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In this paper, we use fully convolutional architectures in AlphaZero-likeself-play training setups to facilitate transfer between variants of boardgames as well as distinct games. We explore how to transfer trained parametersof these architectures based on shared semantics of channels in the state andaction representations of the Ludii general game system. We use Ludii's largelibrary of games and game variants for extensive transfer learning evaluations,in zero-shot transfer experiments as well as experiments with additionalfine-tuning time.

Transfer of Fully Convolutional Policy-Value Networks Between Games and Game Variants