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:
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
Optimal move tie-breaking
When more than one move is optimal, the selected target move is chosen by the following rules:
- Choose the most central optimal move.
- 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:
Threat definitions based on:
- James D. Allen, The Complete Book of Connect Four