Spinning Up in Deep RL is an educational resource from OpenAI designed to demystify deep reinforcement learning for learners and aspiring researchers. It provides foundational knowledge, practical code implementations, and guidance for entering the RL research field.

How It Works

The project offers a curated collection of standalone, well-documented code implementations for key deep RL algorithms. This approach prioritizes clarity and ease of understanding, allowing users to grasp the core mechanics of each algorithm without the complexity of large, integrated frameworks.

Quick Start & Requirements

• Install: pip install -e . (from the cloned repository)
• Prerequisites: Python 3.5+, NumPy, SciPy, TensorFlow 1.15.0 or PyTorch 1.0.
• Resources: Requires significant computational resources for training agents.
• Docs: spinningup.openai.com

Highlighted Details

• Comprehensive introduction to RL terminology, theory, and algorithm categories.
• Curated list of seminal RL papers organized by topic.
• Standalone, modular implementations of algorithms like VPG, TRPO, PPO, DDPG, TD3, SAC.
• Guidance on developing an RL research career.

Maintenance & Community

The project is in maintenance mode, expecting bug fixes and minor updates. It is authored by Joshua Achiam.

Licensing & Compatibility

• License: MIT License.
• Compatibility: Permissive license allows for commercial use and integration into closed-source projects.

Limitations & Caveats

The code is designed for educational purposes and may not be optimized for production-level performance or scalability. It relies on older versions of TensorFlow (1.15.0) and PyTorch (1.0), which may require specific environment configurations.