Future generations of mobile networks are expected to contain more and more
antennas with growing complexity and more parameters. Optimizing these
parameters is necessary for ensuring the good performance of the network. The
scale of mobile networks makes it challenging to optimize antenna parameters
using manual intervention or hand-engineered strategies. Reinforcement learning
is a promising technique to address this challenge but existing methods often
use local optimizations to scale to large network deployments. We propose a new
multi-agent reinforcement learning algorithm to optimize mobile network
configurations globally. By using a value decomposition approach, our algorithm
can be trained from a global reward function instead of relying on an ad-hoc
decomposition of the network performance across the different cells. The
algorithm uses a graph neural network architecture which generalizes to
different network topologies and learns coordination behaviors. We empirically
demonstrate the performance of the algorithm on an antenna tilt tuning problem
and a joint tilt and power control problem in a simulated environment.

该论文提出了一种新的多智能体强化学习算法，使用图神经网络结构进行全局优化移动网络配置，实现对移动网络的优化。