Chess AI based on my Lichess game database.
Inspired by Maia Chess. Objective to use deep learning to mimic personal chess styles, emphasizing capturing human decision-making in rapid games.
Focused personal game datasets to ensure unique results. The dataset (~2,000 games) is optimized for an AMD Ryzen CPU without GPU acceleration. It comprises 1,633 Bullet, 165 Blitz, and 244 Rapid games. Emphasizing Bullet games captures impulsive decisions, providing a comprehensive play style view. All games were split into train, validation, and test sets (75-15-15).
3.0 Board Features:
8x8x12 map representation, where each 8x8 channel denotes a piece (12 total). A '1' indicates the presence of a piece, and '0' its absence
3.1 Move Features:
Moves were translated from the Universal Chess Interface to a numerical system, leading to 4096 potential classes.
3.2 Random Valid Move Model:
A baseline model, achieving 3.1-3.6% accuracy through random move selection.
The base convolutional model uses a convolutional neural network with a variable number of layers.
- Utilizes a convolutional neural network with variable layer counts.
- Each layer maintains consistent input-output channels with a 3x3 kernel and padding.
- Output is flattened and connected to two dense layers, culminating in a 4096-long channel.
- Model trained by comparing cross-entropy loss between output and move classifications.
The best model had losses of 0.9274 (training), 4.616 (validation), and 4.696 (test). This led to a move prediction accuracy of 14.24%.
After hyperparameter adjustments, this model had a top move prediction accuracy of 10.21%. Though inferior to the convolutional model, it outperforms the random baseline. Overall, I am using a small model with a small dataset, which poses training challenges.