feat: Add the second exercise (first throw)

ex2.py

@@ -0,0 +1,121 @@
+import random
+from collections import deque
+
+import gymnasium as gym
+import torch
+import torch.nn as nn
+import torch.optim as optim
+
+
+class DQN(nn.Module):
+    def __init__(self, n_states=4, n_actions=2):
+        """
+        Notre modèle à deux états, et peux faire deux actions (trouver à gauche
+        ou à droite)
+        :param n_state:
+        :param n_action:
+        """
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Linear(n_states, 64), nn.ReLU(),
+            nn.Linear(64, n_actions)
+        )
+
+
+    def forward(self, x):
+        """
+
+        :param x:
+        :return:
+        """
+        return self.net(x)
+
+
+def epsilon_greedy(epsilon: float, s, policy_net: DQN, n_actions: int) -> int:
+    if random.random() < epsilon:
+        return random.randrange(n_actions)
+    else:
+        return torch.argmax(policy_net(s)).item()
+
+
+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
+
+    policy_net = DQN(n_states, n_actions)  # Q Network
+    target_net = DQN(n_states, n_actions)  # Target network
+    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
+    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)
+    batch_size = 64
+
+    for ep in range(episodes):
+        s, _ = env.reset()
+        s = torch.tensor(s, dtype=torch.float32)
+        done, total_r = False, 0
+
+        while not done:
+            a = epsilon_greedy(epsilon, s, policy_net, n_actions)
+
+            ns, r, done, _, _ = env.step(a)
+            ns = torch.tensor(ns, dtype=torch.float32)
+
+            memory.append((s, a, r, ns, done))
+            s, total_r = ns, total_r + r
+
+            if len(memory) >= batch_size:
+                batch = random.sample(memory, batch_size)
+                s_b, a_b, r_b, ns_b, d_b = zip(*batch)
+                s_b = torch.stack(s_b)
+                ns_b = torch.stack(ns_b)
+
+                q_pred = policy_net(s_b).gather(1, torch.tensor(a_b).unsqueeze(1)).squeeze()
+
+                with torch.no_grad():
+                    q_next = target_net(ns_b).max(1)[0]
+                    q_target = torch.tensor(r_b, dtype=torch.float32) + \
+                        gamma * q_next * (1 - torch.tensor(d_b, dtype=torch.float32))
+
+                loss = ((q_pred - q_target)**2).mean()
+                optimizer.zero_grad(); loss.backward(); optimizer.step()
+
+        epsilon = max(eps_min, epsilon * eps_decay)
+
+        if (ep + 1) % update_target_every == 0:
+            target_net.load_state_dict(policy_net.state_dict())
+
+        if (ep + 1) % 20 == 0:
+            print(f'Episode {ep + 1}: total reward {total_r:.1f}, epsilon {epsilon:.2f}')
+    env.close()
+
+    torch.save(policy_net.state_dict(), weights_path)
+    print(f'Training finished. Weights saved to {weights_path}')
+    return policy_net  # <--- trained Q-network
+
+
+def show(weights_path='cartpole_dqn.pth') -> None:
+    env = gym.make('CartPole-v1', render_mode='human')
+    qnet = DQN()
+    qnet.load_state_dict(torch.load(weights_path))
+    qnet.eval()
+
+    s, _ = env.reset()
+    s = torch.tensor(s, dtype=torch.float32)
+    done = False
+    while not done:
+        a = torch.argmax(qnet(s)).item()
+        s_, r, done, _, _ = env.step(a)
+        s = torch.tensor(s_, dtype=torch.float32)
+    env.close()
+    print('Demonstration finished.')
+
+
+if __name__ == '__main__':
+    #trained_model = train_and_save()
+    show()

tp2.py

@@ -38,8 +38,8 @@ def train_and_save(weights_path="acrobot_dqn.pth", episodes=2000, update_target_
     n_states, n_actions = env.observation_space.shape[0], env.action_space.n
 
     # les DQN
-    policy_net = DQN(n_states, n_actions) #
+    policy_net = DQN(n_states, n_actions) # Q Network
-    target_net = DQN(n_states, n_actions)
+    target_net = DQN(n_states, n_actions) # Target network
     target_net.load_state_dict(policy_net.state_dict()) # same weights at start
     target_net.eval()