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Fundamentals of Reinforcement Learning, Ris-Ala, Rafael


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Автор: Ris-Ala, Rafael
Название:  Fundamentals of Reinforcement Learning
ISBN: 9783031373442
Издательство: Springer
Классификация:


ISBN-10: 3031373448
Обложка/Формат: Hardback
Страницы: 88
Вес: 0.30 кг.
Дата издания: 06.09.2023
Язык: English
Издание: 1st ed. 2023
Иллюстрации: 54 tables, color; 87 illustrations, color; 7 illustrations, black and white; xv, 88 p. 94 illus., 87 illus. in color.
Размер: 158 x 243 x 13
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: Artificial intelligence (AI) applications bring agility and modernity to our lives, and the reinforcement learning technique is at the forefront of this technology. It can outperform human competitors in strategy games, creative compositing, and autonomous movement. Moreover, it is just starting to transform our civilization. This book provides an introduction to AI, specifies machine learning techniques, and explores various aspects of reinforcement learning, approaching the latest concepts in a didactic and illustrated manner. It is aimed at students who want to be part of technological advances and professors engaged in the development of innovative applications, helping with academic and industrial challenges. Understanding the Fundamentals of Reinforcement Learning will allow you to: * Understand essential AI concepts * Gain professional experience * Interpret sequential decision problems and solve them with reinforcement learning * Learn how the Q-Learning algorithm works * Practice with commented Python code * Find advantageous directions
Дополнительное описание: Chapter. 1. Introduction.- Chapter. 2. Concepts.- Chapter. 3. Q-Learning algorithm.- Chapter. 4. Development tools.- Chapter. 5. Practice with code.- Chapter. 6. Recent applications and future research.- Index.



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: 16315.00 р.
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.

Deep Reinforcement Learning with Guaranteed Performance

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: 18167.00 р.
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.

Deep Reinforcement Learning

Автор: Mohit Sewak
Название: Deep Reinforcement Learning
ISBN: 9811382840 ISBN-13(EAN): 9789811382840
Издательство: Springer
Рейтинг:
Цена: 18167.00 р.
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.

Reinforcement learning

Автор: Sutton, Richard S. Barto, Andrew G.
Название: Reinforcement learning
ISBN: 0262193981 ISBN-13(EAN): 9780262193986
Издательство: MIT Press
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Цена: 10040.00 р.
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.

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
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Цена: 12707.00 р.
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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.

Reinforcement learning algorithms with python

Автор: Lonza, Andrea
Название: Reinforcement learning algorithms with python
ISBN: 1789131111 ISBN-13(EAN): 9781789131116
Издательство: Неизвестно
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Цена: 7171.00 р.
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.

Learn Unity ML - Agents - Fundamentals of Unity Machine Learning

Автор: Lanham Micheal
Название: Learn Unity ML - Agents - Fundamentals of Unity Machine Learning
ISBN: 1789138132 ISBN-13(EAN): 9781789138139
Издательство: Неизвестно
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Цена: 6068.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: Unity Machine Learning Agents allows researchers and developers to create games and simulations using the Unity Editor which serve as environments where intelligent agents can be trained with machine learning methods through a simple-to-use Python API. This book takes you from the basics of Reinforcement and Q Learning to building Deep ...

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
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Цена: 7918.00 р.
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.

TensorFlow Reinforcement Learning Quick Start Guide

Автор: Balakrishnan Kaushik
Название: TensorFlow Reinforcement Learning Quick Start Guide
ISBN: 1789533589 ISBN-13(EAN): 9781789533583
Издательство: Неизвестно
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Цена: 4964.00 р.
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Описание: This book is an essential guide for anyone interested in Reinforcement Learning. The book provides an actionable reference for Reinforcement Learning algorithms and their applications using TensorFlow and Python. It will help readers leverage the power of algorithms such as Deep Q-Network (DQN), Deep Deterministic Policy Gradients (DDPG), and ...

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)
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Цена: 9286.00 р.
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.

Deep reinforcement learning

Автор: Plaat, Aske
Название: Deep reinforcement learning
ISBN: 9811906378 ISBN-13(EAN): 9789811906374
Издательство: Springer
Рейтинг:
Цена: 6986.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: Deep reinforcement learning has attracted considerable attention recently. Biology has inspired the creation of artificial neural networks and deep learning, while psychology studies how animals and humans learn, and how subjects` desired behavior can be reinforced with positive and negative stimuli.

Transfer Learning for Multiagent Reinforcement Learning Systems

Автор: Da Silva Felipe Leno, Reali Costa Anna Helena
Название: Transfer Learning for Multiagent Reinforcement Learning Systems
ISBN: 1636391346 ISBN-13(EAN): 9781636391342
Издательство: Mare Nostrum (Eurospan)
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Цена: 8039.00 р.
Наличие на складе: Нет в наличии.

Описание:

Learning to solve sequential decision-making tasks is difficult. Humans take years exploring the environment essentially in a random way until they are able to reason, solve difficult tasks, and collaborate with other humans towards a common goal. Artificial Intelligent agents are like humans in this aspect. Reinforcement Learning (RL) is a well-known technique to train autonomous agents through interactions with the environment. Unfortunately, the learning process has a high sample complexity to infer an effective actuation policy, especially when multiple agents are simultaneously actuating in the environment.

However, previous knowledge can be leveraged to accelerate learning and enable solving harder tasks. In the same way humans build skills and reuse them by relating different tasks, RL agents might reuse knowledge from previously solved tasks and from the exchange of knowledge with other agents in the environment. In fact, virtually all of the most challenging tasks currently solved by RL rely on embedded knowledge reuse techniques, such as Imitation Learning, Learning from Demonstration, and Curriculum Learning.

This book surveys the literature on knowledge reuse in multiagent RL. The authors define a unifying taxonomy of state-of-the-art solutions for reusing knowledge, providing a comprehensive discussion of recent progress in the area. In this book, readers will find a comprehensive discussion of the many ways in which knowledge can be reused in multiagent sequential decision-making tasks, as well as in which scenarios each of the approaches is more efficient. The authors also provide their view of the current low-hanging fruit developments of the area, as well as the still-open big questions that could result in breakthrough developments. Finally, the book provides resources to researchers who intend to join this area or leverage those techniques, including a list of conferences, journals, and implementation tools.

This book will be useful for a wide audience; and will hopefully promote new dialogues across communities and novel developments in the area.


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