This　paper　investigates　robust　control　problems　of　nonlinear　continuous-time　multiplayer　systems　with　infinite　horizon　by　utilizing　adaptive　dynamic　programming　algorithms.　Combined　with　pre-training,　an　improved　policy　iteration　(PI)　algorithm　is　developed　to　solve　robust　control　issues　of　multiplayer　nonzero-sum　(NZS)　games　with　actuator　uncertainties.　Pre-training　of　initial　weights　is　added　to　the　PI　algorithm　to　relax　the　requirement　of　the　initial　admissible　control　policy.　It　implies　that　the　admissible　control　pair　can　be　obtained　by　pre-training　of　initial　weights　given　randomly.　Then,　critic　neural　networks　(NNs)　are　utilized　to　approximate　the　optimal　control　pair　by　applying　the　PI　algorithm.　Robust　controllers　can　be　obtained　by　modifying　the　optimal　control　pair.　The　algorithm　accomplishes　robust　stabilization　of　multiplayer　NZS　games　with　uncertainties.　Besides,　initial　weights　of　NNs　can　be　set　arbitrarily.　Finally,　a　simulation　example　is　given　to　demonstrate　the　effectiveness　of　the　developed　algorithm.　©　2023　Technical　Committee　on　Control　Theory,　Chinese　Association　of　Automation.