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Deep learning, reinforcement learning, and world models

Matsuo, Yutaka; LeCun, Yann; Sahani, Maneesh; Precup, Doina; Silver, David; Sugiyama, Masashi; Uchibe, Eiji; (2022) Deep learning, reinforcement learning, and world models. Neural Networks , 152 pp. 267-275. 10.1016/j.neunet.2022.03.037. Green open access

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Abstract

Deep learning (DL) and reinforcement learning (RL) methods seem to be a part of indispensable factors to achieve human-level or super-human AI systems. On the other hand, both DL and RL have strong connections with our brain functions and with neuroscientific findings. In this review, we summarize talks and discussions in the “Deep Learning and Reinforcement Learning” session of the symposium, International Symposium on Artificial Intelligence and Brain Science. In this session, we discussed whether we can achieve comprehensive understanding of human intelligence based on the recent advances of deep learning and reinforcement learning algorithms. Speakers contributed to provide talks about their recent studies that can be key technologies to achieve human-level intelligence.

Type: Article
Title: Deep learning, reinforcement learning, and world models
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.neunet.2022.03.037
Publisher version: https://doi.org/10.1016/j.neunet.2022.03.037
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Deep learning, reinforcement learning, world models, machine learning, artificial intelligence
UCL classification: UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neurosci Unit
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL
URI: https://discovery.ucl.ac.uk/id/eprint/10147351
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