Deep Reinforcement Learning, Mohit Sewak

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
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Цена: 60070.00 T
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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
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Цена: 59130.00 T
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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: Yinyan Zhang; Shuai Li; Xuefeng Zhou
Название: Deep Reinforcement Learning with Guaranteed Performance
ISBN: 3030333833 ISBN-13(EAN): 9783030333836
Издательство: Springer
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Цена: 121110.00 T
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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: F. Richard Yu; Ying He
Название: Deep Reinforcement Learning for Wireless Networks
ISBN: 3030105458 ISBN-13(EAN): 9783030105457
Издательство: Springer
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Цена: 51230.00 T
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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: Ravichandiran Sudharsan
Название: Hands-On Reinforcement Learning with Python
ISBN: 1788836529 ISBN-13(EAN): 9781788836524
Издательство: Неизвестно
Цена: 47810.00 T
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Описание: Reinforcement learning is a self-evolving type of machine learning that takes us closer to achieving true artificial intelligence. This easy-to-follow guide explains everything from scratch using rich examples written in Python.

Автор: Matthew Taylor
Название: Transfer in Reinforcement Learning Domains
ISBN: 3642101860 ISBN-13(EAN): 9783642101861
Издательство: Springer
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Цена: 158380.00 T
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Описание: In reinforcement learning (RL) problems, learning agents sequentially execute actions with the goal of maximizing a reward signal. This book provides an introduction to the RL transfer problem and discusses methods which demonstrate the promise of this exciting area of research.

Автор: Christopher Gatti
Название: Design of Experiments for Reinforcement Learning
ISBN: 3319385518 ISBN-13(EAN): 9783319385518
Издательство: Springer
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Цена: 102480.00 T
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Описание: This thesis takes an empirical approach to understanding of the behavior and interactions between the two main components of reinforcement learning: the learning algorithm and the functional representation of learned knowledge.

Автор: Abhishek Nandy; Manisha Biswas
Название: Reinforcement Learning
ISBN: 1484232844 ISBN-13(EAN): 9781484232842
Издательство: Springer
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Цена: 35390.00 T
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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: 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.

Автор: Lapan Maxim
Название: Deep Reinforcement Learning Hands-On - Second Edition
ISBN: 1838826998 ISBN-13(EAN): 9781838826994
Издательство: Неизвестно
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Цена: 98080.00 T
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Описание: With six new chapters, Deep Reinforcement Learning Hands-On Second edition is completely updated and expanded with the very latest reinforcement learning (RL) tools and techniques, providing you with an introduction to RL, as well as the hands-on ability to code intelligent learning agents to perform a range of practical tasks.

Автор: Mohit Sewak
Название: Deep Reinforcement Learning
ISBN: 9811382840 ISBN-13(EAN): 9789811382840
Издательство: Springer
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Цена: 121110.00 T
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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: Kathryn E. Merrick; Mary Lou Maher
Название: Motivated Reinforcement Learning
ISBN: 364210035X ISBN-13(EAN): 9783642100352
Издательство: Springer
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Цена: 121110.00 T
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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.