Public record
Software health reportschema 0.31.0 · metrics 2.10.0 · 2026-08-12 21:58 UTC

DLR-RM / stable-baselines3

PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.

PythonMIT★ 13,681 stars⑂ 2,168 forkssince May 2020View on GitHub ↗

DLR-RM/stable-baselines3 holds a health index of 91 out of 100, placing it in the Excellent band. It scores highest on Community & Adoption (94/100) and lowest on Security (61/100). It was last updated 18 days ago. A single contributor accounts for most of its recent work.

91
overall / 100
Excellent

Software health index

Metrics are grouped into weighted categories on one standardized 1–100 scale. Overall starts as their weighted mean, calibrated against the distribution of the public record so bands carry percentile meaning; when public evidence triggers the High-Risk Jurisdiction Policy, the rating is adjusted and receives an At Risk ceiling of 34.

91
Exceptional93-100The record's top tier (≈ top 5%); essentially all checked criteria met
Excellent80-92Strong across the board; minor gaps
Good65-79Healthy; gaps are limited and manageable
Moderate50-64Acceptable with notable gaps; review recommended
Weak35-49Material weaknesses across several areas
At Risk20-34Significant weaknesses; adoption warrants caution
Critical1-19Severe problems (abandoned, single-maintainer, no hygiene)
VitalityCommunity &AdoptionSustainability &GovernanceEngineeringQualitySecurityAI Readiness

Score profile

Each axis is a category. The shape matters more than the average — a healthy subject fills the whole shape, while a spike-and-crater profile means strength in one dimension is masking risk in another.

The weighted overall 77 is calibrated to 91 on the published index scale (record calibration 2026-08-02).

Ownership

DLR-RMOrganization
929 followers61 public repossince Jan 2017

This repository is backed by an organization — shared, accountable stewardship that can outlive any single maintainer.

Package ecosystems

Metrics by category

Vitality

Is the project alive — is code being written and are releases shipping?

75Good · 21% of overall
How it's scored
28.8/36Push recencylast push 18 days ago
15.2/36Commit cadence22/52 weeks with commits
14.3/18Commit volume38 commits in the last year
6/10OpenSSF Scorecard: Maintained6 commit(s) and 2 issue activity found in the last 90 days -- score normalized to 6
Inputs used
commits_last_year38
human_commit_share1
days_since_last_push18
active_weeks_last_year22
How it's scored
27/27Ships releases32 releases published
36/36Release recencylatest release 58 days ago
19.8/27Release cadencea release every ~89.5 days
0/10OpenSSF Scorecard: Signed-Releasesno data
Inputs used
releases_count32
latest_release_tagv2.9.0
releases_from_tagsno
days_since_latest_release58
mean_days_between_releases89.5
Excluded from scoring (no data or not applicable): OpenSSF Scorecard: Signed-Releases. Remaining weights renormalized.

Community & Adoption

Does the project have users, downloads, attention, and a welcoming setup for contributors?

94Exceptional · 17% of overall
How it's scored
60/60Stars13,681 stars
25/25Forks2,168 forks
10.1/15Watchers66 watchers
Inputs used
forks2,168
stars13,681
watchers66
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

92Excellent
How it's scored
22.5/22.5README
22.5/22.5Licenserecognized license (MIT)
18/18CONTRIBUTING guide
13.5/13.5Code of conduct
0/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges3
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_servicesgithub.com, readthedocs.org, shields.io
has_pull_request_templateyes

Sustainability & Governance

Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?

75Good · 23% of overall
How it's scored
9/54Bus factor1 contributor(s) cover half of all commits
7.5/22.5Commit distributiontop contributor authored 67% of commits
13.5/13.5Contributor breadth99 contributors
10/10OpenSSF Scorecard: Contributorsproject has 9 contributing companies or organizations
Inputs used
bus_factor1
contributors_sampled99
top_contributor_share0.667
How it's scored
40.4/42Issue resolution96% of issues closed
24.4/30PR acceptance507/624 decided PRs merged
4.3/13Newcomer PR acceptance1/3 first-time contributors' PRs merged in 30d
7.5/15OpenSSF Scorecard: Code-ReviewFound 15/29 approved changesets -- score normalized to 5
Inputs used
merged_prs507
open_issues59
closed_issues1,540
prs_merged_7d0
prs_decided_7d0
prs_merged_30d2
prs_decided_30d4
issue_closed_ratio0.963
closed_unmerged_prs117
first_time_authors_30d2
first_time_prs_merged_30d1
first_time_prs_decided_30d3
How it's scored
30/30Ownership backingorganization-owned
0/20Verified domainverified-domain status not read for this organization
21.3/25Owner reach929 followers of DLR-RM
25/25Track record61 public repos, account ~9 yr old
Inputs used
followers929
owner_typeOrganization
is_verified
owner_loginDLR-RM
public_repos61
account_age_days3,492
Excluded from scoring (no data or not applicable): Verified domain. Remaining weights renormalized.

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable1 package(s) on pypi
35/35Publish recencylatest publish 58 days ago
20/20Version history115 published versions
20/20Not deprecatedactive, not deprecated or yanked
Inputs used
packagesstable_baselines3
ecosystemspypi
any_deprecatedno
min_days_since_publish58

Engineering Quality

Are baseline engineering and documentation practices in place?

81Excellent · 19% of overall
How it's scored
24/24CI workflows2 workflow(s)
24/24Tests present
0/16Linter config
0/9.6Pre-commit hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests28 out of 28 merged PRs checked by a CI test -- score normalized to 10
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configno
has_precommit_configno

Documentation

100Exceptional
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage sitehttps://stable-baselines3.readthedocs.io
10/10Repository description
10/10Topics14 topics
10/10Wiki
Inputs used
topicsreinforcement-learning, reinforcement-learning-algorithms, machine-learning, gym, openai, baselines, toolbox, stable-baselines, python, pytorch, robotics, sde, gsde, sb3
has_wikiyes
homepagehttps://stable-baselines3.readthedocs.io
docs_sitehttps://stable-baselines3.readthedocs.io
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

Are visible security and supply-chain practices strong, without unresolved high-risk jurisdiction exposure?

61Moderate · 16% of overall
How it's scored
7.5/7.5Binary-Artifactsno binaries found in the repo
0/7.5Branch-Protectionno data
2.5/2.5CI-Tests28 out of 28 merged PRs checked by a CI test -- score normalized to 10
0/2.5CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
3.8/7.5Code-ReviewFound 15/29 approved changesets -- score normalized to 5
2.5/2.5Contributorsproject has 9 contributing companies or organizations
10/10Dangerous-Workflowno dangerous workflow patterns detected
0/7.5Dependency-Update-Toolno update tool detected
0/5Fuzzingproject is not fuzzed
2.5/2.5Licenselicense file detected
4.5/7.5Maintained6 commit(s) and 2 issue activity found in the last 90 days -- score normalized to 6
5/5Packagingpackaging workflow detected
0/5Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0/5SASTSAST tool is not run on all commits -- score normalized to 0
0/5Security-Policysecurity policy file not detected
0/7.5Signed-Releasesno data
0/7.5Token-Permissionsdetected GitHub workflow tokens with excessive permissions
7.5/7.5Vulnerabilities0 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated16
scorecard_versionv5.5.0
checks_inconclusive2
scorecard_aggregate5.1
Excluded from scoring (no data or not applicable): Branch-Protection, Signed-Releases. Remaining weights renormalized.

Dependency advisories

100Exceptional
How it's scored
35/35Direct dependencies free of known advisoriesno direct dependency carries a known advisory
25/25Indirect dependencies free of known advisoriesno indirect dependency carries a known advisory
0/40No advisories left outstandingno advisory carries a publication date
Inputs used
sourceosv
advisories0
affected_packages0
assessed_packages6
unassessed_packages0
affected_by_severitynone
direct_affected_packages0
Excluded from scoring (no data or not applicable): No advisories left outstanding. Remaining weights renormalized. Matched the pypi:stable_baselines3@2.9.0 runtime dependency closure — what installing the published package pulls in — 6 packages. Reachability is not analyzed.

AI Readiness

How well is the repo equipped to be developed and maintained with AI coding agents? Carries a deliberately small weight (4%): agent tooling is a real maintenance signal, but a repository with none can still reach 100/100.

62Moderate · 4% of overall
How it's scored
0/45Agent instructionsno CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
40/40Legible commit history96 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url
legible_history_share0.96
agent_instruction_files
agent_instruction_max_bytes
How it's scored
18/18One-command bootstrapMakefile, docs/Makefile
22/22Automated tests
0/11Lint / format config
11/11Static type checkingstable_baselines3/py.typed
10/10Reproducible environmentDockerfile
10/10Demonstrated agent practice7 of the last 100 commits agent-authored or agent-credited
0/8Automated maintenanceno automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsyes
lockfiles
has_dockerfileyes
typed_languageno
bootstrap_filesMakefile, docs/Makefile
has_devcontainerno
has_linter_configno
typecheck_configsstable_baselines3/py.typed
agent_commit_share0.07
toolchain_manifests
dependency_bot_commit_share0
How it's scored
27/45Type-checkable codePython with type-check config (stable_baselines3/py.typed)
55/55Manageable file sizes0/97 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes42,902
source_files_sampled97
oversized_source_files0

Key facts

13,681GitHub stars
99contributors
38commits, last 12 months
18days since last push
32releases
1bus factor
59open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token

More detail

Star and fork history 0 ★ / 2,168 ⇿
0Stars
2,168Forks
13Releases

When each star and fork was added, collected from GitHub and bucketed by day. Cumulative growth sits directly above the daily additions it is made of, so the two read against each other: steady organic accretion looks nothing like an abrupt, short-lived burst. Where that difference is measurable, it is reported as growth authenticity.

Only the most recent history is shown — this repository exceeds the collection window, so the earliest history is not captured.

1,0001,2001,4001,6001,8002,0002,2002,16882023-062025-012026-08
Major 1Minor 8Patch 4

Each point covers 3 days.

OpenSSF Scorecard 5.1 / 10
5.1aggregate

Independent, tool-agnostic security assessment from the open-source OpenSSF Scorecard. Each check rewards a security practice, not a specific vendor's tool. Checks Scorecard could not determine are marked n/a and excluded from the security score (never counted as zero).Scorecard v5.5.0 · 2026-08-12 21:57 UTC

10Binary-Artifactsno binaries found in the repo
n/aBranch-Protectioninternal error: error during branchesHandler.setup: internal error: some github tokens can't read classic branch protection rules: https://github.com/ossf/scorecard-action/blob/main/docs/authentication/fine-grained-auth-token.md
10CI-Tests28 out of 28 merged PRs checked by a CI test -- score normalized to 10
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
5Code-ReviewFound 15/29 approved changesets -- score normalized to 5
10Contributorsproject has 9 contributing companies or organizations
10Dangerous-Workflowno dangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
6Maintained6 commit(s) and 2 issue activity found in the last 90 days -- score normalized to 6
10Packagingpackaging workflow detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0SASTSAST tool is not run on all commits -- score normalized to 0
0Security-Policysecurity policy file not detected
n/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
10Vulnerabilities0 existing vulnerabilities detected
All dependencies 27

Full resolved dependency set from the GitHub dependency graph: 0 direct and 27 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIale-pyindirect
PyPIblackindirect
PyPIcloudpickleindirect
PyPIgymnasiumindirect
PyPImatplotlibindirect
PyPImypyindirect
PyPImyst-parserindirect
PyPInumpyindirect
PyPIopencv-pythonindirect
PyPIpandasindirect
PyPIpillowindirect
PyPIpsutilindirect
PyPIpygame-ceindirect
PyPIpytestindirect
PyPIpytest-covindirect
PyPIpytest-envindirect
PyPIpytest-xdistindirect
PyPIrichindirect
PyPIruffindirect
PyPIsphinxindirect
PyPIsphinx-autobuildindirect
PyPIsphinx-copybuttonindirect
PyPIsphinx-rtd-themeindirect
PyPIsphinxcontrib-spellingindirect
PyPItensorboardindirect
PyPItorchindirect
PyPItqdmindirect
Dependency advisories 0

Installing pypi:stable_baselines3@2.9.0 pulls in 6 packages, direct and transitive: 0 carry known advisories, of which 0 are direct dependencies.

No known advisories affect the assessed dependencies.

An advisory means the version recorded in the dependency graph falls inside an advisory’s affected range. Reachability is not analysed, and the graph includes development and test pins — a finding may concern tooling rather than shipped software.

Raw JSON report machine-readable

Feedback

Spotted something off in this report, or have thoughts to share? Wrong measurements, missed tooling, ideas, questions — anything is welcome. Every message is read and gets a response.

The message is kept through sign-in.

Scores are signals, not warranties. They reflect publicly visible practices on GitHub — not a code audit, and not a security guarantee.

Missing data is excluded and weights renormalized, never scored as zero. Methodology is versioned and open: metrics v2.10.0, schema v0.31.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statisticsPyPI.