Reinforcement learning and trustworthy autonomy

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dc.authoridÇetin Kaya Koç / 0000-0002-2572-9565
dc.authorscopusidÇetin Kaya Koç / 57053693300
dc.authorwosidÇetin Kaya Koç / W-3929-2018
dc.contributor.authorLuo J.
dc.contributor.authorGreen S.
dc.contributor.authorFeghali P.
dc.contributor.authorLegrady G.
dc.contributor.authorKoç, Çetin Kaya
dc.date.accessioned2020-08-30T20:01:38Z
dc.date.available2020-08-30T20:01:38Z
dc.date.issued2018
dc.departmentİstinye Üniversitesi, Meslek Yüksekokulu, Bilgisayar Programcılığı Bölümüen_US
dc.descriptionKoc, Cetin Kaya (isu author)
dc.description.abstractCyber-Physical Systems (CPS) possess physical and software interdependence and are typically designed by teams of mechanical, electrical, and software engineers. The interdisciplinary nature of CPS makes them difficult to design with safety guarantees. When autonomy is incorporated, design complexity and, especially, the difficulty of providing safety assurances are increased. Visionbased reinforcement learning is an increasingly popular family of machine learning algorithms that may be used to provide autonomy for CPS. Understanding how visual stimuli trigger various actions is critical for trustworthy autonomy. In this chapter we introduce reinforcement learning in the context of Microsoft's AirSim drone simulator. Specifically, we guide the reader through the necessary steps for creating a drone simulation environment suitable for experimenting with visionbased reinforcement learning. We also explore how existing vision-oriented deep learning analysis methods may be applied toward safety verification in vision-based reinforcement learning applications. © Springer Nature Switzerland AG 2018.en_US
dc.identifier.citationLuo, J., Green, S., Feghali, P., Legrady, G., & Koç, Ç. K. (2018). Reinforcement learning and trustworthy autonomy. In Cyber-Physical Systems Security (pp. 191-217). Springer, Cham.en_US
dc.identifier.doi10.1007/978-3-319-98935-8_10en_US
dc.identifier.endpage217en_US
dc.identifier.isbn9783319989358; 9783319989341
dc.identifier.scopus2-s2.0-85063927274en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage191en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-319-98935-8_10
dc.identifier.urihttps://hdl.handle.net/20.500.12713/328
dc.indekslendigikaynakScopusen_US
dc.institutionauthorKoç, Çetin Kayaen_US
dc.language.isoenen_US
dc.publisherSpringer International Publishingen_US
dc.relation.ispartofCyber-Physical Systems Securityen_US
dc.relation.publicationcategoryKitap Bölümü - Uluslararasıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.titleReinforcement learning and trustworthy autonomyen_US
dc.typeBook Chapteren_US

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