# 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](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"](https://arxiv.org/abs/2408.13871).