Coordinated Reinforcement Learning for Optimizing Mobile Networks

TL;DR

This paper presents a scalable multi-agent reinforcement learning approach using coordination graphs to optimize complex mobile networks with hundreds of cooperating base stations, outperforming existing methods.

Contribution

It introduces a novel application of coordination graphs combined with reinforcement learning for large-scale mobile network optimization, leveraging expert knowledge and neural network-based edge value functions.

Findings

Coordinated RL outperforms other methods in network optimization.

Graph structure enables explicit learning of coordination behaviors.

Local updates and parameter sharing handle many agents efficiently.

Abstract

Mobile networks are composed of many base stations and for each of them many parameters must be optimized to provide good services. Automatically and dynamically optimizing all these entities is challenging as they are sensitive to variations in the environment and can affect each other through interferences. Reinforcement learning (RL) algorithms are good candidates to automatically learn base station configuration strategies from incoming data but they are often hard to scale to many agents. In this work, we demonstrate how to use coordination graphs and reinforcement learning in a complex application involving hundreds of cooperating agents. We show how mobile networks can be modeled using coordination graphs and how network optimization problems can be solved efficiently using multi- agent reinforcement learning. The graph structure occurs naturally from expert knowledge about the network and allows to explicitly learn coordinating behaviors between the antennas through edge value functions represented by neural networks. We show empirically that coordinated reinforcement learning outperforms other methods. The use of local RL updates and parameter sharing can handle a large number of agents without sacrificing coordination which makes it well suited to optimize the ever denser networks brought by 5G and beyond.