Learning　ability　is　a　typical　characteristic　of　higher　animal　intelligence.　In　order　to　explore　the　learning　mechanism　of　quadruped　motor　skills,　this　paper　studies　the　gait　learning　task　of　quadruped　robots,　and　reproduces　the　rhythmic　gait　learning　process　of　quadruped　animals　from　scratch.　In　recent　years,　proximal　policy　optimization　(PPO)　algorithm,　as　a　typical　representative　algorithm　of　deep　reinforcement　learning,　has　been　widely　used　in　gait　learning　tasks　for　quadruped　robots,　with　good　experimental　results　and　fewer　hyperparameters　required.　However,　in　the　multidimensional　input　and　output　scenario,　it　is　easy　to　converge　to　the　local　optimum　point,　in　the　experimental　environment　of　this　study,　the　gait　rhythm　signals　of　the　trained　quadruped　robot　were　irregular,　and　the　center　of　gravity　oscillates.　To　solve　the　above　problems,　inspired　by　meta-learning,　based　on　the　advantage　of　meta-learning　in　characterizing　the　high-dimensional　abstract　representation　of　learning　processes,　this　paper　proposes　an　meta　proximal　policy　optimization　(MPPO)　algorithm　that　combines　meta-learning　and　PPO　algorithms.　This　algorithm　can　enable　quadruped　robots　to　learn　better　gait.　The　simulation　results　on　the　PyBullet　simulation　platform　show　that　the　algorithm　proposed　in　this　paper　can　enable　quadruped　robots　to　learn　walking　skills.　Compared　with　soft　actor-critic　(SAC)　and　PPO　algorithms,　the　MPPO　algorithm　proposed　in　this　paper　has　advantages　such　as　more　regular　gait　rhythm　signals　and　faster　walking　speed.　©　2024　South　China　University　of　Technology.　All　rights　reserved.