SpringerBriefs in Electrical and Computer Engineering Deep Reinforcement Learning for Wireless Networks - F Richard Yu Ying He Kartoniert (TB)

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 practica l 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.