Complex　dynamic　services　and　heterogeneous　network　environments　make　the　asymmetrical　control　a　curial　issue　to　handle　on　the　Internet.　With　the　advent　of　the　Internet　of　Things　(IoT)　and　the　fifth　generation　(5G),　the　emerging　network　applications　lead　to　the　explosive　growth　of　mobile　traffic　while　bringing　forward　more　challenging　service　requirements　to　future　radio　access　networks.　Therefore,　how　to　effectively　allocate　limited　heterogeneous　network　resources　to　improve　content　delivery　for　massive　application　services　to　ensure　network　quality　of　service　(QoS)　becomes　particularly　urgent　in　heterogeneous　network　environments.　To　cope　with　the　explosive　mobile　traffic　caused　by　emerging　Internet　services,　this　paper　designs　an　intelligent　optimization　strategy　based　on　deep　reinforcement　learning　(DRL)　for　resource　allocation　in　heterogeneous　cloud-edge-end　collaboration　environments.　Meanwhile,　the　asymmetrical　control　problem　caused　by　complex　dynamic　services　and　heterogeneous　network　environments　is　discussed　and　overcome　by　distributed　cooperation　among　cloud-edge-end　nodes　in　the　system.　Specifically,　the　multi-layer　heterogeneous　resource　allocation　problem　is　formulated　as　a　maximal　traffic　offloading　model,　where　content　caching　and　request　aggregation　mechanisms　are　utilized.　A　novel　DRL　policy　is　proposed　to　improve　content　distribution　by　making　cache　replacement　and　task　scheduling　for　arriving　content　requests　in　accordance　with　the　information　about　users＇　history　requests,　in-network　cache　capacity,　available　link　bandwidth　and　topology　structure.　The　performance　of　our　proposed　solution　and　its　similar　counterparts　are　analyzed　in　different　network　conditions.