Deep Reinforcement Learning for Wireless Networks

Sinopse

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