Learning search control for forward state planning has been previously addressed as a relational classification task, where predictions are used to generate action policies. In this paper, we describe a new bagging approach to learn and apply ensembles of relational decision trees to generate more robust policies for planning. Preliminary experimental results demonstrate that new policies produce on average plans of better quality.