original_docstring: 
          Build model by adding all necessary variables
          
docstring: Build model by adding all necessary variables
docstring_tokens: Build model by adding all necessary variables
code: def build(self):
          """
          Build model by adding all necessary variables
          """
          # add placeholders
          self.add_placeholders_op()
  
          # compute Q values of state
          s = self.process_state(self.s)
          self.q = self.get_q_values_op(s, scope="q", reuse=False)
  
          # compute Q values of next state
          sp = self.process_state(self.sp)
          self.target_q = self.get_q_values_op(sp, scope="target_q", reuse=False)
  
          # add update operator for target network
          self.add_update_target_op("q", "target_q")
  
          # add square loss
          self.add_loss_op(self.q, self.target_q)
  
          # add optmizer for the main networks
          self.add_optimizer_op("q")
code_tokens: def build ( self ) : self . add_placeholders_op ( ) s = self . process_state ( self . s ) self . q = self . get_q_values_op ( s , scope = "q" , reuse = False ) sp = self . process_state ( self . sp ) self . target_q = self . get_q_values_op ( sp , scope = "target_q" , reuse = False ) self . add_update_target_op ( "q" , "target_q" ) self . add_loss_op ( self . q , self . target_q ) self . add_optimizer_op ( "q" )
short_docstring: Build model by adding all necessary variables
short_docstring_tokens: Build model by adding all necessary variables
comment: """
          Build model by adding all necessary variables
          """$SEP$# add placeholders$SEP$# compute Q values of state$SEP$# compute Q values of next state$SEP$# add update operator for target network$SEP$# add square loss$SEP$# add optmizer for the main networks