diff --git a/src/simulation/scripts/lanch_one_simu.py b/src/simulation/scripts/lanch_one_simu.py
index 8779f0b..2c309e8 100644
--- a/src/simulation/scripts/lanch_one_simu.py
+++ b/src/simulation/scripts/lanch_one_simu.py
@@ -1,35 +1,31 @@
-raise NotImplementedError("This file is currently begin worked on")
-
 import os
 import sys
 
+from typing import *
+import numpy as np
 import onnxruntime as ort
+import gymnasium as gym
 
-from simulation import (
-    VehicleEnv,
-)
-from simulation import config as c
-from utils import onnx_utils
+from simulation.config import *
+from utils import run_onnx_model
 
-# -------------------------------------------------------------------------
+from extractors import (  # noqa: F401
+    CNN1DResNetExtractor,
+    TemporalResNetExtractor,
+)
 
+from simulation import VehicleEnv
 
-# --- Chemin vers le fichier ONNX ---
+# -------------------------------------------------------------------------
 
-ONNX_MODEL_PATH = "model.onnx"
+ONNX_MODEL_PATH = "/home/exo/Bureau/CoVAPSy/model.onnx"
 
 
-# --- Initialisation du moteur d'inférence ONNX Runtime (ORT) ---
+# --- Launching of inference motor ONNX Runtime (ORT) ---
 def init_onnx_runtime_session(onnx_path: str) -> ort.InferenceSession:
     if not os.path.exists(onnx_path):
-        raise FileNotFoundError(
-            f"Le fichier ONNX est introuvable à : {onnx_path}. Veuillez l'exporter d'abord."
-        )
-
-    # Crée la session d'inférence
-    return ort.InferenceSession(
-        onnx_path
-    )  # On peut modifier le providers afin de mettre une CUDA
+        raise FileNotFoundError(f"Le fichier ONNX est introuvable à : {onnx_path}. Veuillez l'exporter d'abord.")
+    return ort.InferenceSession(onnx_path)
 
 
 if __name__ == "__main__":
@@ -38,7 +34,7 @@ def init_onnx_runtime_session(onnx_path: str) -> ort.InferenceSession:
 
     os.system('if [ -n "$(ls /tmp/autotech)" ]; then rm /tmp/autotech/*; fi')
 
-    # 2. Initialisation de la session ONNX Runtime
+    # Starting of OnnxSession
     try:
         ort_session = init_onnx_runtime_session(ONNX_MODEL_PATH)
         input_name = ort_session.get_inputs()[0].name
@@ -47,33 +43,48 @@ def init_onnx_runtime_session(onnx_path: str) -> ort.InferenceSession:
         print(f"Input Name: {input_name}, Output Name: {output_name}")
     except FileNotFoundError as e:
         print(f"ERREUR : {e}")
-        print(
-            "Veuillez vous assurer que vous avez exécuté une fois le script d'entraînement pour exporter 'model.onnx'."
-        )
         sys.exit(1)
 
-    # 3. Boucle d'inférence (Test)
     env = VehicleEnv(0, 0)
-    obs = env.reset()
+    obs, _ = env.reset()
+
     print("Début de la simulation en mode inférence...")
 
-    max_steps = 5000
     step_count = 0
 
     while True:
-        action = onnx_utils.run_onnx_model(ort_session, obs)
+        # 1. On récupère les logits (probabilités) bruts de l'ONNX
+        raw_action = run_onnx_model(ort_session, obs[None])
+        logits = np.array(raw_action).flatten()
+
+        # 2. On sépare le tableau en deux (Direction et Vitesse)
+        # On utilise n_actions_steering et n_actions_speed venant de config.py
+        steer_logits = logits[:n_actions_steering]
+        speed_logits = logits[n_actions_steering:]
+
+        # 3. L'IA choisit l'action qui a le score (logit) le plus élevé
+        action_steer = np.argmax(steer_logits)
+        action_speed = np.argmax(speed_logits)
 
-        # 4. Exécuter l'action dans l'environnement
-        obs, reward, done, info = env.step(action)
+        # 4. On crée le tableau final parfaitement formaté pour Webots (strictement 2 entiers)
+        action = np.array([action_steer, action_speed], dtype=np.int64)
 
-        # Note: L'environnement Webots gère généralement son propre affichage
-        # env.render() # Décommenter si votre env supporte le rendu externe
+        # 5. Exécuter l'action dans l'environnement
+        next_obs, reward, done, truncated, info = env.step(action)
+
+        step_count += 1
 
         # Gestion des fins d'épisodes
         if done:
             print(f"Épisode(s) terminé(s) après {step_count} étapes.")
-            obs = env.reset()
+            step_count = 0
+
+            fresh_frame = next_obs[:, -1:]
+            obs, _ = env.reset()
+            env.context[:, -1:] = fresh_frame
+            obs = env.context
+        else:
+            obs = next_obs
 
-    # Fermeture propre (très important pour les processus parallèles SubprocVecEnv)
-    envs.close()
-    print("Simulation terminée. Environnements fermés.")
+    env.close()
+    print("Simulation terminée. Environnements fermés.")
\ No newline at end of file
diff --git a/src/simulation/src/simulation/config.py b/src/simulation/src/simulation/config.py
index 1e8864d..7e572eb 100644
--- a/src/simulation/src/simulation/config.py
+++ b/src/simulation/src/simulation/config.py
@@ -11,7 +11,7 @@
 
 n_map = 2
 n_simulations = 1
-n_vehicles = 2
+n_vehicles = 1
 n_stupid_vehicles = 0
 n_actions_steering = 16
 n_actions_speed = 16
@@ -26,3 +26,5 @@
 
 LOG_LEVEL = logging.INFO
 FORMATTER = logging.Formatter("%(asctime)s - %(name)s - %(levelname)s - %(message)s")
+
+B_DEBUG = False
\ No newline at end of file
diff --git a/src/simulation/src/utils/__init__.py b/src/simulation/src/utils/__init__.py
index 4b7ee72..7e56893 100644
--- a/src/simulation/src/utils/__init__.py
+++ b/src/simulation/src/utils/__init__.py
@@ -1,3 +1,8 @@
 from .plot_model_io import PlotModelIO
+import onnxruntime as ort
+import numpy as np
 
 __all__ = ["PlotModelIO"]
+
+def run_onnx_model(session: ort.InferenceSession, x: np.ndarray):
+    return session.run(None, {"input": x})[0]
\ No newline at end of file
diff --git a/uv.lock b/uv.lock
index 7effdcb..7ac586b 100644
--- a/uv.lock
+++ b/uv.lock
@@ -852,6 +852,11 @@ dependencies = [
     { name = "zmq" },
 ]
 
+[package.optional-dependencies]
+controller = [
+    { name = "pygame" },
+]
+
 [package.metadata]
 requires-dist = [
     { name = "adafruit-blinka", specifier = ">=8.0.0" },
@@ -869,6 +874,7 @@ requires-dist = [
     { name = "onnxruntime", specifier = ">=1.8.0" },
     { name = "opencv-python", specifier = ">=4.12.0.88" },
     { name = "picamera2", specifier = ">=0.3.0" },
+    { name = "pygame", marker = "extra == 'controller'", specifier = ">=2.6.1" },
     { name = "pyps4controller", specifier = ">=1.2.0" },
     { name = "rpi-gpio", specifier = ">=0.7.1" },
     { name = "rpi-hardware-pwm", specifier = ">=0.1.0" },
@@ -879,6 +885,7 @@ requires-dist = [
     { name = "websockets", specifier = ">=16.0" },
     { name = "zmq", specifier = ">=0.0.0" },
 ]
+provides-extras = ["controller"]
 
 [[package]]
 name = "humanfriendly"
@@ -2392,6 +2399,9 @@ dependencies = [
 wheels = [
     { url = "https://files.pythonhosted.org/packages/d3/54/a2ba279afcca44bbd320d4e73675b282fcee3d81400ea1b53934efca6462/torch-2.10.0-2-cp312-none-macosx_11_0_arm64.whl", hash = "sha256:13ec4add8c3faaed8d13e0574f5cd4a323c11655546f91fbe6afa77b57423574", size = 79498202, upload-time = "2026-02-10T21:44:52.603Z" },
     { url = "https://files.pythonhosted.org/packages/ec/23/2c9fe0c9c27f7f6cb865abcea8a4568f29f00acaeadfc6a37f6801f84cb4/torch-2.10.0-2-cp313-none-macosx_11_0_arm64.whl", hash = "sha256:e521c9f030a3774ed770a9c011751fb47c4d12029a3d6522116e48431f2ff89e", size = 79498254, upload-time = "2026-02-10T21:44:44.095Z" },
+    { url = "https://files.pythonhosted.org/packages/b3/7a/abada41517ce0011775f0f4eacc79659bc9bc6c361e6bfe6f7052a6b9363/torch-2.10.0-3-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:98c01b8bb5e3240426dcde1446eed6f40c778091c8544767ef1168fc663a05a6", size = 915622781, upload-time = "2026-03-11T14:17:11.354Z" },
+    { url = "https://files.pythonhosted.org/packages/ab/c6/4dfe238342ffdcec5aef1c96c457548762d33c40b45a1ab7033bb26d2ff2/torch-2.10.0-3-cp313-cp313-manylinux_2_28_x86_64.whl", hash = "sha256:80b1b5bfe38eb0e9f5ff09f206dcac0a87aadd084230d4a36eea5ec5232c115b", size = 915627275, upload-time = "2026-03-11T14:16:11.325Z" },
+    { url = "https://files.pythonhosted.org/packages/d8/f0/72bf18847f58f877a6a8acf60614b14935e2f156d942483af1ffc081aea0/torch-2.10.0-3-cp313-cp313t-manylinux_2_28_x86_64.whl", hash = "sha256:46b3574d93a2a8134b3f5475cfb98e2eb46771794c57015f6ad1fb795ec25e49", size = 915523474, upload-time = "2026-03-11T14:17:44.422Z" },
     { url = "https://files.pythonhosted.org/packages/cc/af/758e242e9102e9988969b5e621d41f36b8f258bb4a099109b7a4b4b50ea4/torch-2.10.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:5fd4117d89ffd47e3dcc71e71a22efac24828ad781c7e46aaaf56bf7f2796acf", size = 145996088, upload-time = "2026-01-21T16:24:44.171Z" },
     { url = "https://files.pythonhosted.org/packages/23/8e/3c74db5e53bff7ed9e34c8123e6a8bfef718b2450c35eefab85bb4a7e270/torch-2.10.0-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:787124e7db3b379d4f1ed54dd12ae7c741c16a4d29b49c0226a89bea50923ffb", size = 915711952, upload-time = "2026-01-21T16:23:53.503Z" },
     { url = "https://files.pythonhosted.org/packages/6e/01/624c4324ca01f66ae4c7cd1b74eb16fb52596dce66dbe51eff95ef9e7a4c/torch-2.10.0-cp312-cp312-win_amd64.whl", hash = "sha256:2c66c61f44c5f903046cc696d088e21062644cbe541c7f1c4eaae88b2ad23547", size = 113757972, upload-time = "2026-01-21T16:24:39.516Z" },