  Decision makers often wish to use offline historical data to compare
sequential-action policies at various world states. Importantly, computational
tools should produce confidence values for such offline policy comparison (OPC)
to account for statistical variance and limited data coverage. Nevertheless,
there is little work that directly evaluates the quality of confidence values
for OPC. In this work, we address this issue by creating benchmarks for OPC
with Confidence (OPCC), derived by adding sets of policy comparison queries to
datasets from offline reinforcement learning. In addition, we present an
empirical evaluation of the risk versus coverage trade-off for a class of
model-based baselines. In particular, the baselines learn ensembles of dynamics
models, which are used in various ways to produce simulations for answering
queries with confidence values. While our results suggest advantages for
certain baseline variations, there appears to be significant room for
improvement in future work.
