/drl-zh

Deep Reinforcement Learning: Zero to Hero!

Primary LanguageJupyter NotebookMIT LicenseMIT

Deep Reinforcement Learning: Zero to Hero!

Welcome to the most hands-on reinforcement learning experience!

This is a short and practical introductory course on foundational and classic deep reinforcement learning algorithms. By the end of the course, you will have written from scratch algorithms like DQN, SAC, PPO, as well as understood at a high-level the theory behind them.

We will be able to train an AI to play Atari games and land on the Moon!

Environment Setup

To make sure we can focus on learning, the environment setup is opinionated 😊 Here it is:

  • Install Miniconda
    Why conda? Because it's a full envinronment manager, and we can choose the Python version too.

  • Checkout this Git repository, and cd into its folder.

  • Create and activate the drlzh virtual environment:

    conda create --name drlzh python=3.11
conda activate drlzh

  • Install Poetry and install dependencies:
    Dependencies include gymnasium[accept-rom-license] for Atari. Make sure to accept the license agreement when installing the dependencies of the project via Poetry. 

    pip install poetry
poetry install

  • Install Visual Studio Code

How Do I Start?

Open this repository folder in Visual Studio Code (make sure to keep the .vscode folder for settings consistency, running on Jupyter might require some tweaks to code and imports).

Open the first 00_Intro.ipynb notebook in Visual Studio Code, and follow along! From there, just keep moving on to the next notebooks. If you get stuck, feel free to check the /solution folder.

For an expanded treatment and step-by-step coding, check out the YouTube videos!