In the paper Mastering the game of Go with deep neural networks and tree search, the input features of the networks of AlphaGo contains a plane of constant ones and a plane of constant zeros, as following.
Feature #of planes Description Stone colour 3 Player stone/opponent stone/empty Ones 1 A constant plane ﬁlled with 1 Turns since 8 How many turns since a move was played Liberties 8 Number of liberties (empty adjacent points) Capture size 8 How many opponent stones would be captured Self-atari size 8 How many of own stones would be captured Liberties after move 8 Number of liberties after this move is played Ladder capture 1 Whether a move at this point is a successful ladder capture Ladder escape 1 Whether a move at this point is a successful ladder escape Sensibleness 1 Whether a move is legal and does not ﬁll its own eyes Zeros 1 A constant plane ﬁlled with 0 Player color 1 Whether current player is black
I wonder why these features are necessary, because I think a constant plane contains no information and it makes the the network larger and consequently harder to train.
What's more, I don't understand the sharp sign here. Does it mean "the number"? But one number is enough to represent "the number of turns since a move was played", why eight?
Thank you very much.