  We apply recent advances in deep generative modeling to the task of imitation
learning from biological agents. Specifically, we apply variations of the
variational recurrent neural network model to a multi-agent setting where we
learn policies of individual uncoordinated agents acting based on their
perceptual inputs and their hidden belief state. We learn stochastic policies
for these agents directly from observational data, without constructing a
reward function. An inference network learned jointly with the policy allows
for efficient inference over the agent's belief state given a sequence of its
current perceptual inputs and the prior actions it performed, which lets us
extrapolate observed sequences of behavior into the future while maintaining
uncertainty estimates over future trajectories. We test our approach on a
dataset of flies interacting in a 2D environment, where we demonstrate better
predictive performance than existing approaches which learn deterministic
policies with recurrent neural networks. We further show that the uncertainty
estimates over future trajectories we obtain are well calibrated, which makes
them useful for a variety of downstream processing tasks.
