Compositional Learning for Modular Multi-Agent Self-Organizing Networks

• URL: http://arxiv.org/abs/2506.02616v1
• Date: Tue, 03 Jun 2025 08:33:18 GMT
• Title: Compositional Learning for Modular Multi-Agent Self-Organizing Networks
• Authors: Qi Liao, Parijat Bhattacharjee,
• Abstract summary: Self-organizing networks face challenges from complex parameter interdependencies and conflicting objectives.<n>This study introduces two compositional learning approaches-Compositional Deep Reinforcement Learning (CDRL) and Compositional Predictive Decision-Making (CPDM)<n>We propose a modular, two-tier framework with cell-level and cell-pair-level agents to manage heterogeneous agent granularities while reducing model complexity.
• Score: 0.7122137885660501
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Self-organizing networks face challenges from complex parameter interdependencies and conflicting objectives. This study introduces two compositional learning approaches-Compositional Deep Reinforcement Learning (CDRL) and Compositional Predictive Decision-Making (CPDM)-and evaluates their performance under training time and safety constraints in multi-agent systems. We propose a modular, two-tier framework with cell-level and cell-pair-level agents to manage heterogeneous agent granularities while reducing model complexity. Numerical simulations reveal a significant reduction in handover failures, along with improved throughput and latency, outperforming conventional multi-agent deep reinforcement learning approaches. The approach also demonstrates superior scalability, faster convergence, higher sample efficiency, and safer training in large-scale self-organizing networks.

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