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Improving reinforcement learning with interactive feedback and affordances
conference contribution
posted on 2014-01-01, 00:00 authored by F Cruz Naranjo, S Magg, C Weber, S WermterInteractive reinforcement learning constitutes an alternative for improving convergence speed in reinforcement learning methods. In this work, we investigate inter-agent training and present an approach for knowledge transfer in a domestic scenario where a first agent is trained by reinforcement learning and afterwards transfers selected knowledge to a second agent by instructions to achieve more efficient training. We combine this approach with action-space pruning by using knowledge on affordances and show that it significantly improves convergence speed in both classic and interactive reinforcement learning scenarios.
History
Event
European Society for Cognitive Systems. Conference (4th : 2014 : Genoa, Italy)Series
European Society for Cognitive Systems ConferencePagination
165 - 170Publisher
Institute of Electrical and Electronics EngineersLocation
Genoa, ItalyPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2014-10-13End date
2014-10-16ISBN-13
9781479975402Language
engPublication classification
E1.1 Full written paper - refereedCopyright notice
2014, IEEEEditor/Contributor(s)
[Unknown]Title of proceedings
IEEE ICDL-EPIROB 2014 : Proceedings of the 4th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics 2014Usage metrics
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