Multi-user Computation Offloading for Mobile Edge Computing: A Deep Reinforcement Learning and Game Theory Approach

摘要

At present, with the development of the Internet of Things (IoT) and the Internet of Everything (IoE), Mobile edge computing (MEC) is proposed to provide universal and flexible computing services at the edge of the wireless access network. To use the services provided by the MEC, how to make efficient and reasonable offloading decisions is very important. In this paper, we study the problem of interference-aware multi-user computation offloading in the MEC. For this problem, we formulate a multi-user computation offloading game model and analyze the existence of nash equilibrium. Then, we design the computation offloading algorithm (e.g., Nash-Q-learning) based on nash equilibrium and reinforcement learning to minimize system overhead. Furthermore, in order to avoid Nash-Q-learning suffering from dimensional disaster, we get a deep reinforcement learning algorithm (e.g., Nash-DQN) by adding the neural networks to Nash-Q-learning. The performance of the proposed algorithms has been compared with the other algorithms by simulation. The simulation results show that the performance of the proposed algorithms is superior to the other multi-user computation offloading algorithms.