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Improving reinforcement learning with interactive feedback and affordances

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

20 Scopus citations

Abstract

Interactive 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.

Original languageEnglish
Title of host publicationIEEE ICDL-EPIROB 2014 - 4th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages165-170
Number of pages6
ISBN (Electronic)9781479975402
DOIs
StatePublished - 11 Dec 2014
Externally publishedYes
Event4th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics, IEEE ICDL-EPIROB 2014 - Genoa, Italy
Duration: 13 Oct 201416 Oct 2014

Publication series

NameIEEE ICDL-EPIROB 2014 - 4th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics

Conference

Conference4th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics, IEEE ICDL-EPIROB 2014
Country/TerritoryItaly
CityGenoa
Period13/10/1416/10/14

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