To　reduce　the　investment　on　network　infrastructure,　many　online　service　providers　have　begun　to　adopt　the　shared　inter-DataCenter　Wide　Area　Network　(inter-DC　WAN)　that　connects　different　datacenters　and　Internet　Service　Providers　(ISPs).　The　shared　inter-DC　WAN　accommodates　two　kinds　of　traffic,　i.e.　delay-sensitive　ISP-facing　traffic　and　high-throughput　inter-DC　traffic.　Traffic　Engineering　(TE)　in　the　shared　inter-DC　WAN　should　determine　the　routing　paths　for　all　traffic　to　achieve　link　load　balancing,　while　select　lower-latency　egress　routers　for　ISP-facing　traffic　to　guarantee　the　Quality　of　Service　(QoS).　Therefore,　this　paper　mainly　focuses　on　jointly　optimizing　routing　paths　selection　and　egress　router　selection　to　strike　a　balance　between　QoS　and　link　load　balancing.　Specifically,　we　first　formulate　the　TE　problem　in　the　shared　inter-DC　WAN　as　a　mixed　integer　nonlinear　programming　problem.　Then,　a　TED　method　is　proposed　to　jointly　optimize　the　egress　router　selection　and　routing　path　selection　by　learning　an　intelligent　agent　with　Deep　Reinforcement　Learning　(DRL).　The　learnt　agent　can　self-adaptively　and　rapidly　select　the　optimal　egress　routers　by　considering　the　utilization-latency　balance　when　traffic　demand　changes.　Finally,　we　conduct　extensive　evaluations　on　Alibaba　WAN　with　real　traffic　traces　to　demonstrate　the　effectiveness　and　superiority　of　the　proposed　method.