  Autonomous agents can learn by imitating teacher demonstrations of the
intended behavior. Hierarchical control policies are ubiquitously useful for
such learning, having the potential to break down structured tasks into simpler
sub-tasks, thereby improving data efficiency and generalization. In this paper,
we propose a variational inference method for imitation learning of a control
policy represented by parametrized hierarchical procedures (PHP), a
program-like structure in which procedures can invoke sub-procedures to perform
sub-tasks. Our method discovers the hierarchical structure in a dataset of
observation-action traces of teacher demonstrations, by learning an approximate
posterior distribution over the latent sequence of procedure calls and
terminations. Samples from this learned distribution then guide the training of
the hierarchical control policy. We identify and demonstrate a novel benefit of
variational inference in the context of hierarchical imitation learning: in
decomposing the policy into simpler procedures, inference can leverage acausal
information that is unused by other methods. Training PHP with variational
inference outperforms LSTM baselines in terms of data efficiency and
generalization, requiring less than half as much data to achieve a 24% error
rate in executing the bubble sort algorithm, and to achieve no error in
executing Karel programs.

Machine Learning;Machine Learning