The　appropriate　design　of　a　vehicular　ad　hoc　network　(VANET)　has　become　a　pivotal　way　to　build　an　efficient　smart　transportation　system.　which　enables　various　applications　associated　with　traffic　safety　and　highly-efficient　transportation.　VANETs　are　vulnerable　to　the　threat　of　malicious　nodes　stemming　from　its　dynamicity　and　infrastructure-less　nature　and　causing　performance　degradation.　Recently,　software-defined　networking　(SDN)　has　provided　a　feasible　way　to　manage　VANETs　dynamically.　In　this　article,　we　propose　a　novel　software-defined　trust　based　VANET　architecture　(SD-TDQL)　in　which　the　centralized　SUN　controller　is　served　as　a　learning　agent　to　get　the　optimal　communication　link　policy　using　a　deep　Q-learning　approach.　The　trust　of　each　vehicle　and　the　reverse　delivery　ratio　are　considered　in　a　joint　optimization　problem,　which　is　modeled　as　a　Markov　decision　process　with　state　space,　action　space,　and　reward　function.　Specifically,　we　use　the　expected　transmission　count　(ETX)　as　a　metric　to　evaluate　the　quality　of　the　communication　link　for　the　connected　vehicles＇　communication.　Moreover,　we　design　a　trust　model　to　avoid　the　bad　influence　of　malicious　vehicles.　Simulation　results　prove　that　the　proposed　SD-TDQL　framework　enhances　the　link　quality.