In　this　paper,　the　decentralized　optimal　control　problem　of　continuous-time　input-affine　nonlinear　systems　with　mismatched　interconnections　is　investigated　by　utilizing　data-based　integral　policy　iteration.　Initially,　the　decentralized　mismatched　subsystems　are　converted　into　the　nominal　auxiliary　subsystems.　Then,　we　derive　the　optimal　controllers　of　the　nominal　auxiliary　subsystems　with　a　well-defined　discounted　cost　function　under　the　framework　of　adaptive　dynamic　programming.　In　the　implementation　process,　the　integral　reinforcement　learning　algorithm　is　employed　to　explore　the　partially　or　completely　unknown　system　dynamics.　It　is　worth　mentioning　that　the　actor-critic　structure　is　adopted　based　on　neural　networks,　in　order　to　evaluate　the　control　policy　and　the　performance　of　the　control　system.　Besides,　the　least　squares　method　is　also　involved　in　this　online　learning　process.　Finally,　a　simulation　example　is　provided　to　illustrate　the　validity　of　the　developed　algorithm.　IEEE