EAN: 9783030105457

SPRINGERBRIEFS IN ELECTRICAL AND COMPUTER ENGIN...

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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