d3qn算法

下面是使用

训练UNet进行建筑物变化检测的

。请注意，这只是一个示例，你需要根据你的数据集和模型进行适当的修改。

首先，我们需要导入必要的库：

 import torch import torch.nn as nn import torch.optim as optim import numpy as np import random from collections import deque 

接下来，我们定义

模型。在这个示例中，我们将使用一个简单的全连接神经网络作为

，它将接受UNet的输出并输出一个Q值。

 class DQN (nn.Module): def __init__(self, state_size, action_size): super( DQN , self).__init__() self.fc1 = nn.Linear(state_size, 64) self.fc2 = nn.Linear(64, 64) self.fc3 = nn.Linear(64, action_size)  def forward(self, x): x = nn.functional.relu(self.fc1(x)) x = nn.functional.relu(self.fc2(x)) x = self.fc3(x) return x 

现在我们定义一个ReplayBuffer类，用于存储先前的状态、动作、奖励和下一个状态，以便我们可以使用

从中随机选择一些样本进行训练。

 class ReplayBuffer(): def __init__(self, buffer_size): self.buffer_size = buffer_size self.buffer = deque(maxlen=buffer_size)  def add(self, state, action, reward, next_state, done): experience = (state, action, reward, next_state, done) self.buffer.append(experience)  def sample(self, batch_size): if len(self.buffer) < batch_size: batch = random.sample(self.buffer, len(self.buffer)) else: batch = random.sample(self.buffer, batch_size) states, actions, rewards, next_states, dones = zip(*batch) return states, actions, rewards, next_states, dones 

现在我们定义一个Agent类，它将使用

模型和ReplayBuffer类来实现

。

 class Agent(): def __init__(self, state_size, action_size, buffer_size, batch_size, gamma, epsilon, epsilon_decay): self.state_size = state_size self.action_size = action_size self.buffer_size = buffer_size self.batch_size = batch_size self.gamma = gamma self.epsilon = epsilon self.epsilon_decay = epsilon_decay self.epsilon_min = 0.01 self.tau = 0.1  self.model = DQN (state_size, action_size) self.target_model = DQN (state_size, action_size) self.optimizer = optim.Adam(self.model.parameters(), lr=0.001)  self.memory = ReplayBuffer(buffer_size)  def select_action(self, state): if random.uniform(0, 1) < self.epsilon: return random.randrange(self.action_size) else: state = torch.from_numpy(state).float().unsqueeze(0) q_values = self.model(state) return np.argmax(q_values.detach().numpy())  def train(self): if len(self.memory.buffer) < self.batch_size: return  states, actions, rewards, next_states, dones = self.memory.sample(self.batch_size)  states = torch.from_numpy(np.array(states)).float() actions = torch.from_numpy(np.array(actions)).long() rewards = torch.from_numpy(np.array(rewards)).float() next_states = torch.from_numpy(np.array(next_states)).float() dones = torch.from_numpy(np.array(dones)).float()  q_values = self.model(states).gather(1, actions.unsqueeze(1)).squeeze(1) next_q_values = self.target_model(next_states).max(1)[0].detach() expected_q_values = rewards + self.gamma * next_q_values * (1 - dones)  loss = nn.functional.smooth_l1_loss(q_values, expected_q_values)  self.optimizer.zero_grad() loss.backward() self.optimizer.step()  for param, target_param in zip(self.model.parameters(), self.target_model.parameters()): target_param.data.copy_(self.tau * param.data + (1 - self.tau) * target_param.data)  if self.epsilon > self.epsilon_min: self.epsilon *= self.epsilon_decay 

最后，我们定义一个主函数，它将训练我们的

模型并输出结果。

 def main(): state_size = 64 action_size = 2 buffer_size =  batch_size = 32 gamma = 0.99 epsilon = 1.0 epsilon_decay = 0.995 num_episodes = 1000  agent = Agent(state_size, action_size, buffer_size, batch_size, gamma, epsilon, epsilon_decay)  # 训练循环 for episode in range(num_episodes): # 初始化环境 state = env.reset()  # 记录总奖励 total_reward = 0  # 训练循环 while True: # 选择动作 action = agent.select_action(state)  # 执行动作并观察结果 next_state, reward, done, _ = env.step(action)  # 将经验添加到回放缓冲区 agent.memory.add(state, action, reward, next_state, done)  # 训练 DQN agent.train()  # 更新状态 state = next_state  # 更新总奖励 total_reward += reward  # 如果游戏结束了，退出循环 if done: break  # 输出训练结果 print("Episode: {}/{}, Total Reward: {}, Epsilon: {:.2f}".format(episode+1, num_episodes, total_reward, agent.epsilon))  if __name__ == "__main__": main() 

请注意，上面的

仅用作示例。你需要根据你的数据集和模型进行适当的修改。

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