You need to have a working Linux installation (Unix) for this class. There are primarily three ways you can get Linux:

• MacOS. Oh yeah, native support out of box. The M1-chip MBA is my main-hand weapon for research.
• Dual boot your laptop to run Linux along with Window. This requires some BIOS hacking skills. If you get it work, it will save lots of installation troubles. However, I don’t have many instructions on this approach since different computers come with different BIOS. In this case, Google is your friend who may help you get this done. If you choose to go this route, please choose Ubuntu 20.04.
• Linux on Windows via WSL2. As a virtual machine solution, WSL2 really suprises me with its undiscounted performance, even with native Nvidia GPU support now.

Note that even though a virtual machine (like VMbox) can technically work, with the great performance cost, it is barely usable for deep reinforcement learning development.

If you choose WSL2 - Windows 11 Linux Subsystem

Follow the instructions here:

https://learn.microsoft.com/en-us/windows/wsl/install

Remember to install WSL2 not 1, and use the distro version as Ubuntu 24.04 LTS.

Post-WSL2 Installation

Install Windows Terminal HERE.

Run this command in your Ubuntu to install the necessary packages:

sudo apt update

Test for GUI apps:

• To test if the X11 server installation is successful,
  • Install xeyes by running sudo apt install x11-apps.
  • Open terminal and run xeyes.
  • If the installation is good, you should see a pair of eyes pop up.

Access WSL files via Windows

When you open the Windows File Explorer, at the left navigation bar, you should find the Linux icon (a penguin). From there, go to Ubuntu –> home –> <your_username> to find the files and folders on WSL2. Create a folder named drl. This could be where you put all your code for this class. Bookmark this folder in your Windows File explorer for quick access in the future.