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---
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tags:
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- chess
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- reinforcement-learning
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- pytorch
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- neural-network
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language:
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- en
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license: mit
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---
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# Chess Policy Network
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A neural network-based chess engine that uses a policy network to predict the best moves.
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## Model Details
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- **Architecture**: Convolutional ResNet with policy head
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- **Input**: 12-channel board representation (piece positions for each color)
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- **Output**: 4672 move logits
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- **Total Parameters**: 5,827,456
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## Architecture Configuration
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```python
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input_channels: 12
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num_res_blocks: 8
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filters: 128
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policy_channels: 32
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num_move_classes: 4672
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dropout: 0.1
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activation: relu
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```
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## Training Data
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- Trained on high-ELO chess games (minimum rating: 2200+)
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- Supervised learning on master games
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- Cross-entropy loss with AdamW optimizer
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- Cosine annealing learning rate schedule
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## Usage
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```python
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import torch
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from huggingface_hub import hf_hub_download
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from training.models import PolicyNetwork
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from training.utils import encode_board
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# Load model
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model_path = hf_hub_download("rzhang-7/chesshacks-model", "pytorch_model.bin")
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model = PolicyNetwork.from_config(config)
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model.load(model_path)
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model.eval()
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# Get move predictions for a position
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board_tensor = encode_board(board)
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with torch.no_grad():
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logits = model(board_tensor.unsqueeze(0))
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# Convert to legal moves
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move_probs = filter_policy_to_legal(logits[0].numpy(), board)
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```
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## Performance
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Evaluate with:
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```bash
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python training/scripts/evaluate.py --model-path path/to/model.pt --data-dir training/data
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```
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## License
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MIT
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## Citation
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If you use this model, please cite:
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```bibtex
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@model{chess_policy_net,
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title={Chess Policy Network},
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author={Chess Hacks},
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year={2024},
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url={https://huggingface.co/rzhang-7/chesshacks-model}
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}
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```
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## Disclaimers
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- This model is trained on historical chess games and may reflect biases in those games
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- The model is provided as-is without guarantees
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- For competitive chess, consider using dedicated engines like Stockfish or Leela Chess Zero
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