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Deep reinforcement learning for wireless networks

F. Richard Yu, Ying He (Springer Nature, 2019)

 Abstrak

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.

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 Metadata

No. Panggil : e20507632
Entri utama-Nama orang :
Entri tambahan-Nama orang :
Subjek :
Penerbitan : Switzerland: Springer Nature, 2019
Sumber Pengatalogan: LibUI eng rda
Tipe Konten: text
Tipe Media: computer
Tipe Pembawa: online resource
Deskripsi Fisik: viii, 71 pages : illustration
Tautan: https://doi.org/10.1007/978-3-030-10546-4
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No. Panggil No. Barkod Ketersediaan
e20507632 02-20-512246825 TERSEDIA
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