The　agile　movement　of　quadruped　robot　requires　rich　professional　knowledge　and　tedious　manual　adjustment.　However,　reinforcement　learning　does　not　require　any　professional　knowledge　to　enable　the　quadruped　robot　to　learn　better　movement　gait　and　skills.　The　TD3　algorithm　is　widely　used　in　continuous　motion　control,　but　it　tends　to　converge　to　boundary　actions,　resulting　in　inability　to　learn　the　optimal　strategy　and　overfitting.　Inspired　by　behavior　cloning,　this　paper　proposes　a　policy　constrained　TD3　algorithm(PC-　TD3),　which　adds　behavioral　constraints　during　the　policy　update　process　of　TD3,　updates　the　policy　in　the　direction　of　expected　behavior,　and　reduces　boundary　actions.　The　experiment　was　conducted　on　the　Pybullet　platform.　The　experimental　results　show　that　the　algorithm　proposed　in　this　paper　can　enable　the　quadruped　Robot　learning　to　learn　the　walking　skills.　Compared　with　other　mainstream　algorithms,　the　experiment　shows　that　PC-　TD3　has　better　performance.　©　2023　IEEE.