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Deep Reinforcement Learning for Wireless Networks

Part of the SpringerBriefs in Electrical and Computer Engineering Book Series

DOI: 10.1007/978-3-030-10546-4

ISBN: 978-3-030-10545-7

Publisher: Springer International Publishing - Springer Journals

Jan 18, 2019

Available

Introduction to Machine Learning

Yu, F. Richard; He, Ying

Pages: 1 –13

[Machine learning is evolved from a collection of powerful techniques in AI areas and has been extensively used in data mining, which allows the system to learn the useful structural patterns and models from training data. Machine learning algorithms can be basically classified into four...
Available

Reinforcement Learning and Deep Reinforcement Learning

Yu, F. Richard; He, Ying

Pages: 15 –19

[In order to better understand state-of-the-art reinforcement learning agent, deep Q-network, a brief review of reinforcement learning and Q-learning are first described. Then recent advances of deep Q-network are presented, and double deep Q-network and dueling deep Q-network that go beyond deep...
Available

Deep Reinforcement Learning for Interference Alignment Wireless Networks

Yu, F. Richard; He, Ying

Pages: 21 –44

[Both caching and interference alignment (IA) are promising techniques for next generation wireless networks. Nevertheless, most existing works on cache-enabled IA wireless networks assume that the channel is invariant, which is unrealistic considering the time-varying nature of practical...
Available

Deep Reinforcement Learning for Mobile Social Networks

Yu, F. Richard; He, Ying

Pages: 45 –71

[Social networks have continuously been expanding and trying to be innovative. The recent advances of computing, caching, and communication (3C) can have significant impacts on mobile social networks (MSNs). MSNs can leverage these new paradigms to provide a new mechanism for users to share...

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Deep Reinforcement Learning for Wireless Networks

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