176 lines
5 KiB
Rust
176 lines
5 KiB
Rust
use crate::{BotStrategy, CheckerMove, Color, GameState, PlayerId, PointsRules};
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use std::path::Path;
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use store::MoveRules;
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use super::dqn_common::{
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get_valid_actions, sample_valid_action, SimpleNeuralNetwork, TrictracAction,
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};
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/// Stratégie DQN pour le bot - ne fait que charger et utiliser un modèle pré-entraîné
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#[derive(Debug)]
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pub struct DqnStrategy {
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pub game: GameState,
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pub player_id: PlayerId,
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pub color: Color,
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pub model: Option<SimpleNeuralNetwork>,
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}
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impl Default for DqnStrategy {
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fn default() -> Self {
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Self {
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game: GameState::default(),
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player_id: 2,
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color: Color::Black,
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model: None,
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}
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}
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}
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impl DqnStrategy {
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pub fn new() -> Self {
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Self::default()
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}
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pub fn new_with_model<P: AsRef<Path>>(model_path: P) -> Self {
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let mut strategy = Self::new();
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if let Ok(model) = SimpleNeuralNetwork::load(model_path) {
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strategy.model = Some(model);
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}
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strategy
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}
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/// Utilise le modèle DQN pour choisir une action valide
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fn get_dqn_action(&self) -> Option<TrictracAction> {
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if let Some(ref model) = self.model {
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let state = self.game.to_vec_float();
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let valid_actions = get_valid_actions(&self.game);
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if valid_actions.is_empty() {
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return None;
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}
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// Obtenir les Q-values pour toutes les actions
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let q_values = model.forward(&state);
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// Trouver la meilleure action valide
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let mut best_action = &valid_actions[0];
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let mut best_q_value = f32::NEG_INFINITY;
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for action in &valid_actions {
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let action_index = action.to_action_index();
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if action_index < q_values.len() {
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let q_value = q_values[action_index];
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if q_value > best_q_value {
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best_q_value = q_value;
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best_action = action;
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}
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}
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}
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Some(best_action.clone())
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} else {
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// Fallback : action aléatoire valide
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sample_valid_action(&self.game)
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}
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}
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}
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impl BotStrategy for DqnStrategy {
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fn get_game(&self) -> &GameState {
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&self.game
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}
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fn get_mut_game(&mut self) -> &mut GameState {
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&mut self.game
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}
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fn set_color(&mut self, color: Color) {
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self.color = color;
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}
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fn set_player_id(&mut self, player_id: PlayerId) {
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self.player_id = player_id;
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}
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fn calculate_points(&self) -> u8 {
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self.game.dice_points.0
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}
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fn calculate_adv_points(&self) -> u8 {
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self.game.dice_points.1
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}
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fn choose_go(&self) -> bool {
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// Utiliser le DQN pour décider si on continue
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if let Some(action) = self.get_dqn_action() {
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matches!(action, TrictracAction::Go)
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} else {
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// Fallback : toujours continuer
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true
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}
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}
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fn choose_move(&self) -> (CheckerMove, CheckerMove) {
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// Utiliser le DQN pour choisir le mouvement
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if let Some(action) = self.get_dqn_action() {
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if let TrictracAction::Move {
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dice_order,
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from1,
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from2,
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} = action
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{
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let dicevals = self.game.dice.values;
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let (mut dice1, mut dice2) = if dice_order {
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(dicevals.0, dicevals.1)
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} else {
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(dicevals.1, dicevals.0)
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};
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if from1 == 0 {
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// empty move
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dice1 = 0;
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}
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let mut to1 = from1 + dice1 as usize;
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if 24 < to1 {
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// sortie
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to1 = 0;
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}
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if from2 == 0 {
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// empty move
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dice2 = 0;
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}
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let mut to2 = from2 + dice2 as usize;
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if 24 < to2 {
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// sortie
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to2 = 0;
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}
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let checker_move1 = CheckerMove::new(from1, to1).unwrap_or_default();
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let checker_move2 = CheckerMove::new(from2, to2).unwrap_or_default();
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let chosen_move = if self.color == Color::White {
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(checker_move1, checker_move2)
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} else {
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(checker_move1.mirror(), checker_move2.mirror())
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};
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return chosen_move;
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}
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}
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// Fallback : utiliser la stratégie par défaut
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let rules = MoveRules::new(&self.color, &self.game.board, self.game.dice);
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let possible_moves = rules.get_possible_moves_sequences(true, vec![]);
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let chosen_move = *possible_moves
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.first()
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.unwrap_or(&(CheckerMove::default(), CheckerMove::default()));
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if self.color == Color::White {
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chosen_move
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} else {
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(chosen_move.0.mirror(), chosen_move.1.mirror())
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}
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}
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}
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