as MAML [@finn2017model] which learns a policy that can be adapted to different task parameters. [@pathak2018zero; @nair2018visual] learn policies that include the goal as an input to encourage the policy to generalize across different targets. These methods work for small numbers of task parameters but may have difficulty scaling up as each additional parameter added to a policy will decrease the sample efficiency of the algorithm.

Sufficiently Accurate Model Learning {#sec:model_learning}
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Let us denote the state of the system by ${s} \in \mathcal{S}$ and the control inputs or actions denoted by ${a}\in \mathcal{A}$. The forward dynamics model is defined by a function $f:\mathcal{S}\times\mathcal{A}\to \mathcal{S}$ that takes as inputs the state and action at time $n$ and whose output is the state at time $n+1$ $${s}_{n+1} = f({s}_n, {a}_n).$$ We denote $\phi_\theta : \mathcal{S} \times \mathcal{A} \to \mathcal{S}$ as the neural network approximation of the true model, where $\theta\in\mathbb{R}^N$ represents the network parameters. The classical approach to model learning consists of finding the parameters $\theta$ that minimize the expectation of a defined scalar-valued loss function. Hence, the problem is written as $$\label{eq:model_learning}
     \min_\theta  \mathbb{E}_{( {s},  {a}) \sim D} l(s,a,\phi_\theta),$$ where $D$ denotes the sampling distribution over the state-action space. In 