Deep Reinforcement Learning for Wireless Networks / F. Richard Yu, Ying He
By: Yu, F. Richard, autor
Contributor(s): SpringerLink (Online service) | He, Ying., autor
Series: (Engineering (Springer-11647)); (SpringerBriefs in Electrical and Computer Engineering, 2191-8112).Publisher: Cham : Springer International Publishing : Imprint: Springer, 2019Description: 1 recurso en línea (VIII, 71 páginas) : 28 ilustraciones.ISBN: 9783030105464.Subject: Sistemas de comunicación inalámbricos | Aprendizaje automático | Inteligencia artificialOnline resources: Acceso a este recurso digital (usuarios Universidad Europea de Madrid)
Abstract: 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
| Item type | Current library | Collection | Call number | Status | Date due | Barcode | Item holds | |
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LIBRO-E NO PRÉSTAMO
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Madrid Digital Acceso Electrónico (UEM) | Ciencias e Ingeniería | Q325.6 2019 EB (Browse shelf(Opens below)) | Acceso electrónico | eBooks24062087 |
Browsing Madrid Digital shelves, Shelving location: Acceso Electrónico (UEM) Close shelf browser (Hides shelf browser)
| Q325.5 V476 2017 EB Roadside video data analysis : deep learning | Q325.5 X856 2015 EB Visual Quality Assessment by Machine Learning | Q325.6 2010 EB Algorithms for Reinforcement Learning | Q325.6 2019 EB Deep Reinforcement Learning for Wireless Networks | Q325.6 2021 EB Reinforcement Learning Algorithms : Analysis and Applications | Q325.6 2021 EB Handbook of Reinforcement Learning and Control | Q325.6 2021 EB Transfer Learning for Multiagent Reinforcement Learning Systems |
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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