[theptip]

You're right, that's the technical term for it, thanks. I was referring to the agent's evaluation function being stateless (i.e. it does not _need_ to persist state from previous turns in order to play optimally).

I think that there are two distinct aspects of the incomplete information that are significant; first, ignoring temporality, you have the fog of war, so you can't see the whole board. This is probably easier to address in a RNN, since you can play quite well as an amnesiac that just reacts to things that are currently visible. But you need to scout less if you have a memory of what's out there, so the amnesiac won't be able to play optimally.

Then there's the temporal aspect. The set of previous states of the game is not stored in the game and made available to the player, and so to play optimally you have to have a memory. This is where new techniques will be necessary.

These are separate problems, I think, so it will be interesting to see if DeepMind can make progress without reaching human performance on the second part.