Multi-Labelled Value Networks for Computer Go

Understand

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.