AlphaViT

This repository provides the model weights for AlphaViT, AlphaViD, AlphaVDA, and Alphazero.

Models

This repository contains the weights for the following configurations:

• Single-Task Models: Models trained individually for specific games such as Connect4, Gomoku, and Othello.
• Multi-Task Models: Unified models capable of handling multiple games with varying board sizes.

Each filename includes digits indicating the training iteration. For example, checkpoint_1000.model represents the model saved after 1000 training iterations.

training_dataset_for_the_first_iteration/

Initial training datasets for various games, organized by game type. Files include:

• Board states (*.boards.npy)
• Move probabilities (*.probs.npy)
• Value outputs (*.vs.npy)

GitHub Repository

The full source code for AlphaViT, including training scripts and detailed implementation, is available on GitHub: https://github.com/KazuhisaFujita/AlphaViT

Related Paper

For detailed information on the methodology and experiments, refer to the research paper: "Flexible Game-Playing AI with AlphaViT: Adapting to Multiple Games and Board Sizes".