The following text is quoted from the AlphaGo Zero Paper 2017 from Nature. My question is regarding the eight features.
The input to the neural network is a 19 × 19 × 17 image stack comprising 17 binary feature planes. Eight feature planes, Xt, consist of binary values indicating the presence of the current player’s stones (Xit=1 if intersection i contains a stone of the player’s colour at timestep t;
This sounds like the
t means time and the eight features are the latest 8 board states. If this is the case, this will be my question.
0 if the intersection is empty, contains an opponent stone, or if t < 0). A further 8 feature planes, Yt, represent the corresponding features for the opponent’s stones. The final feature plane, C, represents the colour to play, and has a constant value of either 1 if black is to play or 0 if white is to play. These planes are concatenated together to give input features st = [Xt, Yt, Xt−1, Yt−1,..., Xt−7, Yt−7, C].
I have the feeling the eight features should be the combination of rotated (4) and reflected (2 ) board states of the current board state. Please correct me if I am wrong.
History features Xt, Yt are necessary, because Go is not fully observable solely from the current stones, as repetitions are forbidden; similarly, the colour feature C is necessary, because the komi is not observable.
Board repetition can be easily prevented by keeping tracking of all previous board states and when a new move is attempted, just compare against all previous board states. The same is for komi. Keeping 8 previous board states to prevent repetition doesn't sound right to me. What if the repetition happened to the last 9th board state? If this is not a serious paper published to the Nature, I would highly doubt this is wrong.