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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Computes the RL Reliability Metrics."""
import datasets
import numpy as np
from rl_reliability_metrics.evaluation import eval_metrics
from rl_reliability_metrics.metrics import metrics_offline, metrics_online
import evaluate
logger = evaluate.logging.get_logger(__name__)
DEFAULT_EVAL_POINTS = [
50000,
150000,
250000,
350000,
450000,
550000,
650000,
750000,
850000,
950000,
1050000,
1150000,
1250000,
1350000,
1450000,
1550000,
1650000,
1750000,
1850000,
1950000,
]
N_RUNS_RECOMMENDED = 10
_CITATION = """\
@conference{rl_reliability_metrics,
title = {Measuring the Reliability of Reinforcement Learning Algorithms},
author = {Stephanie CY Chan, Sam Fishman, John Canny, Anoop Korattikara, and Sergio Guadarrama},
booktitle = {International Conference on Learning Representations, Addis Ababa, Ethiopia},
year = 2020,
}
"""
_DESCRIPTION = """\
Computes the RL reliability metrics from a set of experiments. There is an `"online"` and `"offline"` configuration for evaluation.
"""
_KWARGS_DESCRIPTION = """
Computes the RL reliability metrics from a set of experiments. There is an `"online"` and `"offline"` configuration for evaluation.
Args:
timestamps: list of timestep lists/arrays that serve as index.
rewards: list of reward lists/arrays of each experiment.
Returns:
dictionary: a set of reliability metrics
Examples:
>>> import numpy as np
>>> rl_reliability = evaluate.load("rl_reliability", "online")
>>> results = rl_reliability.compute(
... timesteps=[np.linspace(0, 2000000, 1000)],
... rewards=[np.linspace(0, 100, 1000)]
... )
>>> print(results["LowerCVaROnRaw"].round(4))
[0.0258]
"""
@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class RLReliability(evaluate.Metric):
"""Computes the RL Reliability Metrics."""
def _info(self):
if self.config_name not in ["online", "offline"]:
raise KeyError("""You should supply a configuration name selected in '["online", "offline"]'""")
return evaluate.MetricInfo(
module_type="metric",
description=_DESCRIPTION,
citation=_CITATION,
inputs_description=_KWARGS_DESCRIPTION,
features=datasets.Features(
{
"timesteps": datasets.Sequence(datasets.Value("int64")),
"rewards": datasets.Sequence(datasets.Value("float")),
}
),
homepage="https://github.com/google-research/rl-reliability-metrics",
)
def _compute(
self,
timesteps,
rewards,
baseline="default",
freq_thresh=0.01,
window_size=100000,
window_size_trimmed=99000,
alpha=0.05,
eval_points=None,
):
if len(timesteps) < N_RUNS_RECOMMENDED:
logger.warning(
f"For robust statistics it is recommended to use at least {N_RUNS_RECOMMENDED} runs whereas you provided {len(timesteps)}."
)
curves = []
for timestep, reward in zip(timesteps, rewards):
curves.append(np.stack([timestep, reward]))
if self.config_name == "online":
if baseline == "default":
baseline = "curve_range"
if eval_points is None:
eval_points = DEFAULT_EVAL_POINTS
metrics = [
metrics_online.HighFreqEnergyWithinRuns(thresh=freq_thresh),
metrics_online.IqrWithinRuns(
window_size=window_size_trimmed, eval_points=eval_points, baseline=baseline
),
metrics_online.IqrAcrossRuns(
lowpass_thresh=freq_thresh, eval_points=eval_points, window_size=window_size, baseline=baseline
),
metrics_online.LowerCVaROnDiffs(baseline=baseline),
metrics_online.LowerCVaROnDrawdown(baseline=baseline),
metrics_online.LowerCVaROnAcross(
lowpass_thresh=freq_thresh, eval_points=eval_points, window_size=window_size, baseline=baseline
),
metrics_online.LowerCVaROnRaw(alpha=alpha, baseline=baseline),
metrics_online.MadAcrossRuns(
lowpass_thresh=freq_thresh, eval_points=eval_points, window_size=window_size, baseline=baseline
),
metrics_online.MadWithinRuns(
eval_points=eval_points, window_size=window_size_trimmed, baseline=baseline
),
metrics_online.MaxDrawdown(),
metrics_online.StddevAcrossRuns(
lowpass_thresh=freq_thresh, eval_points=eval_points, window_size=window_size, baseline=baseline
),
metrics_online.StddevWithinRuns(
eval_points=eval_points, window_size=window_size_trimmed, baseline=baseline
),
metrics_online.UpperCVaROnAcross(
alpha=alpha,
lowpass_thresh=freq_thresh,
eval_points=eval_points,
window_size=window_size,
baseline=baseline,
),
metrics_online.UpperCVaROnDiffs(alpha=alpha, baseline=baseline),
metrics_online.UpperCVaROnDrawdown(alpha=alpha, baseline=baseline),
metrics_online.UpperCVaROnRaw(alpha=alpha, baseline=baseline),
metrics_online.MedianPerfDuringTraining(window_size=window_size, eval_points=eval_points),
]
else:
if baseline == "default":
baseline = "median_perf"
metrics = [
metrics_offline.MadAcrossRollouts(baseline=baseline),
metrics_offline.IqrAcrossRollouts(baseline=baseline),
metrics_offline.StddevAcrossRollouts(baseline=baseline),
metrics_offline.LowerCVaRAcrossRollouts(alpha=alpha, baseline=baseline),
metrics_offline.UpperCVaRAcrossRollouts(alpha=alpha, baseline=baseline),
metrics_offline.MedianPerfAcrossRollouts(baseline=None),
]
evaluator = eval_metrics.Evaluator(metrics=metrics)
result = evaluator.compute_metrics(curves)
return result
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