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README.md

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+---
+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