[logicrook]

Thank you, for the excellent rephrasing.

>But Go (and tetris etc) are games of perfect information where perception of the game state is not a challenge.

In general, "perception of the game state" is not a challenge, at least according to good game design principles (e.g. in danmaku shmups, perception can be a challenge because of visual effects that are not really part of the game, but this is seen as poor game design, similarly to how being unable to differentiate backgrounds from platforms in a run&jump is bad design). Although there are games where the perception of the game state is a game mechanic, but Doom isn't really one.

But even in Doom, you can separate quite neatly the two tasks. The vision task essentially aims to reconstruct a model of the world. But in a video-game, this model comes for free. You can trivially limit the information an agent get to what he would get as a player (in games like MGS it's already the case, albeit in a very simplistic way). It's fairly easy to make a function that computes what is visible, what sounds a player would hear, etc. You can then rephrase the problem as make an AI that can only access this function, and this wouldn't change anything.

So for the AI community, I think a more interesting question would have been to design an AI over such a function.

[eutectic]

If the contestants are using deep learning, I don't see why it should be any more difficult to generate a meaningful, low-dimensional representation of the game-state from raw pixels than from an abstract view input.