… ers the edge network … learning in edge networks. This allows the possibility of employing
artificial intelligence (AI) techniques at mobile network edges for better understanding of mobile …

… adopt a model-free reinforcement learning scheme called Qlearning [10], which allows us
to learn the optimal control policy without any information of dynamic network statistics. The Q-…

… sets of historical data for training. In this survey, reinforcement learningaided mobile edge
caching solutions are presented and classified, based on the networking architecture and …

… content caching at mobile network edges to alleviate … this circumstance, machine
learning can be used to learn the … propose a multi-agent reinforcement learning (MARL)-based …

… In this paper, we consider a wireless powered MEC network that adopts a … Reinforcement
learning-based Online Offloading (DROO) framework that implements a deep neural network …

… [25] designed an edge network orchestrator to enable fast and accurate object analytics at
the network edge for mobile AR (MAR), and proposed a joint optimization algorithm of server …

Video surveillance systems or Internet of Multimedia Things are playing a more and more
important role in our daily life. To obtain useful surveillance information timely and accurately, …

… We apply value function approximation Q-learning and a deep Q-network (… reinforcement
learning in network caching. The simulation results show that the proposed policy can learn …

… mobile edge networks is illustrated in Fig. 1. We consider a cache-enabled mobile edge
network with N mobile edge … We denote Mc = 11, 2, ..., Nl as the set of mobile edge clouds and …

… mobile edge network. Since network states are unknown random variables, the reinforcement
learning (RL) is adopted to learn the optimal stochastic policy through interactions with the …