Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Reinforcement Learning-Based Trajectory Design for the Aerial Base
  Stations   In this paper, the trajectory optimization problem for a multi-aerial base
station (ABS) communication network is investigated. The objective is to find
the trajectory of the ABSs so that the sum-rate of the users served by each ABS
is maximized. To reach this goal, along with the optimal trajectory design,
optimal power and sub-channel allocation is also of great importance to support
the users with the highest possible data rates. To solve this complicated
problem, we divide it into two sub-problems: ABS trajectory optimization
sub-problem, and joint power and sub-channel assignment sub-problem. Then,
based on the Q-learning method, we develop a distributed algorithm which solves
these sub-problems efficiently, and does not need significant amount of
information exchange between the ABSs and the core network. Simulation results
show that although Q-learning is a model-free reinforcement learning technique,
it has a remarkable capability to train the ABSs to optimize their trajectories
based on the received reward signals, which carry decent information from the
topology of the network.

26
The categories of this paper are: bayes-an
8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an
13
The categories of this paper are: quant-ph
36
The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
15
The categories of this paper are: nucl-ex
6
The categories of this paper are: astro-ph
14
The categories of this paper are: math-ph
3
The categories of this paper are: physics
17
The categories of this paper are: q-fin
10
The categories of this paper are: q-bio
20
The categories of this paper are: acc-phys
24
The categories of this paper are: dg-ga
34
The categories of this paper are: chem-ph