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If I understood correctly you're looking at a Multi-Objective Reinforcement Learning (MORL). Keep in mind however that many scientist will often follow the reward hypothesis (Sutton and Barto) which says that All of what we mean by goals and purposes can be well thought of as the maximization of the expected value of the cumulative sum of a received scalar ...


4

What if a scalar reward is insufficient, or its unclear on how to collapse a multi-dimensional reward to a single dimension. Example, for someone eating a burger, both taste and cost are important. Agents may prioritize taste and cost differently, so its not clear on how to aggregate the two. It is also not clear on how a subjective categorical taste value ...


2

I agree with Tomasz that the approach you are describing falls within the field of MORL. For a solid introduction MORL I would recommend the survey by Roijers, D. M., Vamplew, P., Whiteson, S., & Dazeley, R. (2013). A survey of multi-objective sequential decision-making. Journal of Artificial Intelligence Research, 48, 67-113. https://www.jair.org/index....


2

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 ...


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