Reinforcement Learning for Dynamic Traffic Routing and Optimization

This paper addresses the rapidly evolving and expanding needs of network traffic with new methods, most notably Reinforcement Learning (RL). The RL has shown significant potential in addressing the challenges of dynamic traffic routing and optimization. Despite the complexity of adapting to varying network conditions, techniques such as deep RL and multi-agent systems have been proposed to enhance network efficiency. However, the challenges persist, including the scalability of these algorithms and their adaptability to real-time changes. This research aims to address these challenges by proposing a robust RL framework that not only adapts to dynamic traffic conditions but also scales effectively across large network infrastructures. Our research raises important questions: How effective are RL algorithms? What effect do reward functions have? How do they handle big networks? Are these models flexible enough to adjust? One goal of the research, then is to test all RL methods designed for online traffic direction in real time; examine their merits and problems when applied on large networks; discover whether they could be used under various network conditions or if only certain traffic patterns would work well with them. It should also seek out possible ways RL algorithms might increase overall network performance, as well as look at security issues This study is important because it could revolutionize how we manage network traffic. It means better, stronger and more reliable networks with RL, we can develop routing protocols that are flexible and distributed to overcome changing network circumstances as well as reach different destinations or satisfy various restraints.