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

Understand

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.