You're right that having an optimization system turns the problem from one of solving the problem to one of defining a metric that we directly care about.

For battery optimization use cases, e.g. screen brightness, one metric that works is minimising the amount of times a user goes and changes the settings (with reinforcement learning), and I think that could definitely be a starting point for thermostats.

Obviously, you also care about other things in a thermostat, e.g. energy usage (eg when you're not home), and maybe the amount of changes a user needs to make is not sufficiently tight supervision for a thermostat, but it's a starting point that does take personalization into account.