# DLR-RM/rl-baselines3-zoo

A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.

Repository: https://github.com/DLR-RM/rl-baselines3-zoo
Canonical: https://ross.abutalabs.com/products/rl-baselines3-zoo
Homepage: https://rl-baselines3-zoo.readthedocs.io
Language: Python
License: MIT
License Family: permissive
Topics: rl, reinforcement-learning, stable-baselines, openai, gym, pybullet, hyperparameter-optimization, hyperparameter-tuning, hyperparameter-search, optimization, sde, robotics, lab, pybullet-environments, tuning-hyperparameters, deep-reinforcement-learning, pytorch
Last push: 2026-08-24T09:47:26+00:00

## Health v2 (maintenance only)
Score: 92/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 77, longevity 100
- inputs: {"age_days": 2311, "days_push": 9, "days_rel": 79, "gap_med": 72.5, "n_releases_24m": 7}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2870, forks 603 (observed 2026-08-28T04:07:27.080805+00:00)

## What it is
RL Baselines3 Zoo is a training framework for reinforcement learning agents built on Stable Baselines3. It provides CLI scripts for training, evaluating, hyperparameter tuning, and video recording, plus a collection of tuned hyperparameters and pre-trained agents for common environments.

## Use cases
- train reinforcement learning agents on gym environments
- tune hyperparameters for RL algorithms
- benchmark different RL algorithms
- download pre-trained RL agents
- record videos of trained agents
- run hyperparameter search for SAC or PPO

## When to choose
- you use Stable Baselines3 and want a ready-made training pipeline
- you need tuned hyperparameters for standard Gym/Gymnasium or PyBullet environments
- you want to benchmark RL algorithms consistently

## When to avoid
- you need custom RL algorithms not in Stable Baselines3
- you want a GUI or notebook-first workflow
- you are not using PyTorch-based SB3

## Facets
- artifact type: framework
- maturity: active
- function: machine-learning, reinforcement-learning, benchmarking, cli
- domain: reinforcement-learning, machine-learning, robotics, developer-tools
- platform: python, windows, cli
- tags: stable-baselines3, pytorch, gymnasium, pybullet, hyperparameter-tuning, pretrained-agents, openai-gym, linux, macos

## Member repositories
- DLR-RM/rl-baselines3-zoo (main) score 92

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:27.080805+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:35:52.671528+00:00, confidence not recorded.
  - readme: https://github.com/DLR-RM/rl-baselines3-zoo (fetched 2026-08-28T04:07:27.080805+00:00, sha 052c1ea0bdb4)
- Data as of 2026-08-30T08:39:29.467469+00:00.