A cookbook for training robot drivers in a fake world

What it does

This is a collection of Jupyter notebook tutorials for experimenting with autonomous-driving ML. It packages datasets, helper scripts, and binaries alongside instructions for training models inside Microsoft’s AirSim simulator. The goal is to lower the barrier to entry for beginners, researchers, and industry experts who want to explore behavioral cloning and reinforcement learning without building a full data pipeline from scratch.

The interesting bit

Rather than treating simulation as a toy, the project uses AirSim as a primary data factory—generating the massive volumes of varied scenarios that real-world fleets struggle to log. You train in the sim and fine-tune with a smaller slice of reality, which is practically the only way to reach the hundreds of millions of miles cited as necessary for level-4/5 autonomy.

Key highlights

• Two complete tutorials: end-to-end deep learning and distributed deep reinforcement learning
• Self-contained notebooks that ship with datasets and helper scripts where needed
• Built on open-source tools (Keras, TensorFlow) and Microsoft tech (AirSim, CNTK, Azure Batch AI)
• Explicitly aimed at a wide audience from beginners to industry experts
• Maintained by Microsoft Garage’s Project Road Runner

Caveats

• Only two tutorials are currently live, with more scenarios listed as “coming soon”
• Explicitly labeled a work in progress by the maintainers
• Tutorials lean heavily on AirSim and Microsoft tools such as CNTK and Azure Batch AI alongside open-source frameworks

Verdict

Worth a look if you want a guided, low-friction on-ramp to autonomous-driving ML via simulation. Skip it if you need a mature, framework-agnostic production pipeline or already have your own data collection fleet.