Emerging　latency-sensitive　applications　(e.g.,　industrial　control,　in-vehicle　networks)　require　that　the　networks　guaranteed　data　delivery　with　low,　bounded　latency.　To　meet　this　requirement,　the　IEEE　802.1　Working　Group　developed　the　time-sensitive　networks　(TSN)　standard　to　enable　deterministic　communication　on　standard　Ethernet.　TSN　technology　is　developed　to　enable　deterministic　communication　using　traffic　scheduling　and　shaping　technology.　However,　while　the　TSN　standards　define　the　mechanisms　to　handle　scheduled　traffic,　it　does　not　specify　algorithms　to　compute　fine-grained　traffic　scheduling　policy.　Current　TSN　flow　scheduling　schemes　largely　rely　on　a　manual　process,　requiring　knowledge　of　the　traffic　pattern　and　network　topology　features.　Inspired　by　recent　successes　in　applying　reinforcement　learning　in　online　control,　we　propose　a　deep　reinforcement　learning　aided　no-waiting　flow　scheduling　algorithm　in　TSN.　Extensive　simulations　are　performed　to　verify　that　our　algorithm　can　find　the　optimal　solution　in　an　acceptable　time.　©　2022　IEEE.