Compositional Learning for Modular Multi-Agent Self-Organizing Networks

Abstract--Self-organizing networks face challenges from complex parameter interdependencies and conflicting objec - tives. This study introduces two compositional learning ap - proaches--Compositional Deep Reinforcement Learning (CDR L) and Compositional Predictive Decision-Making (CPDM)--and evaluates their performance under training time and safety constraints in multi-agent systems. We propose a modular, t wo-tier framework with cell-level and cell-pair-level agents to manage heterogeneous agent granularities while reducing model co mplex-ity. Numerical simulations reveal a 37.2% reduction in handover failures, along with improved throughput and latency, outp er-forming conventional multi-agent deep reinforcement lear ning approaches. The approach also demonstrates superior scala bility, faster convergence, higher sample efficiency, and safer tra ining in large-scale self-organizing networks. Self-organizing networks (SON) is a key enabler of autonomous networks, leveraging mechanisms like mobility ro - bustness optimization (MRO) and mobility load balancing (MLB) [1] to dynamically optimize network control parameters using key performance indicators (KPIs).