ismcts 0.3.3

Information Set Monte Carlo Tree Search
Documentation 
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use super::{action_list::ActionList, edge::Edge, state::State};

type ChildArray = Vec<usize>;

#[derive(Clone)]
pub(crate) struct Node<T: State> {
    edge: Option<Edge<T::Action, usize>>,
    parent_id: Option<usize>,
    child_ids: ChildArray,
    tried_actions: T::ActionList,

    num_sims: usize,
    num_avails: usize,
    score: f32,
}

impl<T> Node<T>
where
    T: State,
{
    pub fn new(edge: Option<Edge<T::Action, usize>>, parent_id: Option<usize>) -> Self {
        Node {
            edge,
            parent_id,
            tried_actions: T::ActionList::uninit(),
            child_ids: Default::default(),
            num_sims: 0,
            num_avails: 1,
            score: 0.,
        }
    }

    pub fn add_child(&mut self, child_id: usize) {
        self.child_ids.push(child_id)
    }

    pub fn has_untried_actions(&self, legal_actions: &T::ActionList) -> bool {
        !legal_actions.without(&self.tried_actions).is_empty()
    }

    pub fn pop_action(&mut self, legal_actions: &T::ActionList) -> Option<T::Action> {
        let mut actions = legal_actions.without(&self.tried_actions);
        let action = actions.pop_random();

        if let Some(action) = action.clone() {
            self.tried_actions.push(action);
        }

        action
    }

    pub const fn increase_availability(&mut self) {
        self.num_avails += 1;
    }

    pub fn update(&mut self, reward: f32) {
        self.num_sims += 1;
        self.score += reward;
    }

    pub fn edge(&self) -> Option<Edge<T::Action, usize>> {
        self.edge.clone()
    }

    pub fn child_ids_ref(&self) -> &ChildArray {
        &self.child_ids
    }

    pub const fn parent_id(&self) -> Option<usize> {
        self.parent_id
    }

    pub const fn num_sims(&self) -> usize {
        self.num_sims
    }

    pub const fn avg_score(&self) -> f32 {
        self.score / self.num_sims as f32
    }

    pub fn uct_score(&self, c: f32) -> f32 {
        let n = self.num_sims as f32;
        self.score / n + c * ((self.num_avails as f32).ln() / n).sqrt()
    }

    pub const fn stats(&self) -> NodeStats {
        NodeStats {
            avg_score: self.avg_score(),
            num_sims: self.num_sims,
            num_avails: self.num_avails,
        }
    }
}

#[derive(Debug, Clone, Copy)]
pub struct NodeStats {
    pub avg_score: f32,
    pub num_sims: usize,
    pub num_avails: usize,
}