| --- |
| license: other |
| license_name: mit-attribution |
| task_categories: |
| - reinforcement-learning |
| tags: |
| - "2048" |
| - game-ai |
| - supervised-learning |
| - imitation-learning |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
| # 2048 Expert Gameplay Dataset |
|
|
| State-action pairs from an expert N-Tuple Network agent playing 2048. |
| Can be used for imitation learning / supervised training of 2048 agents. |
|
|
| ## Stats |
|
|
| - Source games: 10,000 |
| - Games after filtering: 9,000 |
| - Total moves: 54,010,983 |
| - Average score: 143,847 |
| - Win rate (>= 2048): 100% (losing games removed) |
| - Score floor: 62,152 (bottom 10% removed) |
|
|
| ## Files |
|
|
| - `train.jsonl` - 8,100 games (48,592,790 moves) for training |
| - `val.jsonl` - 900 games (5,418,193 moves) for validation |
| - `games.jsonl` - original unfiltered dataset (10,000 games) |
|
|
| ## Filtering |
|
|
| The raw dataset was filtered to improve supervised learning quality: |
| 1. Removed games that did not reach the 2048 tile (losing games with desperate end-moves) |
| 2. Removed bottom 10% by score (bad-luck games with messy board patterns) |
| 3. Split 90/10 into train/validation (every 10th kept game to validation) |
|
|
| ## Format |
|
|
| JSONL format, one game per line. Each game object: |
|
|
| ```json |
| { |
| "game_id": 0, |
| "score": 142056, |
| "max_tile": 8192, |
| "num_moves": 2341, |
| "moves": [ |
| { |
| "board": [0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0], |
| "action": 0, |
| "action_name": "up", |
| "reward": 0, |
| "move_number": 0 |
| } |
| ] |
| } |
| ``` |
|
|
| - `board`: 16 tile values in row-major order (0=empty, 2/4/8/...) |
| - `action`: 0=up, 1=right, 2=down, 3=left |
| - `reward`: points from merges on this move |
|
|
