1. Perhaps the most important limitation I can see is the artificial environments used. In games and especially those old Atari ones, audio events can be repeated exaclty the same and it's quite easy for the network to learn to distinguish new sounds, whereas this might not be the case in more realistic environments, where there's more variance and noise in the audio. 2. L.225: "Visiting states with already learned audio-visual pairs is necessary for achieving a high score, even though they may not be crucial for exploration" So that seems like an important limitation, agent won't work well in this sort of environments, which can easily happen in realistic scenarios. 3. L.227 "The game has repetitive background sounds. Games like SpaceInvaders and BeamRider have background sounds at a fixed time interval, but it is hard to visually associate these sounds" Same here, repetitive background sounds might often be the case in real applications. 4. L190: It's a bit strange how the authors use vanilla FFT instead of the more common STFT (overlapping segments and a Hann windowing function). Probably a good idea to try this for consistency with literature. Insufficient ablations: 6. An ablation on the weighting method of the cross-entropy loss would be nice to see. The authors note for example that in Atlantis their method underperforms because "the game has repetitive background sounds". This is a scenario I'd expect the weighting might have helped remedy. 7. An ablation with adding noise to the audio channel would be interesting. 8. An ablation sampling the negatives from unrelated trajectories would also be interesting 9. Some architectural details are missing and some unclear. For example why is the 2D convnet shown in Fig. 2 fixed to random initialization?
6. An ablation on the weighting method of the cross-entropy loss would be nice to see. The authors note for example that in Atlantis their method underperforms because "the game has repetitive background sounds". This is a scenario I'd expect the weighting might have helped remedy.