带池化层和全连接层的卷积神经层后拼接dueling DQN的价值函数、优势函数网络的代码

以下是带池化层和全连接层的卷积神经层后拼接dueling DQN的价值函数、优势函数网络的代码：

import tensorflow as tf

class DuelingDQNAgent:
    def __init__(self, state_size, action_size, learning_rate, name='DuelingDQNAgent'):
        self.state_size = state_size
        self.action_size = action_size
        self.learning_rate = learning_rate
        
        with tf.variable_scope(name):
            self.inputs = tf.placeholder(tf.float32, [None, *state_size], name='inputs')
            self.actions = tf.placeholder(tf.float32, [None, action_size], name='actions')
            self.target_Q = tf.placeholder(tf.float32, [None], name='target')
            
            # Convolutional layers
            self.conv1 = tf.layers.conv2d(inputs=self.inputs, filters=32, kernel_size=[8,8], strides=[4,4], padding='VALID', kernel_initializer=tf.contrib.layers.xavier_initializer_conv2d(), name='conv1')
            self.conv1_out = tf.nn.relu(self.conv1, name='conv1_out')
            self.conv2 = tf.layers.conv2d(inputs=self.conv1_out, filters=64, kernel_size=[4,4], strides=[2,2], padding='VALID', kernel_initializer=tf.contrib.layers.xavier_initializer_conv2d(), name='conv2')
            self.conv2_out = tf.nn.relu(self.conv2, name='conv2_out')
            self.conv3 = tf.layers.conv2d(inputs=self.conv2_out, filters=64, kernel_size=[3,3], strides=[1,1], padding='VALID', kernel_initializer=tf.contrib.layers.xavier_initializer_conv2d(), name='conv3')
            self.conv3_out = tf.nn.relu(self.conv3, name='conv3_out')
            
            # Dueling DQN
            self.flatten = tf.layers.flatten(self.conv3_out)
            
            # Value function
            self.value_fc = tf.layers.dense(inputs=self.flatten, units=512, activation=tf.nn.relu, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='value_fc')
            self.value = tf.layers.dense(inputs=self.value_fc, units=1, activation=None, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='value')
            
            # Advantage function
            self.advantage_fc = tf.layers.dense(inputs=self.flatten, units=512, activation=tf.nn.relu, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='advantage_fc')
            self.advantage = tf.layers.dense(inputs=self.advantage_fc, units=self.action_size, activation=None, kernel_initializer=tf.contrib.layers.xavier_initializer(), name='advantages')
            
            # Combine value and advantage functions to get final Q-values
            self.output = self.value + tf.subtract(self.advantage, tf.reduce_mean(self.advantage, axis=1, keepdims=True))
            
            # Q-value of chosen action
            self.Q = tf.reduce_sum(tf.multiply(self.output, self.actions), axis=1)
            
            # Loss function
            self.loss = tf.reduce_mean(tf.square(self.target_Q - self.Q))
            
            # Optimizer
            self.optimizer = tf.train.AdamOptimizer(self.learning_rate).minimize(self.loss)

在这个代码中，我们首先定义了一个DuelingDQNAgent类，该类包含了一个带池化层和全连接层的卷积神经网络，以及一个Dueling DQN，它将价值函数和优势函数拼接起来，得到最终的Q值。

在卷积神经网络部分，我们使用了三个卷积层，每个卷积层后面跟着一个ReLU激活函数。在最后一个卷积层之后，我们将其展平，并通过两个全连接层，分别计算价值函数和优势函数。

在Dueling DQN部分，我们将价值函数和优势函数拼接起来，得到最终的Q值。我们首先计算出平均优势函数，然后将其从优势函数中减去，以确保优势函数的平均值为0。

最后，我们使用平方损失函数来计算损失，并使用Adam优化器来最小化损失。