In　this　paper,　an　effective　mixed　driven　framework　is　constructed　involving　both　data　and　event　considerations.　The　primary　purpose　lies　in　that　the　mixed　driven　iterative　adaptive　critic　method　is　established　to　address　approximate　optimal　control　towards　discrete-time　nonlinear　dynamics.　The　neural　dynamic　programming　technique　is　inventively　integrated　with　the　mixed　driven　architecture,　such　that　the　knowledge　of　the　controlled　plant　is　needless　and　the　number　for　updating　control　inputs　is　prominently　reduced.　A　triggering　threshold　is　also　designed　with　theoretical　guarantee,　which　renders　that　the　control　signals　can　be　updated　conditionally.　Through　carrying　out　simulation　studies　with　comparisons,　the　superiority　of　the　present　near-optimal　regulation　approach　is　confirmed　at　last.　Copyright　(C)　2020　The　Authors.