The　emergence　of　Software　Defined　Network　(SDN)　provides　a　centralized　and　flexible　approach　to　route　network　flows.　Due　to　the　technical　and　economic　challenges　in　upgrading　to　a　fully　SDN-enabled　network,　hybrid　SDN,　with　a　partial　deployment　of　SDN　switches　in　a　traditional　network,　has　been　a　prevailing　network　architecture.　Meanwhile,　Traffic　Engineering　(TE)　in　the　hydbrid　SDN　has　attracted　wide　attentions　from　academia　and　industry.　Previous　studies　on　TE　in　the　hybrid　SDN　are　either　traffic-oblivious　or　time-consuming,　which　causes　routing　schemes　failed　in　responding　to　the　dynamically-changing　traffic　rapidly　and　intelligently.　Therefore,　in　this　paper,　we　propose　a　Reinforcement　Learning　(RL)　based　method,　which　learns　a　traffic-splitting　agent　to　address　the　dynamically-changing　traffic　and　achieve　the　link　load　balancing　in　the　hybrid　SDN.　Specifically,　to　rapidly　and　intelligently　determine　a　routing　scheme　to　the　new　traffic　demands,　a　traffic-splitting　agent　is　designed　and　learnt　offline　by　exploiting　the　RL　algorithm　to　establish　the　direct　relationship　between　traffic　demands　and　traffic-splitting　policies.　Once　the　traffic-splitting　agent　is　learnt,　the　effective　traffic-splitting　policies,　which　are　used　to　determine　the　traffic-splitting　ratios　on　SDN　switches,　can　be　generated　rapidly.　Additionally,　to　meet　the　interactive　requirements　for　learning　a　traffic-splitting　agent,　a　reasonable　simulation　environment　is　proposed　to　be　constructed　to　avoid　routing　loops　when　traffic-splitting　policies　are　taken.　Extensive　evaluations　on　different　topologies　and　real　traffic　demands　demonstrate　that　the　proposed　method　achieves　the　comparable　network　performance　and　performs　superiorities　in　rapidly　generating　the　satisfying　routing　schemes.　©　2021　Elsevier　Ltd