openai/spinningup resource

Health v2 · maintenance only

Adoption not part of the score

11915 stars · 2464 forks observed · 2026-08-28

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

Spinning Up in Deep RL is an OpenAI-produced educational resource combining tutorials, curated papers, and a Python code repo with short standalone implementations of key deep reinforcement learning algorithms (VPG, TRPO, PPO, DDPG, TD3, SAC). It is aimed at helping people build the background to become deep RL researchers.

Use cases

• learn deep reinforcement learning from scratch
• understand how PPO and policy gradient algorithms work
• read clean minimal implementations of SAC and TD3
• find a curated list of important deep RL papers
• prepare for a career in RL research
• run RL experiments on Gym and MuJoCo environments
• practice RL implementation exercises

When to choose

• you want a structured educational path into deep RL with theory and code
• you need readable reference implementations of core on-policy and off-policy algorithms
• you want algorithm documentation with derivations, pseudocode, and key papers

When to avoid

• you need a production-grade or feature-rich RL training framework
• you need Windows support or the latest maintained algorithm implementations
• you need distributed or highly scalable RL training infrastructure

Facets

learning-resource · maturity maintenance

machine-learning reinforcement-learning developer-tools documentation reinforcement-learning machine-learning deep-learning education tutorials python cli deep-rl policy-gradient openai-gym mujoco pytorch educational research-education algorithm-implementations linux macos

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For agents

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