If you want to prototype autonomous behavior without cloud inference, GPU clusters, or reinforcement learning frameworks, the browser is enough. NeuroDrive is a React + p5.js + TensorFlow.js application where a population of simulated cars learns to navigate a noisy, curved race track via neuroevolution. Why Neuroevolution for This Problem Instead of differentiable rewards and backprop through time, we use an evolutionary loop: Initialize many random brains Run them in a common environment Score by survival/progress (fitness) Preserve elites + mutate offspring Repeat This works especially well for simple control tasks where evaluating many candidate policies is cheap. High-Level Architecture 1) Environment: Procedural Track With Boundaries The track is built from radial points whose radius is perturbed with Perlin noise. Inner and outer walls are derived from local perpendicular vectors, producing a continuous ribbon-like lane. Core track generation pattern: const angle = ( i / numPoints ) * Math .…