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This commit is contained in:
Henri Bourcereau 2025-06-01 20:21:38 +02:00
parent f7eea0ed02
commit ebe98ca229
5 changed files with 73 additions and 66 deletions
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2
bot/src/strategy/dqn.rs
View file

@ -2,7 +2,7 @@ use crate::{BotStrategy, CheckerMove, Color, GameState, PlayerId, PointsRules};
use std::path::Path;
use store::MoveRules;
use super::dqn_common::{DqnConfig, SimpleNeuralNetwork, TrictracAction, get_valid_actions, sample_valid_action};
use super::dqn_common::{SimpleNeuralNetwork, TrictracAction, get_valid_actions, sample_valid_action};
/// Stratégie DQN pour le bot - ne fait que charger et utiliser un modèle pré-entraîné
#[derive(Debug)]

15
bot/src/strategy/dqn_common.rs
View file

@ -1,5 +1,4 @@
use serde::{Deserialize, Serialize};
use crate::{CheckerMove};
/// Types d'actions possibles dans le jeu
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
@ -24,7 +23,7 @@ impl TrictracAction {
TrictracAction::Roll => 0,
TrictracAction::Mark { points } => {
1 + (*points as usize).min(12) // Indices 1-13 pour 0-12 points
},
}
TrictracAction::Go => 14,
TrictracAction::Move { move1, move2 } => {
// Encoder les mouvements dans l'espace d'actions
@ -38,13 +37,15 @@ impl TrictracAction {
pub fn from_action_index(index: usize) -> Option<TrictracAction> {
match index {
0 => Some(TrictracAction::Roll),
1..=13 => Some(TrictracAction::Mark { points: (index - 1) as u8 }),
1..=13 => Some(TrictracAction::Mark {
points: (index - 1) as u8,
}),
14 => Some(TrictracAction::Go),
i if i >= 15 => {
let move_code = i - 15;
let (move1, move2) = decode_move_pair(move_code);
Some(TrictracAction::Move { move1, move2 })
},
}
_ => None,
}
}
@ -236,7 +237,7 @@ impl SimpleNeuralNetwork {
/// Obtient les actions valides pour l'état de jeu actuel
pub fn get_valid_actions(game_state: &crate::GameState) -> Vec<TrictracAction> {
use crate::{Color, PointsRules};
use crate::PointsRules;
use store::{MoveRules, TurnStage};
let mut valid_actions = Vec::new();
@ -287,7 +288,6 @@ pub fn get_valid_actions(game_state: &crate::GameState) -> Vec<TrictracAction> {
});
}
}
_ => {}
}
}
@ -304,10 +304,9 @@ pub fn get_valid_action_indices(game_state: &crate::GameState) -> Vec<usize> {
/// Sélectionne une action valide aléatoire
pub fn sample_valid_action(game_state: &crate::GameState) -> Option<TrictracAction> {
use rand::{thread_rng, seq::SliceRandom};
use rand::{seq::SliceRandom, thread_rng};
let valid_actions = get_valid_actions(game_state);
let mut rng = thread_rng();
valid_actions.choose(&mut rng).cloned()
}

17
bot/src/strategy/dqn_trainer.rs
View file

@ -5,7 +5,7 @@ use serde::{Deserialize, Serialize};
use std::collections::VecDeque;
use store::{GameEvent, MoveRules, PointsRules, Stage, TurnStage};
use super::dqn_common::{DqnConfig, SimpleNeuralNetwork, TrictracAction, get_valid_actions, get_valid_action_indices, sample_valid_action};
use super::dqn_common::{get_valid_actions, DqnConfig, SimpleNeuralNetwork, TrictracAction};
/// Expérience pour le buffer de replay
#[derive(Debug, Clone, Serialize, Deserialize)]
@ -99,7 +99,10 @@ impl DqnAgent {
let mut rng = thread_rng();
if rng.gen::<f64>() < self.epsilon {
// Exploration : action valide aléatoire
valid_actions.choose(&mut rng).cloned().unwrap_or(TrictracAction::Roll)
valid_actions
.choose(&mut rng)
.cloned()
.unwrap_or(TrictracAction::Roll)
} else {
// Exploitation : meilleure action valide selon le modèle
let q_values = self.model.forward(state);
@ -287,7 +290,9 @@ impl TrictracEnv {
let dice_values = (rng.gen_range(1..=6), rng.gen_range(1..=6));
let dice_event = GameEvent::RollResult {
player_id: self.agent_player_id,
dice: store::Dice { values: dice_values },
dice: store::Dice {
values: dice_values,
},
};
if self.game_state.validate(&dice_event) {
self.game_state.consume(&dice_event);
@ -393,8 +398,10 @@ impl DqnTrainer {
pub fn train_episode(&mut self) -> f32 {
let mut total_reward = 0.0;
let mut state = self.env.reset();
// let mut step_count = 0;
loop {
// step_count += 1;
let action = self.agent.select_action(&self.env.game_state, &state);
let (next_state, reward, done) = self.env.step(action.clone());
total_reward += reward;
@ -412,6 +419,9 @@ impl DqnTrainer {
if done {
break;
}
// if step_count % 100 == 0 {
// println!("{:?}", next_state);
// }
state = next_state;
}
@ -429,6 +439,7 @@ impl DqnTrainer {
for episode in 1..=episodes {
let reward = self.train_episode();
print!(".");
if episode % 100 == 0 {
println!(
"Épisode {}/{}: Récompense = {:.2}, Epsilon = {:.3}, Steps = {}",

15
bot/src/strategy/stable_baselines3.rs
View file

@ -1,11 +1,11 @@
use crate::{BotStrategy, CheckerMove, Color, GameState, PlayerId, PointsRules};
use store::MoveRules;
use std::process::Command;
use std::io::Write;
use serde::{Deserialize, Serialize};
use std::fs::File;
use std::io::Read;
use std::io::Write;
use std::path::Path;
use serde::{Serialize, Deserialize};
use std::process::Command;
use store::MoveRules;
#[derive(Debug)]
pub struct StableBaselines3Strategy {
@ -85,7 +85,6 @@ impl StableBaselines3Strategy {
store::TurnStage::HoldOrGoChoice => 3,
store::TurnStage::Move => 4,
store::TurnStage::MarkAdvPoints => 5,
_ => 0,
};
// Récupérer les points et trous des joueurs
@ -170,10 +169,7 @@ with open("{}", "w") as f:
script_file.write_all(python_script.as_bytes()).ok()?;
// Exécuter le script Python
let status = Command::new("python")
.arg(temp_script_path)
.status()
.ok()?;
let status = Command::new("python").arg(temp_script_path).status().ok()?;
if !status.success() {
return None;
@ -274,3 +270,4 @@ impl BotStrategy for StableBaselines3Strategy {
}
}
}

2
store/src/game.rs
View file

@ -174,7 +174,7 @@ impl GameState {
state.push(self.dice.values.0 as i8);
state.push(self.dice.values.1 as i8);
// points length=4 x2 joueurs = 8
// points, trous, bredouille, grande bredouille length=4 x2 joueurs = 8
let white_player: Vec<i8> = self
.get_white_player()
.unwrap()