Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Decentralized Motion Planning for Multi-Robot Navigation using Deep
  Reinforcement Learning   This work presents a decentralized motion planning framework for addressing
the task of multi-robot navigation using deep reinforcement learning. A custom
simulator was developed in order to experimentally investigate the navigation
problem of 4 cooperative non-holonomic robots sharing limited state information
with each other in 3 different settings. The notion of decentralized motion
planning with common and shared policy learning was adopted, which allowed
robust training and testing of this approach in a stochastic environment since
the agents were mutually independent and exhibited asynchronous motion
behavior. The task was further aggravated by providing the agents with a sparse
observation space and requiring them to generate continuous action commands so
as to efficiently, yet safely navigate to their respective goal locations,
while avoiding collisions with other dynamic peers and static obstacles at all
times. The experimental results are reported in terms of quantitative measures
and qualitative remarks for both training and deployment phases.

20
The categories of this paper are: acc-phys
23
The categories of this paper are: solv-int
24
The categories of this paper are: dg-ga
3
The categories of this paper are: physics
29
The categories of this paper are: funct-an
25
The categories of this paper are: patt-sol
0
The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph
2
The categories of this paper are: math
28
The categories of this paper are: alg-geom
22
The categories of this paper are: chao-dyn
37
The categories of this paper are: supr-con
32
The categories of this paper are: atom-ph
4
The categories of this paper are: cond-mat
21
The categories of this paper are: adap-org
15
The categories of this paper are: nucl-ex