  We study the problem of off-policy policy evaluation (OPPE) in RL. In
contrast to prior work, we consider how to estimate both the individual policy
value and average policy value accurately. We draw inspiration from recent work
in causal reasoning, and propose a new finite sample generalization error bound
for value estimates from MDP models. Using this upper bound as an objective, we
develop a learning algorithm of an MDP model with a balanced representation,
and show that our approach can yield substantially lower MSE in common
synthetic benchmarks and a HIV treatment simulation domain.
