Deep Reinforcement Learning with Guaranteed Performance, Yinyan Zhang; Shuai Li; Xuefeng Zhou
Автор: F. Richard Yu; Ying He Название: Deep Reinforcement Learning for Wireless Networks ISBN: 3030105458 ISBN-13(EAN): 9783030105457 Издательство: Springer Рейтинг: Цена: 51230.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This Springerbrief presents a deep reinforcement learning approach to wireless systems to improve system performance. Particularly, deep reinforcement learning approach is used in cache-enabled opportunistic interference alignment wireless networks and mobile social networks. Simulation results with different network parameters are presented to show the effectiveness of the proposed scheme. There is a phenomenal burst of research activities in artificial intelligence, deep reinforcement learning and wireless systems. Deep reinforcement learning has been successfully used to solve many practical problems. For example, Google DeepMind adopts this method on several artificial intelligent projects with big data (e.g., AlphaGo), and gets quite good results.. Graduate students in electrical and computer engineering, as well as computer science will find this brief useful as a study guide. Researchers, engineers, computer scientists, programmers, and policy makers will also find this brief to be a useful tool.
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: 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: Ramsundar Bharath, Zadeh Reza Bosagh Название: Tensorflow for Deep Learning: From Linear Regression to Reinforcement Learning ISBN: 1491980451 ISBN-13(EAN): 9781491980453 Издательство: Wiley Рейтинг: Цена: 59130.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Learn how to solve challenging machine learning problems with TensorFlow, Google`s revolutionary new software library for deep learning. If you have some background in basic linear algebra and calculus, this practical book introduces machine-learning fundamentals.
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: Mohit Sewak Название: Deep Reinforcement Learning ISBN: 9811382875 ISBN-13(EAN): 9789811382871 Издательство: Springer Рейтинг: Цена: 149060.00 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.
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: 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.
Автор: Abhishek Nandy; Manisha Biswas Название: Reinforcement Learning ISBN: 1484232844 ISBN-13(EAN): 9781484232842 Издательство: Springer Рейтинг: Цена: 35390.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Chapter 1: Reinforcement Learning basicsChapter Goal: This chapter covers the basics needed for AI, ML and Deep Learning.Relation between them and differences.No of pages 30Sub -Topics1. Reinforcement Learning2. The flow3. Faces of Reinforcement Learning4. 5. Environments6. The depiction of inter relation between Agents and EnvironmentDeep Learning Chapter 2: Theory and AlgorithmsChapter Goal: This Chapter covers the theory of Reinforcement Learning and Algorithms.No of pages: 60Sub-topics1 . Problem scenarios in Reinforcement Learningins 2. Markov Decision process3. SARSA4.Q learning5.Value Functions6.Dynamic Programming and Policies7.Approaches to RL Chapter 3: Open AI basicsChapter Goal: In this chapter we will cover the basics of Open AI gym and universe and then move forward for installing it. No of pages: 40 Sub - Topics: 1. What are Open AI environments 2. Installation of Open AI Gym and Universe in Ubuntu 3. Difference between Open AI Gym and Universe Chapter 4: Getting to know Open AI and Open AI gym the developers wayChapter Goal: We will use Python to start the programming and cover topics accordinglyNo of pages: 60Sub - Topics: 1. Open AI, Open AI Gym and python2. Setting up the environment3. Examples4 Swarm Intelligence using python 5.Markov Decision process toolbox for Python6.Implementing a Game AI with Reinforcement Learning Chapter 5: Reinforcement learning using Tensor Flow environment and KerasChapter Goal: We cover Reinforcement Learning in terms of Tensorflow and KerasNo of pages: 40Sub - Topics: 1. Tensorflow and Reinforcement Learning2. Q learning with Tensor Flow3. Keras4. Keras and Reinforcement Learning Chapter 6 Google's DeepMind and the future of Reinforcement LearningChapter Goal: We cover the descriptions of the above the content.No of pages: 25Sub - Topics: 1. Google's Deep Mind2. Future of Reinforcement Learning 3. Man VS Machines where is it Heading to.
Автор: Taweh Beysolow II Название: Applied Reinforcement Learning with Python ISBN: 1484251261 ISBN-13(EAN): 9781484251263 Издательство: Springer Рейтинг: Цена: 23280.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Delve into the world of reinforcement learning algorithms and apply them to different use-cases via Python. This book covers important topics such as policy gradients and Q learning, and utilizes frameworks such as Tensorflow, Keras, and OpenAI Gym. Applied Reinforcement Learning with Python introduces you to the theory behind reinforcement learning (RL) algorithms and the code that will be used to implement them. You will take a guided tour through features of OpenAI Gym, from utilizing standard libraries to creating your own environments, then discover how to frame reinforcement learning problems so you can research, develop, and deploy RL-based solutions.What You'll LearnImplement reinforcement learning with Python Work with AI frameworks such as OpenAI Gym, Tensorflow, and KerasDeploy and train reinforcement learning–based solutions via cloud resourcesApply practical applications of reinforcement learning Who This Book Is For Data scientists, machine learning engineers and software engineers familiar with machine learning and deep learning concepts.
Автор: Colome Adria, Torras Carme Название: Reinforcement Learning of Bimanual Robot Skills ISBN: 3030263258 ISBN-13(EAN): 9783030263256 Издательство: Springer Рейтинг: Цена: 93160.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book tackles all the stages and mechanisms involved in the learning of manipulation tasks by bimanual robots in unstructured settings, as it can be the task of folding clothes. The first part describes how to build an integrated system, capable of properly handling the kinematics and dynamics of the robot along the learning process.
Автор: Todd Hester Название: TEXPLORE: Temporal Difference Reinforcement Learning for Robots and Time-Constrained Domains ISBN: 3319375105 ISBN-13(EAN): 9783319375106 Издательство: Springer Рейтинг: Цена: 104480.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book presents and develops new reinforcement learning methods that enable fast and robust learning on robots in real-time. It presents a novel model-based reinforcement learning algorithm.
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