Compositional Learning for Modular Multi-Agent Self-Organizing Networks

Qi Liao; Parijat Bhattacharjee

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arXiv · 2506.02616

Compositional Learning for Modular Multi-Agent Self-Organizing Networks

Qi Liao·Parijat Bhattacharjee

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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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BibTeX RIS

Qi Liao, Parijat Bhattacharjee. 2025-06-03. Compositional Learning for Modular Multi-Agent Self-Organizing Networks. https://arxiv.org/abs/2506.02616

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