(self, agent_mode, stack_size_idx, iter_nr, score, upper_conf95=None, lower_conf95=None)
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)
Log evaluation results by sending these results to the Chief, who will later send them to the Crayon log server.
Log evaluation results by sending these results to the Chief who will later send them to the Crayon log server .
def _log_results(self, agent_mode, stack_size_idx, iter_nr, score, upper_conf95=None, lower_conf95=None):
        """
        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)
        """
        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)
def _log_results ( self , agent_mode , stack_size_idx , iter_nr , score , upper_conf95 = None , lower_conf95 = None ) : 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 )