TY - EJOU
AU - Wei, Yifei
AU - Wang, Zhaoying
AU - Guo, Da
AU - Yu, F. Richard
TI - Deep Q-Learning Based Computation Offloading Strategy for Mobile Edge Computing
T2 - Computers, Materials \& Continua
PY - 2019
VL - 59
IS - 1
SN - 1546-2226
AB - To reduce the transmission latency and mitigate the backhaul burden of the centralized cloud-based network services, the mobile edge computing (MEC) has been drawing increased attention from both industry and academia recently. This paper focuses on mobile usersâ€™ computation offloading problem in wireless cellular networks with mobile edge computing for the purpose of optimizing the computation offloading decision making policy. Since wireless network states and computing requests have stochastic properties and the environmentâ€™s dynamics are unknown, we use the model-free reinforcement learning (RL) framework to formulate and tackle the computation offloading problem. Each mobile user learns through interactions with the environment and the estimate of its performance in the form of value function, then it chooses the overhead-aware optimal computation offloading action (local computing or edge computing) based on its state. The state spaces are high-dimensional in our work and value function is unrealistic to estimate. Consequently, we use deep reinforcement learning algorithm, which combines RL method Q-learning with the deep neural network (DNN) to approximate the value functions for complicated control applications, and the optimal policy will be obtained when the value function reaches convergence. Simulation results showed that the effectiveness of the proposed method in comparison with baseline methods in terms of total overheads of all mobile users.
KW - Mobile edge computing
KW - computation offloading
KW - resource allocation
KW - deep reinforcement learning
DO - 10.32604/cmc.2019.04836