import tensorflow as tf

                        self.rewards_ph_n +
                        (1 - self.terminals_ph_n) *( self.gamma**self.n_step_length ) * self.value_target_n)
                        qf1_loss_n = 0.5 * tf.reduce_mean(((q_backup_n - qf1) ** 2)*self.weight_ph)
                        qf1_loss_n_col = tf.reduce_mean(((q_backup_n - qf1) ** 2),1)
                        qf2_loss_n = 0.5 * tf.reduce_mean(((q_backup_n - qf2) ** 2)*self.weight_ph)
                    if self.n_step:
                        value_for_priority = qf1_loss_col + qf1_loss_n_col
                    else:
