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Learning agents that acquire representations of social groups

Published online by Cambridge University Press:  07 July 2022

Joel Z. Leibo Open the ORCID record for Joel Z. Leibo [Opens in a new window] ,
Alexander Sasha Vezhnevets ,
Maria K. Eckstein ,
John P. Agapiou  and
Edgar A. Duéñez-Guzmán
Joel Z. Leibo
Affiliation:
DeepMind, London EC4A 3TW, UK jzl@deepmind.com vezhnick@deepmind.com mariaeckstein@deepmind.com jagapiou@deepmind.com duenez@deepmind.comwww.jzleibo.com
Alexander Sasha Vezhnevets
Affiliation:
DeepMind, London EC4A 3TW, UK jzl@deepmind.com vezhnick@deepmind.com mariaeckstein@deepmind.com jagapiou@deepmind.com duenez@deepmind.comwww.jzleibo.com
Maria K. Eckstein
Affiliation:
DeepMind, London EC4A 3TW, UK jzl@deepmind.com vezhnick@deepmind.com mariaeckstein@deepmind.com jagapiou@deepmind.com duenez@deepmind.comwww.jzleibo.com
John P. Agapiou
Affiliation:
DeepMind, London EC4A 3TW, UK jzl@deepmind.com vezhnick@deepmind.com mariaeckstein@deepmind.com jagapiou@deepmind.com duenez@deepmind.comwww.jzleibo.com
Edgar A. Duéñez-Guzmán
Affiliation:
DeepMind, London EC4A 3TW, UK jzl@deepmind.com vezhnick@deepmind.com mariaeckstein@deepmind.com jagapiou@deepmind.com duenez@deepmind.comwww.jzleibo.com
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Abstract

Humans are learning agents that acquire social group representations from experience. Here, we discuss how to construct artificial agents capable of this feat. One approach, based on deep reinforcement learning, allows the necessary representations to self-organize. This minimizes the need for hand-engineering, improving robustness and scalability. It also enables “virtual neuroscience” research on the learned representations.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 45 , 2022 , e111
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Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

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