AI人工智能 Agent：零售业中智能体的应用

    graph LR
    A[Agent] --感知--> B[Environment]
    B --反馈--> A
    A --行为--> B
    
      
      
      
    
    代码解读

    graph TD
    A[Agent] -->|状态s| B(Q网络)
    B -->|Q(s,a)| C{选择行为a}
    C -->|行为a| D[环境]
    D -->|奖励r,新状态s'| E[目标Q值计算]
    E -->|y| F[损失函数]
    F -->|梯度下降| G[更新Q网络]
    G --> B
    
      
      
      
      
      
      
      
    
    代码解读

    import torch
    import torch.nn as nn
    import numpy as np
    
    # 定义Q网络
    class QNetwork(nn.Module):
    def __init__(self, state_dim, action_dim):
        super(QNetwork, self).__init__()
        self.fc1 = nn.Linear(state_dim, 64)
        self.fc2 = nn.Linear(64, action_dim)
    
    def forward(self, x):
        x = torch.relu(self.fc1(x))
        return self.fc2(x)
    
    # 定义DQN Agent
    class DQNAgent:
    def __init__(self, state_dim, action_dim):
        self.q_net = QNetwork(state_dim, action_dim)
        self.target_q_net = QNetwork(state_dim, action_dim)
        self.optimizer = torch.optim.Adam(self.q_net.parameters(), lr=0.001)
        self.loss_fn = nn.MSELoss()
        self.replay_buffer = []
        self.buffer_size = 10000
        self.batch_size = 64
        self.gamma = 0.99
    
    def get_action(self, state, epsilon):
        if np.random.rand() < epsilon:
            return np.random.randint(action_dim)
        else:
            state = torch.tensor(state, dtype=torch.float32)
            q_values = self.q_net(state)
            return torch.argmax(q_values).item()
    
    def update(self):
        if len(self.replay_buffer) < self.batch_size:
            return
    
        transitions = random.sample(self.replay_buffer, self.batch_size)
        state_batch, action_batch, reward_batch, next_state_batch = zip(*transitions)
    
        state_batch = torch.tensor(state_batch, dtype=torch.float32)
        action_batch = torch.tensor(action_batch, dtype=torch.int64)
        reward_batch = torch.tensor(reward_batch, dtype=torch.float32)
        next_state_batch = torch.tensor(next_state_batch, dtype=torch.float32)
    
        q_values = self.q_net(state_batch).gather(1, action_batch.unsqueeze(1)).squeeze()
        next_q_values = self.target_q_net(next_state_batch).max(1)[0]
        expected_q_values = reward_batch + self.gamma * next_q_values
    
        loss = self.loss_fn(q_values, expected_q_values)
        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()
    
        if len(self.replay_buffer) > self.buffer_size:
            self.replay_buffer.pop(0)
    
        # 更新目标网络
        if episode % 10 == 0:
            self.target_q_net.load_state_dict(self.q_net.state_dict())
    
    # 训练循环
    env = GridWorld()
    agent = DQNAgent(state_dim, action_dim)
    for episode in range(num_episodes):
    state = env.reset()
    done = False
    while not done:
        action = agent.get_action(state, epsilon)
        next_state, reward, done = env.step(action)
        agent.replay_buffer.append((state, action, reward, next_state))
        agent.update()
        state = next_state
    epsilon = max(epsilon * 0.995, 0.01)
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读