Multilabeled Value Networks for Computer Go.

This paper proposes a new approach to a novel value network architecture for the game Go, called a multilabeled (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: 1) it outputs values for different komi; (2) it...