performs poorly [@diehl2005real]. The aforementioned methods learn models with an objective function similar to $$\min_\theta \mathbb{E}\left\| \phi_\theta(s, a) - f(s, a) \right\|$$ where $f(s, a)$ represents the true model dynamics and $\phi_\theta(s, a)$ is the learned model. @byravan2017se3 design a specific neural network architecture for their forward model which uses a normalized objective. However, there are slack terms introduced to avoid numerical instability caused by dividing with small numbers. [@amos2018differentiable] presents a way to differentiate through the controller so that a model can be learned end to end. However, this method requires that the policy has converged to a fix point which can be hard to achieve in complicated systems.

Learning models is also of great interest in reinforcement learning where a forward model can increase sample efficiency [@sutton1990integrated; @levine2014learning]. In addition, it has been found that learning forward and inverse models can provide additional rewards to help train a reinforcement learning agent [@pathak2017curiosity]. [@achiam2017constrained] introduced a policy search method for reinforcement learning that can impose expectation constraints on states and actions.

There has also been some work in multi task learning that tries to obtain task agnostic policies. One way to do this is with meta learning algorithms such 