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Titel:

Deep Reinforcement Learning for Formation Control

Dokumenttyp:
Konferenzbeitrag
Autor(en):
Aykın, Can; Knopp, Martin; Diepold, Klaus
Seitenangaben Beitrag:
1124-1128
Abstract:
Continuing our work on using reinforcement learning for formation control, we present an end-to-end deep learning system which uses only camera images to learn to control the individual system's correct position within the formation. Mnih et al. created AIs playing video games utilizing the same visual input as a human player by employing convolutional neural networks for automatic feature extraction on images. This published work inspired us to employ a similar approach for processing the c...     »
Dewey-Dezimalklassifikation:
620 Ingenieurwissenschaften
Herausgeber:
Institute of Electrical and Electronics Engineers (IEEE)
Kongress- / Buchtitel:
27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN 2018)
Kongress / Zusatzinformationen:
Nanjing, China
Ausrichter der Konferenz:
Institute of Electrical and Electronics Engineers (IEEE)
Datum der Konferenz:
27.-31. August 2018
Verlag / Institution:
Institute of Electrical and Electronics Engineers (IEEE)
Publikationsdatum:
29.08.2018
Jahr:
2018
Jahr / Monat:
2018-08
Monat:
Aug
Seiten:
1124-1128
Print-ISBN:
978-1-5386-7981-4
E-ISBN:
978-1-5386-7980-7
Reviewed:
ja
Sprache:
en
Volltext / DOI:
doi:10.1109/ROMAN.2018.8525765
TUM Einrichtung:
Lehrstuhl für Datenverarbeitung
Copyright Informationen:
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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