  In this paper, we use fully convolutional architectures in AlphaZero-like
self-play training setups to facilitate transfer between variants of board
games as well as distinct games. We explore how to transfer trained parameters
of these architectures based on shared semantics of channels in the state and
action representations of the Ludii general game system. We use Ludii's large
library of games and game variants for extensive transfer learning evaluations,
in zero-shot transfer experiments as well as experiments with additional
fine-tuning time.
