Joint Optimization of Task Offloading and Resource Allocation via Deep Reinforcement Learning for Augmented Reality in Mobile Edge Network

摘要

Mobile edge computing (MEC) has been recognized as emerging techniques in 5G to provide powerful computing capabilities for the Ultra Reliable Low Latency Communication (URLLC) applications. In this paper, a MEC enable multi-user wireless network is considered by offloading the computation task to MEC server, reducing latency and energy consumption of user terminal for Augmented Reality (AR) application. The joint optimization problem of resource allocation and task offloading is studied to minimize the energy consumption of each user subject to the delay requirement and the limited resources. We propose a deep reinforcement learning algorithm based on a multi-agent deep deterministic policy gradient (MADDPG) to solve this problem. Simulation results show that the proposed algorithm can greatly reduce energy consumption of the users.