Plenty of real world situations have clear objectives with obvious rewards.
Take children to school -> they safely arrive on time.
Autonomous driving -> arrive at destination without crashing.
Call centre -> customers are happy.
Or maybe there is some art to finding happiness in simple things like having folded clothes or surviving the commute?
I guess you can always find some well-specified, measurable goal/reward, but then that choice limits the performance of your model. It's fine when you're building a very specialized system; it gets more difficult the more general you're trying to be.
For a general system meant to operate in human environment, the goal ends up approaching "things that humans like". Case in point, that's what the overall LLM goal function is - continuations that make sense to humans, in fully-general meaning of that.
>> Take children to school -> they safely arrive on time.
>> Autonomous driving -> arrive at destination without crashing.
>> Call centre -> customers are happy.
Define a) "folded", b) "safely", c) "destination", d) "happy".
Also define the reward functions for each of the four objectives above.
Destination -> Like, close to the destination? I don't see how that's hard.
Happy -> you can use customer feedback for this
Folded -> this is indeed the trickiest one, but I think well within the capabilities of modern vision models.
Really? What about fires? Falling off cliffs? Causing others to crash?
Your "examples" are all hand-wavy and vague and no good to train an RL agent. You've also not provided a reward function.
Of course the delay is much bigger with working a job than most RL games but fundamentally it's the same problem.