openai/spinningup

★ 11,769PythonAudience · researcherComplexity · 2/5Setup · moderate

Mindmap

mindmap
  root((SpinningUp RL))
    What it does
      RL education
      Algorithm code
      Reading list
    Algorithms
      PPO
      SAC
      DDPG
      TD3
    Use cases
      Learn RL basics
      Study algorithm code
    Audience
      Students
      ML researchers

mindmap root((SpinningUp RL)) What it does RL education Algorithm code Reading list Algorithms PPO SAC DDPG TD3 Use cases Learn RL basics Study algorithm code Audience Students ML researchers

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Things people build with this

USE CASE 1

Learn the core vocabulary and ideas of deep reinforcement learning from scratch using the included guides

USE CASE 2

Study short, readable Python implementations of algorithms like PPO and SAC to understand how they work

USE CASE 3

Follow a curated reading list to build up knowledge of the most important published papers in the field

USE CASE 4

Complete practice exercises to test and reinforce understanding of reinforcement learning concepts

Tech stack

PythonNumPyMuJoCo

Getting it running

Difficulty · moderate Time to first run · 30min

Requires installing a physics simulation environment such as MuJoCo or a compatible alternative before running most example tasks.

License terms are not stated in the explanation.

In plain English

Spinning Up in Deep RL is an educational resource from OpenAI for people who want to learn about deep reinforcement learning. Reinforcement learning is a branch of machine learning where a computer program learns to make decisions by trying things out and receiving feedback on whether those actions were good or bad. The "deep" part refers to using neural networks as the core of the learning system. The package is designed to lower the barrier to entry for this field, which has historically required a lot of background knowledge to approach. It includes a written introduction to the core concepts and vocabulary, a guide on how to build a career in reinforcement learning research, and a curated reading list of important published papers organized by topic. Beyond the written materials, the repository includes clean, well-documented Python implementations of several key reinforcement learning algorithms. These are written to be short and readable rather than maximally optimized, so someone studying them can follow the logic without getting lost in engineering complexity. A set of exercises is also included as practice. The project is in maintenance mode, meaning the OpenAI team is no longer adding major new features but does address bug fixes. All of the documentation and full guides are hosted at the project's website.

Copy-paste prompts

Prompt 1

Using the SpinningUp PPO implementation, train an agent on CartPole-v1 and log the average reward per episode so I can plot the learning curve.

Prompt 2

Walk me through the SpinningUp SAC implementation line by line. Explain what each section does and why the soft update target is computed that way.

Prompt 3

Using SpinningUp, set up a custom OpenAI Gym environment and connect it to the DDPG algorithm without modifying the core SpinningUp source files.

Prompt 4

I am new to reinforcement learning. Based on the SpinningUp intro materials, explain the difference between on-policy and off-policy learning with a concrete everyday example.

Prompt 5

Which SpinningUp algorithm should a beginner study first, PPO, DDPG, or SAC, and what makes its source code the easiest to follow?

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