niSrSSKrSSKrSSKJrJrJrJrJr SSK r \ R"SS\R55r "SS\ 5r"SS \5rg) aThe metric reporting system used in our env. In the gym environment, there are many variables or quantities that can help researchers to debug, evaluate policy performance. Such quantities may include: Motor torques for quadrupeds, even when they are controlled in POSITION mode; Distance to walls while a wheeled robot is navigating the indoor environment. Often, these metrics are private variables (or can only be computed from private variables). To expose the user interested metrics outside the environment's observations, we designed this Metric system that can be invoked in any modules (robot, tasks, sensors) inside the gym env. N)AnyCallableDictSequenceTextc(\rSrSrSrSrSrSrSrSr g) MetricScopezThe supported scope of metrics.))))N) __name__ __module__ __qualname____firstlineno____doc__ PERFORMANCEDEBUGSAFETY STATISTIC__static_attributes__rj/home/james-whalen/.local/lib/python3.13/site-packages/pybullet_envs/minitaur/envs_v2/evaluation/metric.pyr r s'+ % &)rr c n\rSrSrSrS\S\S\\\ /\ 4S\\\ /\ \\ 444Sjr S\ 4S jr S r g ) MetricCore#z4Aggregates values of interest to compute statistics.namescopesingle_ep_aggregatormulti_ep_aggregatorcXURS-U-UlX0lX@l/Ulg)aInitializes the class. Args: name: The name of the metric, for example "motor_torques", "distance_to_wall", etc. The full name of the metric will have scope name in the prefix, i.e. "scope/name". scope: The scope of this metric. Most metric should be for DEBUG purpose. The scope name will be added to the final name of metric in this way: "scope/name", which is standarded format for Tensorboard to group named variables. single_ep_aggregator: The function to process all aggregated metric values. The derived MetricReporter (see below) will implements reset_episode() which clears the episode data, and will be called during env.reset(). multi_ep_aggregator: The functions to process multi-episode metric values. We assume the inputs to the functions is a list of per episode metric values, i.e. each element of the list is the output from the single_ep_aggregator. /N)r_name_single_ep_aggregator_multi_ep_aggregator _episode_data)selfrr r!r"s r__init__MetricCore.__init__&s-4c!D(DJ!5 3Dr metric_valuec:URRU5 g)zrStores the reported metric in the internal buffer. Args: metric_value: The metric we are interested to report. N)r(append)r)r,s rreportMetricCore.reportEs  l+r)r(r'r%r&N)rrrrrrr rrrrr*r/rrrrrr#sd< %hsm_c%9:  $Xc]OT$)_$DE >,,rrch\rSrSrSrS\\\\44Sjr S\\\\4S\\\44Sjr Sr Sr g ) MetricReporterNz;Reports the metric using the provided aggregator functions.returncvUR(a'URURUR50$0$)zeProcesses and returns episode metric values. Returns: Aggregated metrics for the current episode. )r(r%r&r)s rget_episode_metric!MetricReporter.get_episode_metricQs2  jj$44T5G5GH II irepisodic_metriccURU;a#[R"SUR5 0$0nURXR5R 5Hup4XBURS-U-'M U$)aProcesses the aggregated metrics over many episodes. Will not be affected by reset_episode, since we take multi-episode data as inputs. Args: episodic_metric: The per episode metrics. We expect the inputs to contain the same key as self._name, and that the value is a list of metric values computed using self.get_episode_metrc(). Returns: The processed multi-episode metrics. z>The inputs does not contain the key for the current metric: %s_)r%loggingwarningr'items)r)r9outputskeyvals rget_multi_ep_metric"MetricReporter.get_multi_ep_metric\s| zz( oo J **iG-- #%%*UW-(+djj3$%- Nrc/Ulg)z}Clears the episode data stored. Will be invoked during env.reset(). This will effect how get_episode_metric gets computed. Nr(r6s r reset_episodeMetricReporter.reset_episodevs DrrEN) rrrrrrrrrr7rBrFrrrrr2r2NsOC $tXc]':"; !$ "56;?c ?4rr2)renumr<typingrrrrrginconstants_from_enumEnumr objectrr2rrrrNsY  66  $))  (,(,V/Z/r