Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Lower Bound for the Sample Complexity of Inverse Reinforcement
  Learning   Inverse reinforcement learning (IRL) is the task of finding a reward function
that generates a desired optimal policy for a given Markov Decision Process
(MDP). This paper develops an information-theoretic lower bound for the sample
complexity of the finite state, finite action IRL problem. A geometric
construction of $\beta$-strict separable IRL problems using spherical codes is
considered. Properties of the ensemble size as well as the Kullback-Leibler
divergence between the generated trajectories are derived. The resulting
ensemble is then used along with Fano's inequality to derive a sample
complexity lower bound of $O(n \log n)$, where $n$ is the number of states in
the MDP.

9
The categories of this paper are: nlin
15
The categories of this paper are: nucl-ex
3
The categories of this paper are: physics
24
The categories of this paper are: dg-ga
36
The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
25
The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph
23
The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph
7
The categories of this paper are: hep-th
29
The categories of this paper are: funct-an
33
The categories of this paper are: plasm-ph
17
The categories of this paper are: q-fin
2
The categories of this paper are: math