Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: APRIL: Active Preference-learning based Reinforcement Learning   This paper focuses on reinforcement learning (RL) with limited prior
knowledge. In the domain of swarm robotics for instance, the expert can hardly
design a reward function or demonstrate the target behavior, forbidding the use
of both standard RL and inverse reinforcement learning. Although with a limited
expertise, the human expert is still often able to emit preferences and rank
the agent demonstrations. Earlier work has presented an iterative
preference-based RL framework: expert preferences are exploited to learn an
approximate policy return, thus enabling the agent to achieve direct policy
search. Iteratively, the agent selects a new candidate policy and demonstrates
it; the expert ranks the new demonstration comparatively to the previous best
one; the expert's ranking feedback enables the agent to refine the approximate
policy return, and the process is iterated. In this paper, preference-based
reinforcement learning is combined with active ranking in order to decrease the
number of ranking queries to the expert needed to yield a satisfactory policy.
Experiments on the mountain car and the cancer treatment testbeds witness that
a couple of dozen rankings enable to learn a competent policy.

35
The categories of this paper are: mtrl-th
14
The categories of this paper are: math-ph
10
The categories of this paper are: q-bio
12
The categories of this paper are: nucl-th
28
The categories of this paper are: alg-geom
33
The categories of this paper are: plasm-ph
21
The categories of this paper are: adap-org
23
The categories of this paper are: solv-int
31
The categories of this paper are: ao-sci
15
The categories of this paper are: nucl-ex
18
The categories of this paper are: econ
30
The categories of this paper are: q-alg
5
The categories of this paper are: gr-qc
16
The categories of this paper are: stat
37
The categories of this paper are: supr-con
9
The categories of this paper are: nlin