  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 500 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.
