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DEEP REINFORCEMENT LEARNING FOR WIRELESS NETWORKS IBD

F RICHARD YU / YING HE
SPRINGER
01 / 2019
9783030105457
Inglés

Sinopsis

This Springerbrief presents a deep reinforcement learning approach to wireless systems to improve system performance.áParticularly, deep reinforcement learning approach is used in cache-enabled opportunistic interference alignment wirelessánetworks and mobile social networks. Simulation results with different network parameters areápresented to show the effectiveness of the proposed scheme.áThere is a phenomenal burst of research activities in artificial intelligence, deep reinforcement learning and wireless systems. Deep reinforcementálearning has been successfully used to solve many practical problems. For example, Google DeepMind adopts this method on several artificial intelligentáprojects with big data (e.g., AlphaGo), and gets quite good results..áGraduate students in electrical and computer engineering, as well as computer science will find this brief useful as a study guide. Researchers, engineers, computeráscientists, programmers, and policy makers will also find this brief to be a useful tool.á