Vehicular　ad　hoc　networks　(VANETs)　have　become　a　promising　technology　in　smart　transportation　systems　with　rising　interest　of　expedient,　safe,　and　high-efficient　transportation.　Dynamicity　and　infrastructure-less　of　VANETs　make　it　vulnerable　to　malicious　nodes　and　result　in　performance　degradation.　In　this　paper,　we　propose　a　software-defined　trust　based　deep　reinlOrcement　learning　framework　(TDRL-RP),　deploying　a　deep　Q-learning　algorithm　into　a　logically　centralized　controller　of　software-defined　networking　(SDN).　Specifically,　the　SDN　controller　is　used　as　an　agent　to　learn　the　highest　routing　path　trust　value　of　a　VANET　environment　by　convolution　neural　network,　where　the　trust　model　is　designed　to　evaluate　neighbors＇　behaviour　of　forwarding　packets.　Simulation　results　are　presented　to　show　the　effectiveness　of　the　proposed　TDRL-RP　framework.