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openai/spinningup

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Spinningup

Spinning Up is a deep reinforcement learning curriculum designed to teach the theory and implementation of deep reinforcement learning algorithms. It serves as a guided educational resource for understanding how agents interact with environments through mathematical models and code.

The project provides a research roadmap consisting of a curated collection of influential research papers and theoretical concepts. This literature study is designed to guide a deeper exploration of specific reinforcement learning domains.

The curriculum covers the implementation of reinforcement learning logic through standalone code examples and a study of core terminology and theoretical foundations.

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Features

  • Structured Reinforcement Learning Curricula - Ships a structured educational curriculum covering mathematical concepts and algorithms for reinforcement learning.
  • Educational Implementations - Provides standalone code examples to help learners understand how reinforcement learning agent models are implemented.
  • Algorithm Implementations - Provides clean, readable educational implementations of reinforcement learning algorithms to demonstrate their mathematical foundations.
  • Reinforcement Learning Curricula - Serves as a guided educational resource for learning both the theory and implementation of deep RL.
  • Implementation Guides - Provides code examples and mathematical models that demonstrate agent interaction with environments.
  • Reinforcement Learning Theory - Offers a study of core reinforcement learning terminology and theoretical concepts.
  • Research Papers - Provides a curated collection of influential research papers in the field of reinforcement learning.
  • Research Roadmaps - Includes a research roadmap consisting of a curated collection of influential RL papers and concepts.
11,812 stars·2,454 forks·Python·MIT·28 views

Star history

Star history chart for openai/spinningupStar history chart for openai/spinningup

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

Frequently asked questions

What does openai/spinningup do?

Spinning Up is a deep reinforcement learning curriculum designed to teach the theory and implementation of deep reinforcement learning algorithms. It serves as a guided educational resource for understanding how agents interact with environments through mathematical models and code.

What are the main features of openai/spinningup?

The main features of openai/spinningup are: Structured Reinforcement Learning Curricula, Educational Implementations, Algorithm Implementations, Reinforcement Learning Curricula, Implementation Guides, Reinforcement Learning Theory, Research Papers, Research Roadmaps.

Which projects share features with openai/spinningup?

Projects with overlapping indexed features include: datawhalechina/easy-rl — Easy-RL is an educational resource designed to teach the principles and implementation of reinforcement learning. It… wangshusen/drl — DRL is a curated educational resource that teaches deep reinforcement learning through a structured series of lectures… nlp-love/ml-nlp — This project is a machine learning algorithm reference and implementation guide that provides theoretical foundations… dod-o/statistical-learning-method_code — This project is a reference collection of statistical learning algorithms built from scratch using NumPy for linear… andri27-ts/reinforcement-learning — This project is a collection of reinforcement learning implementations and educational materials written in Python. It… mingchaozhu/deeplearning — This project is a deep learning implementation library and neural network theory repository. It translates…

Projects sharing features with Spinningup

These projects share indexed features with Spinningup. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
  • datawhalechina/easy-rldatawhalechina avatar

    datawhalechina/easy-rl

    14,348View on GitHub↗

    Easy-RL is an educational resource designed to teach the principles and implementation of reinforcement learning. It provides a structured curriculum that guides users from fundamental concepts to advanced algorithmic techniques, focusing on the development and training of autonomous agents that learn through interaction with simulated environments. The project distinguishes itself through a pedagogical framework that utilizes interactive notebooks to bridge the gap between theoretical research and functional code. By organizing complex methods into modular units, it allows for the study of i

    Jupyter Notebooka3cddpgdeep-reinforcement-learning
    View on GitHub↗14,348
  • wangshusen/drlwangshusen avatar

    wangshusen/DRL

    4,512View on GitHub↗

    DRL is a curated educational resource that teaches deep reinforcement learning through a structured series of lectures and videos. It covers the three main families of reinforcement learning methods: actor-critic architectures, value-based algorithms like Q-learning and DQN, and policy-based techniques that directly optimize an agent's action-selection strategy. The curriculum extends beyond these core topics to include imitation learning, multi-agent training, and methods for handling continuous action spaces. Content is organized as markdown-driven documentation that generates static, navig

    View on GitHub↗4,512
  • dod-o/statistical-learning-method_codeDod-o avatar

    Dod-o/Statistical-Learning-Method_Code

    11,621View on GitHub↗

    This project is a reference collection of statistical learning algorithms built from scratch using NumPy for linear algebra and matrix operations. It serves as an educational resource for studying the mathematical foundations and inner workings of machine learning models through manual implementations. The codebase provides hand-coded implementations of both supervised and unsupervised learning. This includes classification and regression models such as support vector machines, decision trees, and Naive Bayes, as well as data clustering and pattern discovery methods like k-means and hierarchi

    Pythoncodemachine-learning-algorithmsstatistical-learning-method
    View on GitHub↗11,621
  • andri27-ts/reinforcement-learningandri27-ts avatar

    andri27-ts/Reinforcement-Learning

    4,722View on GitHub↗

    This project is a collection of reinforcement learning implementations and educational materials written in Python. It provides neural network architectures for solving control tasks through deep reinforcement learning, spanning value-based and policy-gradient methods. The repository includes a library of evolutionary strategies and genetic algorithms as alternatives to gradient-based learning. It also features a model-based system for predicting future environment states and rewards to enable internal simulation and offline planning. The codebase covers a wide range of capabilities, includi

    Jupyter Notebooka2cartificial-intelligencedeep-learning
    View on GitHub↗4,722
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