Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for
  Reinforcement Learning   Off-policy evaluation (OPE) in both contextual bandits and reinforcement
learning allows one to evaluate novel decision policies without needing to
conduct exploration, which is often costly or otherwise infeasible. The
problem's importance has attracted many proposed solutions, including
importance sampling (IS), self-normalized IS (SNIS), and doubly robust (DR)
estimates. DR and its variants ensure semiparametric local efficiency if
Q-functions are well-specified, but if they are not they can be worse than both
IS and SNIS. It also does not enjoy SNIS's inherent stability and boundedness.
We propose new estimators for OPE based on empirical likelihood that are always
more efficient than IS, SNIS, and DR and satisfy the same stability and
boundedness properties as SNIS. On the way, we categorize various properties
and classify existing estimators by them. Besides the theoretical guarantees,
empirical studies suggest the new estimators provide advantages.

19
The categories of this paper are: eess
5
The categories of this paper are: gr-qc
28
The categories of this paper are: alg-geom
31
The categories of this paper are: ao-sci
24
The categories of this paper are: dg-ga
2
The categories of this paper are: math
18
The categories of this paper are: econ
25
The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
30
The categories of this paper are: q-alg
36
The categories of this paper are: cmp-lg
35
The categories of this paper are: mtrl-th
11
The categories of this paper are: hep-lat
3
The categories of this paper are: physics
27
The categories of this paper are: comp-gas
32
The categories of this paper are: atom-ph