Train　station　parking　(TSP)　accuracy　is　important　to　enhance　the　efficiency　of　train　operation　and　the　safety　of　passengers　for　urban　rail　transit.　However,　TSP　is　always　subject　to　a　series　of　uncertain　factors　such　as　extreme　weather　and　uncertain　conditions　of　rail　track　resistances.　To　increase　the　parking　accuracy,　robustness,　and　self-learning　ability,　we　propose　new　train　station　parking　frameworks　by　using　the　reinforcement　learning　(RL)　theory　combined　with　the　information　of　balises.　Three　algorithms　were　developed,　involving　a　stochastic　optimal　selection　algorithm　(SOSA),　a　Q-learning　algorithm　(QLA),　and　a　fuzzy　function　based　Q-learning　algorithm　(FQLA)　in　order　to　reduce　the　parking　error　in　urban　rail　transit.　Meanwhile,　five　braking　rates　are　adopted　as　the　action　vector　of　the　three　algorithms　and　some　statistical　indices　are　developed　to　evaluate　parking　errors.　Simulation　results　based　on　real-world　data　show　that　the　parking　errors　of　the　three　algorithms　are　all　within　the　±30cm,　which　meet　the　requirement　of　urban　rail　transit.　©　2019　Wei　Li　et　al.