rlworkgroup/garage

Health v2 · maintenance only

Adoption not part of the score

2126 stars · 328 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

garage is a Python toolkit for developing and evaluating reinforcement learning algorithms, with a library of state-of-the-art RL algorithm implementations. It provides modular components like replay buffers, samplers, experiment checkpointing, and reproducibility tools supporting both PyTorch and TensorFlow.

Use cases

• implement and benchmark reinforcement learning algorithms
• run reproducible RL experiments with fixed random seeds
• train PPO, DDPG, DQN, SAC and other RL agents
• evaluate RL algorithms on Gym and other benchmark environments
• checkpoint and resume long-running RL training runs
• log RL experiment metrics to TensorBoard

When to choose

• you need reproducible RL research with experiment checkpointing and seeding
• you want ready implementations of many standard RL algorithms in one library
• you want framework flexibility between PyTorch and TensorFlow
• you need high-performance samplers and environment interfaces for RL benchmarks

When to avoid

• you need cutting-edge algorithms actively updated with the latest research
• you want a lightweight single-algorithm library rather than a full toolkit
• your project requires frequent releases or active maintenance
• you need non-Python or production RL deployment tooling

Facets

library · maturity maintenance

machine-learning reinforcement-learning benchmarking logging reinforcement-learning machine-learning artificial-intelligence python reinforcement-learning pytorch tensorflow reproducibility experiment-tracking rl-algorithms research linux macos docker

Member repositories

For agents

markdown · JSON · MCP: product_card(name="rlworkgroup/garage")