jat/utils.py [268:279]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        scores_dict = {"a": np.array(list(norm_scores.values())).T}

        def aggregate_func(x):
            return np.array([metrics.aggregate_iqm(x)])

        aggregate_scores, aggregate_score_cis = rly.get_interval_estimates(scores_dict, aggregate_func)
        iqm, low, high = aggregate_scores["a"][0], aggregate_score_cis["a"][0][0], aggregate_score_cis["a"][1][0]

        eval_results.append(
            EvalResult(
                task_type="reinforcement-learning",
                task_name="Reinforcement Learning",
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



jat/utils.py [296:307]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    scores_dict = {"a": np.array(list(norm_scores.values())).T}

    def aggregate_func(x):
        return np.array([metrics.aggregate_iqm(x)])

    aggregate_scores, aggregate_score_cis = rly.get_interval_estimates(scores_dict, aggregate_func)
    iqm, low, high = aggregate_scores["a"][0], aggregate_score_cis["a"][0][0], aggregate_score_cis["a"][1][0]

    eval_results.append(
        EvalResult(
            task_type="reinforcement-learning",
            task_name="Reinforcement Learning",
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -