  Offline Reinforcement learning is commonly used for sequential
decision-making in domains such as healthcare and education, where the rewards
are known and the transition dynamics $T$ must be estimated on the basis of
batch data. A key challenge for all tasks is how to learn a reliable estimate
of the transition dynamics $T$ that produce near-optimal policies that are safe
enough so that they never take actions that are far away from the best action
with respect to their value functions and informative enough so that they
communicate the uncertainties they have. Using data from an expert, we propose
a new constraint-based approach that captures our desiderata for reliably
learning a posterior distribution of the transition dynamics $T$ that is free
from gradients. Our results demonstrate that by using our constraints, we learn
a high-performing policy, while considerably reducing the policy's variance
over different datasets. We also explain how combining uncertainty estimation
with these constraints can help us infer a partial ranking of actions that
produce higher returns, and helps us infer safer and more informative policies
for planning.
