Title: Steerable Generative Modeling of Playing Style at Scale
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: style, parameter efficient fine-tuning, peft, chess, stylometry, playstyle, representation learning, steerability

Abstract: There has been a growing interest in using AI to model human behavior, particularly in domains where humans interact with this technology. While most existing work models human behavior at an aggregate level, our goal is to model behavior at the individual level. Recent approaches to *behavioral stylometry*---or the task of identifying a person from their actions alone---have shown promise in domains like chess, but these approaches are either not scalable (e.g., fine-tune a separate model for each person) or not generative, in that they cannot actually generate any actions. We address these limitations by framing behavioral stylometry as a multi-task learning problem---where each *task* represents a distinct *person*---and use parameter-efficient fine-tuning (PEFT) methods to learn an explicit *style vector* for each person. Style vectors are generative: they selectively activate shared ``skill" parameters to generate actions in the style of each person. They also induce a latent space that we can interpret and manipulate algorithmically. In particular, we develop a general technique for *style steering* that allows us to steer a player's style vector towards a desired property. We apply our approach to two very different games, at unprecedented scales: chess (47,864 players) and Rocket League (2,000 players).