Network　virtualization　enables　the　share　of　a　physical　network　among　multiple　virtual　networks.　Virtual　network　embedding　determines　the　effectiveness　of　utilization　of　network　resources.　Traditional　heuristic　mapping　algorithms　follow　static　procedures,　thus　cannot　be　optimized　automatically,　leading　to　suboptimal　ranking　and　embedding　decisions.　To　solve　this　problem,　we　introduce　a　reinforcement　learning　method　to　virtual　network　embedding.　In　this　paper,　we　design　and　implement　a　policy　network　based　on　reinforcement　learning　to　make　node　mapping　decisions.　We　use　policy　gradient　to　achieve　optimization　automatically　by　training　the　policy　network　with　the　historical　data　based　on　virtual　network　requests.　To　the　best　of　our　knowledge,　this　work　is　the　first　to　utilize　historical　requests　data　to　optimize　network　embedding　automatically.　The　performance　of　the　proposed　embedding　algorithm　is　evaluated　in　comparison　with　two　other　algorithms　which　use　artificial　rules　based　on　node　ranking.　Simulation　results　show　that　our　reinforcement　learning　is　able　to　learn　from　historical　requests　and　outperforms　the　other　two　embedding　algorithms.　(C)　2018　The　Author(s).　Published　by　Elsevier　B.V.