Abstract: With the rapid development of network applications and the increasing diversification of network scenarios, the design of congestion control algorithms faces unprecedented challenges. Artificial intelligence (AI) methods, leveraging their robust adaptability and decision-making capabilities, have become a focal point for both academia and industry. Consequently, AI-based network congestion control algorithms have emerged. This paper systematically reviews recent advancements in AI-based network congestion control research, analyzing technical approaches, application scenarios, training, and experimentation. Building on this analysis, future research directions are also explored.

Key words: network congestion control, machine learning, reinforcement learning, deep reinforcement learning