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