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Looks cool but a bit confusing: I don't know much of neural networks, but I thought they were based on iterations and trial and error.

Last time I've seen them (elsewhere), they tried to complete a level, died, tried again, died, tried again, etc.

Here I don't know what I'm seeing instead :D although I'm pretty sure someone who does know neural networks will understand it.

That being said, I have an unrelated small piece of advice: adjust the layers so that the player is on top, the bots below, and the yellow agents below the bots.

Hey thanks for leaving such a thorough comment, I really appreciate it!

It's a bit of a mess right now, but it was uploaded to serve as the technical result of a college assignment, that's probably why there's little to no time spent on design and user-friendliness... Sorry about that.

Each car has a neural network with a couple of sensors (6 wall sensors and 1 flow sensor, I will update the page with additional information about this asap). They have a fitness value based on how far they got and how fast. This is a bit different from the usual car racing along a track network as it has to abide by the same rules as the player and finish laps etc. Once all agents turned inactive (10/20 sec of no real progress) they reset and gain neural weights from the best agents of the previous generation and weight values from the overall best agents ever. A really small percentage stays randomized to possibly reset the agents when they have a major screw up near the start or to find a breakthrough with luck.

I'm planning on drawing the sensors of the networks to show a bit better what's going on, but life is quite busy at the moment, so this might take a while to be updated haha

The 'player' aspect of the game was just something that was left over from my first research question, which I changed later on. I just left it in, together with the generic AI. 

Thanks against for checking it out and pointing out some of the many flaws!