Door　opening,　as　one　of　the　common　actions　in　daily　life,　has　become　an　important　direction　for　robotic　arm　applications.　Different　door　handles　open　in　different　ways,　to　enable　the　robotic　arm　to　complete　the　corresponding　door-opening　operation　according　to　the　handle　category,　the　Proximal　Policy　Optimization　algorithm　is　used　to　open　the　door.　Opening　the　door　contains　a　multi-segment　process　such　as　approaching　the　handle,　operating　the　handle　and　pushing　the　door　open.　The　sparse　reward　that　focuses　only　on　the　result　of　opening　the　door　will　lead　to　the　extension　of　the　training　time　of　the　robotic　arm,　or　even　fail　to　converge.　To　address　this　problem,　this　paper　proposes　a　segmented　adaptive　reward.　First,　consider　the　segment　task　of　opening　the　door,　design　the　segmented　reward,　formulate　segmented　training　rules,　and　gradually　guide　the　robotic　arm　to　improve　the　overall　training　effect.　At　the　same　time,　the　reward　adds　an　adaptive　weight　adjustment　mechanism,　which　adaptively　adjusts　the　weights　according　to　the　current　stage　of　attention　to　different　tasks,　and　then　matches　the　segmented　training　to　accelerate　the　training　speed.　In　a　simulation　environment,　the　experimental　results　show　that　the　door　opening　success　rate　of　our　algorithm　is　61.04%　higher　than　that　of　the　original　PPO　algorithm,　and　it　can　achieve　the　round　handle　opening　task　that　cannot　be　solved　by　the　original　algorithm.　©　2024　Technical　Committee　on　Control　Theory,　Chinese　Association　of　Automation.