Reviews: When to use parametric models in reinforcement learning?

This paper broadly considers the use of a learned parametric model. Through (1) toy examples, (2) theoretical analysis of a Dyna-like algorithm, and (3) a large scale study of sample-efficient model-free RL, it arrives at the conclusion that "using an imperfect (e.g., parametric) model to generate fictional experiences from truly observed states… should probably not result in better learning." While the individual pieces described above are all valuable, I am not sure this claim is properly qualified. For example: "More generally, if we use a perfect model to generate experiences only from states that were actually observed, the resulting updates would be indistinguishable from doing experience replay. In a sense, replay is a perfect model, albeit only from the states we have observed." I am not sure this is, as stated, exactly true.