2 research outputs found
Move Evaluation in Go Using Deep Convolutional Neural Networks
The game of Go is more challenging than other board games, due to the
difficulty of constructing a position or move evaluation function. In this
paper we investigate whether deep convolutional networks can be used to
directly represent and learn this knowledge. We train a large 12-layer
convolutional neural network by supervised learning from a database of human
professional games. The network correctly predicts the expert move in 55% of
positions, equalling the accuracy of a 6 dan human player. When the trained
convolutional network was used directly to play games of Go, without any
search, it beat the traditional search program GnuGo in 97% of games, and
matched the performance of a state-of-the-art Monte-Carlo tree search that
simulates a million positions per move.Comment: Minor edits and included captures in Figure
Monte Carlo Game Solver
We present a general algorithm to order moves so as to speedup exact game
solvers. It uses online learning of playout policies and Monte Carlo Tree
Search. The learned policy and the information in the Monte Carlo tree are used
to order moves in game solvers. They improve greatly the solving time for
multiple games