Deep learning, reinforcement learning, and world models

Yutaka Matsuo, Yann LeCun, Maneesh Sahani, Doina Precup, David Silver, Masashi Sugiyama, Eiji Uchibe, Jun Morimoto

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)267-275
Number of pages9
JournalNeural Networks
StatePublished - Aug 2022


  • Artificial intelligence
  • Deep learning
  • Machine learning
  • Reinforcement learning
  • World models

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Artificial Intelligence


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