Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Path Planning for UAV-Mounted Mobile Edge Computing with Deep
  Reinforcement Learning   In this letter, we study an unmanned aerial vehicle (UAV)-mounted mobile edge
computing network, where the UAV executes computational tasks offloaded from
mobile terminal users (TUs) and the motion of each TU follows a Gauss-Markov
random model. To ensure the quality-of-service (QoS) of each TU, the UAV with
limited energy dynamically plans its trajectory according to the locations of
mobile TUs. Towards this end, we formulate the problem as a Markov decision
process, wherein the UAV trajectory and UAV-TU association are modeled as the
parameters to be optimized. To maximize the system reward and meet the QoS
constraint, we develop a QoS-based action selection policy in the proposed
algorithm based on double deep Q-network. Simulations show that the proposed
algorithm converges more quickly and achieves a higher sum throughput than
conventional algorithms.

11
The categories of this paper are: hep-lat
20
The categories of this paper are: acc-phys
35
The categories of this paper are: mtrl-th
12
The categories of this paper are: nucl-th
13
The categories of this paper are: quant-ph
9
The categories of this paper are: nlin
28
The categories of this paper are: alg-geom
33
The categories of this paper are: plasm-ph
31
The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio
17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex
14
The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
7
The categories of this paper are: hep-th