  We propose a multiple-komi modification of the AlphaGo Zero/Leela Zero
paradigm. The winrate as a function of the komi is modeled with a
two-parameters sigmoid function, so that the neural network must predict just
one more variable to assess the winrate for all komi values. A second novel
feature is that training is based on self-play games that occasionally branch
-- with changed komi -- when the position is uneven. With this setting,
reinforcement learning is showed to work on 7x7 Go, obtaining very strong
playing agents. As a useful byproduct, the sigmoid parameters given by the
network allow to estimate the score difference on the board, and to evaluate
how much the game is decided.
