Nowadays,　a　hybrid　Software-Defined　Network　(hybrid　SDN),　which　combines　the　robustness　of　the　distributed　network　and　the　flexibility　of　the　centralized　network,　is　a　prevailing　network　architecture.　The　performance　of　Traffic　Engineering　(TE)　in　a　hybrid　SDN　is　largely　influenced　by　the　location　of　SDN　switches.　To　derive　the　optimal　location　of　SDN　switches,　the　previous　SDN　switches　deployment　strategies　mainly　focused　on　manually　designing　heuristics　to　search　for　the　SDN　deployment　sequence　and　only　take　a　static　Traffic　Matrix　(TM)　into　consideration.　However,　the　manually-designed　heuristics　cannot　capture　the　complex　intrinsic　relations　among　the　location　of　SDN　switches,　network　topology,　and　dynamic　traffic　demands.　In　addition,　the　SDN　deployment　strategy　optimized　under　a　single　TM　exhibits　poor　performance　in　a　dynamic　environment.　Therefore,　in　this　paper,　we　propose　a　Deep　Reinforcement　Learning　(DRL)-based　algorithm　SEED　to　intelligently　learn　the　SDN　deployment　strategy　under　multiple　TMs.　Specifically,　to　capture　the　dynamic　traffic　information,　we　first　cluster　the　historical　traffic　demand　matrices　for　obtaining　the　representative　Traffic　Matrices　(TM)　that　can　depict　the　dynamic　traffic.　Then,　to　intelligently　learn　the　intrinsic　relations　between　the　topology,　TMs,　and　the　location　of　SDN　switches,　we　design　a　DRL　agent　under　multiple　TMs　by　interacting　with　the　environment　in　a　trial　and　error　manner.　The　extensive　experiments　on　three　real　network　topologies　and　traffic　demands　demonstrate　that　our　proposed　SDN　switches　deployment　strategy　can　better　adapt　to　the　dynamic　traffic　and　better　improve　the　TE　performance　than　the　other　deployment　strategies.　©　2022　IEEE.