Neural Networks2022,Vol.1529.DOI:10.1016/j.neunet.2022.03.037

Deep learning, reinforcement learning, and world models

Matsuo, Yutaka LeCun, Yann Sahani, Maneesh Precup, Doina Silver, David Sugiyama, Masashi Uchibe, Eiji Morimoto, Jun
Neural Networks2022,Vol.1529.DOI:10.1016/j.neunet.2022.03.037

Deep learning, reinforcement learning, and world models

Matsuo, Yutaka 1LeCun, Yann 2Sahani, Maneesh 3Precup, Doina 4Silver, David 4Sugiyama, Masashi 1Uchibe, Eiji 5Morimoto, Jun5
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作者信息

  • 1. Univ Tokyo
  • 2. Courant Inst,NYU
  • 3. Gatsby Computat Neurosci Unit,UCL
  • 4. DeepMind
  • 5. Adv Telecommun Res Int ATR
  • 折叠

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. (c) 2022 Published by Elsevier Ltd.

Key words

Deep learning/Reinforcement learning/World models/Machine learning/Artificial intelligence/LINEAR BELLMAN COMBINATION/GO/REPRESENTATION/ACQUISITION/IMITATION/BEHAVIOR/SHOGI/CHESS/GAME

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出版年

2022
Neural Networks

Neural Networks

EISCI
ISSN:0893-6080
被引量57
参考文献量95
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