[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.
Centre de recherche :
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Automation & Robotics Research Group
Disciplines :
Sciences informatiques
Auteur, co-auteur :
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
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
Deep Reinforcement Learning based Continuous Control for Multicopter Systems
Date de publication/diffusion :
26 avril 2019
Nom de la manifestation :
International Conference on Control, Decision and Information CoDIT
Lieu de la manifestation :
Paris, France
Date de la manifestation :
from 23-04-2019 to 26-04-2019
Manifestation à portée :
International
Titre de l'ouvrage principal :
International Conference on Control, Decision and Information CoDIT, Paris 23-26 April 2019
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