| --- |
| license: apache-2.0 |
| task_categories: |
| - reinforcement-learning |
| language: |
| - en |
| - ru |
| tags: |
| - chess |
| - deep-learning |
| - stockfish |
| - pytorch |
| - multi-pv |
| - chess-engine |
| - reinforcement-learning |
| size_categories: |
| - 100K<n<1M |
| --- |
| |
| # Strategic Chess Dataset: Multi-PV (700K+) |
|
|
| Dataset containing 706,000 chess positions evaluated by Stockfish 16.1. Built for training policy-value networks (AlphaZero / Leela Chess Zero style architectures). |
|
|
| ## Dataset Summary |
|
|
| * 706,000 unique board positions with Multi-PV evaluations (top 3 candidate moves per position). |
| * 15-channel board encoding: 12 piece channels, 1 active-color channel, 2 move-history channels. |
| * 5,000 targeted blunder-recovery positions extracted from reinforcement learning self-play. |
| * Normalized evaluations: Stockfish centipawns mapped to [-1, 1] range using tanh(cp / 300.0). |
|
|
| ## Data Structure |
|
|
| Format: compressed `.npz` archive. |
|
|
| * `states`: float32 array of shape (N, 15, 8, 8). |
| * `plans`: int64 array of shape (N, 3, 1) with candidate move indices (`from_square * 64 + to_square`). |
| * `evals`: float32 array of shape (N,) containing normalized position scores. |
|
|
| ## PyTorch Loading Example |
|
|
| ```python |
| import numpy as np |
| import torch |
| from torch.utils.data import Dataset |
| |
| class StrategicChessDataset(Dataset): |
| def __init__(self, npz_path): |
| data = np.load(npz_path) |
| self.states = data["states"] |
| self.evals = data["evals"] |
| self.best_moves = data["plans"][:, 0, 0] |
| |
| def __len__(self): |
| return len(self.states) |
| |
| def __getitem__(self, idx): |
| state = torch.from_numpy(self.states[idx]).float() |
| move = torch.tensor(self.best_moves[idx], dtype=torch.long) |
| val = torch.tensor(self.evals[idx], dtype=torch.float32) |
| return state, move, val |
| ``` |
|
|
| ## License |
|
|
| Apache 2.0. |
|
|
