Despite technological advancements, the significance of interdisciplinary subjects like complex networks has grown. Exploring communication within these networks is crucial, with traffic becoming a key concern due to the expanding population and increased need for connections. Congestion tends to originate in specific network areas but quickly proliferates throughout. Consequently, understanding the transition from a flow-free state to a congested state is vital. Numerous studies have delved into comprehending the emergence and control of congestion in complex networks, falling into three general categories: soft strategies, hard strategies, and resource allocation strategies. This article introduces a routing algorithm leveraging reinforcement learning to address two primary objectives: congestion control and optimizing path length based on the shortest path algorithm, ultimately enhancing network throughput compared to previous methods. Notably, the proposed method proves effective not only in Barab\'asi-Albert scale-free networks but also in other network models such as Watts-Strogatz (small-world) and Erd\"os-R\'enyi (random network). Simulation experiment results demonstrate that, across various traffic scenarios and network topologies, the proposed method can enhance efficiency criteria by up to 30% while reducing maximum node congestion by five times.

翻译：尽管技术进步日新月异，复杂网络等交叉学科的重要性与日俱增。探索这些网络内部的通信机制至关重要，而随着人口增长和连接需求的增加，流量问题已成为关键关注点。拥塞往往起源于特定网络区域，但会迅速蔓延至整个网络。因此，理解从无流状态到拥塞状态的转变至关重要。大量研究致力于理解复杂网络中拥塞的产生与控制，这些研究可分为三大类：软策略、硬策略和资源分配策略。本文提出一种基于强化学习的路由算法，旨在解决两个主要目标：拥塞控制以及基于最短路径算法优化路径长度，从而最终相较先前方法提升网络吞吐量。值得注意的是，所提方法不仅在Barabási-Albert无标度网络中有效，在Watts-Strogatz（小世界）网络和Erdős-Rényi（随机网络）等其他网络模型中也同样有效。仿真实验结果表明，在不同流量场景和网络拓扑下，所提方法可将效率指标提升高达30%，同时将最大节点拥塞程度降低五倍。