Struct MonteCarloTreeSearchBuilder 

pub struct MonteCarloTreeSearchBuilder<T: Board, K: RandomGenerator> { /* private fields */ }

A builder for creating instances of MonteCarloTreeSearch.

This provides a convenient way to configure the MCTS search with different parameters.

Implementations

impl<T: Board, K: RandomGenerator> MonteCarloTreeSearchBuilder<T, K>

pub fn new(board: T) -> Self

Creates a new builder with the given initial board state.

pub fn with_random_generator(self, rg: K) -> Self

Sets the random number generator for the MCTS search.

Examples found in repository

examples/tic_tac_toe.rs (line 14)

fn main() {
    // Create a new Tic-Tac-Toe board
    let board = TicTacToeBoard::default();

    // Create a new MCTS search instance
    let mut mcts = MonteCarloTreeSearch::builder(board)
        .with_alpha_beta_pruning(false)
        .with_random_generator(CustomNumberGenerator::default())
        .build();

    // Run the search for 20,000 iterations
    mcts.iterate_n_times(20000);

    // Print the chances
    let root = mcts.get_root();
    for node in root.children() {
        println!(
            "Move: {:?} = {:.2?}%",
            node.value().prev_move,
            node.value().wins_rate() * 100.0
        );
    }

    // Get the most promising move
    let best_move_node = root.get_best_child().unwrap();
    let best_move = best_move_node.value().prev_move;

    println!("The best move is: {:?}", best_move);
    assert_eq!(best_move, Some(4));
}

pub fn with_alpha_beta_pruning(self, use_abp: bool) -> Self

Enables or disables alpha-beta pruning.

Examples found in repository

examples/tic_tac_toe.rs (line 13)

fn main() {
    // Create a new Tic-Tac-Toe board
    let board = TicTacToeBoard::default();

    // Create a new MCTS search instance
    let mut mcts = MonteCarloTreeSearch::builder(board)
        .with_alpha_beta_pruning(false)
        .with_random_generator(CustomNumberGenerator::default())
        .build();

    // Run the search for 20,000 iterations
    mcts.iterate_n_times(20000);

    // Print the chances
    let root = mcts.get_root();
    for node in root.children() {
        println!(
            "Move: {:?} = {:.2?}%",
            node.value().prev_move,
            node.value().wins_rate() * 100.0
        );
    }

    // Get the most promising move
    let best_move_node = root.get_best_child().unwrap();
    let best_move = best_move_node.value().prev_move;

    println!("The best move is: {:?}", best_move);
    assert_eq!(best_move, Some(4));
}

pub fn build(self) -> MonteCarloTreeSearch<T, K>

Builds the MonteCarloTreeSearch instance with the configured parameters.

Examples found in repository

examples/tic_tac_toe.rs (line 15)

fn main() {
    // Create a new Tic-Tac-Toe board
    let board = TicTacToeBoard::default();

    // Create a new MCTS search instance
    let mut mcts = MonteCarloTreeSearch::builder(board)
        .with_alpha_beta_pruning(false)
        .with_random_generator(CustomNumberGenerator::default())
        .build();

    // Run the search for 20,000 iterations
    mcts.iterate_n_times(20000);

    // Print the chances
    let root = mcts.get_root();
    for node in root.children() {
        println!(
            "Move: {:?} = {:.2?}%",
            node.value().prev_move,
            node.value().wins_rate() * 100.0
        );
    }

    // Get the most promising move
    let best_move_node = root.get_best_child().unwrap();
    let best_move = best_move_node.value().prev_move;

    println!("The best move is: {:?}", best_move);
    assert_eq!(best_move, Some(4));
}