Humanlike Behavior in a Third-Person Shooter with Imitation Learning

We tackle the problem of generating humanlike bot behavior by learning from human demonstrations. We developed a controlled gym environment to collect data on a subset of human behavior-namely aiming and target acquisition in single opponent settings. We introduce an identity-conditioned causal transformer to produce humanlike behavior of a controllable quality on a per-frame basis that captures the differences in skill and style between conditioned players.