… Abstract—In mobile edge computing systems, an edge node may have a high load when
a large number of mobile devices offload their tasks to it. Those offloaded tasks may …

… for mobile devices, mobile-edge computing (MEC) is emerging as a promising paradigm by
providing computing … Illustration of a mobile-edge computing system with wireless charging …

… Her research interests cover smart city, mobile edge computing, and deep reinforcement …
, in 2015, and the MS degree in computer system architecture from Xidian University, in 2018. …

… 3) By further exploring the additive structure of the utility function, we attain a novel online
deep stateaction-reward-state-action-based reinforcement learning algorithm (Deep-SARL) for …

… Towards this end, we propose a deep reinforcement learning-… in virtual edge computing
systems via deep reinforcement … Wu, “Deep reinforcement learning-based joint task offloading …

… , this paper proposes a computing offloading resource allocation strategy based on deep
reinforcement learning in Internet of Vehicles. Firstly, the system architecture for Internet of …

… hierarchical system deployment for edge computing systems,… for mobile edge computing
by deep reinforcement learning … multi-access mobile edge computing via deep reinforcement …

… for mobile edge computing by deep reinforcement learning … multi-access edge computing:
A deep reinforcement learning … Wu, Deep reinforcement learning-based joint task offloading …

… Due to the stochastic and dynamic nature of such a mobile edge computing system, we
leverage reinforcement learning to solve the problem, by reformulating it as a Markov Decision …

… on the deep reinforcement learning (DQN) to tackle the problem. Reinforced learning problems
… DQN provides a stable solution for deep value-based reinforcement learning problems. …