rll/rllab

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

3078 stars · 800 forks observed · 2026-08-28

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

rllab is a Python framework for developing and evaluating reinforcement learning algorithms, with implementations of algorithms like TRPO, DDPG, and REINFORCE, plus continuous control tasks and OpenAI Gym compatibility. It is no longer under active development and has been succeeded by the actively maintained garage codebase from the rlworkgroup alliance.

Use cases

• implement and evaluate reinforcement learning algorithms
• run policy gradient experiments like TRPO on continuous control tasks
• benchmark RL algorithms against OpenAI Gym environments
• run RL experiments at scale on an EC2 cluster
• reproduce classic RL research baselines from older papers

When to choose

• you need to reproduce or extend legacy rllab-based research code
• you are studying the original implementations of TRPO, DDPG, or REPS
• you are working with an old project already built on rllab and cannot migrate

When to avoid

• you are starting a new reinforcement learning project - use garage instead
• you need actively maintained code, updated dependencies, or modern TensorFlow/PyTorch support
• you need Python versions newer than 3.5 or current GPU tooling

Facets

framework · maturity abandoned

machine-learning reinforcement-learning benchmarking reinforcement-learning machine-learning python reinforcement-learning openai-gym policy-gradient robotics deprecated superseded-by-garage research linux macos

Member repositories

For agents

markdown · JSON · MCP: product_card(name="rll/rllab")