Reinforcement learning (RL) algorithms often rely on trial and error for exploring environments, leading to local minima and high sample inefficiency during training. In many cases, leveraging prior knowledge can efficiently construct expert policies, e.g. model predictive control (MPC) techniques. However, the expert might not be optimal and thus, when used as a prior, might introduce bias that can harm the control performance. Thus, in this work, we propose a novel RL method based on a simple options framework that only uses the expert to guide the exploration during training. The exploration is controlled by a learned high-level policy that can decide to follow either an expert policy or a learned low-level policy. In that sense, the high-level skip policy learns when to trust the expert for exploration. As we aim at deploying the low-level policy without accessing the expert after training, we increasingly regularize the usage of the expert during training, to reduce the covariate shift problem. Using different environments combined with potentially sub-optimal experts derived from MPC or RL, we find that our method improves over sub-optimal experts and significantly improves the sample efficiency.