drl-zh - An Introduction to Foundational Algorithms in Deep Reinforcement Learning

Discovering Deep Reinforcement Learning with the "drl-zh" Project

The "drl-zh" project provides an engaging and practical introduction to the world of deep reinforcement learning (RL). Aspiring enthusiasts and seasoned programmers alike are offered the opportunity to delve into foundational and classic deep RL algorithms. By participating in this project, individuals can expect to not only implement essential algorithms like Deep Q-Network (DQN), Soft Actor-Critic (SAC), and Proximal Policy Optimization (PPO) from scratch but also gain a comprehensive understanding of the theoretical concepts behind these advanced tools.

The Exciting Journey

The project's ultimate goal is to empower users with the ability to train artificial intelligence (AI) capable of playing Atari games and even performing complex tasks like landing on the moon. This hands-on approach ensures that participants acquire both practical coding skills and an appreciation for the sophisticated mathematical concepts that drive these algorithms.

Setting Up the Environment

To foster a focused learning experience, the "drl-zh" project adopts a specific setup strategy:

Install Miniconda - The project recommends using Miniconda, a streamlined version of Anaconda. Miniconda functions as a complete environment manager, which allows users to precisely choose their Python version according to their project's needs.
Repository Checkout - Users should checkout the Git repository and navigate to the correct folder for further actions.
Create and Activate Virtual Environment - With the command conda create --name drlzh python=3.11 followed by conda activate drlzh, users can create an isolated environment tailored for this project.
Install Dependencies via Poetry - Poetry, a Python dependency manager, is then used to install all necessary packages. This includes gymnasium[accept-rom-license], a critical package for Atari game simulations, making it essential to agree to license terms during installation.
Use Visual Studio Code - Finally, installing Visual Studio Code is suggested to facilitate a seamless coding and testing experience.

Getting Started with Learning

Once the environment is ready, the journey begins by opening the repository folder within Visual Studio Code. It is advised to retain the .vscode folder to ensure consistency with setup configurations. Participants kick off their learning by exploring the 00_Intro.ipynb notebook, where interactive coding awaits.

Each notebook gradually introduces new concepts, with placeholders (TODO sections) encouraging users to implement certain parts of algorithms. Occasionally, unit tests are incorporated to allow learners to verify their code implementations. This step-by-step process continues throughout the series of notebooks, culminating in a comprehensive understanding and practical proficiency in deep RL.

For those who prefer a more guided approach, upcoming YouTube video tutorials will offer expanded explanations and detailed coding walkthroughs. These resources will cater to visual learners and provide additional context to enhance the learning experience.

Embark on this educational venture with the "drl-zh" project and explore the fascinating intersection of AI and deep reinforcement learning!