Computer File

Optimizing the area coverage of networked UAVs using multi-agent reinforcement learning

Wireless sensor networks (WSNs) have been widely used in various area coverage applications which require the monitoring and surveillance of systems with spatiotemporally varying variables or parameters. One important task in the implementation of WSNs for area coverage and monitoring purposes is the determination of the solution for the optimal coverage problem. This paper describes that the formulation of the area coverage problem can be modeled using Markov game modeling formalism whereas the optimal joint state-action policy for each agent which also takes into consideration the group objective can be computed using multi-agent Q-learning iterative processes using multi-agent reinforcement learning framework. Simulation results are presented to illustrate the proposed iterative learningbased area coverage solution approach.

Ketersediaan

Informasi Detail

Judul Seri

No. Panggil

-

Penerbit

Bandung : IEEE., 2021

Deskripsi Fisik

p. 1-5

Bahasa

English

ISBN/ISSN

2639-5045

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

AREA COVERAGE PROBLEM
NETWORKED UAVS
MULTIAGENT REINFORCEMENT LEARNING

Info Detail Spesifik

Makalah dipresentasikan pada Proceedings of 2021 International Conference on Instrumentation, Control, and Automation (ICA); Bandung, Indonesia, August 25th – August 26th, 2021.

Pernyataan Tanggungjawab

Tua A. Tamba

Versi lain/terkait

Lampiran Berkas

Komentar