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In this paper, the Transformer BBR algorithm is proposed, which is a congestion control algorithm based on BBR deep reinforcement learning combined with Transformer\u2019s excellent long-term prediction ability. In the BBR algorithm bandwidth detection stage, transformer model is used as an agent to detect the network state and map it to the congestion window, so as to find the network changing state in time and make the corresponding decision action. Firstly, the congestion control strategy is learned in the simulation network environment, and then the efficiency is verified in the simulation environment. Finally, the experiment shows that the algorithm can reduce the delay while ensuring good bandwidth performance, and is superior to the main application algorithms in network throughput.<\/jats:p>","DOI":"10.3233\/jcm-226352","type":"journal-article","created":{"date-parts":[[2022,8,23]],"date-time":"2022-08-23T11:31:05Z","timestamp":1661254265000},"page":"2185-2199","source":"Crossref","is-referenced-by-count":0,"title":["Noninvasive real-time traffic and congestion control algorithm based on policy"],"prefix":"10.1177","volume":"22","author":[{"given":"Wenjing","family":"Li","sequence":"first","affiliation":[]},{"given":"Nan","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Jinhong","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Zhu","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Wensheng","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yan","family":"Li","sequence":"additional","affiliation":[]},{"given":"Wenjing","family":"Guo","sequence":"additional","affiliation":[]}],"member":"179","reference":[{"key":"10.3233\/JCM-226352_ref1","doi-asserted-by":"crossref","unstructured":"Jacobsen V, Karels MJ. 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