Deep reinforcement learning for wireless networks

著者

• Yu, F. Richard
• He, Ying

書誌事項

Deep reinforcement learning for wireless networks

F. Richard Yu, Ying He

（Springerbriefs in electrical and computer engineering）

Springer, [2019]

内容説明・目次

内容説明

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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詳細情報

• NII書誌ID(NCID): BB28920651
• ISBN:
  • 9783030105457
• LCCN: 2018967964
• 出版国コード: sz
• タイトル言語コード: eng
• 本文言語コード: eng
• 出版地: Cham
• ページ数/冊数: viii , 71 p.
• 大きさ: 24 cm
• 分類:
  • LCC : Q325.6
  • DC23 : 006.3/1
• 件名:
  • LCSH : Reinforcement learning
  • LCSH : Wireless communication systems
• 親書誌ID:
  • BB06307470