SlowGuess/NeurIPS_2023_OCR huggingface dataset:
(a) correlation  $= 0.7$
(b) correlation  $= 0.8$
(c) correlation  $= 0.9$
(d) correlation  $= 0.99$
Figure 9: Generalisation to reversed correlation at the vertical dotted line with varying correlation strengths on the cartpole swingup task.
Figure 10: Generalisation to reversed correlation at the vertical dotted line on the cartpole swingup task with all image observations converted to greyscale.
on the leading diagonal as the correlation strength. The results for generalisation to the reversed correlation are shown in Figure 9. While the generalisation performance of SVEA decreases as the correlation gets stronger, SVEA-CMID consistently generalises well up to a very strong correlation of 0.99 at which point the performance deteriorates but still significantly improves the performance of SVEA in this setting.
C.4 Greyscale images.
Our experiments use colour correlations to demonstrate the failure to generalise under correlation shifts. So we also demonstrate that the results still hold in greyscale images in Figure 10.
D Environment variations
In Table 5, we provide a description of the differences between the two object variations (A and B) in each task, along with images of example observations for each object and colour combination. The exact specification of the world model for each task is available in our code.
Environment	Variation	Blue	Green
cartpole_swingup	A - original cartpole
B - wider cart, shorter pole
Table 5: Environment images
E Saliency maps
The full set of saliency maps, as described in Section 6, for each representation feature is provided in Figure 11 for a trained SVEA encoder and a trained SVEA-CMID encoder. The features are sorted in order of most active to least active based on the sum of attributions for each feature.
To create the saliency maps, we use the Captum open-source interpretability library for PyTorch (Kokhlikyan et al., 2020) to calculate the integrated gradients (Sundararajan et al., 2017) pixel attributions for each feature in the representation output of the encoder. We use an all black image as the baseline image for integrated gradients which is compared to the input image in Figure 7a. The absolute value of the attributions are overlayed onto the input image to create the saliency maps.
(a) SVEA
(b)SVEA-CMID
Figure 11: Saliency maps for each representation feature of a trained (a) SVEA and (b) SVEA-CMID encoder on the cartpole swingup task, sorted in order of highest total attributions to lowest.