AI人工智能深度学习算法：智能代理在智慧城市中的实践

    import tensorflow as tf
    
    # 构建模型
    model = tf.keras.Sequential([
    tf.keras.layers.Dense(128, activation='relu', input_shape=(input_size,)),
    tf.keras.layers.Dense(64, activation='relu'),
    tf.keras.layers.Dense(output_size, activation='softmax')
    ])
    
    # 编译模型
    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
    
    # 训练模型
    model.fit(x_train, y_train, epochs=10, batch_size=32)

    import torch
    import torch.nn as nn
    
    # 定义模型
    class ConvNet(nn.Module):
    def __init__(self):
        super(ConvNet, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=5)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=5)
        self.fc1 = nn.Linear(1024, 128)
        self.fc2 = nn.Linear(128, 10)
    
    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2(x), 2))
        x = x.view(-1, 1024)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)
    
    # 实例化并训练模型
    model = ConvNet()
    optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
    loss_fn = nn.CrossEntropyLoss()
    
    for epoch in range(10):
    optimizer.zero_grad()
    output = model(x_train)
    loss = loss_fn(output, y_train)
    loss.backward()
    optimizer.step()

    from keras.models import Sequential
    from keras.layers import LSTM, Dense
    
    # 构建模型
    model = Sequential()
    model.add(LSTM(50, return_sequences=True, input_shape=(time_steps, input_dim)))
    model.add(LSTM(50))
    model.add(Dense(output_dim, activation='softmax'))
    
    # 编译模型
    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
    
    # 训练模型
    model.fit(x_train, y_train, epochs=10, batch_size=32)

    import gym
    import numpy as np
    
    env = gym.make('CartPole-v1')
    Q_table = np.zeros([env.observation_space.n, env.action_space.n])
    
    # Q学习参数
    alpha = 0.1
    gamma = 0.99
    epsilon = 0.1
    
    for i_episode in range(1000):
    state = env.reset()
    for t in range(100):
        if np.random.uniform(0, 1) < epsilon:
            action = env.action_space.sample()  # 探索
        else:
            action = np.argmax(Q_table[state])  # 利用
    
        next_state, reward, done, info = env.step(action)
        old_value = Q_table[state, action]
        next_max = np.max(Q_table[next_state])
    
        # 更新Q值
        new_value = (1 - alpha) * old_value + alpha * (reward + gamma * next_max)
        Q_table[state, action] = new_value
    
        if done:
            break
        state = next_state