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Document type:
Konferenzbeitrag 
Author(s):
Aykın, Can; Knopp, Martin; Diepold, Klaus 
Title:
Deep Reinforcement Learning for Formation Control 
Pages contribution:
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 th...    »
 
Dewey Decimal Classification:
620 Ingenieurwissenschaften 
Editor:
Institute of Electrical and Electronics Engineers (IEEE) 
Book / Congress title:
27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN 2018) 
Congress (additional information):
Nanjing, China 
Organization:
Institute of Electrical and Electronics Engineers (IEEE) 
Date of congress:
27.-31. August 2018 
Publisher:
Institute of Electrical and Electronics Engineers (IEEE) 
Date of publication:
29.08.2018 
Year:
2018 
Year / month:
2018-08 
Month:
Aug 
Pages:
1124-1128 
Print-ISBN:
978-1-5386-7981-4 
E-ISBN:
978-1-5386-7980-7 
Reviewed:
ja 
Language:
en 
TUM Institution:
Lehrstuhl für Datenverarbeitung