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Evidence record 420 · automatically gathered

AETDICE: Unified Framework and Offline Optimization for Nonlinear Multi-Objective RL

Optimizing nonlinear preferences in multi-objective reinforcement learning (MORL) is essential for capturing complex trade-offs like risk aversion or fairness. However, such non-linearity has historically bifurcated nonlinear MORL objectives into two distinct paradigms: Scalarized Expected Return (SER) and Expected Scalarized Return (ESR). While SER requires global-level optimization and ESR requires non-Markovian policies, leading to fragmented optimization strategies, we bridge this divide thr

Record details

Published: 30 June 2026
Source: arXiv
Category: Research
Topics: Bias & fairness
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Shared topic: Bias & fairness

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Shared topic: Bias & fairness

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Shared topic: Bias & fairness

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Shared topic: Bias & fairness

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How to cite this record

ethics.ai (30 June 2026), “AETDICE: Unified Framework and Offline Optimization for Nonlinear Multi-Objective RL,” evidence record 420, https://ethics.ai/record/420 (originally published by arXiv).

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