… , we adopt a model-free deep reinforcement learning scheme called deep Q networks (DQN).
In DQN, the optimal policy is obtained by update Q values in a neural network. the neural …

… in wireless MEC. Specifically, the Q… Deep Reinforcement Learning (DRL) [7] as an enhanced
version of RL. Based on DRL, we propose a Deep Q Network (DQN) which can use a Deep …

… offloading strategy for MEC networks. The offloading performance of MEC networks can be
… In this paper, we investigate a multiuser MEC network, where the tasks from users can be …

… This paper aims to reduce the total system latency of MUs in the MEC network while consuming
less energy as possible. The system latency and energy consumption are minimized by …

… have been proposed and applied to MEC systems, such as deep neural network (DNN) [13]…
Yang, “Deep learning based joint resource scheduling algorithms for hybrid MEC networks,” …

… MEC networks based on DRL (DROO), making real-time resource allocation possible for
wireless MEC networks … In the face of a large-scale dynamic MEC network, the convergence of …

… In the MEC-enabled network (“MEC network” is used for convenience in the following), MDs
… UAV scenario in MEC network. Recently, deployment of multi-UAV for MEC has drawn great …

… when new MEC devices join the network [30]. How to improve security and training efficiency
for DRL in dynamic MEC networks still remains an open problem for further investigation. …

… (MEC) network, through a deep reinforcement learning … secure MEC network, based on the
deep reinforcement learning… where a novel feature extraction network is proposed to improve …

… In order to verify the performance of our proposed CCA + DDPG algorithm, we consider a
multi-region MEC network, where each region is equipped with an edge server to assist in the …