This repository provides a comprehensive, hands-on course for learning Deep Reinforcement Learning (DRL) from foundational concepts to advanced techniques like RLHF and AlphaZero. It targets engineers and researchers seeking practical implementation experience, offering a structured path through Jupyter notebooks and a pre-configured VS Code environment.

How It Works

The course utilizes a series of Jupyter notebooks with guided "TODO" sections for implementing DRL algorithms from scratch. This "learn by doing" approach allows users to build classic algorithms (DQN, SAC, PPO) and explore advanced topics. A provided "solution" folder offers completed notebooks for reference, ensuring users can overcome implementation hurdles. The entire setup is designed for a streamlined experience within VS Code.

Quick Start & Requirements

• Installation: Docker is the recommended method. Clone the repo, create a .env file with UID=$(id -u) and GID=$(id -g) (Linux/macOS), then run docker compose up --build -d. For GPU support, use docker compose -f docker-compose.yml -f docker-compose.gpu.yml up --build -d.
• Prerequisites: Docker, Git. NVIDIA drivers and compatible GPU for GPU support.
• Environment: Python 3.12.11 within the Docker container.
• Documentation: drlzh.ai (course content), notebooks available directly on GitHub.

Highlighted Details

• Covers foundational RL algorithms (DQN, SAC, PPO) and advanced topics (AlphaZero, RLHF, MCTS).
• Hands-on implementation through Jupyter notebooks with guided TODOs and solutions.
• Opinionated VS Code environment setup for a focused learning experience.
• Demonstrates applications from Atari games and robotics to LLM fine-tuning.

Maintenance & Community

No specific community links (Discord/Slack) or details on contributors/sponsorships are provided in the README.

Licensing & Compatibility

The repository's license is not specified in the README.

Limitations & Caveats

The README does not specify the license, which may impact commercial use or integration into closed-source projects. While Docker provides a reproducible environment, users without Docker experience may face an initial learning curve.