Toward high-performance, memory-efficient, and fast reinforcement learning-Lessons from decision neuroscience
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Lee, Jee Hang | ko |
| dc.contributor.author | Seymour, Ben | ko |
| dc.contributor.author | Leibo, Joel Z. | ko |
| dc.contributor.author | An, Su Jin | ko |
| dc.contributor.author | Lee, Sang Wan | ko |
| dc.date.accessioned | 2019-02-20T05:13:39Z | - |
| dc.date.available | 2019-02-20T05:13:39Z | - |
| dc.date.created | 2019-02-11 | - |
| dc.date.created | 2019-02-11 | - |
| dc.date.created | 2019-02-11 | - |
| dc.date.issued | 2019-01 | - |
| dc.identifier.citation | SCIENCE ROBOTICS, v.4, no.26 | - |
| dc.identifier.issn | 2470-9476 | - |
| dc.identifier.uri | http://hdl.handle.net/10203/250381 | - |
| dc.description.abstract | Recent insights from decision neuroscience raise hope for the development of intelligent brain-inspired solutions to robot learning in real dynamic environments full of noise and unpredictability. | - |
| dc.language | English | - |
| dc.publisher | AMER ASSOC ADVANCEMENT SCIENCE | - |
| dc.title | Toward high-performance, memory-efficient, and fast reinforcement learning-Lessons from decision neuroscience | - |
| dc.type | Article | - |
| dc.identifier.wosid | 000455967100004 | - |
| dc.identifier.scopusid | 2-s2.0-85060017347 | - |
| dc.type.rims | ART | - |
| dc.citation.volume | 4 | - |
| dc.citation.issue | 26 | - |
| dc.citation.publicationname | SCIENCE ROBOTICS | - |
| dc.identifier.doi | 10.1126/scirobotics.aav2975 | - |
| dc.contributor.localauthor | Lee, Sang Wan | - |
| dc.contributor.nonIdAuthor | Seymour, Ben | - |
| dc.contributor.nonIdAuthor | Leibo, Joel Z. | - |
| dc.description.isOpenAccess | N | - |
| dc.type.journalArticle | Editorial Material | - |
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