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Personalized therapy, in which a therapeutic practice is adapted to anindividual patient, leads to better health outcomes. Typically, this isaccomplished by relying on a therapist's training and intuition along withfeedback from a patient. While there exist approaches to automatically adapttherapeutic content to a patient, they rely on hand-authored, pre-definedrules, which may not generalize to all individuals. In this paper, we proposean approach to automatically adapt therapeutic content to patients based onphysiological measures. We implement our approach in the context ofarachnophobia exposure therapy, and rely on experience-driven proceduralcontent generation via reinforcement learning (EDPCGRL) to generate virtualspiders to match an individual patient. In this initial implementation, and dueto the ongoing pandemic, we make use of virtual or artificial humansimplemented based on prior arachnophobia psychology research. Our EDPCGRLmethod is able to more quickly adapt to these virtual humans with high accuracyin comparison to existing, search-based EDPCG approaches.

Arachnophobia Exposure Therapy using Experience-driven Procedural Content Generation via Reinforcement Learning (EDPCGRL)