Compositional Learning for Modular Multi-Agent Self-Organizing Networks

2025/06/03 by Qi Liao, Liao, Qi, Parijat Bhattacharjee +1
Computer Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Neural Networks and Applications

paper · pdf · doi:10.48550/arxiv.2506.02616

openalex publication_date 2025/06/03 · openalex created_date 2025/10/14 · openalex updated_date 2026/07/28

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.

Citations

• Fast and Scalable Network Slicing by Integrating Deep Learning with Lagrangian Methods
• Modular Deep Learning
• Knowledge Transfer in Deep Reinforcement Learning for Slice-Aware Mobility Robustness Optimization
• Inter-Cell Slicing Resource Partitioning via Coordinated Multi-Agent Deep Reinforcement Learning
• Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled\n Wireless Networks: A Tutorial
• Task-Driven Modular Networks for Zero-Shot Compositional Learning
• Addressing Function Approximation Error in Actor-Critic Methods
• Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
• Выпуклая оптимизация

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