** * Clarity. There are parts of this paper that are a bit unclear. The diagram and caption for KeyQN section are very helpful, but the actual text section could be fleshed out more. It would nice if the text could have a little more detail on how the outputs from the transporter are input to the KeyQN architecture and how the whole thing is trained. The exploration section was well explained for most part, but it took a bit of time to understand. Maybe would help to have an algorithm box. Also, the explanation of training process a bit confusing. Maybe a diagram of the architecture and how the transporter feeds into this would help. Also, I am confused a bit about whether the transporter is pretrained and frozen or fine-tuned. One quote from the paper in this regard confused me: âOur transporter model and all control policies simultaneuosly â so the weights of the Transporter network are not frozen during the downstream task like in KeyQN? * Experiments: They only show these results on a few games (and no error bars), so it would have been nice (but not a dealbreaker) to see results from more Atari games. They do partially justify this by saying they couldnât use a random policy on other games, but Iâd be curious just to see what happens when they try a couple more games. Would be nice to see comparisons to other exploration methods (they only show results compared to random exploration) Nitpicks/Questions * Makes sense to just refer the reader to the PointNet paper instead of re-explaining it, but a short explanation if possible of PointNet (couple sentences) might be helpful, so that one doesnât have to skim that paper to understand this paper * The diagram in figure 5 (h_psi) should show a heat map not keypoints superimposed on raw frame right? * In the appendix âK is handpicked for each game?â How? Validation loss? * The tracking experiments but the section is a bit unclear. I have a few questions on that front: * why is there a need to separating precision and recall? * why not just report overall mean average precision or F1 score? Might be a bit easier for reader to digest one number * Why bucket into diff sequence lengths? what do the different sequence lengths mean? There is no prediction-in-keypoint space model right? So there is no concept of the performance worsening as the trajectory gets longer. Arenât the keypoint guesses just the output of the PointNet at each frame, so why would the results from a 200 frame sequence be much different than 100 or something? Why not just report overall precision and recall on the test set? * In the KeyQN section What is the keypoint mask averaged feature vector? just multiply each feature map element wise by H_psi?
* In the KeyQN section What is the keypoint mask averaged feature vector? just multiply each feature map element wise by H_psi?