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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 beextracted from the game rules. The dataset generation uses MCTS playing insteadof self-play; only the value network is used, and attention layers replace theconvolutional ones. This allows us to abandon any assumptions about the actionspace and board topology. We implement the method within the Regular BoardgamesGGP system and show that we can build models outperforming the UCT baseline formost games efficiently.

Fast and Knowledge-Free Deep Learning for General Game Playing (Student Abstract)