A Cooperative Edge Caching Approach Based on Multi-Agent Deep Reinforcement Learning

Abstract

With the support of 5G technology, mobile edge computing has made the application of industrial IoT and power IoT more and more extensive. By deploying a certain number of edge servers at the edge of the network, network service delay may significantly reduce. For the IoT scenario where the content demand is unpredictable, there are multiple distributed cloud servers and the distributed cloud servers do not communicate directly, a feasible way to improve the network service quality is to dynamically optimize the storage of edge servers and formulate targeted caching strategies. This paper proposes an edge caching approach based on multi-agent deep deterministic policy gradient named MADDPG-C, which regards distributed cloud servers and edge servers as different types of agents and maximizes the efficiency of edge caching in cooperation and competition. Simulation experiments show that the proposed MADDPG-C can further improve the hit rate of the edge cache and reduce the waiting delay of terminal devices.