Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards More Sample Efficiency in Reinforcement Learning with Data
  Augmentation   Deep reinforcement learning (DRL) is a promising approach for adaptive robot
control, but its current application to robotics is currently hindered by high
sample requirements. We propose two novel data augmentation techniques for DRL
in order to reuse more efficiently observed data. The first one called
Kaleidoscope Experience Replay exploits reflectional symmetries, while the
second called Goal-augmented Experience Replay takes advantage of lax goal
definitions. Our preliminary experimental results show a large increase in
learning speed.

27
The categories of this paper are: comp-gas
30
The categories of this paper are: q-alg
4
The categories of this paper are: cond-mat
12
The categories of this paper are: nucl-th
6
The categories of this paper are: astro-ph
5
The categories of this paper are: gr-qc
25
The categories of this paper are: patt-sol
10
The categories of this paper are: q-bio
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
17
The categories of this paper are: q-fin
29
The categories of this paper are: funct-an
20
The categories of this paper are: acc-phys
8
The categories of this paper are: hep-ex
28
The categories of this paper are: alg-geom
23
The categories of this paper are: solv-int