Bionic　gait　learning　of　quadruped　robots　based　on　reinforcement　learning　has　become　a　hot　research　topic.　The　proximal　policy　optimization　(PPO)　algorithm　has　a　low　probability　of　learning　a　successful　gait　from　scratch　due　to　problems　such　as　reward　sparsity.　To　solve　the　problem,　we　propose　a　experience　evolution　proximal　policy　optimization　(EEPPO)　algorithm　which　integrates　PPO　with　priori　knowledge　highlighting　by　evolutionary　strategy.　We　use　the　successful　trained　samples　as　priori　knowledge　to　guide　the　learning　direction　in　order　to　increase　the　success　probability　of　the　learning　algorithm.　To　verify　the　effectiveness　of　the　proposed　EEPPO　algorithm,　we　have　conducted　simulation　experiments　of　the　quadruped　robot　gait　learning　task　on　Pybullet.　Experimental　results　show　that　the　central　pattern　generator　based　radial　basis　function　(CPG-RBF)　network　and　the　policy　network　are　simultaneously　updated　to　achieve　the　quadruped　robot’s　bionic　diagonal　trot　gait　learning　task　using　key　information　such　as　the　robot’s　speed,　posture　and　joints　information.　Experimental　comparison　results　with　the　traditional　soft　actor-critic　(SAC)　algorithm　validate　the　superiority　of　the　proposed　EEPPO　algorithm,　which　can　learn　a　more　stable　diagonal　trot　gait　in　flat　terrain.　©　2023,　Shanghai　Jiao　Tong　University.