%0 Journal Article
%A CHEN Shizhao
%A LI Dunqiao
%A WEI Yifei
%A ZHOU Junhua
%A DU Mei
%T A Joint Intelligent Optimization Scheme of Computation Offloading and Resource Allocation for MEC
%D 
%R 10.13190/j.jbupt.2021-145
%J Journal of Beijing University of Posts and Telecommunications
%P 65-71
%V 45
%N 2
%X Due to the distributed base station deployment, limited server resources and dynamic end-users in mobile edge computing (MEC), the design of computation offloading scheme is extremely challenging. Since the deep reinforcement learning has advantages in terms of dealing with dynamic complex problems, we design the optimal computation offloading and resource allocation strategies based on deep reinforcement learning to minimize the system energy consumption. First, the network framework of cloud edge-end collaboration is considered. Then, the joint computation offloading and resource allocation problem is defined as a Markov decision process.Next, a multi-agent deep deterministic policy gradient-based learning algorithm is proposed to minimize the system energy consumption. The experimental results show that our proposed scheme significantly reduces the energy consumption compared to the deep deterministic policy gradient-based algorithm and the full offloading policy.
%U https://journal.bupt.edu.cn/EN/10.13190/j.jbupt.2021-145