Wireless　sensor　networks　have　become　incredibly　popular　due　to　the　Internet　of　Things’　(IoT)　rapid　development.　IoT　routing　is　the　basis　for　the　efficient　operation　of　the　perception-layer　network.　As　a　popular　type　of　machine　learning,　reinforcement　learning　techniques　have　gained　significant　attention　due　to　their　successful　application　in　the　field　of　network　communication.　In　the　traditional　Routing　Protocol　for　low-power　and　Lossy　Networks　(RPL)　protocol,　to　solve　the　fairness　of　control　message　transmission　between　IoT　terminals,　a　fair　broadcast　suppression　mechanism,　or　Drizzle　algorithm,　is　usually　used,　but　the　Drizzle　algorithm　cannot　allocate　priority.　Moreover,　the　Drizzle　algorithm　keeps　changing　its　redundant　constant　k　value　but　never　converges　to　the　optimal　value　of　k.　To　address　this　problem,　this　paper　uses　a　combination　based　on　reinforcement　learning　(RL)　and　trickle　timer.　This　paper　proposes　an　RL　Intelligent　Adaptive　Trickle-Timer　Algorithm　(RLATT)　for　routing　optimization　of　the　IoT　awareness　layer.　RLATT　has　triple-optimized　the　trickle　timer　algorithm.　To　verify　the　algorithm＇s　effectiveness,　the　simulation　is　carried　out　on　Contiki　operating　system　and　compared　with　the　standard　trickling　timer　and　Drizzle　algorithm.　Experiments　show　that　the　proposed　algorithm　performs　better　in　terms　of　packet　delivery　ratio　(PDR),　power　consumption,　network　convergence　time,　and　total　control　cost　ratio.　©　2023　The　Authors