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.

Weight norm $\|w\|$ and margin $\gamma$ participate in learning theory via the normalized margin $\gamma/\|w\|$. Since standard neural net optimizers do not control normalized margin, it is hard to test whether this quantity causally relates to generalization. This paper designs a series of experimental studies that explicitly control normalized margin and thereby tackle two central questions. First: does normalized margin always have a causal effect on generalization? The paper finds that no—networks can be produced where normalized margin has seemingly no relationship with generalization, counter to the theory of Bartlett et al. (2017). Second: does normalized margin ever have a causal effect on generalization? The paper finds that yes—in a standard training setup, test performance closely tracks normalized margin. The paper suggests a Gaussian process model as a promising explanation for this behavior.