Reinforcement Learning based Embodied Agents Modelling Human Users Through Interaction and Multi-Sen...
Reinforcement Learning based Embodied Agents Modelling Human Users Through Interaction and Multi-Sensory Perception
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Ithaca: Cornell University Library, arXiv.org
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English
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Ithaca: Cornell University Library, arXiv.org
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This paper extends recent work in interactive machine learning (IML) focused on effectively incorporating human feedback. We show how control and feedback signals complement each other in systems which model human reward. We demonstrate that simultaneously incorporating human control and feedback signals can improve interactive robotic systems' per...
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Reinforcement Learning based Embodied Agents Modelling Human Users Through Interaction and Multi-Sensory Perception
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TN_cdi_proquest_journals_2074531363
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2074531363
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2331-8422
