26 citations · 63 across the 7 of their papers we have counts for
3 papers · 1 filter
Score vs. Winrate in Score-Based Games: which Reward for Reinforcement Learning?
Luca Pasqualini, Gianluca Amato, Marco Fantozzi +5
In the last years, the DeepMind algorithm AlphaZero has become the state of the art to efficiently tackle perfect information two-player zero-sum games with a win/lose outcome. How…
SAI: a Sensible Artificial Intelligence that plays with handicap and targets high scores in 9x9 Go (extended version)
Francesco Morandin, Gianluca Amato, Marco Fantozzi +3
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 Mon…
SAI, a Sensible Artificial Intelligence that plays Go
Francesco Morandin, Gianluca Amato, Rosa Gini +3
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 th…