  This paper proposes a new approach to a novel value network architecture for
the game Go, called a multi-labelled (ML) value network. In the ML value
network, different values (win rates) are trained simultaneously for different
settings of komi, a compensation given to balance the initiative of playing
first. The ML value network has three advantages, (a) it outputs values for
different komi, (b) it supports dynamic komi, and (c) it lowers the mean
squared error (MSE). This paper also proposes a new dynamic komi method to
improve game-playing strength. This paper also performs experiments to
demonstrate the merits of the architecture. First, the MSE of the ML value
network is generally lower than the value network alone. Second, the program
based on the ML value network wins by a rate of 67.6% against the program based
on the value network alone. Third, the program with the proposed dynamic komi
method significantly improves the playing strength over the baseline that does
not use dynamic komi, especially for handicap games. To our knowledge, up to
date, no handicap games have been played openly by programs using value
networks. This paper provides these programs with a useful approach to playing
handicap games.
