Reference : Deep Reinforcement Learning based Continuous Control for Multicopter Systems

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/10993/41999

Title :	Deep Reinforcement Learning based Continuous Control for Multicopter Systems
Language :	English
Author, co-author :	Manukyan, Anush [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Olivares Mendez, Miguel Angel [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Geist, Matthieu []
	Voos, Holger [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > Engineering Research Unit >]
Publication date :	26-Apr-2019
Main document title :	International Conference on Control, Decision and Information CoDIT, Paris 23-26 April 2019
Publisher :	IEEE
Peer reviewed :	Yes
On invitation :	No
Audience :	International
Event name :	International Conference on Control, Decision and Information CoDIT
Event date :	from 23-04-2019 to 26-04-2019
Event place (city) :	Paris
Event country :	France
Keywords :	[en] Reinforcement Learning ; UAV ; TRPO
Abstract :	[en] In this paper we apply deep reinforcement learning techniques on a multicopter for learning a stable hovering task in a continuous action state environment. We present a framework based on OpenAI GYM, Gazebo and RotorS MAV simulator, utilized for successfully training different agents to perform various tasks. The deep reinforcement learning method used for the training is model-free, on-policy, actor-critic based algorithm called Trust Region Policy Optimization (TRPO). Two neural networks have been used as a nonlinear function approximators. Our experiments showed that such learning approach achieves successful results, and facilitates the process of controller design.
Research centres :	Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Automation & Robotics Research Group
Target :	Researchers ; Professionals ; Students ; General public ; Others
Permalink :	http://hdl.handle.net/10993/41999

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