import tensorflow as tf

            return update_scale_expr

        # Functionality to update the threshold for parameter space noise.
        update_param_noise_threshold_expr = param_noise_threshold.assign(tf.cond(update_param_noise_threshold_ph >= 0,
            lambda: update_param_noise_threshold_ph, lambda: param_noise_threshold))

        # Put everything together.
        deterministic_actions = tf.argmax(q_values_perturbed, axis=1)
        batch_size = tf.shape(observations_ph.get())[0]
        random_actions = tf.random_uniform(tf.stack([batch_size]), minval=0, maxval=num_actions, dtype=tf.int64)
        chose_random = tf.random_uniform(tf.stack([batch_size]), minval=0, maxval=1, dtype=tf.float32) < eps
        stochastic_actions = tf.where(chose_random, random_actions, deterministic_actions)

        output_actions = tf.cond(stochastic_ph, lambda: stochastic_actions, lambda: deterministic_actions)
        update_eps_expr = eps.assign(tf.cond(update_eps_ph >= 0, lambda: update_eps_ph, lambda: eps))
        updates = [
            update_eps_expr,
            tf.cond(reset_ph, lambda: perturb_vars(original_scope="q_func", perturbed_scope="perturbed_q_func"), lambda: tf.group(*[])),
