Multi-Agent Reinforcement Learning for Power Control in Wireless
Networks via Adaptive Graphs
- URL: http://arxiv.org/abs/2311.15858v1
- Date: Mon, 27 Nov 2023 14:25:40 GMT
- Title: Multi-Agent Reinforcement Learning for Power Control in Wireless
Networks via Adaptive Graphs
- Authors: Lorenzo Mario Amorosa, Marco Skocaj, Roberto Verdone, and Deniz
G\"und\"uz
- Abstract summary: Multi-agent deep reinforcement learning (MADRL) has emerged as a promising method to address a wide range of complex optimization problems like power control.
We present the use of graphs as communication-inducing structures among distributed agents as an effective means to mitigate these challenges.
- Score: 1.1861167902268832
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: 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.
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