A networked multi-agent reinforcement learning approach for cooperative FemtoCaching assisted wireless heterogeneous networks

Abstract

To meet the explosive growth of mobile traffic requirement in the 5th generation (5 G) mobile system, FemtoCaching at the network edge has been regarded as a promising technique for the 5 G mobile system. In this paper, we focus on studying the cooperative FemtoCaching problem in wireless heterogeneous networks (HetNets), which is aimed to minimize the overall fetching delays of all users. Owing to the NP-hardness of the problem, we formulate the cooperative FemtoCaching problem as a Networked Multi-Agent Reinforcement Learning (NMARL) problem and accordingly propose a Soft Attentional Networked Multi-Agent Actor-Critic (SAN-AC) Reinforcement Learning algorithm. Simulation results demonstrate that the proposed algorithm can significantly increase the overall performance compared with existing work.