用强化学习根据生理数据自动生成蜘蛛，实现恐惧症个性化治疗。
Personalizing Exposure Therapy via Reinforcement Learning
- 基于生理信号驱动的强化学习生成虚拟蜘蛛内容
- 真人实验显示优于传统规则方法，提升治疗效果
- 适合需要动态调整刺激强度的心理治疗场景
个性化治疗通过针对个体患者调整治疗方案可提升健康成效。目前通常依赖治疗师的经验与直觉，并结合患者反馈，但需治疗师掌握技术细节，如虚拟现实暴露疗法（VRET）。现有自动适应方法多依赖人工预设规则，难以泛化。本文提出一种基于生理测量自动适配治疗内容的方法，应用于虚拟现实恐蛛症暴露治疗中，利用经验驱动的程序化内容生成强化学习（EDPCGRL）生成匹配个体患者的虚拟蜘蛛。通过真人实验验证，该系统显著优于常见规则基方法，展现了其在增强个性化治疗干预中的潜力。
原文摘要 · Abstract (English)
Personalized therapy, in which a therapeutic practice is adapted to an individual patient, can lead to improved health outcomes. Typically, this is accomplished by relying on a therapist's training and intuition along with feedback from a patient. However, this requires the therapist to become an expert on any technological components, such as in the case of Virtual Reality Exposure Therapy (VRET). While there exist approaches to automatically adapt therapeutic content to a patient, they generally rely on hand-authored, pre-defined rules, which may not generalize to all individuals. In this paper, we propose an approach to automatically adapt therapeutic content to patients based on physiological measures. We implement our approach in the context of virtual reality arachnophobia exposure therapy, and rely on experience-driven procedural content generation via reinforcement learning (EDPCGRL) to generate virtual spiders to match an individual patient. Through a human subject study, we demonstrate that our system significantly outperforms a more common rules-based method, highlighting its potential for enhancing personalized therapeutic interventions.
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