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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