[iliketrains]

I see, integrating image inputs can be very challenging in this case as the models work with text input. I was not even thinking about the full isometric image, but just some simple 2D map where each pixel can be color-coded based on the entity type. I guess the problem is that these maps would look like nothing the models were trained on, so as you say, it might not provide any value.

The reason I was suggesting this is that I worked in robotics making RL policies, and supplying image data (be it maps, lidar scans, etc.) was a common practice. But our networks were custom made to ingest these data and trained from scratch, which is quite different from this approach.

[martbakler]

Indeed I think the trade-off here is the more "pure factorio" types of images we give to the agents, the more likely it is that they've seen it during training (from google etc), however the signal-to-noise ratio is low and hence the current models get confused as the map complexity (amount of entities) and level of detail grows. If we start to create custom images, we can reduce the unneeded noise, but then risk giving something completely OOD to the agent (unless we train a visual encoder) and the performance also tanks