checkmate-chess

Checkmate Chess Engine

A neural network trained to play chess using MCTS (Monte Carlo Tree Search) guidance.

Model Description

This model evaluates chess positions and suggests moves. It outputs:

• Policy (P): Probability distribution over legal moves
• Value (V): Position evaluation from -1 (losing) to +1 (winning)

Architecture

• Input: 773-dimensional board encoding (pieces, turn, castling rights)
• Hidden layers: 3x512 with ReLU + BatchNorm + Dropout
• Output heads:
  • Policy head: 4672-dim output (all possible moves)
  • Value head: Single scalar (-1 to +1)

Training Data Format

The model is trained on game positions with format:

{"fen": "...", "move": "e2e4", "value": -1}

Usage

from inference import ChessModelInference
import chess

# Load model
model = ChessModelInference("checkmate_model.pt")

# Get predictions
board = chess.Board()
P, V = model.predict(board.fen(), board)

print(f"Position value: {V}")
print(f"Best moves: {sorted(P.items(), key=lambda x: x[1], reverse=True)[:3]}")

Integration with MCTS

This model is designed to work with MCTS for move selection. The policy priors guide the search, while value estimates help evaluate unvisited positions.

License

MIT License