A　policy-iteration-based　algorithm　is　presented　in　this　article　for　optimal　control　of　unknown　continuous-time　nonlinear　systems　subject　to　bounded　inputs　by　utilizing　the　adaptive　dynamic　programming　(ADP).　Three　neural　networks　(NNs),　called　critic　network,　actor　network,　and　quasi-model　network,　are　utilized　in　the　proposed　algorithm　to　give　approximations　of　the　control　law,　the　cost　function,　and　the　function　constituted　by　partial　derivatives　of　value　functions　with　respect　to　states　and　unknown　input　gain　dynamics,　respectively.　At　each　iteration,　based　on　the　least　sum　of　squares　method,　the　parameters　of　critic　and　quasi-model　networks　will　be　tuned　simultaneously,　which　eliminates　the　necessity　of　separately　learning　the　system　model　in　advance.　Then,　the　control　law　is　improved　by　satisfying　the　necessary　optimality　condition.　Then,　the　proposed　algorithm＇s　optimality　and　convergence　properties　are　exhibited.　Finally,　the　simulation　results　demonstrate　the　availability　of　the　proposed　algorithm.