… factory framework based on edge computing, and further … , the deep Q network (DQN), which
combines deep learning and reinforcement learning, has showed its great computing power …

… a deep reinforcement learning approach for computation offloading decision issue with mobile
edge computing. The … In order to solve this problem, we apply deep neural network in RL …

… deployment problem (MMDP) in edge computing. MMDP aims to minimize the … between
edge nodes. Without the requirement for domain experts, we propose Reward Sharing Deep Q …

… , in response to dynamic network conditions. Multi-access edge computing (MEC) is a … -scaling
method using deep Q-networks (DQN), which is a reinforcement learning algorithm, …

… , called “DL inference in Edge”. At last, we classify all techniques, which adapts edge
computing frameworks and networks to better serve Edge DL, as “Edge computing for DL”. …

By offloading storage and computing resources to the edge of networks, mobile edge
computing (MEC) is emerged as a promising architecture to reduce the transmission delay and …

… allocation policy for the IoT edge computing system to improve the … deep reinforcement
learning approach is applied to solve the problem. We also propose an improved deep Q-network …

… To break the curse of high dimensionality in state space, we propose a deep Q-network-… a
revolution in terms of computing infrastructure [2]. Mobile-edge computing (MEC) is envisioned …

… resource optimization of edge computing, an intelligent task dispatching model using a deep
Q-network, which can efficiently use the computing resource of edge nodes is proposed to …

… gateways, we allow an edge gateway to offload tasks to a nearby edge gateway further. We
propose a deep Q-learning-based joint optimization approach for both device-level and edge…