RL excels at learning control problems. It is mathematically guaranteed to provide an optimal solution for the state and controls you provide it, given enough runtime. For some problems (playing computer games), that runtime is surprisingly short.
There is a reason self-driving cars use RL, and don't use GPTs.
Some part of it, but I would argue with a lot of guardrail in place and not as common as you think. I don't think the majority of the planner/control stack out there in SDC is based. I also don't think any production SDCs are RL-based.
Apparently AI sets the best time even better than the pros It is really useful when it comes to controlled environment optimizations
Control theory and reinforcement learning are different ways of looking at the same problem. They traditionally and culturally focussed on different aspects.