Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: VILD: Variational Imitation Learning with Diverse-quality Demonstrations   The goal of imitation learning (IL) is to learn a good policy from
high-quality demonstrations. However, the quality of demonstrations in reality
can be diverse, since it is easier and cheaper to collect demonstrations from a
mix of experts and amateurs. IL in such situations can be challenging,
especially when the level of demonstrators' expertise is unknown. We propose a
new IL method called \underline{v}ariational \underline{i}mitation
\underline{l}earning with \underline{d}iverse-quality demonstrations (VILD),
where we explicitly model the level of demonstrators' expertise with a
probabilistic graphical model and estimate it along with a reward function. We
show that a naive approach to estimation is not suitable to large state and
action spaces, and fix its issues by using a variational approach which can be
easily implemented using existing reinforcement learning methods. Experiments
on continuous-control benchmarks demonstrate that VILD outperforms
state-of-the-art methods. Our work enables scalable and data-efficient IL under
more realistic settings than before.

2
The categories of this paper are: math
17
The categories of this paper are: q-fin
7
The categories of this paper are: hep-th
3
The categories of this paper are: physics
5
The categories of this paper are: gr-qc
32
The categories of this paper are: atom-ph
11
The categories of this paper are: hep-lat
36
The categories of this paper are: cmp-lg
8
The categories of this paper are: hep-ex
30
The categories of this paper are: q-alg
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The categories of this paper are: chao-dyn
6
The categories of this paper are: astro-ph
12
The categories of this paper are: nucl-th
18
The categories of this paper are: econ
31
The categories of this paper are: ao-sci
28
The categories of this paper are: alg-geom