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Agent-advising approaches in an interactive reinforcement learning scenario
conference contribution
posted on 2017-01-01, 00:00 authored by F Cruz Naranjo, P Wuppen, S Magg, A Fazrie, S WermterReinforcement learning has become one of the fundamental topics in the field of robotics and machine learning. In this paper, we expand the classical reinforcement learning framework by the idea of external interaction to support the learning process. To this end, we review a number of proposed advising approaches for interactive reinforcement learning and discuss their implications, namely, probabilistic advising, early advising, importance advising, and mistake correcting. Moreover, we implement the advice strategies for interactive reinforcement learning based on a simulated robotic scenario of a domestic cleaning task. The obtained results show that the mistake correcting approach outperforms a purely probabilistic advice approach as well as the early and importance advising approaches allowing to collect more reward and also to converge faster.
History
Event
IEEE Computational Intelligence Society. Conference (2017 : Lisbon, Portugal)Series
IEEE Computational Intelligence Society ConferencePagination
209 - 214Publisher
Institute of Electrical and Electronics EngineersLocation
Lisbon, PortugalPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2017-09-18End date
2017-09-21ISBN-13
9781538637159Language
engPublication classification
E1.1 Full written paper - refereedCopyright notice
2017, IEEEEditor/Contributor(s)
[Unknown]Title of proceedings
IEEE ICDL-EPIROB 2017 : Proceedings of the 7th Joint International Confernce on Development and Learning and on Epigenetic Robotics 2017Usage metrics
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