Cart-Pole Guide (Written Tutorial)
This guide will cover everything from creating the Cart-Pole USD to applying Reinforcement Learning Algorithms to balance it effectively, serving as template for more complex tasks later on. We’ll follow theTraining with an RL Agent — Isaac Lab Documentation as a foundation, but go way deeper, breaking down concepts and steps in the simplest way possible to make it easy to follow along.
We start by the main head script train.py [Tutorial] and systematically work our way through all its functionalities, exploring each part step by step from top to bottom.
The Full-Script can be found here but can be ignored as we walk through it anyway:
Note: - Comments are marked in # red - Hyperlinks are marked with a blue background - These are also accessible inside the code, indicated by: # Hyperlink Example - Also quite useful is the “Copy Code” Button on top of the code snippets
Top Level Script (train.py) in digestible parts
AppLauncher
Starting with the AppLauncher, which sets up a command-line interface (CLI) to configure and launch the simulation. The process initializes Isaac Sim 
Isaac Sim Introduction, which Isaac Lab is built on.
Imports
After having learnt on how to configure and start the simulation using the CLI, we now take a look the used libraries and modules. Each import is explained and include Hyperlinks to the library documentations, as well as the paths for Isaac Lab Modules to ease the process of finding the information you may need.