The learning part is done by training on the output of an existing simulator. Obviously, if you already have the output of a simulator, you don't need to learn a simulator so the only real use of this approach is to train a simulator by watching a human agent interacting with the environment. So the idea is to eventually apply this thing to training robots to interact in the real world by watching how humans do it (this is not clearly stated in the paper; it's my charitable interpretation of the very poor motivation of learning a simulator from the output of an existing simulator).
The only problem is that creating an accurate simulation of a human's interaction with the real world (let alone an accurate simulation of the real world) is prohibitively expensive. Thus, for the time being, this kind of idea works only in very, very simple environments like PacMan.
So we don't yet have an expensive prototype electic car that nevertheless can do everything an er other kind of car can do. We have... not sure what. The description of an idea with an example of how such a thing could be achieved in the far future, when we can train robots to act like humans by watching us.
But eventually we reached the point where they are not only competitive, they may be positioned to fully overtake combustion cars.
I wonder how many discoveries on electric cars ended up being fully dead ends along the path that took us to the point we are now?