I believe the claim is true. Here is my attempt at a proof. Let us consider the optimal infinite horizon value function $V_\alpha^*$ of $\mathcal{M}_\alpha$ at an arbitrary state $s \in S$. The value $V_\alpha^*(s)$ is the expected sum of discounted rewards under an optimal policy $\pi_\alpha^*$, i.e., \begin{equation} V_\alpha^*(s) = \mathbb{E}_{\rho_\...


One of the used is to use a neural interpolation, this is using a pretrained model to zoom our images, in some sense training with this kind of images is like combining this pretrainined model with our new data, and can be seen as transfer learning technique

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