Handbook of Reinforcement Learning and Control, Vamvoudakis Kyriakos G., Wan Yan, Lewis Frank L.
Автор: Sutton Richard S., Barto Andrew G. Название: Reinforcement Learning: An Introduction, 2 ed. ISBN: 0262039249 ISBN-13(EAN): 9780262039246 Издательство: MIT Press Рейтинг: Цена: 18850.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
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 for 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.
Описание: This book is a practical, developer-oriented introduction to deep reinforcement learning (RL). Explore the theoretical concepts of RL, before discovering how deep learning (DL) methods and tools are making it possible to solve more complex and challenging problems than ever before. Apply deep RL methods to training your agent to beat arcade ...
Описание: Shows how to avoid or correct typical behaviour problems, including jumping, barking, and lead-pulling. This title covers hand-feeding; crate and potty training; and basic cues - sit, stay, come here - as well as complex goals, such as bite inhibition and water safety.
Автор: Mindess, Sidney Название: Developments in the Formulation and Reinforcement of Concrete ISBN: 0081026161 ISBN-13(EAN): 9780081026168 Издательство: Elsevier Science Рейтинг: Цена: 38739.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
In the first edition of this book, Developments in the Formulation and Reinforcement of Concrete, a number of what are often referred to as "specialty" concretes were discussed. These concretes through specialty mix design offer high physical properties and enhanced performance. The information discussed in these chapters is still relevant today, but in the ten years since the first edition was published, a number of other key areas have become increasingly important in modern concrete technology. There is now much more emphasis on sustainability within the cement and concrete industries. This requires a greater emphasis and understanding of some of the major durability problems that the scientist or engineer may encounter, such as chloride corrosion of steel, and alkali-aggregate reactions. There is also increasing use of specifications involving explicit service life requirements (100 years being common), but this is a concept that is still widely misunderstood.
In this second edition of the book all material previously covered on specialty concretes is brought fully up-to-date taking into consideration latest developments and with the addition of new chapters on supplementary cementitious materials; mass concrete; the sustainably of concrete; service life prediction; limestone cements; corrosion of steel in concrete; alkali-aggregate reactions and concrete as a multiscale material the chapters will introduce the reader to some of the most important issues facing the concrete industry today. In keeping with the previous edition, the international team of contributors, are all at the cutting edge in their own areas of research and have wide industrial experience.
With its distinguished editor and international team of contributors, Developments in the Formulation and Reinforcement of Concrete, Second Edition is a standard reference for civil and structural engineers.
Summarizes a wealth of recent research on structural concrete including material microstructure, concrete types, variation and construction techniques
Emphasizes concrete mixture design and applications in civil and structural engineering
Reviews modern concrete materials to novel construction systems, such as the precast industry and structures requiring high-performance concrete
Автор: Kyriakos G. Vamvoudakis, Nick-Marios T. Kokolakis Название: Synchronous Reinforcement Learning-Based Control for Cognitive Autonomy ISBN: 1680837443 ISBN-13(EAN): 9781680837445 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 12197.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Describes the use of principles of reinforcement learning (RL) to design feedback policies for continuous-time dynamical systems that combine features of adaptive control and optimal control. The authors give an insightful introduction to reinforcement learning techniques that can address various control problems.
Автор: Bertsekas, Dimitri P. Название: Reinforcement learning and optimal control ISBN: 1886529396 ISBN-13(EAN): 9781886529397 Издательство: Amazon Internet Цена: 23100.00 р. Наличие на складе: Невозможна поставка.
Описание: With the help of practical examples and engaging activities, The Reinforcement Learning Workshop takes you through reinforcement learning`s core techniques and frameworks. Following a hands-on approach, it allows you to learn reinforcement learning at your own pace to develop your own intelligent applications with ease.
Science based training is not just a way of modifying behavior, but an entire way of living with animals. A solid understanding of our species' natural history helps us best meet their needs and bring out their fullest potential. With an understanding of learning theory and behavioral science we can really begin to influence our companion's choices in the kindest way possible. Positive Reinforcement techniques allow us to develop a strong bond based on mutual understanding, empathy, and compassion. With the help of ethology, biology, and neuroscience, we can better understand how our horses think and feel. Using all this objective, science-based information, as well as our desire to be kinder to horses, we can reshape the horse-human connection.
This book dives deep into the sciences behind how horses behave, learn, and feel with many custom designed charts and diagrams for visual learners to enjoy. There are over 70 instructional worksheets for you to apply this information to real life situations and individual equines. In these, we've detailed how to train a wide variety of ground and mounted behaviors as well as how to address emotional and behavioral problems.
Автор: Wolfgang Ahnert, Frank Steffen Название: Sound Reinforcement Engineering: Fundamentals and Practice ISBN: 0415238706 ISBN-13(EAN): 9780415238700 Издательство: Taylor&Francis Рейтинг: Цена: 56654.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Sound reinforcement is the increasing of the power of sound signals and reproducing them as acoustic signals. This book introduces the fundamentals of sound reinforcement engineering, and explains its relationship to other disciplines.
Powertrain electrification, fuel decarburization, and energy diversification are techniques that are spreading all over the world, leading to cleaner and more efficient vehicles.
Hybrid electric vehicles (HEVs) are considered a promising technology today to address growing air pollution and energy deprivation. To realize these gains and still maintain good performance, it is critical for HEVs to have sophisticated energy management systems. Supervised by such a system, HEVs could operate in different modes, such as full electric mode and power split mode. Hence, researching and constructing advanced energy management strategies (EMSs) is important for HEVs performance. There are a few books about rule- and optimization-based approaches for formulating energy management systems. Most of them concern traditional techniques and their efforts focus on searching for optimal control policies offline. There is still much room to introduce learning-enabled energy management systems founded in artificial intelligence and their real-time evaluation and application.
In this book, a series hybrid electric vehicle was considered as the powertrain model, to describe and analyze a reinforcement learning (RL)-enabled intelligent energy management system. The proposed system can not only integrate predictive road information but also achieve online learning and updating. Detailed powertrain modeling, predictive algorithms, and online updating technology are involved, and evaluation and verification of the presented energy management system is conducted and executed.
Powertrain electrification, fuel decarburization, and energy diversification are techniques that are spreading all over the world, leading to cleaner and more efficient vehicles.
Hybrid electric vehicles (HEVs) are considered a promising technology today to address growing air pollution and energy deprivation. To realize these gains and still maintain good performance, it is critical for HEVs to have sophisticated energy management systems. Supervised by such a system, HEVs could operate in different modes, such as full electric mode and power split mode. Hence, researching and constructing advanced energy management strategies (EMSs) is important for HEVs performance. There are a few books about rule- and optimization-based approaches for formulating energy management systems. Most of them concern traditional techniques and their efforts focus on searching for optimal control policies offline. There is still much room to introduce learning-enabled energy management systems founded in artificial intelligence and their real-time evaluation and application.
In this book, a series hybrid electric vehicle was considered as the powertrain model, to describe and analyze a reinforcement learning (RL)-enabled intelligent energy management system. The proposed system can not only integrate predictive road information but also achieve online learning and updating. Detailed powertrain modeling, predictive algorithms, and online updating technology are involved, and evaluation and verification of the presented energy management system is conducted and executed.
A comprehensive exploration of the control schemes of human-robot interactions
In Human-Robot Interaction Control Using Reinforcement Learning, an expert team of authors delivers a concise overview of human-robot interaction control schemes and insightful presentations of novel, model-free and reinforcement learning controllers. The book begins with a brief introduction to state-of-the-art human-robot interaction control and reinforcement learning before moving on to describe the typical environment model. The authors also describe some of the most famous identification techniques for parameter estimation.
Human-Robot Interaction Control Using Reinforcement Learning offers rigorous mathematical treatments and demonstrations that facilitate the understanding of control schemes and algorithms. It also describes stability and convergence analysis of human-robot interaction control and reinforcement learning based control.
The authors also discuss advanced and cutting-edge topics, like inverse and velocity kinematics solutions, H2 neural control, and likely upcoming developments in the field of robotics.
Readers will also enjoy:
A thorough introduction to model-based human-robot interaction control
Comprehensive explorations of model-free human-robot interaction control and human-in-the-loop control using Euler angles
Practical discussions of reinforcement learning for robot position and force control, as well as continuous time reinforcement learning for robot force control
In-depth examinations of robot control in worst-case uncertainty using reinforcement learning and the control of redundant robots using multi-agent reinforcement learning
Perfect for senior undergraduate and graduate students, academic researchers, and industrial practitioners studying and working in the fields of robotics, learning control systems, neural networks, and computational intelligence, Human-Robot Interaction Control Using Reinforcement Learning is also an indispensable resource for students and professionals studying reinforcement learning.
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