code: import tensorflow as tf
  from keras.activations import relu
  from keras.initializers import VarianceScaling
  from keras.layers import Dense, Conv2D, Flatten
  from keras.losses import logcosh
  
  
  class DDQN:
      """ Implements a Dueling Dual Deep Q-Network based on the frames of the Retro Environment """
  
      def __init__(self, n_actions, frame_height=63, frame_width=113, stacked_frames=4, learning_rate=0.00001):
          self.n_actions = n_actions
          self.frame_height = frame_height
          self.frame_width = frame_width
          self.stacked_frames = stacked_frames
          self.learning_rate = learning_rate
  
          self.input = tf.placeholder(shape=[None, self.frame_height, self.frame_width, self.stacked_frames],
                                      dtype=tf.float32)
          self.input = self.input / 255
  
          # Convolutional layers
          self.conv1 = self.conv_layer(self.input, 32, [8, 8], 4, 'conv1')
          self.conv2 = self.conv_layer(self.conv1, 64, [4, 4], 2, 'conv2')
          self.conv3 = self.conv_layer(self.conv2, 64, [3, 3], 1, 'conv3')
          self.flat = Flatten()(self.conv3)
          self.dense1 = self.dense_layer(self.flat, 512, 'dense1', relu)
  
          # Splitting into value and advantage streams
          self.v_stream, self.a_stream = tf.split(self.dense1, 2, 1)
          self.value = self.dense_layer(self.v_stream, 1, 'value')
          self.advantage = self.dense_layer(self.a_stream, self.n_actions, 'advantage')
  
          # Getting Q-values from value and advantage streams
          self.q_values = self.value + tf.subtract(self.advantage, tf.reduce_mean(self.advantage, axis=1, keepdims=True))
          self.prediction = tf.argmax(self.q_values, 1)
  
          # targetQ according to Bellman equation
          self.target_q = tf.placeholder(shape=[None], dtype=tf.float32)
          self.action = tf.placeholder(shape=[None], dtype=tf.uint8)
          self.action_one_hot = tf.one_hot(self.action, self.n_actions, dtype=tf.float32)
          self.Q = tf.reduce_sum(tf.multiply(self.q_values, self.action_one_hot), axis=1)
  
          # Parameter updates
          self.error = logcosh(self.target_q, self.Q)
          self.loss = tf.reduce_mean(self.error)
          self.optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate)
          self.update = self.optimizer.minimize(self.loss)
  
      @staticmethod
      def conv_layer(_inputs, _filters, _kernel_size, _strides, _name):
          return Conv2D(filters=_filters, kernel_size=_kernel_size, strides=_strides,
                        kernel_initializer=VarianceScaling(scale=2.0), padding="valid",
                        activation=relu, use_bias=False, name=_name)(_inputs)
  
      @staticmethod
      def dense_layer(_inputs, _units, _name, _activation=None):
          return Dense(activation=_activation, units=_units,
                       kernel_initializer=VarianceScaling(scale=2.0), name=_name)(_inputs)
  
  
  class TargetNetworkUpdater:
      """ Updates the variables and the weights of the target network based on the main network """
  
      def __init__(self, main_vars, target_vars):
          self.main_vars = main_vars
          self.target_vars = target_vars
  
      def update_target_vars(self):
          update_ops = []
          for i, var in enumerate(self.main_vars):
              copy_op = self.target_vars[i].assign(var.value())
              update_ops.append(copy_op)
          return update_ops
  
      def update_networks(self, sess):
          update_ops = self.update_target_vars()
          for copy_op in update_ops:
              sess.run(copy_op)
  
apis: tensorflow.placeholder tensorflow.train.AdamOptimizer tensorflow.reduce_mean tensorflow.multiply tensorflow.one_hot tensorflow.argmax tensorflow.split