import tensorflow as tf

        q_tp1_best_masked = (1.0 - done_mask_ph) * q_tp1_best

        # compute RHS of bellman equation
        q_t_selected_target = rew_t_ph + gamma * q_tp1_best_masked

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

        # compute optimization op (potentially with gradient clipping)
        if grad_norm_clipping is not None:
