original_docstring: 
          Assumes the graph has been constructed
          Creates a tf Session and run initializer of variables
          
docstring: Assumes the graph has been constructed
  Creates a tf Session and run initializer of variables
docstring_tokens: Assumes the graph has been constructed Creates a tf Session and run initializer of variables
code: def initialize(self):
          """
          Assumes the graph has been constructed
          Creates a tf Session and run initializer of variables
          """
          # create tf session
          self.sess = tf.Session()
  
          # tensorboard stuff
          self.add_summary()
  
          # initiliaze all variables
          init = tf.global_variables_initializer()
          self.sess.run(init)
  
          # synchronise q and target_q networks
          self.sess.run(self.update_target_op)
  
          # for saving networks weights
          self.saver = tf.train.Saver()
code_tokens: def initialize ( self ) : self . sess = tf . Session ( ) self . add_summary ( ) init = tf . global_variables_initializer ( ) self . sess . run ( init ) self . sess . run ( self . update_target_op ) self . saver = tf . train . Saver ( )
short_docstring: Assumes the graph has been constructed
  Creates a tf Session and run initializer of variables
short_docstring_tokens: Assumes the graph has been constructed Creates a tf Session and run initializer of variables
comment: """
          Assumes the graph has been constructed
          Creates a tf Session and run initializer of variables
          """$SEP$# create tf session$SEP$# tensorboard stuff$SEP$# initiliaze all variables$SEP$# synchronise q and target_q networks$SEP$# for saving networks weights