[PeterisP]

The link between imagining and deep learning is rather in the opposite direction - it has always been obvious that imagining possible outcomes before executing would be useful, but the novelty is that deep learning has allowed them to actually make "imagination" that works.

MPC is an useful concept if you have a predictive model that's at least vaguely close to the actual behavior. In some contexts (e.g. modeling of particular industrial systems) programmers could build such a model, but in the general case that's absolutely not feasible, the world is full with problems where, practically speaking, you can not manually build a forward model of the system under control.

So this article is about initial research on systems that can construct such a predictive model/imagination from experience, with a proof of concept that the current deep learning approaches allow us to build systems that can learn such predictive models (which wasn't really possible before) and further development of this concept seems to be the way how we can actually apply things like MPC to problems where we won't build a forward model ourselves; and in the long run, that means pretty much all problems.

[habitue]

> systems that can construct such a predictive model/imagination from experience

I just want to emphasize this point as the crux here. We have many many techniques for AI that involve doing roll-outs once a smart human with domain knowledge hands the system a fully-formed model of the dynamics. Not so many where the dynamics are learned