Replanning via determinization is a recent, popular approach for online planning in MDPs. In this paper we adapt this idea to classical, non-stochastic domains with partial information and sensing actions. At each step we generate a candidate plan which solves a classical planning problem induced by the original problem. We execute this plan as long as it is safe to do so. When this is no longer the case, we replan. The classical planning problem we generate is based on the T0 translation, in which the classical state captures the knowledge state of the agent. We overcome the non-determinism in sensing actions, and the large domain size introduced by T0 by using state sampling. Our planner also employs a novel, lazy, regression-based method for querying the belief state.