123 lines
3.8 KiB
Python
123 lines
3.8 KiB
Python
import random
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from collections import deque
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import gymnasium as gym
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import torch
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import torch.nn as nn
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import torch.optim as optim
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import matplotlib.pyplot as plt
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class DQN(nn.Module):
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def __init__(self, n_states=4, n_actions=2):
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"""
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Notre modèle à deux états, et peux faire deux actions (trouver à gauche
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ou à droite)
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:param n_state:
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:param n_action:
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"""
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super().__init__()
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self.net = nn.Sequential(
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nn.Linear(n_states, 64), nn.ReLU(),
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nn.Linear(64, n_actions)
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)
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def forward(self, x):
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"""
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:param x:
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:return:
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"""
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return self.net(x)
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def epsilon_greedy(epsilon: float, s, policy_net: DQN, n_actions: int) -> int:
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if random.random() < epsilon:
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return random.randrange(n_actions)
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else:
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return torch.argmax(policy_net(s)).item()
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def train_and_save(weights_path="cartpole_dqn.pth", episodes=2_000, update_target_every=20) -> DQN:
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env = gym.make('CartPole-v1')
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n_states, n_actions = env.observation_space.shape[0], env.action_space.n
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policy_net = DQN(n_states, n_actions) # Q Network
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target_net = DQN(n_states, n_actions) # Target network
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target_net.load_state_dict(policy_net.state_dict()) # same weights at start
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target_net.eval()
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optimizer = optim.Adam(policy_net.parameters(), lr=1e-2) #1e-3
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gamma = 0.99 # discount factor
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epsilon = 1.0 # Fréquence d'exploration initiale
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eps_min = 0.05 # Fréquence d'exploration minimale
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eps_decay = 0.995 # Facteur de réduction d'epsilon
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memory = deque(maxlen=5_000)
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batch_size = 64
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for ep in range(episodes):
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print(f'Episode: {ep}/{episodes}')
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s, _ = env.reset()
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s = torch.tensor(s, dtype=torch.float32)
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done, total_r = False, 0
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while not done:
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a = epsilon_greedy(epsilon, s, policy_net, n_actions)
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ns, r, done, _, _ = env.step(a)
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ns = torch.tensor(ns, dtype=torch.float32)
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memory.append((s, a, r, ns, done))
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s, total_r = ns, total_r + r
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if len(memory) >= batch_size:
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batch = random.sample(memory, batch_size)
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s_b, a_b, r_b, ns_b, d_b = zip(*batch)
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s_b = torch.stack(s_b)
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ns_b = torch.stack(ns_b)
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q_pred = policy_net(s_b).gather(1, torch.tensor(a_b).unsqueeze(1)).squeeze()
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with torch.no_grad():
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q_next = target_net(ns_b).max(1)[0]
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q_target = torch.tensor(r_b, dtype=torch.float32) + \
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gamma * q_next * (1 - torch.tensor(d_b, dtype=torch.float32))
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loss = ((q_pred - q_target)**2).mean()
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optimizer.zero_grad(); loss.backward(); optimizer.step()
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epsilon = max(eps_min, epsilon * eps_decay)
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if (ep + 1) % update_target_every == 0:
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target_net.load_state_dict(policy_net.state_dict())
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if (ep + 1) % 20 == 0:
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print(f'Episode {ep + 1}: total reward {total_r:.1f}, epsilon {epsilon:.2f}')
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env.close()
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torch.save(policy_net.state_dict(), weights_path)
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print(f'Training finished. Weights saved to {weights_path}')
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return policy_net # <--- trained Q-network
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def show(weights_path='cartpole_dqn.pth') -> None:
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env = gym.make('CartPole-v1', render_mode='human')
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qnet = DQN()
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qnet.load_state_dict(torch.load(weights_path))
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qnet.eval()
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s, _ = env.reset()
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s = torch.tensor(s, dtype=torch.float32)
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done = False
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while not done:
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a = torch.argmax(qnet(s)).item()
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s_, r, done, _, _ = env.step(a)
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s = torch.tensor(s_, dtype=torch.float32)
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env.close()
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print('Demonstration finished.')
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if __name__ == '__main__':
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trained_model = train_and_save()
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show()
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