How does Q-Learning handle continuous state spaces?

It uses function approximation like neural networks or discretization techniques.

Since Q-tables cannot handle infinite state spaces, continuous inputs are mapped using function approximators or discretized into bins. Deep Q-Networks are the most common solution.