parameters: (self, observ, reward, length)
docstring: Compute the loss function for the value baseline.
  
      The value loss is the difference between empirical and approximated returns
      over the collected episodes. Returns the loss tensor and a summary strin.
  
      Args:
        observ: Sequences of observations.
        reward: Sequences of reward.
        length: Batch of sequence lengths.
  
      Returns:
        Tuple of loss tensor and summary tensor.
docstring_summary: Compute the loss function for the value baseline.
docstring_tokens: Compute the loss function for the value baseline .
function: def _value_loss(self, observ, reward, length):
      """Compute the loss function for the value baseline.
  
      The value loss is the difference between empirical and approximated returns
      over the collected episodes. Returns the loss tensor and a summary strin.
  
      Args:
        observ: Sequences of observations.
        reward: Sequences of reward.
        length: Batch of sequence lengths.
  
      Returns:
        Tuple of loss tensor and summary tensor.
      """
      with tf.name_scope('value_loss'):
        value = self._network(observ, length).value
        return_ = utility.discounted_return(reward, length, self._config.discount)
        advantage = return_ - value
        value_loss = 0.5 * self._mask(advantage**2, length)
        summary = tf.summary.merge([
            tf.summary.histogram('value_loss', value_loss),
            tf.summary.scalar('avg_value_loss', tf.reduce_mean(value_loss))
        ])
        value_loss = tf.reduce_mean(value_loss)
        return tf.check_numerics(value_loss, 'value_loss'), summary
function_tokens: def _value_loss ( self , observ , reward , length ) : with tf . name_scope ( 'value_loss' ) : value = self . _network ( observ , length ) . value return_ = utility . discounted_return ( reward , length , self . _config . discount ) advantage = return_ - value value_loss = 0.5 * self . _mask ( advantage ** 2 , length ) summary = tf . summary . merge ( [ tf . summary . histogram ( 'value_loss' , value_loss ) , tf . summary . scalar ( 'avg_value_loss' , tf . reduce_mean ( value_loss ) ) ] ) value_loss = tf . reduce_mean ( value_loss ) return tf . check_numerics ( value_loss , 'value_loss' ) , summary