This repository provides intuitive, hands-on tutorials for reinforcement learning and decision-making, targeting newcomers to the field. It aims to demystify complex concepts through clear explanations and interactive Jupyter Notebooks, fostering community contributions for improvement.

How It Works

The project employs a structured curriculum covering foundational concepts to advanced topics like discrete/continuous states/actions, partial observability, and multi-agent systems. It utilizes Jupyter Notebooks for practical implementation and visualization, with a Dockerized environment to ensure reproducibility and ease of setup for users.

Quick Start & Requirements

• Install: Clone the repository and use Docker.

  git clone git@github.com:mimoralea/applied-reinforcement-learning.git
  cd applied-reinforcement-learning
  docker pull mimoralea/openai-gym:v1
  docker run -it --rm -p 8888:8888 -p 6006:6006 -v $PWD/notebooks/:/mnt/notebooks/ mimoralea/openai-gym:v1
  

• Prerequisites: Git, Docker.
• Access: Notebooks at http://localhost:8888, TensorBoard at http://localhost:6006.
• Docs: Installation, Run Notebooks

Highlighted Details

• Comprehensive curriculum from basic decision-making to multi-agent systems.
• Docker image (mimoralea/openai-gym:v1) pre-configures the environment.
• Includes TensorBoard integration for visualizing neural networks.
• Encourages community contributions for content and code improvements.

Maintenance & Community

The project is actively seeking community collaboration for typos, fixes, and new content. Further community engagement channels are not explicitly listed in the README.

Licensing & Compatibility

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

Limitations & Caveats

The README does not specify the project's license, which may impact commercial use or closed-source integration. The content is presented as an initial attempt, implying potential for ongoing refinement and potential for breaking changes as the project evolves.