High　cost　of　environmental　interaction　and　low　data　efficiency　limit　the　development　of　reinforcement　learning　in　robotic　grasping.　This　paper　proposes　an　end-to-end　robotic　grasping　method　based　on　offline　reinforcement　learning　via　sequence　modeling.　It　considers　the　most　recent　n-step　history　to　assist　the　agent　in　making　decisions,　where　a　predictive　model　learns　to　directly　predict　actions　from　raw　image　inputs.　The　experimental　results　show　that　our　method　can　achieve　higher　grasping　success　rate　with　less　training　data　than　traditional　reinforcement　learning　algorithms　in　offline　setting.　©　2023　IEEE.