The　current　metro　train　control　system　has　achieved　automatic　operation,　but　the　degree　of　intelligence　needs　to　be　enhanced.　To　improve　the　intelligence　of　train　driving,　this　paper　adopts　the　proximal　policy　optimization　(PPO)　algorithm　to　study　the　intelligent　train　operation　(ITO)　of　metro　trains　by　drawing　on　the　successful　application　of　deep　reinforcement　learning　in　games.　We　propose　an　adaptive　model-free　control　(MFAC)　method　for　train　speed　profile　tracking,　named　as　intelligent　train　operation　based　on　PPO　(ITOP),　and　design　reinforcement　learning　policies,　actions,　and　rewards　to　ensure　the　accuracy　of　the　train　tracking　speed　profile,　passenger　comfort,　and　stopping　accuracy.　Simulation　experiments　are　conducted　using　real　railroad　data　from　the　Yizhuang　Line　of　Beijing　Metro　(YLBS).　The　results　show　that　the　tracking　curve　generated　by　ITOP　is　highly　coincident　with　the　target　curve　with　good　parking　accuracy　and　comfort,　and　responds　positively　to　the　changes　of　the　target　curve　during　the　operation.　This　provides　a　new　solution　for　the　intelligent　control　of　trains.　©　2023,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Switzerland　AG.