Hybrid　Software　Defined　Networks　(Hybrid　SDNs),　with　a　partial　upgrade　of　legacy　routers　to　SDN　switches　in　traditional　distributed　networks,　currently　stand　as　a　prevailing　network　architecture.　Traffic　Engineering　(TE)　in　hybrid　SDN　requires　the　efficient　and　timely　acquisition　of　a　routing　policy　to　adapt　to　dynamically　changing　traffic　demands,　which　has　recently　become　a　hot　topic.　Ignoring　the　hidden　relations　of　consecutive　states　and　the　compact　representation　of　the　network　environment,　previous　Deep　Reinforcement　Learning　(DRL)-based　studies　suffer　from　the　convergence　problem　by　only　establishing　the　direct　relationship　between　individual　Traffic　Matrix　(TM)　and　routing　policy.　Therefore,　to　enhance　TE　performance　in　hybrid　SDNs　under　dynamically　changing　traffic　demands,　we　propose　to　integrate　the　Transformer　model　with　DRL　to　establish　the　relationship　between　consecutive　states　and　routing　policies.　The　temporal　characteristic　among　consecutive　states　can　effectively　assist　DRL　in　solving　the　convergence　problem.　To　obtain　a　compact　and　accurate　description　of　the　network　environment,　we　propose　to　jointly　consider　TM,　routing　action,　and　reward　in　designing　the　state　of　the　network　environment.　To　better　capture　the　temporal　relations　among　consecutive　states　of　the　network　environment,　we　design　a　multi-feature　embedding　module　and　achieve　positional　encodings　in　the　Transformer　model.　The　extensive　experiments　demonstrate　that　once　the　convergence　problem　is　solved,　the　proposed　Transformer-based　DRL　method　can　efficiently　generate　routing　policies　that　adapt　well　to　dynamic　network　traffic.　©　2024　Elsevier　B.V.