With　the　rapid　growth　and　widespread　usage　of　smart　devices,　the　emerging　Internet　service　represented　by　face　recognition　and　video　streaming　have　brought　great　traffic　pressure　for　the　existing　mobile　communication　networks.　Moreover,　the　users＇　mobility　makes　traffic　engineering　more　complicated.　Cloud-edgeend　coordination　has　recently　been　regarded　as　effective　solution　to　improve　traffic　distribution.　In　order　to　reduce　redundant　content　transmission　and　improve　end-users＇　quality　of　experience　in　mobile　cloud-edge-end　cooperation　environments,　we　propose　a　traffic　engineering　algorithm　based　on　deep　reinforcement　learning　(DRL)　in　this　paper　to　tackle　these　challenges.　We　model　the　optimal　network　traffic　problem　as　a　maximal　traffic　offloading　model,　where　network　devices＇　caching　capacity　is　considered　and　the　mobile　users＇　same　requests　will　be　aggregated.　We　design　a　new　DRL　scheme　to　solve　the　maximal　traffic　offloading　model　based　on　request　history　and　timely　network　status　in　the　system.　Numerical　results　show　that　the　proposed　policy　demonstrates　much　better　compared　to　the　existing　popular　counterparts　in　cloud-edge-side　collaboration　networks.　©　2022　IEEE.