The　existing　coflow　scheduling　schemes　minimize　coflow　completion　time　(CCT)　based　on　the　information　of　previous　coflows,　which　makes　them　hard　to　use　in　practice.　Thus,　new　scheduling　mechanisms　without　considering　prior　knowledge　are　proposed,　such　as　Aalo.　In　general,　these　algorithms　demote　coflows　from　the　highest　priority　queue　into　several　lower　priority　queues　when　their　sent-bytes　exceed　predefined　thresholds.　However,　most　of　the　information-agnostic　algorithms　use　static　thresholds,　and　these　coflow　scheduling　mechanisms　may　suffer　a　performance　penalty　when　threshold　settings　mismatch　traffic.　In　this　paper,　an　information-agnostic　coflow　scheduler　DeepAalo　is　proposed　to　minimize　CCT　by　automatically　adjusting　thresholds　of　queues.　DeepAalo　applies　Deep　Reinforcement　Learning(DRL)　techniques　to　translate　the　design　of　thresholds　into　a　continuous　learning　process.　Specifically,　DeepAalo　can　collect　network　information,　learn　from　past　decisions,　and　automatically　update　the　thresholds　of　queues　every　t　time.　Therefore,　DeepAalo　has　better　self-adaptability　when　traffic　changes.　A　flow-level　simulator　using　python　is　developed,　and　the　simulation　results　show　that　DeepAalo　improves　the　average　CCT　up　to　1.37×　over　Aalo.　©　2020　IEEE.