[nmca]

(disclaimer: work on RL, have trained models for simulated tasks)

I'm fairly sure that people work on control because general algorithms for control would be very useful (e.g., robot that can skin a cat and drive a car by holding the steering wheel). Such a robot would exist in our 3d physical world, so simulations of of our 3d world are used for training. If this could be done with radically less compute, it would be.

[anon_tor_12345]

sure but it doesn't hurt that you have infinite data too (i.e. the thing most other ML research is bound by). like you can't argue that it's not a very comfortable corner to be in wrt being able to publish.

[kadoban]

Sounds quite a bit like you're complaining that they chose/engineered a fruitful field of study. I think I'm missing what the problem with that is.

[anon_tor_12345]

>I think I'm missing what the problem with that is.

I'm complaining that publishing endless papers on your methods that are trained on endless amounts of synthetic data is more about paper churn than contributing something novel. like the person below says: no real control system uses an RL controller (e.g. boston dynamics uses only classical controls).

[kadoban]

Oh I see. I would have guessed that that was because this way relatively new. If it really doesn't translate to anything real then I definitely get your point.