are developed in the context of quantum state preparation, quantum gate construction, and ultrafast control of molecules. Researchers and graduates studying systems and control theory, quantum control, and quantum engineering, especially from backgrounds in electrical engineering, applied mathematics and quantum information will find Learning and Robust Control in Quantum Technology to be a valuable reference for the investigation of learning and robust control of quantum systems. The material contained in this book will also interest chemists and physicists working on chemical physics, quantum optics, and quantum information technology.