torques, friction) is often impossible. This is especially true for systems with contact dynamics.]{}]{} With the advent of deep learning, an alternative paradigm has emerged which proposes learning the controller directly [@ho2016generative; @khan2017memory; @silver2017predictron]. The drawbacks with this approach are twofold; supervised expert data is often needed and the learned controller may not generalize to new tasks.

[An alternative to directly learning a controller is to instead learn a model for the complex dynamical system under consideration. One may learn a *forward* model that produces a mapping from the control inputs and current state to predicted behavior of the system. This is in contrast with the previously described direct controller learning approach where an *inverse* model is learned that maps current state and desired behaviour to control signals. One of the advantages of a forward model is that it eliminates the need for a carefully curated set of expert demonstrations across different tasks. This is due to the fact that the behaviour of the controller is completely defined by the model and the optimization problem posed to solve for the controller. ]{}

![**Optimizing a sufficiently accurate model:** The solid line represents the true trajectory of the ball, while the dotted 