Combinations of Monte-Carlo tree search and Deep Neural Networks, trained through self-play, have produced state-of-the-art results for automated game-playing in many board games. The training and search algorithms are not game-specific, but every individual game that these approaches are applied to still requires domain knowledge for the implementation of the game’s rules, and constructing the neural network’s architecture – in particular the shapes of its input and output tensors. Ludii is a general game system that already contains over 1,000 different games, which can rapidly grow thanks to its powerful and user-friendly game description language. Polygames is a framework with training and search algorithms, which has already produced superhuman players for several board games. This paper describes the implementation of a bridge between Ludii and Polygames, which enables Polygames to train and evaluate models for games that are implemented and run through Ludii. We do not require any game-specific domain knowledge anymore, and instead leverage our domain knowledge of the Ludii system and its abstract state and move representations to write functions that can automatically determine the appropriate shapes for input and output tensors for any game implemented in Ludii. We describe experimental results for short training runs in a wide variety of different board games, and discuss several open problems and avenues for future research.

中文翻译：

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使用 Ludii 和 Polygames 进行一般游戏的深度学习

蒙特卡洛树搜索和深度神经网络的结合，通过自我对弈进行训练，已经为许多棋盘游戏中的自动游戏产生了最先进的结果。训练和搜索算法不是特定于游戏的，但是应用这些方法的每个单独游戏仍然需要领域知识来实现​​游戏规则和构建神经网络的架构——尤其是其输入和输出张量的形状. Ludii 是一个通用的游戏系统，已经包含超过 1000 种不同的游戏，得益于其强大且用户友好的游戏描述语言，它可以迅速发展。Polygames 是一个包含训练和搜索算法的框架，它已经为几个棋盘游戏产生了超人玩家。本文描述了 Ludii 和 Polygames 之间桥梁的实现，它使 Polygames 能够为通过 Ludii 实现和运行的游戏训练和评估模型。我们不再需要任何特定于游戏的领域知识，而是利用我们对 Ludii 系统及其抽象状态的领域知识并移动表示来编写函数，这些函数可以自动确定 Ludii 中实现的任何游戏的输入和输出张量的适当形状. 我们描述了在各种不同棋盘游戏中进行短期训练的实验结果，并讨论了几个未解决的问题和未来研究的途径。而是利用我们对 Ludii 系统及其抽象状态的领域知识和移动表示来编写函数，这些函数可以自动确定 Ludii 中实现的任何游戏的输入和输出张量的适当形状。我们描述了在各种不同棋盘游戏中进行短期训练的实验结果，并讨论了几个未解决的问题和未来研究的途径。而是利用我们对 Ludii 系统及其抽象状态的领域知识和移动表示来编写函数，这些函数可以自动确定 Ludii 中实现的任何游戏的输入和输出张量的适当形状。我们描述了在各种不同棋盘游戏中进行短期训练的实验结果，并讨论了几个未解决的问题和未来研究的途径。