Review for NeurIPS paper: Experimental design for MRI by greedy policy search

Weaknesses: The main claim of the paper is the hypothesis that the noise in the non-greedy objective's paper is the reason why the greedy method can outperform it. However, I think that the empirical methodology is not strong enough to back this claim up, as the experiments are carried out on a single dataset, using a single network architecture and reported with a single performance metric. I think that the hypothesis would be much more clearly substantiated if the noise in the gradients were shown to be a consistent trend in various setting; I am afraid that, in the current state, the conclusion could be an anecdotical performance of the given setting. In addition, if I'm correct, RL models are prone to unstable training and are generally hard to train well. How can you confidently ensure that this behaviour isn't due to the RL policy not being trained for long enough?