Compositional Learning for Modular Multi-Agent Self-Organizing Networks

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.

@article{liao2025_2506.02616,
  title={ Compositional Learning for Modular Multi-Agent Self-Organizing Networks },
  author={ Qi Liao and Parijat Bhattacharjee },
  journal={arXiv preprint arXiv:2506.02616},
  year={ 2025 }
}