Multi-Agent Reinforcement Learning for Power Control in Wireless
  Networks via Adaptive Graphs
Lorenzo Mario Amorosa Marco Skocaj Roberto Verdone Deniz Gündüz
The ever-increasing demand for high-quality and heterogeneous wireless
communication services has driven extensive research on dynamic optimization
strategies in wireless networks. Among several possible approaches, multi-agent
deep reinforcement learning (MADRL) has emerged as a promising method to
address a wide range of complex optimization problems like power control.
However, the seamless application of MADRL to a variety of network optimization
problems faces several challenges related to convergence. In this paper, we
present the use of graphs as communication-inducing structures among
distributed agents as an effective means to mitigate these challenges.
Specifically, we harness graph neural networks (GNNs) as neural architectures
for policy parameterization to introduce a relational inductive bias in the
collective decision-making process. Most importantly, we focus on modeling the
dynamic interactions among sets of neighboring agents through the introduction
of innovative methods for defining a graph-induced framework for integrated
communication and learning. Finally, the superior generalization capabilities
of the proposed methodology to larger networks and to networks with different
user categories is verified through simulations.