Deep reinforcement learning for wireless networks
F. Richard Yu, Ying He
(Springer Nature, 2019)
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| 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. |
 Deep Reinforcement Learning for Wireless Networks.pdf :: Unduh
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| No. Panggil : | e20507632 |
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| 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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| Tidak ada ulasan pada koleksi ini: 20507632 |
