Title: Learning to Play Othello with Deep Neural Networks,
Abstract: Achieving superhuman playing level by AlphaGo corroborated the capabilities
of convolutional neural architectures (CNNs) for capturing complex spatial
patterns. This result was to a great extent due to several analogies between Go
board states and 2D images CNNs have been designed for, in particular
translational invariance and a relatively large board. In this paper, we verify
whether CNN-based move predictors prove effective for Othello, a game with
significantly different characteristics, including a much smaller board size
and complete lack of translational invariance. We compare several CNN
architectures and board encodings, augment them with state-of-the-art
extensions, train on an extensive database of experts' moves, and examine them
with respect to move prediction accuracy and playing strength. The empirical
evaluation confirms high capabilities of neural move predictors and suggests a
strong correlation between prediction accuracy and playing strength. The best
CNNs not only surpass all other 1-ply Othello players proposed to date but
defeat (2-ply) Edax, the best open-source Othello player.