import tensorflow as tf


        # compute the error (potentially clipped)
        td_error = q_t_selected - tf.stop_gradient(q_t_selected_target)
        errors = tf_util.huber_loss(td_error)
        weighted_error = tf.reduce_mean(importance_weights_ph * errors)

        tf.summary.scalar("td_error", tf.reduce_mean(td_error))
        tf.summary.scalar("loss", weighted_error)
