Registro públicoBueno
Informe de salud del softwareesquema 0.11.0 · métricas 1.13.0 · 2026-07-15 02:55 UTC
pytorch / rl
A modular, primitive-first, python-first PyTorch library for Reinforcement Learning.
pytorch/rl tiene un índice de salud de 79 sobre 100, lo que lo sitúa en la banda Bueno. Su puntuación más alta es Vitality (94/100) y la más baja, Security (46/100). Se actualizó por última vez hoy. Una sola persona concentra la mayor parte del trabajo reciente.
79
global / 100Índice de salud del software
Las métricas se agrupan en categorías ponderadas sobre una escala de 1 a 100. El resultado global parte de su media; cuando la evidencia pública activa la Política de Jurisdicciones de Alto Riesgo, la calificación se ajusta y recibe el límite 49 (En riesgo). Preparación para IA queda fuera.
79
Excelente85-100Ejemplar; cumple prácticamente todos los criterios evaluados
Bueno70-84Saludable; carencias menores
Moderado50-69Aceptable con carencias notables; se recomienda revisión
En riesgo30-49Debilidades significativas; su adopción exige cautela
Crítico1-29Problemas graves (proyecto abandonado, un solo mantenedor, sin higiene)
Perfil de puntuación
Cada eje es una categoría. La forma importa más que la media: un proyecto sano llena toda la figura, mientras que un perfil de picos y cráteres indica que la fortaleza en una dimensión enmascara el riesgo en otra.
Titularidad
pytorchOrganización
13.385 seguidores70 repositorios públicosdesde ago 2016
Este repositorio está respaldado por una organización: una custodia compartida y responsable que puede sobrevivir a cualquier mantenedor individual.
Ecosistemas de paquetes
| Registro | Paquete | Versión | Descargas / mes | Versiones | Última publicación | Etiquetas |
|---|---|---|---|---|---|---|
| PyPI | torchrl | 0.13.3 | - | 34 | hace 0 días | reinforcement-learningpytorchrlmachine-learning |
Métricas por categoría
Vitalidad
¿Está vivo el proyecto: se escribe código y se publican versiones?
94
Actividad de desarrollo
97Excelente
Cómo se puntúa
| 36/36 | Recencia de push — último push hace 0 días |
| 33.2/36 | Cadencia de commits — 48/52 semanas con commits |
| 18/18 | Volumen de commits — 678 commits en el último año |
| 10/10 | OpenSSF Scorecard: Maintained — 30 commit(s) and 5 issue activity found in the last 90 days -- score normalized to 10 |
Datos de entrada utilizados
| commits_last_year | 678 |
| human_commit_share | — |
| days_since_last_push | 0 |
| active_weeks_last_year | 48 |
Disciplina de publicación
90Excelente
Cómo se puntúa
| 27/27 | Publica versiones — 35 versiones publicadas |
| 36/36 | Recencia de las versiones — última versión hace 0 días |
| 27/27 | Cadencia de publicación — una versión cada ~40,2 días |
| 0/10 | OpenSSF Scorecard: Signed-Releases — Project has not signed or included provenance with any releases. |
Datos de entrada utilizados
| releases_count | 35 |
| latest_release_tag | v0.13.3 |
| releases_from_tags | no |
| days_since_latest_release | 0 |
| mean_days_between_releases | 40,2 |
Comunidad y Adopción
¿Tiene el proyecto usuarios, descargas, atención y unas condiciones acogedoras para quienes contribuyen?
90
Popularidad y adopción
89Excelente
Cómo se puntúa
| 57.5/60 | Estrellas — 3491 estrellas |
| 22.2/25 | Forks — 468 forks |
| 9.1/15 | Observadores — 44 observadores |
Datos de entrada utilizados
| forks | 468 |
| stars | 3491 |
| watchers | 44 |
| growth_state | unverified |
| growth_factor_pct | 100 |
| growth_unverified_reason | no_history |
Salud de la comunidad
92Excelente
Cómo se puntúa
| 22.5/22.5 | README |
| 22.5/22.5 | Licencia — licencia reconocida (MIT) |
| 18/18 | Guía CONTRIBUTING |
| 13.5/13.5 | Código de conducta |
| 0/7.2 | Plantilla de issues |
| 6.3/6.3 | Plantilla de PR |
Datos de entrada utilizados
| has_readme | sí |
| has_license | sí |
| has_contributing | sí |
| has_issue_template | no |
| has_code_of_conduct | sí |
| has_pull_request_template | sí |
Sostenibilidad y Gobernanza
¿Sobrevivirá el proyecto a sus personas: factor bus, capacidad de respuesta, quién lo respalda y mantenimiento del paquete?
70
Resiliencia de mantenedores (factor bus)
41En riesgo
Cómo se puntúa
| 9/54 | Factor bus — la mitad de los commits recae en 1 contribuyente(s) |
| 8.3/22.5 | Distribución de commits — el principal contribuyente firma el 63% de los commits |
| 13.5/13.5 | Amplitud de contribuyentes — 100 contribuyentes |
| 10/10 | OpenSSF Scorecard: Contributors — project has 21 contributing companies or organizations |
Datos de entrada utilizados
| bus_factor | 1 |
| contributors_sampled | 100 |
| top_contributor_share | 0,631 |
Cómo se puntúa
| 35.8/46.8 | Resolución de issues — 77% de issues cerradas |
| 33.8/38.3 | Aceptación de PR — 2684/3035 PR decididos fusionados |
| 1.5/15 | OpenSSF Scorecard: Code-Review — Found 4/29 approved changesets -- score normalized to 1 |
Datos de entrada utilizados
| merged_prs | 2684 |
| open_issues | 192 |
| closed_issues | 629 |
| issue_closed_ratio | 0,766 |
| closed_unmerged_prs | 351 |
Propiedad y custodia
80Bueno
Cómo se puntúa
| 30/30 | Respaldo de la propiedad — propiedad de una organización |
| 0/20 | Dominio verificado |
| 25/25 | Alcance del propietario — 13.385 seguidores de pytorch |
| 25/25 | Trayectoria — 70 repos públicos, cuenta de ~9 años |
Datos de entrada utilizados
| followers | 13.385 |
| owner_type | Organization |
| is_verified | — |
| owner_login | pytorch |
| public_repos | 70 |
| account_age_days | 3622 |
Mantenimiento de paquetes
100Excelente
Cómo se puntúa
| 25/25 | Publicado y resoluble — 1 paquete(s) en pypi |
| 35/35 | Recencia de publicación — última publicación hace 0 días |
| 20/20 | Historial de versiones — 34 versiones en el registro |
| 20/20 | No obsoleto — activo, ni obsoleto ni retirado |
Datos de entrada utilizados
| packages | torchrl |
| ecosystems | pypi |
| any_deprecated | no |
| min_days_since_publish | 0 |
Calidad de Ingeniería
¿Existen unas prácticas mínimas de ingeniería y documentación?
92
Prácticas de ingeniería
94Excelente
Cómo se puntúa
| 24/24 | Flujos de trabajo de CI — 28 flujo(s) de trabajo |
| 24/24 | Pruebas presentes |
| 16/16 | Configuración de linter |
| 9.6/9.6 | Hooks de pre-commit |
| 0/6.4 | .editorconfig |
| 20/20 | OpenSSF Scorecard: CI-Tests — 14 out of 14 merged PRs checked by a CI test -- score normalized to 10 |
Datos de entrada utilizados
| has_ci | sí |
| has_tests | sí |
| has_editorconfig | no |
| has_linter_config | sí |
| has_precommit_config | sí |
Documentación
90Excelente
Cómo se puntúa
| 30/30 | README |
| 25/25 | Directorio de documentación |
| 15/15 | Sitio de documentación / página del proyecto — https://pytorch.org/rl |
| 10/10 | Descripción del repositorio |
| 10/10 | Topics — 13 topics |
| 0/10 | Wiki |
Datos de entrada utilizados
| topics | ai, control, decision-making, distributed-computing, machine-learning, marl, model-based-reinforcement-learning, multi-agent-reinforcement-learning, pytorch, reinforcement-learning, rl, robotics, torch |
| has_wiki | no |
| homepage | https://pytorch.org/rl |
| has_readme | sí |
| has_docs_dir | sí |
| has_description | sí |
Seguridad
¿Son sólidas las prácticas visibles de seguridad y de cadena de suministro, sin exposición jurisdiccional de alto riesgo sin resolver?
46
Postura de seguridad
46En riesgo
Cómo se puntúa
| 7.5/7.5 | Binary-Artifacts — no binaries found in the repo |
| 0/7.5 | Branch-Protection — branch protection not enabled on development/release branches |
| 2.5/2.5 | CI-Tests — 14 out of 14 merged PRs checked by a CI test -- score normalized to 10 |
| 0/2.5 | CII-Best-Practices — no effort to earn an OpenSSF best practices badge detected |
| 0.8/7.5 | Code-Review — Found 4/29 approved changesets -- score normalized to 1 |
| 2.5/2.5 | Contributors — project has 21 contributing companies or organizations |
| 10/10 | Dangerous-Workflow — no dangerous workflow patterns detected |
| 7.5/7.5 | Dependency-Update-Tool — update tool detected |
| 0/5 | Fuzzing — project is not fuzzed |
| 2.5/2.5 | Licencia — license file detected |
| 7.5/7.5 | Maintained — 30 commit(s) and 5 issue activity found in the last 90 days -- score normalized to 10 |
| 5/5 | Packaging — packaging workflow detected |
| 0/5 | Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0 |
| 3/5 | SAST — SAST tool is not run on all commits -- score normalized to 6 |
| 0/5 | Security-Policy — security policy file not detected |
| 0/7.5 | Signed-Releases — Project has not signed or included provenance with any releases. |
| 0/7.5 | Token-Permissions — detected GitHub workflow tokens with excessive permissions |
| 0/7.5 | Vulnerabilities — 16 existing vulnerabilities detected |
Datos de entrada utilizados
| source | openssf_scorecard |
| checks_evaluated | 18 |
| scorecard_version | v5.5.0 |
| checks_inconclusive | 0 |
| scorecard_aggregate | 4,6 |
Preparación para IA
¿Hasta qué punto está el repositorio preparado para desarrollarse y mantenerse con agentes de codificación de IA? Es una insignia independiente y experimental — peso 0,0, de modo que se presenta por separado y no afecta a la puntuación de salud global.
71
Contexto y guía para agentes
75Bueno
Cómo se puntúa
| 45/45 | Instrucciones para agentes — AGENTS.md, CLAUDE.md |
| 0/15 | Documentación legible por máquinas (llms.txt) |
| 0/40 | Historial de commits legible — sin datos |
Datos de entrada utilizados
| has_llms_txt | no |
| legible_history_share | — |
| agent_instruction_files | AGENTS.md, CLAUDE.md |
| agent_instruction_max_bytes | 8363 |
Excluidos de la puntuación (sin datos o no aplicable): Historial de commits legible. Los pesos restantes se han renormalizado.
Cómo se puntúa
| 18/18 | Arranque con un solo comando — Makefile, docs/Makefile |
| 22/22 | Pruebas automatizadas |
| 11/11 | Configuración de lint / formato |
| 11/11 | Verificación estática de tipos — mypy.ini |
| 0/10 | Entorno reproducible |
| 0/10 | Práctica demostrada con agentes — sin datos |
| 0/8 | Mantenimiento automatizado — sin datos |
| 0/10 | OpenSSF Scorecard: Pinned-Dependencies — dependency not pinned by hash detected -- score normalized to 0 |
Datos de entrada utilizados
| has_nix | no |
| has_tests | sí |
| lockfiles | — |
| has_dockerfile | no |
| typed_language | no |
| bootstrap_files | Makefile, docs/Makefile |
| has_devcontainer | no |
| has_linter_config | sí |
| typecheck_configs | mypy.ini |
| agent_commit_share | — |
| toolchain_manifests | — |
| dependency_bot_commit_share | — |
Excluidos de la puntuación (sin datos o no aplicable): Práctica demostrada con agentes, Mantenimiento automatizado. Los pesos restantes se han renormalizado.
Cómo se puntúa
| 27/45 | Código verificable por tipos — Python con configuración de verificación de tipos (mypy.ini) |
| 51/55 | Tamaños de archivo manejables — 58/796 archivos fuente de más de 60 KB |
Datos de entrada utilizados
| primary_language | Python |
| largest_source_bytes | 298.120 |
| source_files_sampled | 796 |
| oversized_source_files | 58 |
Interfaces legibles por máquina
40En riesgo
Cómo se puntúa
| 0/40 | Esquema de API (OpenAPI/GraphQL/proto) |
| 0/20 | Servidor MCP |
| 40/40 | Ejemplos ejecutables — examples, recipes |
Datos de entrada utilizados
| example_dirs | examples, recipes |
| has_mcp_signal | no |
| api_schema_files | — |
Datos clave
3491estrellas de GitHub
100contribuidores
678commits en los últimos 12 meses
0días desde el último push
35versiones publicadas
1factor bus
192issues abiertas
PyPIecosistemas de paquetes
Más detalle
OpenSSF Scorecard 4.6 / 10
4.6agregado
Evaluación de seguridad independiente y agnóstica en cuanto a herramientas, procedente del proyecto de código abierto OpenSSF Scorecard. Cada comprobación premia una práctica de seguridad, no la herramienta de un proveedor concreto. Las comprobaciones que Scorecard no pudo determinar se marcan como n/d y se excluyen de la puntuación de seguridad (nunca se cuentan como cero).
| 10 | Binary-Artifacts | no binaries found in the repo |
| 0 | Branch-Protection | branch protection not enabled on development/release branches |
| 10 | CI-Tests | 14 out of 14 merged PRs checked by a CI test -- score normalized to 10 |
| 0 | CII-Best-Practices | no effort to earn an OpenSSF best practices badge detected |
| 1 | Code-Review | Found 4/29 approved changesets -- score normalized to 1 |
| 10 | Contributors | project has 21 contributing companies or organizations |
| 10 | Dangerous-Workflow | no dangerous workflow patterns detected |
| 10 | Dependency-Update-Tool | update tool detected |
| 0 | Fuzzing | project is not fuzzed |
| 10 | License | license file detected |
| 10 | Maintained | 30 commit(s) and 5 issue activity found in the last 90 days -- score normalized to 10 |
| 10 | Packaging | packaging workflow detected |
| 0 | Pinned-Dependencies | dependency not pinned by hash detected -- score normalized to 0 |
| 6 | SAST | SAST tool is not run on all commits -- score normalized to 6 |
| 0 | Security-Policy | security policy file not detected |
| 0 | Signed-Releases | Project has not signed or included provenance with any releases. |
| 0 | Token-Permissions | detected GitHub workflow tokens with excessive permissions |
| 0 | Vulnerabilities | 16 existing vulnerabilities detected |
Dependencias directas 7
| Registro | Paquete | Restricción de versión | Manifiesto |
|---|---|---|---|
| PyPI | torch | >=2.1.0 | pyproject.toml |
| PyPI | pyvers | >=0.2.3 | pyproject.toml |
| PyPI | hoptorch | >=0.1.4 | pyproject.toml |
| PyPI | numpy | — | pyproject.toml |
| PyPI | packaging | — | pyproject.toml |
| PyPI | cloudpickle | — | pyproject.toml |
| PyPI | tensordict | >=0.13.0,<0.14.0 | pyproject.toml |
Todas las dependencias 103
Conjunto completo de dependencias resueltas según el grafo de dependencias de GitHub: 9 paquetes directos y 94 indirectos (transitivos). El cierre transitivo es completo cuando el repositorio incluye un lockfile.
| Registro | Paquete | Versión | Relación |
|---|---|---|---|
| PyPI | cloudpickle | — | directa |
| PyPI | hoptorch | — | directa |
| PyPI | numpy | — | directa |
| PyPI | packaging | — | directa |
| PyPI | pyvers | — | directa |
| PyPI | tensordict | 0.9.0 | directa |
| PyPI | tensordict | — | directa |
| PyPI | torch | 2.12.0 | directa |
| PyPI | torch | — | directa |
| PyPI | accelerate | 1.7.0 | indirecta |
| PyPI | accelerate | — | indirecta |
| PyPI | ale-py | — | indirecta |
| PyPI | bitsandbytes | 0.46.0 | indirecta |
| PyPI | bitsandbytes | — | indirecta |
| PyPI | coverage | — | indirecta |
| PyPI | datasets | 3.6.0 | indirecta |
| PyPI | datasets | — | indirecta |
| PyPI | dm-control | — | indirecta |
| PyPI | docutils | — | indirecta |
| PyPI | expecttest | — | indirecta |
| PyPI | future | — | indirecta |
| PyPI | gymnasium | — | indirecta |
| PyPI | gymnasium-robotics | — | indirecta |
| PyPI | hydra-core | — | indirecta |
| PyPI | hydra-core | 1.3.2 | indirecta |
| PyPI | hydra-submitit-launcher | — | indirecta |
| PyPI | hypothesis | — | indirecta |
| PyPI | imageio | — | indirecta |
| PyPI | immutabledict | 4.2.1 | indirecta |
| PyPI | immutabledict | — | indirecta |
| PyPI | ipython | — | indirecta |
| PyPI | jax | — | indirecta |
| PyPI | jinja2 | 3.1.4 | indirecta |
| PyPI | langdetect | 1.0.9 | indirecta |
| PyPI | langdetect | — | indirecta |
| PyPI | lerobot | — | indirecta |
| PyPI | matplotlib | — | indirecta |
| PyPI | memory-profiler | — | indirecta |
| PyPI | minari | — | indirecta |
| PyPI | mujoco | — | indirecta |
| PyPI | myst-parser | — | indirecta |
| PyPI | nltk | 3.9.4 | indirecta |
| PyPI | nltk | — | indirecta |
| PyPI | omegaconf | — | indirecta |
| PyPI | onnx | — | indirecta |
| PyPI | onnxruntime | — | indirecta |
| PyPI | onnxscript | — | indirecta |
| PyPI | open-spiel | — | indirecta |
| PyPI | opencv-python-headless | — | indirecta |
| PyPI | pandas | — | indirecta |
| PyPI | peft | 0.15.2 | indirecta |
| PyPI | peft | — | indirecta |
| PyPI | pettingzoo | — | indirecta |
| PyPI | pillow | — | indirecta |
| PyPI | playwright | — | indirecta |
| PyPI | procgen | — | indirecta |
| PyPI | psutil | — | indirecta |
| PyPI | pybind11 | — | indirecta |
| PyPI | pygame | — | indirecta |
| PyPI | pyglet | — | indirecta |
| PyPI | pytest | — | indirecta |
| PyPI | pytest-asyncio | — | indirecta |
| PyPI | pytest-benchmark | — | indirecta |
| PyPI | pytest-cov | — | indirecta |
| PyPI | pytest-error-for-skips | — | indirecta |
| PyPI | pytest-instafail | — | indirecta |
| PyPI | pytest-mock | — | indirecta |
| PyPI | pytest-rerunfailures | — | indirecta |
| PyPI | pytest-timeout | — | indirecta |
| PyPI | pytest-xdist | — | indirecta |
| PyPI | pytorch-sphinx-theme2 | — | indirecta |
| PyPI | pyyaml | — | indirecta |
| PyPI | ray | 2.55.0 | indirecta |
| PyPI | ray | — | indirecta |
| PyPI | requests | — | indirecta |
| PyPI | safetensors | — | indirecta |
| PyPI | safety-gymnasium | — | indirecta |
| PyPI | scipy | — | indirecta |
| PyPI | sentencepiece | — | indirecta |
| PyPI | setuptools | — | indirecta |
| PyPI | sphinx | — | indirecta |
| PyPI | sphinx-autodoc-typehints | — | indirecta |
| PyPI | sphinx-copybutton | — | indirecta |
| PyPI | sphinx-design | — | indirecta |
| PyPI | sphinx-gallery | — | indirecta |
| PyPI | sphinx-serve | 1.0.1 | indirecta |
| PyPI | sphinxcontrib-htmlhelp | — | indirecta |
| PyPI | sphinxcontrib-video | — | indirecta |
| PyPI | tenacity | — | indirecta |
| PyPI | tiktoken | — | indirecta |
| PyPI | torchcodec | — | indirecta |
| PyPI | torchvision | — | indirecta |
| PyPI | tqdm | 4.67.1 | indirecta |
| PyPI | tqdm | — | indirecta |
| PyPI | transformers | 5.5.0 | indirecta |
| PyPI | transformers | — | indirecta |
| PyPI | vllm | 0.22.0 | indirecta |
| PyPI | vllm | — | indirecta |
| PyPI | vmas | — | indirecta |
| PyPI | wandb | 0.19.11 | indirecta |
| PyPI | wandb | — | indirecta |
| PyPI | xformers | 0.0.30 | indirecta |
| PyPI | xformers | — | indirecta |
Informe JSON sin procesar legible por máquina
{
"data": {
"repo": {
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"created_at": "2022-02-01T17:17:36Z",
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"description": "A modular, primitive-first, python-first PyTorch library for Reinforcement Learning.",
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"Intended Audience :: Science/Research",
"Operating System :: OS Independent",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12",
"Programming Language :: Python :: 3.13",
"Programming Language :: Python :: 3.14",
"Topic :: Scientific/Engineering :: Artificial Intelligence"
],
"ecosystem": "pypi",
"matches_repo": true,
"registry_url": "https://pypi.org/project/torchrl/",
"is_deprecated": false,
"latest_version": "0.13.3",
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"versions_count": 34,
"total_downloads": null,
"dependents_count": null,
"deprecation_note": null,
"maintainers_count": null,
"monthly_downloads": null,
"first_published_at": "2022-07-06T13:20:35.808578Z",
"latest_published_at": "2026-07-14T17:51:12.793028Z",
"latest_version_yanked": null,
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]
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"watchers": 44,
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"ai_readiness": {
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"example_dirs": [
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],
"has_llms_txt": false,
"has_dockerfile": false,
"has_mcp_signal": false,
"bootstrap_files": [
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],
"api_schema_files": [],
"has_devcontainer": false,
"typecheck_configs": [
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],
"largest_source_bytes": 298120,
"source_files_sampled": 796,
"oversized_source_files": 58,
"agent_instruction_files": [
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],
"agent_instruction_max_bytes": 8363
},
"dependencies": {
"manifests": [
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"description": "How well is the repo equipped to be developed and maintained with AI coding agents? An independent, experimental badge — weight 0.0, so it is surfaced on its own and does not affect the overall health score."
}
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"full_name": "pytorch/rl",
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"license_spdx": "MIT"
}Las puntuaciones son señales, no garantías. Reflejan prácticas públicamente visibles en GitHub; no son una auditoría de código ni una garantía de seguridad.
Los datos ausentes se excluyen y los pesos se renormalizan; nunca se puntúan como cero. La metodología es versionada y abierta: métricas v1.13.0, esquema v0.11.0 — metodología completa · wiki de métricas.
Cómo se sitúa un resultado dentro del registro general: estadísticas agregadas — PyPI.