openai/baselines

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

16764 stars · 4932 forks observed · 2026-08-28

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

OpenAI Baselines is a collection of high-quality reference implementations of reinforcement learning algorithms such as DQN, PPO, and ACKTR, built on TensorFlow. It is intended for the research community to replicate published results, compare new methods against standard baselines, and build new ideas on top of well-tested algorithm implementations.

Use cases

• implement reinforcement learning algorithms in python
• replicate published RL paper results like DQN scores
• compare a new RL algorithm against standard baselines
• train agents on Atari and MuJoCo environments
• get reference implementations of PPO and DQN
• build RL research on top of tested algorithm code

When to choose

• you need canonical, paper-accurate RL algorithm implementations for research
• you want to benchmark a new RL method against established baselines
• you are working with TensorFlow 1.x and classic RL environments

When to avoid

• you need actively maintained code or modern TensorFlow/PyTorch support
• you want production-ready RL training infrastructure rather than research reference code
• you need the latest RL algorithms or ecosystem integrations like Gymnasium

Facets

library · maturity maintenance

machine-learning reinforcement-learning benchmarking reinforcement-learning machine-learning artificial-intelligence python reinforcement-learning dqn ppo openai tensorflow research-baselines mujoco research linux macos gpu

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markdown · JSON · MCP: product_card(name="openai/baselines")