main function

void main()

Implementation

void main() {
  print("--- Conceptual Chess Reinforcement Learning Example ---");

  // Model parameters
  final embedSize = 128; // Embedding dimension for pieces/squares
  final numLayers = 2; // Number of Transformer Encoder layers
  final numHeads = 8; // Number of attention heads

  // Instantiate the Chess Transformer model
  final chessModel = ChessTransformer(
    embedSize: embedSize,
    numLayers: numLayers,
    numHeads: numHeads,
  );

  final optimizer = SGD(chessModel.parameters(), 0.001); // Learning rate

  // --- Conceptual Board State ---
  // A simplified starting board (e.g., standard chess starting position for white to move)
  // This is a flattened list of 64 integers, each representing a piece ID.
  // This is for demonstration; a real system would generate this from a Chess environment.
  List<int> initialBoardState = [
    // Rank 1 (White pieces)
    WHITE_ROOK, WHITE_KNIGHT, WHITE_BISHOP, WHITE_QUEEN, WHITE_KING,
    WHITE_BISHOP, WHITE_KNIGHT, WHITE_ROOK,
    // Rank 2 (White pawns)
    WHITE_PAWN, WHITE_PAWN, WHITE_PAWN, WHITE_PAWN, WHITE_PAWN, WHITE_PAWN,
    WHITE_PAWN, WHITE_PAWN,
    // Ranks 3-6 (Empty)
    ...List.filled(4 * BOARD_SIZE, EMPTY_SQUARE_ID),
    // Rank 7 (Black pawns)
    BLACK_PAWN, BLACK_PAWN, BLACK_PAWN, BLACK_PAWN, BLACK_PAWN, BLACK_PAWN,
    BLACK_PAWN, BLACK_PAWN,
    // Rank 8 (Black pieces)
    BLACK_ROOK, BLACK_KNIGHT, BLACK_BISHOP, BLACK_QUEEN, BLACK_KING,
    BLACK_BISHOP, BLACK_KNIGHT, BLACK_ROOK,
  ];

  print(
      "Conceptual Board State loaded (first 8 squares): ${initialBoardState.sublist(0, 8)}");
  print("Total possible moves the model can predict: $NUM_POSSIBLE_MOVES");

  // --- Conceptual RL Training Loop (Offline Behavioral Cloning / Imitation Learning) ---
  // In a real RL setup, this would involve interaction with a chess environment.
  // Here, we simulate learning from a "target move" from an "expert."
  final epochs = 100;
  final random = Random();

  // Pick a random target move for this simplified example (e.g., e2-e4 is (48, 36))
  // In a real scenario, this would come from an expert game or an RL algorithm.
  final int dummyTargetMoveIndex = random
      .nextInt(NUM_POSSIBLE_MOVES); // e.g., for e2-e4 it's 48*64 + 36 = 3100
  print(
      "\nDummy Target Move Index (from expert/target policy): $dummyTargetMoveIndex");

  print("\nTraining Chess Transformer (simulated behavioral cloning)...");
  for (int epoch = 0; epoch < epochs; epoch++) {
    // 1. Forward pass: Get logits for all possible moves
    final moveLogits = chessModel.forward(
        initialBoardState); // Returns List<Value> of size NUM_POSSIBLE_MOVES

    // 2. Calculate Cross-Entropy Loss against the target move
    final targetMoveVector = ValueVector(List.generate(
      NUM_POSSIBLE_MOVES,
      (i) => Value(i == dummyTargetMoveIndex
          ? 1.0
          : 0.0), // One-hot encoding of the target move
    ));
    final logitsVector = ValueVector(moveLogits);
    final loss = logitsVector.softmax().crossEntropy(targetMoveVector);

    // 3. Backward pass and optimization step
    chessModel.zeroGrad(); // Clear gradients
    loss.backward(); // Compute gradients
    optimizer.step(); // Update parameters

    if (epoch % 1 == 0 || epoch == epochs - 1) {
      print("Epoch $epoch | Loss: ${loss.data.toStringAsFixed(4)}");
    }
  }
  print("✅ Chess Transformer (policy network) training complete.");

  // --- Inference Example: Predicting a Move ---
  print("\n--- Chess Inference ---");
  final inferenceLogits = chessModel.forward(initialBoardState);
  final predictedMoveProbs = ValueVector(inferenceLogits).softmax();

  // Find the predicted move (index with highest probability)
  double maxProb = -1.0;
  int predictedMoveIndex = -1;
  for (int i = 0; i < predictedMoveProbs.values.length; i++) {
    if (predictedMoveProbs.values[i].data > maxProb) {
      maxProb = predictedMoveProbs.values[i].data;
      predictedMoveIndex = i;
    }
  }

  // Convert predictedMoveIndex back to (from_square, to_square)
  final int fromSquare = predictedMoveIndex ~/ NUM_SQUARES;
  final int toSquare = predictedMoveIndex % NUM_SQUARES;

  print(
      "Inference Logits (first 10): ${inferenceLogits.sublist(0, 10).map((v) => v.data.toStringAsFixed(4)).toList()}...");
  print(
      "Predicted Move Index: $predictedMoveIndex (Prob: ${maxProb.toStringAsFixed(4)})");
  print(
      "Conceptual Predicted Move: From square $fromSquare to square $toSquare");

  print("\n--- Important Considerations for Real Chess RL ---");
  print(
      "1. **Actual RL:** This example only shows the policy network. A full RL setup needs an environment, reward functions, and algorithms like PPO or AlphaZero for training.");
  print(
      "2. **Legal Moves:** The current model predicts over ALL 64*64 possible (start, end) pairs. A real chess engine would filter these to only legal moves or mask illegal moves during training/inference.");
  print(
      "3. **Board Representation:** More complex representations might include castling rights, en passant, turn to move, halfmove clock, etc.");
  print(
      "4. **Action Space:** More precise action encoding for special moves (promotion) would be needed.");
}