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Reinforcement Learning for Sequential Decision and Optimal Control, Shengbo Eben Li


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Автор: Shengbo Eben Li   (Ли)
Название:  Reinforcement Learning for Sequential Decision and Optimal Control
Перевод названия: Ли: Обучение с подкреплением для последовательного принятия решений и оптимального управления
ISBN: 9789811977831
Издательство: Springer
Классификация:




ISBN-10: 9811977836
Обложка/Формат: Hardback
Страницы: 472
Вес: 0.33 кг.
Дата издания: 01.12.2022
Язык: English
Издание: 1st ed. 2023
Иллюстрации: 213 illustrations, color; 4 illustrations, black and white; xxx, 462 p. 217 illus., 213 illus. in color.
Размер: 174 x 247 x 34
Читательская аудитория: Professional & vocational
Основная тема: Computer Science
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: Have you ever wondered how AlphaZero learns to defeat the top human Go players? Do you have any clues about how an autonomous driving system can gradually develop self-driving skills beyond normal drivers? What is the key that enables AlphaStar to make decisions in Starcraft, a notoriously difficult strategy game that has partial information and complex rules? The core mechanism underlying those recent technical breakthroughs is reinforcement learning (RL), a theory that can help an agent to develop the self-evolution ability through continuing environment interactions. In the past few years, the AI community has witnessed phenomenal success of reinforcement learning in various fields, including chess games, computer games and robotic control. RL is also considered to be a promising and powerful tool to create general artificial intelligence in the future. As an interdisciplinary field of trial-and-error learning and optimal control, RL resembles how humans reinforce their intelligence by interacting with the environment and provides a principled solution for sequential decision making and optimal control in large-scale and complex problems. Since RL contains a wide range of new concepts and theories, scholars may be plagued by a number of questions: What is the inherent mechanism of reinforcement learning? What is the internal connection between RL and optimal control? How has RL evolved in the past few decades, and what are the milestones? How do we choose and implement practical and effective RL algorithms for real-world scenarios? What are the key challenges that RL faces today, and how can we solve them? What is the current trend of RL research? You can find answers to all those questions in this book. The purpose of the book is to help researchers and practitioners take a comprehensive view of RL and understand the in-depth connection between RL and optimal control. The book includes not only systematic and thorough explanations of theoretical basics but also methodical guidance of practical algorithm implementations. The book intends to provide a comprehensive coverage of both classic theories and recent achievements, and the content is carefully and logically organized, including basic topics such as the main concepts and terminologies of RL, Markov decision process (MDP), Bellman’s optimality condition, Monte Carlo learning, temporal difference learning, stochastic dynamic programming, function approximation, policy gradient methods, approximate dynamic programming, and deep RL, as well as the latest advances in action and state constraints, safety guarantee, reference harmonization, robust RL, partially observable MDP, multiagent RL, inverse RL, offline RL, and so on.
Дополнительное описание: Chapter 1 Introduction of Reinforcement Learning.- Chapter 2 Principles of RL Problems.- Chapter 3 Model-free Indirect RL: Monte Carlo.- Chapter 4 Model-Free Indirect RL: Temporal-Difference.- Chapter 5 Model-based Indirect RL: Dynamic Programming.- Chapt


Model-based reinforcement learning :

Автор: Farsi, Milad,
Название: Model-based reinforcement learning :
ISBN: 111980857X ISBN-13(EAN): 9781119808572
Издательство: Wiley
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Цена: 108770.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book is for researchers and students in statistics, data mining, computer science, machine learning, marketing and also practitioners who implement recommender systems. It provides an in-depth discussion of challenges encountered in deploying real-life large-scale systems and state-of-the-art solutions in personalization, explore/exploit, dimension reduction and multi-objective optimization.

Reinforcement Learning for Cyber-Physical Systems with Cybersecurity Case Studies

Автор: Li, Chong
Название: Reinforcement Learning for Cyber-Physical Systems with Cybersecurity Case Studies
ISBN: 1138543535 ISBN-13(EAN): 9781138543539
Издательство: Taylor&Francis
Рейтинг:
Цена: 84710.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book introduces reinforcement learning, and provides novel ideas and use cases to demonstrate the benefits of using reinforcement learning for Cyber Physical Systems. Two important case studies on applying reinforcement learning to cybersecurity problems are included.

Deep Reinforcement Learning

Автор: Mohit Sewak
Название: Deep Reinforcement Learning
ISBN: 9811382840 ISBN-13(EAN): 9789811382840
Издательство: Springer
Рейтинг:
Цена: 121110.00 T
Наличие на складе: Поставка под заказ.
Описание: 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.


Deep Reinforcement Learning with Guaranteed Performance

Автор: Yinyan Zhang; Shuai Li; Xuefeng Zhou
Название: Deep Reinforcement Learning with Guaranteed Performance
ISBN: 3030333868 ISBN-13(EAN): 9783030333867
Издательство: Springer
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Цена: 121110.00 T
Наличие на складе: Поставка под заказ.
Описание: This book is devoted to the description of the most widely used classifications of the most frequent fractures in clinical practice. For each type of fracture one or several classifications are described.This edition will include new classifications and classifications that have gained popularity in the last 3 years, resulting in 25% new material.

Handbook of Reinforcement Learning and Control

Автор: Vamvoudakis
Название: Handbook of Reinforcement Learning and Control
ISBN: 3030609928 ISBN-13(EAN): 9783030609924
Издательство: Springer
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Цена: 214280.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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.

Reinforcement Learning-Enabled Intelligent Energy Management for Hybrid Electric Vehicles

Автор: Liu Teng
Название: Reinforcement Learning-Enabled Intelligent Energy Management for Hybrid Electric Vehicles
ISBN: 1681736209 ISBN-13(EAN): 9781681736204
Издательство: Mare Nostrum (Eurospan)
Рейтинг:
Цена: 61910.00 T
Наличие на складе: Нет в наличии.
Описание:

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.


The Reinforcement Learning Workshop: Learn how to apply cutting-edge reinforcement learning algorithms to a wide range of control problems

Автор: Palmas Alessandro, Ghelfi Emanuele, Petre Alexandra Galina
Название: The Reinforcement Learning Workshop: Learn how to apply cutting-edge reinforcement learning algorithms to a wide range of control problems
ISBN: 1800200455 ISBN-13(EAN): 9781800200456
Издательство: Неизвестно
Рейтинг:
Цена: 50670.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
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.

Control systems and reinforcement learning

Автор: Meyn, Sean (university Of Florida)
Название: Control systems and reinforcement learning
ISBN: 1316511960 ISBN-13(EAN): 9781316511961
Издательство: Cambridge Academ
Рейтинг:
Цена: 52790.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The book is written for newcomers to reinforcement learning who wish to write code for various applications, from robotics to power systems to supply chains. It also contains advanced material designed to prepare graduate students and professionals for both research and application of reinforcement learning and optimal control techniques.

Output Feedback Reinforcement Learning Control for Linear Systems

Автор: Rizvi
Название: Output Feedback Reinforcement Learning Control for Linear Systems
ISBN: 3031158571 ISBN-13(EAN): 9783031158575
Издательство: Springer
Рейтинг:
Цена: 139750.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This monograph explores the analysis and design of model-free optimal control systems based on reinforcement learning (RL) theory, presenting new methods that overcome recent challenges faced by RL. New developments in the design of sensor data efficient RL algorithms are demonstrated that not only reduce the requirement of sensors by means of output feedback, but also ensure optimality and stability guarantees. A variety of practical challenges are considered, including disturbance rejection, control constraints, and communication delays. Ideas from game theory are incorporated to solve output feedback disturbance rejection problems, and the concepts of low gain feedback control are employed to develop RL controllers that achieve global stability under control constraints. Output Feedback Reinforcement Learning Control for Linear Systems will be a valuable reference for graduate students, control theorists working on optimal control systems, engineers, and applied mathematicians.

Reinforcement Learning Aided Performance Optimization of Feedback Control Systems

Автор: Hua, Changsheng
Название: Reinforcement Learning Aided Performance Optimization of Feedback Control Systems
ISBN: 3658330333 ISBN-13(EAN): 9783658330330
Издательство: Springer
Рейтинг:
Цена: 65210.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Changsheng Hua proposes two approaches, an input/output recovery approach and a performance index-based approach for robustness and performance optimization of feedback control systems.

Reinforcement Learning and Dynamic Programming Using Function Approximators

Автор: Busoniu, Lucian
Название: Reinforcement Learning and Dynamic Programming Using Function Approximators
ISBN: 1439821089 ISBN-13(EAN): 9781439821084
Издательство: Taylor&Francis
Рейтинг:
Цена: 112290.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.

Motivated Reinforcement Learning

Автор: Kathryn E. Merrick; Mary Lou Maher
Название: Motivated Reinforcement Learning
ISBN: 364210035X ISBN-13(EAN): 9783642100352
Издательство: Springer
Рейтинг:
Цена: 121110.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book covers the design, application and evaluation of computational models of motivation in reinforcement learning. The performance of these models is demonstrated by applications in simulated game scenarios and a live, open-ended, virtual world.


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