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https://github.com/Noratrieb/monte-carlo-tree-search.git
synced 2026-01-15 07:45:05 +01:00
104 lines
2.2 KiB
Rust
104 lines
2.2 KiB
Rust
#![allow(dead_code)]
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use bumpalo::collections::Vec;
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use bumpalo::Bump;
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use rand::Rng;
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mod basic_search;
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pub trait GameState {
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fn points(&self) -> i32;
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fn next(&self) -> &[Self]
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where
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Self: Sized;
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fn is_finished(&self) -> bool;
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}
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type Tree<'tree, S> = Node<'tree, S>;
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struct Node<'tree, S> {
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state: S,
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visited: u64,
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won: u64,
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parent: Option<&'tree Node<'tree, S>>,
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children: Vec<'tree, Node<'tree, S>>,
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}
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impl<'tree, S: GameState> Node<'tree, S> {
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fn new(state: S, alloc: &'tree Bump) -> Node<S> {
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Node {
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state,
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visited: 0,
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won: 0,
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parent: None,
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children: Vec::new_in(alloc),
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}
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}
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fn random_child(&self) -> &Self {
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&self.children[rand::thread_rng().gen_range(0..self.children.len())]
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}
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fn into_child_with_max_score(self) -> Self {
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todo!()
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}
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}
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mod mcts {
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use crate::{GameState, Node};
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use bumpalo::Bump;
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const MAX_TRIES: u64 = 10000;
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pub fn find_next_move<S: GameState>(current_state: S) -> S {
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let alloc = Bump::new();
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let root_node = Node::new(current_state, &alloc);
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for _ in 0..MAX_TRIES {
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let promising_node = select_promising_node(&root_node);
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if !promising_node.state.is_finished() {
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expand_node(promising_node);
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}
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let node_to_explore = if !node_to_explore.children.is_empty() {
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promising_node.random_child()
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} else {
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promising_node
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};
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let playout_result = simulate_random_playout(node_to_explore);
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back_propagation(node_to_explore, playout_result);
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}
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let winner_node = root_node.into_child_with_max_score();
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winner_node.state
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}
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fn select_promising_node<'tree, S>(node: &'tree Node<'_, S>) -> &'tree Node<'tree, S> {
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todo!()
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}
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fn expand_node<S>(node: &Node<S>) {
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todo!()
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}
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fn simulate_random_playout<S>(node: &Node<'_, S>) -> u64 {
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todo!()
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}
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fn back_propagation<S>(node: &Node<'_, S>, playout_result: u64) {
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todo!()
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}
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}
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#[cfg(test)]
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mod test {
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#[test]
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fn t() {}
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}
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