  A primary difficulty with unsupervised discovery of structure in large data
sets is a lack of quantitative evaluation criteria. In this work, we propose
and investigate several metrics for evaluating and comparing generative models
of behavior learned using imitation learning. Compared to the commonly-used
model log-likelihood, these criteria look at longer temporal relationships in
behavior, are relevant if behavior has some properties that are inherently
unpredictable, and highlight biases in the overall distribution of behaviors
produced by the model. Pointwise metrics compare real to model-predicted
trajectories given true past information. Distribution metrics compare
statistics of the model simulating behavior in open loop, and are inspired by
how experimental biologists evaluate the effects of manipulations on animal
behavior. We show that the proposed metrics correspond with biologists'
intuitions about behavior, and allow us to evaluate models, understand their
biases, and enable us to propose new research directions.
