unity-technologies/ml-agents
Grade a repo

90

/ 100

GradeA

Excellent work. The main thing left to polish is a reproducible setup.

Top 12% of 5,156 graded repos

The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning.

Documentation86
Engineering89
Health100
C#19,713Other9d ago

Top fixes

Highest-impact changes first, ranked by point weight

6 to address
  1. 1
    README12pt

    Add a GIF, screenshot, or logo image. It is the fastest way to show what your project does.

  2. 2
    Install and run instructions9pt

    Add a .env.example listing all required environment variables so contributors know what to set up.

  3. 3
    Reproducibility6pt

    Commit poetry.lock, uv.lock, pdm.lock, Pipfile.lock, conda-lock.yml, or another lockfile for your Python dependency manager.

  4. 4
    Reproducibility6pt

    Add .github/dependabot.yml with at least one package-ecosystem entry, or a renovate.json, so dependencies are updated automatically.

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Scorecard

Every check, grouped by category and sorted worst-first

Documentation

86

Contributing guide5pt77

CONTRIBUTING guide found.

README12pt80

README is present.

Install and run instructions9pt90

README documents how to install the project.

License6pt100

Licensed under Other.

Engineering

89

Reproducibility6pt10

No dependency lockfile found (−70 pts).

→ Commit poetry.lock, uv.lock, pdm.lock, Pipfile.lock, conda-lock.yml, or another lockfile for your Python dependency manager.

Tests18pt100

Test files detected (DevProject/Assets/ML-Agents/Scripts/Tests).

CI/CD14pt100

CI is configured (.github/workflows/pre-commit.yml).

Linting and formatting5pt100

.NET formatting configured (.editorconfig).

Issue and PR templates6pt100

Issue or PR templates present.

Project health

100

Dependency manifest6pt100

Dependency manifest found (setup.cfg).

Repository metadata5pt100

Repository has a description.

Activity5pt100

Actively maintained (pushed within the last month).

Housekeeping3pt100

.gitignore present.

Repository health signals

Activity, community, and responsiveness at scan time

Activity

  • 1 / 8
    Commits (30d / 90d)
  • 4,501
    Forks
  • 63
    Releaseslatest 8y ago

Community

  • 62% - Fair
    Community health
  • -
    authors own >50% of commits
  • 19,713
    Watchers

Responsiveness

  • 2h
    Median issue response
  • 14h
    Median PR merge time
  • 19
    Open issues
Repository files42 root entries
  • .github
    Good: CI is configured (.github/workflows/pre-commit.yml).
    Good: Issue or PR templates present.
  • .yamato
  • colab
  • com.unity.ml-agents
    Good: CONTRIBUTING guide found.
    Issue: CONTRIBUTING guide contents could not be read (−28 pts vs a readable file).Fix: Move the file to the repo root or docs/CONTRIBUTING.md so its setup, style, test, and PR sections can be graded.
  • com.unity.ml-agents.tests
  • config
  • DevProject
    Good: Test files detected (DevProject/Assets/ML-Agents/Scripts/Tests).
  • docs
  • localized_docs
  • ml-agents
  • ml-agents-envs
  • ml-agents-plugin-examples
  • ml-agents-trainer-plugin
  • PerformanceProject
  • Project
  • protobuf-definitions
  • Tools
  • unity-volume
  • utils
  • .editorconfig
    Good: .NET formatting configured (.editorconfig).
  • .gitattributes
  • .gitignore
    Good: .gitignore present.
  • .gitmodules
  • .pre-commit-config.yaml
  • .pre-commit-search-and-replace.yaml
  • catalog-info.yaml
  • CODE_OF_CONDUCT.md
    Good: Code of conduct present.
  • CODEOWNERS
  • colab_requirements.txt
  • conftest.py
  • Dockerfile
    Good: Environment pinned via Dockerfile.
  • LICENSE.md
    Good: Licensed under Other.
  • markdown-link-check.fast.json
  • markdown-link-check.full.json
  • mkdocs.yml
  • pytest.ini
  • Readme.md
    Good: README is present.
    Good: README is well structured with multiple sections.
    Issue: No screenshots or images in the README (−20 pts).Fix: Add a GIF, screenshot, or logo image. It is the fastest way to show what your project does.
    Good: README has code examples.
    Good: README links to a live demo or deployed app.
    Good: README includes status badges.
    Good: README documents how to install the project.
    Good: README documents how to run the project.
  • setup.cfg
    Good: Dependency manifest found (setup.cfg).
  • SURVEY.md
  • test_constraints_version.txt
  • test_requirements.txt
  • Third Party Notices.md
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