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dc.contributor.authorÖztürkçü, Özgür Baran
dc.contributor.authorUğur, E.
dc.contributor.authorÖztop, E.
dc.description.abstractHow the sensorimotor experience of an agent can be organized into abstract symbol-like structures to enable effective planning and control is an open question. In the literature, there are many studies that start by assuming the existence of some symbols and 'ground' those onto continuous sensorimotor signals. There are also works that aim to facilitate the emergence of symbol-like representations by using specially designed machine learning architectures. In this paper, we investigate whether a deep reinforcement learning system that learns a dynamic task would facilitate the formation of high-level neural representations that might be considered as precursors of symbolic representation, which could be exploited by higher level neural circuits for better control and planning. The results indicate that without even explicit design to promote such representations, neural responses emerge that may serve as the basis of abstract symbol-like representations.en_US
dc.relation.ispartof2020 Joint IEEE 10th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)
dc.titleHigh-level representations through unconstrained sensorimotor learningen_US
dc.typeConference paperen_US
dc.contributor.departmentÖzyeğin University
dc.subject.keywordsReinforcement learning symbol generationen_US
dc.subject.keywordsSymbol emergeen_US
dc.subject.keywordsSymbol groundingen_US
dc.contributor.ozugradstudentÖztürkçü, Özgür Baran
dc.relation.publicationcategoryConference Paper - International - Institutional Graduate Student

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