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New research highlights limitations in multi-objective RL timescales

By PulseAugur Editorial · [1 sources] ·

This paper introduces a new perspective on multi-objective reinforcement learning (RL) and successor features, arguing that current approaches are insufficient. The authors contend that existing methods fail to account for how different effects occurring on varying timescales can simultaneously impact a decision problem. They illustrate this gap with an example, highlighting a significant and previously unaddressed challenge in the field. AI

IMPACT Addresses theoretical limitations in multi-objective reinforcement learning, potentially impacting future agent design.

RANK_REASON Research paper published on arXiv detailing novel theoretical contributions to multi-objective reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New research highlights limitations in multi-objective RL timescales

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Research paper published on arXiv detailing novel theoretical contributions to multi-objective reinforcement learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Liam P. H. Mertens, Lucas N. Alegre, Florent Delgrange, Diederik M. Roijers, Ann Now\'e, Peter Vamplew ·

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

    arXiv:2608.25723v1 Announce Type: new 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 eac…

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