Towards Energy Efficient Resource Allocation: When Green Mobile Edge Computing Meets Multi-Agent Deep Reinforcement Learning
Yang Xiao, Yuqian Song, Jun Liu · 2022
Mobile edge computing (MEC) extends the computing power to the edge of communication networks, which has been considered as a promising technology to further improve the quality of communication services in the near future. Nevertheless, the issue of MEC-empowered energy efficient resource allocation has not been well studied. To maximize the longterm energy efficiency for green MEC-enabled heterogeneous networks (HetNets), we proposed a decentralized multi-agent deep reinforcement learning (MADRL) resource allocation algorithm. Based on the proximal policy optimization (PPO) framework, our proposed algorithm enables observation exchange to coordinate the policies of multiple agents. Simulation results show that our proposed algorithm significantly outperforms three baseline methods in terms of effectiveness, robustness, and scalability.