I believe that there is no clear answer to your question. It essentially boils down to whether you are a reductionist – whether you believe that quantitative measurements can truly give justice to the complexity of the real world, and that a framework such as expectation maximization can losslessly capture what we care about as humans in the performing of tasks.
From a non-reductionist perspective, one would be aware that almost any mathematical representation of complex real-world goals will necessarily be a proxy rather than the true goal (as many goals are not mathematically formalizable, such as what we perceive as "good music" or "meaning"), and thus the reward hypothesis is at best an approximation. Based on this, a non-reductionist's reward hypothesis could be rephrased as:
that all of what we mean by goals and purposes can be
well thought of approximately operationalized (albeit at a certain domain-dependent loss) as the maximization of the expected value of the cumulative sum of a received scalar signal (called reward)
Clearly the original (stricter) version of the reward hypothesis does apply to some cases, such as purely-quantitative domains (e.g. maximizing $ earned on the stock market, or maximizing score in a video game), but as soon as the problem involves enough "complexity" (e.g. humans, or wherever you think the boundary should be), a non-reductionist would say that mathematics is clearly not fit to the task to truly capture the intended goal.
More info on the reward hypothesis (as presented by Michael Littman himself) is here. I would have added it as a comment to the question but do not have enough reputation.