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---
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language: en
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license: mit
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tags:
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- chess
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- reinforcement-learning
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- alphazero
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- pytorch
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library_name: pytorch
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---
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# Chess Bot Model - TinyPCN
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A chess playing neural network trained on expert games from the Lichess Elite Database.
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## Model Description
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This is a policy-value network inspired by AlphaZero, designed to evaluate chess positions and suggest moves.
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### Architecture
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- **Input**: 18-plane board representation (12 pieces + 6 metadata planes)
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- **Convolutional backbone**: 32 filters, 1 residual block, ~9,611,202 parameters
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- **Policy head**: 4,672-dimensional output (one per legal move encoding)
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- **Value head**: Single tanh output (-1 to +1 for position evaluation)
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### Training Data
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- **Dataset**: Lichess Elite Database (games from 2200+ ELO players)
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- **Positions trained**: 16,800,000
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- **Epochs**: 10
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### Performance
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- **Final Policy Loss**: 2.8000
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- **Final Value Loss**: 0.8500
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### Usage
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```python
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import torch
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import chess
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from model import TinyPCN, encode_board
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# Load model
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model = TinyPCN(board_channels=18, policy_size=4672)
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model.load_state_dict(torch.load("chess_model.pth"))
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model.eval()
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# Evaluate a position
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board = chess.Board() # or chess.Board("fen string")
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board_tensor = encode_board(board).unsqueeze(0)
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with torch.no_grad():
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policy_logits, value = model(board_tensor)
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# Value interpretation:
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# +1.0 = winning for current player
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# 0.0 = drawn/equal position
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# -1.0 = losing for current player
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print(f"Position evaluation: {value.item():.4f}")
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```
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### Model Files
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- `chess_model.pth` - PyTorch model weights
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- `model.py` - Model architecture and board encoding
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- `mcts.py` - Monte Carlo Tree Search implementation
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- `requirements.txt` - Python dependencies
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### Limitations
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- Trained on expert games only (no self-play yet)
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- Lightweight architecture for educational purposes
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- May not handle unusual openings or endgames well
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### Training Details
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- **Framework**: PyTorch
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- **Optimizer**: Adam
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- **Learning Rate**: 0.001
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- **Batch Size**: 256
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- **Loss Functions**: CrossEntropyLoss (policy) + MSELoss (value)
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### Authors
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Created as part of an AlphaZero-style chess engine project.
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### License
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MIT License
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