An Adaptive Hirarchical Meta Agent for Intelligent Congestion Control in IP Networks using Machine Intelligence

Abstract

Modern IP networks face significant challenges in maintaining performance under dynamic and diverse traffic conditions. Traditional congestion control algorithms, such as TCP Reno, Cubic, and even recent reinforcement learning (RL) methods like PPO and DQN, often respond uniformly to packet loss, failing to distinguish between congestion-induced losses and those arising from wireless interference or hardware failures. This paper introduces AHMA (Adaptive Hierarchical Meta-Agent) — a novel, two-stage intelligent congestion control framework that integrates a Bayesian Transformer-based classifier with a Meta-Evolutionary Reinforcement Learning (Meta-ES-RL) controller. AHMA first classifies the cause of packet loss in real-time and then dynamically selects an optimized control strategy based on classification confidence. Using a synthetically generated NS-3 dataset of 1000 labeled flow samples, we evaluate AHMA’s performance against PPO, DQN, TCP Cubic, and TCP Reno across key metrics. Experimental results show that AHMA achieves a decision accuracy of 92%, reduces packet loss to 8.56% with improved throughput, and decreased latency, outperforming all baseline methods. This approach represents a significant advancement in adaptive, cause-aware congestion management, with strong potential for deployment in next-generation high-performance IP and 5G networks.