Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Framework for Learning to Request Rich and Contextually Useful
  Information from Humans   When deployed, AI agents will encounter problems that are beyond their
autonomous problem-solving capabilities. Leveraging human assistance can help
agents overcome their inherent limitations and robustly cope with unfamiliar
situations. We present a general interactive framework that enables an agent to
determine and request contextually useful information from an assistant, and to
incorporate rich forms of responses into its decision-making process. We
demonstrate the practicality of our framework on a simulated human-assisted
navigation problem. Aided with an assistance-requesting policy learned by our
method, a navigation agent achieves up to a 7x improvement in success rate on
tasks that take place in previously unseen environments, compared to fully
autonomous behavior. We show that the agent can take advantage of different
types of information depending on the context, and analyze the benefits and
challenges of learning the assistance-requesting policy when the assistant can
recursively decompose tasks into subtasks.

21
The categories of this paper are: adap-org
10
The categories of this paper are: q-bio
16
The categories of this paper are: stat
13
The categories of this paper are: quant-ph
29
The categories of this paper are: funct-an
2
The categories of this paper are: math
20
The categories of this paper are: acc-phys
31
The categories of this paper are: ao-sci
24
The categories of this paper are: dg-ga
11
The categories of this paper are: hep-lat
9
The categories of this paper are: nlin
6
The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas
32
The categories of this paper are: atom-ph
18
The categories of this paper are: econ
12
The categories of this paper are: nucl-th