OpenAI has released Spinning Up in Deep RL, an educational resource designed to teach deep reinforcement learning to newcomers. The resource includes a guide on becoming a deep RL researcher, a curated list of key papers organized by topics such as model-free RL, exploration, transfer learning, hierarchy, memory, model-based RL, meta-RL, scaling RL, real-world RL, safety, imitation learning, and reproducibility, along with exercises and benchmarks for implementations.
Spinning Up as a Deep RL ResearcherKey Papers in Deep RL1. Model-Free RL2. Exploration3. Transfer and Multitask RL4. Hierarchy5. Memory6. Model-Based RL7. Meta-RL8. Scaling RL9. RL in the Real World10. Safety11. Imitation Learning and Inverse Reinforcement Learning12. Reproducibility, Analysis, and Critique13. Bonus: Classic Papers in RL Theory or Review
ExercisesBenchmarks for Spinning Up Implementations
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