  We introduce a novel apprenticeship learning algorithm to learn an expert's
underlying reward structure in off-policy model-free \emph{batch} settings.
Unlike existing methods that require a dynamics model or additional data
acquisition for on-policy evaluation, our algorithm requires only the batch
data of observed expert behavior. Such settings are common in real-world
tasks---health care, finance or industrial processes ---where accurate
simulators do not exist or data acquisition is costly. To address challenges in
batch settings, we introduce Deep Successor Feature Networks(DSFN) that
estimate feature expectations in an off-policy setting and a
transition-regularized imitation network that produces a near-expert initial
policy and an efficient feature representation. Our algorithm achieves superior
results in batch settings on both control benchmarks and a vital clinical task
of sepsis management in the Intensive Care Unit.
