Depending on the context, 'turn the wheel left two degrees' can be a non-rampant, a very rampant, or a rampage-compensating action.
One would expect the output of the neural nets to be encoded in those.
1. "Keep the car in a straight line" => The road is closed or there is a detour, or the lane markers aren't visible (or incorrectly marked like on the Palo Alto stretch of the 101 where there are two sets of lane markers; one new (the "actual" markers) and another old but barely faded and still very visible.
2. "Turn right, 10 degree" => With what steering radius? What if the road is bumpy or sloped and the driver has to compensate with the wheel?
etc.
These corner cases are everyday occurrences and lend a necessity for something far more sophisticated than a mere ad hoc model to represent and control human-level driving behaviors with a finite set of rudimentary actions.
Actuator inputs like steering and pedals are almost always represented using continuous values rather than discrete sets for these very reasons.