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Deep Reinforcement Learning for Wireless Networks (SpringerBriefs in Electrical and Computer Engineering) - Tapa blanda

Libro 183 de 209: SpringerBriefs in Electrical and Computer Engineering

Yu, F. Richard; He, Ying

 
9783030105457: Deep Reinforcement Learning for Wireless Networks (SpringerBriefs in Electrical and Computer Engineering)
Tapa blanda
ISBN 10: 3030105458 ISBN 13: 9783030105457
Editorial: Springer, 2019

Sinopsis

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. 

"Sinopsis" puede pertenecer a otra edición de este libro.

Reseña del editor

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. 

"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
Springer
Año de publicación
2019
Idioma
Inglés
ISBN 10 
3030105458
ISBN 13 
9783030105457
Encuadernación
Tapa blanda
Número de edición
1
Número de páginas
80
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