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

arXiv:2606.31178v1 Announce Type: new Abstract: 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 through the Aggregation-Expectation-Transformation (AET

Source: arXiv cs.LG — read the full report at the original publisher.

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