Stable-Baselines3

PyTorch implementations of RL algorithms (PPO, SAC, DQN, etc.) — research-grade reliability; widely used as a reference benchmark.

Active · Verified 2026-10-27 · fresh as of 2026-10-27 · 18 cited facts, 1 with a third-party source · 1 recorded change · Suggest a correction

At a glance

Type: Reliable RL algorithm implementations repo fix
Tier: T1
Section: Training infrastructure
Created: 2020 repo fix
Latest release: v2.9.2 (2026-10-26) — stable-baselines3 registry fix
License: MIT repo fix
GitHub: DLR-RM/stable-baselines3 — ~13.9k stars, last commit 2026-10 repo fix
Pricing: searched not found fix
Funding: searched not found fix
Freshness: 2026-10-27 fix
Layer: model-infra repo fix
Licensing: open-source repo fix
Deployment locus: local repo fix
Platform vs product: platform repo fix
Developer activity: OpenRank 1.46 (12-mo avg 3.61) · 8 participants — 2026-09 third-party fix
Maintainer health: 8 issues in 90 days, 100% answered or closed · 4 releases in 12 months (latest 2026-10-26) · bus factor 1 — as of 2026-10-31 repo

Optimised for: searched not found

Anti-fit: searched not found

searched not found

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Standard reference for RL.

Other training infrastructure in the catalog, ranked by inbound references.

MLflow T1
Databricks-incubated open platform for ML lifecycle — tracking, projects, models, model registry. Standard ML-ops baseline. MLflow 3.0 (2024) added LLM-eval features.
AWS SageMaker T1
AWS's managed ML platform — Studio, Training, HyperPod (large-scale clusters), Endpoints. Industry-standard for enterprise AWS-hosted training.
Weights & Biases T1
Experiment-tracking and ML-ops platform (metrics, artifacts, sweeps, Weave tracing/evals). Acquired by CoreWeave (completed 2025-05-05, ~$1.7B); now part of CoreWeave Forge.
Comet ML T2
ML experiment tracking + LLM evaluation platform (Opik for LLM eval). Comet competes head-to-head with Weights & Biases.
Azure Machine Learning T1
Microsoft Azure's managed ML platform — pipelines, AutoML, Designer, prompt flow (LLM). Heavy integration with Azure OpenAI and Azure AI Studio.
LoRA T3
Microsoft Research's Low-Rank Adapters method — trains rank-r matrices added to attention weights; 10000x parameter reduction. Foundation of all adapter-style fine-tuning.

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2026-10-27
- Latest release first recorded as “v2.9.2 (2026-10-26) — stable-baselines3” source
- Deployment first recorded as “Self-hosted only — library” source
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