Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Reinforcement Learning based Automatic Exploration for Navigation
  in Unknown Environment   This paper investigates the automatic exploration problem under the unknown
environment, which is the key point of applying the robotic system to some
social tasks. The solution to this problem via stacking decision rules is
impossible to cover various environments and sensor properties. Learning based
control methods are adaptive for these scenarios. However, these methods are
damaged by low learning efficiency and awkward transferability from simulation
to reality. In this paper, we construct a general exploration framework via
decomposing the exploration process into the decision, planning, and mapping
modules, which increases the modularity of the robotic system. Based on this
framework, we propose a deep reinforcement learning based decision algorithm
which uses a deep neural network to learning exploration strategy from the
partial map. The results show that this proposed algorithm has better learning
efficiency and adaptability for unknown environments. In addition, we conduct
the experiments on the physical robot, and the results suggest that the learned
policy can be well transfered from simulation to the real robot.

27
The categories of this paper are: comp-gas
14
The categories of this paper are: math-ph
25
The categories of this paper are: patt-sol
20
The categories of this paper are: acc-phys
18
The categories of this paper are: econ
23
The categories of this paper are: solv-int
37
The categories of this paper are: supr-con
15
The categories of this paper are: nucl-ex
29
The categories of this paper are: funct-an
6
The categories of this paper are: astro-ph
17
The categories of this paper are: q-fin
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
5
The categories of this paper are: gr-qc
21
The categories of this paper are: adap-org
11
The categories of this paper are: hep-lat