  Offline policy optimization could have a large impact on many real-world
decision-making problems, as online learning may be infeasible in many
applications. Importance sampling and its variants are a commonly used type of
estimator in offline policy evaluation, and such estimators typically do not
require assumptions on the properties and representational capabilities of
value function or decision process model function classes. In this paper, we
identify an important overfitting phenomenon in optimizing the importance
weighted return, in which it may be possible for the learned policy to
essentially avoid making aligned decisions for part of the initial state space.
We propose an algorithm to avoid this overfitting through a new
per-state-neighborhood normalization constraint, and provide a theoretical
justification of the proposed algorithm. We also show the limitations of
previous attempts to this approach. We test our algorithm in a
healthcare-inspired simulator, a logged dataset collected from real hospitals
and continuous control tasks. These experiments show the proposed method yields
less overfitting and better test performance compared to state-of-the-art batch
reinforcement learning algorithms.
