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Note that the order of the final frames is determined in advance of the experiment. By picking $\beta=1000$ and $\delta=0.01$ we are able to successfully fit the algorithm to the video data. The two modes for each model can also be seen as a reward for each mode (Fig. \[fig:entropy\]b and c). We reward modes with lower entropy (more idling) and modes with higher entropy (movement) when the entropy is high. This gives motivation to the agents to move around the scene.

![Histogram of the reward values during training. Values close to 0 represent a mode with low entropy and values close to 1 represent a mode with high entropy. Therefore, modes with low entropy are rewarded with low values and modes with high entropy with high values. []{data-label=”fig:entropy”}](figures/entropy){width=”\textwidth”}

In Fig. \[fig:descriptive\]c we show a qualitative comparison between the movement and the entropy mode. We see how the agent learns to move towards high entropy areas which results in movement. Our assumption is that idling modes (near the corners) are rewarding and move towards the center of the scene (view). From this experiment we can also see that models will sometimes halt at a good idling spot (and will continue to idle even without a single clear concept in the view) and sometimes they will move and do some basic actions (like reaching for a cup). This behavior can be explained by the reward function of our model and the initial seed location of the agent. In the former case we observe that the agent learns to exploit this behavior by constantly moving
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