Obtaining fault tolerance avoidance behavior using deep reinforcement learning

Please use this identifier to cite or link to this item: http://hdl.handle.net/10045/91549
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Title: Obtaining fault tolerance avoidance behavior using deep reinforcement learning
Authors: Aznar Gregori, Fidel | Pujol, Mar | Rizo, Ramón
Research Group/s: Informática Industrial e Inteligencia Artificial
Center, Department or Service: Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial
Keywords: Deep reinforcement learning | Obstacle avoidance | Fault tolerance
Knowledge Area: Ciencia de la Computación e Inteligencia Artificial
Issue Date: 14-Jun-2019
Publisher: Elsevier
Citation: Neurocomputing. 2019, 345: 77-91. doi:10.1016/j.neucom.2018.11.090
Abstract: In this article, a mapless movement policy for mobile agents, designed specifically to be fault-tolerant, is presented. The provided policy, which is learned using deep reinforcement learning, has advantages compared to the usual mapless policies: this policy is capable of handling a robot even when some of its sensors are broken. It is an end-to-end policy based on three neuronal models capable not only of moving the robot and maximizing the coverage of the environment but also of learning the best movement behavior to adapt it to its perception needs. A custom robot, for which none of the readings of the sensors overlap each other, has been used. This setup makes it possible to determine the operation of a robust failure policy, since the failure of a sensor unequivocally affects the perceptions. The proposed system exhibits several advantages in terms of robustness, extensibility and utility. The system has been trained and tested exhaustively in a simulator, obtaining very good results. It has also been transferred to real robots, verifying the generalization and the good functioning of our model in real environments.
Sponsor: This work has been supported by the Ministerio de Economia y Competitividad (Spain), project TIN2013-40982-R (project co-financed with FEDER funds) and by the Erasmus + EUROBOTIQUE Project with reference 2016-1-ES01-KA201-024990.
URI: http://hdl.handle.net/10045/91549
ISSN: 0925-2312 (Print) | 1872-8286 (Online)
DOI: 10.1016/j.neucom.2018.11.090
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2019 Elsevier B.V.
Peer Review: si
Publisher version: https://doi.org/10.1016/j.neucom.2018.11.090
Appears in Collections:INV - i3a - Proyecto Erasmus+ EUROBOTIQUE
INV - i3a - Artículos de Revistas

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