A Deep Reinforcement Learning-Based TCP Congestion Control Algorithm: Design, Simulation, and Evaluation

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.