openai/spinningup
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.
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.
