… Abstract—The computational tasks at multi-access edge com… nonlinear optimization
with computational and latency … we apply a model-based deep reinforcement learning (MDRL). …

… IoT system with main contributions summarized as follows: 1) We propose a vehicle edge
computing … traditional edge server. 2) We propose an efficient offloading scheme for the vehicle …

… At this point, how to allocate computing resources and network resources … , Deep
Reinforcement Learning based Resource Allocation (DRLRA) scheme, which can allocate …

… IoT facilities, edge computing can satisfy various IoT applications’ resource and latency
requirements. Sensing-data-driven IoT applications are prevalent in IoT … in an edge-enabled IoT …

… and resource allocation for mobile edge computing by deep reinforcement learning based
on … mobile edge computing via deep reinforcement learning for Industrial Internet of Things,” …

… In this paper, a deep reinforcement learning algorithm is … Edge Computing Server(ECS)
collaborative computing. The … are IoT device layer, heterogeneous edge computing layer …

… devices, these tasks can be offloaded to mobile edge computing (MEC) and cloud for …
reformulate it as an MDP-based dynamic task offloading problem. We design a deep reinforcement …

… equipped with edge computing server and blockchain systems, it is defined as APm in this
article. The edge computing servers enable computation and storage of computing tasks and …

… To make the layer distribution decisions on cloudlets, cloud, and IoT devices, we use
improved deep reinforcement learning. The main contributions of this paper are as follows: …

… and computing capacity, while the IoT devices are usually resource constrained. As a potential
solution, mobile edge computing (… In this work, we propose a deep reinforcement learning …