Cloud　manufacturing　promotes　the　transformation　of　intelligence　for　the　traditional　manufacturing　mode.　In　a　cloud　manufacturing　environment,　the　task　scheduling　plays　an　important　role.　However,　as　the　number　of　problem　instances　increases,　the　solution　quality　and　computation　time　always　go　against.　Existing　task　scheduling　algorithms　can　get　local　optimal　solutions　with　the　high　computational　cost,　especially　for　large　problem　instances.　To　tackle　this　problem,　a　task　scheduling　algorithm　based　on　a　deep　reinforcement　learning　architecture　(RLTS)　is　proposed　to　dynamically　schedule　tasks　with　precedence　relationship　to　cloud　servers　to　minimize　the　task　execution　time.　Meanwhile,　the　Deep-Q-Network,　as　a　kind　of　deep　reinforcement　learning　algorithms,　is　employed　to　consider　the　problem　of　complexity　and　high　dimension.　In　the　simulation,　the　performance　of　the　proposed　algorithm　is　compared　with　other　four　heuristic　algorithms.　The　experimental　results　show　that　RLTS　can　be　effective　to　solve　the　task　scheduling　in　a　cloud　manufacturing　environment.