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Toward high-performance, memory-efficient, and fast reinforcement learning-Lessons from decision neuroscience.

Accepted version
Peer-reviewed

Repository URI

https://www.repository.cam.ac.uk/handle/1810/289266

Repository DOI

https://doi.org/10.17863/CAM.36528
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Accepted version (124.16 KB)

Type

Article

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Authors

Lee, Jee Hang  ORCID logo  https://orcid.org/0000-0002-4337-2774
Seymour, Ben  ORCID logo  https://orcid.org/0000-0003-1724-5832
Leibo, Joel Z  ORCID logo  https://orcid.org/0000-0002-3153-916X
An, Su Jin  ORCID logo  https://orcid.org/0000-0003-4232-2799
Lee, Sang Wan  ORCID logo  https://orcid.org/0000-0001-6266-9613

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.

Description

Keywords

46 Information and Computing Sciences, 4602 Artificial Intelligence, 4611 Machine Learning, Neurosciences, Behavioral and Social Science, Basic Behavioral and Social Science, Mental Health, 1.1 Normal biological development and functioning, Mental health, Neurological

Journal Title

Sci Robot

Conference Name

Journal ISSN

2470-9476
2470-9476

Volume Title

4

Publisher

American Association for the Advancement of Science (AAAS)

Publisher DOI

https://doi.org/10.1126/scirobotics.aav2975

Rights and licensing

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Except where otherwised noted, this item's license is described as http://www.rioxx.net/licenses/all-rights-reserved
Sponsorship
Wellcome Trust (097490/Z/11/Z)
Arthritis Research UK (21192)
Arthritis Research UK (21537)

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University of Cambridge Research Outputs (Articles and Conferences)