Foundations of reinforcement learning with applications in finance, Rao, Ashwin (stanford University, Usa) Jelvis, Tikhon
Автор: Palanisamy Praveen Название: TensorFlow 2 Reinforcement Learning Cookbook: Over 50 recipes to help you build, train, and deploy learning agents for real-world applications ISBN: 183898254X ISBN-13(EAN): 9781838982546 Издательство: Неизвестно Рейтинг: Цена: 60070.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This cookbook will help you to gain a solid understanding of deep reinforcement learning (RL) algorithms with the help of concise, easy-to-follow implementations from scratch. You`ll learn how to implement these algorithms with minimal code and develop AI applications to solve real-world and business problems using RL.
Автор: Crompton, T.R. Название: Plastics Reinforcement and Industrial Applications ISBN: 0367377462 ISBN-13(EAN): 9780367377465 Издательство: Taylor&Francis Рейтинг: Цена: 63280.00 T Наличие на складе: Нет в наличии. Описание:
When combined with reinforcing agents, plastics can be used for a number of high-temperature applications. Plastics Reinforcement and Industrial Applications provides a detailed discussion on plastics, polymers, and reinforcing agents (including organic and natural biomaterials). Focused specifically on improving the mechanical, thermal, and electrical stability of plastics by combining them with reinforcing agents, this book explains the background of reinforced plastics and describes how they work.
The book examines reinforcing agents that include glass fibers, carbon fibers, carbon nanotubes, graphite, talc, and minerals, and commonly used plastics such as polyamides, polyesters, polyethylene terephthalate, and epoxy resins. It also introduces newer plastics such as polyimides, polysulfones, polyethersulfone, polyphenylene sulfide, and polyether ether ketones. It highlights recent developments in the field that include the use of nanocomposites for manufacturing sports equipment, and other applications of nanoparticles in polymer reinforcement. In addition, use of this material can aid in the production of plastics utilized in the construction of aircraft and light weight automobiles. The author covers a wide range of applications that may be applied in general engineering, automotive, aerospace, building materials, electronics and microelectronics, power sources, medical, and bioengineering. He also includes material on natural fibers used for reinforcement and green chemistry applications.
Suitable for use in the metals and plastics industries, Plastics Reinforcement and Industrial Applications is an ideal resource for polymer and material scientists, and chemical and mechanical engineers.
Автор: Sylvia-Stasiewicz Dawn, Kay Larry Название: Training the Best Dog Ever: A 5-Week Program Using the Power of Positive Reinforcement ISBN: 0761168850 ISBN-13(EAN): 9780761168850 Издательство: Неизвестно Рейтинг: Цена: 16550.00 T Наличие на складе: Есть у поставщика Поставка под заказ. 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: Yeuching Название: Deep Reinforcement Learning-based Energy Management for Hybrid Electric Vehicles ISBN: 3031791940 ISBN-13(EAN): 9783031791949 Издательство: Springer Рейтинг: Цена: 55890.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: 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.
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.
Автор: Hao Dong, Zihan Ding, Shanghang Zhang Название: Deep Reinforcement Learning ISBN: 9811540942 ISBN-13(EAN): 9789811540943 Издательство: Springer Рейтинг: Цена: 130430.00 T Наличие на складе: Невозможна поставка. Описание: Deep reinforcement learning (DRL) is the combination of reinforcement learning (RL) and deep learning. The first part introduces the foundations of deep learning, reinforcement learning (RL) and widely used deep RL methods and discusses their implementation.
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: 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.
Автор: Vamvoudakis Kyriakos G., Wan Yan, Lewis Frank L. Название: Handbook of Reinforcement Learning and Control ISBN: 3030609898 ISBN-13(EAN): 9783030609894 Издательство: Springer Цена: 214280.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The Cognitive Dialogue: A New Architecture for Perception and Cognition.- Rooftop-Aware Emergency Landing Planning for Small Unmanned Aircraft Systems.- Quantum Reinforcement Learning in Changing Environment.- The Role of Thermodynamics in the Future Research Directions in Control and Learning.- Mixed Density Reinforcement Learning Methods for Approximate Dynamic Programming.- Analyzing and Mitigating Link-Flooding DoS Attacks Using Stackelberg Games and Adaptive Learning.- Learning and Decision Making for Complex Systems Subjected to Uncertainties: A Stochastic Distribution Control Approach.- Optimal Adaptive Control of Partially Unknown Linear Continuous-time Systems with Input and State Delay.- Gradient Methods Solve the Linear Quadratic Regulator Problem Exponentially Fast.- Architectures, Data Representations and Learning Algorithms: New Directions at the Confluence of Control and Learning.- Reinforcement Learning for Optimal Feedback Control and Multiplayer Games.- Fundamental Principles of Design for Reinforcement Learning Algorithms Course Titles.- Long-Term Impacts of Fair Machine Learning.- Learning-based Model Reduction for Partial Differential Equations with Applications to Thermo-Fluid Models' Identification, State Estimation, and Stabilization.- CESMA: Centralized Expert Supervises Multi-Agents, for Decentralization.- A Unified Framework for Reinforcement Learning and Sequential Decision Analytics.- Trading Utility and Uncertainty: Applying the Value of Information to Resolve the Exploration-Exploitation Dilemma in Reinforcement Learning.- Multi-Agent Reinforcement Learning: Recent Advances, Challenges, and Applications.- Reinforcement Learning Applications, An Industrial Perspective.- A Hybrid Dynamical Systems Perspective of Reinforcement Learning.- Bounded Rationality and Computability Issues in Learning, Perception, Decision-Making, and Games Panagiotis Tsiotras.- Mixed Modality Learning.- Computational Intelligence in Uncertainty Quantification for Learning Control and Games.- Reinforcement Learning Based Optimal Stabilization of Unknown Time Delay Systems Using State and Output Feedback.- Robust Autonomous Driving with Humans in the Loop.- Boundedly Rational Reinforcement Learning for Secure Control.
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.
Автор: Lonza, Andrea Название: Reinforcement learning algorithms with python ISBN: 1789131111 ISBN-13(EAN): 9781789131116 Издательство: Неизвестно Рейтинг: Цена: 47810.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: With this book, you will understand the core concepts and techniques of reinforcement learning. You will take a look into each RL algorithm and will develop your own self-learning algorithms and models. You will optimize the algorithms for better precision, use high-speed actions and lower the risk of anomalies in your applications.
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.
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