Decision-making in brains and robots - the case for an interdisciplinary approach

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Reinforcement Learning describes a general method for trial-and-error learning, and it has emerged as a dominant framework both for optimal control in autonomous robots, and understanding decision-making in the brain. Despite their common roots, however, these two fields have evolved largely independently. In this perspective, we consider how each now face problems that could potentially be addressed by insights from the other, and argue that an interdisciplinary approach could greatly accelerate progress in both.
Publisher
ELSEVIER SCIENCE BV
Issue Date
2019-04
Language
English
Article Type
Review
Citation

CURRENT OPINION IN BEHAVIORAL SCIENCES, v.26, pp.137 - 145

ISSN
2352-1546
DOI
10.1016/j.cobeha.2018.12.012
URI
http://hdl.handle.net/10203/261878
Appears in Collection
BiS-Journal Papers(저널논문)
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