The　multi-task　particle　swarm　optimization　(MTPSO)　algorithm　is　widely　used　to　solve　multi-task　multi-objective　problems　due　to　its　rapid　convergence　via　knowledge　transfer　learning.　However,　the　MTPSO　has　strong　randomness　and　is　lack　of　guideness　during　search　process,　which　is　prone　to　fall　into　local　optimum　and　has　poor　convergence　performance.　This　paper　proposes　a　Q-learning-based　multi-task　multi-objective　particle　swarm　optimization　algorithm　(QM2PSO)　via　using　learning　and　prediction　of　reinforcement　learning　to　guide　optimization.　Firstly,　we　design　the　adaptive　parameter　adjustment　method,　which　can　update　the　inertia　weight　and　acceleration　parameters　online　based　on　Q-learning　to　improve　the　convergence　ability.　Secondly,　we　develop　a　mutation　strategy　based　on　Cauchy　distribution,　which　can　balance　exploration　and　exploitation　to　avoid　falling　into　local　optimum.　Finally,　we　design　a　knowledge　transfer　method　based　on　the　positive　transfer　criterion　via　updating　the　knowledge　transfer　rate　based　on　Q-learning　to　avoid　negative　knowledge　transfer.　The　comparative　results　demonstrate　that　the　QM2PSO　is　superior　to　the　existing　algorithms　on　convergence　performance.　©　2023　Northeast　University.　All　rights　reserved.