Paper ID: 2408.13871 • Published Aug 25, 2024
AlphaViT: A Flexible Game-Playing AI for Multiple Games and Variable Board Sizes
Kazuhisa Fujita
TL;DR
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This paper presents novel game-playing AI agents based on the AlphaZero
framework, enhanced with Vision Transformer (ViT): AlphaViT, AlphaViD, and
AlphaVDA. These agents are designed to play multiple board games of various
sizes using a single network with shared weights, thereby overcoming
AlphaZero's limitation of fixed-board-size constraints. AlphaViT employs only a
transformer encoder, whereas AlphaViD and AlphaVDA incorporate both transformer
encoders and decoders. In AlphaViD, the decoder processes outputs from the
encoder, whereas AlphaVDA uses a learnable embeddings as the decoder input. The
additional decoder layers in AlphaViD and AlphaVDA provide flexibility to adapt
to various action spaces and board sizes. Experimental results show that the
proposed agents, trained on either individual games or multiple games
simultaneously, consistently outperform traditional algorithms such as Minimax
and Monte Carlo Tree Search and approach the performance of AlphaZero, despite
using a single deep neural network (DNN) with shared weights. In particular,
AlphaViT shows strong performance across all tested games. Furthermore,
fine-tuning the DNN using pre-trained weights from small-board games
accelerates convergence and improves performance, particularly in Gomoku.
Interestingly, simultaneous training on multiple games yields performance
comparable to, or even surpassing, single-game training. These results indicate
the potential of transformer-based architectures to develop more flexible and
robust game-playing AI agents that excel in multiple games and dynamic
environments.