Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Reinforcement Learning for Decentralized Multi-Robot Exploration
  with Macro Actions   Cooperative multi-robot teams need to be able to explore cluttered and
unstructured environments together while dealing with communication challenges.
Specifically, during communication dropout, local information about robots can
no longer be exchanged to maintain robot team coordination. Therefore, robots
need to consider high-level teammate intentions during action selection. In
this paper, we present the first Macro Action Decentralized Exploration Network
(MADE-Net) using multi-agent deep reinforcement learning to address the
challenges of communication dropouts during multi-robot exploration in unseen,
unstructured, and cluttered environments. Simulated robot team exploration
experiments were conducted and compared to classical and deep reinforcement
learning methods. The results showed that our MADE-Net method was able to
outperform all benchmark methods in terms of computation time, total travel
distance, number of local interactions between robots, and exploration rate
across various degrees of communication dropouts; highlighting the
effectiveness and robustness of our method.

20
The categories of this paper are: acc-phys
6
The categories of this paper are: astro-ph
34
The categories of this paper are: chem-ph
3
The categories of this paper are: physics
31
The categories of this paper are: ao-sci
28
The categories of this paper are: alg-geom
26
The categories of this paper are: bayes-an
14
The categories of this paper are: math-ph
36
The categories of this paper are: cmp-lg
35
The categories of this paper are: mtrl-th
30
The categories of this paper are: q-alg
18
The categories of this paper are: econ
9
The categories of this paper are: nlin
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