ISBN: 9783030105457 - deep reinforcement learning for wireless networks (19 risultati)

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Condizione: New.

Condizione: New.

Condizione: New. In English.

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Paperback. Condizione: Brand New. 80 pages. 9.25x6.10x0.32 inches. In Stock.

Condizione: New.

Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 wirelessnetworks and mobile social networks. Simulation results with different network parameters arepresented 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 reinforcementlearning has been successfully used to solve many practical problems. For example, Google DeepMind adopts this method on several artificial intelligentprojects 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, computerscientists, programmers, and policy makers will also find this brief to be a useful tool.…

Taschenbuch. Condizione: Neu. Deep Reinforcement Learning for Wireless Networks | F. Richard Yu (u. a.) | Taschenbuch | SpringerBriefs in Electrical and Computer Engineering | viii | Englisch | 2019 | Springer | EAN 9783030105457 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Condizione: new. Questo è un articolo print on demand.

PF. Condizione: New. This item is printed on demand. Brand new book, sourced directly from publisher. Dispatch time is 6-7 days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 wirelessnetworks and mobile social networks. Simulation results with different network parameters arepresented 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 reinforcementlearning has been successfully used to solve many practical problems. For example, Google DeepMind adopts this method on several artificial intelligentprojects 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, computerscientists, programmers, and policy makers will also find this brief to be a useful tool. 80 pp. Englisch.…

Condizione: New. Print on Demand.

Kartoniert / Broschiert. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This Springerbrief presents a deep reinforcement learning approach to wireless systems to improve system performance.&nbspParticularly, deep reinforcement learning approach is used in cache-enabled opportunistic interference alignment wireless&nbspnetw.…

Condizione: New. PRINT ON DEMAND.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 80 pp. Englisch.…