---
language: en
license: mit
tags:
- chess
- reinforcement-learning
- alphazero
- pytorch
library_name: pytorch
---

# Chess Bot Model - TinyPCN

A chess playing neural network trained on expert games from the Lichess Elite Database.

## Model Description

This is a policy-value network inspired by AlphaZero, designed to evaluate chess positions and suggest moves.

### Architecture

- **Input**: 18-plane board representation (12 pieces + 6 metadata planes)
- **Convolutional backbone**: 32 filters, 1 residual block, ~9,611,202 parameters
- **Policy head**: 4,672-dimensional output (one per legal move encoding)
- **Value head**: Single tanh output (-1 to +1 for position evaluation)

### Training Data

- **Dataset**: Lichess Elite Database (games from 2200+ ELO players)
- **Positions trained**: 16,800,000
- **Epochs**: 10

### Performance

- **Final Policy Loss**: 2.8000
- **Final Value Loss**: 0.8500

### Usage

```python
import torch
import chess
from model import TinyPCN, encode_board

# Load model
model = TinyPCN(board_channels=18, policy_size=4672)
model.load_state_dict(torch.load("chess_model.pth"))
model.eval()

# Evaluate a position
board = chess.Board()  # or chess.Board("fen string")
board_tensor = encode_board(board).unsqueeze(0)

with torch.no_grad():
    policy_logits, value = model(board_tensor)

# Value interpretation:
# +1.0 = winning for current player
#  0.0 = drawn/equal position
# -1.0 = losing for current player

print(f"Position evaluation: {value.item():.4f}")
```

### Model Files

- `chess_model.pth` - PyTorch model weights
- `model.py` - Model architecture and board encoding
- `mcts.py` - Monte Carlo Tree Search implementation
- `requirements.txt` - Python dependencies

### Limitations

- Trained on expert games only (no self-play yet)
- Lightweight architecture for educational purposes
- May not handle unusual openings or endgames well

### Training Details

- **Framework**: PyTorch
- **Optimizer**: Adam
- **Learning Rate**: 0.001
- **Batch Size**: 256
- **Loss Functions**: CrossEntropyLoss (policy) + MSELoss (value)

### Authors

Created as part of an AlphaZero-style chess engine project.

### License

MIT License