Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Reinforcement Learning for Navigation of Mobile Robot with LiDAR   This paper presents a technique for navigation of mobile robot with Deep
Q-Network (DQN) combined with Gated Recurrent Unit (GRU). The DQN integrated
with the GRU allows action skipping for improved navigation performance. This
technique aims at efficient navigation of mobile robot such as autonomous
parking robot. Framework for reinforcement learning can be applied to the DQN
combined with the GRU in a real environment, which can be modeled by the
Partially Observable Markov Decision Process (POMDP). By allowing action
skipping, the ability of the DQN combined with the GRU in learning key-action
can be improved. The proposed algorithm is applied to explore the feasibility
of solution in real environment by the ROS-Gazebo simulator, and the simulation
results show that the proposed algorithm achieves improved performance in
navigation and collision avoidance as compared to the results obtained by DQN
alone and DQN combined with GRU without allowing action skipping.

25
The categories of this paper are: patt-sol
33
The categories of this paper are: plasm-ph
20
The categories of this paper are: acc-phys
29
The categories of this paper are: funct-an
17
The categories of this paper are: q-fin
11
The categories of this paper are: hep-lat
10
The categories of this paper are: q-bio
12
The categories of this paper are: nucl-th
34
The categories of this paper are: chem-ph
3
The categories of this paper are: physics
13
The categories of this paper are: quant-ph
36
The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin
30
The categories of this paper are: q-alg
18
The categories of this paper are: econ
2
The categories of this paper are: math