It's a matter of timescale: non-linear utility in successor features and multi-objective planning and learning

Abstract:Time is of the essence when dealing with multiple reward signals and non-linear utility. In this paper we argue that the current main approaches in multi-objectiveRL (SER and ESR), and successor features, are insufficient. While each approach deals with non-linear effects on user utility on different timescales, none of them take into account that different effects happening on different timescales can happen within the same decision problem. We motivate that this can indeed be the case by an example, both intuitively and numerically, leading to a new perspective, and a significant and non-trivial gap in the literature.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2608.25723 [cs.LG]

(or arXiv:2608.25723v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.25723 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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