Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fully General Online Imitation Learning   In imitation learning, imitators and demonstrators are policies for picking
actions given past interactions with the environment. If we run an imitator, we
probably want events to unfold similarly to the way they would have if the
demonstrator had been acting the whole time. No existing work provides formal
guidance in how this might be accomplished, instead restricting focus to
environments that restart, making learning unusually easy, and conveniently
limiting the significance of any mistake. We address a fully general setting,
in which the (stochastic) environment and demonstrator never reset, not even
for training purposes. Our new conservative Bayesian imitation learner
underestimates the probabilities of each available action, and queries for more
data with the remaining probability. Our main result: if an event would have
been unlikely had the demonstrator acted the whole time, that event's
likelihood can be bounded above when running the (initially totally ignorant)
imitator instead. Meanwhile, queries to the demonstrator rapidly diminish in
frequency.

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The categories of this paper are: chem-ph
27
The categories of this paper are: comp-gas
7
The categories of this paper are: hep-th
29
The categories of this paper are: funct-an
15
The categories of this paper are: nucl-ex
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The categories of this paper are: supr-con
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The categories of this paper are: mtrl-th
23
The categories of this paper are: solv-int
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The categories of this paper are: eess
10
The categories of this paper are: q-bio
9
The categories of this paper are: nlin
5
The categories of this paper are: gr-qc
21
The categories of this paper are: adap-org
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The categories of this paper are: alg-geom
6
The categories of this paper are: astro-ph
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The categories of this paper are: chao-dyn