通过经验驱动的强化学习设计者闭环情感循环
Closing the Affective Loop via Experience-Driven Reinforcement Learning Designers
AI总结：
本研究提出经验驱动强化学习（EDRL）框架，以唤醒度轨迹语料库构建奖励函数，可根据目标情感模式生成定制化赛车赛道，性能优于搜索式方法，可推广至各类情感适配内容生成场景。
摘要：
自主根据一组预设的情感模式定制内容，长期以来一直被视为广义上的情感感知人机交互领域的圣杯。经验驱动的程序化内容生成框架通过搜索能为用户唤起特定体验模式的内容，实现了这一愿景。本文提出了一种用于生成情感定制内容的新型强化学习（RL）框架，并在赛车游戏领域对其进行了测试。具体而言，经验驱动强化学习（EDRL）框架接收目标唤醒度轨迹，随后生成能为特定类型玩家唤起所需情感反应的赛道。EDRL 利用一种奖励函数，该函数可基于唤醒度轨迹语料库评估任意生成赛道的情感模式。研究结果表明，EDRL 能够根据设计者的风格准确生成情感驱动的赛车游戏关卡，其表现优于基于搜索的个性化内容生成方法。该方法不仅可直接应用于游戏内容生成任务，还可广泛应用于任何利用内容进行情感适配的领域。
英文摘要：
Autonomously tailoring content to a set of predetermined affective patterns has long been considered the holy grail of affect-aware human-computer interaction at large. The experience-driven procedural content generation framework realises this vision by searching for content that elicits a certain experience pattern to a user. In this paper, we propose a novel reinforcement learning (RL) framework for generating affect-tailored content, and we test it in the domain of racing games. Specifically, the experience-driven RL (EDRL) framework is given a target arousal trace, and it then generates a racetrack that elicits the desired affective responses for a particular type of player. EDRL leverages a reward function that assesses the affective pattern of any generated racetrack from a corpus of arousal traces. Our findings suggest that EDRL can accurately generate affect-driven racing game levels according to a designer's style and outperforms search-based methods for personalised content generation. The method is not only directly applicable to game content generation tasks but also employable broadly to any domain that uses content for affective adaptation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
