r/deeplearning

I built Router Rumble, a small Python experiment that compares gradient descent and NSGA-II by placing a Wi-Fi router in a simulated home.

The GIF replays recorded search states. The objective counts locations where the signal reaches −57 dBm. Gradient descent tests four nearby positions, gets the same count each time, and estimates a zero slope. It stays at 100 covered locations. NSGA-II explores a population of 32 positions and reaches 209 of the 560 sampled locations.

Both methods get 1,200 objective evaluations, including initialization and local probes. Their starts differ: gradient descent starts at one position, while NSGA-II starts with a population spread across the room. This example uses NSGA-II with a single objective.

The repo includes smooth-objective comparisons, results across 20 seeds, and an interactive replay you can scrub through. You can change the walls, signal target, seed, or evaluation budget and rerun the experiment locally:

git clone https://github.com/austin-starks/router-rumble.git
cd router-rumble
uv run run_demo.py

It uses NumPy and pymoo and runs without a GPU or API key.