1) yes you are correct. the point i was making is that, in the context of the discovery/research, that's outside the scope, and 'easier' to do, as it has been done in other verticals (ie.: e2e self driving)
2) yep, aligned here
3) I'm not fully following here, but agree this is not NeurIPS, and no Schmidhuber's bickering.
4) The network does store information, it just doesn't store a gameplay information, which could be forced, but as per point 1, it is , and I think it is the right approach, beyond the scope of this research
1) I'm not sure this is outside scope. It's also not something I'd use to reject a paper were I to review this in a conference. I mean you got to start somewhere and unlike reviewer 2 I don't think any criticism is rejection criteria. That'd be silly since lack of globally optimal solutions. But I'm also unconvinced this is proven my self-driving vehicles but I'm also not an RL expert.
3) It's always hard to evaluate. I was thinking about the ripping the game and so a reasonable metric is a comparison of ability to perform the task by a human. Of course I'm A LOT faster than my dishwasher at cleaning dishes but I'm not occupied while it is going, so it still has high utility. (Someone tell reviewer 2 lol)
4) Why should we believe that it doesn't store gameplay? The model was fed "user" inputs and frames. So it has this information and this information appears useful for learning the task.
1) yes you are correct. the point i was making is that, in the context of the discovery/research, that's outside the scope, and 'easier' to do, as it has been done in other verticals (ie.: e2e self driving)
2) yep, aligned here
3) I'm not fully following here, but agree this is not NeurIPS, and no Schmidhuber's bickering.
4) The network does store information, it just doesn't store a gameplay information, which could be forced, but as per point 1, it is , and I think it is the right approach, beyond the scope of this research