In　this　paper,　a　simplified　reinforcement　learning　(RL)　of　identifier-actor-critic　architecture　is　proposed　to　solve　the　formation　problem　of　multiagent　state-delay　system.　The　dynamics　of　multi-agent　systems　includes　the　uncertainties　and　state　delay,　which　is　more　practical　in　applications.　In　the　multiagent　system　formation　control,　the　system　uncertainties　are　counteracted　through　the　fuzzy　logic　system;　the　state　delay　is　offset　by　applying　a　Lyapunov-Krasovskii　functional.　The　updating　laws　of　RL　are　derived　from　a　simple　equation,　which　is　equivalent　to　the　gradient　of　the　HJB　equation.　Finally,　a　simulation　example　is　given　to　demonstrate　the　satisfactory　performance　of　the　proposed　method.　©　2021　Technical　Committee　on　Control　Theory,　Chinese　Association　of　Automation.