Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sample-efficient Deep Reinforcement Learning with Imaginary Rollouts for
  Human-Robot Interaction   Deep reinforcement learning has proven to be a great success in allowing
agents to learn complex tasks. However, its application to actual robots can be
prohibitively expensive. Furthermore, the unpredictability of human behavior in
human-robot interaction tasks can hinder convergence to a good policy. In this
paper, we present an architecture that allows agents to learn models of
stochastic environments and use them to accelerate learning. We descirbe how an
environment model can be learned online and used to generate synthetic
transitions, as well as how an agent can leverage these synthetic data to
accelerate learning. We validate our approach using an experiment in which a
robotic arm has to complete a task composed of a series of actions based on
human gestures. Results show that our approach leads to significantly faster
learning, requiring much less interaction with the environment. Furthermore, we
demonstrate how learned models can be used by a robot to produce optimal plans
in real world applications.

4
The categories of this paper are: cond-mat
31
The categories of this paper are: ao-sci
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
32
The categories of this paper are: atom-ph
34
The categories of this paper are: chem-ph
20
The categories of this paper are: acc-phys
30
The categories of this paper are: q-alg
29
The categories of this paper are: funct-an
16
The categories of this paper are: stat
9
The categories of this paper are: nlin
6
The categories of this paper are: astro-ph
21
The categories of this paper are: adap-org
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
17
The categories of this paper are: q-fin