diff --git a/ex2.py b/ex2.py
index f0573d7..1c06516 100644
--- a/ex2.py
+++ b/ex2.py
@@ -17,8 +17,8 @@ class DQN(nn.Module):
         """
         super().__init__()
         self.net = nn.Sequential(
-            nn.Linear(n_states, 64), nn.ReLU(),
-            nn.Linear(64, n_actions)
+            nn.Linear(n_states, 128), nn.ReLU(),
+            nn.Linear(128, n_actions)
         )
 
 
@@ -47,12 +47,12 @@ def train_and_save(weights_path="cartpole_dqn.pth", episodes=2_000, update_targe
     target_net.load_state_dict(policy_net.state_dict())  # same weights at start
     target_net.eval()
 
-    optimizer = optim.Adam(policy_net.parameters(), lr=1e-3) # <- erreur ici
+    optimizer = optim.Adam(policy_net.parameters(), lr=1e-3)
     gamma = 0.99  # discount factor
     epsilon = 1.0  # Fréquence d'exploration initiale
-    eps_min = 0.05  # Fréquence d'exploration minimale
-    eps_decay = 0.995  # Facteur de réduction d'epsilon
-    memory = deque(maxlen=5000)
+    eps_min = 0.01  # Fréquence d'exploration minimale
+    eps_decay = 0.999  # Facteur de réduction d'epsilon
+    memory = deque(maxlen=100_000)
     batch_size = 64
 
     for ep in range(episodes):
@@ -108,14 +108,16 @@ def show(weights_path='cartpole_dqn.pth') -> None:
     s, _ = env.reset()
     s = torch.tensor(s, dtype=torch.float32)
     done = False
+    total_r = 0.0
     while not done:
         a = torch.argmax(qnet(s)).item()
         s_, r, done, _, _ = env.step(a)
+        total_r += r
         s = torch.tensor(s_, dtype=torch.float32)
     env.close()
-    print('Demonstration finished.')
+    print(f'Demonstration finished. {total_r:.1f}')
 
 
 if __name__ == '__main__':
-    #trained_model = train_and_save()
+    trained_model = train_and_save()
     show()