I try to create autonomous car using keyboard data so this is a multi class classification problem. I have keys W,A,S and D. So I have four categories. My model should decide what key should be pressed based on the screenshot (or some other data, I have some ideas). I have some API that I can use to capture keyboard data and screen (while gathering data) and also to simulate keyboard events (in autonomous mode when the car is driven by neural network).
Should I create another category called for example "NOKEY"? I will use sigmoid function on each output neuron (instead of using softmax on the all neurons) to have probabilities from 0 to one for each category. But I could have very low probabilities for each neuron. And it can mean either that no key should be pressed or that the network doesn't know what to do. So maybe I should just create additional "artificial" category? What is the standard way to deal with such situations?