yes, model-based (ie - learning physics and predicting future states and planning accordingly, rather than just choosing actions that you've learned will give y

yes, model-based (ie - learning physics and predicting future states and planning accordingly, rather than just choosing actions that you've learned will give you rewards) RL has been in the literature for many years, see Sutton's DYNA for example. But I think the model-based part is still considered relatively hard in real-world environments due to the large search space of possible actions/consequences. And note that here they didn't actually use the model at runtime, they just used it as an 'expert' (rather than getting a human to provide guidance) to give a model-free agent policy a head start (see DAGGER algorithm) and reduce the number of training samples.