DLR-RM/stable-baselines3

Health v2 · maintenance only

Adoption not part of the score

13728 stars · 2171 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Stable Baselines3 is a Python library providing reliable, well-tested PyTorch implementations of reinforcement learning algorithms with a unified API. It serves as a standard baseline toolbox for RL research and applications, supporting custom environments, policies, and TensorBoard logging.

Use cases

• train reinforcement learning agents in python
• compare my RL algorithm against standard baselines
• implement PPO or SAC without writing it from scratch
• train agents on custom gymnasium environments
• learn reinforcement learning with a simple high-level library
• benchmark RL algorithm performance

When to choose

• you want trusted, tested PyTorch RL algorithm implementations with a common interface
• you need reproducible baselines for RL research
• you want beginner-friendly access to state-of-the-art RL methods

When to avoid

• you need the very latest experimental RL algorithms (see SB3 Contrib or SBX)
• you have no prior reinforcement learning knowledge and want a fully guided tutorial
• you need JAX-based training rather than PyTorch

Facets

library · maturity stable

machine-learning reinforcement-learning benchmarking reinforcement-learning machine-learning robotics python cross-platform pytorch gymnasium rl-algorithms baselines openai-gym training-agents

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markdown · JSON · MCP: product_card(name="DLR-RM/stable-baselines3")