基于自注意力深度强化学习的特定流路由选择算法OA北大核心

To effectively mitigate the negative impact of rerouting on the network in traditional traffic engineering mechanisms,this paper proposes a specific flow routing selection algorithm based on Self-Attention deep reinforcement learning,leveraging the global network perspective and management capabilities of software-defined networking,to reroute a small amount of traffic and achieve near-optimal performance.A neural network model with multi-scale fusion attention mechanism is used to extract features of traffic,and a centralized training distributed execution architecture is adopted to make real-time decisions based on the observed network state.The theoretical research and experimental results show that compared with traditional deep reinforcement learning algorithms and heuristic algorithms,the proposed algorithm has significant improvements in average load and end-to-end delay performance.