# Spiders Based on Anxiety: How Reinforcement Learning Can Deliver Desired User Experience in Virtual Reality Personalized Arachnophobia Treatment

arXiv: https://arxiv.org/abs/2409.17406 · HTML: https://mlsanity.com/paper/2409.17406/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 127 references that are in the map.
- First posted on arXiv 25 Sep 2024.

## Builds on (most cited first, 6 of 6)

- [PCGRL: Procedural Content Generation via Reinforcement Learning](https://mlsanity.com/paper/2001.09212/) (2020)
- [Neurosymbolic Reinforcement Learning with Formally Verified Exploration](https://mlsanity.com/paper/2009.12612/) (2020)
- [Friend, Collaborator, Student, Manager: How Design of an AI-Driven Game Level Editor Affects Creators](https://mlsanity.com/paper/1901.06417/) (2019)
- [A Comparison of Methods for Player Clustering via Behavioral Telemetry](https://mlsanity.com/paper/1407.3950/) (2014)
- [Arachnophobia Exposure Therapy using Experience-driven Procedural Content Generation via Reinforcement Learning (EDPCGRL)](https://mlsanity.com/paper/2110.04146/) (2021)
- [Designer Modeling through Design Style Clustering](https://mlsanity.com/paper/2004.01697/) (2020)

## Abstract (arXiv)

The need to generate a spider to provoke a desired anxiety response arises in the context of personalized virtual reality exposure therapy (VRET), a treatment approach for arachnophobia. This treatment involves patients observing virtual spiders in order to become desensitized and decrease their phobia, which requires that the spiders elicit specific anxiety responses. However, VRET approaches tend to require therapists to hand-select the appropriate spider for each patient, which is a time-consuming process and takes significant technical knowledge and patient insight. While automated methods exist, they tend to employ rules-based approaches with minimal ability to adapt to specific users. To address these challenges, we present a framework for VRET utilizing procedural content generation (PCG) and reinforcement learning (RL), which automatically adapts a spider to elicit a desired anxiety response. We demonstrate the superior performance of this system compared to a more common rules-based VRET method.