Paper:
Model-based reinforcement learning has been empirically demonstrated as a successful strategy to improve sample efficiency. Particularly, Dyna architecture, as an elegant model-based architecture integrating learning and planning, provides huge flexibility of using a model. One of the most important components in Dyna is called search-control, which refers to the process of generating state or stateaction pairs from which we query the model to acquire simulated experiences. Search-control is critical to improve learning efficiency. In this work, we propose a simple and novel search-control strategy by searching high frequency region on value function. Our main intuition is built on Shannon sampling theorem from signal processing, which indicates that a high frequency signal requires more samples to reconstruct. We empirically show that a high frequency function is more difficult to approximate. This suggests a search-control strategy: we should use states in high frequency region of the value function to query the model to acquire more samples. We develop a simple strategy to locally measure the frequency of a function by gradient norm, and provide theoretical justification for this approach. We then apply our strategy to search-control in Dyna, and conduct experiments to show its property and effectiveness on benchmark domains.

Review:
Disclaimer: 
This is an emergency review that I got assigned on Nov 5th, so I unfortunately had only a few hours to assess the paper.
Summary:
This paper proposes a new mechanism for “search control”, that is, choosing the planning-start-states from which to roll out the model, in the context of Dyna-style updates. The key idea is to favour updates to states where the the value functions changes a lot, because those need more information to become accurate.
The topic is very interesting, and not studied enough, the proposed method looks novel, and the experimental results look promising. However, the clarity of the paper is not great, the theoretical result (Theorem 1) seems to be incorrect, the narrative from Claude Shannon to Joseph Fourier to gradient norms to practical choices is somewhat confusing, the empirical results are not really conclusive, and despite a large appendix there are missing implementation details. I think this paper is currently one or two major revisions away from the acceptance threshold.
Comments:
1. Theorem 1, equation (3): consider a constant function f(x) = K. Then its derivative is zero, so the left-hand side is zero, yet the right-hand side is the product of two strictly positive factors (each of them proportional to K^2), so how can the equation hold?
2. Sine experiment: How can you reasonably do *linear* regression onto a sine function? What are your input features?
3. Figure 2 is odd: panels (b) and (c) look very similar in how they bias the sampling toward the left side, yet the performance difference (a) is very stark, how come? Also, how can there be such a high contrast in (b)/(c) if 60% of all samples are chosen uniformly, as stated in the text?
4. The paper has numerous grammatical mistakes, to the extent it becomes difficult to read. I know this can happen in deadline mode, but please revise the draft thoroughly (special call-out to the dozens of missing definite/indefinite articles and plural forms). Also, use “\citep” where appropriate.
5. The objective g as the sum of a gradient norm and a Hessian norm seems odd, as these terms have completely different scales, so usually one of them will dominate, can you explain and motivate this further, and compare empirically to the two terms in isolation?
7. For the prioritizedER baseline, what variant and hyper-parameters are you using?
8. Please describe “out of boundary” mentioned in Algorithm 1.
9. Section 5.2 states that the “variance of the evaluation curve” is smaller, indicating robustness, yet Figure 3(a) appears to have high instability in the (early) learning curve?
10. Section 5.2 states that Figure 5 should show that the bottleneck areas are sampled more densely, but that’s a dubious claim. Please quantify the claim or drop it.
11. Figure 4(b) has oddly wide error-bars for DQN-Value, which looks suspiciously like a single failed run. Can you add a plot to the appendix with median/quantiles instead of mean/std statistics?
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Update Nov 17: most of my concerns have been addressed, and I have thus increased my rating.