In　view　of　the　prevalence　of　dynamic　uncertainties,　we　study　the　robust　policy　learning　control　of　nonlinear　plants　in　this　paper.　The　auxiliary　system　and　policy　learning　techniques　are　integrated　to　accomplish　robust　stabilization　of　mismatched　nonlinear　systems.　First,　the　uncertain　dynamics　is　handled　by　proper　transformation,　so　as　to　construct　an　optimal　regulation　problem　with　respect　to　an　augmented　auxiliary　system.　Then,　the　integral　policy　iteration　algorithm　is　employed　for　optimal　control　design　without　requiring　system　dynamics.　The　equivalence　results　involved　in　problem　transformation　and　algorithm　improvement　are　analyzed.　After　that,　the　actor-critic　structure　is　adopted　with　least　squares　implementation　for　approximate　calculation.　Finally,　the　experimental　simulation　with　an　application　to　a　power　system　is　provided,　which　demonstrates　the　validity　of　the　adaptive　robust　control　strategy.　The　present　policy　learning　algorithm　does　not　rely　on　whole　information　of　system　dynamics　and　the　established　robust　control　technique　is　applicable　for　nonlinear　plants　subjected　to　mismatched　uncertainties.