A Novel Reinforcement Learning Routing Algorithm for Congestion Control
  in Complex Networks
Seyed Hassan Yajadda Farshad Safaei
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.