Source code for ray.rllib.evaluation.metrics

import logging
import numpy as np
import collections

import ray
from ray.rllib.evaluation.rollout_metrics import RolloutMetrics
from ray.rllib.policy.sample_batch import DEFAULT_POLICY_ID
from ray.rllib.offline.off_policy_estimator import OffPolicyEstimate
from ray.rllib.policy.policy import LEARNER_STATS_KEY
from ray.rllib.utils.annotations import DeveloperAPI

logger = logging.getLogger(__name__)


@DeveloperAPI
def get_learner_stats(grad_info):
    """Return optimization stats reported from the policy.

    Example:
        >>> grad_info = worker.learn_on_batch(samples)
        {"td_error": [...], "learner_stats": {"vf_loss": ..., ...}}
        >>> print(get_stats(grad_info))
        {"vf_loss": ..., "policy_loss": ...}
    """

    if LEARNER_STATS_KEY in grad_info:
        return grad_info[LEARNER_STATS_KEY]

    multiagent_stats = {}
    for k, v in grad_info.items():
        if type(v) is dict:
            if LEARNER_STATS_KEY in v:
                multiagent_stats[k] = v[LEARNER_STATS_KEY]

    return multiagent_stats


[docs]@DeveloperAPI def collect_metrics(local_worker=None, remote_workers=[], to_be_collected=[], timeout_seconds=180): """Gathers episode metrics from RolloutWorker instances.""" episodes, to_be_collected = collect_episodes( local_worker, remote_workers, to_be_collected, timeout_seconds=timeout_seconds) metrics = summarize_episodes(episodes, episodes) return metrics
@DeveloperAPI def collect_episodes(local_worker=None, remote_workers=[], to_be_collected=[], timeout_seconds=180): """Gathers new episodes metrics tuples from the given evaluators.""" if remote_workers: pending = [ a.apply.remote(lambda ev: ev.get_metrics()) for a in remote_workers ] + to_be_collected collected, to_be_collected = ray.wait( pending, num_returns=len(pending), timeout=timeout_seconds * 1.0) if pending and len(collected) == 0: logger.warning( "WARNING: collected no metrics in {} seconds".format( timeout_seconds)) metric_lists = ray.get(collected) else: metric_lists = [] if local_worker: metric_lists.append(local_worker.get_metrics()) episodes = [] for metrics in metric_lists: episodes.extend(metrics) return episodes, to_be_collected @DeveloperAPI def summarize_episodes(episodes, new_episodes=None): """Summarizes a set of episode metrics tuples. Arguments: episodes: smoothed set of episodes including historical ones new_episodes: just the new episodes in this iteration. This must be a subset of `episodes`. If None, assumes all episodes are new. """ if new_episodes is None: new_episodes = episodes episodes, estimates = _partition(episodes) new_episodes, _ = _partition(new_episodes) episode_rewards = [] episode_lengths = [] policy_rewards = collections.defaultdict(list) custom_metrics = collections.defaultdict(list) perf_stats = collections.defaultdict(list) hist_stats = collections.defaultdict(list) for episode in episodes: episode_lengths.append(episode.episode_length) episode_rewards.append(episode.episode_reward) for k, v in episode.custom_metrics.items(): custom_metrics[k].append(v) for k, v in episode.perf_stats.items(): perf_stats[k].append(v) for (_, policy_id), reward in episode.agent_rewards.items(): if policy_id != DEFAULT_POLICY_ID: policy_rewards[policy_id].append(reward) for k, v in episode.hist_data.items(): hist_stats[k] += v if episode_rewards: min_reward = min(episode_rewards) max_reward = max(episode_rewards) avg_reward = np.mean(episode_rewards) else: min_reward = float("nan") max_reward = float("nan") avg_reward = float("nan") if episode_lengths: avg_length = np.mean(episode_lengths) else: avg_length = float("nan") # Show as histogram distributions. hist_stats["episode_reward"] = episode_rewards hist_stats["episode_lengths"] = episode_lengths policy_reward_min = {} policy_reward_mean = {} policy_reward_max = {} for policy_id, rewards in policy_rewards.copy().items(): policy_reward_min[policy_id] = np.min(rewards) policy_reward_mean[policy_id] = np.mean(rewards) policy_reward_max[policy_id] = np.max(rewards) # Show as histogram distributions. hist_stats["policy_{}_reward".format(policy_id)] = rewards for k, v_list in custom_metrics.copy().items(): filt = [v for v in v_list if not np.isnan(v)] custom_metrics[k + "_mean"] = np.mean(filt) if filt: custom_metrics[k + "_min"] = np.min(filt) custom_metrics[k + "_max"] = np.max(filt) else: custom_metrics[k + "_min"] = float("nan") custom_metrics[k + "_max"] = float("nan") del custom_metrics[k] for k, v_list in perf_stats.copy().items(): perf_stats[k] = np.mean(v_list) estimators = collections.defaultdict(lambda: collections.defaultdict(list)) for e in estimates: acc = estimators[e.estimator_name] for k, v in e.metrics.items(): acc[k].append(v) for name, metrics in estimators.items(): for k, v_list in metrics.items(): metrics[k] = np.mean(v_list) estimators[name] = dict(metrics) return dict( episode_reward_max=max_reward, episode_reward_min=min_reward, episode_reward_mean=avg_reward, episode_len_mean=avg_length, episodes_this_iter=len(new_episodes), policy_reward_min=policy_reward_min, policy_reward_max=policy_reward_max, policy_reward_mean=policy_reward_mean, custom_metrics=dict(custom_metrics), hist_stats=dict(hist_stats), sampler_perf=dict(perf_stats), off_policy_estimator=dict(estimators)) def _partition(episodes): """Divides metrics data into true rollouts vs off-policy estimates.""" rollouts, estimates = [], [] for e in episodes: if isinstance(e, RolloutMetrics): rollouts.append(e) elif isinstance(e, OffPolicyEstimate): estimates.append(e) else: raise ValueError("Unknown metric type: {}".format(e)) return rollouts, estimates