  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.
