Closing the Affective Loop via Experience-Driven Reinforcement Learning Designers

TL;DR

This paper introduces a reinforcement learning framework that autonomously generates game content, specifically racetracks, tailored to elicit specific emotional responses, advancing affect-aware human-computer interaction.

Contribution

It presents a novel experience-driven RL approach for affective content generation, demonstrating its effectiveness in racing game level design and outperforming search-based methods.

Findings

EDRL accurately generates affect-driven racing levels

Outperforms search-based content generation methods

Applicable to various domains requiring affective content adaptation

Abstract

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.