research
The Higher Goal Reweighted the Lower Ones
A reinforcement-learning controller generates state-dependent preferences among competing objectives instead of fixing their weights in advance.
Summary
A reinforcement-learning controller generates state-dependent preferences among competing objectives instead of fixing their weights in advance.
The framework pairs a multi-objective inner controller with an outer preference generator trained on a higher-level goal. In constructed exploration environments, the learned preferences switched priorities by context, made graded trade-offs and persisted over time while outperforming fixed and handcrafted strategies. The paper defines a computational mechanism inspired by emotion; it does not demonstrate feelings or subjective experience.
Why it matters
A reinforcement-learning controller generates state-dependent preferences among competing objectives instead of fixing their weights in advance.
Limits and context
- The paper defines a computational mechanism inspired by emotion; it does not demonstrate feelings or subjective experience.
Key claims
A reinforcement-learning controller generates state-dependent preferences among competing objectives instead of fixing their weights in advance.
Qualification: The paper defines a computational mechanism inspired by emotion; it does not demonstrate feelings or subjective experience.
Evidence: source-2026-08-29-012
Sources
- arXiv preprint 2608.27072arXiv · primary research
Corrections
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