Reinforcement　Learning　(RL)　has　been　applied　to　robotic　arm　control,　which　enables　the　agent　to　learn　an　effective　policy　to　solve　complex　tasks.　However,　it　requires　constant　interaction　with　the　environment　leading　to　low　sample　efficiency.　In　this　paper,　we　propose　a　robotic　arm　control　approach　based　on　planning　via　lookahead　search,　which　is　a　model-based　RL　algorithm　to　improve　the　sample　efficiency.　The　approach　builds　an　environment　model　in　order　to　obtain　the　dynamics　of　the　environment.　Thus　the　model　can　be　used　to　plan　future　actions　by　a　tree-based　search.　The　experiments　show　that　our　approach　can　solve　the　task　of　robotic　arm　control　with　less　environmental　samples.　　©　2023　IEEE.