Reinforcement Learning for Maritime Communications, Liang Xiao , Helin Yang , Weihua Zhuang , Minghui
Автор: Shengbo Eben Li Название: Reinforcement Learning for Sequential Decision and Optimal Control ISBN: 9811977836 ISBN-13(EAN): 9789811977831 Издательство: Springer Рейтинг: Цена: 74530.00 T Наличие на складе: Нет в наличии. 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.
Автор: Fuchs Marjorie, Bonner Margo, Adelson-Goldstein Название: Oxford Picture Dictionary Low-Intermediate Workbook ISBN: 019474048X ISBN-13(EAN): 9780194740487 Издательство: Oxford University Press Рейтинг: Цена: 11330.00 T Наличие на складе: Нет в наличии. Описание: Exercises, games and puzzles map page-for-page to the 163 topics and 4,000 words in the Oxford Picture Dictionary, for teenage and adult ESL students of American English.
Автор: Farsi, Milad, Название: Model-based reinforcement learning : ISBN: 111980857X ISBN-13(EAN): 9781119808572 Издательство: Wiley Рейтинг: 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.
Автор: Fuchs Marjorie Название: Oxford Picture Dictionary High Beginning Workbook +CD ISBN: 0194740447 ISBN-13(EAN): 9780194740449 Издательство: Oxford University Press Рейтинг: Цена: 13790.00 T Наличие на складе: Нет в наличии. Описание: Exercises, games and puzzles map page-for-page to the 163 topics and 4,000 words in the Oxford Picture Dictionary, for teenage and adult ESL students of American English.
Автор: Rieser, Verena Название: Reinforcement Learning for Adaptive Dialogue Systems ISBN: 3642249418 ISBN-13(EAN): 9783642249419 Издательство: Springer Рейтинг: Цена: 111790.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The past decade has seen a revolution in the field of spoken dialogue systems. As in other areas of Computer Science and Artificial Intelligence, data-driven methods are now being used to drive new methodologies for system development and evaluation. This book is a unique contribution to that ongoing change. A new methodology for developing spoken dialogue systems is described in detail. The journey starts and ends with human behaviour in interaction, and explores methods for learning from the data, for building simulation environments for training and testing systems, and for evaluating the results. The detailed material covers: Spoken and Multimodal dialogue systems, Wizard-of-Oz data collection, User Simulation methods, Reinforcement Learning, and Evaluation methodologies. The book is a research guide for students and researchers with a background in Computer Science, AI, or Machine Learning. It navigates through a detailed case study in data-driven methods for development and evaluation of spoken dialogue systems. Common challenges associated with this approach are discussed and example solutions are provided. This work provides insights, lessons, and inspiration for future research and development – not only for spoken dialogue systems in particular, but for data-driven approaches to human-machine interaction in general.
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.
Автор: Liu Yuxi (Hayden) Название: PyTorch 1.0 Reinforcement Learning Cookbook ISBN: 1838551964 ISBN-13(EAN): 9781838551964 Издательство: Неизвестно Рейтинг: Цена: 53940.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book presents practical solutions to the most common reinforcement learning problems. The recipes in this book will help you understand the fundamental concepts to develop popular RL algorithms. You will gain practical experience in the RL domain using the modern offerings of the PyTorch 1.x library.
Автор: Lapan Maxim Название: Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more ISBN: 1788834240 ISBN-13(EAN): 9781788834247 Издательство: Неизвестно Рейтинг: Цена: 60070.00 T Наличие на складе: Есть у поставщика Поставка под заказ. 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 ...
Автор: Sutton, Richard S. Barto, Andrew G. Название: Reinforcement learning ISBN: 0262193981 ISBN-13(EAN): 9780262193986 Издательство: MIT Press Рейтинг: Цена: 66930.00 T Наличие на складе: Нет в наличии. Описание: An account of key ideas and algorithms in reinforcement learning. The discussion ranges from the history of the field`s intellectual foundations to recent developments and applications. Areas studied include reinforcement learning problems in terms of Markov decision problems and solution methods.
Автор: Sadhu Arup Kumar, Konar Amit Название: Multi-Agent Coordination: A Reinforcement Learning Approach ISBN: 1119699037 ISBN-13(EAN): 9781119699033 Издательство: Wiley Рейтинг: Цена: 115050.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
Discover the latest developments in multi-robot coordination techniques with this insightful and original resource
Multi-Agent Coordination: A Reinforcement Learning Approach delivers a comprehensive, insightful, and unique treatment of the development of multi-robot coordination algorithms with minimal computational burden and reduced storage requirements when compared to traditional algorithms. The accomplished academics, engineers, and authors provide readers with both a high-level introduction to, and overview of, multi-robot coordination, and in-depth analyses of learning-based planning algorithms.
You'll learn about how to accelerate the exploration of the team-goal and alternative approaches to speeding up the convergence of TMAQL by identifying the preferred joint action for the team. The authors also propose novel approaches to consensus Q-learning that address the equilibrium selection problem and a new way of evaluating the threshold value for uniting empires without imposing any significant computation overhead. Finally, the book concludes with an examination of the likely direction of future research in this rapidly developing field.
Readers will discover cutting-edge techniques for multi-agent coordination, including:
An introduction to multi-agent coordination by reinforcement learning and evolutionary algorithms, including topics like the Nash equilibrium and correlated equilibrium
Improving convergence speed of multi-agent Q-learning for cooperative task planning
Consensus Q-learning for multi-agent cooperative planning
The efficient computing of correlated equilibrium for cooperative q-learning based multi-agent planning
A modified imperialist competitive algorithm for multi-agent stick-carrying applications
Perfect for academics, engineers, and professionals who regularly work with multi-agent learning algorithms, Multi-Agent Coordination: A Reinforcement Learning Approach also belongs on the bookshelves of anyone with an advanced interest in machine learning and artificial intelligence as it applies to the field of cooperative or competitive robotics.
The urgent need for vehicle electrification and improvement in fuel efficiency has gained increasing attention worldwide. Regarding this concern, the solution of hybrid vehicle systems has proven its value from academic research and industry applications, where energy management plays a key role in taking full advantage of hybrid electric vehicles (HEVs). There are many well-established energy management approaches, ranging from rules-based strategies to optimization-based methods, that can provide diverse options to achieve higher fuel economy performance. However, the research scope for energy management is still expanding with the development of intelligent transportation systems and the improvement in onboard sensing and computing resources. Owing to the boom in machine learning, especially deep learning and deep reinforcement learning (DRL), research on learning-based energy management strategies (EMSs) is gradually gaining more momentum. They have shown great promise in not only being capable of dealing with big data, but also in generalizing previously learned rules to new scenarios without complex manually tunning.
Focusing on learning-based energy management with DRL as the core, this book begins with an introduction to the background of DRL in HEV energy management. The strengths and limitations of typical DRL-based EMSs are identified according to the types of state space and action space in energy management. Accordingly, value-based, policy gradient-based, and hybrid action space-oriented energy management methods via DRL are discussed, respectively. Finally, a general online integration scheme for DRL-based EMS is described to bridge the gap between strategy learning in the simulator and strategy deployment on the vehicle controller.
Автор: Yinyan Zhang; Shuai Li; Xuefeng Zhou Название: Deep Reinforcement Learning with Guaranteed Performance ISBN: 3030333833 ISBN-13(EAN): 9783030333836 Издательство: Springer Рейтинг: Цена: 121110.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: 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.
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