Creating Diverse Play-Style-Centric Agents through Behavioural Cloning

Abstract

Developing diverse and realistic agents in terms of behaviour and skill is crucial for game developers to enhance player satisfaction and immersion. Traditional game design approaches involve hand-crafted solutions, while learning game-playing agents often focuses on optimizing for a single objective, or play-style. These processes typically lack intuitiveness, fail to resemble realistic behaviour, and do not encompass a diverse spectrum of play-styles at varying levels of skill. To this end, our goal is to learn a set of policies that exhibit diverse behaviours or styles while also demonstrating diversity in skill level. In this paper, we propose a novel pipeline, called PCPG (Play-style-Centric Policy Generation), which combines unsupervised play-style identification and policy learning techniques to generate a diverse set of play-style-centric agents. The agents generated by the pipeline can effectively capture the richness and diversity of gameplay experiences in multiple video game domains, showcasing identifiable and diverse play-styles at varying levels of proficiency.