There are plenty of domains where the objective is quite well defined. Video games are excellent examples. An agent that achieved superhuman performance on, say, Starcraft, would have a very impressive suite of capabilities involving some significant advances from the current state of the art. As such these tasks are great drivers of research.
But you're absolutely right that humans don't optimize any 'simple' reward function, and achieving human-like behavior in real-world domains will likely require learning reward functions. There are a few people starting to think about this, e.g. this paper https://arxiv.org/abs/1606.03137 (disclaimer: work from my research group though I'm not involved) proposing a framework by which a robot can learn a human's (implicit, complex) values through interaction. This is also related to concerns over AI safety, since naive reward-optimizing agents are (like drug addicts) willing to do arbitrarily bad things in service of maximizing their prescribed "reward", while agents that maintain uncertainty over reward functions are willing to ask for guidance when confused or unsure. However this line of research is much more preliminary and academic -- there are probably people at DeepMind thinking along these lines, but certainly their focus is more on directions that will produce new breakthroughs and practical capabilities within forseeable timeframes.
But you're absolutely right that humans don't optimize any 'simple' reward function, and achieving human-like behavior in real-world domains will likely require learning reward functions. There are a few people starting to think about this, e.g. this paper https://arxiv.org/abs/1606.03137 (disclaimer: work from my research group though I'm not involved) proposing a framework by which a robot can learn a human's (implicit, complex) values through interaction. This is also related to concerns over AI safety, since naive reward-optimizing agents are (like drug addicts) willing to do arbitrarily bad things in service of maximizing their prescribed "reward", while agents that maintain uncertainty over reward functions are willing to ask for guidance when confused or unsure. However this line of research is much more preliminary and academic -- there are probably people at DeepMind thinking along these lines, but certainly their focus is more on directions that will produce new breakthroughs and practical capabilities within forseeable timeframes.