  We develop a method of adapting the AlphaZero model to General Game Playing
(GGP) that focuses on faster model generation and requires less knowledge to be
extracted from the game rules. The dataset generation uses MCTS playing instead
of self-play; only the value network is used, and attention layers replace the
convolutional ones. This allows us to abandon any assumptions about the action
space and board topology. We implement the method within the Regular Boardgames
GGP system and show that we can build models outperforming the UCT baseline for
most games efficiently.
