Reinforcement　learning　algorithm　represented　by　flexible　action　evaluation　(SAC)　has　been　successful　in　reproducing　the　motor　skills　of　higher　animals.　This　framework　combines　strategy　search　and　state　action　value　function.　However,　the　agent　use　strategy　exploration　is　greedy,　and　the　Q　value　function　of　evaluation　network　estimation　uses　low　valuation.　This　paper　proposes　a　policy　distillation　(PD)　soft　actor-critic　(PDSAC)　algorithm　that　integrates　PD　and　SAC　algorithms　to　enable　agents　to　adopt　better　policies.　This　algorithm　allows　the　agent　to　explore　using　hybrid　policies　and　speeds　up　the　convergence　of　the　reward　function　from　reinforcement　learning.　To　validate　the　proposed　algorithm,　Theoretical　proof　that　the　PDSAC　algorithm　improves　the　efficiency　of　policy　exploration　and　validation　in　quadruped　robot　gait　learning　tasks.　According　to　simulation　results,　the　PDSAC　outperforms　the　SAC　in　the　gait　learning　task,　achieving　a　40%　increase　in　convergence　speed　and　a　26.7%　improvement　in　the　reward　value　function.　©　2025　Beijing　University　of　Aeronautics　and　Astronautics　(BUAA).　All　rights　reserved.