Reinforcement Learning for Cyber-Physical Systems with Cybersecurity Case Studies, Li, Chong
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: Li, Chong Qiu, Meikang Название: Reinforcement learning for cyber-physical systems ISBN: 0367656639 ISBN-13(EAN): 9780367656638 Издательство: Taylor&Francis Рейтинг: Цена: 45930.00 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: Balakrishnan Kaushik Название: TensorFlow Reinforcement Learning Quick Start Guide ISBN: 1789533589 ISBN-13(EAN): 9781789533583 Издательство: Неизвестно Рейтинг: Цена: 33090.00 T Наличие на складе: Есть у поставщика Поставка под заказ. 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 ...
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.
Автор: Lanham Micheal Название: Hands-On Reinforcement Learning for Games ISBN: 1839214937 ISBN-13(EAN): 9781839214936 Издательство: Неизвестно Рейтинг: Цена: 53940.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: The AI revolution is here and it is embracing games. Game developers are being challenged to enlist cutting edge AI as part of their games. In this book, you will look at the journey of building capable AI using reinforcement learning algorithms and techniques. You will learn to solve complex tasks and build next-generation games using a ...
Автор: Bilgin Enes Название: Mastering Reinforcement Learning with Python: Build next-generation, self-learning models using reinforcement learning techniques and best practices ISBN: 1838644148 ISBN-13(EAN): 9781838644147 Издательство: Неизвестно Рейтинг: Цена: 60070.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This book focuses on expert-level explanations and implementations of scalable reinforcement learning algorithms and approaches. Starting with the fundamentals, the book covers state-of-the-art methods from bandit problems to meta-reinforcement learning. You`ll also explore practical examples inspired by real-life problems from the industry.
Автор: Sugiyama Название: Statistical Reinforcement Learning ISBN: 1439856893 ISBN-13(EAN): 9781439856895 Издательство: Taylor&Francis Рейтинг: Цена: 86760.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
Reinforcement learning is a mathematical framework for developing computer agents that can learn an optimal behavior by relating generic reward signals with its past actions. With numerous successful applications in business intelligence, plant control, and gaming, the RL framework is ideal for decision making in unknown environments with large amounts of data.
Supplying an up-to-date and accessible introduction to the field, Statistical Reinforcement Learning: Modern Machine Learning Approaches presents fundamental concepts and practical algorithms of statistical reinforcement learning from the modern machine learning viewpoint. It covers various types of RL approaches, including model-based and model-free approaches, policy iteration, and policy search methods.
Covers the range of reinforcement learning algorithms from a modern perspective
Lays out the associated optimization problems for each reinforcement learning scenario covered
Provides thought-provoking statistical treatment of reinforcement learning algorithms
The book covers approaches recently introduced in the data mining and machine learning fields to provide a systematic bridge between RL and data mining/machine learning researchers. It presents state-of-the-art results, including dimensionality reduction in RL and risk-sensitive RL. Numerous illustrative examples are included to help readers understand the intuition and usefulness of reinforcement learning techniques.
This book is an ideal resource for graduate-level students in computer science and applied statistics programs, as well as researchers and engineers in related fields.
Автор: Lapan Maxim Название: Deep Reinforcement Learning Hands-On - Second Edition ISBN: 1838826998 ISBN-13(EAN): 9781838826994 Издательство: Неизвестно Рейтинг: Цена: 98080.00 T Наличие на складе: Есть у поставщика Поставка под заказ. 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: Francois-Lavet Vincent, Henderson Peter, Islam Riashat Название: An Introduction to Deep Reinforcement Learning ISBN: 1680835386 ISBN-13(EAN): 9781680835380 Издательство: Неизвестно Рейтинг: Цена: 91040.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Provides a starting point for understanding deep reinforcement learning. Although written at a research level it provides a comprehensive and accessible introduction to deep reinforcement learning models, algorithms and techniques.
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: 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.
Название: Recent advances in reinforcement learning ISBN: 0792397053 ISBN-13(EAN): 9780792397052 Издательство: Springer Рейтинг: Цена: 130430.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Addresses research in the Artificial Intelligence and Neural Network communities. This book includes topics such as the theoretical foundations of dynamic programming approaches, the role of prior knowledge, and methods for improving performance of reinforcement-learning techniques.
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