With　the　increasing　integration　level　of　flow-based　microfluidics,　fully　programmable　valve　arrays　(FPVAs)　have　emerged　as　the　next　generation　of　microfluidic　devices.　Microvalves　in　an　FPVA　are　typically　managed　by　a　control　logic,　where　valves　are　connected　to　a　core　input　via　control　channels　to　receive　control　signals　that　guide　states　switching.　The　critical　valves　that　suffer　from　asynchronous　actuation　leading　to　chip　malfunctions,　however,　need　to　be　switched　simultaneously　in　a　specific　bioassay.　As　a　result,　the　channel　lengths　from　the　core　input　to　these　valves　are　required　to　be　equal　or　similar,　which　poses　a　challenge　to　the　channel　routing　of　the　control　logic.　To　solve　this　problem,　we　propose　a　deep　reinforcement　learning-based　adaptive　routing　flow　for　the　control　logic　of　FPVAs.　With　the　proposed　routing　flow,　an　efficient　control　channel　network　can　be　automatically　constructed　to　realize　accurate　control　signals　propagation.　Meanwhile,　the　timing　skews　among　synchronized　valves　and　the　total　length　of　control　channels　can　be　minimized,　thus　generating　an　optimized　control　logic　with　excellent　timing　behavior.　Simulation　results　on　multiple　benchmarks　demonstrate　the　effectiveness　of　the　proposed　routing　flow.