Transfer of Fully Convolutional Policy-Value Networks Between Games and Game Variants
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Abstract (arXiv)
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.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 17 of its 39 references that are in the map. First posted on arXiv 24 Feb 2021.
Builds on
17 of 39 references.
References · 17
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5 more
- 2020Polygames: Improved Zero Learningcited by 11
- 2018Innateness, AlphaZero, and Artificial Intelligencecited by 10
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Built on by
Citing papers per year: 2021: 1; 2022: 2; 2023: 2; 2024: 1.
6 papers.
- 2021General Board Game Conceptscited by 8
- 2023Measuring Board Game Distancecited by 5
- 2022Spatial State-Action Features for General Gamescited by 3
- 2022Brick Tic-Tac-Toe: Exploring the Generalizability of AlphaZero to Novel Test Environmentscited by 2
- 2023Language-Informed Transfer Learning for Embodied Household Activitiescited by 1
- 2024AlphaViT: A flexible game-playing AI for multiple games and variable board sizescited by 0
- 2024AlphaViT: A flexible game-playing AI for multiple games and variable board sizes25 Aug 2024
- 2023Language-Informed Transfer Learning for Embodied Household Activities12 Jan 2023
- 2023Measuring Board Game Distance10 Jan 2023
- 2022Brick Tic-Tac-Toe: Exploring the Generalizability of AlphaZero to Novel Test Environments13 Jul 2022
- 2022Spatial State-Action Features for General Games17 Jan 2022
- 2021General Board Game Concepts2 Jul 2021