diff --git a/bot/Cargo.toml b/bot/Cargo.toml
index 135deae..4a0a95c 100644
--- a/bot/Cargo.toml
+++ b/bot/Cargo.toml
@@ -9,6 +9,14 @@ edition = "2021"
 name = "train_dqn_burn_valid"
 path = "src/dqn/burnrl_valid/main.rs"
 
+[[bin]]
+name = "train_dqn_burn_big"
+path = "src/dqn/burnrl_big/main.rs"
+
+[[bin]]
+name = "train_dqn_burn_before"
+path = "src/dqn/burnrl_before/main.rs"
+
 [[bin]]
 name = "train_dqn_burn"
 path = "src/dqn/burnrl/main.rs"
diff --git a/bot/scripts/train.sh b/bot/scripts/train.sh
index 9e54c7a..a3be831 100755
--- a/bot/scripts/train.sh
+++ b/bot/scripts/train.sh
@@ -4,20 +4,23 @@ ROOT="$(cd "$(dirname "$0")" && pwd)/../.."
 LOGS_DIR="$ROOT/bot/models/logs"
 
 CFG_SIZE=12
+# BINBOT=train_dqn_burn
+BINBOT=train_dqn_burn_big
+# BINBOT=train_dqn_burn_before
 OPPONENT="random"
 
 PLOT_EXT="png"
 
 train() {
-  cargo build --release --bin=train_dqn_burn
-  NAME="train_$(date +%Y-%m-%d_%H:%M:%S)"
+  cargo build --release --bin=$BINBOT
+  NAME=$BINBOT"_$(date +%Y-%m-%d_%H:%M:%S)"
   LOGS="$LOGS_DIR/$NAME.out"
   mkdir -p "$LOGS_DIR"
-  LD_LIBRARY_PATH="$ROOT/target/release" "$ROOT/target/release/train_dqn_burn" | tee "$LOGS"
+  LD_LIBRARY_PATH="$ROOT/target/release" "$ROOT/target/release/$BINBOT" | tee "$LOGS"
 }
 
 plot() {
-  NAME=$(ls -rt "$LOGS_DIR" | tail -n 1)
+  NAME=$(ls -rt "$LOGS_DIR" | grep $BINBOT | tail -n 1)
   LOGS="$LOGS_DIR/$NAME"
   cfgs=$(head -n $CFG_SIZE "$LOGS")
   for cfg in $cfgs; do
diff --git a/bot/src/dqn/burnrl_before/dqn_model.rs b/bot/src/dqn/burnrl_before/dqn_model.rs
new file mode 100644
index 0000000..02646eb
--- /dev/null
+++ b/bot/src/dqn/burnrl_before/dqn_model.rs
@@ -0,0 +1,211 @@
+use crate::dqn::burnrl_before::environment::TrictracEnvironment;
+use crate::dqn::burnrl_before::utils::soft_update_linear;
+use burn::module::Module;
+use burn::nn::{Linear, LinearConfig};
+use burn::optim::AdamWConfig;
+use burn::tensor::activation::relu;
+use burn::tensor::backend::{AutodiffBackend, Backend};
+use burn::tensor::Tensor;
+use burn_rl::agent::DQN;
+use burn_rl::agent::{DQNModel, DQNTrainingConfig};
+use burn_rl::base::{Action, ElemType, Environment, Memory, Model, State};
+use std::fmt;
+use std::time::SystemTime;
+
+#[derive(Module, Debug)]
+pub struct Net<B: Backend> {
+    linear_0: Linear<B>,
+    linear_1: Linear<B>,
+    linear_2: Linear<B>,
+}
+
+impl<B: Backend> Net<B> {
+    #[allow(unused)]
+    pub fn new(input_size: usize, dense_size: usize, output_size: usize) -> Self {
+        Self {
+            linear_0: LinearConfig::new(input_size, dense_size).init(&Default::default()),
+            linear_1: LinearConfig::new(dense_size, dense_size).init(&Default::default()),
+            linear_2: LinearConfig::new(dense_size, output_size).init(&Default::default()),
+        }
+    }
+
+    fn consume(self) -> (Linear<B>, Linear<B>, Linear<B>) {
+        (self.linear_0, self.linear_1, self.linear_2)
+    }
+}
+
+impl<B: Backend> Model<B, Tensor<B, 2>, Tensor<B, 2>> for Net<B> {
+    fn forward(&self, input: Tensor<B, 2>) -> Tensor<B, 2> {
+        let layer_0_output = relu(self.linear_0.forward(input));
+        let layer_1_output = relu(self.linear_1.forward(layer_0_output));
+
+        relu(self.linear_2.forward(layer_1_output))
+    }
+
+    fn infer(&self, input: Tensor<B, 2>) -> Tensor<B, 2> {
+        self.forward(input)
+    }
+}
+
+impl<B: Backend> DQNModel<B> for Net<B> {
+    fn soft_update(this: Self, that: &Self, tau: ElemType) -> Self {
+        let (linear_0, linear_1, linear_2) = this.consume();
+
+        Self {
+            linear_0: soft_update_linear(linear_0, &that.linear_0, tau),
+            linear_1: soft_update_linear(linear_1, &that.linear_1, tau),
+            linear_2: soft_update_linear(linear_2, &that.linear_2, tau),
+        }
+    }
+}
+
+#[allow(unused)]
+const MEMORY_SIZE: usize = 8192;
+
+pub struct DqnConfig {
+    pub min_steps: f32,
+    pub max_steps: usize,
+    pub num_episodes: usize,
+    pub dense_size: usize,
+    pub eps_start: f64,
+    pub eps_end: f64,
+    pub eps_decay: f64,
+
+    pub gamma: f32,
+    pub tau: f32,
+    pub learning_rate: f32,
+    pub batch_size: usize,
+    pub clip_grad: f32,
+}
+
+impl fmt::Display for DqnConfig {
+    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
+        let mut s = String::new();
+        s.push_str(&format!("min_steps={:?}\n", self.min_steps));
+        s.push_str(&format!("max_steps={:?}\n", self.max_steps));
+        s.push_str(&format!("num_episodes={:?}\n", self.num_episodes));
+        s.push_str(&format!("dense_size={:?}\n", self.dense_size));
+        s.push_str(&format!("eps_start={:?}\n", self.eps_start));
+        s.push_str(&format!("eps_end={:?}\n", self.eps_end));
+        s.push_str(&format!("eps_decay={:?}\n", self.eps_decay));
+        s.push_str(&format!("gamma={:?}\n", self.gamma));
+        s.push_str(&format!("tau={:?}\n", self.tau));
+        s.push_str(&format!("learning_rate={:?}\n", self.learning_rate));
+        s.push_str(&format!("batch_size={:?}\n", self.batch_size));
+        s.push_str(&format!("clip_grad={:?}\n", self.clip_grad));
+        write!(f, "{s}")
+    }
+}
+
+impl Default for DqnConfig {
+    fn default() -> Self {
+        Self {
+            min_steps: 250.0,
+            max_steps: 2000,
+            num_episodes: 1000,
+            dense_size: 256,
+            eps_start: 0.9,
+            eps_end: 0.05,
+            eps_decay: 1000.0,
+
+            gamma: 0.999,
+            tau: 0.005,
+            learning_rate: 0.001,
+            batch_size: 32,
+            clip_grad: 100.0,
+        }
+    }
+}
+
+type MyAgent<E, B> = DQN<E, B, Net<B>>;
+
+#[allow(unused)]
+pub fn run<E: Environment + AsMut<TrictracEnvironment>, B: AutodiffBackend>(
+    conf: &DqnConfig,
+    visualized: bool,
+) -> DQN<E, B, Net<B>> {
+    // ) -> impl Agent<E> {
+    let mut env = E::new(visualized);
+    env.as_mut().min_steps = conf.min_steps;
+    env.as_mut().max_steps = conf.max_steps;
+
+    let model = Net::<B>::new(
+        <<E as Environment>::StateType as State>::size(),
+        conf.dense_size,
+        <<E as Environment>::ActionType as Action>::size(),
+    );
+
+    let mut agent = MyAgent::new(model);
+
+    // let config = DQNTrainingConfig::default();
+    let config = DQNTrainingConfig {
+        gamma: conf.gamma,
+        tau: conf.tau,
+        learning_rate: conf.learning_rate,
+        batch_size: conf.batch_size,
+        clip_grad: Some(burn::grad_clipping::GradientClippingConfig::Value(
+            conf.clip_grad,
+        )),
+    };
+
+    let mut memory = Memory::<E, B, MEMORY_SIZE>::default();
+
+    let mut optimizer = AdamWConfig::new()
+        .with_grad_clipping(config.clip_grad.clone())
+        .init();
+
+    let mut policy_net = agent.model().as_ref().unwrap().clone();
+
+    let mut step = 0_usize;
+
+    for episode in 0..conf.num_episodes {
+        let mut episode_done = false;
+        let mut episode_reward: ElemType = 0.0;
+        let mut episode_duration = 0_usize;
+        let mut state = env.state();
+        let mut now = SystemTime::now();
+
+        while !episode_done {
+            let eps_threshold = conf.eps_end
+                + (conf.eps_start - conf.eps_end) * f64::exp(-(step as f64) / conf.eps_decay);
+            let action =
+                DQN::<E, B, Net<B>>::react_with_exploration(&policy_net, state, eps_threshold);
+            let snapshot = env.step(action);
+
+            episode_reward +=
+                <<E as Environment>::RewardType as Into<ElemType>>::into(snapshot.reward().clone());
+
+            memory.push(
+                state,
+                *snapshot.state(),
+                action,
+                snapshot.reward().clone(),
+                snapshot.done(),
+            );
+
+            if config.batch_size < memory.len() {
+                policy_net =
+                    agent.train::<MEMORY_SIZE>(policy_net, &memory, &mut optimizer, &config);
+            }
+
+            step += 1;
+            episode_duration += 1;
+
+            if snapshot.done() || episode_duration >= conf.max_steps {
+                let envmut = env.as_mut();
+                println!(
+                    "{{\"episode\": {episode}, \"reward\": {episode_reward:.4}, \"steps count\": {episode_duration}, \"epsilon\": {eps_threshold:.3}, \"goodmoves\": {}, \"rollpoints\":{}, \"duration\": {}}}",
+                    envmut.goodmoves_count,
+                    envmut.pointrolls_count,
+                    now.elapsed().unwrap().as_secs(),
+                );
+                env.reset();
+                episode_done = true;
+                now = SystemTime::now();
+            } else {
+                state = *snapshot.state();
+            }
+        }
+    }
+    agent
+}
diff --git a/bot/src/dqn/burnrl_before/environment.rs b/bot/src/dqn/burnrl_before/environment.rs
new file mode 100644
index 0000000..9925a9a
--- /dev/null
+++ b/bot/src/dqn/burnrl_before/environment.rs
@@ -0,0 +1,449 @@
+use crate::dqn::dqn_common_big;
+use burn::{prelude::Backend, tensor::Tensor};
+use burn_rl::base::{Action, Environment, Snapshot, State};
+use rand::{thread_rng, Rng};
+use store::{GameEvent, GameState, PlayerId, PointsRules, Stage, TurnStage};
+
+/// État du jeu Trictrac pour burn-rl
+#[derive(Debug, Clone, Copy)]
+pub struct TrictracState {
+    pub data: [i8; 36], // Représentation vectorielle de l'état du jeu
+}
+
+impl State for TrictracState {
+    type Data = [i8; 36];
+
+    fn to_tensor<B: Backend>(&self) -> Tensor<B, 1> {
+        Tensor::from_floats(self.data, &B::Device::default())
+    }
+
+    fn size() -> usize {
+        36
+    }
+}
+
+impl TrictracState {
+    /// Convertit un GameState en TrictracState
+    pub fn from_game_state(game_state: &GameState) -> Self {
+        let state_vec = game_state.to_vec();
+        let mut data = [0; 36];
+
+        // Copier les données en s'assurant qu'on ne dépasse pas la taille
+        let copy_len = state_vec.len().min(36);
+        data[..copy_len].copy_from_slice(&state_vec[..copy_len]);
+
+        TrictracState { data }
+    }
+}
+
+/// Actions possibles dans Trictrac pour burn-rl
+#[derive(Debug, Clone, Copy, PartialEq)]
+pub struct TrictracAction {
+    // u32 as required by burn_rl::base::Action type
+    pub index: u32,
+}
+
+impl Action for TrictracAction {
+    fn random() -> Self {
+        use rand::{thread_rng, Rng};
+        let mut rng = thread_rng();
+        TrictracAction {
+            index: rng.gen_range(0..Self::size() as u32),
+        }
+    }
+
+    fn enumerate() -> Vec<Self> {
+        (0..Self::size() as u32)
+            .map(|index| TrictracAction { index })
+            .collect()
+    }
+
+    fn size() -> usize {
+        1252
+    }
+}
+
+impl From<u32> for TrictracAction {
+    fn from(index: u32) -> Self {
+        TrictracAction { index }
+    }
+}
+
+impl From<TrictracAction> for u32 {
+    fn from(action: TrictracAction) -> u32 {
+        action.index
+    }
+}
+
+/// Environnement Trictrac pour burn-rl
+#[derive(Debug)]
+pub struct TrictracEnvironment {
+    pub game: GameState,
+    active_player_id: PlayerId,
+    opponent_id: PlayerId,
+    current_state: TrictracState,
+    episode_reward: f32,
+    pub step_count: usize,
+    pub min_steps: f32,
+    pub max_steps: usize,
+    pub pointrolls_count: usize,
+    pub goodmoves_count: usize,
+    pub goodmoves_ratio: f32,
+    pub visualized: bool,
+}
+
+impl Environment for TrictracEnvironment {
+    type StateType = TrictracState;
+    type ActionType = TrictracAction;
+    type RewardType = f32;
+
+    fn new(visualized: bool) -> Self {
+        let mut game = GameState::new(false);
+
+        // Ajouter deux joueurs
+        game.init_player("DQN Agent");
+        game.init_player("Opponent");
+        let player1_id = 1;
+        let player2_id = 2;
+
+        // Commencer la partie
+        game.consume(&GameEvent::BeginGame { goes_first: 1 });
+
+        let current_state = TrictracState::from_game_state(&game);
+        TrictracEnvironment {
+            game,
+            active_player_id: player1_id,
+            opponent_id: player2_id,
+            current_state,
+            episode_reward: 0.0,
+            step_count: 0,
+            min_steps: 250.0,
+            max_steps: 2000,
+            pointrolls_count: 0,
+            goodmoves_count: 0,
+            goodmoves_ratio: 0.0,
+            visualized,
+        }
+    }
+
+    fn state(&self) -> Self::StateType {
+        self.current_state
+    }
+
+    fn reset(&mut self) -> Snapshot<Self> {
+        // Réinitialiser le jeu
+        self.game = GameState::new(false);
+        self.game.init_player("DQN Agent");
+        self.game.init_player("Opponent");
+
+        // Commencer la partie
+        self.game.consume(&GameEvent::BeginGame { goes_first: 1 });
+
+        self.current_state = TrictracState::from_game_state(&self.game);
+        self.episode_reward = 0.0;
+        self.goodmoves_ratio = if self.step_count == 0 {
+            0.0
+        } else {
+            self.goodmoves_count as f32 / self.step_count as f32
+        };
+        println!(
+            "info: correct moves: {} ({}%)",
+            self.goodmoves_count,
+            (100.0 * self.goodmoves_ratio).round() as u32
+        );
+        self.step_count = 0;
+        self.pointrolls_count = 0;
+        self.goodmoves_count = 0;
+
+        Snapshot::new(self.current_state, 0.0, false)
+    }
+
+    fn step(&mut self, action: Self::ActionType) -> Snapshot<Self> {
+        self.step_count += 1;
+
+        // Convertir l'action burn-rl vers une action Trictrac
+        let trictrac_action = Self::convert_action(action);
+
+        let mut reward = 0.0;
+        let mut is_rollpoint = false;
+        let mut terminated = false;
+
+        // Exécuter l'action si c'est le tour de l'agent DQN
+        if self.game.active_player_id == self.active_player_id {
+            if let Some(action) = trictrac_action {
+                (reward, is_rollpoint) = self.execute_action(action);
+                if is_rollpoint {
+                    self.pointrolls_count += 1;
+                }
+                if reward != Self::ERROR_REWARD {
+                    self.goodmoves_count += 1;
+                }
+            } else {
+                // Action non convertible, pénalité
+                reward = -0.5;
+            }
+        }
+
+        // Faire jouer l'adversaire (stratégie simple)
+        while self.game.active_player_id == self.opponent_id && self.game.stage != Stage::Ended {
+            reward += self.play_opponent_if_needed();
+        }
+
+        // Vérifier si la partie est terminée
+        let max_steps = self.min_steps
+            + (self.max_steps as f32 - self.min_steps)
+                * f32::exp((self.goodmoves_ratio - 1.0) / 0.25);
+        let done = self.game.stage == Stage::Ended || self.game.determine_winner().is_some();
+
+        if done {
+            // Récompense finale basée sur le résultat
+            if let Some(winner_id) = self.game.determine_winner() {
+                if winner_id == self.active_player_id {
+                    reward += 50.0; // Victoire
+                } else {
+                    reward -= 25.0; // Défaite
+                }
+            }
+        }
+        let terminated = done || self.step_count >= max_steps.round() as usize;
+
+        // Mettre à jour l'état
+        self.current_state = TrictracState::from_game_state(&self.game);
+        self.episode_reward += reward;
+
+        if self.visualized && terminated {
+            println!(
+                "Episode terminé. Récompense totale: {:.2}, Étapes: {}",
+                self.episode_reward, self.step_count
+            );
+        }
+
+        Snapshot::new(self.current_state, reward, terminated)
+    }
+}
+
+impl TrictracEnvironment {
+    const ERROR_REWARD: f32 = -1.12121;
+    const REWARD_RATIO: f32 = 1.0;
+
+    /// Convertit une action burn-rl vers une action Trictrac
+    pub fn convert_action(action: TrictracAction) -> Option<dqn_common_big::TrictracAction> {
+        dqn_common_big::TrictracAction::from_action_index(action.index.try_into().unwrap())
+    }
+
+    /// Convertit l'index d'une action au sein des actions valides vers une action Trictrac
+    fn convert_valid_action_index(
+        &self,
+        action: TrictracAction,
+        game_state: &GameState,
+    ) -> Option<dqn_common_big::TrictracAction> {
+        use dqn_common_big::get_valid_actions;
+
+        // Obtenir les actions valides dans le contexte actuel
+        let valid_actions = get_valid_actions(game_state);
+
+        if valid_actions.is_empty() {
+            return None;
+        }
+
+        // Mapper l'index d'action sur une action valide
+        let action_index = (action.index as usize) % valid_actions.len();
+        Some(valid_actions[action_index].clone())
+    }
+
+    /// Exécute une action Trictrac dans le jeu
+    // fn execute_action(
+    //     &mut self,
+    //     action:dqn_common_big::TrictracAction,
+    // ) -> Result<f32, Box<dyn std::error::Error>> {
+    fn execute_action(&mut self, action: dqn_common_big::TrictracAction) -> (f32, bool) {
+        use dqn_common_big::TrictracAction;
+
+        let mut reward = 0.0;
+        let mut is_rollpoint = false;
+
+        let event = match action {
+            TrictracAction::Roll => {
+                // Lancer les dés
+                reward += 0.1;
+                Some(GameEvent::Roll {
+                    player_id: self.active_player_id,
+                })
+            }
+            // TrictracAction::Mark => {
+            //     // Marquer des points
+            //     let points = self.game.
+            //     reward += 0.1 * points as f32;
+            //     Some(GameEvent::Mark {
+            //         player_id: self.active_player_id,
+            //         points,
+            //     })
+            // }
+            TrictracAction::Go => {
+                // Continuer après avoir gagné un trou
+                reward += 0.2;
+                Some(GameEvent::Go {
+                    player_id: self.active_player_id,
+                })
+            }
+            TrictracAction::Move {
+                dice_order,
+                from1,
+                from2,
+            } => {
+                // Effectuer un mouvement
+                let (dice1, dice2) = if dice_order {
+                    (self.game.dice.values.0, self.game.dice.values.1)
+                } else {
+                    (self.game.dice.values.1, self.game.dice.values.0)
+                };
+                let mut to1 = from1 + dice1 as usize;
+                let mut to2 = from2 + dice2 as usize;
+
+                // Gestion prise de coin par puissance
+                let opp_rest_field = 13;
+                if to1 == opp_rest_field && to2 == opp_rest_field {
+                    to1 -= 1;
+                    to2 -= 1;
+                }
+
+                let checker_move1 = store::CheckerMove::new(from1, to1).unwrap_or_default();
+                let checker_move2 = store::CheckerMove::new(from2, to2).unwrap_or_default();
+
+                reward += 0.2;
+                Some(GameEvent::Move {
+                    player_id: self.active_player_id,
+                    moves: (checker_move1, checker_move2),
+                })
+            }
+        };
+
+        // Appliquer l'événement si valide
+        if let Some(event) = event {
+            if self.game.validate(&event) {
+                self.game.consume(&event);
+
+                // Simuler le résultat des dés après un Roll
+                if matches!(action, TrictracAction::Roll) {
+                    let mut rng = thread_rng();
+                    let dice_values = (rng.gen_range(1..=6), rng.gen_range(1..=6));
+                    let dice_event = GameEvent::RollResult {
+                        player_id: self.active_player_id,
+                        dice: store::Dice {
+                            values: dice_values,
+                        },
+                    };
+                    if self.game.validate(&dice_event) {
+                        self.game.consume(&dice_event);
+                        let (points, adv_points) = self.game.dice_points;
+                        reward += Self::REWARD_RATIO * (points - adv_points) as f32;
+                        if points > 0 {
+                            is_rollpoint = true;
+                            // println!("info: rolled for {reward}");
+                        }
+                        // Récompense proportionnelle aux points
+                    }
+                }
+            } else {
+                // Pénalité pour action invalide
+                // on annule les précédents reward
+                // et on indique une valeur reconnaissable pour statistiques
+                reward = Self::ERROR_REWARD;
+            }
+        }
+
+        (reward, is_rollpoint)
+    }
+
+    /// Fait jouer l'adversaire avec une stratégie simple
+    fn play_opponent_if_needed(&mut self) -> f32 {
+        let mut reward = 0.0;
+
+        // Si c'est le tour de l'adversaire, jouer automatiquement
+        if self.game.active_player_id == self.opponent_id && self.game.stage != Stage::Ended {
+            // Utiliser la stratégie default pour l'adversaire
+            use crate::BotStrategy;
+
+            let mut strategy = crate::strategy::random::RandomStrategy::default();
+            strategy.set_player_id(self.opponent_id);
+            if let Some(color) = self.game.player_color_by_id(&self.opponent_id) {
+                strategy.set_color(color);
+            }
+            *strategy.get_mut_game() = self.game.clone();
+
+            // Exécuter l'action selon le turn_stage
+            let event = match self.game.turn_stage {
+                TurnStage::RollDice => GameEvent::Roll {
+                    player_id: self.opponent_id,
+                },
+                TurnStage::RollWaiting => {
+                    let mut rng = thread_rng();
+                    let dice_values = (rng.gen_range(1..=6), rng.gen_range(1..=6));
+                    GameEvent::RollResult {
+                        player_id: self.opponent_id,
+                        dice: store::Dice {
+                            values: dice_values,
+                        },
+                    }
+                }
+                TurnStage::MarkPoints => {
+                    panic!("in play_opponent_if_needed > TurnStage::MarkPoints");
+                    let opponent_color = store::Color::Black;
+                    let dice_roll_count = self
+                        .game
+                        .players
+                        .get(&self.opponent_id)
+                        .unwrap()
+                        .dice_roll_count;
+                    let points_rules =
+                        PointsRules::new(&opponent_color, &self.game.board, self.game.dice);
+                    let (points, adv_points) = points_rules.get_points(dice_roll_count);
+                    // reward -= Self::REWARD_RATIO * (points - adv_points) as f32; // Récompense proportionnelle aux points
+
+                    GameEvent::Mark {
+                        player_id: self.opponent_id,
+                        points,
+                    }
+                }
+                TurnStage::MarkAdvPoints => {
+                    let opponent_color = store::Color::Black;
+                    let dice_roll_count = self
+                        .game
+                        .players
+                        .get(&self.opponent_id)
+                        .unwrap()
+                        .dice_roll_count;
+                    let points_rules =
+                        PointsRules::new(&opponent_color, &self.game.board, self.game.dice);
+                    let points = points_rules.get_points(dice_roll_count).1;
+                    // pas de reward : déjà comptabilisé lors du tour de blanc
+                    GameEvent::Mark {
+                        player_id: self.opponent_id,
+                        points,
+                    }
+                }
+                TurnStage::HoldOrGoChoice => {
+                    // Stratégie simple : toujours continuer
+                    GameEvent::Go {
+                        player_id: self.opponent_id,
+                    }
+                }
+                TurnStage::Move => GameEvent::Move {
+                    player_id: self.opponent_id,
+                    moves: strategy.choose_move(),
+                },
+            };
+
+            if self.game.validate(&event) {
+                self.game.consume(&event);
+            }
+        }
+        reward
+    }
+}
+
+impl AsMut<TrictracEnvironment> for TrictracEnvironment {
+    fn as_mut(&mut self) -> &mut Self {
+        self
+    }
+}
diff --git a/bot/src/dqn/burnrl_before/main.rs b/bot/src/dqn/burnrl_before/main.rs
new file mode 100644
index 0000000..602ff51
--- /dev/null
+++ b/bot/src/dqn/burnrl_before/main.rs
@@ -0,0 +1,53 @@
+use bot::dqn::burnrl_before::{
+    dqn_model, environment,
+    utils::{demo_model, load_model, save_model},
+};
+use burn::backend::{Autodiff, NdArray};
+use burn_rl::agent::DQN;
+use burn_rl::base::ElemType;
+
+type Backend = Autodiff<NdArray<ElemType>>;
+type Env = environment::TrictracEnvironment;
+
+fn main() {
+    // println!("> Entraînement");
+
+    // See also MEMORY_SIZE in dqn_model.rs : 8192
+    let conf = dqn_model::DqnConfig {
+        //                   defaults
+        num_episodes: 40, // 40
+        min_steps: 500.0, // 1000 min of max steps by episode (mise à jour par la fonction)
+        max_steps: 3000,  // 1000 max steps by episode
+        dense_size: 256,  // 128  neural network complexity (default 128)
+        eps_start: 0.9,   // 0.9  epsilon initial value (0.9 => more exploration)
+        eps_end: 0.05,    // 0.05
+        // eps_decay higher = epsilon decrease slower
+        // used in : epsilon = eps_end + (eps_start - eps_end) * e^(-step / eps_decay);
+        // epsilon is updated at the start of each episode
+        eps_decay: 2000.0, // 1000 ?
+
+        gamma: 0.999, // 0.999 discount factor. Plus élevé = encourage stratégies à long terme
+        tau: 0.005, // 0.005 soft update rate. Taux de mise à jour du réseau cible. Plus bas = adaptation
+        // plus lente moins sensible aux coups de chance
+        learning_rate: 0.001, // 0.001 taille du pas. Bas : plus lent, haut : risque de ne jamais
+        // converger
+        batch_size: 32, // 32 nombre d'expériences passées sur lesquelles pour calcul de l'erreur moy.
+        clip_grad: 100.0, // 100 limite max de correction à apporter au gradient (default 100)
+    };
+    println!("{conf}----------");
+    let agent = dqn_model::run::<Env, Backend>(&conf, false); //true);
+
+    let valid_agent = agent.valid();
+
+    println!("> Sauvegarde du modèle de validation");
+
+    let path = "models/burn_dqn_40".to_string();
+    save_model(valid_agent.model().as_ref().unwrap(), &path);
+
+    println!("> Chargement du modèle pour test");
+    let loaded_model = load_model(conf.dense_size, &path);
+    let loaded_agent = DQN::new(loaded_model.unwrap());
+
+    println!("> Test avec le modèle chargé");
+    demo_model(loaded_agent);
+}
diff --git a/bot/src/dqn/burnrl_before/mod.rs b/bot/src/dqn/burnrl_before/mod.rs
new file mode 100644
index 0000000..f4380eb
--- /dev/null
+++ b/bot/src/dqn/burnrl_before/mod.rs
@@ -0,0 +1,3 @@
+pub mod dqn_model;
+pub mod environment;
+pub mod utils;
diff --git a/bot/src/dqn/burnrl_before/utils.rs b/bot/src/dqn/burnrl_before/utils.rs
new file mode 100644
index 0000000..6c25c5d
--- /dev/null
+++ b/bot/src/dqn/burnrl_before/utils.rs
@@ -0,0 +1,114 @@
+use crate::dqn::burnrl_before::{
+    dqn_model,
+    environment::{TrictracAction, TrictracEnvironment},
+};
+use crate::dqn::dqn_common_big::get_valid_action_indices;
+use burn::backend::{ndarray::NdArrayDevice, Autodiff, NdArray};
+use burn::module::{Module, Param, ParamId};
+use burn::nn::Linear;
+use burn::record::{CompactRecorder, Recorder};
+use burn::tensor::backend::Backend;
+use burn::tensor::cast::ToElement;
+use burn::tensor::Tensor;
+use burn_rl::agent::{DQNModel, DQN};
+use burn_rl::base::{Action, ElemType, Environment, State};
+
+pub fn save_model(model: &dqn_model::Net<NdArray<ElemType>>, path: &String) {
+    let recorder = CompactRecorder::new();
+    let model_path = format!("{path}_model.mpk");
+    println!("Modèle de validation sauvegardé : {model_path}");
+    recorder
+        .record(model.clone().into_record(), model_path.into())
+        .unwrap();
+}
+
+pub fn load_model(dense_size: usize, path: &String) -> Option<dqn_model::Net<NdArray<ElemType>>> {
+    let model_path = format!("{path}_model.mpk");
+    // println!("Chargement du modèle depuis : {model_path}");
+
+    CompactRecorder::new()
+        .load(model_path.into(), &NdArrayDevice::default())
+        .map(|record| {
+            dqn_model::Net::new(
+                <TrictracEnvironment as Environment>::StateType::size(),
+                dense_size,
+                <TrictracEnvironment as Environment>::ActionType::size(),
+            )
+            .load_record(record)
+        })
+        .ok()
+}
+
+pub fn demo_model<B: Backend, M: DQNModel<B>>(agent: DQN<TrictracEnvironment, B, M>) {
+    let mut env = TrictracEnvironment::new(true);
+    let mut done = false;
+    while !done {
+        // let action = match infer_action(&agent, &env, state) {
+        let action = match infer_action(&agent, &env) {
+            Some(value) => value,
+            None => break,
+        };
+        // Execute action
+        let snapshot = env.step(action);
+        done = snapshot.done();
+    }
+}
+
+fn infer_action<B: Backend, M: DQNModel<B>>(
+    agent: &DQN<TrictracEnvironment, B, M>,
+    env: &TrictracEnvironment,
+) -> Option<TrictracAction> {
+    let state = env.state();
+    // Get q-values
+    let q_values = agent
+        .model()
+        .as_ref()
+        .unwrap()
+        .infer(state.to_tensor().unsqueeze());
+    // Get valid actions
+    let valid_actions_indices = get_valid_action_indices(&env.game);
+    if valid_actions_indices.is_empty() {
+        return None; // No valid actions, end of episode
+    }
+    // Set non valid actions q-values to lowest
+    let mut masked_q_values = q_values.clone();
+    let q_values_vec: Vec<f32> = q_values.into_data().into_vec().unwrap();
+    for (index, q_value) in q_values_vec.iter().enumerate() {
+        if !valid_actions_indices.contains(&index) {
+            masked_q_values = masked_q_values.clone().mask_fill(
+                masked_q_values.clone().equal_elem(*q_value),
+                f32::NEG_INFINITY,
+            );
+        }
+    }
+    // Get best action (highest q-value)
+    let action_index = masked_q_values.argmax(1).into_scalar().to_u32();
+    let action = TrictracAction::from(action_index);
+    Some(action)
+}
+
+fn soft_update_tensor<const N: usize, B: Backend>(
+    this: &Param<Tensor<B, N>>,
+    that: &Param<Tensor<B, N>>,
+    tau: ElemType,
+) -> Param<Tensor<B, N>> {
+    let that_weight = that.val();
+    let this_weight = this.val();
+    let new_weight = this_weight * (1.0 - tau) + that_weight * tau;
+
+    Param::initialized(ParamId::new(), new_weight)
+}
+
+pub fn soft_update_linear<B: Backend>(
+    this: Linear<B>,
+    that: &Linear<B>,
+    tau: ElemType,
+) -> Linear<B> {
+    let weight = soft_update_tensor(&this.weight, &that.weight, tau);
+    let bias = match (&this.bias, &that.bias) {
+        (Some(this_bias), Some(that_bias)) => Some(soft_update_tensor(this_bias, that_bias, tau)),
+        _ => None,
+    };
+
+    Linear::<B> { weight, bias }
+}
diff --git a/bot/src/dqn/burnrl_big/dqn_model.rs b/bot/src/dqn/burnrl_big/dqn_model.rs
new file mode 100644
index 0000000..f50bf31
--- /dev/null
+++ b/bot/src/dqn/burnrl_big/dqn_model.rs
@@ -0,0 +1,211 @@
+use crate::dqn::burnrl_big::environment::TrictracEnvironment;
+use crate::dqn::burnrl_big::utils::soft_update_linear;
+use burn::module::Module;
+use burn::nn::{Linear, LinearConfig};
+use burn::optim::AdamWConfig;
+use burn::tensor::activation::relu;
+use burn::tensor::backend::{AutodiffBackend, Backend};
+use burn::tensor::Tensor;
+use burn_rl::agent::DQN;
+use burn_rl::agent::{DQNModel, DQNTrainingConfig};
+use burn_rl::base::{Action, ElemType, Environment, Memory, Model, State};
+use std::fmt;
+use std::time::SystemTime;
+
+#[derive(Module, Debug)]
+pub struct Net<B: Backend> {
+    linear_0: Linear<B>,
+    linear_1: Linear<B>,
+    linear_2: Linear<B>,
+}
+
+impl<B: Backend> Net<B> {
+    #[allow(unused)]
+    pub fn new(input_size: usize, dense_size: usize, output_size: usize) -> Self {
+        Self {
+            linear_0: LinearConfig::new(input_size, dense_size).init(&Default::default()),
+            linear_1: LinearConfig::new(dense_size, dense_size).init(&Default::default()),
+            linear_2: LinearConfig::new(dense_size, output_size).init(&Default::default()),
+        }
+    }
+
+    fn consume(self) -> (Linear<B>, Linear<B>, Linear<B>) {
+        (self.linear_0, self.linear_1, self.linear_2)
+    }
+}
+
+impl<B: Backend> Model<B, Tensor<B, 2>, Tensor<B, 2>> for Net<B> {
+    fn forward(&self, input: Tensor<B, 2>) -> Tensor<B, 2> {
+        let layer_0_output = relu(self.linear_0.forward(input));
+        let layer_1_output = relu(self.linear_1.forward(layer_0_output));
+
+        relu(self.linear_2.forward(layer_1_output))
+    }
+
+    fn infer(&self, input: Tensor<B, 2>) -> Tensor<B, 2> {
+        self.forward(input)
+    }
+}
+
+impl<B: Backend> DQNModel<B> for Net<B> {
+    fn soft_update(this: Self, that: &Self, tau: ElemType) -> Self {
+        let (linear_0, linear_1, linear_2) = this.consume();
+
+        Self {
+            linear_0: soft_update_linear(linear_0, &that.linear_0, tau),
+            linear_1: soft_update_linear(linear_1, &that.linear_1, tau),
+            linear_2: soft_update_linear(linear_2, &that.linear_2, tau),
+        }
+    }
+}
+
+#[allow(unused)]
+const MEMORY_SIZE: usize = 8192;
+
+pub struct DqnConfig {
+    pub min_steps: f32,
+    pub max_steps: usize,
+    pub num_episodes: usize,
+    pub dense_size: usize,
+    pub eps_start: f64,
+    pub eps_end: f64,
+    pub eps_decay: f64,
+
+    pub gamma: f32,
+    pub tau: f32,
+    pub learning_rate: f32,
+    pub batch_size: usize,
+    pub clip_grad: f32,
+}
+
+impl fmt::Display for DqnConfig {
+    fn fmt(&self, f: &mut fmt::Formatter) -> fmt::Result {
+        let mut s = String::new();
+        s.push_str(&format!("min_steps={:?}\n", self.min_steps));
+        s.push_str(&format!("max_steps={:?}\n", self.max_steps));
+        s.push_str(&format!("num_episodes={:?}\n", self.num_episodes));
+        s.push_str(&format!("dense_size={:?}\n", self.dense_size));
+        s.push_str(&format!("eps_start={:?}\n", self.eps_start));
+        s.push_str(&format!("eps_end={:?}\n", self.eps_end));
+        s.push_str(&format!("eps_decay={:?}\n", self.eps_decay));
+        s.push_str(&format!("gamma={:?}\n", self.gamma));
+        s.push_str(&format!("tau={:?}\n", self.tau));
+        s.push_str(&format!("learning_rate={:?}\n", self.learning_rate));
+        s.push_str(&format!("batch_size={:?}\n", self.batch_size));
+        s.push_str(&format!("clip_grad={:?}\n", self.clip_grad));
+        write!(f, "{s}")
+    }
+}
+
+impl Default for DqnConfig {
+    fn default() -> Self {
+        Self {
+            min_steps: 250.0,
+            max_steps: 2000,
+            num_episodes: 1000,
+            dense_size: 256,
+            eps_start: 0.9,
+            eps_end: 0.05,
+            eps_decay: 1000.0,
+
+            gamma: 0.999,
+            tau: 0.005,
+            learning_rate: 0.001,
+            batch_size: 32,
+            clip_grad: 100.0,
+        }
+    }
+}
+
+type MyAgent<E, B> = DQN<E, B, Net<B>>;
+
+#[allow(unused)]
+pub fn run<E: Environment + AsMut<TrictracEnvironment>, B: AutodiffBackend>(
+    conf: &DqnConfig,
+    visualized: bool,
+) -> DQN<E, B, Net<B>> {
+    // ) -> impl Agent<E> {
+    let mut env = E::new(visualized);
+    env.as_mut().min_steps = conf.min_steps;
+    env.as_mut().max_steps = conf.max_steps;
+
+    let model = Net::<B>::new(
+        <<E as Environment>::StateType as State>::size(),
+        conf.dense_size,
+        <<E as Environment>::ActionType as Action>::size(),
+    );
+
+    let mut agent = MyAgent::new(model);
+
+    // let config = DQNTrainingConfig::default();
+    let config = DQNTrainingConfig {
+        gamma: conf.gamma,
+        tau: conf.tau,
+        learning_rate: conf.learning_rate,
+        batch_size: conf.batch_size,
+        clip_grad: Some(burn::grad_clipping::GradientClippingConfig::Value(
+            conf.clip_grad,
+        )),
+    };
+
+    let mut memory = Memory::<E, B, MEMORY_SIZE>::default();
+
+    let mut optimizer = AdamWConfig::new()
+        .with_grad_clipping(config.clip_grad.clone())
+        .init();
+
+    let mut policy_net = agent.model().as_ref().unwrap().clone();
+
+    let mut step = 0_usize;
+
+    for episode in 0..conf.num_episodes {
+        let mut episode_done = false;
+        let mut episode_reward: ElemType = 0.0;
+        let mut episode_duration = 0_usize;
+        let mut state = env.state();
+        let mut now = SystemTime::now();
+
+        while !episode_done {
+            let eps_threshold = conf.eps_end
+                + (conf.eps_start - conf.eps_end) * f64::exp(-(step as f64) / conf.eps_decay);
+            let action =
+                DQN::<E, B, Net<B>>::react_with_exploration(&policy_net, state, eps_threshold);
+            let snapshot = env.step(action);
+
+            episode_reward +=
+                <<E as Environment>::RewardType as Into<ElemType>>::into(snapshot.reward().clone());
+
+            memory.push(
+                state,
+                *snapshot.state(),
+                action,
+                snapshot.reward().clone(),
+                snapshot.done(),
+            );
+
+            if config.batch_size < memory.len() {
+                policy_net =
+                    agent.train::<MEMORY_SIZE>(policy_net, &memory, &mut optimizer, &config);
+            }
+
+            step += 1;
+            episode_duration += 1;
+
+            if snapshot.done() || episode_duration >= conf.max_steps {
+                let envmut = env.as_mut();
+                println!(
+                    "{{\"episode\": {episode}, \"reward\": {episode_reward:.4}, \"steps count\": {episode_duration}, \"epsilon\": {eps_threshold:.3}, \"goodmoves\": {}, \"rollpoints\":{}, \"duration\": {}}}",
+                    envmut.goodmoves_count,
+                    envmut.pointrolls_count,
+                    now.elapsed().unwrap().as_secs(),
+                );
+                env.reset();
+                episode_done = true;
+                now = SystemTime::now();
+            } else {
+                state = *snapshot.state();
+            }
+        }
+    }
+    agent
+}
diff --git a/bot/src/dqn/burnrl_big/environment.rs b/bot/src/dqn/burnrl_big/environment.rs
new file mode 100644
index 0000000..9925a9a
--- /dev/null
+++ b/bot/src/dqn/burnrl_big/environment.rs
@@ -0,0 +1,449 @@
+use crate::dqn::dqn_common_big;
+use burn::{prelude::Backend, tensor::Tensor};
+use burn_rl::base::{Action, Environment, Snapshot, State};
+use rand::{thread_rng, Rng};
+use store::{GameEvent, GameState, PlayerId, PointsRules, Stage, TurnStage};
+
+/// État du jeu Trictrac pour burn-rl
+#[derive(Debug, Clone, Copy)]
+pub struct TrictracState {
+    pub data: [i8; 36], // Représentation vectorielle de l'état du jeu
+}
+
+impl State for TrictracState {
+    type Data = [i8; 36];
+
+    fn to_tensor<B: Backend>(&self) -> Tensor<B, 1> {
+        Tensor::from_floats(self.data, &B::Device::default())
+    }
+
+    fn size() -> usize {
+        36
+    }
+}
+
+impl TrictracState {
+    /// Convertit un GameState en TrictracState
+    pub fn from_game_state(game_state: &GameState) -> Self {
+        let state_vec = game_state.to_vec();
+        let mut data = [0; 36];
+
+        // Copier les données en s'assurant qu'on ne dépasse pas la taille
+        let copy_len = state_vec.len().min(36);
+        data[..copy_len].copy_from_slice(&state_vec[..copy_len]);
+
+        TrictracState { data }
+    }
+}
+
+/// Actions possibles dans Trictrac pour burn-rl
+#[derive(Debug, Clone, Copy, PartialEq)]
+pub struct TrictracAction {
+    // u32 as required by burn_rl::base::Action type
+    pub index: u32,
+}
+
+impl Action for TrictracAction {
+    fn random() -> Self {
+        use rand::{thread_rng, Rng};
+        let mut rng = thread_rng();
+        TrictracAction {
+            index: rng.gen_range(0..Self::size() as u32),
+        }
+    }
+
+    fn enumerate() -> Vec<Self> {
+        (0..Self::size() as u32)
+            .map(|index| TrictracAction { index })
+            .collect()
+    }
+
+    fn size() -> usize {
+        1252
+    }
+}
+
+impl From<u32> for TrictracAction {
+    fn from(index: u32) -> Self {
+        TrictracAction { index }
+    }
+}
+
+impl From<TrictracAction> for u32 {
+    fn from(action: TrictracAction) -> u32 {
+        action.index
+    }
+}
+
+/// Environnement Trictrac pour burn-rl
+#[derive(Debug)]
+pub struct TrictracEnvironment {
+    pub game: GameState,
+    active_player_id: PlayerId,
+    opponent_id: PlayerId,
+    current_state: TrictracState,
+    episode_reward: f32,
+    pub step_count: usize,
+    pub min_steps: f32,
+    pub max_steps: usize,
+    pub pointrolls_count: usize,
+    pub goodmoves_count: usize,
+    pub goodmoves_ratio: f32,
+    pub visualized: bool,
+}
+
+impl Environment for TrictracEnvironment {
+    type StateType = TrictracState;
+    type ActionType = TrictracAction;
+    type RewardType = f32;
+
+    fn new(visualized: bool) -> Self {
+        let mut game = GameState::new(false);
+
+        // Ajouter deux joueurs
+        game.init_player("DQN Agent");
+        game.init_player("Opponent");
+        let player1_id = 1;
+        let player2_id = 2;
+
+        // Commencer la partie
+        game.consume(&GameEvent::BeginGame { goes_first: 1 });
+
+        let current_state = TrictracState::from_game_state(&game);
+        TrictracEnvironment {
+            game,
+            active_player_id: player1_id,
+            opponent_id: player2_id,
+            current_state,
+            episode_reward: 0.0,
+            step_count: 0,
+            min_steps: 250.0,
+            max_steps: 2000,
+            pointrolls_count: 0,
+            goodmoves_count: 0,
+            goodmoves_ratio: 0.0,
+            visualized,
+        }
+    }
+
+    fn state(&self) -> Self::StateType {
+        self.current_state
+    }
+
+    fn reset(&mut self) -> Snapshot<Self> {
+        // Réinitialiser le jeu
+        self.game = GameState::new(false);
+        self.game.init_player("DQN Agent");
+        self.game.init_player("Opponent");
+
+        // Commencer la partie
+        self.game.consume(&GameEvent::BeginGame { goes_first: 1 });
+
+        self.current_state = TrictracState::from_game_state(&self.game);
+        self.episode_reward = 0.0;
+        self.goodmoves_ratio = if self.step_count == 0 {
+            0.0
+        } else {
+            self.goodmoves_count as f32 / self.step_count as f32
+        };
+        println!(
+            "info: correct moves: {} ({}%)",
+            self.goodmoves_count,
+            (100.0 * self.goodmoves_ratio).round() as u32
+        );
+        self.step_count = 0;
+        self.pointrolls_count = 0;
+        self.goodmoves_count = 0;
+
+        Snapshot::new(self.current_state, 0.0, false)
+    }
+
+    fn step(&mut self, action: Self::ActionType) -> Snapshot<Self> {
+        self.step_count += 1;
+
+        // Convertir l'action burn-rl vers une action Trictrac
+        let trictrac_action = Self::convert_action(action);
+
+        let mut reward = 0.0;
+        let mut is_rollpoint = false;
+        let mut terminated = false;
+
+        // Exécuter l'action si c'est le tour de l'agent DQN
+        if self.game.active_player_id == self.active_player_id {
+            if let Some(action) = trictrac_action {
+                (reward, is_rollpoint) = self.execute_action(action);
+                if is_rollpoint {
+                    self.pointrolls_count += 1;
+                }
+                if reward != Self::ERROR_REWARD {
+                    self.goodmoves_count += 1;
+                }
+            } else {
+                // Action non convertible, pénalité
+                reward = -0.5;
+            }
+        }
+
+        // Faire jouer l'adversaire (stratégie simple)
+        while self.game.active_player_id == self.opponent_id && self.game.stage != Stage::Ended {
+            reward += self.play_opponent_if_needed();
+        }
+
+        // Vérifier si la partie est terminée
+        let max_steps = self.min_steps
+            + (self.max_steps as f32 - self.min_steps)
+                * f32::exp((self.goodmoves_ratio - 1.0) / 0.25);
+        let done = self.game.stage == Stage::Ended || self.game.determine_winner().is_some();
+
+        if done {
+            // Récompense finale basée sur le résultat
+            if let Some(winner_id) = self.game.determine_winner() {
+                if winner_id == self.active_player_id {
+                    reward += 50.0; // Victoire
+                } else {
+                    reward -= 25.0; // Défaite
+                }
+            }
+        }
+        let terminated = done || self.step_count >= max_steps.round() as usize;
+
+        // Mettre à jour l'état
+        self.current_state = TrictracState::from_game_state(&self.game);
+        self.episode_reward += reward;
+
+        if self.visualized && terminated {
+            println!(
+                "Episode terminé. Récompense totale: {:.2}, Étapes: {}",
+                self.episode_reward, self.step_count
+            );
+        }
+
+        Snapshot::new(self.current_state, reward, terminated)
+    }
+}
+
+impl TrictracEnvironment {
+    const ERROR_REWARD: f32 = -1.12121;
+    const REWARD_RATIO: f32 = 1.0;
+
+    /// Convertit une action burn-rl vers une action Trictrac
+    pub fn convert_action(action: TrictracAction) -> Option<dqn_common_big::TrictracAction> {
+        dqn_common_big::TrictracAction::from_action_index(action.index.try_into().unwrap())
+    }
+
+    /// Convertit l'index d'une action au sein des actions valides vers une action Trictrac
+    fn convert_valid_action_index(
+        &self,
+        action: TrictracAction,
+        game_state: &GameState,
+    ) -> Option<dqn_common_big::TrictracAction> {
+        use dqn_common_big::get_valid_actions;
+
+        // Obtenir les actions valides dans le contexte actuel
+        let valid_actions = get_valid_actions(game_state);
+
+        if valid_actions.is_empty() {
+            return None;
+        }
+
+        // Mapper l'index d'action sur une action valide
+        let action_index = (action.index as usize) % valid_actions.len();
+        Some(valid_actions[action_index].clone())
+    }
+
+    /// Exécute une action Trictrac dans le jeu
+    // fn execute_action(
+    //     &mut self,
+    //     action:dqn_common_big::TrictracAction,
+    // ) -> Result<f32, Box<dyn std::error::Error>> {
+    fn execute_action(&mut self, action: dqn_common_big::TrictracAction) -> (f32, bool) {
+        use dqn_common_big::TrictracAction;
+
+        let mut reward = 0.0;
+        let mut is_rollpoint = false;
+
+        let event = match action {
+            TrictracAction::Roll => {
+                // Lancer les dés
+                reward += 0.1;
+                Some(GameEvent::Roll {
+                    player_id: self.active_player_id,
+                })
+            }
+            // TrictracAction::Mark => {
+            //     // Marquer des points
+            //     let points = self.game.
+            //     reward += 0.1 * points as f32;
+            //     Some(GameEvent::Mark {
+            //         player_id: self.active_player_id,
+            //         points,
+            //     })
+            // }
+            TrictracAction::Go => {
+                // Continuer après avoir gagné un trou
+                reward += 0.2;
+                Some(GameEvent::Go {
+                    player_id: self.active_player_id,
+                })
+            }
+            TrictracAction::Move {
+                dice_order,
+                from1,
+                from2,
+            } => {
+                // Effectuer un mouvement
+                let (dice1, dice2) = if dice_order {
+                    (self.game.dice.values.0, self.game.dice.values.1)
+                } else {
+                    (self.game.dice.values.1, self.game.dice.values.0)
+                };
+                let mut to1 = from1 + dice1 as usize;
+                let mut to2 = from2 + dice2 as usize;
+
+                // Gestion prise de coin par puissance
+                let opp_rest_field = 13;
+                if to1 == opp_rest_field && to2 == opp_rest_field {
+                    to1 -= 1;
+                    to2 -= 1;
+                }
+
+                let checker_move1 = store::CheckerMove::new(from1, to1).unwrap_or_default();
+                let checker_move2 = store::CheckerMove::new(from2, to2).unwrap_or_default();
+
+                reward += 0.2;
+                Some(GameEvent::Move {
+                    player_id: self.active_player_id,
+                    moves: (checker_move1, checker_move2),
+                })
+            }
+        };
+
+        // Appliquer l'événement si valide
+        if let Some(event) = event {
+            if self.game.validate(&event) {
+                self.game.consume(&event);
+
+                // Simuler le résultat des dés après un Roll
+                if matches!(action, TrictracAction::Roll) {
+                    let mut rng = thread_rng();
+                    let dice_values = (rng.gen_range(1..=6), rng.gen_range(1..=6));
+                    let dice_event = GameEvent::RollResult {
+                        player_id: self.active_player_id,
+                        dice: store::Dice {
+                            values: dice_values,
+                        },
+                    };
+                    if self.game.validate(&dice_event) {
+                        self.game.consume(&dice_event);
+                        let (points, adv_points) = self.game.dice_points;
+                        reward += Self::REWARD_RATIO * (points - adv_points) as f32;
+                        if points > 0 {
+                            is_rollpoint = true;
+                            // println!("info: rolled for {reward}");
+                        }
+                        // Récompense proportionnelle aux points
+                    }
+                }
+            } else {
+                // Pénalité pour action invalide
+                // on annule les précédents reward
+                // et on indique une valeur reconnaissable pour statistiques
+                reward = Self::ERROR_REWARD;
+            }
+        }
+
+        (reward, is_rollpoint)
+    }
+
+    /// Fait jouer l'adversaire avec une stratégie simple
+    fn play_opponent_if_needed(&mut self) -> f32 {
+        let mut reward = 0.0;
+
+        // Si c'est le tour de l'adversaire, jouer automatiquement
+        if self.game.active_player_id == self.opponent_id && self.game.stage != Stage::Ended {
+            // Utiliser la stratégie default pour l'adversaire
+            use crate::BotStrategy;
+
+            let mut strategy = crate::strategy::random::RandomStrategy::default();
+            strategy.set_player_id(self.opponent_id);
+            if let Some(color) = self.game.player_color_by_id(&self.opponent_id) {
+                strategy.set_color(color);
+            }
+            *strategy.get_mut_game() = self.game.clone();
+
+            // Exécuter l'action selon le turn_stage
+            let event = match self.game.turn_stage {
+                TurnStage::RollDice => GameEvent::Roll {
+                    player_id: self.opponent_id,
+                },
+                TurnStage::RollWaiting => {
+                    let mut rng = thread_rng();
+                    let dice_values = (rng.gen_range(1..=6), rng.gen_range(1..=6));
+                    GameEvent::RollResult {
+                        player_id: self.opponent_id,
+                        dice: store::Dice {
+                            values: dice_values,
+                        },
+                    }
+                }
+                TurnStage::MarkPoints => {
+                    panic!("in play_opponent_if_needed > TurnStage::MarkPoints");
+                    let opponent_color = store::Color::Black;
+                    let dice_roll_count = self
+                        .game
+                        .players
+                        .get(&self.opponent_id)
+                        .unwrap()
+                        .dice_roll_count;
+                    let points_rules =
+                        PointsRules::new(&opponent_color, &self.game.board, self.game.dice);
+                    let (points, adv_points) = points_rules.get_points(dice_roll_count);
+                    // reward -= Self::REWARD_RATIO * (points - adv_points) as f32; // Récompense proportionnelle aux points
+
+                    GameEvent::Mark {
+                        player_id: self.opponent_id,
+                        points,
+                    }
+                }
+                TurnStage::MarkAdvPoints => {
+                    let opponent_color = store::Color::Black;
+                    let dice_roll_count = self
+                        .game
+                        .players
+                        .get(&self.opponent_id)
+                        .unwrap()
+                        .dice_roll_count;
+                    let points_rules =
+                        PointsRules::new(&opponent_color, &self.game.board, self.game.dice);
+                    let points = points_rules.get_points(dice_roll_count).1;
+                    // pas de reward : déjà comptabilisé lors du tour de blanc
+                    GameEvent::Mark {
+                        player_id: self.opponent_id,
+                        points,
+                    }
+                }
+                TurnStage::HoldOrGoChoice => {
+                    // Stratégie simple : toujours continuer
+                    GameEvent::Go {
+                        player_id: self.opponent_id,
+                    }
+                }
+                TurnStage::Move => GameEvent::Move {
+                    player_id: self.opponent_id,
+                    moves: strategy.choose_move(),
+                },
+            };
+
+            if self.game.validate(&event) {
+                self.game.consume(&event);
+            }
+        }
+        reward
+    }
+}
+
+impl AsMut<TrictracEnvironment> for TrictracEnvironment {
+    fn as_mut(&mut self) -> &mut Self {
+        self
+    }
+}
diff --git a/bot/src/dqn/burnrl_big/environmentDiverge.rs b/bot/src/dqn/burnrl_big/environmentDiverge.rs
new file mode 100644
index 0000000..6706163
--- /dev/null
+++ b/bot/src/dqn/burnrl_big/environmentDiverge.rs
@@ -0,0 +1,459 @@
+use crate::dqn::dqn_common_big;
+use burn::{prelude::Backend, tensor::Tensor};
+use burn_rl::base::{Action, Environment, Snapshot, State};
+use rand::{thread_rng, Rng};
+use store::{GameEvent, GameState, PlayerId, PointsRules, Stage, TurnStage};
+
+/// État du jeu Trictrac pour burn-rl
+#[derive(Debug, Clone, Copy)]
+pub struct TrictracState {
+    pub data: [i8; 36], // Représentation vectorielle de l'état du jeu
+}
+
+impl State for TrictracState {
+    type Data = [i8; 36];
+
+    fn to_tensor<B: Backend>(&self) -> Tensor<B, 1> {
+        Tensor::from_floats(self.data, &B::Device::default())
+    }
+
+    fn size() -> usize {
+        36
+    }
+}
+
+impl TrictracState {
+    /// Convertit un GameState en TrictracState
+    pub fn from_game_state(game_state: &GameState) -> Self {
+        let state_vec = game_state.to_vec();
+        let mut data = [0; 36];
+
+        // Copier les données en s'assurant qu'on ne dépasse pas la taille
+        let copy_len = state_vec.len().min(36);
+        data[..copy_len].copy_from_slice(&state_vec[..copy_len]);
+
+        TrictracState { data }
+    }
+}
+
+/// Actions possibles dans Trictrac pour burn-rl
+#[derive(Debug, Clone, Copy, PartialEq)]
+pub struct TrictracAction {
+    // u32 as required by burn_rl::base::Action type
+    pub index: u32,
+}
+
+impl Action for TrictracAction {
+    fn random() -> Self {
+        use rand::{thread_rng, Rng};
+        let mut rng = thread_rng();
+        TrictracAction {
+            index: rng.gen_range(0..Self::size() as u32),
+        }
+    }
+
+    fn enumerate() -> Vec<Self> {
+        (0..Self::size() as u32)
+            .map(|index| TrictracAction { index })
+            .collect()
+    }
+
+    fn size() -> usize {
+        1252
+    }
+}
+
+impl From<u32> for TrictracAction {
+    fn from(index: u32) -> Self {
+        TrictracAction { index }
+    }
+}
+
+impl From<TrictracAction> for u32 {
+    fn from(action: TrictracAction) -> u32 {
+        action.index
+    }
+}
+
+/// Environnement Trictrac pour burn-rl
+#[derive(Debug)]
+pub struct TrictracEnvironment {
+    pub game: GameState,
+    active_player_id: PlayerId,
+    opponent_id: PlayerId,
+    current_state: TrictracState,
+    episode_reward: f32,
+    pub step_count: usize,
+    pub min_steps: f32,
+    pub max_steps: usize,
+    pub pointrolls_count: usize,
+    pub goodmoves_count: usize,
+    pub goodmoves_ratio: f32,
+    pub visualized: bool,
+}
+
+impl Environment for TrictracEnvironment {
+    type StateType = TrictracState;
+    type ActionType = TrictracAction;
+    type RewardType = f32;
+
+    fn new(visualized: bool) -> Self {
+        let mut game = GameState::new(false);
+
+        // Ajouter deux joueurs
+        game.init_player("DQN Agent");
+        game.init_player("Opponent");
+        let player1_id = 1;
+        let player2_id = 2;
+
+        // Commencer la partie
+        game.consume(&GameEvent::BeginGame { goes_first: 1 });
+
+        let current_state = TrictracState::from_game_state(&game);
+        TrictracEnvironment {
+            game,
+            active_player_id: player1_id,
+            opponent_id: player2_id,
+            current_state,
+            episode_reward: 0.0,
+            step_count: 0,
+            min_steps: 250.0,
+            max_steps: 2000,
+            pointrolls_count: 0,
+            goodmoves_count: 0,
+            goodmoves_ratio: 0.0,
+            visualized,
+        }
+    }
+
+    fn state(&self) -> Self::StateType {
+        self.current_state
+    }
+
+    fn reset(&mut self) -> Snapshot<Self> {
+        // Réinitialiser le jeu
+        self.game = GameState::new(false);
+        self.game.init_player("DQN Agent");
+        self.game.init_player("Opponent");
+
+        // Commencer la partie
+        self.game.consume(&GameEvent::BeginGame { goes_first: 1 });
+
+        self.current_state = TrictracState::from_game_state(&self.game);
+        self.episode_reward = 0.0;
+        self.goodmoves_ratio = if self.step_count == 0 {
+            0.0
+        } else {
+            self.goodmoves_count as f32 / self.step_count as f32
+        };
+        println!(
+            "info: correct moves: {} ({}%)",
+            self.goodmoves_count,
+            (100.0 * self.goodmoves_ratio).round() as u32
+        );
+        self.step_count = 0;
+        self.pointrolls_count = 0;
+        self.goodmoves_count = 0;
+
+        Snapshot::new(self.current_state, 0.0, false)
+    }
+
+    fn step(&mut self, action: Self::ActionType) -> Snapshot<Self> {
+        self.step_count += 1;
+
+        // Convertir l'action burn-rl vers une action Trictrac
+        let trictrac_action = Self::convert_action(action);
+
+        let mut reward = 0.0;
+        let is_rollpoint;
+
+        // Exécuter l'action si c'est le tour de l'agent DQN
+        if self.game.active_player_id == self.active_player_id {
+            if let Some(action) = trictrac_action {
+                (reward, is_rollpoint) = self.execute_action(action);
+                if is_rollpoint {
+                    self.pointrolls_count += 1;
+                }
+                if reward != Self::ERROR_REWARD {
+                    self.goodmoves_count += 1;
+                }
+            } else {
+                // Action non convertible, pénalité
+                reward = -0.5;
+            }
+        }
+
+        // Faire jouer l'adversaire (stratégie simple)
+        while self.game.active_player_id == self.opponent_id && self.game.stage != Stage::Ended {
+            reward += self.play_opponent_if_needed();
+        }
+
+        // Vérifier si la partie est terminée
+        let max_steps = self.min_steps
+            + (self.max_steps as f32 - self.min_steps)
+                * f32::exp((self.goodmoves_ratio - 1.0) / 0.25);
+        let done = self.game.stage == Stage::Ended || self.game.determine_winner().is_some();
+
+        if done {
+            // Récompense finale basée sur le résultat
+            if let Some(winner_id) = self.game.determine_winner() {
+                if winner_id == self.active_player_id {
+                    reward += 50.0; // Victoire
+                } else {
+                    reward -= 25.0; // Défaite
+                }
+            }
+        }
+        let terminated = done || self.step_count >= max_steps.round() as usize;
+
+        // Mettre à jour l'état
+        self.current_state = TrictracState::from_game_state(&self.game);
+        self.episode_reward += reward;
+
+        if self.visualized && terminated {
+            println!(
+                "Episode terminé. Récompense totale: {:.2}, Étapes: {}",
+                self.episode_reward, self.step_count
+            );
+        }
+
+        Snapshot::new(self.current_state, reward, terminated)
+    }
+}
+
+impl TrictracEnvironment {
+    const ERROR_REWARD: f32 = -1.12121;
+    const REWARD_RATIO: f32 = 1.0;
+
+    /// Convertit une action burn-rl vers une action Trictrac
+    pub fn convert_action(action: TrictracAction) -> Option<dqn_common_big::TrictracAction> {
+        dqn_common_big::TrictracAction::from_action_index(action.index.try_into().unwrap())
+    }
+
+    /// Convertit l'index d'une action au sein des actions valides vers une action Trictrac
+    fn convert_valid_action_index(
+        &self,
+        action: TrictracAction,
+        game_state: &GameState,
+    ) -> Option<dqn_common_big::TrictracAction> {
+        use dqn_common_big::get_valid_actions;
+
+        // Obtenir les actions valides dans le contexte actuel
+        let valid_actions = get_valid_actions(game_state);
+
+        if valid_actions.is_empty() {
+            return None;
+        }
+
+        // Mapper l'index d'action sur une action valide
+        let action_index = (action.index as usize) % valid_actions.len();
+        Some(valid_actions[action_index].clone())
+    }
+
+    /// Exécute une action Trictrac dans le jeu
+    // fn execute_action(
+    //     &mut self,
+    //     action: dqn_common_big::TrictracAction,
+    // ) -> Result<f32, Box<dyn std::error::Error>> {
+    fn execute_action(&mut self, action: dqn_common_big::TrictracAction) -> (f32, bool) {
+        use dqn_common_big::TrictracAction;
+
+        let mut reward = 0.0;
+        let mut is_rollpoint = false;
+
+        let event = match action {
+            TrictracAction::Roll => {
+                // Lancer les dés
+                reward += 0.1;
+                Some(GameEvent::Roll {
+                    player_id: self.active_player_id,
+                })
+            }
+            // TrictracAction::Mark => {
+            //     // Marquer des points
+            //     let points = self.game.
+            //     reward += 0.1 * points as f32;
+            //     Some(GameEvent::Mark {
+            //         player_id: self.active_player_id,
+            //         points,
+            //     })
+            // }
+            TrictracAction::Go => {
+                // Continuer après avoir gagné un trou
+                reward += 0.2;
+                Some(GameEvent::Go {
+                    player_id: self.active_player_id,
+                })
+            }
+            TrictracAction::Move {
+                dice_order,
+                from1,
+                from2,
+            } => {
+                // Effectuer un mouvement
+                let (dice1, dice2) = if dice_order {
+                    (self.game.dice.values.0, self.game.dice.values.1)
+                } else {
+                    (self.game.dice.values.1, self.game.dice.values.0)
+                };
+                let mut to1 = from1 + dice1 as usize;
+                let mut to2 = from2 + dice2 as usize;
+
+                // Gestion prise de coin par puissance
+                let opp_rest_field = 13;
+                if to1 == opp_rest_field && to2 == opp_rest_field {
+                    to1 -= 1;
+                    to2 -= 1;
+                }
+
+                let checker_move1 = store::CheckerMove::new(from1, to1).unwrap_or_default();
+                let checker_move2 = store::CheckerMove::new(from2, to2).unwrap_or_default();
+
+                reward += 0.2;
+                Some(GameEvent::Move {
+                    player_id: self.active_player_id,
+                    moves: (checker_move1, checker_move2),
+                })
+            }
+        };
+
+        // Appliquer l'événement si valide
+        if let Some(event) = event {
+            if self.game.validate(&event) {
+                self.game.consume(&event);
+
+                // Simuler le résultat des dés après un Roll
+                if matches!(action, TrictracAction::Roll) {
+                    let mut rng = thread_rng();
+                    let dice_values = (rng.gen_range(1..=6), rng.gen_range(1..=6));
+                    let dice_event = GameEvent::RollResult {
+                        player_id: self.active_player_id,
+                        dice: store::Dice {
+                            values: dice_values,
+                        },
+                    };
+                    if self.game.validate(&dice_event) {
+                        self.game.consume(&dice_event);
+                        let (points, adv_points) = self.game.dice_points;
+                        reward += Self::REWARD_RATIO * (points - adv_points) as f32;
+                        if points > 0 {
+                            is_rollpoint = true;
+                            // println!("info: rolled for {reward}");
+                        }
+                        // Récompense proportionnelle aux points
+                    }
+                }
+            } else {
+                // Pénalité pour action invalide
+                // on annule les précédents reward
+                // et on indique une valeur reconnaissable pour statistiques
+                reward = Self::ERROR_REWARD;
+            }
+        }
+
+        (reward, is_rollpoint)
+    }
+
+    /// Fait jouer l'adversaire avec une stratégie simple
+    fn play_opponent_if_needed(&mut self) -> f32 {
+        let mut reward = 0.0;
+
+        // Si c'est le tour de l'adversaire, jouer automatiquement
+        if self.game.active_player_id == self.opponent_id && self.game.stage != Stage::Ended {
+            // Utiliser la stratégie default pour l'adversaire
+            use crate::BotStrategy;
+
+            let mut strategy = crate::strategy::random::RandomStrategy::default();
+            strategy.set_player_id(self.opponent_id);
+            if let Some(color) = self.game.player_color_by_id(&self.opponent_id) {
+                strategy.set_color(color);
+            }
+            *strategy.get_mut_game() = self.game.clone();
+
+            // Exécuter l'action selon le turn_stage
+            let mut calculate_points = false;
+            let opponent_color = store::Color::Black;
+            let event = match self.game.turn_stage {
+                TurnStage::RollDice => GameEvent::Roll {
+                    player_id: self.opponent_id,
+                },
+                TurnStage::RollWaiting => {
+                    let mut rng = thread_rng();
+                    let dice_values = (rng.gen_range(1..=6), rng.gen_range(1..=6));
+                    // calculate_points = true; // comment to replicate burnrl_before
+                    GameEvent::RollResult {
+                        player_id: self.opponent_id,
+                        dice: store::Dice {
+                            values: dice_values,
+                        },
+                    }
+                }
+                TurnStage::MarkPoints => {
+                    panic!("in play_opponent_if_needed > TurnStage::MarkPoints");
+                    let dice_roll_count = self
+                        .game
+                        .players
+                        .get(&self.opponent_id)
+                        .unwrap()
+                        .dice_roll_count;
+                    let points_rules =
+                        PointsRules::new(&opponent_color, &self.game.board, self.game.dice);
+                    GameEvent::Mark {
+                        player_id: self.opponent_id,
+                        points: points_rules.get_points(dice_roll_count).0,
+                    }
+                }
+                TurnStage::MarkAdvPoints => {
+                    let opponent_color = store::Color::Black;
+                    let dice_roll_count = self
+                        .game
+                        .players
+                        .get(&self.opponent_id)
+                        .unwrap()
+                        .dice_roll_count;
+                    let points_rules =
+                        PointsRules::new(&opponent_color, &self.game.board, self.game.dice);
+                    // pas de reward : déjà comptabilisé lors du tour de blanc
+                    GameEvent::Mark {
+                        player_id: self.opponent_id,
+                        points: points_rules.get_points(dice_roll_count).1,
+                    }
+                }
+                TurnStage::HoldOrGoChoice => {
+                    // Stratégie simple : toujours continuer
+                    GameEvent::Go {
+                        player_id: self.opponent_id,
+                    }
+                }
+                TurnStage::Move => GameEvent::Move {
+                    player_id: self.opponent_id,
+                    moves: strategy.choose_move(),
+                },
+            };
+
+            if self.game.validate(&event) {
+                self.game.consume(&event);
+                if calculate_points {
+                    let dice_roll_count = self
+                        .game
+                        .players
+                        .get(&self.opponent_id)
+                        .unwrap()
+                        .dice_roll_count;
+                    let points_rules =
+                        PointsRules::new(&opponent_color, &self.game.board, self.game.dice);
+                    let (points, adv_points) = points_rules.get_points(dice_roll_count);
+                    // Récompense proportionnelle aux points
+                    reward -= Self::REWARD_RATIO * (points - adv_points) as f32;
+                }
+            }
+        }
+        reward
+    }
+}
+
+impl AsMut<TrictracEnvironment> for TrictracEnvironment {
+    fn as_mut(&mut self) -> &mut Self {
+        self
+    }
+}
diff --git a/bot/src/dqn/burnrl_big/main.rs b/bot/src/dqn/burnrl_big/main.rs
new file mode 100644
index 0000000..3b72ef8
--- /dev/null
+++ b/bot/src/dqn/burnrl_big/main.rs
@@ -0,0 +1,53 @@
+use bot::dqn::burnrl_big::{
+    dqn_model, environment,
+    utils::{demo_model, load_model, save_model},
+};
+use burn::backend::{Autodiff, NdArray};
+use burn_rl::agent::DQN;
+use burn_rl::base::ElemType;
+
+type Backend = Autodiff<NdArray<ElemType>>;
+type Env = environment::TrictracEnvironment;
+
+fn main() {
+    // println!("> Entraînement");
+
+    // See also MEMORY_SIZE in dqn_model.rs : 8192
+    let conf = dqn_model::DqnConfig {
+        //                   defaults
+        num_episodes: 40, // 40
+        min_steps: 500.0, // 1000 min of max steps by episode (mise à jour par la fonction)
+        max_steps: 3000,  // 1000 max steps by episode
+        dense_size: 256,  // 128  neural network complexity (default 128)
+        eps_start: 0.9,   // 0.9  epsilon initial value (0.9 => more exploration)
+        eps_end: 0.05,    // 0.05
+        // eps_decay higher = epsilon decrease slower
+        // used in : epsilon = eps_end + (eps_start - eps_end) * e^(-step / eps_decay);
+        // epsilon is updated at the start of each episode
+        eps_decay: 2000.0, // 1000 ?
+
+        gamma: 0.999, // 0.999 discount factor. Plus élevé = encourage stratégies à long terme
+        tau: 0.005, // 0.005 soft update rate. Taux de mise à jour du réseau cible. Plus bas = adaptation
+        // plus lente moins sensible aux coups de chance
+        learning_rate: 0.001, // 0.001 taille du pas. Bas : plus lent, haut : risque de ne jamais
+        // converger
+        batch_size: 32, // 32 nombre d'expériences passées sur lesquelles pour calcul de l'erreur moy.
+        clip_grad: 100.0, // 100 limite max de correction à apporter au gradient (default 100)
+    };
+    println!("{conf}----------");
+    let agent = dqn_model::run::<Env, Backend>(&conf, false); //true);
+
+    let valid_agent = agent.valid();
+
+    println!("> Sauvegarde du modèle de validation");
+
+    let path = "models/burn_dqn_40".to_string();
+    save_model(valid_agent.model().as_ref().unwrap(), &path);
+
+    println!("> Chargement du modèle pour test");
+    let loaded_model = load_model(conf.dense_size, &path);
+    let loaded_agent = DQN::new(loaded_model.unwrap());
+
+    println!("> Test avec le modèle chargé");
+    demo_model(loaded_agent);
+}
diff --git a/bot/src/dqn/burnrl_big/mod.rs b/bot/src/dqn/burnrl_big/mod.rs
new file mode 100644
index 0000000..f4380eb
--- /dev/null
+++ b/bot/src/dqn/burnrl_big/mod.rs
@@ -0,0 +1,3 @@
+pub mod dqn_model;
+pub mod environment;
+pub mod utils;
diff --git a/bot/src/dqn/burnrl_big/utils.rs b/bot/src/dqn/burnrl_big/utils.rs
new file mode 100644
index 0000000..9159d57
--- /dev/null
+++ b/bot/src/dqn/burnrl_big/utils.rs
@@ -0,0 +1,114 @@
+use crate::dqn::burnrl_big::{
+    dqn_model,
+    environment::{TrictracAction, TrictracEnvironment},
+};
+use crate::dqn::dqn_common_big::get_valid_action_indices;
+use burn::backend::{ndarray::NdArrayDevice, Autodiff, NdArray};
+use burn::module::{Module, Param, ParamId};
+use burn::nn::Linear;
+use burn::record::{CompactRecorder, Recorder};
+use burn::tensor::backend::Backend;
+use burn::tensor::cast::ToElement;
+use burn::tensor::Tensor;
+use burn_rl::agent::{DQNModel, DQN};
+use burn_rl::base::{Action, ElemType, Environment, State};
+
+pub fn save_model(model: &dqn_model::Net<NdArray<ElemType>>, path: &String) {
+    let recorder = CompactRecorder::new();
+    let model_path = format!("{path}_model.mpk");
+    println!("Modèle de validation sauvegardé : {model_path}");
+    recorder
+        .record(model.clone().into_record(), model_path.into())
+        .unwrap();
+}
+
+pub fn load_model(dense_size: usize, path: &String) -> Option<dqn_model::Net<NdArray<ElemType>>> {
+    let model_path = format!("{path}_model.mpk");
+    // println!("Chargement du modèle depuis : {model_path}");
+
+    CompactRecorder::new()
+        .load(model_path.into(), &NdArrayDevice::default())
+        .map(|record| {
+            dqn_model::Net::new(
+                <TrictracEnvironment as Environment>::StateType::size(),
+                dense_size,
+                <TrictracEnvironment as Environment>::ActionType::size(),
+            )
+            .load_record(record)
+        })
+        .ok()
+}
+
+pub fn demo_model<B: Backend, M: DQNModel<B>>(agent: DQN<TrictracEnvironment, B, M>) {
+    let mut env = TrictracEnvironment::new(true);
+    let mut done = false;
+    while !done {
+        // let action = match infer_action(&agent, &env, state) {
+        let action = match infer_action(&agent, &env) {
+            Some(value) => value,
+            None => break,
+        };
+        // Execute action
+        let snapshot = env.step(action);
+        done = snapshot.done();
+    }
+}
+
+fn infer_action<B: Backend, M: DQNModel<B>>(
+    agent: &DQN<TrictracEnvironment, B, M>,
+    env: &TrictracEnvironment,
+) -> Option<TrictracAction> {
+    let state = env.state();
+    // Get q-values
+    let q_values = agent
+        .model()
+        .as_ref()
+        .unwrap()
+        .infer(state.to_tensor().unsqueeze());
+    // Get valid actions
+    let valid_actions_indices = get_valid_action_indices(&env.game);
+    if valid_actions_indices.is_empty() {
+        return None; // No valid actions, end of episode
+    }
+    // Set non valid actions q-values to lowest
+    let mut masked_q_values = q_values.clone();
+    let q_values_vec: Vec<f32> = q_values.into_data().into_vec().unwrap();
+    for (index, q_value) in q_values_vec.iter().enumerate() {
+        if !valid_actions_indices.contains(&index) {
+            masked_q_values = masked_q_values.clone().mask_fill(
+                masked_q_values.clone().equal_elem(*q_value),
+                f32::NEG_INFINITY,
+            );
+        }
+    }
+    // Get best action (highest q-value)
+    let action_index = masked_q_values.argmax(1).into_scalar().to_u32();
+    let action = TrictracAction::from(action_index);
+    Some(action)
+}
+
+fn soft_update_tensor<const N: usize, B: Backend>(
+    this: &Param<Tensor<B, N>>,
+    that: &Param<Tensor<B, N>>,
+    tau: ElemType,
+) -> Param<Tensor<B, N>> {
+    let that_weight = that.val();
+    let this_weight = this.val();
+    let new_weight = this_weight * (1.0 - tau) + that_weight * tau;
+
+    Param::initialized(ParamId::new(), new_weight)
+}
+
+pub fn soft_update_linear<B: Backend>(
+    this: Linear<B>,
+    that: &Linear<B>,
+    tau: ElemType,
+) -> Linear<B> {
+    let weight = soft_update_tensor(&this.weight, &that.weight, tau);
+    let bias = match (&this.bias, &that.bias) {
+        (Some(this_bias), Some(that_bias)) => Some(soft_update_tensor(this_bias, that_bias, tau)),
+        _ => None,
+    };
+
+    Linear::<B> { weight, bias }
+}
diff --git a/bot/src/dqn/mod.rs b/bot/src/dqn/mod.rs
index ab75746..ebc01a4 100644
--- a/bot/src/dqn/mod.rs
+++ b/bot/src/dqn/mod.rs
@@ -1,4 +1,6 @@
 pub mod burnrl;
+pub mod burnrl_before;
+pub mod burnrl_big;
 pub mod dqn_common;
 pub mod dqn_common_big;
 pub mod simple;