In　this　paper,　the　Q-learning　method　for　quadratic　optimal　control　problem　of　discrete-time　linear　systems　is　reconsidered.　The　theoretical　results　prove　that　the　quadratic　optimal　controller　cannot　be　solved　directly　due　to　the　linear　correlation　of　the　data　sets.　The　following　corollaries　have　been　made:　(1)　The　correlation　of　data　is　the　key　factor　in　the　success　for　the　calculation　of　quadratic　optimal　control　laws　by　Q-learning　method;　(2)　The　control　laws　for　linear　systems　cannot　be　derived　directly　by　the　existing　Q-learning　method;　(3)　For　nonlinear　systems,　there　are　some　doubts　about　the　data　independence　of　current　method.　Therefore,　it　is　necessary　to　discuss　the　probability　of　the　controllers　established　by　the　existing　Q-learning　method.　To　solve　this　problem,　based　on　the　ridge　regression,　an　improved　model-free　Q-learning　quadratic　optimal　control　method　for　discrete-time　linear　systems　is　proposed　in　this　paper.　Therefore,　the　computation　process　can　be　implemented　correctly,　and　the　effective　controller　can　be　solved.　The　simulation　results　show　that　the　proposed　method　can　not　only　overcome　the　problem　caused　by　the　data　correlation,　but　also　derive　proper　control　laws　for　discrete-time　linear　systems.　©　2020　by　the　authors.