Reviews: Robust exploration in linear quadratic reinforcement learning

The paper is very well written and organized and its contributions are quite original as it proposes a novel coarse-ID method for robust model-based reinforcement learning in which both exploration AND exploitation are optimized jointly (which was not the case in previous similar works). The method proposed to solve the robust Reinforcement Learning problem is all the more original as it does not rely on Stochastic Dynamic Programming, but rather on Semidefinite Programming. Concerning clarity, the only element that is not clear for me is related to equation (1) in page 2: do you consider in the system model some uncertainty in the measurements of the states x? For example, it is said in the supplemental material that the velocity of the servo-motor of your second experiment is estimated using a high pass-filter, and is hence not perfectly known. If it is modeled, is it included in the process noise w or how do you deal with it?