Nowadays,　Edge　Information　System　(EIS)　has　received　a　lot　of　attentions.　In　EIS,　Distributed　Machine　Learning　(DML),　which　requires　fewer　computing　resources,　can　implement　many　artificial　intelligent　applications　efficiently.　However,　due　to　the　dynamical　network　topology　and　the　fluctuating　transmission　quality　at　the　edge,　work　node　selection　affects　the　performance　of　DML　a　lot.　In　this　paper,　we　focus　on　the　Internet　of　Vehicles　(IoV),　one　of　the　typical　scenarios　of　EIS,　and　consider　the　DML-based　High　Definition　(HD)　mapping　and　intelligent　driving　decision　model　as　the　example.　The　worker　selection　problem　is　modeled　as　a　Markov　Decision　Process　(MDP),　maximizing　the　DML　model　aggregate　performance　related　to　the　timeliness　of　the　local　model,　the　transmission　quality　of　model　parameters　uploading,　and　the　effective　sensing　area　of　the　worker.　A　Deep　Reinforcement　Learning　(DRL)　based　solution　is　proposed,　called　the　Worker　Selection　based　on　Policy　Gradient　(PG-WS)　algorithm.　The　policy　mapping　from　the　system　state　to　the　worker　selection　action　is　represented　by　a　deep　neural　network.　The　episodic　simulations　are　built　and　the　REINFORCE　algorithm　with　baseline　is　used　to　train　the　policy　network.　Results　show　that　the　proposed　PG-WS　algorithm　outperforms　other　comparation　methods.　©　All　articles　included　in　the　journal　are　copyrighted　to　the　ITU　and　TUP.