def _log_results(self, agent_mode, stack_size_idx, iter_nr, score, upper_conf95=None, lower_conf95=None):
    '\n        Log evaluation results by sending these results to the Chief, who will later send them to the Crayon log server.\n\n        Args:\n            agent_mode:             Evaluation mode of the agent whose performance is logged\n            stack_size_idx:         If evaluating multiple starting stack sizes, this is an index describing which one\n                                    this data is from.\n            iter_nr:                Algorithm Iteration of this data\n            score:                  Score in this evaluation (e.g. exploitability)\n        '
    graph_name = ('Evaluation/' + self._eval_env_bldr.env_cls.WIN_METRIC)
    self._ray.remote(self._chief_handle.add_scalar, self._exp_name_total[agent_mode][stack_size_idx], graph_name, iter_nr, score)
    if self._log_conf_interval:
        assert (upper_conf95 is not None)
        assert (lower_conf95 is not None)
        self._ray.remote(self._chief_handle.add_scalar, self._exp_names_conf[agent_mode][stack_size_idx][0], graph_name, iter_nr, lower_conf95)
        self._ray.remote(self._chief_handle.add_scalar, self._exp_names_conf[agent_mode][stack_size_idx][1], graph_name, iter_nr, upper_conf95)
Log evaluation results by sending these results to the Chief, who will later send them to the Crayon log server.

Args:
    agent_mode:             Evaluation mode of the agent whose performance is logged
    stack_size_idx:         If evaluating multiple starting stack sizes, this is an index describing which one
                            this data is from.
    iter_nr:                Algorithm Iteration of this data
    score:                  Score in this evaluation (e.g. exploitability)
def _log_results(self, agent_mode, stack_size_idx, iter_nr, score, upper_conf95=None, lower_conf95=None):
    '\n        Log evaluation results by sending these results to the Chief, who will later send them to the Crayon log server.\n\n        Args:\n            agent_mode:             Evaluation mode of the agent whose performance is logged\n            stack_size_idx:         If evaluating multiple starting stack sizes, this is an index describing which one\n                                    this data is from.\n            iter_nr:                Algorithm Iteration of this data\n            score:                  Score in this evaluation (e.g. exploitability)\n        '
    graph_name = ('Evaluation/' + self._eval_env_bldr.env_cls.WIN_METRIC)
    self._ray.remote(self._chief_handle.add_scalar, self._exp_name_total[agent_mode][stack_size_idx], graph_name, iter_nr, score)
    if self._log_conf_interval:
        assert (upper_conf95 is not None)
        assert (lower_conf95 is not None)
        self._ray.remote(self._chief_handle.add_scalar, self._exp_names_conf[agent_mode][stack_size_idx][0], graph_name, iter_nr, lower_conf95)
        self._ray.remote(self._chief_handle.add_scalar, self._exp_names_conf[agent_mode][stack_size_idx][1], graph_name, iter_nr, upper_conf95)