On the Importance of Representation in Imitating Human-Like Gameplay
Ville Tanskanen, Arto Klami, Ville Hautamäki · 2025
Being able to synthesize human-like game play is highly useful for automating play-testing, generating naturally behaving bots, and for assisting game design in general. However, for complex games that require long-term planning this remains a major challenge. Imitation learning (IL) algorithms offer one approach for learning to replicate human-like behavior by training models that imitate the behavior observed in human demonstrations, and in fact much of the research in IL is done in context of games due to fast feedback loops and relatively easy access to the required data. One recent approach employing IL is Video PreTraining (VPT) that leverages representation models trained on massive unlabeled video collection of humans playing a complex game, specifically Minecraft. Despite promising results on a complex task, in this work we empirically demonstrate a failure mode of VPT as a representation learner in Minecraft, showing how the pretrained model is insufficient for providing useful representations for new tasks. We then explain how the fundamental challenge remains even in a substantially simplified game environment. We argue that for many practitioners finetuning the representation models for the tasks of interest is unfeasible, making the overall approach of limited interest for use in game design applications.