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.

翻译：我们提出了一种将AlphaZero模型适配至通用博弈（GGP）的方法，该方法侧重于更快的模型生成速度，且无需从博弈规则中提取大量知识。数据集生成采用蒙特卡洛树搜索（MCTS）对弈而非自对弈；仅使用价值网络，并以注意力层替代卷积层。这使得我们能够摒弃对动作空间和棋盘拓扑结构的任何假设。我们在Regular Boardgames通用博弈系统中实现了该方法，并证明能够高效构建出在多数博弈中优于UCT基线的模型。