Fast and Knowledge-Free Deep Learning for General Game Playing (Student Abstract)
DOI:
https://doi.org/10.1609/aaai.v38i21.30480Keywords:
Game Playing, Reinforcement Learning, Applications Of AIAbstract
We develop a method of adapting the AlphaZero model to General Game Playing (GGP) that focuses on faster model generation and requires less knowledge to be extracted from the game rules. The dataset generation uses MCTS playing instead of self-play; only the value network is used, and attention layers replace the convolutional ones. This allows us to abandon any assumptions about the action space and board topology. We implement the method within the Regular Boardgames GGP system and show that we can build models outperforming the UCT baseline for most games efficiently.Downloads
Published
2024-03-24
How to Cite
Maras, M., Kępa, M., Kowalski, J., & Szykuła, M. (2024). Fast and Knowledge-Free Deep Learning for General Game Playing (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 23576-23578. https://doi.org/10.1609/aaai.v38i21.30480
Issue
Section
AAAI Student Abstract and Poster Program