[xthestreams]

> Instead of doing anything fancy, my program generates the coefficients at random to explore the space. If I wanted to generate a good driver for a course, I’d run a few thousand of these and pick the coefficients that complete the course in the shortest time.

It is worth pointing out that this strategy can likely overfit on the data that you have used for training: when you change the track, your car may not behave as good as before. In other words: the coefficients are only good for that specific track(s).

The author is still using Machine Learning, even if not with neural networks: the need for rigorous strategies for model selection doesn't disappear.

[firethief]

> It is worth pointing out that this strategy can likely overfit on the data that you have used for training: when you change the track, your car may not behave as good as before. In other words: the coefficients are only good for that specific track(s).

More generally, I think this approach is only suited to "static" courses, which only matter in contrived demos. Any real use of a steering algorithm requires reacting to conditions that can't be predicted a priori; e.g. if you have to avoid collisions with other cars, and one car is controlled by a human player, every run is effectively a different track and overfitting like this would not be an option.