🤖 AI Summary
Traditional TCP congestion control mechanisms (e.g., NewReno) suffer from poor adaptability, high latency, and low throughput in dynamic network environments. To address these limitations, this paper proposes an end-to-end, deep reinforcement learning–based approach for adaptive congestion window (cWnd) control. It introduces the Deep Q-Network (DQN) into TCP congestion control for the first time, leveraging a co-simulation framework integrating NS-3 and OpenGym to enable real-time online perception and decision-making based on dynamic network states—including round-trip delay, packet loss, and bandwidth fluctuations. Departing from fixed heuristic rules, the method employs a weighted reward function combining throughput and latency to guide policy learning, significantly enhancing generalization and responsiveness across heterogeneous network scenarios. Experimental results demonstrate that, compared to NewReno, the proposed scheme reduces average end-to-end latency by 32%, improves throughput by approximately 28%, and exhibits superior stability.
📝 Abstract
This paper presents a novel TCP congestion control algorithm based on Deep Reinforcement Learning. The proposed approach utilizes Deep Q-Networks to optimize the congestion window (cWnd) by observing key network parameters and taking real-time actions. The algorithm is trained and evaluated within the NS-3 network simulator using the OpenGym interface. The results demonstrate significant improvements over traditional TCP New Reno in terms of latency and throughput, with better adaptability to changing network conditions. This study emphasizes the potential of reinforcement learning techniques for solving complex congestion control problems in modern networks.