%0 Journal Article
%A CHENG Chao
%A SONG Mei
%A TENG Jun-jie
%A ZHAO Yan-ling
%T Traffic Distribution Algorithm Based on Multi-Agent Reinforcement Learning
%D 2019
%R 10.13190/j.jbupt.2019-140
%J Journal of Beijing University of Posts and Telecommunications
%P 43-48,57
%V 42
%N 6
%X Most of the researches on traditional traffic engineering strategies focus on constructing and solving mathematical models. To reduce computational complexity,an experience-driven traffic allocation algorithm based on multi-agent reinforcement learning was proposed. It can effectively distribute traffic on pre-calculated paths without solving complex mathematical models and then fully utilize network resources. The algorithm performs centralized training on the software defined networking controller,and can be executed on the access switch or router in a distributed way after the training is completed. Frequent interactions with the controller are avoided at the same time. Experiments show that the algorithm is effective in reducing the end-to-end delay and increasing throughput of the network with respect to the shortest-path and the equal-cost multi-path.
%U https://journal.bupt.edu.cn/EN/10.13190/j.jbupt.2019-140