This paper introduces a Deep Reinforcement Learning (DRL) based TCP congestion-control algorithm that uses a Deep Q-Network (DQN) to adapt the congestion window (cWnd) dynamically based on observed network state. The proposed approach utilizes DQNs to optimize the congestion window 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 that the DRL-based algorithm provides a superior balance between throughput and latency compared to both traditional TCP New Reno and TCP Cubic algorithms. Specifically: Compared to TCP Cubic, the DRL algorithm achieved comparable throughput (statistically insignificant difference of -3.79%, $p>0.05$) while delivering a massive 46.29% reduction in Round-Trip Time (RTT). Furthermore, the DRL agent maintained near-zero packet loss, whereas Cubic suffered from significant buffer overflow. Compared to TCP New Reno, the DRL algorithm achieved comparable throughput (+0.38%) with a 32.40% reduction in RTT. Results from NS-3 simulations indicate that the proposed DRL agent effectively mitigates bufferbloat without compromising bandwidth utilization. This study emphasizes the potential of reinforcement learning techniques for solving complex congestion control problems in modern networks by learning the network capacity rather than saturating it.

翻译：本文介绍了一种基于深度强化学习（DRL）的TCP拥塞控制算法，该算法使用深度Q网络（DQN）根据观测到的网络状态动态调整拥塞窗口（cWnd）。所提出的方法利用DQN通过观测关键网络参数并采取实时行动来优化拥塞窗口。该算法在NS-3网络模拟器中使用OpenGym接口进行训练和评估。结果表明，与传统的TCP New Reno和TCP Cubic算法相比，基于DRL的算法在吞吐量和延迟之间提供了更优的平衡。具体而言：与TCP Cubic相比，DRL算法实现了可比的吞吐量（统计上不显著的差异为-3.79%，$p>0.05$），同时实现了高达46.29%的往返时间（RTT）降低。此外，DRL智能体保持了接近零的丢包率，而Cubic则遭受了显著的缓冲区溢出。与TCP New Reno相比，DRL算法实现了可比的吞吐量（+0.38%），同时RTT降低了32.40%。NS-3仿真结果表明，所提出的DRL智能体在不损害带宽利用率的情况下，有效缓解了缓冲区膨胀问题。本研究强调了强化学习技术通过感知网络容量而非使其饱和，来解决现代网络中复杂拥塞控制问题的潜力。