Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Curiosity-driven Exploration for Mapless Navigation with Deep
  Reinforcement Learning   This paper investigates exploration strategies of Deep Reinforcement Learning
(DRL) methods to learn navigation policies for mobile robots. In particular, we
augment the normal external reward for training DRL algorithms with intrinsic
reward signals measured by curiosity. We test our approach in a mapless
navigation setting, where the autonomous agent is required to navigate without
the occupancy map of the environment, to targets whose relative locations can
be easily acquired through low-cost solutions (e.g., visible light
localization, Wi-Fi signal localization). We validate that the intrinsic
motivation is crucial for improving DRL performance in tasks with challenging
exploration requirements. Our experimental results show that our proposed
method is able to more effectively learn navigation policies, and has better
generalization capabilities in previously unseen environments. A video of our
experimental results can be found at https://goo.gl/pWbpcF.

14
The categories of this paper are: math-ph
19
The categories of this paper are: eess
9
The categories of this paper are: nlin
12
The categories of this paper are: nucl-th
36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
25
The categories of this paper are: patt-sol
31
The categories of this paper are: ao-sci
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
15
The categories of this paper are: nucl-ex
21
The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
3
The categories of this paper are: physics