diff --git a/requirements.txt b/requirements.txt
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diff --git a/tp3.py b/tp3.py
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+import random
+from collections import deque
+
+import gymnasium as gym
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.optim as optim
+
+ACTION_SET = [
+    np.zeros(17),
+    np.full(17, -0.4),
+    np.full(17, 0.4),
+    np.concatenate([np.full(8, 0.4), np.full(9, -0.4)])
+]
+
+class DQN(nn.Module):
+    def __init__(self, n_states=348, n_actions=4):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Linear(n_states, 64), nn.ReLU(),
+            nn.Linear(64, n_actions)
+        )
+
+    def forward(self, x):
+        """
+        Forward pass of the network.
+        :param x:  torch.Tensor of shape [n_states]
+        :return: torch.Tensor of shape [n_actions] with Q-Values for each action
+        """
+        return self.net(x)
+
+
+def train_and_save(weights_path="humanoid_dqn.pth", episodes=20_000, update_target_every=20):
+    """
+    Train a DQN agent on the Humanoid-v5 environnement.
+    :param weights_path: file path to save learned network weights
+    :param episodes: number of training episodes (complete games)
+    :param update_target_every: how many episodes to wait before syncing the target network
+    :return: trained Q-Network ready to be used for inference
+    """
+
+    # environnement setup
+    env = gym.make("Humanoid-v5")
+    n_states, n_actions = env.observation_space.shape[0], len(ACTION_SET)
+
+    # les DQN
+    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 et hyperparameters
+    optimizer = optim.Adam(policy_net.parameters(), lr=1e-3)
+    gamma = 0.99 # discount factor
+    epsilon = 1.0 # Fréquence d'exploration initiale
+    eps_min = 0.01 # Fréquence d'exploration minimale
+    eps_decay = 0.9999 # Facteur de réduction d'epsilon
+    memory = deque(maxlen=int(1e9))
+    batch_size = 64
+
+    # main training loop
+    for ep in range(episodes):
+        # env.reset() returns a tuple (initial_state, info_dict)
+        s, _ = env.reset()
+        s = torch.tensor(s, dtype=torch.float32)
+        done, total_r = False, 0
+
+        while not done:
+            # epsilon-greedy à chaque prévision d'action pour une exploration plus fine (a = indice d'action, a_vecteur)
+            if random.random() < epsilon:
+                a = random.randrange(n_actions)
+            else:
+                a = torch.argmax(policy_net(s)).item()
+            a_vector = ACTION_SET[a]
+            # env.step(s) returns (next_state, reward, terminated, truncated, info)
+            ns, r, done, _, _ = env.step(a_vector)
+            ns = torch.tensor(ns, dtype=torch.float32)
+
+            memory.append((s, a ,r ,ns, done))
+            s, total_r = ns, total_r + r
+
+            # learning phase
+            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 values for chosen actions
+                q_pred = policy_net(s_b).gather(1, torch.tensor(a_b).unsqueeze(1)).squeeze()
+
+                # Target values using target network
+                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))
+
+                # MSE
+                loss = ((q_pred - q_target)**2).mean()
+                optimizer.zero_grad(); loss.backward(); optimizer.step()
+
+        # decay epsilon to gradually reduce exploration
+        epsilon = max(eps_min, epsilon * eps_decay)
+
+        # Periodically synchronise target network with policy network
+        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()
+
+    # save trained policy network
+    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="humanoid_dqn.pth") -> None:
+    """
+    Load trained Q network and run a single episode to visually
+    demonstrate the learned policy
+    :param weights_path: path to the saved network weights
+    :return:
+    """
+    env = gym.make("Humanoid-v5", 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
+    total_r = 0.0
+    while not done:
+        a = torch.argmax(qnet(s)).item()
+        s_, r, done, _, _ = env.step(ACTION_SET[a])
+        s = torch.tensor(s_, dtype=torch.float32)
+        total_r += r
+    env.close()
+    print(f'Demonstration finished. Reward: {total_r:.2f}')
+
+
+if __name__ == '__main__':
+    trained_model = train_and_save()
+    show()