Deep Reinforcement Learning for Wireless Networks

Autor: F. Richard Yu, Ying He

Jazyk:

Vazba: Brožovaná

Vydavatel: Springer Nature Switzerland AG

Dostupnost: Skladem u dodavatele

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This Springerbrief presents a deep reinforcement learning approach to wireless systems to improve sy...

Informace o knize

Autor

F. Richard Yu, Ying He

Jazyk

Angličtina

Vazba

Kniha - Brožovaná

Vydáno

2019

Stránek

EAN

9783030105457

Enbook ID

20755333

Vydavatel

Springer Nature Switzerland AG

Hmotnost

141

Rozměry

155 x 235 x 5

Kompletní popis

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.

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Deep Reinforcement Learning for Wireless Networks

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