Dynamic Routing via Reinforcement Learning for Network Traffic Optimization

With the rapid development of the Internet, network traffic has shown explosive growth, which puts forward higher requirements for the network routing system. Traditional static routing methods are no longer able to meet the needs of today's complex and ever-changing network environment, as they cannot be flexibly adjusted according to real-time network conditions. In order to address this challenge, this paper proposes an innovative dynamic routing method. This method is based on reinforcement learning, especially Q-learning algorithm, which realizes the dynamic adjustment of routing decisions through continuous learning and adaptation to changes in the network environment. Our goal is to minimize root mean square error (RMSE) to improve routing accuracy, while at the same time improving load balancing efficiency to ensure that network resources are fully utilized. In order to verify the effectiveness of this method, we conducted detailed simulation experiments. Experimental results show that compared with the baseline method, our dynamic routing method significantly improves the throughput of the network, which increases by 30%, effectively reduces the delay, and reduces 25%. These positive results not only prove the effectiveness of our method in network traffic optimization, but also provide new ideas for the development of network routing system in the future