The　gait　control　of　the　quadruped　robot　has　always　been　a　hot　topic　in　the　field　of　robot　research.　At　present,　the　traditional　control　methods　have　many　limitations　such　as　low　intelligence　and　poor　autonomy.　With　the　development　of　artificial　intelligence　technology,　the　application　of　reinforcement　learning　to　the　quadruped　robot　autonomous　learning　strategy　provides　a　promising　solution.　Deep　deterministic　policy　gradient　(DDPG)　algorithm　has　achieved　good　performance　in　continuous　control　tasks,　but　such　value-based　reinforcement　learning　algorithms　have　the　problem　of　too　high　epoch　estimates　when　performing　function　approximation,　then　reached　a　bad　strategy　actually.　In　order　to　solve　the　above-mentioned　problem,　this　paper　proposed　a　heuristic　gait　learning　method　for　quadruped　robot　based　on　DDPG,　inspired　by　the　Double　Q-learning　algorithm,　two　independent　critics　were　used　to　select　the　smaller　value　to　update　the　parameters.　The　Open　AI　Gym　platform　was　used　for　experimental　verification,　which　proved　that　the　proposed　improved　DDPG　algorithm　had　better　performance.