Merge branch 'v1.0.0'

# Conflicts:
#	ex2.py

ex2.py

@@ -5,7 +5,6 @@ import gymnasium as gym
 import torch
 import torch.nn as nn
 import torch.optim as optim
-import matplotlib.pyplot as plt
 
 
 class DQN(nn.Module):
@@ -18,10 +17,11 @@ 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)
         )
 
+
     def forward(self, x):
         """
 
@@ -38,7 +38,7 @@ def epsilon_greedy(epsilon: float, s, policy_net: DQN, n_actions: int) -> int:
         return torch.argmax(policy_net(s)).item()
 
 
-def train_and_save(weights_path="cartpole_dqn.pth", episodes=2_000, update_target_every=20) -> DQN:
+def train_and_save(weights_path="cartpole_dqn.pth", episodes=2_000, update_target_every=20):
     env = gym.make('CartPole-v1')
     n_states, n_actions = env.observation_space.shape[0], env.action_space.n
 
@@ -47,16 +47,15 @@ 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-2) #1e-3
+    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=5_000)
+    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):
-        print(f'Episode: {ep}/{episodes}')
         s, _ = env.reset()
         s = torch.tensor(s, dtype=torch.float32)
         done, total_r = False, 0
@@ -109,12 +108,14 @@ 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__':