Deep Reinforcement Learning With Guaranteed Performance
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Deep Reinforcement Learning With Guaranteed Performance
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Author : Yinyan Zhang
language : en
Publisher: Springer Nature
Release Date : 2019-11-09
Deep Reinforcement Learning With Guaranteed Performance written by Yinyan Zhang and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-09 with Technology & Engineering categories.
This book discusses methods and algorithms for the near-optimal adaptive control of nonlinear systems, including the corresponding theoretical analysis and simulative examples, and presents two innovative methods for the redundancy resolution of redundant manipulators with consideration of parameter uncertainty and periodic disturbances. It also reports on a series of systematic investigations on a near-optimal adaptive control method based on the Taylor expansion, neural networks, estimator design approaches, and the idea of sliding mode control, focusing on the tracking control problem of nonlinear systems under different scenarios. The book culminates with a presentation of two new redundancy resolution methods; one addresses adaptive kinematic control of redundant manipulators, and the other centers on the effect of periodic input disturbance on redundancy resolution. Each self-contained chapter is clearly written, making the book accessible to graduate students as well as academic and industrial researchers in the fields of adaptive and optimal control, robotics, and dynamic neural networks.
Reinforcement Learning Unlocked A Deep Dive Into Advanced Methodologies
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Author : Adam Jones
language : en
Publisher: Walzone Press
Release Date : 2025-01-04
Reinforcement Learning Unlocked A Deep Dive Into Advanced Methodologies written by Adam Jones and has been published by Walzone Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-04 with Computers categories.
"Reinforcement Learning Unlocked: A Deep Dive into Advanced Methodologies" is an essential resource for those eager to enhance their mastery in reinforcement learning (RL). This in-depth book comprehensively covers the foundational concepts and theoretical aspects of RL, progressing to the latest techniques and transformative applications shaping the future of artificial intelligence. Delve into chapters that unravel the intricacies of RL, providing detailed exploration of model-based and model-free methodologies, deep reinforcement learning, policy gradient techniques, advanced exploration methods, multi-agent systems, and the empowering capabilities of transfer and meta-learning. Ideal for graduate students, researchers, and industry professionals, this book offers a thorough dive into the strategies that drive intelligent decision-making in dynamic, uncertain environments. With lucid explanations, algorithmic insights, and practical illustrations, readers will gain a nuanced understanding of constructing sophisticated RL models adept at tackling real-world challenges. Embark on a journey with "Reinforcement Learning Unlocked: A Deep Dive into Advanced Methodologies" and unlock the potential to innovate and excel in the ever-evolving field of AI.
Deep Learning For Unmanned Systems
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Author : Anis Koubaa
language : en
Publisher: Springer Nature
Release Date : 2021-10-01
Deep Learning For Unmanned Systems written by Anis Koubaa and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-10-01 with Technology & Engineering categories.
This book is used at the graduate or advanced undergraduate level and many others. Manned and unmanned ground, aerial and marine vehicles enable many promising and revolutionary civilian and military applications that will change our life in the near future. These applications include, but are not limited to, surveillance, search and rescue, environment monitoring, infrastructure monitoring, self-driving cars, contactless last-mile delivery vehicles, autonomous ships, precision agriculture and transmission line inspection to name just a few. These vehicles will benefit from advances of deep learning as a subfield of machine learning able to endow these vehicles with different capability such as perception, situation awareness, planning and intelligent control. Deep learning models also have the ability to generate actionable insights into the complex structures of large data sets. In recent years, deep learning research has received an increasing amount of attention from researchers in academia, government laboratories and industry. These research activities have borne some fruit in tackling some of the challenging problems of manned and unmanned ground, aerial and marine vehicles that are still open. Moreover, deep learning methods have been recently actively developed in other areas of machine learning, including reinforcement training and transfer/meta-learning, whereas standard, deep learning methods such as recent neural network (RNN) and coevolutionary neural networks (CNN). The book is primarily meant for researchers from academia and industry, who are working on in the research areas such as engineering, control engineering, robotics, mechatronics, biomedical engineering, mechanical engineering and computer science. The book chapters deal with the recent research problems in the areas of reinforcement learning-based control of UAVs and deep learning for unmanned aerial systems (UAS) The book chapters present various techniques of deep learning for robotic applications. The book chapters contain a good literature survey with a long list of references. The book chapters are well written with a good exposition of the research problem, methodology, block diagrams and mathematical techniques. The book chapters are lucidly illustrated with numerical examples and simulations. The book chapters discuss details of applications and future research areas.
Reinforcement Learning Second Edition
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Author : Richard S. Sutton
language : en
Publisher: MIT Press
Release Date : 2018-11-13
Reinforcement Learning Second Edition written by Richard S. Sutton and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-13 with Computers categories.
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.
A Companion To Applied Philosophy Of Ai
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Author : Martin Hähnel
language : en
Publisher: John Wiley & Sons
Release Date : 2025-08-12
A Companion To Applied Philosophy Of Ai written by Martin Hähnel and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-12 with Philosophy categories.
A comprehensive guide to AI's ethical, epistemological, and legal impacts through applied philosophy Inartificial intelligence (AI) influences nearly every aspect of society. A Companion to Applied Philosophy of AI provides a critical philosophical framework for understanding and addressing its complexities. Edited by Martin Hähnel and Regina Müller, this volume explores AI's practical implications in epistemology, ethics, politics, and law. Moving beyond a narrow ethical perspective, the authors advocate for a multi-faceted approach that synthesizes diverse disciplines and perspectives, offering readers a nuanced and integrative understanding of AI's transformative role. The Companion explores a broad range of topics, from issues of transparency and expertise in AI-driven systems to discussions of ethical theories and their relevance to AI, such as consequentialism, deontology, and virtue ethics. Filling a significant gap in the current academic literature, this groundbreaking volume also addresses AI's broader social, political, and legal dimensions, equipping readers with practical frameworks to navigate this rapidly evolving field. Offering fresh and invaluable insights into the interplay between philosophical thought and technological innovation, A Companion to Applied Philosophy of AI: Features contributions from leading philosophers and interdisciplinary experts Offers a unique applied philosophy perspective on artificial intelligence Covers diverse topics including ethics, epistemology, politics, and law Encourages interdisciplinary dialogue to better understand AI's profound implications for humanity A Companion to Applied Philosophy of AI is ideal for undergraduate and graduate courses in applied philosophy, AI ethics, political theory, and legal philosophy. It is also a vital reference for those working in areas including AI policy, governance, and interdisciplinary research.
Machine Learning For Future Wireless Communications
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Author : Fa-Long Luo
language : en
Publisher: John Wiley & Sons
Release Date : 2020-02-10
Machine Learning For Future Wireless Communications written by Fa-Long Luo and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-02-10 with Technology & Engineering categories.
A comprehensive review to the theory, application and research of machine learning for future wireless communications In one single volume, Machine Learning for Future Wireless Communications provides a comprehensive and highly accessible treatment to the theory, applications and current research developments to the technology aspects related to machine learning for wireless communications and networks. The technology development of machine learning for wireless communications has grown explosively and is one of the biggest trends in related academic, research and industry communities. Deep neural networks-based machine learning technology is a promising tool to attack the big challenge in wireless communications and networks imposed by the increasing demands in terms of capacity, coverage, latency, efficiency flexibility, compatibility, quality of experience and silicon convergence. The author – a noted expert on the topic – covers a wide range of topics including system architecture and optimization, physical-layer and cross-layer processing, air interface and protocol design, beamforming and antenna configuration, network coding and slicing, cell acquisition and handover, scheduling and rate adaption, radio access control, smart proactive caching and adaptive resource allocations. Uniquely organized into three categories: Spectrum Intelligence, Transmission Intelligence and Network Intelligence, this important resource: Offers a comprehensive review of the theory, applications and current developments of machine learning for wireless communications and networks Covers a range of topics from architecture and optimization to adaptive resource allocations Reviews state-of-the-art machine learning based solutions for network coverage Includes an overview of the applications of machine learning algorithms in future wireless networks Explores flexible backhaul and front-haul, cross-layer optimization and coding, full-duplex radio, digital front-end (DFE) and radio-frequency (RF) processing Written for professional engineers, researchers, scientists, manufacturers, network operators, software developers and graduate students, Machine Learning for Future Wireless Communications presents in 21 chapters a comprehensive review of the topic authored by an expert in the field.
Neural Information Processing
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Author : Long Cheng
language : en
Publisher: Springer
Release Date : 2018-12-03
Neural Information Processing written by Long Cheng and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-03 with Computers categories.
The seven-volume set of LNCS 11301-11307, constitutes the proceedings of the 25th International Conference on Neural Information Processing, ICONIP 2018, held in Siem Reap, Cambodia, in December 2018. The 401 full papers presented were carefully reviewed and selected from 575 submissions. The papers address the emerging topics of theoretical research, empirical studies, and applications of neural information processing techniques across different domains. The third volume, LNCS 11303, is organized in topical sections on embedded learning, transfer learning, reinforcement learning, and other learning approaches.
Proceedings Of The 11th International Conference On Advanced Intelligent Systems And Informatics Aisi 2025
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Author : Aboul Ella Hassanien
language : en
Publisher: Springer Nature
Release Date : 2025-02-20
Proceedings Of The 11th International Conference On Advanced Intelligent Systems And Informatics Aisi 2025 written by Aboul Ella Hassanien and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-20 with Computers categories.
This book includes recent research on intelligent systems and informatics. It constitutes the proceedings of the 11th International Conference on Advanced Intelligent Systems and Informatics. It presents scientific research on all aspects of informatics and intelligent systems including current research in informatics, machine and deep learning, real-time system, and business intelligence.
Distributed Computing And Artificial Intelligence Special Sessions I 20th International Conference
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Author : Rashid Mehmood
language : en
Publisher: Springer Nature
Release Date : 2023-07-25
Distributed Computing And Artificial Intelligence Special Sessions I 20th International Conference written by Rashid Mehmood and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-25 with Technology & Engineering categories.
The present book brings together experience, current work, and promising future trends associated with distributed computing, artificial intelligence, and their application in order to provide efficient solutions to real problems. DCAI 2023 is a forum to present applications of innovative techniques for studying and solving complex problems in artificial intelligence and computing areas. This year’s technical program presents both high quality and diversity, with contributions in well-established and evolving areas of research. Specifically, 108 papers were submitted, by authors from 31 different countries representing a truly “wide area network” of research activity. The DCAI’23 technical program has selected 50 full papers in the Special Sessions (ASET, AIMPM, AI4CS, CLIRAI, TECTONIC, PSO-ML, SmartFoF, IoTalentum) and, as in past editions, it will be special issues in ranked journals. This symposium is organized by the LASI and Centro Algoritmi of the University of Minho (Portugal). The authors like to thank all the contributing authors, the members of the Program Committee, National Associations (AEPIA, APPIA), and the sponsors (AIR Institute).
Advances In Neural Computation Machine Learning And Cognitive Research Vii
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Author : Boris Kryzhanovsky
language : en
Publisher: Springer Nature
Release Date : 2023-10-11
Advances In Neural Computation Machine Learning And Cognitive Research Vii written by Boris Kryzhanovsky and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-11 with Technology & Engineering categories.
This book describes new theories and applications of artificial neural networks, with a special focus on answering questions in neuroscience, biology and biophysics and cognitive research. It covers a wide range of methods and technologies, including deep neural networks, large-scale neural models, brain–computer interface, signal processing methods, as well as models of perception, studies on emotion recognition, self-organization and many more. The book includes both selected and invited papers presented at the XXV International Conference on Neuroinformatics, held on October 23-27, 2023, in Moscow, Russia.