Emotional Preferences as Goal-Priority Regulation

📅 2026-08-27
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🤖 AI Summary
本文探讨了如何通过强化学习实现由高层次目标自主生成的情绪偏好来调节竞争性低层次目标的优先级问题。
📝 Abstract
A core question in decision-making for agents is whether the relative priorities of competing lower-level objectives can be determined by emotional preferences autonomously generated by higher-level goals, rather than being externally prespecified. Under changing external environments and evolving internal states, emotions play an important functional role in regulating the relative priorities of competing goals. Inspired by the goal-directed theory of emotion, this paper studies how such preference regulation can be computationally realized through reinforcement learning. We first propose a conception of emergent emotional preference: a high-level goal autonomously induces state-dependent preferences over competing lower-level objectives. This conception is built upon a framework consisting of a multi-objective reinforcement learning inner controller and an outer preference generator. The inner controller provides a repertoire of preference-conditioned goal-directed behaviors, while the outer preference generator learns a mapping from the current state to objective preferences through reinforcement learning on a high-level goal. We operationalize emotional preference as a state-dependent regulation of relative goal priorities that emerges through optimization. Furthermore, we characterize the policy space induced by preference regulation and derive an upper bound on the optimality gap in terms of the representation error of the inner behavioral repertoire. We show that the gap vanishes when the optimal policy can be represented by the available preference-conditioned policies. Experiments in self-constructed multi-objective exploration environments show that the learned preference function exhibits contextual priority switching, graded trade-offs, and temporal persistence, and outperforms the evaluated fixed-preference and handcrafted-preference strategies.
Problem

Research questions and friction points this paper is trying to address.

Emotional Preferences
Goal-Priority Regulation
Reinforcement Learning
Multi-Objective Decision Making
Autonomous Preference Generation
Innovation

Methods, ideas, or system contributions that make the work stand out.

emotional preference
reinforcement learning
goal-priority regulation
multi-objective reinforcement learning
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