Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13087/161
Title: Evolutionary Reinforcement Learning for the Coordination of Swarm UAVs
Authors: Altın, Umut Can
Keywords: deep reinforcement learning
evolutionary algorithms
unmanned aerial vehicles
formation task
Issue Date: 2020
Publisher: IEEE
Abstract: Deep Reinforcement Learning (DRL) algorithms are used in many challenging tasks and their usage areas are rapidly increasing. One of these areas is the formation flights of Unmanned Aerial Vehicles (UAVs). The rising of Reinforcement Learning (RL) algorithms performances is directly proportional to the development of environments. This paper presents a new environment developed through software (Ardupilot, Mavlink, drone-kit) that is frequently used in open source UAV simulation and programming, and the performance of the Evolutionary Reinforcement Learning (ERL) agent in this environment. The difference of this environment is that, unlike other environments, the model can be operated directly on a drone-kit supported vehicle and is specifically defined on the centralised formation task. The aim of this study is; in order to question the performance of the Evolutionary Reinforcement Learning (ERL) algorithm which has better results than other algorithms in DRL training environments,in this environment, and increasing the usage of the algorithm in this direction.
Description: 28th Signal Processing and Communications Applications Conference (SIU) -- OCT 05-07, 2020 -- ELECTR NETWORK
URI: https://hdl.handle.net/20.500.13087/161
ISBN: 978-1-7281-7206-4
ISSN: 2165-0608
Appears in Collections:Makine Mühendisliği Bölümü Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu

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