use crate::policy::backprop::BackpropagationPolicy;
use crate::policy::decision::DecisionPolicy;
use crate::policy::selection::SelectionPolicy;
use crate::policy::simulation::SimulationPolicy;
use crate::state::GameState;
use std::time::Duration;

/// Configuration for the MCTS algorithm
#[derive(Debug)]
pub struct MCTSConfig<S: GameState> {
    /// The maximum number of iterations to run when searching
    ///
    /// The search will stop after the given number of iterations, even if there
    /// is search time has not exceeded `max_time`.
    pub max_iterations: usize,

    /// The maximum time to run the search
    ///
    /// If set, the search will stop after this duration even if the maximum
    /// iterations hasn't been reached.
    pub max_time: Option<Duration>,

    /// The size to initially allocate for the search tree
    ///
    /// This pre-allocates memory for the search tree which ensures contiguous
    /// memory and improves performance by preventing the resizing of tree
    /// as we explore.
    pub tree_size_allocation: usize,

    /// The selection policy
    ///
    /// This dictates the path through which the game tree is searched. As such
    /// the policy has a large impact on the overall aglorthm exeuction
    pub selection_policy: SelectionPolicy<S>,

    /// The simulation policy
    ///
    /// This dictates the game siluation when expanding and evaluating the
    /// search tree. Random is generally a good default.
    pub simulation_policy: SimulationPolicy<S>,

    /// The backpropagation policy
    ///
    /// This dictates how the results of the simulation playouts are propagated
    /// back up the tree.
    pub backprop_policy: BackpropagationPolicy<S>,

    /// The decision policy
    ///
    /// This dictates how the MCTS algorithm determines its final decision
    /// after iterating through the search tree
    pub decision_policy: DecisionPolicy,
}

impl<S: GameState> Default for MCTSConfig<S> {
    fn default() -> Self {
        MCTSConfig {
            max_iterations: 10_000,
            max_time: None,
            tree_size_allocation: 10_000,
            selection_policy: SelectionPolicy::UCB1Tuned(1.414),
            simulation_policy: SimulationPolicy::Random,
            backprop_policy: BackpropagationPolicy::Standard,
            decision_policy: DecisionPolicy::MostVisits,
        }
    }
}