| --- |
| dataset_info: |
| features: |
| - name: state |
| dtype: string |
| - name: turn |
| dtype: string |
| - name: negamax |
| list: string |
| - name: optimal |
| dtype: string |
| - name: stage |
| dtype: string |
| - name: outcome |
| dtype: string |
| - name: final_move |
| dtype: int64 |
| - name: depth |
| dtype: int64 |
| - name: assistant_threats |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 505934787 |
| num_examples: 2000000 |
| - name: test |
| num_bytes: 19273018 |
| num_examples: 76145 |
| download_size: 183716944 |
| dataset_size: 525207805 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| - split: test |
| path: data/test-* |
| license: apache-2.0 |
| tags: |
| - connect4 |
| - board_game |
| --- |
| |
|
|
| # Parsenal/c4_v2 |
| |
| The dataset contains about **2.076M positions**, with **76.1k positions reserved for the test split**. |
| |
| ## Overview |
| |
| This dataset was created to provide rich supervision for Connect Four models. |
| For each board position, the action value of every valid move is computed using a solver: |
| |
| - Solver: https://github.com/ChristopheSteininger/c4.git |
| |
| The core signal is the solver-produced **negamax list** over valid moves. |
| Aside from threat annotations, most other targets can be derived directly from this list. |
| |
| ## Data generation |
| |
| - Games were generated by solver play with **epsilon-greedy** action selection. |
| - This produced a distribution of positions from roughly **250k games**. |
| - Positions were shuffled. |
| - **76.1k positions** were selected for the test split. |
| - **Mirror duplicates were intentionally excluded**. |
| |
| ## Labels and supervision |
| |
| For each position, the dataset provides supervision that can support several training objectives: |
| |
| - **Optimal move** |
| - **Threat masks** |
| - **Per-move action values** for valid moves |
| - Signals that can be reduced to **position value** or related targets from the action-value / negamax outputs |
| |
| ### Threat definitions |
| |
| <p align="left"> |
| <img src="threats.png" alt="Connect Four example position" width="300"> |
| </p> |
| Threat annotations follow the definitions from **James D. Allen, _The Complete Book of Connect Four_**. |
| |
| ### Optimal move tie-breaking |
| |
| When more than one move is optimal, the selected target move is chosen by the following rules: |
| |
| 1. Choose the **most central** optimal move. |
| 2. If there is still a tie, choose the **leftmost** move. |
| |
| ## Intended use |
| |
| This dataset was used to train **`granite_4_in_a_row`** by teaching the model to: |
| |
| - output **threat masks** |
| - predict the **optimal move** |
| |
| However, the dataset contains much richer supervision than that setup alone. It can also be used for: |
| |
| - **value prediction** |
| - **action-value prediction** |
| - multi-task training over **threats + policy + value** |
| |
| ## Notes and limitations |
| |
| - Because games are generated with **epsilon-greedy** play, the position distribution is not the same as pure optimal play or human play. |
| - **Mirrored positions are excluded**, so to try symmetry augmentation you should add it separately. |
| |
| ## Attribution |
| |
| Solver used for annotation and game generation: |
| |
| - https://github.com/ChristopheSteininger/c4.git |
| |
| Threat definitions based on: |
| |
| - James D. Allen, _The Complete Book of Connect Four_ |
