---
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 & RL-Refined (700K+)

This is a high-performance dataset designed for training and pre-training state-of-the-art chess neural networks. It contains over **706,000 unique board positions** generated and evaluated by Stockfish 16.1.

The dataset is specifically optimized for models using **Policy & Value heads**, providing rich metadata for each state.

## 🌟 Key Features

* **Multi-PV Intelligence:** Each position includes not just the single best move, but **3 strong alternative plans**. This allows models to learn strategic variability and fine-grained positional judgment.
* **15-Channel Encoding:** Data is pre-structured for advanced architectures. It includes 12 piece layers, 1 side-to-move layer, and **2 temporal layers** (from/to squares of the last move) to eliminate tactical blindness.
* **RL-Refined Accuracy:** Includes a specialized subset of **5,000+ positions** derived from Reinforcement Learning sessions. These capture "hard-to-learn" tactical blunders that were corrected by Stockfish during active self-play.
* **High-Performance Processing:** The entire dataset was generated and processed using a cluster with **128+ CPU cores**, ensuring consistent and deep engine evaluation for every frame.
* **Ready-to-Train Eval:** Position evaluations are pre-normalized using the $tanh(x / 300.0)$ function, mapping Stockfish centipawns to a perfect $[-1, 1]$ range for Stable MSE training.

---

## 📊 Data Structure

The dataset is provided in a compressed `.npz` format:

* **`states`**: `(N, 15, 8, 8)` float32 tensors representing the board state.
* **`plans`**: `(N, 3, 1)` int64 array containing Multi-PV move indices ($from\_square \times 64 + to\_square$).
* **`evals`**: `(N,)` float32 array of normalized position evaluations.

---

## 🛠️ Usage (PyTorch 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']
        # Extract the primary best move from Multi-PV plans
        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

```
📈 Intended Use
This dataset is ideal for:

Pre-training Chess Policy-Value networks (like AlphaZero or LC0 clones).

Fine-tuning models to reduce tactical blunders.

Researching Reinforcement Learning and MCTS-based agents.

📜 License
This dataset is licensed under the Apache 2.0 License. You are free to use, modify, and distribute it for any purpose, including commercial projects.