Deep learning, reinforcement learning, and world models

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 t...

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Bibliographic Details
Published inNeural networks Vol. 152; pp. 267 - 275
Main Authors Matsuo, Yutaka, LeCun, Yann, Sahani, Maneesh, Precup, Doina, Silver, David, Sugiyama, Masashi, Uchibe, Eiji, Morimoto, Jun
Format Journal Article
LanguageEnglish
Published United States Elsevier Ltd 01.08.2022
Subjects
Artificial intelligence
Deep learning
Machine learning
Reinforcement learning
World models
Deep learning
World models
Artificial intelligence
Reinforcement learning
Machine learning
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Summary: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.
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ISSN:0893-6080
1879-2782
1879-2782
DOI:10.1016/j.neunet.2022.03.037