  We develop a new model that can be applied to any perfect information
two-player zero-sum game to target a high score, and thus a perfect play. We
integrate this model into the Monte Carlo tree search-policy iteration learning
pipeline introduced by Google DeepMind with AlphaGo. Training this model on 9x9
Go produces a superhuman Go player, thus proving that it is stable and robust.
We show that this model can be used to effectively play with both positional
and score handicap, and to minimize suboptimal moves. We develop a family of
agents that can target high scores against any opponent, and recover from very
severe disadvantage against weak opponents. To the best of our knowledge, these
are the first effective achievements in this direction.
