MACC: Cross-Layer Multi-Agent Congestion Control with Deep Reinforcement Learning
This addresses congestion control for Internet communication applications such as 5G and IoT, representing an incremental improvement through a novel hybrid method.
The paper tackles the challenge of modeling and coordinating dynamic Active Queue Management (AQM) and Transmission Control Protocol (TCP) congestion control algorithms across network layers by proposing MACC, a multi-agent deep reinforcement learning approach, which in NS3 simulations outperforms other combinations in metrics like throughput and delay.
Congestion Control (CC), as the core networking task to efficiently utilize network capacity, received great attention and widely used in various Internet communication applications such as 5G, Internet-of-Things, UAN, and more. Various CC algorithms have been proposed both on network and transport layers such as Active Queue Management (AQM) algorithm and Transmission Control Protocol (TCP) congestion control mechanism. But it is hard to model dynamic AQM/TCP system and cooperate two algorithms to obtain excellent performance under different communication scenarios. In this paper, we explore the performance of multi-agent reinforcement learning-based cross-layer congestion control algorithms and present cooperation performance of two agents, known as MACC (Multi-agent Congestion Control). We implement MACC in NS3. The simulation results show that our scheme outperforms other congestion control combination in terms of throughput and delay, etc. Not only does it proves that networking protocols based on multi-agent deep reinforcement learning is efficient for communication managing, but also verifies that networking area can be used as new playground for machine learning algorithms.